H. Talkad¹, Y. Chen², A.P. Bress³, J.B. Langbaum², P.N. Tariot^1,2, J.J. Pruzin^1,2

1. University of Arizona College of Medicine – Phoenix, Phoenix, AZ, USA; 2. Banner Alzheimer’s Institute, Phoenix, AZ, USA; 3. University of Utah, Salt Lake City Utah

Corresponding Author: Harshita Talkad, University of Arizona College of Medicine – Phoenix, Phoenix, AZ, USA, htalkad@arizona.edu

J Aging Res & Lifestyle 2024;13:126-134
Published online December 16, 2024, http://dx.doi.org/10.14283/jarlife.2024.19

Abstract

BACKGROUND: Dementia disproportionately affects Hispanic communities, which may be partially attributable to disparities in resources to address modifiable risk factors. Addressing risk factors at younger ages would likely confer greater benefit than at older ages. Interest among Hispanic and younger persons participating in a dementia prevention program is unknown.
OBJECTIVES: To understand knowledge of dementia risk factors and attitudes toward prevention program participation among Arizona residents.
DESIGN: Cross-sectional study.
SETTING: Online survey conducted of Arizona residents in either English or Spanish between July 13, 2021 and August 2, 2021.
PARTICIPANTS: 1,303 persons age 35 and older; 332 (25.5%) were Hispanic.
MEASUREMENTS: Survey questions about knowledge of dementia risk factors and dementia prevention program interest. Comparisons between Hispanic and non-Hispanic White and younger and older respondents were made using chi-squared tests.
RESULTS: Overall, 30.7% of respondents were aware of any risk factors that increased risk for dementia with no differences between Hispanic and non-Hispanic White respondents. 76.4% of all respondents were “very” or “somewhat” interested in a dementia prevention program, interest was significantly higher in Hispanic (83.0% vs 73.3% “very” or “somewhat interested,” X2 (3, N=1226) = 14.8, p=0.002) and younger respondents (82.2% vs 72.1% “very” or “somewhat” interested X2 (1, N=1302) = 20.0, p<0.001).
CONCLUSION: General knowledge of risk factors for dementia is low, contrasting with high interest in a prevention program. Interest is higher in Hispanic and younger persons compared with older or non-Hispanic White persons. A dementia prevention program accessible to younger and Hispanic populations could help narrow dementia outcome disparities.

Key words: Alzheimer’s disease, health equity, risk factor.

