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ASSOCIATION BETWEEN MODIFIABLE RISK FACTORS AND LEVELS OF BLOOD-BASED BIOMARKERS OF ALZHEIMER’S AND RELATED DEMENTIAS IN THE LOOK AHEAD COHORT

 

K.M. Hayden1, M.M. Mielke2, J.K. Evans3, R. Neiberg3, D. Molina-Henry4,5, M. Culkin1, S. Marcovina6,  K.C. Johnson7, O.T. Carmichael8, S.R. Rapp1,9, B.C. Sachs10,11, J. Ding11, H. Shappell3, L. Wagenknecht2,  J.A. Luchsinger12, M.A. Espeland3,11

 

1. Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, USA; 2. Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA; 3. Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA; 4. Winston-Salem State University, Winston-Salem, NC, USA; 5. Alzheimer’s Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, CA, USA; 6. Medpace Reference Laboratories, Cincinnati, OH, USA; 7. Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN, USA; 8. Biomedical Imaging Center, Pennington Biomedical Research Center, Baton Rouge, LA, USA; 9. Department of Psychiatry & Behavioral Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA; 10. Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, NC, USA; 11. Sticht Division of Gerontology and Geriatric Medicine Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA; 12. Departments of Medicine and Epidemiology, Columbia University Irving Medical Center, New York, NY, USA

Corresponding Author: K.M. Hayden, Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, USA, khayden@wakehealth.edu

J Aging Res & Lifestyle 2024;13:1-21
Published online January 5, 2024, http://dx.doi.org/10.14283/jarlife.2024.1

 


Abstract

BACKGROUND: Emerging evidence suggests that a number of factors can influence blood-based biomarker levels for Alzheimer’s disease (AD) and Alzheimer’s related dementias (ADRD). We examined the associations that demographic and clinical characteristics have with AD/ADRD blood-based biomarker levels in an observational continuation of a clinical trial cohort of older individuals with type 2 diabetes and overweight or obesity.
METHODS: Participants aged 45-76 years were randomized to a 10-year Intensive Lifestyle Intervention (ILI) or a diabetes support and education (DSE) condition. Stored baseline and end of intervention (8-13 years later) plasma samples were analyzed with the Quanterix Simoa HD-X Analyzer. Changes in Aβ42, Aβ40, Aβ42/Aβ40, ptau181, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) were evaluated in relation to randomization status, demographic, and clinical characteristics.
RESULTS: In a sample of 779 participants from the Look AHEAD cohort, we found significant associations between blood-based biomarkers for AD/ADRD and 15 of 18 demographic (age, gender, race and ethnicity, education) and clinical characteristics (APOE, depression, alcohol use, smoking, body mass index, HbA1c, diabetes duration, diabetes treatment, estimated glomerular filtration rate, hypertension, and history of cardiovascular disease) .
CONCLUSIONS: Blood-based biomarkers of AD/ADRD are influenced by common demographic and clinical characteristics. These factors should be considered carefully when interpreting these AD/ADRD blood biomarker values for clinical or research purposes.

Key words: Alzheimer’s disease, blood-based biomarkers of AD, comorbidities, obesity, diabetes.


 

Introduction

Alzheimer’s disease (AD) blood-based biomarkers have evolved rapidly over the last decade with the development of more sensitive technologies, such as single molecular array (SIMOA) immunoassays, to more accurately measure biomarkers of AD pathology (Aβ42/Aβ40 ratio and phosphorylated tau at threonine 181 [ptau181]), and neurodegeneration (neurofilament light chain, [NfL] and glial fibrillary acidic protein [GFAP]) (1-3). However, with the increasing availability of blood biomarker assays for clinical use, there is an urgent need to understand their utility in large, heterogeneous populations. Several recent studies have reported that multiple chronic conditions can affect the interpretation of the blood biomarkers, causing increases or decreases in levels due to peripheral physiological factors such as obesity or renal function (4-8). Understanding the impact of multiple chronic conditions on AD biomarker levels in diverse populations will inform their use and interpretation in clinical settings.
The Action for Health in Diabetes (Look AHEAD) MIND cohort provides an opportunity for a unique contribution to this emerging body of literature in the high AD and Alzheimer’s related dementia (ADRD) risk group of persons with type 2 diabetes (T2D) (9). Look AHEAD participants represent a diverse group of individuals with T2D and overweight or obesity, who were recruited from 16 sites across the US who were randomized 1:1 to a lifestyle intervention vs. a control condition (10). To date there have been few, if any, studies that have comprehensively examined the AD/ADRD blood-based biomarkers in this type of cohort. Also, associations between diabetes duration and the AD/ADRD blood-based biomarkers are not known. Herein we present descriptive data on baseline levels of Aβ42, Aβ40, Aβ42/Aβ40, ptau181, NfL, and GFAP, and factors associated with their levels, as well as change over time from baseline to the end of the intervention.

 

Methods

Study design

Look AHEAD was a randomized controlled clinical trial of participants with T2D and overweight or obesity. The study design, methods (10), and CONSORT diagram (11) for Look AHEAD have been published previously. The trial was designed to determine whether intentional weight loss for older adults with diabetes and overweight/obesity provided health benefits. The primary outcome of the trial was a composite of fatal and major not-fatal cardiovascular events. Eligibility criteria required that participants have body mass index (BMI) >25 kg/m2 (>27 kg/m2 if on insulin), glycated hemoglobin (HbA1c) <11%, systolic/diastolic blood pressure <160/100 mmHg, and triglycerides <600 mg/dl. Participants were required to demonstrate over a two-week run-in period, the ability to make a daily record of their diet and physical activity. A behavioral psychologist or interventionist met with each participant to confirm that the intervention requirements were understood and that participants did not have competing life stressors that would impair adherence to the protocol. Enrollment spanned 2001 to 2004. The study was halted in September 2012 for futility with respect to the primary outcome and follow-up has continued as an observational study (see timeline, Figure 1). Local Institutional Review Boards approved the protocols and all participants provided written informed consent.

Setting, Participants, and Intervention

Participants (n=5,145) aged 45-76 years were recruited from 16 sites across the United States and were randomized to either an Intensive Lifestyle Intervention (ILI) or a Diabetes Support and Education (DSE) condition. The ILI was a multidomain intervention including dietary modification and physical activity with a goal of inducing an average of ≥7% weight loss at one year and maintenance of weight loss over the course of the study (12). Participants in the ILI arm had a daily calorie goal of 1200 to 1800 kcal based on initial weight. The diet specified <30% total calories from fat (<10% saturated fat) and a minimum of 15% total calories from protein. The physical activity goal was similar in intensity to brisk walking for at least 175 minutes per week. The participants randomized to the DSE condition were invited, but not required, to attend three yearly group sessions. These sessions focused on diet, physical activity, and social support (13). There were no specific instructions or goals for weight loss, physical activity, or dietary modification. The current analyses include those who participated in blood draws at baseline and in 2010-2013 participated in cognitive interviews. Preference for inclusion was given to those who also participated in a brain MRI ancillary study. Compared to those included in the study, participants who were not included were younger, more were women, and more often self-identified as American Indian/Native American/Alaskan Native or Hispanic. Excluded participants had higher levels of education, higher baseline BMI, and fewer had a baseline history of cardiovascular disease (CVD), as seen in Supplemental Table 1.

Biomarkers

Blood samples were drawn at baseline and proximal to the end of the intervention, approximately 8-13 years later. All assays were performed using EDTA plasma samples stored at Medpace Reference Laboratories, Cincinnati, OH. Concentrations of Aβ42, Aβ40, GFAP, and NfL were measured using the Simoa Human Neurology 4-Plex E Advantage Kit on a Quanterix Simoa HD-X bead-based immunoassay analyzer (Quanterix Corporation, Billerica, MA). The determination of the concentration of the pTau-181 was performed using the Simoa pTau-181 V2 Advantage kit. For all assays, within-run coefficients of variation (CVs) ranged from 3-19% and between-run CVs ranged from 6-13%. Biomarkers were converted to z-scores using baseline sample means and standard deviations for comparability.

Baseline demographic and clinical characteristics

Demographic characteristics were collected at baseline and included age; self-identified gender, race, and ethnicity; years of education; alcohol use; and smoking status. Participants provided self-reports of clinical characteristics including the duration of their diabetes, type of diabetes treatment (diet, oral medications, oral medications and insulin), hypertension, high cholesterol, history of CVD (including MI, heart bypass surgery, coronary artery bypass graft, carotid endarterectomy, lower leg angioplasty, aortic aneurysm, congestive heart failure, or stroke), peripheral neuropathy, and use of antidepressants. Additional variables measured at baseline included BMI, systolic and diastolic blood pressure, and depressive symptoms (Beck Depression Inventory, BDI (14)). Blood specimens were analyzed centrally at the Northwest Lipid Metabolism and Diabetes Research Laboratories at the University of Washington for HbA1c and creatinine. Estimated glomerular filtration rate (eGFR) was calculated. APOE ε4 carrier status was determined for participants who provided consent (80% of women versus 86% of men, p<0.001), using TaqMan genotyping (rs7412 and rs429358) (15).

Statistical Methods

Demographic and clinical characteristics of the sample were compared by intervention assignment. Continuous variables were evaluated using t-tests and categorical variables were evaluated with χ2 tests. Spearman correlations between individual biomarkers at baseline and change from baseline to the end of the intervention were calculated. Spearman correlations were also calculated for associations between each of the biomarkers and age, BMI, and diabetes duration at baseline and for change between these two times. For each characteristic, we evaluated associations with biomarker levels at baseline and change from baseline to the end of the intervention, adjusted for age and gender using linear regression models. We present these data in two ways, forest plots with each individual measure compared to a ‘standard’ referent category that most resembles existing biomarker studies, and in tables where the least squares means are reported without fixed reference categories.

 

Results

There were 779 participants (383 DSE and 396 ILI) with available blood samples and cognitive data for this analysis. Baseline characteristics are shown in Table 1. The mean (standard deviation [SD]) age was 61.4 (6.2); 438 (56.2%) were women; 199 (25.5%) had education <13 years; 130 (16.7%) were African American; and 97 (12.5%) identified as Hispanic. Mean ±SD BMI at baseline was 34.8 ±5.3 kg/m2; 441 (57.2%) had diabetes for at least 5 years; and 668 (86.4%) were taking a diabetes medication. The only difference between randomization groups included in this analysis was in baseline peripheral neuropathy: more participants in the ILI group reported baseline peripheral neuropathy than among those in the DSE group (18.9% vs 12.5%, p=0.01). There were no significant randomization group differences in either baseline or end of intervention levels of blood-based biomarkers (data not shown), thus subsequent analyses of biomarkers were not stratified by intervention arm.

Table 1. Baseline Characteristics of Participants by Randomization Group

Abbreviations Aβ, Amyloid beta; APOE ε4, Apolipoprotein E gene, ε4 carrier status; CVD, cardiovascular disease; DSE, diabetes support and education; GFAP, glial fibrillary acidic protein; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; Hx, history; ILI, intensive lifestyle intervention; kg/m², kilogram per square meter; Meds, medications; NfL, neurofilament light chain; oz, ounces; SD, standard deviation, Trt, treatment; μg/DL, microgram per deciliter; wk, week; Yrs, years

 

Spearman correlations between unadjusted plasma biomarker levels at baseline and change are shown in Tables 2a and 2b. At baseline, all biomarkers significantly correlated with each other, with two exceptions: the Aβ42/40 ratio only correlated with Aβ42 and Aβ40; and ptau181 and GFAP were not correlated. Next, we examined correlations between change in biomarker levels from baseline to the end of the intervention (Table 2b); nearly all biomarkers correlated, except for pTau181 or GFAP with Aβ42/40.

Table 2a. Spearman Correlations between Biomarkers at Baseline

Table 2b. Spearman Correlations between Biomarker Change from Baseline to End of the Intervention

 

We then examined correlations between baseline biomarker levels and baseline values of age, BMI, and diabetes duration; we also examined correlations between these variables and longitudinal change in biomarker levels (Table 3). At baseline, Aβ42, Aβ40, NfL and GFAP positively correlated with age; Aβ42/40, NfL, and GFAP were inversely correlated with baseline BMI; and diabetes duration positively correlated with NfL. Change in NfL and GFAP positively correlated with age, while change in Aβ42/40 negatively correlated with age.

Table 3. Spearman Correlations between Biomarkers, Age, BMI, and Diabetes Duration at Baseline, and Change from Baseline to the End of the Intervention

Figure 2b. pTau181, NfL, and GFAP Levels by Age Groups: Baseline and Change over 8-13 years

 

In Figures 2a and 2b, forest plots depict the associations between each biomarker by age groups (45-55, 56-65, 65-76). Both Aβ42 and Aβ40 levels differed significantly among age groups at baseline and change over time with higher biomarker levels in older age groups at baseline and greater increases in biomarker levels over time among the youngest and oldest age groups. Age group was not significantly associated with Aβ42/40 ratio at baseline, but it was associated with change in Aβ42/40 ratio from baseline to years 8-13 (p<0.001). Figure 2b shows no significant differences in pTau181 by age groups at baseline, but significant differences by age group emerged when considering change from baseline (p=0.005). NfL and GFAP demonstrated significant associations with age groups at baseline and change (all p<0.05).

Figure 2a. Aβ42, Aβ40 and Aβ42/40 Levels by Age Groups: Baseline and Change over 8-13 years

 

Figure 3a and 3b show forest plots of each biomarker by gender at baseline and change. Women had significantly lower levels of Aβ40 compared to men (p=0.02) at baseline. Women had lower NfL levels compared to men at baseline and change (all p<0.05). However, women had higher levels of GFAP at baseline (p=0.009; Figure 3b).

Figure 3a. Aβ42, Aβ40 and Aβ42/40 Levels by gender: Baseline and Change over 8-13 years

Figure 3b. pTau181, NfL, and GFAP Levels by Gender: Baseline and Change over 8-13 years

 

Given the effect of age and gender on biomarker levels, subsequent analyses were adjusted for these demographics. Results are presented two ways: 1) visually for baseline and change values (from baseline to end of intervention) as forest plots (Figures 4-9) with comparisons to a common reference category set at zero; and 2) as tables in which least squares means of the z-scores are reported without reference categories and including baseline and change from baseline to the end of the intervention (Tables 4-9).

Figure 4. Aβ42 levels at baseline and change over 8-13 years

Table 4. Aβ42 levels by individual baseline characteristics adjusted for age and gender

Each categorical group of characteristics was evaluated individually, controlling for age and gender. Significant associations are bolded; p-values are presented for each group. Abbreviations: Aβ, Amyloid beta; APOE ε4, Apolipoprotein E gene, ε4 carrier status; BDI, Beck Depression Inventory; BMI, body mass index; CVD, cardiovascular disease; GFR, glomerular filtration rate; HbA1c, hemoglobin A1c; Hx, history; kg/m², kilogram per meters squared; LDL, low density lipoprotein; LS, least-squares; OM, oral medication; oz, ounces; Trt, treatment; wk, week; w/, with; w/out, without; Yrs, years

 

Aβ42

Aβ42 levels differed by race and ethnicity in change from baseline (p=0.007); Hispanic and Black participants had lower levels of change than White or other/mixed race participants (Table 4 and Figure 4). Never smokers had the lowest increase in Aβ42 levels (p=0.004) compared to past or current smokers. Participants with elevated baseline HbA1c levels (≥7%) had lower baseline levels of Aβ42 (p=0.002) and a greater increase over time (p=0.004). Participants with lower eGFR had higher levels of Aβ42 at baseline (p<0.0001). Hypertension at baseline was associated with higher Aβ42 levels (p=0.0002). Those with a history of CVD at baseline had greater increase in Aβ42 over time than those without a baseline history of CVD (p=0.021).

Aβ40

White participants and those reporting other/mixed race and ethnicity had higher Aβ40 levels than Black or Hispanic participants at baseline (p=0.018); and greater increases from baseline (p=0.003) (Table 5 and Figure 5). Participants who never smoked had had less of an increase in Aβ40 over time (p=0.008) than past or current smokers. Participants with HbA1c levels ≥7% had lower Aβ40 levels at baseline (p=0.002) and a greater increase over time (p=0.025). Participants with lower baseline eGFR had higher levels of Aβ40 (p<0.0001) at baseline compared to those with higher eGFR. Participants with hypertension at baseline had higher levels of Aβ40 at baseline than those without hypertension (p=0.004); participants with a baseline history of CVD had a greater increase in Aβ40 levels than those without a baseline history of CVD (p=0.009).

Table 5. Aβ40 levels by individual baseline characteristics adjusted for age and gender

Each categorical group of characteristics was evaluated individually, controlling for age and gender. Significant associations are bolded; p-values are presented for each group. Abbreviations: Aβ, Amyloid beta; APOE ε4, Apolipoprotein E gene, ε4 carrier status; BDI, Beck Depression Inventory; BMI, body mass index; CVD, cardiovascular disease; GFR, glomerular filtration rate; HbA1c, hemoglobin A1c; Hx, history; kg/m², kilogram per meters squared; LDL, low density lipoprotein; LS, least-squares; OM, oral medication; oz, ounces; Trt, treatment; wk, week; w/, with; w/out, without; Yrs, years

 

Figure 5. Aβ40 levels at baseline and change over 8-13 years

 

Aβ42/40

At baseline, Black participants had a higher Aβ42/40 ratio compared to White participants and those reporting other/mixed race (p=0.029) (Table 6 and Figure 6). APOE ε4 carriers had a lower Aβ42/40 ratio at baseline (p=0.003). Participants who had diabetes treated with medications had a higher Aβ42/40 ratio compared to those who were not treated with oral medications or insulin (p=0.038). Higher eGFR was associated with a lower Aβ42/40 ratio at baseline (p=0.018). Participants who self-reported hypertension at baseline had greater decrease in Aβ42/40 ratio compared to those who did not (p=0.012).

Table 6. Aβ42/Aβ40 levels by individual baseline characteristics adjusted for age and gender

Each categorical group of characteristics was evaluated individually, controlling for age and gender. Significant associations are bolded; p-values are presented for each group. Abbreviations: Aβ, Amyloid beta; APOE ε4, Apolipoprotein E gene, ε4 carrier status; BDI, Beck Depression Inventory; BMI, body mass index; CVD, cardiovascular disease; GFR, glomerular filtration rate; HbA1c, hemoglobin A1c; Hx, history; kg/m², kilogram per meters squared; LDL, low density lipoprotein; LS, least-squares; OM, oral medication; oz, ounces; Trt, treatment; wk, week; w/, with; w/out, without; Yrs, years

 

 

Figure 6. Aβ42/ Aβ40 ratio levels at baseline and change over 8-13 years

 

pTau181

At baseline, there were no significant associations between any characteristic and ptau181 levels, adjusting for age and gender (Table 7 and Figure 7). In the time from baseline to the end of the intervention, White participants had the greatest increase in pTau181 levels over time compared to the other groups (p=0.0002).

able 7. Ptau181 levels by individual baseline characteristics adjusted for age and gender

Each categorical group of characteristics was evaluated individually, controlling for age and gender. Significant associations are bolded; p-values are presented for each group. Abbreviations: Aβ, Amyloid beta; APOE ε4, Apolipoprotein E gene, ε4 carrier status; BDI, Beck Depression Inventory; BMI, body mass index; CVD, cardiovascular disease; GFR, glomerular filtration rate; HbA1c, hemoglobin A1c; Hx, history; kg/m², kilogram per meters squared; LDL, low density lipoprotein; LS, least-squares; OM, oral medication; oz, ounces; Trt, treatment; wk, week; w/, with; w/out, without; Yrs, years

 

Figure 7. pTau181 levels at baseline and change over 8-13 years

 

NfL

Baseline NfL levels (Table 8 and Figure 8), adjusted for age and gender, are associated with race and ethnicity, with Black participants having the lowest NfL levels at baseline (p=0.010). Participants who had diabetes <5 years at baseline had lower NfL levels than those who had diabetes ≥5 years (p<0.0001). Participants with lower HbA1c levels at baseline had less increase in NfL levels by the end of the intervention (p=0.032). Participants who treated their diabetes without medication had the lowest NfL levels at baseline (p=0.003). Lower eGFR levels were associated with higher NfL levels at baseline (p<0.0001).

Table 8. NfL levels by individual baseline characteristics adjusted for age and gender

Each categorical group of characteristics was evaluated individually, controlling for age and gender. Significant associations are bolded; p-values are presented for each group. Abbreviations: Aβ, Amyloid beta; APOE ε4, Apolipoprotein E gene, ε4 carrier status; BDI, Beck Depression Inventory; BMI, body mass index; CVD, cardiovascular disease; GFR, glomerular filtration rate; HbA1c, hemoglobin A1c; Hx, history; kg/m², kilogram per meters squared; LDL, low density lipoprotein; LS, least-squares; OM, oral medication; oz, ounces; Trt, treatment; wk, week; w/, with; w/out, without; Yrs, years

 

Figure 8. NfL levels at baseline and change over 8-13 years

 

GFAP

Lower GFAP levels (Table 9, Figure 9) were associated with lower education levels at baseline (p=0.006). BDI scores ≥10 were associated with lower GFAP levels at baseline (p=0.001). Alcohol use at baseline ≥21 oz/week was associated with higher GFAP levels at baseline (p=0.019). Higher baseline BMI was associated with lower GFAP levels at baseline (p=0.002). Participants with baseline eGFR of 60-90 had the highest GFAP levels at baseline (p=0.006). Finally, having a history of CVD at baseline was associated with higher GFAP levels at baseline (p=0.032).

Table 9. GFAP levels by individual baseline characteristics adjusted for age and gender

Each categorical group of characteristics was evaluated individually, controlling for age and gender. Significant associations are bolded; p-values are presented for each group. Abbreviations: Aβ, Amyloid beta; APOE ε4, Apolipoprotein E gene, ε4 carrier status; BDI, Beck Depression Inventory; BMI, body mass index; CVD, cardiovascular disease; GFR, glomerular filtration rate; HbA1c, hemoglobin A1c; Hx, history; kg/m², kilogram per meters squared; LDL, low density lipoprotein; LS, least-squares; OM, oral medication; oz, ounces; Trt, treatment; wk, week; w/, with; w/out, without; Yrs, years

 

Figure 9. GFAP levels at baseline and change over 8-13 years

 

Intervention

As noted above, there were no significant intervention group differences at baseline in biomarker levels. Change in biomarker levels from baseline to the end of the intervention also did not differ by intervention arm (Table 1). Interactions between intervention arm and age, gender, BMI, history of CVD, and APOE ε4 carrier status were tested. There was a significant interaction between intervention group and age such that those over age 65 at baseline and randomized to DSE had greater increases in NfL than those under age 65. Those over age 65 at baseline and randomized to DSE also had a greater increase in NfL than those in the ILI group who were aged 65 or older. No other interactions by randomization group emerged.

 

Discussion

Blood-based AD biomarkers have complex associations with each other, with age and gender, and with a number of common conditions that are themselves risk factors for dementia. Some of these conditions may influence blood biomarker levels because they are risk factors for AD whereas others may influence the biomarker levels because they physiologically impact peripheral levels (4, 8). Most studies of blood-based AD biomarkers conducted to date, have focused on relatively healthy and homogenous populations which do not reflect the reality of older populations with multiple chronic conditions and cognitive impairment, who will be most likely to have blood biomarker testing. There is an urgent need to understand the impact of factors that may affect the interpretation of the blood markers in the general population, especially now that some of these biomarkers are clinically available for use in forming diagnoses at the population level. In this study, we examined associations between AD-related plasma biomarkers (Aβ42, Aβ40, Aβ42/Aβ40, pTau181, NfL, and GFAP) and common conditions in older adults with T2D and overweight or obesity. The cohort comprised clinical trial participants, so we also evaluated the legacy effect of the intensive lifestyle intervention on biomarker levels. Of the 18 factors tested, 15 had significant associations with at least one of the AD blood-based biomarkers. We discuss and place into context our results for each factor studied below. We have chosen to focus on comparisons to studies with similar methods, age ranges, and those that mostly included participants who were cognitively normal at their baseline assessment. See Supplemental Table 2 for basic information on the comparison studies including biomarker methods, biomarkers targeted, sample size, age, and cognitive status.

Age

At baseline, Aβ42, Aβ40, NfL, and GFAP were positively associated with age. At the end of the intervention, Aβ42/Aβ40 was inversely associated with age, while NfL and GFAP were positively associated with age. Our findings largely correspond with some, but not all other studies. For example, the Mayo Clinic Study of Aging (MCSA) showed higher levels of Aβ42 with older ages (4); but the Health, Aging and Body Composition Study (Health ABC) study found no association between Aβ42 and age (16). MCSA also found increases in Aβ40 levels with age, as did Health ABC (16) and the Systolic Blood Pressure Intervention Trial (SPRINT) trial (5). In the Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL) (17), similar to our study, there was no association between baseline Aβ42/Aβ40 and age whereas MCSA showed a negative association between Aβ42/Aβ40 and age. We found no association between pTau181 and baseline age, but the AIBL study found higher pTau181 levels with older ages. Our NfL and GFAP findings were more consistent with findings from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study (18), AIBL (17), the Austrian Stroke Prevention Family Study (ASPS-Fam) (19), the National Health and Nutrition Examination Survey (NHANES) (20), and SPRINT (5), all showing higher NfL and GFAP levels with increased age. Taken together, these findings suggest that Aβ42, Aβ40, NfL, and GFAP levels increase with age. Relationships between Aβ42/Aβ40, pTau181, and age are less consistent at this time. Of note, one study reported an increase of pTau181 with age only among individuals who were amyloid PET positive (8). Thus, the relationship between pTau181 and age may appear stronger among cohorts who have a higher prevalence of amyloid positivity.

Gender

In the present study, women had lower levels of Aβ40 than men at baseline. Our results corresponded with the lower Aβ40 levels found in women in the UK Medical Research Council (MRC) National Survey for Health and Development (NSHD) (21). Conversely, there were no gender differences in Aβ40 levels between women and men in the Health ABC cohort (16). While we reported no gender differences in baseline or levels of change over time in Look AHEAD, AIBL (17) similarly showed no differences in Aβ42/Aβ40 by gender; but the MCSA showed lower levels of Aβ42/Aβ40 among women (4). These findings do not paint a clear picture and suggest that more work is needed to understand gender and sex differences in blood-based biomarkers for AD. The various studies we have cited have adjusted for different sets of comorbidities and samples were drawn from different populations with different characteristics which could contribute to different findings.

Race and Ethnicity

Lower levels of Aβ42 were observed among Black and Hispanic participants compared to White participants. Aβ40 at baseline and change in biomarker levels over the interval followed the same pattern with Black and Hispanic participants having lower levels and less change over time. The opposite trend emerged for the Aβ42/Aβ40 ratio at baseline and in change over time with White and other/mixed race groups having lower levels and less change over time. Ptau181 levels increased the most over time among White participants compared to other groups. Black participants had the lowest NfL levels on average at baseline. The only biomarker in our study that was not associated with race and ethnicity was GFAP. Others have found similar associations between race and biomarker levels. Hajjar et al. (22), found race differences in biomarkers Aβ42, Aβ40, pTau181, and NfL with African Americans having lower levels than their White counterparts in the Brain, Stress, Hypertension, and Aging Research Program (B-SHARP). In the Health ABC study, investigators similarly found lower levels of Aβ42 and Aβ40 in African American participants compared to White participants (16). Mexican Americans were found to have lower levels of Aβ40, total tau, and Aβ42/Aβ40, in the Health & Aging Brain study among Latino Elders (HABS-HD) (7). However, a study using the NHANES data found no differences in NfL levels by race in models that controlled for age, sex, stroke, diabetes, eGFR, and alcohol consumption (20). More work needs to be done to get a better grasp on these associations as race and ethnicity are social constructs and represent proxies for systemic racism. Associations between biomarkers and race and ethnicity could change depending on the confounders or social determinants of health that are addressed in any given analysis.

Education

Education levels were associated with GFAP at baseline with those in the lowest education group having the lowest GFAP levels. In the Health ABC study, low education was associated with lower Aβ42 levels (16). It is unclear whether associations between education level and blood-based biomarkers are biologically based or whether higher education is an effective proxy for health advantages due to socio-economic status.

APOE

APOE ε4 carrier status was only associated with lower Aβ42/Aβ40 ratio at baseline in Look AHEAD. In Health ABC, APOE ε4 carrier status was associated with lower Aβ42 levels (16). In the AIBL study, APOE ε4 carrier status was not associated with biomarker levels after adjustment for age, sex, diagnosis, and amyloid PET Aβ status (17).

Depressive Symptoms and Antidepressant Usage

Depressive symptoms as measured by the BDI (stratified as BDI<10 vs BDI ≥10), were associated with GFAP levels at baseline. A recent systematic review of GFAP studies indicated associations between GFAP and a number of neurological diseases and disorders, including major depressive disorder as well as Alzheimer’s disease (23). Although there seems to be some association with depression, we were unable to find studies that also investigated antidepressant usage.

Alcohol and Smoking

Higher alcohol use was associated with higher baseline GFAP levels in our study. In the NHANES study, alcohol use was associated with NfL levels instead, in models that controlled for age, sex, stroke, race, diabetes, and eGFR (20). A history of smoking was associated with an increase in Aβ42 and Aβ40 levels over time in our cohort. We were only able to find one other study that referenced smoking and blood-based AD biomarkers. That study, conducted among patients with heart failure, reported former smokers had lower GFAP levels (24). More work should be done to better understand the interplay between alcohol use, smoking and AD biomarker levels as these are common exposures in older cohorts.

BMI

Given that Look AHEAD recruited participants with overweight or obesity (mean baseline BMI=34.8±5.3 kg/m2), there was less variation in BMI compared to other studies so we expected to have limited ability to identify differences by BMI. Higher BMI was only significantly associated with lower GFAP levels at baseline, and this association was consistent with a report using BioFINDER data where BMI was inversely correlated with NfL and GFAP (25). In a cohort of 327 community dwelling participants in the ASPS-Fam study, Koini et al. found that BMI was a significant predictor of lower NfL among younger participants (<60) (19). In the AIBL study, lower BMI was correlated with higher pTau181, NfL, and GFAP levels (17).

Diabetes Duration

More than half of our participants (57%) had diabetes for 5 years or more at baseline. A longer duration of diabetes was associated with higher NfL levels at baseline. Serum NfL levels were also elevated among diabetics in an HNANES analysis compared to those without diabetes (20). Using data from the HABS-HD study, O’Bryant et al. found relationships between the presence of diabetes (not duration) and higher levels of Aβ42, Aβ40, and NfL (7). The NHANES study examined the association between NfL and the presence of diabetes (also not considering duration), and found that diabetes was associated with higher NfL levels in models that controlled for age, sex, stroke, race, diabetes, eGFR, and alcohol consumption (20).

HbA1c

HbA1c levels <7% were associated with higher Aβ42 and Aβ40 at baseline and lower levels of increase from baseline to end of intervention. HbA1c levels ≥7% were associated with greater NfL increases from baseline. In the ACCORD Study, NfL was similarly positively associated with HbA1c (18). In the HABS-HD study, higher HbA1c was also related to higher Aβ42, Aβ40, and NfL (7).

Diabetes Treatment

Participants not using diabetes medications had lower Aβ42/Aβ40 and NfL levels at baseline. While few studies have reported specifically on the relationship between diabetes treatment and blood-based biomarkers for AD, several studies have investigated the associations between diabetes treatment and cognitive outcomes (26). Future work should examine more closely the relationship between specific diabetes medication classes and blood-based biomarkers as they have different effects (27).

Peripheral Neuropathy

We found no significant relationships between any of the biomarkers and neuropathy. In contrast, a meta-analysis of 36 studies found significant associations between neuropathy and higher NfL levels (28). It is possible our data failed to reflect this association because the generally higher BMI levels may dilute NfL levels in our cohort.

eGFR

Estimated glomerular filtration rate (eGFR) has been consistently associated with biomarker levels in the literature (8). A higher filtration rate (i.e., higher eGFR) indicates better kidney function and tends to be associated with lower levels of biomarkers in the blood. In Look AHEAD, higher eGFR was associated with lower baseline levels of Aβ42, Aβ40, Aβ42/Aβ40 ratio, NfL, and GFAP. In the ASPS-Fam study, ACCORD, and NHANES, renal function measured by eGFR was similarly associated with NfL levels (18-20). A higher eGFR was also significantly related to lower Aβ42, Aβ40, Tau, and NfL; and higher levels of Aβ42/Aβ40 in the HABS-HD study (7). In SPRINT, after adjustment for age, both Aβ40, and NfL were negatively associated with eGFR. Of all the factors studied here, eGFR seems to have consistent associations with biomarkers across studies similar to increased age.

Blood Pressure

Self-reported hypertension was associated with higher baseline Aβ42 and Aβ40 and greater decline in the Aβ42/Aβ40 ratio from baseline to end of intervention in our study. In the HABS-HD and ACCORD studies, hypertension was related to NfL (7, 18). In SPRINT, greater increases in NfL were seen in the intensive treatment (blood pressure lowering) arm of the study, however the association was attenuated when adjusted for eGFR (5).

Dyslipidemia

We examined potential associations with self-reported high cholesterol and found no significant associations. In the HABS-HD Study, dyslipidemia was related to Aβ42, Aβ40, and NfL; and higher levels of Aβ42/Aβ40 in models that were adjusted for age, sex, and education (7).

History of CVD

A history of CVD at baseline in Look AHEAD, defined as self-reported myocardial infarction, heart bypass surgery, coronary artery bypass graft, carotid endarterectomy, lower leg angioplasty, aortic aneurysm, congestive heart failure and stroke, was associated with a greater increase in Aβ42 and Aβ40 over time. History of CVD was also associated with higher baseline GFAP levels. Syrjanan et al. examined history of stroke and myocardial infarction and found that stroke was related to higher baseline NfL and total tau, while myocardial infarction was only related to total tau (4).

There are several key findings from this exercise. First, blood-based biomarker levels at baseline in our cohort were associated with 15 out of 18 variables, many of which represent conditions that are very common in older adults. Interestingly, in this study of participants with diabetes and overweight or obesity, diabetes treatment was associated with Aβ42, Aβ40, Aβ42/Aβ40, and NfL, while BMI was associated only with GFAP. As we have outlined, these associations are in general agreement with the limited number of studies that have evaluated these biomarkers in other cohorts (4, 5, 7, 16-22, 25, 29, 30). For example, when considering our work and others, we observe general agreement that Aβ42, Aβ40, NfL, and GFAP levels go up with age. There is a strong suggestion that levels of Aβ42, Aβ40, pTau181 are lower among persons with self-reported Black and Hispanic race and ethnicity. Second, biomarker levels were not associated with legacy effects of our ILI intervention. Third, changes in the biomarker levels over time are associated with race (Aβ42, Aβ40, Aβ42/Aβ40, and NfL), smoking (Aβ42, Aβ40), HbA1c levels (Aβ42, Aβ40, pTau181), and history of CVD (Aβ42, Aβ40). Evaluation of variables that are associated with change in biomarker levels over time suggests potential for malleability. Whether this translates into modifiable lifestyle behaviors is currently unknown.
There has been much progress in the technology to evaluate blood-based biomarkers for neurodegenerative disease. As blood samples are more distal than CSF, the assays for blood samples have to be particularly sensitive and are subject to additional complexities inherent in their measurement in blood (31). As the field begins to use these markers as screening tools for trials or as clinical indicators of brain health, it is critically important to understand all the patient/participant characteristics that can influence their measurement. In our study, all the participants have T2D and all of them had overweight or obesity at baseline, both of which are characteristics associated with increased risk of cognitive impairment and dementia in later life.

Limitations

This study is not without limitations. There was no baseline cognitive assessment as it was not a primary focus of the original Look AHEAD clinical trial. Therefore, participants were not excluded based on the presence of cognitive impairment at baseline. Cognitive impairment was not assessed until after the end of the intervention. Nonetheless, our rigorous screening procedures, designed to ascertain whether participants could execute the protocol, would have effectively excluded those with clear impairment. Our self-reported designations of race and ethnicity were not collected in a mutually exclusive fashion, thereby conflating the two. Randomization facilitated comparable demographic and health characteristics across study arms at baseline, and there is no reason to suspect that the two groups would have differed in cognitive performance at baseline either.
There may be other age-related chronic diseases that influence plasma levels of biomarkers for which we did not account. Our findings are perhaps only generalizable to a high-risk subset of the population, i.e., older adults with T2D and overweight/obesity. However, this group represents a growing at-risk portion of the population. While we tried to put our results in context with other studies, we acknowledge that there are differences in study designs including the methods used to ascertain biomarker level, the proportions of people of different races and ethnicities included, and assumptions about cognitive status at baseline. We also acknowledge that while we are looking at differences among important clinical subgroups, we are unable to determine the degree to which these reflect underlying differences in neuropathology. Finally, a strength of the work is the fact that Look AHEAD was a randomized controlled clinical trial, conducted using rigorous methods.

 

Conclusions

Blood-based biomarkers for neurodegenerative disorders have great potential for identifying individuals at risk for cognitive decline and dementia, however more work needs to be done in this area before they can be used clinically. Much of the work to date in biomarker studies has focused on clinical samples, comparing participants with and without clinically diagnosed dementing illness, often with confirmatory and Aβ and Tau PET imaging. Emerging work similar to results we present suggests that a number of participant or patient characteristics need to be considered when interpreting blood-based biomarkers. Our study of biomarkers in the Look AHEAD cohort reveals a number of participant characteristics that correlate with biomarker levels in a sample of individuals with diabetes and overweight or obesity, which should be considered when developing clinical applications for these biomarkers.

 

Clinical Sites: The Johns Hopkins University: Jeanne M. Clark, MD, MPH1; Lee Swartz2; Dawn Jiggetts2; Jeanne Charleston, RN3; Lawrence Cheskin, MD3; Nisa M. Maruthur, MD, MHS 3; Scott J. Pilla, MD, MHS3; Danielle Diggins; Mia Johnson. Pennington Biomedical Research Center George: A. Bray, MD1; Frank L. Greenway, MD1; Donna H. Ryan, MD3; Catherine Champagne, PhD, RD3; Valerie Myers, PhD3; Jeffrey Keller, PhD3; Tiffany Stewart, PhD3; Jennifer Arceneaux, RN2; Karen Boley, RD, LDN2; Greta Fry, LPN; Lisa Jones; Kim Landry; Melissa Lingle; Marisa Smith2. The University of Alabama at Birmingham: Cora E. Lewis, MD, MSPH1; Sheikilya Thomas, PhD, MPH2; Stephen Glasser, MD3; Gareth Dutton, PhD3; Amy Dobelstein; Sara Hannum; Anne Hubbell, MS; DeLavallade Lee; Phyllis Millhouse, L. Christie Oden; Cathy Roche, PhD, RN, BSN; Jackie Grant; Janet Turman. Harvard Center: Massachusetts General Hospital. David M. Nathan, MD1; Valerie Goldman, MS, RDN2; Linda Delahanty, MS, RDN3; Mary Larkin, MS, RN; Kristen Dalton, BS; Roshni Singh, BS; Melanie Ruazol, BS. Joslin Diabetes Center: Joslin Diabetes Center: Medha N Munshi, MD1; Sharon D. Jackson, CCRC, MS, RD, CDE2; Roeland J.W. Middelbeek MD3; A. Enrique Caballero, MD, Anthony Rodriguez. Beth Israel Deaconess Medical Center: George Blackburn, MD, PhD1*; Christos Mantzoros, MD, DSc3; Ann McNamara, RN. University of Colorado Anschutz Medical Campus: Holly Wyatt, MD1; James O. Hill, PhD1; Jeanne Anne Breen, MS2; Marsha Miller, MS, RD2; Debbie Bochert; Suzette Bossart; Paulette Cohrs, RN, BSN; Susan Green; April Hamilton, BS, CCRC; Eugene Leshchinskiy; Loretta Rome, TRS. The University of Tennessee Health Science Center: University of Tennessee East. Karen C. Johnson, MD, MPH1; Beate Griffin, RN, BS2; Mace Coday, PhD3; Donna Valenski, Linda Jones; Karen Johnson, RN. University of Tennessee Downtown: Karen C. Johnson, MD, MPH1; Beate Griffin, RN, BS2; Helmut Steinburg, MD3. University of Minnesota: Robert W. Jeffery, PhD1; Tricia Skarphol, MA2; John P. Bantle, MD3; J. Bruce Redmon, MD3; Kerrin Brelje, MPH, RD; Carolyne Campbell; Mary Ann Forseth, BA; Soni Uccellini, BS; Mary Susan Voeller, BA. Columbia University Medical Center: Blandine Laferrère, MD, PhD1; Xavier Pi-Sunyer, MD1; Jennifer Patricio, MS2; Jose Luchsinger, MD1; Priya Palta, PhD,MHS3; Jennifer Patricio, MS2; Sarah Lyon, Kim Kelly. University of Pennsylvania: Thomas A. Wadden, PhD1; Barbara J. Maschak-Carey, MSN, CDE2; Robert I. Berkowitz, MD3; Ariana Chao, PhD, CRNP3; Renee Davenport; Katherine Gruber, CRNP; Sharon Leonard, RD; Olivia Walsh, BA. University of Pittsburgh: John M. Jakicic, PhD1; Jacqueline Wesche-Thobaben, RN, BSN, CDE2; Lin Ewing, PhD, RN3; Andrea Hergenroeder, PhD, PT, CCS3; Mary Korytkowski, MD3; Susan Copelli, BS, CTR; Rebecca Danchenko, BS; Diane Ives, MPH; Juliet Mancino, MS, RD, CDE, LDN; Lisa Martich, BS, RD, LDN; Meghan McGuire, MS; Tracey Y. Murray, BS; Linda Semler, MS, RD, LDN; Kathy Williams, RN, MHA. The Miriam Hospital/Brown Medical School: Rena R. Wing, PhD1; Caitlin Egan, MS2; Elissa Jelalian, PhD3; Jeanne McCaffery, PhD3 ; Kathryn Demos McDermott, PhD3; Jessica Unick, PhD3; Kirsten Annis, BA; Jose DaCruz; Ariana Rafanelli, BA. The University of Texas Health Science Center at San Antonio: Helen P. Hazuda, PhD1; Juan Carlos Isaac, CCRC, BSN2; Prepedigna Hernandez, RN. VA Puget Sound Health Care System / University of Washington: Steven E. Kahn, MB, ChB1; Edward J. Boyko, MD, MPH3; Elaine Tsai, MD3; Lorena Wright, MD3; Karen Atkinson, RN, BSN2; Ivy Morgan-Taggart; Jolanta Socha, BS; Heidi Urquhart, RN. Southwestern American Indian Center, Phoenix, Arizona and Shiprock, New Mexico: William C. Knowler, MD, DrPH1; Paula Bolin, RN, MC2; Harelda Anderson, LMSW2, Sara Michaels, MD3; Ruby Johnson; Patricia Poorthunder; Janelia Smiley. University of Southern California: Anne L. Peters, MD1; Siran Ghazarian, MD2; Elizabeth Beale, MD3; Edgar Ramirez; Gabriela Rodriguez, MA; Valerie Ruelas MSW, LCSW; Sara Serafin-Dokhan; Martha Walker, RD; Marina Perez.

Coordinating Center: Wake Forest University Mark A. Espeland, PhD1; Kathleen Hayden, PhD1; Judy L. Bahnson, BA, CCRP3; Lynne E. Wagenknecht, DrPH3; David Reboussin, PhD3; Nicholas Pajewski, PhD3; Jingzhong Ding, PhD3; Gagan Deep, PhD3; Stephen R. Rapp, PhD3; Bonnie C. Sachs, PhD3; Jerry M. Barnes, MA; Tara D. Beckner; Delilah R. Cook; Joni Evans, MS; Katie Garcia, MS; Sarah A. Gaussoin, MS; Carol Kittel, MS; Lea Harvin, BS; Marjorie Howard, MS; Rebecca H. Neiberg, MS; Jennifer Walker, MS; Michael P. Walkup, MS

Federal Sponsors: National Institute of Diabetes and Digestive and Kidney Diseases: Mary Evans, PhD; Robert Kuczmarski, DrPH; Rebecca Van Raaphorst, MPH; Susan Z. Yanovski, MD. National Institute on Aging: Marcel Salive, MD, MPH

Funding and Support: Funded by the National Institutes of Health through cooperative agreements with the National Institute on Aging: AG058571 and National Institute of Diabetes and Digestive and Kidney Diseases: DK57136, DK57149, DK56990, DK57177, DK57171, DK57151, DK57182, DK57131, DK57002, DK57078, DK57154, DK57178, DK57219, DK57008, DK57135, and DK56992. Additional funding was provided by the National Heart, Lung, and Blood Institute; National Institute of Nursing Research; National Center on Minority Health and Health Disparities; NIH Office of Research on Women’s Health; and the Centers for Disease Control and Prevention. This research was supported in part by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases. The Indian Health Service (I.H.S.) provided personnel, medical oversight, and use of facilities. The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the I.H.S. or other funding sources. Additional support was received from The Johns Hopkins Medical Institutions Bayview General Clinical Research Center (M01RR02719); the Massachusetts General Hospital Mallinckrodt General Clinical Research Center and the Massachusetts Institute of Technology General Clinical Research Center (M01RR01066); the Harvard Clinical and Translational Science Center (RR025758-04); the University of Colorado Health Sciences Center General Clinical Research Center (M01RR00051) and Clinical Nutrition Research Unit (P30 DK48520); the University of Tennessee at Memphis General Clinical Research Center (M01RR0021140); the University of Pittsburgh General Clinical Research Center (GCRC) (M01RR000056), the Clinical Translational Research Center (CTRC) funded by the Clinical & Translational Science Award (UL1 RR 024153) and NIH grant (DK 046204); the VA Puget Sound Health Care System Medical Research Service, Department of Veterans Affairs; and the Frederic C. Bartter General Clinical Research Center (M01RR01346). The Look AHEAD Mind ancillary study was funded by R01AG058571. Dr. Meilke’s contributions were covered in part by U24AG082930. The following organizations have committed to make major contributions to Look AHEAD: FedEx Corporation; Health Management Resources; LifeScan, Inc., a Johnson & Johnson Company; OPTIFAST® of Nestle HealthCare Nutrition, Inc.; Hoffmann-La Roche Inc.; Abbott Nutrition; and Slim-Fast Brand of Unilever North America. Some of the information contained herein was derived from data provided by the Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene. 1 Principal Investigator; 2 Program Coordinator; 3 Co-Investigator; *Deceased; All other Look AHEAD staffs are listed alphabetically by site.

Statement of Ethics: This study was conducted ethically, in accordance with the World Medical Association Declaration of Helsinki. The study protocol was approved by the Wake Forest School of Medicine (Look AHEAD study Coordinating Center) Institutional Review Board as well as the Institutional Review Boards of all the data collection sites. The ClinicalTrials.gov identifier is NCT00017953.

Conflict of Interest Statement: The authors report no conflicts of interest.

Author Contributions: Drs. Hayden, Mielke, Espeland, and Luchsinger made substantial contributions to the design of this study. Drs. Espeland, Johnson, and Wagenknecht made substantial contributions for the collection of Look AHEAD data for this study. Drs. Espeland, Hayden, and Luchsinger made substantial contributions for the acquisition of Look AEHAD MIND data. Drs. Hayden, Espeland, Mielke, and Ms. Evans and Ms. Neiberg made substantial contributions to the analysis of the data for this study. All took part in drafting and revising this manuscript and all gave final approval for this manuscript prior to submission.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

SUPPLEMENTARY MATERIAL

 

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© The Authors 2024

DISCRIMINATION OF FRAILTY PHENOTYPE BY KINECTTM-BASED STEPPING PARAMETERS

 

Y. Osuka1,2, N. Takeshima3, N. Kojima2, T. Kohama4, E. Fujita5, M. Kusunoki4, Y. Kato6, W.F. Brechue7, H. Sasai1

 

1. Department of Frailty Research, Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Aichi, Japan; 2. Research Team for Promoting Independence and Mental Health, Tokyo Metropolitan Institute for Geriatrics and Gerontology, Tokyo, Japan; 3. Department of Health and Sports Sciences, Asahi University, Gifu, Japan; 4. Faculty of Biology-Oriented Science and Technology, Kindai University, Wakayama, Japan; 5. Department of Sports and Life Science, National Institute of Fitness and Sports in Kanoya, Kagoshima, Japan; 6. Department of Physical Therapy, Nagoya Women’s University, Aichi, Japan; 7. Department of Physiology, Kirksville College of Osteopathic Medicine, A.T. Still University of Health Sciences, Missouri, USA

Corresponding Author: Yosuke Osuka, PhD, Associate Professor, Department of Frailty Research, Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, 7-430 Morioka, Obu, Aichi 474-8511, Japan. Tel: +81-562-46-2311; Email: osuka@ncgg.go.jp

J Aging Res & Lifestyle 2023;12:100-104
Published online December 20, 2023, http://dx.doi.org/10.14283/jarlife.2023.17

 


Abstract

BACKGROUND: Frailty increases the risk of falling, hospitalization, and premature death, necessitating practical early-detection tools.
OBJECTIVES: To examine the discriminative ability of KinectTM-based stepping parameters for identifying frailty phenotype
DESIGN: Population-based cross-sectional study
SETTING: Eighteen neighborhoods near Tokyo Metropolitan Institute for Geriatrics and Gerontology, Itabashi, Tokyo, Japan.
PARTICIPANTS: In total, 563 community-dwelling older adults aged ≥75 years without mobility limitations, neurological disease, or dementia were included.
MEASUREMENTS: Step number (SN) and knee total movement distance (KMD) during a 20-s stepping test were evaluated using the KinectTM infrared depth sensor.
RESULTS: The number (%) of participants with frailty were 51 (9.1). The area under the receiver operating characteristic curves (95% confidence interval) of a parameter consisting of SN and KMD for frailty was 0.72 (0.64, 0.79).
CONCLUSIONS: Stepping parameters evaluated using KinectTM provided acceptable ability in identifying frailty phenotype, making it a practical screening tool in primary care and home settings.

Key words: aging, digital biomarker, frailty, mobility, KinectTM.


 

Introduction

Frailty increases the risk of falling, hospitalization, and premature death (1), necessitating practical early-detection tools (2). Fried’s frailty phenotype is the most frequently used assessment in clinical practice; however, its practicality is limited in busy or remote clinical settings because of space and time requirements and the necessity for trained health professionals (3). Inexpensive and easily available screening tools for frailty, utilizing digital health technology (e.g., smartphones and wearable devices) may help bridge this gap (4).
Digital parameters generated with the KinectTM infrared depth sensor provide a simple method for assessing postural control during standing (5) and specific spatiotemporal gait parameters (6). Recently, four physical performance tests (gait analysis, 30-s arm curl, 30-s chair sit-to-stand, and 2-min step) were quantified using skeletal data acquired from KinectTM sensors and a machine learning approach to identify the frailty level with 97.5% accuracy (7). These approaches provide accurate assessments but require significant human, place, and time resources to conduct four performance tests.
Mobility, defined as “the ability to move or walk freely and easily (8),” can be assessed through various performance tests, including 5-chair sit-to-stand, timed up-and-go [TUG], gait speed, and alternate stepping tests, indicating that these parameters help identify frailty risk (9). Among these, step-in-place tests, such as the alternative step test, are the most feasible for home or clinical settings, given their minimal space requirements. Our group has shown that KinectTM sensor-generated variables, such as number of steps and head movement during a 20-s step test (ST), identify older adults requiring care as defined by Japan’s long-term care insurance system (area under the receiver operating characteristic [AUC]: 0.76–0.82) (10). Thus, these stepping parameters evaluated using KinectTM technology may provide a more practical frailty screening tool in busy clinical or remote environments. However, the extent to which stepping performance assessed by KinectTM can identify frailty remains unclear. Thus, this study aimed to examine the discriminative ability of a 20-s ST using KinectTM to identify the frailty phenotype.

 

Methods

Setting and participants

This study involved community-dwelling older adults residing in 18 neighborhoods near the Tokyo Metropolitan Institute for Geriatrics and Gerontology, Itabashi, Tokyo, Japan. Itabashi Ward is a special ward located in the northwestern part of Tokyo. The population of Itabashi Ward in July 2019 was 570,522 (males: 279,919; 49.0%), while that of older adults aged ≥65 years was 131,167 (23.0%) (11).
Information regarding the names and addresses of all individuals aged 75–85 years, registered in the Basic Resident Registry for 18 areas, was collected, totaling 4,233 individuals. After excluding 88 participants who had joined other studies, invitations were extended to 4,145 potential candidates. Ultimately, 757 individuals participated in the study, with 639 undergoing a 20-s ST evaluation using KinectTM. Exclusion criteria were as follows: 1) inability to walk independently (n=12, 1.9%); 2) presence of neurological disease, Mini-Mental State Examination-Japanese score <10 points, or dementia (n=13, 2.0%); and 3) missing variables from either the frailty phenotype or KinectTM parameters (n=51, 8.0%). Ultimately, 563 participants (88.1% of the original population) were included (Fig 1).

Figure 1. Study flow

Frailty phenotype

Frailty phenotype was assessed using the revised Japanese version of the Cardiovascular Health Study criteria (12). Frailty and prefrailty were defined as the presence of ≥3 and 1–2 of the five limitations (weight loss, exhaustion, slowness, weakness, and inactivity), respectively (12). “Weight loss” and “exhaustion” were assessed using two Kihon Checklist questions: “Have you lost ≥2 kg body weight in the past 6 months?” and “In the last 2 weeks, have you felt tired for no reason?” Individuals answering “yes” were defined as having “weight loss” and/or “exhaustion,” respectively. Walking speed was measured as time to walk 5 m at the usual pace, with “slowness” defined as <1.0 m/sec. Handgrip strength was assessed using a handheld dynamometer, with “weakness” defined by values <28 kg for men and <18 kg for women. Finally, inactivity was assessed by asking participants 1) “Do you engage in light-intensity exercise or calisthenics?” and 2) “Do you engage in exercise or sports activities?” Participants who answered “not at all” to both questions were defined as “inactive.”