Introduction

Alzheimer’s disease (AD), the most common cause of dementia, results from a long and complex pathophysiological process that begins at least two decades before the appearance of symptoms (1). While the accumulation of beta-amyloid (Aβ) plaques followed by hyperphosphorylated tau-containing neurofibrillary tangles define AD and are likely largely responsible for symptoms, the degree to which, and precisely how the presence of plaque and tangle pathology influence cognitive and functional symptoms remain incompletely understood. Many additional factors significantly influence the absolute risk and age of onset of both AD and AD-related dementias (AD/ADRD), many of which are modifiable (2). Previous studies highlight the importance of these risk factors, estimating that about 40% of dementia cases are attributable to them, and thus potentially preventable if fully addressed (2). Physical activity slows cognitive decline (3, 4) and lowers risk for AD/ADRD (5, 6). Sleep disturbances in middle age increase risk of dementia (7, 8). Loneliness increases risk, while strong social engagement is protective (9). Although recent evidence is mixed, eating a Mediterranean diet may lower risk (10, 11). Avoiding smoking (12), moderating alcohol consumption, treating hearing loss, obesity, and depression, and avoiding traumatic brain injury and polluted air also reduce the risk of developing dementia later in life (2, 10).
While risk factor modification in older adults is important in addressing AD/ADRD, epidemiological evidence shows that midlife may represent a crucial window to address many risk factors. For example, for diabetes and hypertension, the longer one has the condition and the more poorly controlled, the greater the later life dementia risk (13-15). Early intervention on modifiable risk factors will likely be more effective in reducing dementia risk compared to interventions at ages more proximate to a typical dementia diagnosis.
Next, interventions accessible to minoritized communities could help narrow AD/ADRD outcome disparities. Unaddressed modifiable risk factors and inequities in social determinants of health likely play an important role in driving disparities. Black and Hispanic persons are 1.5-2 times more likely than non-Hispanic persons to develop AD/ADRD (16). They have less brain Aβ (17) but more cerebral small vessel disease (18), indicative of differences in the etiology of AD/ADRD among ethnicities, likely reflecting the degree to which conditions like hypertension and diabetes are adequately treated (14, 15, 19). Additionally, risk factors like diet and physical activity level vary in relationship to race and socioeconomic status, which may be due to differences in the built environment, including access to parks and quality food, and neighborhood walkability (20, 21).
A small number of cognitive health clinics aimed at providing care and recommendations to delay or even prevent the onset of dementia have been established to address AD/ADRD through optimization of modifiable risk factors, with early evidence suggestive of a benefit (22, 23). However, the focus of these programs is often on older individuals and does not emphasize including marginalized populations. While dementia prevention programs may provide the most benefit to younger persons and those in disadvantaged communities, the degree of interest in cognitive health clinics aimed at dementia prevention in general and attitudes about participation in such programs among younger persons and those in marginalized Hispanic communities are unknown. To address this knowledge gap, we surveyed Arizona residents age 35 and over about knowledge of modifiable risk factors for dementia and attitudes toward prevention programs to address risk factors. We next examined differences in responses between Hispanic and non-Hispanic White respondents as well as between younger and older respondents (age 35-54 vs 55 and over) to determine the interest and feasibility of establishing a cognitive health clinic to reduce dementia risk serving middle-aged residents, with an emphasis on Hispanic community participation, in Arizona.

Methods

Survey Data Collection

We partnered with SSRS, a professional market and survey research firm, to survey Arizona residents age 35 or older using an online self-administered questionnaire in either English or Spanish between July 13, 2021 and August 2, 2021. The questions were developed as a practical, exploratory approach to gather information about knowledge of AD/ADRD risk factors and attitudes toward participation in dementia prevention and research. The questions of interest are shown in Table 1. The study utilized third-party opt-in web unpaid panels with potential respondents being invited via email. Within the survey, respondents were screened to ensure that they met qualification criteria. Multiple panels were used to ensure adequate representation of hard-to-reach respondents, including Hispanic and Black households as well as those with income <$25,000 per year. Extensive program checks were conducted to ensure that skip patterns followed the design of the questionnaire, and the program was tested via desktop computer as well as mobile devices to ensure consistent visualization across devices prior to execution. The final program used various quality checks, including questions asking respondents to select a specific answer to be sure they were reading each question, a “low incidence item” question and a “speeder trap” to ensure the final sample included only high-quality surveys.

Table 1. Survey Questions

The survey sample sought to mirror the demographic composition of Arizona, the geographic area of interest. We employed weighting to compensate for sample designs and patterns of non-response that might bias results. To handle missing data among some of the self-reported demographic variables for purposes of weighting only, we employed a technique termed “hot decking,” which replaces the missing values of a respondent randomly with another similar respondent without missing data. These are further determined by variables predictive of non-response that are present in the entire file (see technical appendix for more details)(24, 25). Weighting was accomplished using SPSS INC RAKE, an SPSS extension module that simultaneously balances the distributions of all variables. The weighting parameters were sex, age, education, race/ethnicity, region of Arizona, civic engagement, marital status, and household income, derived from the September 2019 Current Population Survey Volunteering and Civic Life Supplement (civic engagement) or the 2019 American Community Survey (sex, age, education, race/ethnicity, region of Arizona, marital status, and household income). Weights were trimmed at the 2nd and 98th percentiles to prevent individual interviews from having too much influence on the final results.