Stepping parameters assessed using KinectTM

Participants were instructed to step for 20 s with their eyes open. This ST was conducted based on our unique protocol that included two modifications to the Fukuda stepping test (13): 1) participants stepped with their eyes open, and 2) the evaluation protocol was time-based rather than step count-based. Step cadence was determined by the individual and was not controlled. The KinectTM sensor was fixed using a tripod such that the sensor center was 1.0 m and 3.0 m from the floor and participants, respectively.
Step number (SN) and knee total movement distance (KMD) were evaluated during a 20-s ST using a KinectTM V2 infrared depth sensor (Microsoft Corporation, WA, US) as described previously (10, 14). During the final 10 s of the test, knee joint point coordinate data were recorded with the KinectTM depth sensor. It employs the “Time of Flight” technique, where an infrared projector emits pulse-modulated infrared light, determining the depth of the participant’s joints by analyzing the timing of the reflected light detected by the infrared camera. Motion analysis with KinectTM sensors is sufficiently accurate, with approximately 95% of valid measurement data used for accuracy verification within 100 mm error and approximately 88% within 40 mm standard deviation (15).
SN and KMD were calculated by processing knee joint coordinate data. For SN, smoothing was performed by applying a 9-point moving average filter to left and right knee joint coordinate data. Signals from the left and right knee joints were then added, and a 7-point low-differential filter was applied to convert them into velocity signals. Finally, these parameters were z-scored, and the zero-crossing points were extracted and defined as SN.
KMD was defined as the average total movement distance of both knee joints. First, joint coordinate point vectors of both knees at any given time (t) were defined as KL (t) and KR (t), respectively. Next, total movement distance in three-dimensional (3D) space, KsumL and KsumR were calculated from equations i) and ii), respectively. Finally, KMD was calculated by averaging KsumLand KsumR R (iii). SN and KMD were corrected for sensor collection time and used for statistical analysis.

Statistical analysis

All analyses were performed using IBM SPSS version 25.0 (IBM Corp., Armonk, New York, USA) and STATA 17.0 (StataCorp LLC, Texas, USA), with P-values <0.05 considered statistically significant.
First, Jonckheere–Terpstra trend tests (16, 17) were applied to analyze the dose-response relationships between the severity of frailty, SN, and KMD. Next, a binomial logistic regression model, with frailty phenotype as the dependent variable and SN and KMD as independent variables, was applied to construct a predictive model of frailty phenotype using the composite stepping parameters of SN and KMD. Finally, receiver operator characteristic analysis was applied to demonstrate the discriminative ability of SN and KMD for identifying frailty phenotype. Sensitivity and 1-specificity were plotted, and the AUC was calculated.

 

Results

The characteristics of the study participants are presented in Table 1. The number (%) of participants with frailty and prefrailty were 51 (9.1) and 319 (56.7), respectively. Figure 1 shows that higher frailty severity was significantly associated with lower medians [interquartile ranges] of SN (robust, 1.81 [1.67–1.97]; prefrailty, 1.78 [1.63–1.92]; and frailty, 1.71 [1.53–1.82], p for trend <0.05) and KMD (robust, 0.49 [0.40–0.62]; prefrailty, 0.43 [0.34–0.53]; and frailty, 0.35 [0.25–0.44], p for trend <0.05). AUCs (95% confidence interval [CI]) for the frailty phenotype of the composite stepping parameter were 0.72 (0.64, 0.79), 0.73 (0.60, 0.86), and 0.73 (0.64, 0.81) for all participants, men, and women, respectively.

Table 1. Characteristics of study participants

Note. The data are shown as median [interquartile range], mean ± standard deviation, or n (%). BMI: body mass index, MMSE-J: Mini Mental State Examination-Japanese, KMD: knee total movement distance. Smoker or drinker were defined as participants who had a history of such behaviors.

Figure 2. Dose-response relationships between frailty severity, step number (SN), and knee total movement distance (KMD)

Note: The figure shows the data distribution of SN and KMD based on frailty severity categories by using a violin plot. The width of the density curves corresponds to the frequency of the data. The box plots drawn in the center of each density curve show the first and third quartile ends, and the central circle shows the median.

 

Discussion/Conclusion

This study quantifies the discriminative ability of stepping performance during a 20-s ST using the KinectTM infrared depth sensor for identifying frailty phenotype in a cross-section sample. Several mobility assessment instruments, such as gait speed and TUG tests, have been reported to discriminate frailty phenotype (gait speed, AUC [95% CI]: 0.73 [0.65, 0.80]; TUG, AUC [95% CI]: 0.76 [0.68, 0.83]) (18); however, the discriminative ability of the composite stepping parameter assessed by KinectTM may provide added benefit in the assessment such as providing information regarding balance function (5, 10) or identifying potentially negative movement compensations as observed with an increased torso angle during the chair-stand test when using KinectTM (19) or lower SN during the ST (10) in discriminating assisted-living from independent living older adults. These results indicate that the composite stepping parameter using KinectTM may have a clinically acceptable discriminatory performance as a simple screening tool for frailty phenotype.
Stepping performance can be accurately quantified using a 3D motion capture system; however, such systems are expensive and, hence, limited for use in primary care or in-home settings. KinectTM may be able to fill this gap as an inexpensive, portable, and easy-to-set-up system in such settings (5). In addition, Kinect-based exergames have been reported to be as beneficial as traditional structured exercises (20) in improving physical function. In the future, performance analysis with the 20-s ST and KinectTM may provide integrated assessment towards designing and assessing interventions, as well as developing practical and sustainable frailty coping strategies in primary care and at-home environments.
As this study was conducted with older adults living in metropolitan areas in Japan, there may be limitations in the generalizability of results to other regions or populations. This study demonstrates the discriminative ability of the ST and KinectTM-based composite stepping parameters as a screening tool for frailty. However, it is unclear whether it 1) predicts the onset of frailty or subsequent important health outcomes and 2) is acutely responsive to care interventions. These remain important future longitudinal and interventional study questions that will clarify these points and the clinical utility of KinectTM-based composite stepping parameters towards assessing frailty, its onset, and remediation interventions.
In summary, stepping parameters derived from a 20-s ST assessed using a KinectTM infrared depth sensor can discriminate frailty, indicating its utility as an inexpensive and widely available screening tool for frailty. Longitudinal studies and studies involving different sample populations will strengthen the generalizability and clinical utility of this tool.

 

Acknowledgments: The authors thank the participants for their voluntary involvement in this study and Dr. A. Imai and Professor Y. Kitabayashi for cooperating in the measurements.

Statement of Ethics: The study protocol was reviewed and approved by the Tokyo Metropolitan Institute for Geriatrics and Gerontology Ethics Committee (approval number [gen-kei-i-ji-1924]). All the participants provided written informed consent.

Conflict of Interest Statement: The authors have no conflicts of interest to declare.

Funding : This work was supported by JSPS KAKENHI (grant Numbers JP20K11656 and JP21H03283). The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.

Author Contributions: Study concept and design: All authors. Data collection: Yosuke Osuka, Nobuo Takeshima, Narumi Kojima, Eiji Fujita, Yoshiji Kato, and Hiroyuki Sasai. Data analysis: Yosuke Osuka, Nobuo Takeshima, Takeshi Kohama, and Masanobu Kusunoki. Data interpretation: All authors. Manuscript preparation: All authors.

Data Availability Statement: Research data are not shared.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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© The Authors 2023

RELATIONSHIP BETWEEN LIFESTYLE AND FRAILTY AMONG IRANIAN COMMUNITY-DWELLING OLDER ADULTS: PILOT STUDY

 

S. Nazari1, M. Bakhtiyary1, A.N. Shabestari2, F. Sharifi3, P.F. Afshar4

 

1. School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran; 2. Department of Geriatric Medicine, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; 3. Elderly Health Research Center, Endocrinology and Metabolism Research Institute, Tehran University of Medical Science, Tehran, Iran; 4. Department of Gerontology, School of Behavioral Sciences and Mental Health (Tehran Institute of Psychiatry), Iran University of Medical Sciences, Tehran, Iran

Corresponding Author: Pouya Farokhnezhad Afshar, School of Behavioral Sciences and Mental Health (Tehran Institute of Psychiatry), Shahid Mansouri Street, Niyayesh Street, Satarkhan Avenue, Tehran, Iran. Postal code: 1445613111, Tel: 0098 21 63471352). ORCID: 0000-0002-6450-7084; E-mail: farokhnezhad.p@iums.ac.ir

J Aging Res & Lifestyle 2023;12:93-99
Published online November 28, 2023, http://dx.doi.org/10.14283/jarlife.2023.16

 


Abstract

BACKGROUND: Aging affects physical, mental, and social functions, which can lead to an increase in frailty. Old adults with frailty syndrome are prone to disabilities and hospitalization. Lifestyle is a context-based factor that has the potential to prevent frailty.
OBJECTIVES: This study aimed to assess the relationship between lifestyle and frailty among Iranian community-dwelling older adults.
DESIGN, SETTING: This is a descriptive-analytical cross-sectional study. The participants were 513 older adults over 60 years by the convenience sampling method from the retirement center.
MEASUREMENTS: Data were collected using Tilberg’s frailty index, the Iranian elderly lifestyle questionnaire, and the Mini-Cog test. Data were analyzed with SPSS v.26 software by chi-square and logistic regression tests.
RESULTS: The age of the participants was 66.43 ± 4.69 years. The male-to-female sex ratio was 1.5 (39.2% women). The lifestyle of 96 (19.3%) old adults was unfavorable. 18.7 percent of older adults had Frailty syndrome. The logistic regression test showed that moderate and favorable lifestyle (OR= 0.06; 95% CI: 0.02-0.16), age over 75 years (OR= 5.25; 95% CI: 2.35-11.69), retired employment status (OR= 0.13; 95% CI: 0.29-0.05) are factors that have a significant relationship with frailty (P< 0.05).
CONCLUSION: The findings showed that lifestyle can predict frailty. Therefore, it seems that an optimal lifestyle can prevent the frailty of older adults.

Key words: Aged, life style, healthy lifestyle, frail elderly, frailty.


 

Introduction

Age-related changes adversely affect normal functions such as physical, psychological, and social functioning (1-3). Frailty syndrome is a set of defects that ultimately causes a decrease in physiological reserve capacities and fragility against stressful factors (4). The incidence of frailty varies among older adults. The prevalence of frailty syndrome varies between 0.4% and 59.1%, depending on the criteria. The prevalence of frailty in low and middle-income countries is around 18%, in high-income countries is 10% (5-8).
The prevalence of CI in Nigeria is less studied than in high income countries . In a survey of cognitive impairment among Yoruba speaking sample from Ibadan Nigeria, 152 (62%) out of 423 individuals studied were diagnosed with cognitive impairment no dementia (CIND) while 28 (6.61%) were diagnosed with dementia (7). In northern Nigeria (8), survey of 323 older adults showed dementia prevalence at 2.79% (CI 1– 4.58%) representing 66.67% of all the cases of dementia in the sample. In south-west Nigeria, 10.1% prevalence of probable dementia were found (9) using the 10 Word Delay Recall test adapted from Consortium to Establish a Registry for Alzheimer’s Disease CERAD (10) . In the North Central Nigeria, Ochayi and Thatcher (11) using the Community Screening Instrument for Dementia (CSID), showed a 6.4% overall prevalence of dementia and in south east Nigeria, 23.1% depression prevalence was shown in older adult sample with 20.7% complaining of forgetfulness (12).
Old adults with frailty syndrome are more vulnerable to health-related problems, including falls, delirium, fractures, disabilities, hospitalizations, and death (9-11). Frailty is associated with energy imbalance, sarcopenia, and reduced function and strengh (12). Some studies have shown that several risk factors can increase the incidence of frailty syndrome, including demographic characteristics (such as old age, female, low educational status, and unfavorable economic status), multiple chronic diseases, malnutrition, and insufficient physical activity, cognitive disorders, and poor function (13-16). Some of these factors are in the lifestyle field. Successful aging is the opposite of frailty, and a healthy lifestyle can predict successful aging (17). Lifestyle is related to the dimensions of nutrition, physical activity, sleep and daily patterns, so it is possible to improve the organs reserve and prevent vulnerability (18).
A person’s lifestyle includes physical, mental, and social domains (19, 20). World Health Organization (WHO) stated lifestyle is approximately 60% of the quality of life related to health (19). Lifestyle is defined in two levels macro (society) and micro (individual-level). The micro level refers to diet and physical activity, alcohol use, smoking, habits, choices, goals, and beliefs (21). The macro level refers to consumption behaviors, social support, social cohesion. People choose their own lifestyle and generally people’s behavior is the result of their choices in the available opportunities (22). The lifestyle is very culture-oriented and varies according to different societies. An unhealthy lifestyle is associated with an increase in mortality (23). It has been stated that a healthy lifestyle can reduce the death rate from chronic diseases by 50% (24).
Lifestyle is influenced by culture and environmental conditions (23, 25). On the other hand, a healthy lifestyle can predict successful aging. Therefore, it can be assumed that frailty maybe is influenced by lifestyle, and it is necessary to examine lifestyle in a context-based method. Lifestyle is a behavioral and situational framework in every person’s life. But first, it is necessary to assess these questions: Is lifestyle related to frailty? Can lifestyle affect frailty? This study cannot answer a comprehensive response to these questions, but it can be a start for future studies. So, this study aimed to assess the relationship between lifestyle and frailty among Iranian community-dwelling older adults.

 

Method

Design Study

This is a descriptive-analytical cross-sectional study. This is a pilot study. The research population was elderly people aged 60 and above from the retirement center of the Tehran University of Medical Sciences.

Sampling Method

The sampling method was convenient in this study. The sample size was calculated using the formula n= .

The prevalence of frailty is about 14.3% (26, 27). Z= 1.96 and d is considered to be 0.3. The sample size was 523 people. Five questionnaires were incomplete and five people were excluded from the study due to cognitive impairment. The sample size was 513 people.

Inclusion and exclusion criteria

The inclusion criteria included the willingness to participate in the study, and the ability to communicate, and the exclusion criteria included movement limitations, hearing and vision impairments, and cognitive disorders (according to Mini-Cog), incomplete questionnaire.

Measurements

Demographic characteristics

Demographic characteristics include age, sex, education, employment status (employed, retired, unemployed), the number of co-morbidities.

Tilburg Frailty Indicator (TFI)

Gobbens et al. developed TFI in 2010. TFI consists of two parts. Part A contains ten questions including age, sex, education and income, marital status, country of birth, types of Stressful Life Events in the past year, comorbidities, place satisfaction, and self-evaluation of living conditions. Part B refers to the main factors of frailty and includes fifteen questions that are divided into three physical, psychological, and social dimensions. Eleven questions are answered with two options (yes and no) and four questions with three options (yes, no, and sometimes). The physical dimension includes eight questions about physical health (physical function), unwanted weight loss, difficulty walking, difficulty maintaining balance, hearing impairment, visual impairment, reduction (lack of) strength in hands, and physical fatigue. The psychological dimension includes four questions related to cognitive status, depression, neurological symptoms, as well as coping with problems, and finally, the social dimension also includes three questions related to living alone, social relationships, and social support (28). The scoring of TFI is from zero to fifteen and the cut point is five. A score of five or more is considered to mean an elderly person is frail. Cronbach’s alpha was 0.81 in the Persian version of TFI and its validity has confirmed the existence of all three dimensions using the construct validity method. The accuracy of this index was 0.88. Its sensitivity and specificity in the point 4.5 cut-offs were obtained as 0.95 and 0.86 in a study by Mazzuchi et al. (2020) (29). Cronbach’s alpha was 0.71 in this study.

The Healthy lifestyle assessment questionnaire

The Healthy lifestyle assessment questionnaire was designed by Eshaghi et al. in 2007. This questionnaire contains 46 questions, which include fifteen questions about prevention, fourteen questions about healthy nutrition, five questions about stress management, seven questions about social and interpersonal relationships, and five questions about physical activity, exercise, recreation, and entertainment. The face and content validity has been confirmed and its Cronbach’s alpha was 0.76. The scoring of this questionnaire is done in the form of a Likert scale from one to five. The lowest score of the questionnaire is 42 and the highest score is 211. The total score is placed in one of three levels « undesirable, medium, and optimal». Score 42-98: undesirable lifestyle, score 99-155: medium lifestyle, and score 156-211: optimal lifestyle. This tool could be used in the Iranian elderly population due to its simplicity of sentences, as well as appropriate validity and reliability (30). Cronbach’s alpha was 0.97 in this study.

Mini-Cog test

The Mini-Cog test is a screening test used to identify people with cognitive disorders (31). The evaluation time is about three to five minutes (32). The older adult is taught to memorize three unrelated words together, and we ask him to repeat those three words. Then the clock-drawing test is assessed by drawing the clock. After that, we asked older adults those three words again. To calculate the score of this test, we will give one point for each correctly remembered word out of the three. If the older adults cannot remember the three words, they may have a cognitive disorder category (score = zero). Still, if they remember all three words correctly, they will be in the non-cognitive disorder category (score = 3). Older adults who only remember one or two words are divided into two groups based on the results of the clock drawing test: if the clock test is correct, the older adult is considered to have no cognitive impairment, but if his/her clock test was also impaired, then it means that he has a cognitive disorder (33). Rezaei et al. psychometrically evaluated Mini-Cog in Iranian older adults. Cronbach’s alpha was 0.83. Its sensitivity and specificity were 88% and 63%, respectively (34).

Ethical considerations

We confirm that this study was following the guidelines and regulations of the Declaration of Helsinki. This study was approved by the research ethics committee of the Tehran University of Medical Sciences (ref.: IR.TUMS.MEDICINE.REC.1400.638). We explained the objectives to the participants and obtained informed written consent.

Data analysis

Descriptive statistics were shown by frequency, mean, and standard deviation. Data were analyzed using chi-square tests and logistic regression. The normality of the data was also determined using the Kolmogorov-Smirnov test. Data were analyzed using SPSS v.26.

 

Results

The age of the participants was 66.43 ± 4.69 years. The participants included 201 (39.2%) women and 312 (60.8%) men. 96 old people (18.7%) have Frailty syndrome, and 99 people (19.3%) have an unfavorable lifestyle (other information is shown in Table 1).

Table 1. Demographic characteristics of study participants

The average scores of frailty and lifestyle of the elderly in this study were 3.69 ± 2.579 and 146.15 ± 40.174, respectively. The Mean and standard deviation of their dimensions are shown in Table 2. The distribution was non-normal in all frailty and lifestyle subscales based on the Kolmogorov-Smirnov test (P< 0.01).

 

Table 2. Mean and standard deviation of frailty and lifestyle dimensions

 

The highest frailty was seen in over 75 years of age (30.1%), women (18.9%), single (61.4%), and illiterate (46.4%) (Table 3).

Table 3. Frequency of frailty based on demographic characteristics

The results of the logistic regression showed that lifestyle, age, employment status are factors that have a significant relationship with frailty (Table 4). Above 75 years of age is a risk factor for frailty (OR= 5.25; 95% CI: 2.35-11.69). A medium and optimal lifestyle (OR= 0.06; 95% CI: 0.02-0.16), retired employment status (OR= 0.13; 95% CI: 0.05-0.29) were protective factors. The result of the Hosmer and Lemeshow Test was (P= 0.35).

 

Table 4. Logistic regression of frailty and related factors

 

Discussion

This study showed that 18.7% of the old participants had frailty. The findings showed that there is a significant relationship between frailty syndrome and lifestyle. An optimal lifestyle is associated with a decrease in the frailty of old people.
The prevalence of frailty in other studies was estimated as 14.3% to 33.4% (35, 36). A study found that the prevalence of frailty was about 24% among community-dwelling older adults (37). Many reasons can explain these differences in the studies. The first reason is the different frailty measurement tools because each of these tools can measure various components of frailty and even focus on a series of specific dimensions of frailty. Also, this difference could be the sampling method. The second reason is the statistical population; if nursing homes or hospitals are selected for sampling, we will likely see a higher prevalence of frailty.
Participants who had an optimal lifestyle were less likely to suffer from frailty syndrome, optimal lifestyle can be one of the protective factors to prevent this syndrome. For example, an old person who does not comply with risk prevention and personal hygiene or does not have a proper diet, or does not have enough daily physical activity, has a high chance of suffering from frailty. On the contrary, those who have an optimal lifestyle, that is, follow health and preventive measures well, have proper nutrition and physical activity, and have good psychological and social conditions, are less likely to get frailty syndrome. Gobens et al. concluded that lifestyle cannot predict frailty (38). the results of the research by Khodamoradi et al. show the existence of modifiable risk factors such as obesity and insufficient physical activity, which are important. It is necessary to use appropriate strategies to prevent frailty, due to the complications and high costs of frailty.
Katayama et al. found that elderly with physical frailty have reduced any activity in their lifestyle including social activities, physical and cognitive activities. Older adults with frailty showed a significant relationship with fewer activity patterns compared to non-frail elderly. Katayama et al stated that frail elderly suffer from disturbances in activity patterns (19).
Abe et al found that it was seen with a lower probability of frailty and its related consequences in participants who did agriculture, sports, activity, and social participation (39). The results of Wang et al.’s study also indicated that participation in social activities was less among people who were frail than non-frail old people. In addition, frailty risk decreased with a healthy diet in old age (40).
This study showed that there is a significant relationship between age and frailty. People with frailty in this study are generally in the age range of 60 to 75 years. The highest prevalence of frailty is seen in people over 75 years old. We can conclude that physical and mental capacities decrease with aging and the possibility of suffering from frailty syndrome increases (41-43).
This study showed that there is no significant difference in frailty between women and men. Some studies have stated that the prevalence of frailty is higher in women (5, 44, 45), and some studies found that frailty is higher in men than women (43, 46). On the other hand, some studies showed that there is no significant relationship between gender and frailty (41, 47, 48). Demographic and community differences can partially explain these variable results.
The frequency of frail old people in retirees was higher than in other employment statuses, but this is due to the larger number of people in this category. The highest percentage of frailty is in the employed category. Employed elderly probably have jobs that are not suitable for their physical and mental conditions due to their financial needs. Unsuitable working conditions can put the elderly under all kinds of physical and mental pressures, and as a result, put them in conditions where they are prone to or suffer from frailty syndrome. Previous studies had found that there is a significant relationship between employment status and frailty, they found that the employed elderly have the least frailty, and this disparity could be due to the difference in people’s jobs or volunteer activities (26, 49).

 

Conclusion

According to the results of this study, the prevalence of frailty was 18.7%. Lifestyle is related to all physical, mental, and social aspects of people. The state of frailty, especially in the elderly, is directly related to lifestyle. Probably, frailty is reduced by improving lifestyle.

Limitations

This study coincided with the covid-19 epidemic, which led to reduced cooperation of participants, which may have affected the data and results. This is a pilot study and it is necessary to conduct it in the future in a larger and more diverse population. It would have been better to separate the lifestyle dimensions, but the Healthy lifestyle assessment questionnaire did not have this possibility.

 

Acknowledgments: We are grateful to the participants and staff of the Tehran University of Medical Sciences Retired Center who helped us in this study.

Conflicts of interest and source of funding: The authors declare that they have no conflicts of interest. There was no specific funding for this study.

Ethical standard: We confirm that this study was following the guidelines and regulations of the Declaration of Helsinki. This study was approved by the research ethics committee of the Tehran University of Medical Sciences (ref.: IR.TUMS.MEDICINE.REC.1400.638). We explained the objectives to the participants and obtained informed written consent.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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© The Authors 2023

PREVALENCE AND PATTERNS OF COGNITIVE IMPAIRMENT IN A SAMPLE OF COMMUNITY DWELLING OLDER PEOPLE IN NIGERIA

V. Ucheagwu1, B. Giordani2

 

1. Nnamdi Azikiwe University Nigeria Nigeria; 2. University of Michigan USA Michigan, USA

Corresponding Author: Dr. Valentine Ucheagwu, Nnamdi Azikiwe University Nigeria Nigeria, Valentine.ucheagwu@gbhi.org

J Aging Res & Lifestyle 2023;12:85-92
Published online November 16, 2023, http://dx.doi.org/10.14283/jarlife.2023.15

 


Abstract

OBJECTIVE: Prevalence and patterns of cognitive impairment were studied in older people from Nigeria.
METHOD: Four hundred and forty one participants (263 females; age: 60-87) were recruited from community dwelling adults in Anambra state Nigeria. Five domains of cognition were tested using the Uniform Data Set Version 3 (UDS-3).
RESULT: Prevalence: 49.7% were classified as normal cognition, 34% as borderline, 12.9% as MCI (2.72% with amnesic MCI) and 3.4% as dementia. We showed in descending order in that 13% of the participants were impaired on visual-spatial index; 6.8% on memory index; 5.2% on attention/concentration index; 2.7% were impaired on executive function index and 34.80% (based on mean) of the participants were impaired on processing speed index. There were significant interaction effects for gender and education on visual spatial and attention domains respectively. Significant effects of education were seen on executive function and processing speed while interaction effect was found on executive function alone. 8% scored 1.5 SD below the mean on MoCA. There was a significant effect of education on MoCA with the pairwise comparison showing a significant difference between tertiary education and other two levels of education. The groups did differ significantly for hypertension on MoCA.
CONCLUSION: This study showed a high prevalence of cognitive impairment among older adult population from Nigeria. A significant proportion of the sample were impaired on the visual spatial domain and at least half of the participants were impaired on one cognitive domain. Hypertensive participants performed significantly poor on MoCA compared to non-hypertensive group.

Key words: Dementia, prevalence, Nigeria, older adults, neuropsychological assessment.


 

Introduction

In a classic article by Folstein and colleagues (1), cognitive impairment (CI) was defined as a diminished capacity to know the world which could result from many clinical disorders like dementia, mental retardation, aphasia and traumatic brain injury. CI cuts across age spectrums but is most likely seen in older adults as its prevalence increases with aging. Changes in cognition occur as part of the aging process but could form a clinical disorder to the extent that it becomes debilitating and affecting individuals’ functioning. Central to this diagnostic scheme is the clinical construct of mild cognitive impairment (MCI) (2-3) that is generally regarded as the borderland between the cognitive changes of aging and very early dementia (4). In other words, older adults could either show cognitive changes related to aging or graded abnormal cognitive related changes from mild to severe forms of cognitive impairment. CI in older people could progress to dementia particularly dementia of Alzheimer’s type (DAT) depending on the severity. The major risk factor for dementia is age, with the prevalence doubling every 5 years after the age of 65 and stabilizing to around 50% in the 8th decade (5-7). The annual rate in which MCI progresses to dementia varies between 8% and 15% per year, indicating that it is an important condition to identify and treat (4). However, it must be noted that not all CI represent incipient Alzheimer’s disease nor did all patients with CI have just a memory impairment. Cognitive impairment could occur in either domains of cognitive functions like memory, attention/concentration, language, executive and visuospatial functions.
The prevalence of CI in Nigeria is less studied than in high income countries. In a survey of cognitive impairment among Yoruba speaking sample from Ibadan Nigeria, 152 (62%) out of 423 individuals studied were diagnosed with cognitive impairment no dementia (CIND) while 28 (6.61%) were diagnosed with dementia (7). In northern Nigeria (8), survey of 323 older adults showed dementia prevalence at 2.79% (CI 1– 4.58%) representing 66.67% of all the cases of dementia in the sample. In south-west Nigeria, 10.1% prevalence of probable dementia were found (9) using the 10 Word Delay Recall test adapted from Consortium to Establish a Registry for Alzheimer’s Disease CERAD (10) . In the North Central Nigeria, Ochayi and Thatcher (11) using the Community Screening Instrument for Dementia (CSID), showed a 6.4% overall prevalence of dementia and in south east Nigeria, 23.1% depression prevalence was shown in older adult sample with 20.7% complaining of forgetfulness (12).
Although there are few studies that have examined cognitive impairment in Nigeria, these studies have two major limitations. First, none of the studies used a comprehensive neuropsychological battery to characterize the domains of CI. The majority of the studies utilized short mental status tests that represented measures of global cognition while others used a single test to determine dementia. This methodology could lead to several problems. For instance, shortening a test and detaching it from its standardized administration, scoring, and normative referencing may make it less sensitive or reliable violating the statistical maxim that multiple measures provide a more reliable estimate of a cognitive construct than any single measure (13). Also, the lack of objective tests from other cognitive domains reduces the ability to detect cognitive impairment profiles that might identify distinct subtypes of CI that vary in clinical and biological characteristics (14-15). Second, the studies in Nigeria did not show the domains of cognitive impairment that were most affected in the ageing population. Similarly, they were not able to clearly characterize graded levels of CI (from normal cognitive ageing to severe CI) in the participants based on the number of cognitive domains and or patterns of cognitive impairment (amnestic and non-amnestic) observed.
The present study aimed to examine the prevalence and patterns of cognitive impairments using actuarial neuropsychological test batteries that tap 5 major domains of cognition. These could be the best way to start estimating the prevalence of CI in the Nigerian ageing population and provide a clear clinical picture of the patterns of cognitive decline and a more comprehensive understanding of people at risk for AD and dementia. Also, we examined the association of some modifiable risk factors and demographics on cognitive decline. We hypothesized that a greater percentage of older adults would perform poorly on memory and attention domains relative to other domains. We further hypothesized significant differences respectively in education, gender, diabetes and hypertension on cognitive performance.

 

Method

Participants

Four hundred and forty one older adults (263 females; age: 65-87; mean: 67.99; SD : 3.67) participated in the study. Participants were recruited from the adult population in Ukpo, Dunukofia local government area of Anambra state. Participants were participating in longitudinal cohort study for blood based biomarkers and dementia in Nigeria (See procedure section for details). One hundred and twelve had tertiary education, 104 had secondary education, while 165 had primary and 109 had no formal education. Our university clinic in the area is running a cognitive screen for every healthy adult over age 65 using the door to door knocking approach and clinical visits. The cognitive screening further involves measures of metabolic syndrome including lipids, diabetes and hypertension.
Instrument

Neuropsychological test battery

The Uniform Data Set-Version 3 (UDS-3) (2015) created and published by the Alzheimer’s Disease Center Clinical Task Force of the National Alzheimer’s Co-ordinating Center United States was used for cognitive testing. UDS-3 measures domains of cognition using selected cognitive tests. For the present study, 5 cognitive domains were assessed using the following tests : attention/concentration (number span test: forward and backward), memory (craft story immediate and delayed (16), visuo-spatial (Benson design: immediate and delayed), processing speed (TMT: A & B) and executive function (category fluency: animal and vegetable; control oral word: FAS). Among the UDS tests used, all were non-verbal except the craft story test (CST). CST was a story test written in English language describing a story of young boy Ricky playing football. CST was translated to the participants’ language (Igbo language) and back translated to English by 2 experts in Igbo and English languages respectively. The two forms were administered to 150 older adults to determine the level of agreement between the forms. The correlation of 0.89 was obtained between the two forms. For full details of the UDS -3 battery see National Alzheimer’s Co-ordinating Center University of Washington. Montreal Cognitive Assessment (MoCA) (17) was used to measure global cognition in the participants. MoCA is a widely used screening assessment for detecting cognitive impairment and has been used in a similar cohort in Nigeria (18). In addition to the cognitive measures, depression status was assessed using the Geriatric Depression Scale (GDS) (19).

Procedure

Participants were recruited through local community advertisement at faith-based organisations, elderly retirement groups and town criers within Ukpo town in the Dunukofia local government area. Interested participants were invited to the community health care center for initial history taking and informed consent. The neuropsychological test battery was administered to participants on second visit. The study was carried out in accordance with the Helsinki Declaration on human participants’ involvement in research studies and ethical approval (IRB) granted by Nnamdi Azikiwe University Ethical Review Board.

Design and Statistics

The study was a longitudinal study to evaluate AD blood based-biomarkers and cognition in older adults (Alzheimer’s Disease Biomarker-Nigeria). The present report was the baseline data on cognition collected at baseline (year 1). Descriptive statistics and multiple analysis of variance were used for data analysis. We developed a measure of impairment called: Cognitive impairment severity index (CISI) by classifying participants’ performance into normal cognition (NC), borderline cognition (BC), mild cognitive impairment (MCI), and severe cognitive impairment (SCI). This was developed by summing up the performance of participants on the 5 cognitive domains. Cut off score of 1.5 SD below the mean (using sample norms) was used as a measure of impairment on that domain. Hence, participants were classified in normal cognition (NC) category if they score did not score below the cut-off on any of the domains. Borderline cognition (BC) were participants that scored below the cut-off on only 1 domain. MCI group was classified as participants that scored below the cut-off on 2 domains while dementia group was classified as participants that scored below the cut-off on 3 or more cognitive domains. This model represents actuarial neuropsychological criteria for MCI and dementia diagnoses (20-21). We calculated percentages for the means and SDs of the 5 cognitive domains, CISI and UDS subscales to demonstrate the prevalence of cognitive impairments in our sample. Multivariate analysis of variance was used to determine the roles of independent variables: education, gender, depression and metabolic syndrome (hypertension and diabetes) on 5 cognitive domains. The independent variables were nominally scaled (yes/no) based on self-report. The norms for the study were derived from the sample data.

 

Results

The results of participants’ neuropsychological assessment are presented below. First we presented their performances based on 5 cognitive domains. Then we presented results showing participants’ cognitive impairment severity index (CISI) using Jack and Bondi method (20-21). CISI was classified as follows: normal cognition (participants that performed above 1.5 SD on all 5 cognitive domains), borderline impairment (participants that performed below 1.5 SD on one cognitive domain), mild impairment (participants with less than 1.5 SD on two domains) and severe cognitive impairment (those with less than 1.5 SD on more than 2 cognitive domains).

Table 1 shows the mean and standard deviation score of the participants on five cognitive domains and percentages of participants that performed below mean and SD scores respectively. At 1.5 SD level, participants performed worst on the visuo-spatial domain followed by the memory domain. While using a mean cut point, they performed more poorly on the processing speed followed by executive function domain.

Table 1. Mean, SD and Percentage of Participants that Scored below the Mean and SD on Cognitive Domains

Note: a— Percentage of participants that fell below the mean score on each cognitive domain; b— Percentage of participants that fell below 1 SD from the mean on each cognitive domain; c— Percentage of participants that fell below 1.5 SD from the mean on each cognitive domain.

 

Table 2 shows the number of participants that were characterized on the cognition impairment severity index.

Table 2. Frequency and Percentage of Participants on Cognitive Impairment Severity Index

Note: MCI— Mild cognitive impairment; aMCI— Amnestic mild cognitive impairment.

 

We performed series of multivariate analysis to determine the roles of gender, education and depression on five cognitive domains. Our findings showed significant differences between gender and education on visual spatial and attention domains: Visual spatial; Gender: F (1,398) = 6.37 (ES: 0.02; Mean: Male = 13.20, Female = 8.15); Education: F (3,398) = 6.70 (ES: 0.05; Mean: Tertiary = 13.60, Secondary = 13.04, Primary = 10.89, Non-educated = 8.44); attention; Gender: Gender: F (1,398) = 5.52 (ES: 0.01; Mean: Male = 21.61, Female = 18.81); Education: F (3,398) = 23.57 (ES: 0.15; Mean: Tertiary = 24.47, Secondary = 20.58, Primary = 18.82, Non-educated = 14.93). There were significant interaction effects of gender and education on visual spatial: F (3,398) = 3.95 (ES: 0.03) and attention: F (3,398) = 3.52 (ES: 0.03).

Figure 1. Interaction of gender and education on visual-spatial index

 

We found no significant effect of gender on executive function: F (1,398) = 1.90 (ES: 0.01) and processing speed: F (1,398) = 0.001 (ES: 0.001). Significant effects of education were seen on executive function: F (3,398) = 39.87 (ES: 0.23; Mean: Tertiary = 42.08, Secondary = 33.51, Primary = 29.36, Non-educated = 19.88) and processing speed: F (3,398) = 6.89 (ES: 0.05; Mean: Tertiary = 4.13, Secondary = 2.36, Primary = 5.58, Non-educated = 6.16). Significant interaction effect of gender and education was found on executive function index: F (3, 399) = 6.25 (ES: 0.05). Our result showed no significant difference of gender and education on memory index; Gender: F (1,398) = 0.43 (ES: 0.01); Education: F (3,398) = 2.17 (ES: 0.01; Mean: Tertiary = 43.54, Secondary = 40.90, Primary = 39.12, Non-educated = 33.22). Although no significant difference was found among levels of education, multiple comparisons using the scheffe method showed significant difference for tertiary education as compared with non-educated level. We found no significant difference of histories of hypertension and diabetes on processing speed and visual-spatial domains; Hypertension: Processing speed: F (1,398) = 0.11 (ES:0.001); visual-spatial index: F (1,398) = 2.50 (ES: 0.006)); diabetes: Processing speed: F (1,398) = 0.61 (ES:0.002); visual-spatial index: F (1,398) = 0.55 (ES: 0.001)). No interaction effects were found.

Table 3. Mean, SD and Percentage of Participants that Scored below the Mean on UDS Measures

Note: a— Percentage of participants that fell below the mean score on each UDS measure; b— Percentage of participants that fell below 1 SD from the mean on each UDS measure; c— Percentage of participants that fell below 1.5 SD from the mean on each UDS measure.

 

Table 4. Multiple Comparisons of Education on Visual spatial and Attention Domains for Education and Gender

 

Equally no significant differences were seen on hypertension and diabetes on the attention and executive function domains; hypertension: attention: F (1,398) = 0.03 (ES:0.001); executive function: F (1,398) = 0.43 (ES: 0.01)); diabetes: attention: F (1,398) = 3.47 (ES:0.01); visual-spatial: F (1,398) = 1.18 (ES: 0.003)). No interaction effects were found. For memory index, no significant differences were found for hypertension and diabetes respectively, though non-diabetic and non-hypertensives had higher mean scores than others. Hypertension: (memory index: F (1,398) = 0.84 (ES: 0.002; Mean: hypertensives = 38.08, non-hypertensives = 40.00) and diabetes: (memory index: F (1,398) = 2.15 (ES: 0.005, diabetics = 36.60, non-diabetics = 40.02). No interaction effects were found. We also showed no significant effects of dementia history and clinical depression on attention and visual spatial index. Dementia history: attention index: F (1,196) = 0.10 (ES:0.001); visual-spatial index: F (1,196) = 0.49 (ES: 0.003)) ; depression: attention index: F (1,196) = 2.69 (ES:0.01); visual-spatial index: F (1,196) = 0.82 (ES: 0.004)). No interaction effects were found. On processing speed and executive function, we found no significant and interaction effects except that of depression on executive function. For dementia history: processing speed: F (1,196) = 0.39 (ES:0.002); executive function: F (1,196) = 0.83 (ES: 0.004)); depression: processing speed: F (1,196) = 2.53 (ES:0.01); executive function: F (1,196) = 4.12 (ES: 0.02, Mean: depressed = 39.34, not depressed = 41.23). No interaction effects were found.

Figure 2. Interaction of gender and education on attention index

 

For the memory domain, no significant differences were found for dementia history and depression respectively, though individuals high on depression scale had higher mean scores than others; dementia history: memory index: F (1,196) = 0.07 (ES: 0.001); depression: memory domain: F (1,196) = 0.003 (ES: 0.001; high on depression scale = 39.94, low on depression scale = 40.02). No interaction effects were found. We evaluated the effects of gender and education on global cognition using the MoCA. Our findings showed significant effect of education, F (2,194) = 5.96 (ES: 0.06; Tertiary = 21.47, secondary = 19.18 , Primary = 20.31 ). The pairwise comparison showed significant difference between tertiary education and other two levels of education. No significant gender, F (1,196) = 1.30 (ES: 0.007) and interaction effects, F (2,194) = 1.48 (ES: 0.02) were found. We further showed significant effect of hypertension on MoCA, F (1,196) = 8.52 (ES: 0.04; Mean: hypertensives = 21.34, non-hypertensives = 19.73), but no significant difference was found on diabetes, F (1,196) = 0.05 (ES: 0.001) as well as no interaction of hypertension and diabetes, F (1,196) = 2.64 (ES: 0.01).

Figure 3. Interaction of education on executive function index

 

Discussion

We were able to demonstrate prevalence of cognitive impairment in an older adult sample from our community population. To the best of our knowledge, this is the first study in sub-Sahara Africa to investigate the prevalence of cognitive impairment using composite scores derived from multi domain neuropsychological assessment. Previous studies have used single cognitive tools measuring global cognition. Among the tools used were the Mini Mental State Examination and Community Screening Instrument for Dementia (CSID). We calculated the cognitive impairment severity index (CISI) by summing up the performance of participants on the 5 cognitive domains. CISI was classified into normal, borderline, mild and severe cognitive impairments based on performance using 1 and 1.5 SD from the mean respectively. This model represents actuarial neuropsychological criteria as proposed by Jak & Bondi and colleagues (20-21) for the MCI and dementia diagnoses.
Thirty four percent of our sample (representing 150 out of 441 participants) had borderline cognitive impairment, 12.90% (57/441) had mild cognitive impairment while 3.40% (15/441) were severely impaired and 47.90% (219/441) had normal cognition. Taken together, the data indicates that 50.30% of the participants fell into one or more levels of cognitive impairments. This suggests a high prevalence of cognitive impairment and probable dementia in our sample. There were differences between our study and results of other studies reported in Nigeria. For example, while our study shows 3.40% prevalence of severe cognitive impairment, Ocha and Thatcher (11) were reporting 6.40% in northern Nigeria and Baiyewu and colleagues (7) in south-west Nigeria were reporting 6.61% prevalence. Conversely, while we reported 34% borderline cognitive impairment and 12.40% mild cognitive impairment, Baiyewu at.al., (7) were reporting 62% of cognitive impairment without dementia in south-west Nigeria. The difference in outcome between our study and few others (7, 11) was the use of multi domain composite score and stricter cut-off point. Previous studies predominantly used a single measure of global cognition with a mean criteria for determining cut-off levels. Composite scores based on multi domain cognitive assessment, done in this study, reduce the chances of making Type I Error given increased sensitivity and specificity as compared with a single measure of global cognition. Also, the use of 1.5 SD as cut-off point would be more sensitive and specific than the use of mean scores. Use of mean score for clinical evaluation more importantly in neuropsychological assessment would significantly affect test specificity and predictive value. Another plausible explanation for the difference between our finding and that (7) could be nature of assessments used for the two studies. Baiyewu and coworkers (7) included informant and physician interview with low weighted neuropsychological test while our study did not include informant and clinician interview but had high weighted neuropsychological tests.
We were also able to characterize the performance of our participants on multiple domains of cognition. We presented the percentage of participants that fell below the mean scores by 1 and 1.5 SDs from the mean on 5 cognitive domains respectively. Using the mean score criterion for cut off, processing speed domain had the highest percentage that fell below the mean score followed by executive function, attention/concentration, memory and visual spatial domains. Using a stricter cut-off level (1.5 SD), processing speed had the highest percentage of participants falling below 1.5 SD, followed by visual spatial, memory, attention/concentration and executive function domains. When we translate the mean percentages into actual numbers, it shows that among 441 older adults studied, 217 had problem with attention/concentration, 215 had problem with memory, 183 had problem with visual spatial, 284 had problem with processing speed and 249 older adults had problem with executive function. On 1.5 SD, 23 older adults had problem with attention/concentration, 30 had problem with memory, 57 had problem with visual spatial and 12 had problem with executive function. The above finding provides evidence of the prevalence of cognitive impairments at two levels of cut-offs. The use of mean score cut-off could represent evidence of cognitive impairment at general level of epidemiology while the 1.5 SD represents evidence of cognitive impairment at a clinical level. In neuropsychology clinics, cut-off of 1.5 SD suggests significant impairment that warrants referral to a memory clinic. Using estimation of 1000 participants, our result would show that per 1000 older adults within our community of interest, and on a mean score cut-off, 492 among them would have problem with attention, 487 with memory, 415 with visual spatial, 644 with processing speed and 565 with executive function problem. On 1.5 SD per 1000 older adults, 52 among them would have problem with attention/concentration, 68 with memory, 129 with visual spatial and 27 with executive function problem. This again suggests overwhelming evidence of impairments on domains of cognition in our sample. We found a fair amount of memory impairment, but still less than other studies generally have reported. A point of discussion is on how we classified memory. In this study memory domain was measured using the Craft Story Test (Immediate and delayed). Participants with memory impairment in this population will be higher if we had included visuo-spatial memory as measured by the Benson Design Delayed which could go for visual memory. Given the fact that our participants performed very poorly on Benson Design Delayed, it is a pointer that memory impairment could rank higher if we had classified memory domain using this formular.
The finding is very important towards our understanding of dementia prevalence in Nigeria. Cognitive domain impairments are among hallmarks of AD and dementia in clinical and community settings. They appear to be the last symptom presentation following underlying neurobiological mechanisms in dementia. The presence of cognitive impairments suggests strong neurobiological underpinnings within an individual. To our knowledge this is first study in Nigeria to examine cognitive impairments in older adults using multi domains of cognitive performance while characterizing participants on both mean score and standard deviation from the mean. Majority of studies (7-9) have categorized individual into cognitively impaired and non-cognitively impaired without giving detailed description of the domains of cognitive impairment. In the present study we were able to show how older adults presents on various domains of cognition. It is striking to note the changes in cognitive impairment presentation as we move from mean score cut-off to 1.5 SD cut-off. For example, at mean score level, processing speed and executive function domains had highest percentage while at 1.5 SD representing clinical level assessment, the visuo-spatial domain came up and the executive function was the lowest. This suggests that speed of deteriorations in domains of cognition differ. Our study suggests that older adults present general problem in executive function but would not deteriorate significantly to clinical level. However, they could show less general problem on visuo-spatial domain but could deteriorate faster on this domain. It is interesting to note that visuo-spatial domain was more affected because of participants’ poor performance on the Benson Design Delayed task, signifying underlying visual memory problem. It suggests that visuo-spatial domain presenting greater impairment in our sample at 1.5 SD cut-off is determined by visual memory.
We were able to show significant gender and education differences on visuo-spatial and attention domains in our sample. Our study shows that male older adults performed better than their female counterparts on the two cognitive domains and more educated individuals performed better, all of which might be expected. In addition, there were significant interaction effects of education and gender on visual spatial, attention and executive function domains respectively. From the interaction, less educated females were the most affected group overall. Our study strongly suggests that being female with less education are risk factors for impairment in these three cognitive domains. We found no significant differences in histories of hypertension or diabetes on the 5 cognitive domains. This is contrary to other studies showing differences in cognitive scores in those with hypertension and diabetes. One reason for this result could be the way we categorized hypertension and diabetes. Participants were categorized based on self -report. No objective measures of blood pressure and sugar were taken to confirm their self-report at this time. There is very high possibility that some clinically hypertensive and diabetic participants may have been misclassified. That said, our findings showed differences in memory performance among hypertensives and diabetes with normal participants performing better. Also, we found that participants that reported higher symptoms of depression performed worse on measures of executive function. We also found a significant effect of education on MoCA scores as a measure of global cognition with participants having tertiary education performing better than others. This is in line with previous studies on the effects of education on global cognition (18, 22-23). Equally, hypertensive participants significantly performed worse than non-hypertensives on MoCA, suggesting broader difficulties in this group. Ucheagwu and colleagues (18) also showed systolic blood pressure as predictor of performance on MoCA in middle age adults from Nigeria.

Limitations of the Study

There are some limitations in the study. First, participants’ diagnoses on hypertension and diabetes were based on self-report. There is likelihood of under diagnoses in the population and that may account for our findings on differences among hypertension and diabetes respectively on cognitive domains. Second, we did not adjust for education on the cognitive severity index. Though there are few educational differences on cognitive domains, adjusting for education level while constructing the cognitive severity index would account for such variations. Our study was an epidemiological survey of community sample, there could be tendency of under reporting of cognitive impairment because we did not use clinical samples. Future, studies are encouraged to compare clinical samples and community population.

 

Conclusion

Our study suggests high prevalence of cognitive impairment in older adult population from Nigeria. We were able to show the prevalence of cognitive domains at a general level (mean score cut-off) and at clinical level (1.5 SD) with different cognitive domains assuming dominance at each level. We further showed significant interaction of gender and education on cognition with females being affected the most.

Acknowledgement: The authors contributed equally to the research and none of us had conflicting interest. The study was supported by research grant from the Alzheimer’s Association AACSF grant: 22-926130

Conflict of Interest: On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical standards: The ethical approval for the study was granted by Nnamdi Azikiwe University Teaching Hospital Nnewi. The study was conducted in line with the Helsinki declaration on human participation in research.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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© The Authors 2023

ERRATUM TO: BLUEBERRY SUPPLEMENTATION EFFECTS ON NEURONAL AND PATHOLOGICAL BIOMARKERS IN SUBJECTS AT RISK FOR ALZHEIMER’S DISEASE: A PILOT STUDY

 

Erratum to: The Journal of Aging Research & Lifestyle DOI 10.14283/jarlife.2023.13

 

P.M. Doraiswamy1,2,3, M.G. Miller2, C.A. Hellegers1, A. Nwosu1, J. Choe4, D.M. Murdoch4

 

1. Neurocognitive Disorders Program, Department of Psychiatry and Behavioural Sciences, Duke University School of Medicine, Durham, NC, USA; 2. Duke Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, NC, USA; 3. Duke Institute for Brain Sciences, Duke University School of Medicine, Durham, NC, USA; 4. Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University School of Medicine, Durham, NC, USA

Corresponding Author: P. Murali Doraiswamy, Neurocognitive Disorders Program, Department of Psychiatry and Behavioural Sciences, Duke University School of Medicine, Durham, NC, USA, murali.doraiswamy@duke.edu

J Aging Res & Lifestyle 2023;12:84
Published online September 25, 2023, http://dx.doi.org/10.14283/jarlife.2023.14


 

The authors from «Blueberry Supplementation Effects on Neuronal and Pathological Biomarkers in Subjects at Risk for Alzheimer’s Disease: A Pilot Study”, J Aging Res & Lifestyle 2023;12:77-83, advise an error in Figure 2. The original Figure 2 has been corrected to display the corect SE bars.

Figure 2. Effect of Blueberry supplementation on Blood biomarkers

Figure 2A and Figure 2B depict mean (SE) plasma biomarker values at baseline (depicted in blue) and week 12/endpoint (depicted in purple). There were no statistically significant differences for any of the biomarkers measured. Abbreviations: Aβ40 = amyloid-beta 40; Aβ42 = amyloid-beta 42; ptau181 = phosphorylated Tau181; NfL = neurofilament light; GFAP = Glial Fibrillary Acidic Protein; BDNF = Brain Derived Neurotrophic Factor

© The Authors 2023

BLUEBERRY SUPPLEMENTATION EFFECTS ON NEURONAL AND PATHOLOGICAL BIOMARKERS IN SUBJECTS AT RISK FOR ALZHEIMER’S DISEASE: A PILOT STUDY

 

P.M. Doraiswamy1,2,3, M.G. Miller2, C.A. Hellegers1, A. Nwosu1, J. Choe4, D.M. Murdoch4

 

1. Neurocognitive Disorders Program, Department of Psychiatry and Behavioural Sciences, Duke University School of Medicine, Durham, NC, USA; 2. Duke Center for the Study of Aging and Human Development, Duke University Medical Center, Durham, NC, USA; 3. Duke Institute for Brain Sciences, Duke University School of Medicine, Durham, NC, USA; 4. Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University School of Medicine, Durham, NC, USA

Corresponding Author: P. Murali Doraiswamy, Neurocognitive Disorders Program, Department of Psychiatry and Behavioural Sciences, Duke University School of Medicine, Durham, NC, USA, murali.doraiswamy@duke.edu

J Aging Res & Lifestyle 2023;12:77-83
Published online August 23, 2023, http://dx.doi.org/10.14283/jarlife.2023.13

 


Abstract

BACKGROUND: There is a need to develop non-invasive practical lifestyle interventions for preventing Alzheimer’s disease (AD) in people at risk, such as those with mild cognitive impairment (MCI). Blueberry consumption has been associated with reduced risk of dementia in some epidemiologic studies and with improvements in cognition in healthy aging adults. Blood-based biomarkers have emerged at the forefront of AD therapeutics research spurred by the development of reliable ultra-sensitive “single-molecule array” assays with 100-1000-fold greater sensitivity over traditional platforms.
OBJECTIVE: The purpose of this study was to examine the effect of blueberry supplementation in MCI on six blood biomarkers: amyloid-beta 40 (Aβ40), amyloid-beta 42 (Aβ42), phosphorylated Tau181 (ptau181), neurofilament light (NfL), Glial Fibrillary acidic protein (GFAP), and Brain-Derived Neurotrophic Factor (BDNF).
METHODS: This was a 12-week, open-label, pilot trial of 10 participants with MCI (mean age 80.2 years + 5.16). Subjects consumed 36 grams per day of lyophilized blueberry powder in a split dose consumed with breakfast and dinner. Baseline and endpoint venous blood was analyzed using an ultrasensitive SIMOA assay. Our aim was to test if blueberry supplementation would particularly impact p-tau181, NfL, and GFAP elevations associated with the neurodegenerative process.
RESULTS: There were no statistically significant (p < 0.05) changes from baseline to endpoint for any of the biomarker values or in the ratios of Aβ42 / Aβ40 and ptau181/ Aβ42. Adverse effects were mild and transient; supplementation was relatively well tolerated with all subjects completing the study.
CONCLUSION: To our knowledge, this is the first study to prospectively examine the effects of blueberry supplementation on a panel of blood biomarkers reflecting the neurodegenerative process. Our findings raise two possibilities – a potential stabilization of the neurodegenerative process or a lack of a direct and acute effect on beta-amyloid/tau/glial markers. A larger controlled study is warranted.

Key words: Dementia, Alzheimer’s, biomarkers, anthocyanins, longevity, neurons, microglia.