Statistical Analysis

All data analyses were performed in SPSS version 27. We calculated descriptive statistics for survey questions of interest in the whole group, then by ethnicity and by age group for questions of interest. We then compared the distribution of answers between Hispanic and non-Hispanic White respondents at all ages with additional comparisons made between the ethnic groups in only those at younger ages, between 35-54 years-old, using chi-squared tests. We also examined differences in responses between all participants by age, comparing younger respondents (age 35-54) to older respondents (55 and over), again using chi-squared tests.

Results

A total of 1,303 persons aged 35 and older, of whom 332 (25.5%) were Hispanic and 842 (64.6%) non-Hispanic White, responded to the survey between July 13th and August 2nd, 2021. 63.3% of respondents were female. The characteristics of the survey population are shown in Table 2.

Table 2. Respondent Characteristics

Attitudes in Hispanic Compared to Non-Hispanic White Respondents

Hispanic respondents were more likely to report having a close friend or relative who had been diagnosed with AD or dementia compared to non-Hispanic White respondents (66.8% vs 51.9%, X2 (1, N=1226) = 24.1, p<0.001). Hispanic respondents also reported a higher degree of concern about a close relative (73.1% vs 63.2% being “very” or “somewhat worried”, X2 (3, N=1226) = 40.3, p<0.001) as well as themselves (71.3% vs 55.6% being “very” or “somewhat worried”, X2 (3, N=1226) = 63.1, p<0.001) being diagnosed with AD or dementia in the future compared to Non-Hispanic White respondents. The concern about a future diagnosis being more prominent among Hispanic respondents remained evident when limiting analyses to younger individuals 35-54 years old (75.4% vs 69.8% “very” or “somewhat interested,” X2 (3, N=500) = 16.5, p<0.001).
When asked, “are you aware of any risk factors that increase the risk for developing AD or dementia later in life?” (Table 1, Question 4), 30.7% of respondents reported being aware of such factors and there was no significant difference between non-Hispanic White and Hispanic respondents (31.6% vs 29.8%, X2 (1, N=1226) = 0.41, p=0.52). When the general question about risk factors was followed up by asking whether a specific item such as diabetes or physical inactivity are risk factors for dementia (Table 1, Question 5), the percentage of persons reporting awareness of individual risk factors increased significantly, generally doubling or more with the exception of hearing loss. Hearing loss was the risk factor with the least awareness, with Hispanic respondents being more likely to be aware of this risk factor. There were no statistically significant differences between the two groups in awareness of any other risk factors. Figure 1 summarizes the percentages of persons reporting awareness of individual risk factors for AD/ADRD in the overall group, as well as by ethnicity and age.

Figure 1. Awareness of risk factors by demographic group

We next asked about interest in a dementia prevention program (Table 1, Question 6). 76.4% of respondents reported being “very” or “somewhat interested” in such a program. Interest was significantly higher in Hispanic compared to non-Hispanic White respondents (83.0% vs 73.3% “very” or “somewhat interested,” X2 (3, N=1226) = 14.8, p=0.002). Reponses of the entire sample and comparisons between Hispanic and non-Hispanic White respondents for this and other survey questions are summarized in Figure 2. Hispanic male respondents were significantly more likely than non-Hispanic White male respondents to have interest in a dementia prevention program (86.2% vs. 71.9% being “very” or “somewhat interested,” X2 (3, N=444) = 15.2, p=0.002), while Hispanic female respondents were numerically more interested in a program compared to non-Hispanic White females, the difference in interest was not significant (80.7% vs. 74.1%, X2 (3, N=778) = 3.9, p=0.269). Stratifying by income level did not significantly alter results, Hispanic respondents expressed greater interest in a dementia prevention program in both the low (<$50,000/year) and greater (>$50,000/year) income groups (83.0% vs. 71.9% being “somewhat” or “very interested” for the income <$50,000 group, X2 (3, N=450) = 8.0, p=0.046, and 84.7% vs. 71.4% X2 (3, N=590) = 13.2, p=0.004 for the income >$50,000 group). Hispanic participants remained more interested in a prevention program regardless of education level (X2 (3, N=40) = 12.9, p=0.005 for those with a high school degree or less, X2 (3, N=1186) = 12.6, p=0.005) for those with more than a high school degree).