 

Introduction

Worldwide, some 50 million adults have Alzheimer’s disease (AD), and several more million may be at elevated risk for AD by virtue of mild cognitive impairment (MCI) and/or silent accumulation of cortical AD pathology (1-2).
Practical, well-tolerated, and non-invasive lifestyle interventions are highly desirable for AD prevention in at-risk middle-aged and older adults (3-9). Evidence from epidemiological, pre-clinical and pilot clinical studies suggests that regular consumption of blueberries may protect against cognitive decline or dementia (7-17). Blueberries contain high levels of micronutrients and antioxidants (such as anthrocyanins) which in preclinical studies have been found to impact many pathways involved in cognitive function or dementia risk – such as reduced oxidative stress, improved inflammatory response, reversed age-related decrements in cognition, increased cerebral blood flow, enhanced microglial clearance of Aβ, inhibited aggregation of Aβ42, suppressed microglial activation and protection against Aβ-induced neurotoxicity (reviewed in 7, 12-17). Some, but not all, epidemiological studies have found evidence favoring a protective role of blueberry consumption on cognition or AD risk. For example, in the longitudinal Nurses Health Study of 16,010 participants, aged ≥70 years; greater intakes of blueberries was associated with slower rates of cognitive decline – the effect estimates suggested that berry intake delayed cognitive aging by up to 2.5 years (15). Pilot randomized controlled trials have also shown that blueberry consumption improved cognition among older adults with MCI (16, 17).
While the overall body of evidence suggests a beneficial effect of blueberry consumption on AD risk, this has not yet been proven in a large, rigorous, randomized controlled trial. Given the limitations of in-vitro or pre-clinical studies of blueberry mechanisms, there is also a need for additional human biomarker studies to understand whether cognitive benefits arise from antioxidant effects, metabolic effects, anti-inflammatory effects, protection from neurotoxins or through altering other pathways implicated in AD/MCI such as beta-amyloid and tau.
Blood biomarkers have emerged at the forefront of MCI/AD clinical therapeutic development research (18-31), spurred by the development of reliable ultra-sensitive “single-molecule array” assays with 100-1000-fold greater sensitivity over traditional platforms. Neurofilament light (NfL) is a marker of neuronal dysfunction whose elevation and rate of change are associated with cognitive decline and hippocampal atrophy (19-23). Studies in some neurodegenerative disorders have also shown that elevated blood NfL levels reduce or stabilize following therapy (22). Likewise, a promising marker of glial activation is GFAP (glial fibrillary acidic protein). There is increased expression of GFAP in reactive glial cells surrounding amyloid plaques and blood GFAP is elevated in AD (24). Other blood markers (Aβ42, Aβ40, t-au, p-tau) reflect neuropathology (18, 25-30). For example, Schindler et al showed that the plasma Aβ42/Aβ40 ratio predicts current and future brain amyloidosis (18). Park et al found plasma t-tau/ Aβ42 predicted an AD pattern of neurofibrillary tangle deposition (25) as well as longitudinal changes in cerebral amyloid deposition, brain glucose metabolism, and hippocampal atrophy. Overall, the evidence makes a strong case for biomarker-guided pilot trials in AD and MCI (31).
Despite this promising body of evidence, to our knowledge, no clinical study has evaluated the effects of blueberry supplementation on in-vivo pathological and neurodegeneration blood biomarkers in MCI. Such studies would be important to extend findings from in-vitro and pre-clinical studies and rule in or rule out potential mechanisms such as reducing glial activation (GFAP) or reducing axonal degeneration (NfL). Since beta-amyloid and tau are conceptually considered as the hallmarks of MCI due to AD, it would be also be important to know whether blueberry consumption affects these pathways.
This open trial examined the effect of blueberry supplementation on neuronal, glial, and pathology blood biomarkers in 10 subjects with MCI. The blood markers measured were Brain Derived Neurotrophic Factor (BDNF), Neurofilament light (NfL), Glial fibrillary acidic protein (GFAP), Amyloid-beta 42 (Aβ42), Amyloid-beta 40 (Aβ40), and p-tau181. Given the small pilot design of this study, a secondary aim of the study was to examine biomarker performance in a clinical trial setting as to generate better estimates of variance and performance of these assays in a clinical trial representative MCI sample.

 

Materials and Methods

Recruitment, Consent, Inclusion/Exclusion Criteria

The study was approved by the Duke institutional review board, and all participants gave written informed consent prior to participation. The study was registered on clinicaltrials.gov (ID: NCT05172128). Key inclusion criteria were that the subject was between 55-85 years of age, had English-speaking ability, was medically stable, and met criteria for amnestic mild cognitive impairment (impaired delayed verbal recall, normal or near normal overall cognition and function). Early and late MCI diagnosis was based on all available information at intake including history, mental status exam, neurological exams, neuropsychological evaluation (including Wechsler Scale-III Logical Memory and Mini-Mental State Examination), and operationalized using accepted criteria from the national ADNI study. Key exclusion criteria included dementia, significant confounding active neurological/psychiatric disease, unwillingness to restrict consumption of anthocyanin-rich foods, allergy or intolerance to blueberries, significant gastrointestinal disorders or surgery that influences digestion and absorption, history of frequent urinary tract or Clostridium difficile infections, and the presence of unstable, acutely symptomatic, or life-limiting illness.

Trial Design

Ten participants with MCI were recruited. Participants underwent a two-week “washout” period, where they refrained from consuming anthocyanin-containing foods. Participants were required to complete this two-week washout prior to their baseline blood draw. The following day after the blood draw, the participants began their 12-week blueberry supplementation, maintaining abstention from other anthocyanin-rich foods and drinks. After 12 weeks, participants returned to the clinic for an exit blood draw.

Blueberry Dosing

Supplementation was in the form of lyophilized blueberry powder packets, mixed with water (18 grams, equivalent to 3/4ths cup of fresh blueberries) with 2 daily meals (36 g/d blueberry powder total; approx. 1.5 servings/d), before breakfast and dinner. The blueberry dose of 36 grams per day in a split dose consumed with meals was based on 1) a 33% increased dose that was previously used in a longer (6-month) trial; 2) a desire to deliver the most effective dose of blueberry bioactives, and 3) a reduced likelihood of any gastrointestinal symptoms. Participants were also instructed to log their times of consumption for compliance monitoring, as well as any inadvertent anthocyanin intake. Blueberry powders were packaged in sealed single-serving packets (18 g/packet) to prevent exposure to light and moisture. Participants were instructed to store packets in home refrigerator to avoid degradation of blueberry bioactives. Nutrient and berry intake function and protocol adherence was assessed by telephone at 1-, 4-, 8-, and 12-week safety calls.

Quanterix Simoa Ultrasensitive Biomarker Assay

Biomarker assays were done using Quanterix HD-X instrument, a fully automated digital immunoassay platform for biomarker assay testing. The Simoa (Single Molecule Array) technology at the heart of this platform enables the detection and quantification of biomarkers previously difficult or impossible to measure, opening up new applications to address significant unmet needs in life science research. The ultrasensitivity of Simoa assays sets it apart from all other immunoassays available today, offering PCR-like limits of detection with both existing and novel protein biomarkers. The sensitivity of the platform is 1000-fold higher than traditional ELISAs and chemiluminescence and electroluminescence (Luminex, Mesoscale) platforms.

Plasma Collection

For each participant, peripheral venous blood was drawn in EDTA vacutainers (BD Biosciences, Franklin Lakes, NJ USA). Within 30 minutes after collection, blood was centrifuged at 1800 x g for 10 min to obtain plasma. Plasma was aliquoted in 0.5 mL polypropylene cryotubes and stored at -80°C until the time of assay.

Plasma Biomarker Assays

Plasma samples were thawed at room temperature and centrifuged at 10,000 x g for 5min prior to assay. Plasma Aβ40, Aβ42, BDNF, GFAP, pTau-181, and NfL concentrations were measured with Simoa (Single Molecule Array) assay kits on an HD-X analyzer (Quanterix, Billerica, MA USA). BDNF (BDNF Discovery Kit, # 102039), pTau-181 (pTau-181 Advantage V2.1 Kit, #104111), and Aβ40, Aβ42, NfL, GFAP (Neurology 4-Plex E Advantage Kit, #103670) kits were assayed according to manufacturer protocol (Quanterix, Billerica, MA USA). All samples were run in duplicate and each assay performed in the same batch. Assays were performed by the same technician blinded to the participant’s state and clinical data. Data were exported from the instrument for biomarker analysis. Subjects were not informed of their biomarker results due to their experimental nature, and this was pre-specified in the consent.

Severity of MCI

Both early MCI (EMCI) and late MCI (LMCI) were eligible. EMCI was defined by a WMS-III Logical memory delayed recall score of 3-6 with 0-7 years of education, a score of 5-9 with 8-15 years of education, and a score of 9-11 with 16 or more years of education. LMCI was defined by a WMS-III Logical memory delayed recall score ≤ 2 with 0-7 years of education, a score ≤ 4 with 8-15 years of education, and a score ≤ 8 with ≥ 16 years of education. Other inclusion criterion was a Folstein Mini-Mental State Examination score of ≥ 23/30.

Dietary Assessment

At the initial screening visit, subjects completed a dietary questionnaire, as well as a diet history questionnaire, for berry consumption during the past year. Subject diaries were used to check for compliance during the study.

Safety

Adverse events (AEs) were monitored and serious AEs were reported to the IRB as appropriate.

Statistical Analysis

Statistical analysis was done using Stata/MP 13.0 and RStudio Version 2022.12.0. The effect of blueberry supplementation on change from baseline in biomarker levels was assessed via paired t-tests. Posthoc responder analyses were performed to compare participants who experienced an increase or decrease in specific biomarkers. Pearson’s correlation coefficients were calculated to test for associations among baseline biomarker measurements. A correlation network was created amongst key baseline and endpoint variables.

 

Results

Forty-three individuals were prescreened for the study. Of these, twelve participated in in-clinic screening, with ten being eligible for enrollment (Figure 1). Table 1 shows the baseline characteristics. The mean age was 80.2 years (SD 5.16). While the study was open to all genders, the first ten participants who met enrollment criteria were male. No outcome data were excluded. Following initial neuropsychological testing administered by the study site coordinator, nine subjects were classified as having LMCI and one with EMCI.

Table 1. Baseline characteristics of the study subjects

Figure 1. Consort Diagram

 

Effect of Blueberry Supplementation on Biomarker Outcomes

There were no statistically significant (p < 0.05) changes from baseline at 3 months in any of the biomarker values (Table 2, Figure 2). Ratios of biomarkers also did not significantly change from baseline to 3 months following supplementation.

Figure 2. Effect of Blueberry Supplementation on Blood Biomarkers

Figure 2A and Figure 2B depict mean (SD) plasma biomarker values at baseline (depicted in blue) and week 12/endpoint (depicted in orange). There were no statistically significant differences for any of the biomarkers measured. Abbreviations: Aβ40 = amyloid-beta 40; Aβ42 = amyloid-beta 42; ptau181 = phosphorylated Tau181; NfL = neurofilament light; GFAP = Glial Fibrillary acidic protein; BDNF = Brain Derived Neurotrophic Factor

Table 2. Effect of Blueberry Supplementation on Neuronal and Pathological Biomarkers (mean ± SE)

1SE, standard error; Aβ40, amyloid-beta 40; Aβ42, amyloid-beta 42; ptau181, phosphorylated Tau181; NfL, neurofilament light; GFAP, Glial Fibrillary acidic protein; BDNF, Brain Derived Neurotrophic Factor

 

Biomarker Responder Analyses

Post-hoc responder analyses showed that those who experienced an improvement in NfL values following supplementation tended to be older. Five subjects each showed numeric improvements in NfL or GFAp or pTau181. Two subjects showed numeric improvements in both NfL and GFAP and in both NfL and pTau181.

Correlations amongst Biomarker and Cognitive Variables

As shown in Figure 3, there was a significant positive correlation between baseline GFAP and NfL levels (r = 0.8026; p = 0.0052) as well as between baseline Aβ42 and Aβ40 levels (r = 0.8344; p = 0.0027) (Figure 3). There was a significant negative correlation between MMSE and baseline GFAP (r = -0.6417; p = 0.0455), as well as endpoint GFAP (r = -0.7103; p = 0.0213) (Figure 4). These correlations were expected, and support the validity of the assays.

Figure 3. Heat Map of Baseline Biomarker Correlations

Figure 3 shows a correlation heat map among the baseline biomarker values for MCI subjects. The index on the right shows the strength of the correlations. The ones in darker colors depict stronger correlations. Abbreviations: Aβ40_B = amyloid-beta 40 at baseline; Aβ42_B = amyloid-beta 42 at baseline; pTau181_B = phosphorylated Tau181 at baseline; NfL_B = neurofilament light at baseline; GFAP_B = Glial Fibrillary acidic protein at baseline; BDNF_B = Brain Derived Neurotrophic Factor at baseline

Figure 4. Correlation Network of Cognitive and Biomarker Variables in MCI

Figure 4 depicts a correlation network amongst age, baseline cognition (MMSE and Logical Memory Immediate and Delayed), baseline plasma biomarker variables, and endpoint plasma biomarker variables. The green lines depict positive correlations, and the red lines depict negative correlations. The thickness of the lines connecting the nodes depicts the strength of the correlations. Please see the text for details. Abbreviations: Aβ40_B = amyloid-beta 40 at baseline; Aβ40_E = amyloid-beta 40 at endpoint; Aβ42_B= amyloid-beta 42 at baseline; Aβ42_E = amyloid-beta 42 at endpoint; pTau181_B = phosphorylated Tau181 at baseline; pTau181_E = phosphorylated Tau181 at endpoint; NfL_B = neurofilament light at baseline; NfL_E = neurofilament light at endpoint; GFAP_B = Glial Fibrillary acidic protein at baseline; GFAP_E = Glial Fibrillary acidic protein at endpoint; BDNF_B = Brain Derived Neurotrophic Factor at baseline; BDNF_E = Brain Derived Neurotrophic Factor at endpoint; LM-I = Logical Memory-Immediate; LM-R = Logical Memory-Recall; MMS = Mini Mental Status Examination

 

Variance Estimates for the Biomarkers

Table 2 presents the variance (SD) estimates for the biomarkers before and after treatment.

Adverse Events

Supplementation was relatively well tolerated, and all subjects completed the study. Observed adverse events were largely mild and transient. One subject had constipation and stool changes at week 3 which resolved after a week of withholding. One subject had mild constipation which resolved on its own. One subject had transient diarrhea at week 12 which resolved on its own. One subject reported a single episode of abdominal pain at week 10 which resolved on its own. One subject had a scalp abrasion and ankle sprain from a fall but recovered fully. One person had hand tremors and lightheadedness at week 2 which lasted just a day and resolved on its own. One subject was hospitalized overnight for altered mental status (on Day 6) but was ruled out for stroke with brain imaging. Following discharge, this subject had full resolution of symptoms, and this event was not considered to be study related. A few months later, this subject also experienced possible hematemesis (weeks 8 -9) attributed to his pre-existing GERD and Barrett’s esophagus, and it resolved on its own. At Week 12, this subject also developed a COVID-19 infection, and their exit visit was delayed until the infection was resolved. The infection was not considered to be supplement related.

Compliance

Compliance (% doses) for blueberry powder consumption and adherence to the requirement to avoid other sources of dietary berries was high (>90%). Two subjects withheld dosing transiently for adverse effects as noted above.

Concomitant cognitive enhancer medications /vaccines

One subject started donepezil at week 1 and also received a COVID-19 vaccine at week 10 both at his doctor’s recommendation.

 

Discussion

To our knowledge, this is the first pilot study to examine the effects of blueberry supplementation on neuronal, glial, and pathology blood biomarkers in subjects with MCI. The blood markers measured were neurofilament light (NfL), glial fibrillary acidic protein (GFAP), Aβ42, and p-tau181. The former are markers of neuronal and glial function whereas the latter are markers of pathological changes. As stated previously, biomarkers have come to the forefront of neurodegenerative disorders therapeutic research (18-31) with the US FDA having given accelerated approval to two immunotherapies for Alzheimer’s and one drug for ALS based on reduction in pathological or neurodegenerative biomarkers, respectively.

Blueberry supplementation has been shown in some prior studies to benefit cognitive aging and preclinical studies have reported that blueberry bioactives may provide anti-oxidant benefits, enhance microglial clearance of Aβ, inhibit aggregation of Aβ42, or suppress microglial activation and provide protection against Aβ-induced neurotoxicity (reviewed in 7-17). Blueberry supplementation has also been reported to improve cognition in pilot studies of MCI (16, 17). However, no prior study had examined blueberry supplementation effects on in-vivo biomarkers of neurodegeneration, glial activation as well as amyloid or tau pathways in MCI patients at risk for Alzheimer’s.
In our study, over the 3 months of blueberry supplementation, the MCI subjects did not experience any significant change (decline or improvement) in neuronal, glial, and pathological blood biomarkers. There are two possible interpretations of this finding. The natural course of MCI, especially late MCI, is usually that of progressive neurodegeneration accompanied by elevations of NfL, GFAP, and pTau181 (25-27); hence, there is a possibility that blueberry supplementation stabilized this process. The alternate interpretation is that blueberry supplementation does not acutely (over 3 months) impact beta-amyloid or tau pathways and/or that it’s potential neuroprotective effects may occur through other pathways such as through reduced oxidative stress, improved inflammatory response, and/or increased cerebral blood flow.
The strengths of our study are the careful selection of MCI subjects, the relatively high compliance with diet and the use of sensitive state-of-the-art biomarker assays to measure neuronal, glial, and pathological changes.
Limitations of the study include a small sample size, relatively short duration of dosing, and lack of a control group. While direct examination of brain changes using PET scans or obtaining cerebrospinal fluid biomarkers may be more optimal than blood biomarkers, such studies are more invasive and very expensive. In addition, future studies should also examine the long-term performance effects of dosing on blood biomarkers and how measures will vary. This work should be conducted in different geographical areas among diverse populations. It should also be noted that all subjects had consumed some form of berry fruit within in the last 12 months prior to their enrollment, although 80% of subjects consumed less than ¼ cup per time of consumption. It is difficult to get blueberry naïve subjects for such a trial. Last but not least, two biomarkers in our panel (Aβ42 and pTau) were selected on the assumption of cortical beta-amyloid and tau as a causal driver for cognitive impairment in MCI/AD; future research should also examine the conceptual basis for cognitive impairment in MCI driven by other factors, such as calcium regulation, metabolism, vascular risks, or inflammatory pathways independent of and/or upstream of beta-amyloid and tau’s effect on cognitive impairment. Such research should also examine other markers of neuronal dysfunction as well as the antioxidant role of blueberry supplementation.
Despite these limitations, the observations and biomarker variance estimates from this study may help guide the design of future randomized trials. Given the possible need for longer follow-up time, as well as the need for a larger sample size in prevention trials, future clinical research should explore the role of pragmatic trial designs such as “randomized group design” where the randomization unit is no longer an individual, but a cluster or group that could increase trial conduct efficiency.
In summary, to our knowledge, this is the first study to prospectively examine the effects of blueberry supplementation on a panel of blood biomarkers reflecting the MCI/AD neurodegenerative process. Our findings warrant replication in a larger controlled study over a longer period.

 

Funding statement: This work is supported by the US Highbush Blueberry Council.

Competing interests: PMD has received research grants from the National Institute on Aging, DARPA, DOD, ONR, Salix, Avanir, Avid, Cure Alzheimer’s Fund, Karen L. Wrenn Trust, Steve Aoki Foundation, US Highbush Blueberry Council and advisory fees from UMethod, Clearview, Lumos, Neuroglee, Otsuka, Lundbeck, Compass, Vitakey, Sermo, Lilly, Nutricia, and Transposon. PMD is a co-inventor of patents for the diagnosis or treatment of Alzheimer’s disease and a patent for infection detection. PMD owns shares in several biotechnology companies whose products are not discussed here. PMD serves on boards of healthsystems and advocacy groups. MGM has received research grants from the Durham Veterans Affairs Health Care System, the US Highbush Blueberry Council, the NIH via work for the Alzheimer’s Disease Research Center, and the Egg Nutrition Center. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgements: PMD and AN drafted the article. PMD and MGM designed the study. PMD, CAH, and AN conducted the clinical study. DMM and JC oversaw the biomarker assays. AN and MGM performed the statistical analyses. MGM, DMM, JC, and CAH provided critical edits. All authors contributed to the article, assisted in data interpretation for the present and cited studies, and approved the submitted version.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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© The Authors 2023

NOVEL SCREENING TOOL USING NON-LINGUISTIC VOICE FEATURES DERIVED FROM SIMPLE PHRASES TO DETECT MILD COGNITIVE IMPAIRMENT AND DEMENTIA

 

D. Mizuguchi1, T. Yamamoto1, Y. Omiya1, K. Endo1, K. Tano2, M. Oya2, S. Takano3

 

1. PST Inc., Yokohama, Japan; 2. Takeyama Hospital, Yokohama, Japan; 3. Honjo Kodama Hospital, Honjo, Japan

Corresponding Author: Daisuke Mizuguchi, Industry & Trade Center Building 905, 2 Yamashita-cho, Naka-ku, Yokohama, Kanagawa, 231-0023, Japan, E-mail: mizuguchi@medical-pst.com, Phone: (+81) 45-263-9346, Fax: (+81) 45-263-9348

J Aging Res & Lifestyle 2023;12:72-76
Published online August 22, 2023, http://dx.doi.org/10.14283/jarlife.2023.12

 


Abstract

Appropriate intervention and care in detecting cognitive impairment early are essential to effectively prevent the progression of cognitive deterioration. Diagnostic voice analysis is a noninvasive and inexpensive screening method that could be useful for detecting cognitive deterioration at earlier stages such as mild cognitive impairment. We aimed to distinguish between patients with dementia or mild cognitive impairment and healthy controls by using purely acoustic features (i.e., nonlinguistic features) extracted from two simple phrases. Voice was analyzed on 195 recordings from 150 patients (age, 45–95 years). We applied a machine learning algorithm (LightGBM; Microsoft, Redmond, WA, USA) to test whether the healthy control, mild cognitive impairment, and dementia groups could be accurately classified, based on acoustic features. Our algorithm performed well: area under the curve was 0.81 and accuracy, 66.7% for the 3-class classification. Thus, our vocal biomarker is useful for automated assistance in diagnosing early cognitive deterioration.

Key words: Mild cognitive impairment, cognitive disorders, diagnosis, vocal biomarker, machine learning.


 

Introduction

Alzheimer’s disease and other dementias (AD/D) are the most common chronic neurodegenerative disease worldwide. Mild cognitive impairment (MCI) is the prodromal phase of cognitive decline, a condition that can be reverted with proper interventions detected with neuropsychological tests (1). For all of these cognitive issues, early detection is essential to ensure effective and timely treatment and slow the progression of cognitive deterioration. For example, a growing consensus is that pharmaceutical interventions may be most effective at the earliest stages of dementia before serious and irreversible neuropathological changes begin (2).
Various screening techniques have been developed for detecting cognitive decline. Cognitive function tests such as the mini-mental state examination (MMSE) (3) and Montreal Cognitive Assessment (4) are conventional methods widely used to screen for AD/D and MCI. In addition, fluid biomarkers collected from cerebrospinal fluid, blood, saliva, and tears (5), and brain imaging with magnetic resonance imaging (MRI) (6) and positron emission tomography (PET) (7) are utilized as reliable clinical examinations to detect pathological findings such as the accumulation of amyloid β, which is a causative agent of AD. However, these methods have several disadvantages such as their time-consuming nature, high inspection cost, invasiveness, and the need for dedicated equipment.
As a relatively new approach, diagnostic assistance with the analysis of patient’s voice to detect cognitive deterioration (i.e., vocal biomarkers) has been extensively studied over the last decade (8). This approach is non-invasive, does not require specific or expensive equipment, and can be efficiently conducted remotely. In addition, voice data collection and analysis is reasonable price-wise, compared to brain imaging or fluid tests. Many studies have successfully detected cognitive impairments using voice data as vocal biomarkers. Most of the studies focused on binary classification tasks (healthy control vs. MCI or vs. AD); the accuracy for predicting AD ranged from 80 to 97%, while MCI ranged from 73 and 86%. Few studies tried 3-class classification tasks (healthy control, MCI, and mild stage of AD) in a model, which achieved an accuracy of 61% (9).
Most of the voice biomarker studies extract the prosodic and/or temporal features from the voice recorded during cognitive tasks such as picture descriptions (using the “cookie theft” picture in most instances) (10–13), sentence-reading tasks (14–17), and telling stories or having a conversation with a clinician (18–21), all of which are slightly time-consuming and require a skilled examinator to impose a task. In addition, when a patient is examined by using the same task repetitively to monitor the patient’s cognitive function, the task-based recordings could be highly affected by the “learning effect”. Thus, repeated exposure to the same task could mask cognitive decline (e.g., an individual remembers the answers in a task) (22).
Another common method is a machine-learning model with linguistic features that primarily uses natural language processing (NLP) (9,11,12,23). Although these methods offer high performance in dementia detection, their linguistic features are highly language-dependent. Thus, text-based models can be applied to limited regions where patients use the same language as that used in the regions in which the model is trained.
In this study, we aimed to test the performance of prediction models for detecting cognitive dysfunction using purely acoustic features (i.e., without linguistic features). Our model uses prosodic and temporal features from two simple phrases, that could be applied to patients in different regions with various languages.

 

Methods

Ethics statements

This study was approved by the local Ethics Committee for Research on Human Subjects in Japan (approval numbers, #000005 and #000006).

Study participants

The participants of this prospective, observational study comprised 150 patients who were aged ≥45 years (up to 95 years) at the time of examination at two hospitals in Japan. All study participants provided informed consent and the research procedures were designed in accordance with the ethical standards of the committee described above and with the Helsinki Declaration of 1975 (as revised in 2000). Patients with respiratory infections and patients who did not understand or complete the assessment process were excluded. The participants were requested to complete two or three cognitive assessments: the Japanese version of the Montreal Cognitive Assessment (MoCA-J) (4,24), the revised version of the Hasegawa’s Dementia Scale (HDS-R) (25), and/or the mini-mental state examination (MMSE) (3). Based on the scores of these assessments, the participants were classified into one of three cognitive groups: healthy control (HE), MCI, and AD/D. The detailed classification criteria are listed in Table 1.

Table 1. Statistics of the demographic information and cognitive scores in the three groups (HE: healthy control, MCI: mild cognitive impairment, AD/D: dementia, MoCA-J: the Japanese version of the Montreal Cognitive Assessment, HDS-R: the revised version of Hasegawa’s Dementia Scale, MMSE: the Mini-Mental State Examination). Note that there was no subject with both MoCA-J ≥ 26 and HDS-R ≤ 20 (or MMSE ≤ 23)

HE: healthy control, MCI: mild cognitive impairment, AD/D: Alzheimer’s disease and other dementias, MoCA-J: Japanese version of the Montreal Cognitive Assessment, HDS-R: the revised version of Hasegawa’s Dementia Scale, MMSE: Mini-Mental State Examination, SD: standard deviation; Note: No participant had both MoCA-J score ≥26 and HDS-R score ≤20 (or MMSE score ≤23).

 

Sound recording

Sound recordings were obtained by using a directional pin microphone (ME-52W; OLYMPUS, Tokyo, Japan) connected to a portable, linear pulse-code modulation recorder (TASCAM DR-100mkIII; TEAC Corporation, Tokyo, Japan) at a sampling rate of 96 kHz with a 24-bit resolution. The microphone was attached to the patient’s clothes at the chest level, approximately 15 cm from the mouth. The patients were asked to utter two simple phrases: 1) sustain the vowel sound (/a/) for more than three seconds and 2) repeat the trisyllable (/pa-ta-ka/) five times or more as quickly as possible. We chose these two phrases because they have been used for various clinical assessments (26) and because such language-independent phrases have great usefulness in prediction models to be applied in different countries. In some instances, the patient’s voice was recorded more than twice on different days (2–5 times, with an adequate interval between recordings), thereby resulting in 195 sound recordings from 150 participants.

Feature extraction

After the audio signals were downsampled to 16 kHz with 16-bit resolution, 17 acoustic features were extracted, including the statistics of pitch(F0)-related or voice quality-related features (e.g., shimmer, jitter, and harmonics-to-noise ratio) derived from the sustained vowel (/a/) and peak intensity-related features derived from the waveform of the repeating trisyllable (/pa-ta-ka/).
For the calculation of pitch-related or voice quality-related features, the audio signal was processed for each 10 msec window length. For the intensity-related features, peaks in the waveforms were extracted by calculating the relative maxima in the time series data of intensity values.

Machine learning

LightGBM (Microsoft, Redmond, WA, USA), a gradient-boosting tree algorithm for classification, was used to create the machine-learning models. The objective function of the LightGBM was set to “multiclass” to predict the three classes: HE, MCI, and AD/D. The sample size in the HE group was smaller than that in the other two groups; therefore, we applied the synthetic minority oversampling technique (SMOTE) (27) to balance the sample size between targets in the training dataset. The hyperparameters for the LightGBM classifiers were optimized using the Optuna hyperparameter optimization framework (Preferred Networks, Tokyo, Japan). The following optimized parameters were used to build and evaluate the models: “learning_rate”, 0.01; “lambda_l1”, 0.0188; “lambda_l2”, 0.00361; “num_leaves”, 31; “feature_fraction”, 0.4; “bagging_fraction”, 1.0; “bagging_freq”, 0; “min_child_samples”, 5.
For the model evaluation, we applied five-fold group cross-validation. The data were randomized and split into five folds, one of which was used iteratively as the test set. The rest were used as the training set. All data from a given participant were categorized in the test set or training set, but not in both, to eliminate potential bias owing to identity-confounding factors. The area under the receiver operating characteristic curve (AUC) was analyzed to evaluate model performance. The average of the three one-vs-rest (OvR) AUCs and the classification accuracy, based on the confusion matrix, were calculated to test the overall performance of the prediction model in discriminating between the three classes. For each recording, the prediction class (shown in the confusion matrix) exhibited the highest prediction probability.

Statistical analysis

Statistical analyses were performed by using R (version 4.1.2; R Foundation for Statistical Computing, Vienna, Austria). Chi-squared test and one-way ANOVA were used to test the difference in sex ratio and age between the three classes, respectively. A p-value less than 0.05 after the Holm-Bonferroni adjustment was considered statistically significant.

 

Results

Figure 1 shows the three receiver operating characteristic curves derived from the three-class prediction model.

Figure 1. Three-class (one-vs-rest) receiver operating curves and confusion matrix derived from the machine learning model for the prediction of the three cognitive classes: healthy control (HE), mild cognitive impairment (MCI), and Alzheimer’s disease and other dementias (AD/D)

TPR: true positive rate; AUC: area under the curve, FPR: false positive rate

 

The average AUC (i.e., OvR discrimination) was 0.81. Among the three OvR AUCs, the highest was 0.95 when discriminating between HE and the other classes (i.e., MCI and AD/D). No significant differences existed between the three classes in sex ratio [chi-squared test, χ2 (1) = 2.46e-31, 0.84, and 5.69; p = 1, 0.72, and 0.051, for the HE vs MCI, HE vs AD/D, and MCI vs AD/D, respectively] or age [ANOVA, F(2) = 2.26, p = 0.11] (Table 1). The AD/D group predominantly consisted of patients with AD, followed by dementia with Lewy bodies and frontotemporal dementia.
All 17 acoustic features contributed to the prediction model. The LightGBM importance (gain) value ranged from 1021 to 2370 (AVE±SD = 1531±369), and the voice quality-related features (i.e., harmonics-to-noise ratio) showed the highest importance.
The accuracy score of the three-class prediction model was 66.7%, which was twice the chance level of the performance (33.3%). Given two-class prediction, predicting HE and the other classes (i.e., MCI and AD/D) achieved an accuracy of 93.8%, whereas predicting AD/D and the other classes (i.e., HE and MCI) achieved an accuracy of 69.7%.

 

Discussion

In this study, we aimed to distinguish between patients with AD/D, MCI, and HE by using purely acoustic features, extracted from two simple phrases, and applying a machine-learning algorithm. We found that our algorithm performed well in distinguishing between the three groups. Increasing evidence indicates that pathological changes in dementia begin much earlier than the appearance of the clinical symptoms used to determine the onset of dementia (28). Speech alterations may be one of the earliest signs of such changes and are observed before other cognitive impairments become apparent (29). Previous studies have shown that voice quality-related features of speech (e.g., number of voice breaks, shimmers, jitter, and noise-to-harmonics ratio) reflect cognitive decline (15). Furthermore, changes in these features begin earlier during disease progression, and during the MCI stage. Our model also used such voice quality features and performed well in discriminating between the three classes (HE, MCI, and AD/D), which supports previous findings. Of note, although the sample size of the HE group was relatively small, our model showed the highest performance in discriminating healthy controls from the MCI and AD/D groups, given the binary classification. Thus, our model could be particularly useful for the early detection of cognitive decline during MCI.
To the best of our knowledge, this study imposed the most straightforward and simple task (utterance of two short phrases) to extract acoustic features and build a machine-learning model to predict cognitive impairments. Recording the two phrases (/a/ and /pa-ta-ka/) generally took less than 10 s, which is much shorter than cognitive tasks in previous studies (e.g., picture descriptions, sentence-reading tasks, and telling stories or having a conversation with a clinician). For the early detection of cognitive decline, monitoring cognitive changes frequently and continuously is essential, which is challenging in terms of adherence (30). Therefore, our simple task might contribute to maintaining the motivation of users to record their voices repeatedly, thereby leading to an assessment of trends in their cognitive function.
In conclusion, our findings demonstrate that purely acoustic features derived from two simple phrases have the potential to be one of the efficient tools for automatically assessing future dementia risk before other cognitive symptoms appear. Further research is required to test whether these acoustic features can discriminate between types of dementia (e.g., AD, dementia with Lewy bodies, and frontotemporal dementia) using larger and balanced datasets of audio samples. In addition, since our model was built with the data from only two hospitals in Japan, further validation should be conducted using sounds from patients whose first language is not Japanese, to test whether our model is not affected by the differences in the intonation or accents when uttering the two phrases (i.e., language-independent).

 

Conflict of interest: D.M., T.Y., K.E., and Y.O. were employed by PST Inc. The remaining authors, K.T., M.O., and S.T. declare that the research was conducted in the absence of any commercial or financial relationships. This study was conducted in collaboration between PST Inc., Takeyama Hospital, and Honjo Kodama Hospital, but no funding for this study was received from PST.

Data availability statement: The data are not publicly available due to personal information contained within.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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© The Authors 2023

 

BETA 1,3-1,6 GLUCANS PRODUCED BY TWO NOVEL STRAINS OF AUREOBASIDIUM PULLULANS EXERT IMMUNE AND METABOLIC BENEFICIAL EFFECTS IN HEALTHY MIDDLE-AGED JAPANESE MEN: RESULTS OF AN EXPLORATORY RANDOMIZED CONTROL STUDY

 

N. Ikewaki1,2, T. Sonoda2, G. Kurosawa3,4, M. Iwasaki5, V. Devaprasad Dedeepiya6, R. Senthilkumar7,8, S. Preethy7, S.J.K. Abraham5,6,8,9,10

 

1. Dept. of Medical Life Science, Kyushu University of Health and Welfare, Japan; 2. Institute of Immunology, Junsei Educational Institute, Nobeoka, Miyazaki, Japan;
3. Department of Academic Research Support Promotion Facility, Center for Research Promotion and Support, Fujita Health University, Aichi, Japan; 4. MabGenesis KK, Nagoya, Japan; 5. Centre for Advancing Clinical Research (CACR), University of Yamanashi – School of Medicine, Chuo, Japan; 6. Mary-Yoshio Translational Hexagon (MYTH), Nichi-In Centre for Regenerative Medicine (NCRM), Chennai, India; 7. Fujio-Eiji Academic Terrain (FEAT), Nichi-In Centre for Regenerative Medicine (NCRM), Chennai, India; 8. Antony- Xavier Interdisciplinary Scholastics (AXIS), GN Corporation Co. Ltd., Kofu, Japan; 9. R & D, Sophy Inc., Japan; 10. Levy-Jurgen Transdisciplinary Exploratory (LJTE), Global Niche Corp, Wilmington, DE, USA

Corresponding Author: Dr. Samuel JK Abraham, University of Yamanashi – School of Medicine, Chuo, Japan, 3-8, Wakamatsu, Kofu, Yamanashi 400-0866, Japan. Email id- drsam@nichimail.jp ; Alternate email id: drspp@nichimail.jp, Phone: +81-55-235-7527

J Aging Res & Lifestyle 2023;12:61-71
Published online July 28, 2023, http://dx.doi.org/10.14283/jarlife.2023.11

 


Abstract

OBJECTIVES: In this pilot study, we have evaluated the specific metabolic and immune-related benefits of the AFO-202 strain and N-163 strain of black yeast Aureobasidium pullulans-produced beta 1,3-1,6 glucan in healthy human subjects.
METHODS: Sixteen healthy Japanese male volunteers (aged 40 to 60 years) took part in this clinical trial. They were divided into four groups (n = 4 each): Group I consumed AFO-202 beta-glucan (2 sachets of 1 g each per day), IA for 35 days and IB for 21 days; Group II consumed a combination of AFO-202 beta-glucan (2 sachets of 1 g each) and N-163 beta-glucan (1 sachet of 15 g gel each per day), IIA for 35 days and IIB for 21 days.
RESULTS: Decrease in HbA1C and glycated albumin (GA), significant increase of eosinophils and monocytes and marginal decrease in D-dimer levels, decrease in neutrophil-to-lymphocyte ratio (NLR), with an increase in the lymphocyte-to-CRP ratio (LCR) and leukocyte-to-CRP ratio (LeCR) was observed in Group I between pre- and post-treatment. Decrease in total and LDL cholesterol, a decrease of CD11b, serum ferritin, galectin-3 and fibrinogen were profound in Group II between pre- and post-treatment. However, there was no statistically significant difference between day 21 and day 35 among the groups.
CONCLUSION: This outcome warrants larger clinical trials to explore the potentials of these safe food supplements in the prevention and prophylaxis of diseases due to dysregulated metabolism, such as fatty liver disease, and infections such as COVID-19 in which balanced immunomodulation are of utmost importance, besides their administration as an adjunct to existing therapeutic approaches of both communicable and non-communicable diseases.

Key words: AFO-202, N-163 strains of black yeast, Aureobasidium pullulans, beta glucans, immune enhancement, immunomodulation, glucotoxicity, lipotoxicity, metabolism, COVID-19, fatty liver disease.


Graphical Abstract

Introduction

Metabolism imbalance is a gradually occurring condition leading to diabetes, heart disease, stroke, etc., and the risk varies between populations based on their genetic predisposition, diet, lifestyle, and environmental influences (1). When an individual is diagnosed with any lifestyle illness requiring medication, further prevention and deceleration of the pathogenesis is an uphill task. To address this, exercise, dietary modifications such as intake of foods with low glycaemic index or fats, and medications are advised which are temporary and not definitive solutions (1). This study aims to study the effects of Aureobasidium pullulans AFO-202 and N-163 strains-produced biological response modifier beta glucan food supplements in middle-aged, healthy subjects. The rationale behind this objective is explained below.
The liver, being the metabolic centre of the body, often becomes the key target (2), and coronary artery and cerebral systemic coagulopathies (1) may also occur. Additionally, the immune reserves may be depleted in handling, and the circulating high levels of advanced glycation end products (AGEs) and lipids affect the functional capability of the immune cells, leading to high risk of disease severity (3) when COVID-19-like infections occur (4). The fibrosis that ensues after a chronic inflammation-metabolic-immune dysregulation can lead to pulmonary or liver fibrosis, such as non-alcoholic steatohepatitis (NASH) (2), which could eventually culminate in carcinogenesis (5). Glucotoxicity and lipotoxicity also cause gut dysbiosis (6), which is now increasingly considered the key factor influencing the progression of infections, inflammations, and fibrosis, creating a vicious cycle.
Against the given background, as a remedy, what we require is an agent which should be safe and possesses the following potentials.

Regarding glucotoxicity and lipotoxicity

At an early stage or before onset of disease

It should be able to balance the blood glucose levels, especially the post-prandial spike and balance the blood cholesterol level without any side effects.

Post-disease onset stage

During and after onset of the glucotoxicity and/or lipotoxicity, it should be able to control abnormal glucose and cholesterol levels without any side effects and without any adverse interactions with other drugs prescribed. It should be able to beneficially regulate LDL and VLDL without adversely affecting HDL and should be able to control inflammation and the accumulation of free fatty acids (FFA).

After progression of disease with chronic sequalae stage

It should be able to control organ inflammatory reaction to avoid fibrosis and also balance micro-inflammation of the gut.

Regarding systemic wellness and immune balance, throughout the various stage, mentioned above, it should support the immune system, especially during aging, by enhancing it to prevent illnesses from disease-affected weakness. It should be able to promote immune modulation to avoid hyper-activation and cytokine storm. It should have potential to balance immune enhancement and modulation to avoid pre-disposing factors to carcinogenesis and also reverse gut dysbiosis.
Although a single such prophylactic measure or component is almost impossible, we selected two products of strains from the black yeast A. pullulans which have a track record of safety (7-9) and potential to restore the gut microbiome (10, 11).

AFO-202 benefits

The AFO-202 strain-produced beta glucan has been shown to normalize Hba1c and fasting, post-prandial blood glucose levels in patients with type II diabetes (7). It has been shown to decrease elevated LDL and VLDL cholesterol and triglycerides in clinical studies of metabolic syndrome (8). Enhancement of immune cells such as natural killer (NK) cells and macrophages, apart from suppression of pro-inflammatory cytokines while enhancing beneficial cytokines and antibodies has been reported (9). Apart from these beneficial immune and metabolic modulations, a decrease in the neutrophil-to-lymphocyte ratio (NLR) and increase in lymphocyte-to-C-reactive protein (CRP) ratio (LCR) and leukocyte-to-CRP ratio (LeCR) are particularly significant in COVID-19 (12), as the dysregulation of these parameters has been correlated with progression of the disease and higher odds of mortality (13).

N-163 benefits

While AFO-202 is relevant to both metabolic and immune regulation, the anti-inflammatory, anti-fibrotic potential of N-163 has been reported with significance in a NASH animal model (14), along with a decrease in inflammation-associated lipid parameters such as non-esterified free fatty acids (NEFAs) (15). Thus, N-163 is more relevant in the stages of progressed disease status.
The potential of the AFO-202 and N-163 beta glucan as an immune adjuvant in the prophylaxis of COVID-19, along with beneficial anti-coagulopathy benefits in clinical trials, has been described (16-20). These beta glucans have also been effective as inti-infective agents against viral infections such as dengue, influenza, rabies apart from beneficial immune-modulation in sepsis (21-24).
Before addressing specific disease targets, we sought to study the effects of AFO-202 and N-163-produced beta glucans in the middle-aged, healthy subjects, as they have been the most vulnerable population for metabolic diseases (25) and severe COVID-19.

 

Methods

The study was conducted in compliance with the ethical principles based on the Declaration of Helsinki. The study protocol was approved by the institutional review board (IRB) of Chiyoda Paramedical Care Clinic, Tokyo, Japan (study protocol number GNC20C1), and registered with the University Hospital Medical Information Network-Clinical Trial Registry (UMIN-CTR) of Japan, Trial registration Number UMIN: 000040882 (26). The study was conducted at the Chiyoda Paramedical Care Clinic, Tokyo, Japan.

Patient and Public involvement

The subjects and the public were involved in the design and conduct of this research. During the feasibility stage, priority of the research question, choice of outcome measures, and methods of recruitment were informed by discussions with the subjects through a focus group session and structured interviews. Once the trial is published, participants will be informed of the results through a study newsletter suitable for a non-specialist audience.

Study Subjects

The study was designed as an exploratory study in sixteen healthy Japanese male volunteers aged 40 to 60 years with four intervention conditions: two test food groups and two durations of intake in each test food group.
The person in charge of the allocation, as specified in the study protocol, allocated the study subjects to the four groups as evenly as possible, giving first priority to pre-test BMI, second priority to weight, and third priority to height.
Subjects who met the selection criteria (26) and did not fall under any of the exclusion criteria were eligible for the study.
The CONSORT flow diagram of the study is available in the supplementary material.

Intervention

The duration of the study food intake and the schedule of visits for each group was:

Group I

AFO-202 beta glucan (1g containing 42 mg active ingredient) – 2 sachets with each meal
• IA: Intervention for 35 days
• IB: Intervention for 21 days

Group II

AFO-202 beta glucan (1g containing 42 mg active ingredient) – 2 sachets with each meal + N-163 (15 g gel sachet containing 90 mg of the active ingredient) – 1 sachet with any one of the meals
• IIA: Intervention for 35 days
• IIB: Intervention for 21 days

Primary endpoints

1. Immune activation effect
2. WBC, RBC, Hb, Ht, PLT, MCV, MCH, MCHC
3. Basophils, eosinophils, neutrophils, lymphocytes, monocyte counts
4. CRP, IgG in blood, IgM in blood, IgA in blood
5. IL-2, IL-6, IL-7, IL-8, IFN-γ, sFas ligand

Secondary endpoints

1. Coagulopathy related markers
i. Ferritin, D-dimer, PT, Fib, CD11b in monocyte fraction, galectin-3
2. Blood glucose level
3. HbA1c, GA
4. Cholesterol level
i. TG, T-Cho, HDL-Cho, LDL-Cho

Safety evaluation items

Incidence of adverse effects.

Evaluations

At pre-test and before intake

Blood sampling volume: 34 mL
Background survey was performed to gather information on the gender, date of birth, age, smoking habits, drinking habits, eating habits, current medical history, medication, treatment, previous history, allergies (to drugs and food), regular use of food for specified health uses, functional foods, health foods, intake of foods rich in β-glucan foods containing beta-glucan, intake of immunity-boosting foods, and blood donation (within 1 year).
The following assessments were performed,
– Medical history and physical measurements: medical history, height, weight, BMI, temperature
– Physiological examination: systolic blood pressure, diastolic blood pressure, pulse rate
– Haematology, cellular immunology assessments and blood biochemistry

Day 21 of intake and Day 35 of intake

– Blood collection volume: 31 mL
– History and physical examination: history, weight, BMI
– Physiological examination: systolic blood pressure, diastolic blood pressure, pulse rate
– Haematology, cellular immunology assessments and blood biochemistry

Daily diary

The diary was maintained from the day of the start of the consumption of the test food until the 35th day of consumption. The following items were recorded in the diary.
Intake of test foods, body temperature, intake of food for specified health uses, functional foods, and health foods, intake of restricted foods, subjective symptoms, visits to medical institutions, treatment, and use of medicines.

Examples of restricted foods

Supplements rich in beta-glucan: supplements containing beta-glucan extracted and concentrated from yeast, barley, mushrooms and seaweed.
Foods claiming to stimulate the immune system: yoghurt, lactobacillus beverages, bifidobacteria powder, propolis, lactoferrin, etc.

Statistical analysis

The statistical significance level was set at 5%, two-sided. SPSS26.0 (IBM Japan, Ltd.) and Microsoft Excel (Microsoft Corporation) were used as analysis software. An unpaired t-test, Fisher’s exact test (Bonferroni correction), Dunnett certification, and a correspondence t-test were performed.

 

Results

One study subject (No. 4) with leukocyte abnormalities (suspected leukaemia) discontinued or dropped out of the study. In addition, two study subjects (Nos. 11 and 16) were excluded because they fell under “6) Other obvious reasons for omission” in the “Exclusion criteria for PPS analysis” (26) section. After excluding these two subjects from the FAS, 13 subjects were included in the PPS.
Comparisons between the test food groups using the change from pre-consumption values showed statistically significant differences in the parameters outlined in Table 1.

Table 1. AFO-202 vs AFO-202+N-163 Beta glucans in healthy volunteers; results in a nutshell

Abbreviations: CRP- C-reactive protein; IL-Interleukin; Ig-Immunoglobin; LCR-Lymphocyte to C-reactive protein ratio; NLR- Neutrophil to Lymphocyte ratio; LeCR- Leukocyte to C-reactive protein ratio; LDL-Low density lipoprotein

 

AFO-202 beta glucan

Glucose metabolism

HbA1C

In Group I, the decrease post-intervention was greater by -0.23 ± 0.06% after 35 days of intake compared with Group II (-0.08 ± 0.05%), which showed a statistically significant higher value (p < 0.05) (Figure 1A).

Glycated albumin (GA)

After 21 days of consumption, the GA decrease in Group I (-0.53 ± 0.15%) was statistically significantly higher than that of the Group II (-0.10 ± 0.18%) (p < 0.05), (Figure 1B).

Figure 1. Decrease in A. HbA1c; B. Glycated albumin (GA); significantly greater in Group I (AFO-202 beta glucan) compared to Group II (AFO-202+N-163 beta glucan); Decrease in C. total cholesterol (T-Cho) and; D. LDL-cholesterol significantly greater in Group II (AFO-202+N-163 beta glucan) compared to Group I (AFO-202 beta glucan)

 

Haematological indices of immune stimulation

RBC

After 21 days of consumption, the RBC was statistically significantly higher (p < 0.05) in Group I (4.0 ± 5.3 x 104/µL) compared with test Group II (-8.8 ±5.6 x 104/µL).

Hb

After 21 days of consumption, the value in Group I (0.13 ± 0.12 g/dL) (p < 0.01) was statistically significantly higher compared with that of Group II (-0.38 ± 0.15 g/dL). Haematocrit (Ht)
After 21 days of intake, Group I (-0.03 ± 0.40%) showed statistically significant higher Ht values than did Group II (-1.50 ± 0.29%) (p < 0.01).

Eosinophils

A statistically significant difference was found between the test food groups in terms of the change from pre-treatment to post-treatment (p < 0.05). Eosinophil count (0.50 ± 0.54%) was higher in Group I compared with Group II (-0.36 ± 0.61%) (Figure 2A).

Monocytes

After 7 days of consumption, Group I (6.63 ± 0.51%) showed a statistically significantly higher monocyte value than did Group II (5.00 ± 0.82%) (p < 0.05). After 21 days of consumption, Group I (1.93 ± 0.47%) also showed a statistically significant increase compared with Group 2 (0.87 ± 0.21%) (p < 0.05) (Figure 2B).

Figure 2. Increase in A. Eosinophil count; B. Monocytes count significantly greater in Group I (AFO-202 beta glucan) compared to Group II (AFO-202+N-163 beta glucan); decrease in C. CRP and D. D-Dimer, significantly greater in Group I (AFO-202 beta glucan) compared to Group II (AFO-202+N-163 beta glucan)

CRP

At 21 days, the decrease in CRP was greater in Group I (level= 0.0517 mg/dl) compared with Group II (0.1329 mg/dl), which was statistically significant (p < 0.05) (Figure 2C).

IL-7

After 7 days of consumption, the IL-7 level was statistically significantly higher (p < 0.05) in Group I (4.33 ±0.87 pg/mL) compared with Group II (2.67 ±0.55 pg/mL).

IL-8

Group I’s IL-8 values (7.003 ±0.929 pg/mL) were statistically significantly higher than those of Group II (5.230 ±0.469 pg/mL) after 7 days of intake (p < 0.05).

D-dimer

After 35 days of intake, the D-dimer decrease in Group I (-0.30 ±0.10 µg/mL) was statistically significantly higher than that of the test food Group II (0.00 ±0.10 µg/mL) (p < 0.05) (Figure 2D).

NLR, LCR, and LeCR

The decrease in NLR was greater in Group I at day 21, but at day 35, the decrease was higher in Group II. In terms of LCR and LeCR, at day 35, the increase from baseline value was greater in Group I compared with Group II (Figure 3A-C). The results however were not statistically significant.

Figure 3. Increase in A.LCR; B. LeCR and decrease in C. NLR significantly greater in Group I (AFO-202 beta glucan) compared to Group II (AFO-202+N-163 beta glucan)

 

N-163

Regulation of lipid parameters

Total cholesterol (T-Cho)

After 21 days of intake, the T-Cho decrease in Group II (-12.8 ± 4.0 mg/dL) was statistically significantly higher than that of the test food Group I (9.0 ± 12.3 mg/dL) (p < 0.05) (Figure 1C).

LDL cholesterol (LDL-Cho)

There was a statistically significant decrease in LDL-Cho in Group II, at 124.0 ±25.3 mg/dL, after 21 days of consumption, compared with 134.0 ±25.2 mg/dL before consumption (p < 0.01) (Figure 1D).
Immuno-modulation and anti-inflammatory effects

IL-2

The increase to 0.3743 ± 0.1165 pg/mL after 14 days of post-observation in Group II was statistically significant higher (p < 0.05) than the 0.1220 ± 0.0635 pg/mL value in Group 1.

Blood IgA

After 21 days of intake, Group II (340.3 ±64.9 mg/dL) had a statistically significantly higher blood IgA value than did Group I (175.0 ±9.5 mg/dL) (p < 0.01) (Table 1).

MCHC

After 7 days of consumption, the MCHC was statistically significantly higher (p < 0.05) in Group II (32.56 ± 0.55%) compared with Group I (31.85 ± 0.55%).

Serum galectin, ferritin, and fibrinogen

The decrease in serum fibrinogen, ferritin and galectin-3 was greater in Group II compared with Group I, but the difference was not significant (Figure 4A-C).

CD11b

An increase in CD11b in the monocyte fraction was observed in Group II after 21 days of ingestion compared with Group I but it was not statistically significant (Figure 4D).

Figure 4. Decrease in A. Fibrinogen and; B. Ferritin C. Galectin-3 and D. increase in CD11b greater in Group II (AFO-202+N-163 beta glucan) compared to Group I (AFO-202 beta glucan)

Other parameters

No statistically significant difference was observed in the other parameters. There was no discernible difference in the food consumption practices in the individuals, post-intervention.

Safety endpoints (incidence of adverse reactions)

No adverse reactions occurred in this study.

 

Discussion

The results of the study has proven the hypothesis suggest that A.pullulans produced beta glucans exert beneficial metabolic and immune effects, with the AFO-202 beta glucan capable of eliciting beneficial effects in balancing blood glucose, alleviating glucotoxicity with immune activation, while a combination of AFO-202 and N-163 beta glucans has significant anti-inflammatory and lipid profile regulating potential, thereby alleviating lipotoxicity.
Metabolic syndrome (MeTS) is a significant health issue in today’s world, affecting one quarter of the global population, which amounts to over a billion people (27). Although lifestyle changes remain the primary modality of therapy, several drugs, including statins and anti-diabetic medications, are major agents used in therapy, which do little to treat the secondary symptoms and are associated with side effects (28, 29).
In addition, these therapeutic approaches focus on either glucotoxicity resulting from irregular and unmanageable blood glucose levels, or lipotoxicity caused by an imbalanced lipid profile. However, they do not tackle the immune system disturbances triggered by MeTS (1, 4). Advancing metabolic disruption, enhanced by aging-induced inflammatory disorders (inflammaging), leads to accumulation of lipids in the aging organs, coupled with immunosenescence (3) which further increases individuals’ risk of contracting infectious diseases. It is important to note that therapeutic strategies are used after the disease has already started and are not given as a preventative measure.
A continuous safe supplementation approach, which could serve as a prophylaxis before the onset of disease and an adjunct to existing treatments after disease onset, could be a holistic solution for which the A. pullulans’ novel strains-produced exopolysaccharide beta glucan-based biological response modifiers could be of potential use. The A. pullulans is a polyextremotolerant generalist black yeast belonging to the phylum Ascomycota, class Dothideomycete and order Dothideales having high levels of genetic recombination (30). The AFO-202 strain of this black yeast produced beta glucan, having been documented to alleviate glucotoxicity and enhance immunity (8-10), when combined with the N-163 strain produced beta glucan has significant balancing effects on the lipid profile, anti-inflammatory and anti-fibrotic effects with immunomodulation (12, 14-16), are further substantiated by the results of the present study.
In the present study in healthy Japanese men, AFO-202 has been shown to enhance the immune system, as observed from the increase in eosinophils and monocytes. A decrease in CRP observed in Group I (Figure 2C) with CRP being an acute phase reactant (31) and known to increase rapidly with the onset of cell injury and inflammation shows that the AFO-202 beta glucan has anti-inflammatory and immune enhancement potential. A significant increase in CD11b in the monocyte fraction was observed after 21 days of ingestion (Figure 4D). CD11b is expressed on monocytes, macrophages, dendritic cells, granulocytes, and NK cells and is an LPS receptor (32). It is associated with the bacteriophagocytic activity of phagocytes. The increase in CD11b in the monocyte fraction of Group II after 21 days of consumption compared with before can be considered as a manifestation of immune activation by N-163 beta glucan. There was no change in IgG or IgM levels in the blood throughout the study period in both test groups, but an increase in IgA levels along with the decrease in D-dimer by AFO-202 and of galectin, fibrinogen, and ferritin by the combination of AFO-202 and N-163 beta glucan (Group II) offers evidence in favour of the combined approach for addressing immune associated coagulopathy-associated risks in diseases such as COVID-19, in which a hyperactivated immune response affects the clotting pathway (13). The decrease in NLR with an increase in LCR and LeCR, all having been reported to be potential biomarkers of the underlying inflammation and hyperactivated immune response in COVID-19 (13), further substantiate the potent anti-inflammatory potential of these beta glucans. In terms of the secondary endpoints of glycaemic control and normalization of cholesterol levels, a longitudinal comparison showed a significant decrease in HbA1c and GA after 21 days of consumption in Group I and in T-Cho and LDL-Cho in Group II, suggesting that the synergistic intake of these beta glucans suppresses the increase in blood glucose level and lowers the cholesterol level, which could be useful in the context of metabolic disorders with underlying immune dysregulation, which again is a key factor associated with disease severity and mortality in COVID-19 (13), apart from application in management of MeTS and associated cardiac/cardiovascular abnormalities (33, 34). Though some of the parameters showed only a minor difference in values, the multi-system effects of these beta glucans are the main reason for recommending their use for health and wellness. The proposed mechanisms by which the AFO-202 and N-163 beta glucans produce beneficial effects include their recognition as pathogen-associated molecular patterns (PAMPs), through which they modulate the function of immune cells (35). The cytochrome P450 enzyme 7-hydroxylase (CYP7A1) catalyses the formation of primary bile acids and thereby regulates cholesterol synthesis and liver cholesterol excretion. Beta glucan regulates CYP7A1 and HMG-CoA, which in turn regulate cholesterol synthesis and its decomposition into bile acids. By regulating enzyme activity in the liver, the lipogenic effects of beta-glucans are elucidated (36, 37). Some of the metabolic effects of the beta glucans may also be mediated by the gut microbiota. As beta-glucans are resistant to digestion by gastric and pancreatic enzymes, they are fermented by the host’s microbiome in the colon and exert their effects in this manner. Viscosity-dependent health benefits of highly viscous fibres such as beta glucans also contribute to cholesterol-lowering and improved glycemic control. Through short-chain fatty acid (SCFA)-induced production of gut hormones, beta-glucan suppresses appetite and increases insulin sensitivity. Gastric emptying peptide and GLP-1 are hypothesised to be related to these alterations (38). Immune-mediated effects of glucan are primarily induced by pattern recognition receptors (PRRs). These include Dectin-1, CR3, TLRs, lactosylceramides, and scavenger receptors. Dectin-1 is the key beta glucan receptor. The recognition and binding of TLR and Dectin-1 control the immune response by modulating the release of pro- and anti-inflammatory cytokines (39). A so far unidentified beta-glucan receptor that induces an Akt/P13K-dependent anti-inflammatory response also contributes to the metabolic and immune effects [40]. Beta glucan is also a potent inducer of epigenetic and functional reprogramming of innate immune cells, a process known as «trained immunity» that improves the host’s response to infections (41). Beta glucan induces acquired immunity via histone modifications at gene promoters in human monocytes, which is accompanied by increased production of proinflammatory cytokines in response to a microbial challenge. The expansion of hematopoietic stem and progenitor cells in the bone marrow and the increase in myelopoiesis provide significant protection against infection. This protective signature of beta glucan is mediated by IL-1 signalling. Beta-glucans activate a variety of immune cells, including macrophages, neutrophils, monocytes, natural killer cells, and dendritic cells, by binding to immune receptors such as Dectin-1, complement receptor 3 (CR3), and TLR-2/6 (42). Beta glucans can also modulate the tumour microenvironment by bridging the innate and adaptive arms of the immune system and by altering the phenotype of immunosuppressive cells to make them immune-stimulatory, contributing to the effects against cancer (42).

Two critical areas of further research are essential for a holistic understanding of their benefits. One is the process of aging, against which all mechanisms must act, as aging is an inevitable phenomenon causing gradual loss of optimal functioning capability of the whole human body, from the cellular to organ level, and age-related cumulative pathogenesis, especially the immune system. The second essential component of research must be on the gut microbiota, also called the “second genome” (43), as their involvement and contribution to both metabolism balancing and immune modulation, besides neuronal implications for aging apoptosis, chronic micro-inflammation, and carcinogenesis (44), have been gaining strong evidence in the past decade. With the earlier studies of immune cell enhancement in young healthy volunteers (45) and elderly cancer patients (46) having been earlier proven as well with these beta glucans, a large population involvement to document the same would strengthen such findings. Also, the effects of the beta glucan supplementation on the food dynamics of the participants, post-intervention needs further research. As beta glucans are known for their pre-biotic effects, and their beneficial effects could be proven to correlate with the gut microbiota, we should be able to see an amalgamation of all these and their mechanisms of interaction to explain technically their various potentials in terms of prevention, prophylaxis, and as a therapeutic adjunct for both communicable and non-communicable diseases.
It an important limitation is that this study was performed in healthy volunteers as an exploratory study, which warrants further validation in translational models designed for specific diseases to confirm the efficacy in specific pathogenesis and in human clinical studies in target illnesses. Further, because these beta glucans have been in consumption for long, AFO-202 since 1996 and N-163 since 2018 with safety track record as a food supplement, apart from pre-clinical and clinical studies (7, 8, 10-12, 15), we did not include a control group, as the main objective was to compare the immunological benefits of AFO-202 beta glucan alone versus when it is combined with N-163 beta-glucan. Although the study sample was small, this study paves the way for future research on the effects of these safe nutritional supplements in prophylaxis and prevention of disease in high-risk individuals, such as those with MeTS, as well as in infectious and non-infectious immune-metabolic dysfunction-associated diseases such as COVID-19.

 

Conclusion

In summary, this study has demonstrated that the AFO-202 beta glucan is capable of eliciting beneficial effects in balancing blood glucose, alleviating glucotoxicity with immune activation, while a combination of AFO-202 and N-163 beta glucans has significant anti-inflammatory and lipid profile regulating potential, thereby alleviating lipotoxicity. Therefore, these beta glucans, being safe to consume, may be incorporated as a new beneficial adjunct therapy for individuals with developing and established MeTS. Future studies in subjects with relevant illnesses are warranted to evaluate these beta glucans’ application as agents for prophylaxis and management of fibrosis-induced non-communicable diseases such as fatty liver disease and immune hyperactivation-related diseases, especially in communicable diseases such as COVID-19. Though the factors capable of determining a reduction in severity and mortality are different and their interactions are not yet fully understood, further elaborate research on evaluation of these beta glucans for their pre-biotic potentials in gut microbiota and related outcomes in managing chronic microinflammation, apoptotic mechanisms, and carcinogenesis could lead to evolution of knowledge and henceforth applications.

 

Acknowledgement: The authors wish to acknowledge: a. M/s CPCC Co. Ltd. and M/s Chiyoda Paramedical Care Clinic, Tokyo, Japan for their assistance in planning and execution of the entire study. b. Dr. Mitsuru Nagataki and Late Mr. Takashi Onaka, (Sophy Inc, Kochi, Japan), for necessary technical clarifications. c. Dr. Kadalraja Raghavan, Research & Development Division, Sarvee Integra Pvt Ltd, Chennai, India and Dr. Vaddi Suryaprakash, Department of Urology, Yashoda Hospitals, Hyderabad, India for their technical inputs. d. Ms. Yoshiko Amikura of GN Corporation, Japan for their liaison assistance with the conduct of the study. e. Loyola-ICAM College of Engineering and Technology (LICET) for their support to our research work.

Ethics Approval: The study protocol was approved by the institutional review board (IRB) of Chiyoda Paramedical Care Clinic, Tokyo, Japan (Study protocol number GNC20C1) and registered with the University hospital Medical Information Network- Clinical Trial Registry UMIN-CTR of Japan (Ref No UMIN: 000040882: https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000046681). The study was conducted at the Chiyoda Paramedical Care Clinic, Tokyo, Japan.

Sources of Support: No external funding was received for the study.

Author Declarations: Author Samuel Abraham is a shareholder in GN Corporation, Japan which in turn is a shareholder in the manufacturing company of novel beta glucans using different strains of Aureobasidium pullulans. The other authors don’t report any potential conflict of interests.

Author Contributions: CRediT author statement: Nobunao Ikewaki and Vidyasagar Devaprasad Dedeepiya; Conceptualization and Investigation: Rajappa Senthilkumar; Formal analysis: Sonoda, Gene Kurosawa and Masaru Iwasaki; Reviewing and editing: Senthilkumar Preethy; Writing original draft: Samuel JK Abraham; Conceptualization and writing original draft.

Data Availability: All data generated during the study are available in the manuscript itself.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

SUPPLEMENTARY MATERIAL

 

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© The Authors 2023

 

COMBINED EXERCISE AND EDUCATION PROGRAM: EFFECT OF SMALLER GROUP SIZE AND LONGER DURATION ON PHYSICAL FUNCTION AND SOCIAL ENGAGEMENT AMONG COMMUNITY-DWELLING OLDER ADULTS

 

S. Yamamoto1, D. Ishii1,2, K. Ishibashi1, Y. Okamoto3, K. Kawamura1, Y. Takasaki1, M. Tagami4, K. Tanamachi5,6, Y. Kohno1

 

1. Ibaraki Prefectural University of Health Sciences, Ibaraki, Japan; 2. Department of Cognitive Behavioral Physiology, Chiba University Graduate School of Medicine, Chiba, Japan; 3. University of Tsukuba Hospital, Tsukuba, Japan; 4. Osaka University, Osaka, Japan; 5. Keio University, Tokyo, Japan; 6. Tokyo Metropolitan University, Tokyo, Japan

Corresponding Author: Satoshi Yamamoto, Daisuke Ishii, Ibaraki Prefectural University of Health Sciences, 4669-2 Ami, Ami-machi, Inashiki-gun, Ibaraki 300-0394, Japan, Phone: (+81)-29-888-4000, Fax number: (+81)-29-840-2935, E-mail: yamamotos@ipu.ac.jp and ishiid@ipu.ac.jp

J Aging Res & Lifestyle 2023;12:56-60
Published online July 25, 2023, http://dx.doi.org/ 10.14283/jarlife.2023.10

 


Abstract

BACKGROUND: Exercise, education, and social engagement are critical interventions for older adults for a healthy life expectancy and to improve their physical function.
OBJECTIVE: To conduct a combined exercise and education (CEE) program for improved social engagement and physical function of older adults.
DESIGN: Based on a short-term program we conducted in our previous study, in this study, the program was conducted for half the number of participants of the earlier study but for a longer duration.
SETTING: A community of older adults in Ami, Japan, was the setting of the study.
PARTICIPANTS: 23 healthy older adults >65 years living in the community were the participants in the study.
INTERVENTIONS: Five 80-minute sessions conducted once in two weeks comprised 60-min exercise instruction and 20-min educational lectures per session on health. We examined the improvement in physical and social engagement before and after participation. Physical function and health-related questionnaire data were collected before and after the program.
RESULTS: Data analysis from 15 participants showed improved physical performance but no effect on social engagement.
CONCLUSIONS: A higher program frequency, rather than program duration, may be vital to improving exercise performance and social engagement and maximizing the effects of high group cohesion in small groups. Further studies are needed to develop more effective interventions to extend healthy life expectancy.

Key words: Community-dwelling older adults, exercise, educational program, physical function, social engagement.


 

Introduction

Japan is currently faced with the various challenges necessitating adaptation to one of the highest rates of aging worldwide (1), one among which is addressing the health problems associated with aging. To extend the healthy life expectancy of older adults, developing effective preventive methods is essential.
Exercise is a crucial intervention for improving physical function and life expectancy in older adults (2-8), apart from educational programs that increase patient adherence to treatment (9) and demonstrate how patient education improves medication adherence rates.
Social engagement is another critical factor in extending healthy life expectancy in older adults, with higher social engagement reported to maintain cognitive function, reduce depressive symptoms, and lower the risk of dementia in community-dwelling older adults (10-14).
In a previous study, we have shown that a combined exercise and education (CEE) program (one 80-min session /week for five weeks) can improve the physical function and social engagement of older adults in a community-dwelling (15). However, the improvement in social engagement resulting from the CEE program was not sustained over a long period (lasting only a month after the last session), suggesting that a short-term intervention program cannot maintain increased social engagement and may require further involvement, such as scheduling opportunities for participants to gather regularly. Further, group cohesiveness often is greater in smaller groups (16), suggesting that a smaller group program may maintain increased social engagement.
Therefore, in this study, we examined the effects of a CEE program conducted for a longer period with half the number of participants on the physical and social engagement of community-dwelling older adults.

 

Materials and Methods

Participants

Participants were recruited in the same manner as in the previous study (15) through advertisements placed in town newsletters (Ami, Japan; population approximately 49,000) and distributed to existing mailing lists of the community site at which the study lectures were to be held. The advertisement mentioned that exercise, health condition measurements, and educational lectures would be included in the program.
Twenty-three healthy older adults aged >65 years who responded to the advertisements were assessed for eligibility and enrolled. The inclusion criteria for this study were the same as those of our previous study (15): the participants had to be (a) able to walk without the use of a cane or any other assistive device, (b) independent in activities of daily living (ADL), and (c) able to communicate. Those with cognitive decline were excluded, and participation was purely voluntary. The participants were briefed on the study instructions and requested to provide written informed consent to participate. The study was performed in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the Ibaraki Prefectural University of Health Sciences (approval no. e195).

Experimental design and procedure

The frequency and total duration of the sessions differed from that of our previous study (15), but the length of each session was the same. Participants enrolled in one 80-minute session every two weeks (one week between the first and second sessions) for seven weeks. In the first session, each participant was given baseline measures of physical function (physical performance, body weight, height of body) and asked to respond to a health-related questionnaire. The second to fourth sessions consisted of 60 minutes of exercise instruction and a 20-min lecture about a health-related topic (comprising one session of the CEE program, described below). At the fifth/last session, post-intervention measurements of the above-mentioned aspects of physical function were conducted, and the participants again filled out the questionnaire about health-related matters.

The combined exercise and education program (CEE program)

In the CEE program, participants received 60 minutes of exercise instruction and 20 minutes of educational lectures per session. Exercise instruction consisted of warm-up stretching (10 minutes), strength training (20 minutes), and balance exercises (10 minutes) followed by dual exercise (combined mental/physical exertion, 20 minutes). The educational lecture was presented in intervals between exercises. In the educational aspect of the program, lectures about health-related topics (the importance of maintaining muscle strength, nutritional intake, fall prevention, community development, and the impact of social participation on well-being) were presented by a physical therapist under the supervision of specialists in nutrition and gerontology.

Baseline and post-CEE program assessments

The baseline and post-CEE programs relating to body weight measurement, skeletal muscle mass index, body mass index (BMI), physical performance, and health-related questionnaire matters were carried out by experienced physical and occupational therapists.
Body weight and height were measured, and BMI was calculated. For skeletal muscle mass index, body composition measurements were carried out by the bioelectric impedance method with a body fat analyzer (Inbody Dial H20B, Inbody Japan, Tokyo). The skeletal muscle mass index was calculated as follows: skeletal muscle mass [kg] / square of height [m].
For physical performance measurements, standardized protocols were used for the measurement of the 30-sec chair stand test (CS-30), and the timed up-and-go (TUG) test (17, 18). To measure grip strength, the participants’ dominant hand was used to measure maximum grip strength using a Smedley-type grip strength dynamometer (T.K.K.5401, Takei Scientific Instruments, Japan). The higher of the two measurements was used for analysis.
In the questionnaire about health-related matters, each participant’s level of social engagement, mobility, and fear of falling was assessed with the Elderly-status Assessment Set (E-SAS) developed by the Japanese Physical Therapy Association (19, 20). The E-SAS is based on the Lubben Social Network Scale-6 (LSNS-6) (21), the Life-Space Assessment (22), and the Falls Efficacy Scale (23). The number of times they were made to exercise per week and the number of falls per year was also noted for each participant before and after the 7-week program. For more detailed information, see our previous study (15).

Statistical analysis

Data distribution was assessed using the Shapiro–Wilk normality test. The Wilcoxon signed-rank test was performed to compare the median value of the TUG, fear of falling, and number of falls per year before and after the CEE program because data were non-normally distributed. When the data were normally distributed, paired t-test was performed. Statistical significance was set at p < 0.05. All analyses were conducted with SPSS® 27.0 for Windows (SPSS, Inc., Chicago, IL, USA). The sample size was calculated after conducting the study using data from the primary outcomes CS-30, grip strength, and LSNS-6, as this information is necessary for planning the next study. The input parameters were effect size (calculated with data) = 1.67, 0.96, 0.08; correlation (calculated with data) = 0.81, 0.96, 0.84; error probability = 0.05; and power = 0.8; G* Power 3.1.9.7 for Windows (24, 25) was used.

Multiple imputation

There were missing values for 1.5% of the measured parameters. We used multiple imputations (MI) as a statistical plan to account for missing data values. MI is a procedure used to replace missing values with other plausible values by creating multiple filling-in patterns to avert bias caused by missing data. In the present study, we replaced each missing value with a set of substituted plausible values by creating 50 filled-in complete data sets using MI by the chained equation method (26). In the imputation process, the following covariates were used to create 50 complete datasets: age, CS-30, TUG, grip strength, skeletal muscle index, number of exercise sessions per week, number of falls per year, LSA, FES, and LSNS-6. Multiple imputation analyses were conducted with R version 3.5.1 (27).

 

Results

We evaluated 23 participants (10 males and 13 females) in the first session. Of these, 17 participants (8 males and 9 females) participated in the fifth session (6 dropouts). Finally, data from 15 participants (6 males and 9 females) who participated in the first and fifth sessions were analyzed. Baseline characteristics of participants and changes in assessment parameters post-CEE program are shown in Table 1. The mean values of CS30 and grip strength after the CEE program were significantly higher than those before the CEE program (t(14) = 4.737, p < 0.001, d = 0.535, and t(14) = 2.671, p = 0.018, d = 0.114, respectively). However, the skeletal muscle mass index after the CEE program was significantly lower than that before the CEE program (t(14) =3.329, p = 0.005, d = 1.445).
No significant differences were found before and after the CEE program in body weight, BMI, TUG, LSA, FES, the number of times of exercise, number of falls per year, and social engagement (p > 0.05).
We calculated the sample size after conducting the study, using data from the primary outcomes CS-30, grip strength, and LSNS-6, as this information is necessary in planning the next study. The results revealed the need for 4, 6, and 927 participants in each group, respectively.

Table 1. Baseline characteristics of participants and changes in assessment parameters

The data are (mean ± std. dev.) or median (interquartile range). CS-30 test: 30-sec chair stand test, FES: Falls Efficacy Scale, LSA: Life-Space Assessment, LSNS-6: Lubben Social Network Scale-6, TUG: Timed up-and-go test. *p < 0.05

 

Discussion

In this study, we investigated the effects of the CEE program with a longer period and half the number of participants than our previous CEE program on the physical and social engagement of community-dwelling older adults (15). The major findings of this study were as follows: (1) the current CEE program (with an extended period and half the number of participants than our previous CEE program) improved their physical performance, (2) but had no effect on their social engagement.
The previous CEE program (one 80-min session 1x/week for 5 weeks) increased exercise habit (>3 times/week) and improved the physical function of elderly individuals living in a community dwelling (15). In this study, we found that the current CEE program did not increase exercise habits, but it did improve physical performance. The participants in the present study exercised >3 times/week, including in our program. Our current and previous results suggest that physical performance may be improved by an exercise habit of at least three times per week and participation in the CEE program at least once every two weeks.
We have previously shown that the CEE program (80 minutes x 1 session/week x 5 weeks) improves social engagement of older adults living in a community dwelling (15). However, this improvement in social engagement with the CEE program was not found to sustain in the long term (one month after the last session), suggesting that a short-term intervention program may not be sufficient to sustain improvement in social engagement and that additional engagement, such as setting up opportunities for participants to meet regularly may be necessary for longer-term improvement in social engagement. Nevertheless, in the current study, the CEE program with a longer period than our previous CEE program did not change the social engagement of community-dwelling older adults, suggesting the need for a higher program frequency rather than a longer period of the program for improving social engagement.
Perceptions of task and group cohesiveness have been reported to be greater in smaller groups (16). Members of cohesive groups are more likely to readily join and remain in the group (28). A review of studies on cohesiveness in sports teams and exercise groups found that team success, collective efficacy, and group communication are positively related to performance (29). The present study consisted of a small number of participants to increase group cohesion, but the effect of this in improving exercise performance was lesser than in previous studies; social engagement also showed no increase. These previous studies and our current result suggest that a higher frequency program may be necessary to increase group cohesion in small groups and effectively improve exercise performance and social engagement.
Although the study contributes to the literature by demonstrating how higher program frequency, rather than program duration, may be vital to improving exercise performance and social engagement and maximizing the effects of high group cohesion in small groups, it also has several limitations. Being a nonrandomized controlled trial, it can introduce bias (30). Participant self-selection bias can be another limitation since participation was voluntary, and only those willing to exercise were included. Future work should include a randomized controlled trial to compare the effectiveness of this CEE program directly. In this study, the sample size was calculated after the study, but not a priori, using data from the primary outcomes CS-30, grip strength, and LSNS-6, as this information is necessary for planning the next study. This resulted in the need for 4, 6, and 927 participants in each group, respectively. Thus, future planned programs similar to this study would require smaller sample sizes for the primary outcome of exercise performance, such as CS-30 and grip strength, and larger sample sizes for the outcome of social participation.
In summary, a CEE program with a longer period and half the number of participants than our previous CEE program improved physical performance but did not affect social engagement, suggesting the need for a higher program frequency, rather than a longer duration, for improving exercise performance and social engagement. A higher frequency program can help maximize the effects of high group cohesion in small groups on exercise performance and social engagement. Further studies are needed to develop more effective interventions to extend healthy life expectancy.

 

Declaration of any potential financial and non-financial conflicts of interest: There is no relevant conflict of interest.

Ethical standard: This study was approved by the Ethics Committee of the Ibaraki Prefectural University of Health Sciences (approval no. e195).

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

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© The Authors 2023

DAYTIME SLEEPINESS IS ASSOCIATED WITH LOWER COGNITIVE SCORES: THE LOOK AHEAD STUDY

 

K.M. Hayden1, A. Anderson2, A.P. Spira3,4,5, M.-P. St-Onge6, J. Ding7, M. Culkin1, D. Molina-Henry8,9,
A.H. Sanderlin7,10, D. Reboussin2, J. Bahnson2, M.A. Espeland2,7

 

1. Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, USA; 2. Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA; 3. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 4. Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA; 5. Johns Hopkins Center on Aging and Health, Baltimore, MD, USA; 6. Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA; 7. Department of Internal Medicine, Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA; 8. Winston-Salem State University, Winston-Salem, NC, USA; 9. University of Southern California, Alzheimer’s Therapeutic Research Institute, San Diego, CA, USA; 10. Department of Biology, North Carolina Agricultural and Technical State University, Greensboro, NC, USA

Corresponding Author: Kathleen M. Hayden, Department of Social Sciences and Health Policy, Wake Forest University School of Medicine, 1 Medical Center Boulevard Winston-Salem, NC 27157, Tel: (336) 716-2918, E-mail: khayden@wakehealth.edu

J Aging Res & Lifestyle 2023;12:46-55
Published online June 26, 2023, http://dx.doi.org/10.14283/jarlife.2023.9

 


Abstract

BACKGROUND: Daytime sleepiness is common in older adults and may result from poor nighttime sleep due to sleep disordered breathing, fragmented sleep, or other sleep disorders. Daytime sleepiness may be associated with cognition in older adults.
OBJECTIVES: We investigated the association between self-reported daytime sleepiness and cognitive function in the Look AHEAD clinical trial.
DESIGN: Observational follow-up of a randomized clinical trial of an intensive lifestyle intervention.
SETTING: Clinic.
PARTICIPANTS: Participants (n=1,778) aged 45-76 years at baseline with type 2 diabetes and overweight or obesity.
INTERVENTIONS: Participants were randomized to an intensive lifestyle intervention for weight loss or a control condition of diabetes support and education.
MEASUREMENTS: Participants provided self-reported levels of daytime sleepiness at baseline and years 12-13. Cognitive function was assessed with a neurocognitive battery at years 12-13 and 18-20.
RESULTS: Participants who reported having frequent daytime sleepiness (often or always) performed significantly worse than others on the cognitive composite (-0.35; p-value=0.014) after controlling for covariates. When stratified by intervention arm, participants assigned to the intensive lifestyle intervention who reported often/always having daytime sleepiness performed worse on Digit Symbol Coding (-0.63; p-value=0.05) and Trail Making Part-B (-0.56; p-value=0.02) after controlling for covariates. Statistical interactions revealed associations between daytime sleepiness and the following covariates: race and ethnicity, APOE ε4 carrier status, baseline history of cardiovascular disease, and depression.
CONCLUSIONS: Daytime sleepiness over ~13 years predicted poorer cognitive performance in older individuals who, by virtue of having diabetes and overweight/obesity, are at high risk for sleep disorders and cognitive impairment.

Key words: Sleep disorders, diabetes mellitus, type 2, cognition disorders, aging, obesity, overweight.


 

Introduction

Daytime sleepiness is common in older adults (1, 2) and may signal health problems (3). Daytime sleepiness may result from sleep-disordered breathing, fragmented sleep or other sleep disorders (2), or from insufficient sleep, and it has been associated with comorbidities, medication use, and metabolic syndrome (4). The Cardiovascular Health Study showed associations between daytime sleepiness and incident cardiovascular disease (CVD) and mortality (3), and other studies have shown associations with cognitive decline (5, 6) and increased dementia risk (7, 8). Daytime sleepiness may signify disruption of circadian rhythms and may occur due to disorder in neural circuitry that may be affected in neurodegenerative disease including impairment in arousal systems (9). The locus coeruleus noradrenergic arousal system is associated with wakefulness and attention (10) and is affected early in the progression of neurodegeneration in Alzheimer’s disease (AD) (11). Self-reported excessive daytime sleepiness has been associated with later beta amyloid deposition, a pathological feature of AD, in the Baltimore Longitudinal Study of Aging (12).
Conditions associated with excessive daytime sleepiness and AD risk include obesity and diabetes (13). Among those with obesity and metabolic syndrome, heightened sympathetic activity can potentially cause fragmented sleep, leading to daytime sleepiness (14). Another factor, sleep-disordered breathing, could play a role, however daytime sleepiness among those with obesity or diabetes is not always associated with sleep-disordered breathing. Indeed, in a large study (n=16,583) of men and women, excessive daytime sleepiness was more strongly associated with greater body mass index (BMI), diabetes, and depression, than with sleep disordered breathing (13).
Previously in the Look AHEAD study, weight loss was shown to improve indices of sleep-disordered breathing and even remission of obstructive sleep apnea (OSA) (15), however daytime sleepiness was not studied. Look AHEAD provides the opportunity to evaluate the potential effects of a lifestyle intervention on daytime sleepiness and subsequent effects on cognitive function in a large, well-characterized cohort. Further, the large sample with cognitive assessments in Look AHEAD facilitates sub-analyses of gender differences and potential differences by APOE ε4 status, two important dementia risk factors, as well as subgroups for which we have found significant interactions in prior work (16-18) including age, baseline BMI, and baseline history of CVD. The objective of this analysis was to determine the degree to which self-reported daytime sleepiness was associated with the intervention and with cognition.

 

Methods

The study design, methods (19), and CONSORT diagram (20) for Look AHEAD have been published. Briefly, Look AHEAD was a randomized controlled clinical trial of (n=5,145) participants aged 45-74 with diabetes and overweight/obesity. The trial was designed to determine whether intentional weight loss is appropriate for older adults with diabetes and overweight/obesity, with primary end points of fatal and nonfatal cardiovascular events. Eligibility criteria required that participants have BMI >25 kg/m2 (>27
kg/m2 if on insulin), glycated hemoglobin (HbA1c) <11%, systolic/diastolic blood pressure <160/100 mmHg, and triglycerides <600 mg/dl. Participants were required to demonstrate over a two-week run-in period, the ability to record daily, their diet and physical activity. Each participant met with a behavioral psychologist or interventionist to confirm that intervention requirements were understood and that participants did not have any competing life stressors that would impair adherence to the protocol. Study data were collected by certified, trained staff who were masked to intervention assignments (19). Participants were randomly assigned with equal probability to either the intensive lifestyle intervention (ILI) or diabetes support and education (DSE) arm of the trial. Enrollment and initiation of intervention delivery occurred between 2001 and 2004. Interventions continued until 2011, at which time participants were invited to join a follow-up observational study to determine the longer-term effects of the intervention on outcomes. The average intervention duration for participants in this study was 9.8 years [8.4-11.1y]. Local Institutional Review Boards approved the protocols and all participants provided written informed consent.
The ILI was a multidomain intervention including dietary modification and increased physical activity with a goal of inducing an average of ≥7% weight loss at one year and maintenance of weight loss over the course of the study (21). Participants in the ILI arm were given a daily calorie goal of 1200-1800 kcal based on initial weight. The diet specified <30% total calories from fat (<10% saturated fat) and a minimum of 15% total calories from protein. The physical activity goal was similar in intensity to brisk walking for at least 175 minutes/week. Participants randomized to the DSE condition were invited, but not required, to attend three group sessions/year. Sessions focused on diet, physical activity, and social support (22). There were no specific instructions or goals for weight loss, physical activity, or dietary modification.

Daytime Sleepiness

At baseline and during extended post-intervention follow-up (12-13 years later), participants were asked about daytime sleepiness: “How often do you feel excessively(overly) sleepy during the day.” Responses included never(1 day/month or less), sometimes(2-4 days/month), often(5-15 days/month), and almost always(16-30 days/month). Baseline and extended follow-up reports of daytime sleepiness were each and classified into three groups: 1) never, 2) sometimes, and 3) often or almost always.

Cognitive Function

Cognitive assessments were conducted 1-4 times during follow-up during years 8-18 as part of the study follow-up protocol and participation of subsets of the cohort in ancillary studies (16). We used the most recent cognitive scores for the current evaluation (2018-2020). Staff were centrally trained and certified in administration of the standardized cognitive assessments and were masked to participant’s randomization status (23). The cognitive battery included the Rey Auditory Verbal Learning Test (RAVLT)(24), Digit Symbol Coding (DSC)(25), the Modified Stroop Color and Word Test (Stroop)(26), and the Trail Making Test Parts A&B (27). The Modified Mini-Mental Status Exam (3MS)(28) was used to assess global cognitive function. Test scores were standardized as z-scores which were averaged to derive a cognitive composite score (23). Trail Making Test scores were re-ordered so that higher values indicate better performance.

Other measures

Staff collected demographic and clinical characteristics including age, gender, race and ethnicity, education level, and smoking status at baseline. Weight was measured with digital scales. Diabetes treatments (insulin, sulfonylureas, other) were recorded at baseline. Hypertension was defined by treatment or measured blood pressure >140/90 mmHg. Baseline CVD included self-report of myocardial infarction, heart bypass surgery, coronary artery bypass graft, carotid endarterectomy, lower leg angioplasty, aortic aneurysm, congestive heart failure, or stroke. The Beck Depression Inventory (BDI)(29) was assessed annually until year 14. Subsequently, the Patient Health Questionnaire-9 (PHQ-9) (30) was administered to assess depressive symptoms. We dichotomized depressive symptoms, with BDI scores of ≥11 and PHQ-9 scores of ≥5. APOE ε4 status, a dementia risk factor, was determined for participants who provided consent (80% of women versus 86% of men, p<0.001), using TaqMan genotyping (rs7412 and rs429358)(31).

Analytic Design

Descriptive statistics were prepared by intervention group. Continuous variables were compared with t-tests and categorical variables compared with χ2 tests. Comparisons of continuous variables across three levels of daytime sleepiness were made using ANOVA. Categorical variables were derived to represent daytime sleepiness at baseline and follow-up 12-13 years later (2013-2014). The daytime sleepiness variable ranged from 1- 3 based on the categories as described above (i.e., never; sometimes; often/almost always). We compared individuals who were included in the analysis to those who were not included due to attrition or missing data.
Regression analysis assessed the association between daytime sleepiness in 2013-2014 and cognitive performance in 2018-2020 adjusting for: intervention arm, age, gender, race and ethnicity, education, baseline levels of daytime sleepiness, BMI, and hypertension. We adjusted for prior cognitive scores and depressive symptoms at baseline and as assessed concurrently with each cognitive measure. We did not use time-varying covariates for risk factors to allow the evaluation of the impact of the intervention. As prior work suggested heterogeneous effects of the intervention by subgroups (18, 32), we tested interactions by intervention arm, age (+/-65 at baseline), gender, race and ethnicity, APOE ε4 status, baseline BMI, and baseline history of CVD; we tested for an interaction by baseline depression as depression is associated with both cognition and daytime sleepiness.

 

Results

Participants who completed daytime sleepiness questions at baseline and proximal to the end of the intervention, and who completed cognitive evaluation at the most recent visit (n=1,778) were included in the analyses (Figure 1). A total of 3,367 participants were not included due either to being lost to follow-up or missing data for covariates of interest. These participants tended to be older, had lower levels of education, and included more APOE ε4 carriers; more of them had a baseline history of CVD, hypertension, insulin use, longer duration of diabetes, higher average HbA1c levels, and reported more depressive symptoms (Supplemental Table 1).

Figure 1. Sample Selection Flowchart

DSE=Diabetes Support and Education; ILI=Intensive Lifestyle Intervention

 

Among those included in the analysis, the average baseline age was 57 (standard deviation [SD] 6.3). The sample included more women (61%) than men, and was mostly White (63.9%) and highly educated (45.3% having college degree or higher). There were no significant differences by intervention arms among covariates listed in Table 1 except baseline BMI: the DSE group had a slightly higher average BMI (36.4 vs. 35.8 kg/m2) at baseline (p=0.02).

Table 1. Baseline characteristics of 1,778 Look AHEAD participants by Intervention Arm

Abbreviations: APOE ε4=Apolipoprotein E gene, ε4 carrier status; BDI=Beck Depression Inventory; CVD=cardiovascular disease; DSE=diabetes support and education; ILI=intensive lifestyle intervention; SD=standard deviation.

 

Participant characteristics are reported in Table 2 by daytime sleepiness status at the 2013-2014 follow-up. A higher proportion of White participants reported often/always having daytime sleepiness and more Hispanic participants reporting sometimes having daytime sleepiness (p<0.001). Those with higher baseline BMIs reported more frequent daytime sleepiness (p=0.02). A higher proportion of participants with hypertension reported having daytime sleepiness sometimes as opposed to never or often/always (p=0.006). Depressive symptoms were more common among those who reported often/always having daytime sleepiness (p<0.001).
Figure 2 shows forest plots for least square means (±standard error) and p-values for associations between cognitive scores and frequency of self-reported daytime sleepiness. Models were adjusted for potential confounders including age, gender, race, ethnicity, education, randomization arm, and baseline values of BMI, hypertension, daytime sleepiness, and depressive symptoms. Models were also adjusted for 2013-2014 cognitive scores and concurrent assessments of depressive symptoms. Diabetes duration and smoking were considered but did not significantly contribute to the models and were dropped. Although the only statistically significant result is in the cognitive composite (p=0.014), forest plots demonstrate general dose-response effects such that participants reporting daytime sleepiness occurring often or always, had lower mean scores than those who reported lower levels of daytime sleepiness.

Table 2. Baseline Characteristics of 1,778 Look AHEAD participants by Daytime Sleepiness at 2013-2014

Abbreviations: APOE ε4=Apolipoprotein E gene, ε4 carrier status; BDI=Beck Depression Inventory; CVD=cardiovascular disease; DSE=diabetes support and education; ILI=intensive lifestyle intervention; SD=standard deviation.

Figure 2. Cognitive performance (2018-2020) by daytime sleepiness (2013-2014)

Abbreviations: 3MS=Modified Mini-mental State Exam; DSC=Digit Symbol Coding; RAVLT Delayed=Rey Auditory Verbal Learning Test Delayed; Trails A=Trail Making Test Part A; Trails B=Trail Making Test Part B. *Models are adjusted for treatment arm, the 2013-2014 value of the outcome, depressive symptoms at baseline, 2013-2014, and 2018-2020 visits, and baseline values of daytime sleepiness, age, gender, race and ethnicity, education, BMI, and hypertension.

 

In sensitivity analyses, we re-fitted models without controlling for cognitive scores at years 13-14 and similar but stronger associations between daytime sleepiness groups and some cognitive scores emerged (Supplemental Table 2). The composite z-score was in the same direction but lost significance (p=0.06), while the DSC (p=0.01) and Trails B (p=0.05) became significant when we did not control for prior cognitive scores.
We tested interactions by intervention arm, age, gender, race and ethnicity, APOE ε4 status, baseline BMI, history of CVD, and depression as suggested by prior work in Look AHEAD(16-18) and the literature on daytime sleepiness(33), using a p-value threshold of p=0.10. Associations between daytime sleepiness and cognitive scores varied across intervention groups (Table 3) with significant findings among those in the ILI group on DSC (p=0.05) and Trails B (p=0.02) such that those reporting daytime sleepiness often/always performed worse than the other two sleep groups (i.e., never or sometimes). We show similar results, albeit with stronger effects in Supplemental Table 3 which does not include adjustment for prior cognitive scores.

Table 3. Cognitive Performance in 2018-2020 by Daytime Sleepiness at 2013-2014 Stratified by Intervention Arm [LS Mean (SE)]

Abbreviations: 3MS=Modified Mini-mental State Exam; DSC=Digit Symbol Coding; RAVLT Delayed=Rey Auditory Verbal Learning Test Delayed; Trails A=Trail Making Test Part A; Trails B=Trail Making Test Part B. *Stratified models are adjusted for the 2013-2014 value of the outcome, depressive symptoms at baseline, 2013-2014 and 2018-2020 visits, and baseline values of daytime sleepiness, age, gender, race and ethnicity, education, BMI, and hypertension.

 

There were no significant interactions by age, gender, or baseline BMI. However, there were significant interactions between race and ethnicity categories and daytime sleepiness on nearly all our measures (shown in Supplemental Table 4). The trend across scores suggests lower performance on the composite (p=0.01), Stroop (p=0.03), DSC (p=0.02), Trails A (p=0.09) and Trails B (p=0.08), and RAVLT Delayed (p=0.07) with greater levels of daytime sleepiness. In most cases, African American and White participants demonstrated worse scores with more self-reported daytime sleepiness (sometimes or often/always). However, some apparent inconsistencies could be due to small numbers for groups including Hispanic participants and ‘other’ (which includes American Indian/Native American, mixed race, and others).
Interactions were found between daytime sleepiness and APOE ε4 status on cognitive scores on Trails A (p=0.05) and RAVLT Delayed (p=0.08) such that those who reported more daytime sleepiness and have one or more APOE ε4 allele(s), tended to perform worse than those without APOE ε4 allele(s). These interactions should be interpreted with caution however, because the number of participants with one or more APOE ε4 allele(s) is relatively small.
An interaction between baseline history of CVD and daytime sleepiness was apparent on the Stroop, with worse performance corresponding to higher levels of daytime sleepiness among those without a baseline history of CVD. Those who reported a baseline history of CVD and no daytime sleepiness performed worse than all the other groups (never: LS Mean= -0.69; p<0.01) on the Stroop. On the Trail Making Test Part A, participants with a baseline history of CVD demonstrated a trend toward worse performance with greater levels of self-reported daytime sleepiness (often/always: LS Mean=-0.68; p<0.08). On the Trail Making Test Part B, a similar trend was apparent with participants who had a baseline history of CVD performing worse with greater levels of daytime sleepiness (often/always: LS Mean=-0.63; p<0.09).
Finally, because there are established associations between depression and cognitive performance (34) as well as between depression and daytime sleepiness (35, 36), we tested for interactions between daytime sleepiness and depressive symptoms. Those with BDI score≥11 and no self-reported daytime sleepiness performed worse on DSC (never: LS Mean=-0.59) than those scoring <11; while those with BDI<11 and daytime sleepiness often or always performed nearly the same (LS Mean=-0.58). On Trails A, participants with BDI≥11 and reported daytime sleepiness sometimes (LS Mean=-0.59) and participants with BDI<11 reporting daytime sleepiness often/always (LS Mean=-0.43) performed worse than others (p<0.01). On Trails B the same pattern emerged where those with BDI≥11 and reported daytime sleepiness sometimes (LS Mean=-0.44) and participants with BDI<11 reporting daytime sleepiness often/always (LS Mean=-0.53) performed worse than others (p=0.02).

 

Discussion

We sought to test the degree to which self-reported daytime sleepiness was associated with the Look AHEAD intervention and cognitive scores. Participants who self-reported daytime sleepiness often or always in 2013-2014 performed significantly worse than those who reported sometimes or never having daytime sleepiness on the cognitive composite. Individual tests suggested a dose-response relationship, with greater levels of daytime sleepiness associated with worse performance. We further stratified by intervention arm, showing poorer scores on executive function tests were driven by the ILI group. This is aligned with prior reports showing no long-term cognitive benefit from the intervention (16). Randomization to ILI was not associated with self-reported daytime sleepiness, although it is feasible that any benefits accrued as a result of the intervention were subsequently lost over time.
To further probe drivers of these associations, we tested interactions based on prior work (16-18) and found differences by racial and ethnic groups on most tests, with African American and White participants demonstrating more consistent associations between greater levels of daytime sleepiness and poor performance on various tests compared to participants from Hispanic and Other (American Indian/Native American, mixed race, and others) groups. Participants with one or more APOE ε4 allele(s) and greater levels of daytime sleepiness performed worse on Trails A and RAVLT than non- APOE ε4 carriers. Poorer cognitive performance was observed among those with more frequent daytime sleepiness and a history of CVD compared to those with less frequent daytime sleepiness and no history of CVD. These results support earlier findings in Look AHEAD that suggested that participants reporting a baseline history of CVD experienced fewer cognitive benefits compared to others in the cohort (16). This result is also not surprising as CVD is associated with daytime sleepiness (37). Finally, tests for interactions with depressive symptoms showed consistent associations between higher daytime sleepiness levels and lower cognitive function among those reporting low levels of depression. However, among participants with greater levels of depressive symptoms, associations between daytime sleepiness and cognition were complex. This may be due to the bidirectional association between depression and disrupted sleep patterns (38).
Daytime sleepiness has been associated with adverse health outcomes in addition to CVD (37), including cognitive decline (5), cognitive impairment and dementia (7), as well as amyloid deposition (12, 39). In our study, daytime sleepiness was associated with poorer scores on the cognitive composite overall; on DSC and Trails B (executive function) among participants in the ILI group; and poorer scores on the Stroop, Trails A, and Trails B among those with a baseline history of CVD.
Our study has some limitations and strengths to note. Cognitive function was not measured at baseline as it was not a primary focus of the trial; therefore, we could not exclude participants based on cognitive impairment at baseline. However, our rigorous screening procedures effectively excluded those with clear impairment, and randomization facilitated comparable demographic and health characteristics across study arms at baseline. Therefore, there is no reason to suspect that the two groups would have differed in cognitive performance at baseline had it been measured. Our findings are generalizable to only a high-risk subset of the population, i.e., older adults with diabetes and overweight/obesity. However, this group, a growing segment of the population, has an increased risk of cognitive impairment, making this work valuable for individuals with diabetes and overweight/obesity. A strength of the work is the fact that Look AHEAD was a long-term randomized controlled clinical trial and was conducted using rigorous methods. Participants have been closely followed for nearly twenty years, providing deep phenotyping and well-characterized outcomes.
Our study adds to this body of literature by illustrating complex relationships between daytime sleepiness and cognitive performance among Look AHEAD participants who all had diabetes and overweight or obesity. Findings expand upon prior findings linking sleepiness to later amyloid deposition (12), and raise questions about the potential for daytime sleepiness (40) as an early indicator of cognitive decline, perhaps tied to atrophy in the locus coeruleus. Future longitudinal studies examining modifiable risk factors for dementia should include early measures of daytime sleepiness together with AD biomarkers to investigate these associations more thoroughly and determine their temporality. New targets for intervention that emerge early in the disease process are crucial to making advances in AD research.

 

Funding: Look AHEAD was funded by the National Institutes of Health through cooperative agreements with the National Institute on Aging and National Institute of Diabetes and Digestive and Kidney Diseases: DK57136, DK57149, DK56990, DK57177, DK57171, DK57151, DK57182, DK57131, DK57002, DK57078, DK57154, DK57178, DK57219, DK57008, DK57135, and DK56992. Additional funding was provided by the National Heart, Lung, and Blood Institute; National Institute of Nursing Research; National Center on Minority Health and Health Disparities; NIH Office of Research on Women’s Health; and the Centers for Disease Control and Prevention. This research was supported in part by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases. The Indian Health Service (I.H.S.) provided personnel, medical oversight, and use of facilities. The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the I.H.S. or other funding sources. Additional support was received from The Johns Hopkins Medical Institutions Bayview General Clinical Research Center (M01RR02719); the Massachusetts General Hospital Mallinckrodt General Clinical Research Center and the Massachusetts Institute of Technology General Clinical Research Center (M01RR01066); the Harvard Clinical and Translational Science Center (RR025758-04); the University of Colorado Health Sciences Center General Clinical Research Center (M01RR00051) and Clinical Nutrition Research Unit (P30 DK48520); the University of Tennessee at Memphis General Clinical Research Center (M01RR0021140); the University of Pittsburgh General Clinical Research Center (GCRC) (M01RR000056), the Clinical Translational Research Center (CTRC) funded by the Clinical & Translational Science Award (UL1 RR 024153) and NIH grant (DK 046204); the VA Puget Sound Health Care System Medical Research Service, Department of Veterans Affairs; and the Frederic C. Bartter General Clinical Research Center (M01RR01346). The National Institute of Aging provided funding for the Look AHEAD Mind ancillary study (R01AG058571) and the Look AHEAD Sleep ancillary (R01AG074562). MSPO was funded from R35HL155670. The following organizations have committed to make major contributions to Look AHEAD: FedEx Corporation; Health Management Resources; LifeScan, Inc., a Johnson & Johnson Company; OPTIFAST® of Nestle HealthCare Nutrition, Inc.; Hoffmann-La Roche Inc.; Abbott Nutrition; and Slim-Fast Brand of Unilever North America. Some of the information contained herein was derived from data provided by the Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene.

Acknowledgements: Clinical Sites: The Johns Hopkins University Jeanne M. Clark, MD, MPH1; Lee Swartz2; Dawn Jiggetts2; Jeanne Charleston, RN3; Lawrence Cheskin, MD3; Nisa M. Maruthur, MD, MHS3; Scott J. Pilla, MD, MHS3; Danielle Diggins; Mia Johnson; Pennington Biomedical Research Center George A. Bray, MD1; Frank L. Greenway, MD1; Donna H. Ryan, MD3; Catherine Champagne, PhD, RD3; Valerie Myers, PhD3; Jeffrey Keller, PhD3; Tiffany Stewart, PhD3; Jennifer Arceneaux, RN2; Karen Boley, RD, LDN2; Greta Fry, LPN; Lisa Jones; Kim Landry; Melissa Lingle; Marisa Smith2. The University of Alabama at Birmingham Cora E. Lewis, MD, MSPH1; Sheikilya Thomas, PhD, MPH2; Stephen Glasser, MD3; Gareth Dutton, PhD3; Amy Dobelstein; Sara Hannum; Anne Hubbell, MS; DeLavallade Lee; Phyllis Millhouse, L. Christie Oden; Cathy Roche, PhD, RN, BSN; Jackie Grant; Janet Turman. Harvard Center: Massachusetts General Hospital. David M. Nathan, MD1; Valerie Goldman, MS, RDN2; Linda Delahanty, MS, RDN3; Mary Larkin, MS, RN; Kristen Dalton, BS; Roshni Singh, BS; Melanie Ruazol, BS. Joslin Diabetes Center: Joslin Diabetes Center: Medha N Munshi, MD1; Sharon D. Jackson, CCRC, MS, RD, CDE2; Roeland J.W. Middelbeek MD3; A. Enrique Caballero, MD, Anthony Rodriguez. Beth Israel Deaconess Medical Center: George Blackburn, MD, PhD1*; Christos Mantzoros, MD, DSc3; Ann McNamara, RN; University of Colorado Anschutz Medical Campus Holly Wyatt, MD1; James O. Hill, PhD1; Jeanne Anne Breen, MS2; Marsha Miller, MS, RD2; Debbie Bochert; Suzette Bossart; Paulette Cohrs, RN, BSN; Susan Green; April Hamilton, BS, CCRC; Eugene Leshchinskiy; Loretta Rome, TRS. The University of Tennessee Health Science Center: University of Tennessee East. Karen C. Johnson, MD, MPH; Beate Griffin, RN, BS; Mace Coday, PhD3; Donna Valenski, Linda Jones; Karen Johnson, RN. University of Tennessee Downtown: Karen C. Johnson, MD, MPH; Beate Griffin, RN, BS; Helmut Steinburg, MD3. University of Minnesota: Robert W. Jeffery, PhD1; Tricia Skarphol, MA2; John P. Bantle, MD3; J. Bruce Redmon, MD3; Kerrin Brelje, MPH, RD; Carolyne Campbell; Mary Ann Forseth, BA; Soni Uccellini, BS; Mary Susan Voeller, BA. Columbia University Medical Center: Blandine Laferrère, MD, PhD1; Xavier Pi-Sunyer, MD1; Jennifer Patricio, MS2; Jose Luchsinger, MD1; Priya Palta, PhD,MHS3; Jennifer Patricio, MS2; Sarah Lyon, Kim Kelly. University of Pennsylvania: Thomas A. Wadden, PhD1; Barbara J. Maschak-Carey, MSN, CDE2; Robert I. Berkowitz, MD3; Ariana Chao, PhD, CRNP3; Renee Davenport; Katherine Gruber, CRNP; Sharon Leonard, RD; Olivia Walsh, BA. University of Pittsburgh: John M. Jakicic, PhD1; Jacqueline Wesche-Thobaben, RN, BSN, CDE2; Lin Ewing, PhD, RN3; Andrea Hergenroeder, PhD, PT, CCS3; Mary Korytkowski, MD3; Susan Copelli, BS, CTR; Rebecca Danchenko, BS; Diane Ives, MPH; Juliet Mancino, MS, RD, CDE, LDN; Lisa Martich, BS, RD, LDN; Meghan McGuire, MS; Tracey Y. Murray, BS; Linda Semler, MS, RD, LDN; Kathy Williams, RN, MHA. The Miriam Hospital/Brown Medical School: Rena R. Wing, PhD1; Caitlin Egan, MSv; Elissa Jelalian, PhD3; Jeanne McCaffery, PhD3; Kathryn Demos McDermott, PhD3; Jessica Unick, PhD3; Kirsten Annis, BA; Jose DaCruz; Ariana Rafanelli, BA. The University of Texas Health Science Center at San Antonio: Helen P. Hazuda, PhD1; Juan Carlos Isaac, CCRC, BSN2; Prepedigna Hernandez, RN. VA Puget Sound Health Care System / University of Washington: Steven E. Kahn, MB, ChB1; Edward J. Boyko, MD, MPH3; Elaine Tsai, MD3; Lorena Wright, MD3; Karen Atkinson, RN, BSN2; Ivy Morgan-Taggart; Jolanta Socha, BS; Heidi Urquhart, RN. Southwestern American Indian Center, Phoenix, Arizona and Shiprock, New Mexico: William C. Knowler, MD, DrPH1; Paula Bolin, RN, MCv; Harelda Anderson, LMSWv, Sara Michaels, MD3; Ruby Johnson; Patricia Poorthunder; Janelia Smiley . University of Southern California: Anne L. Peters, MD1; Siran Ghazarian, MD2; Elizabeth Beale, MD3; Edgar Ramirez; Gabriela Rodriguez, MA; Valerie Ruelas MSW, LCSW; Sara Serafin-Dokhan; Martha Walker, RD; Marina Perez.

Coordinating Center: Wake Forest University Mark A. Espeland, PhD1; Kathleen Hayden, PhD1; Judy L. Bahnson, BA, CCRP3; Lynne E. Wagenknecht, DrPH3; David Reboussin, PhD3; Nicholas Pajewski, PhD3; Jingzhong Ding, PhD3; Gagan Deep, PhD3; Stephen R. Rapp, PhD3; Bonnie C. Sachs, PhD3; Jerry M. Barnes, MA; Tara D. Beckner; Delilah R. Cook; Joni Evans, MS; Katie Garcia, MS; Sarah A. Gaussoin, MS; Carol Kittel, MS; Lea Harvin, BS; Marjorie Howard, MS; Rebecca H. Neiberg, MS; Jennifer Walker, MS; Michael P. Walkup, MS. 1Principal Investigator; 2Program Coordinator; 3Co-Investigator; All other Look AHEAD staffs are listed alphabetically by site.

Federal Sponsors: National Institute of Diabetes and Digestive and Kidney Diseases Mary Evans, PhD; Robert Kuczmarski, DrPH; Rebecca Van Raaphorst, MPH; Susan Z. Yanovski, MD; National Institute on Aging Marcel Salive, MD, MPH.

Conflict of Interest Statement: Adam Spira received honoraria for serving as a consultant to Merck and from Springer Nature Switzerland AG for guest editing special issues of Current Sleep Medicine Reports. The other authors report no conflicts of interest.

Statement of Ethics: This study was conducted ethically, in accordance with the World Medical Association Declaration of Helsinki. The study protocol was approved by the Wake Forest School of Medicine (Look AHEAD study Coordinating Center) Institutional Review Board (IRB #BG99-042) as well as the Institutional Review Boards of all the data collection sites. All participants provided written informed consent to participate. The ClinicalTrials.gov identifier is NCT00017953.

Author Contributions: Drs. Hayden Spira, St-Onge, Ding, Molina-Henry, Sanderlin, Reboussin, Espeland and Ms. Anderson and Bahnson made substantial contributions to the design of this study. Drs. Reboussin, Espeland, and Ms. Anderson and Bahnson made substantial contributions for the collection of Look AHEAD data for this study. Drs. Espeland and Hayden made substantial contributions for the acquisition of Look AEHAD MIND data. Drs. Hayden and Espeland, and Ms. Anderson made substantial contributions to the analysis of the data for this study. All authors took part in drafting and revising this manuscript and gave final approval for this manuscript prior to submission.

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

 

SUPPLEMENTARY MATERIAL1

 

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