Figure 2. Distribution and Comparison of Responses of Hispanic and Non-Hispanic White Participants

Next, we asked about interest in participation in a clinical trial aimed at preventing or delaying Alzheimer’s disease or dementia (Table 1, Question 7). Hispanic respondents had greater interest in participating in AD/ADRD prevention research (78.1% Hispanic vs 62.8% Non-Hispanic White were “very” or “somewhat interested”, X2 (3, N=1226) = 35.1, p<0.001). This finding was consistent when limiting analyses to younger respondents, age 35-54 (80.7% vs. 68.4%, X2 (3, N=500) = 15.6, p=0.001) as well as both only male (82.4% vs. 61.8%, X2 (3, N=444) = 30.3, p<0.001) and only female respondents (74.9% vs. 63.1%, X2 (3, N=778) = 10.4, p=0.015).

Attitudes in Younger Compared to Older Respondents

Younger participants (age 35-54) were more worried about themselves being diagnosed with AD or another dementia in the future (72.4% vs. 51.5%, X2 (3, N=1302) = 74.9, p <0.001) compared to older respondents (age 55 and older). This remained the case when comparing younger with older Hispanic respondents, with 74.8% of the younger group versus 61.1% of the older group reporting that they were either “very” or “somewhat worried” (X2 (3, N=383) = 9.9, p=0.019). Comparisons between younger and older respondents are shown in Figure 3.

Figure 3. Distribution and Comparison of Responses of Participants Ages 35-54 Compared to Those 55+

There were no significant differences between younger and older persons in awareness of risk factors that increase the risk of developing AD/ADRD later in life (33.4% vs. 28.8%, X2 (1, N=1302) = 3.1, p =0.076) though again surprisingly the trend was for younger persons to be more aware of risk factors. Interest in participating in a program targeting dementia prevention was greater in the younger population. 83.0% of younger participants were either “very interested” or “somewhat interested” in participating in a dementia prevention program versus 72.1% of older participants (X2 (3, N=1302) = 20.0, p<0.001). Younger respondents also reported significantly greater interest in being referred for a drug or clinical trial to prevent, delay, or treat AD or dementia if one were available (75.0% vs. 61.7% being “somewhat” or “very interested,” X2 (3, N=1302) = 33.6, p<0.001).

Discussion

This study provides insight into the degree of awareness of risk factors for dementia, interest in participating in a prevention program, and respective differences between Hispanic and non-Hispanic White persons as well as younger and older persons in Arizona. We found that a significant majority of respondents reported not knowing about any risk factors for dementia, with no difference between Hispanic and non-Hispanic White participants. While less than 1/3 of respondents reported being aware of risk factors, about 3/4 were interested in dementia prevention programs, indicating that an important disconnect exists between the desire for addressing modifiable risk factors to lower dementia risk and the ability to accomplish this goal.
Next, we found that Hispanic respondents had a higher degree of interest in a dementia prevention program than non-Hispanic White respondents. This may follow from the fact that Hispanic respondents were also more likely to know someone with dementia and be more worried both about a relative and themselves developing dementia in the future. This interest, paired with the fact that Hispanic persons have more prevalent and poorly addressed modifiable risk factors, suggests dementia prevention programs have the potential to provide greater benefit to Hispanic communities, which could narrow outcome disparities. It is important to point out that, at a minimum, access to such clinics would need to be equitable, if not somewhat designed, tailored, and with recruitment skewed toward underrepresented communities. While interest in a dementia program is encouraging, interest alone does nothing to address existing barriers to implementation (28). Increasing healthcare resources, both in terms of education and delivery, are required to meet the existing interest in underrepresented communities to make an impact. Increasing investment in preventative and early intervention programs for common but addressable risk factors for dementia may be one such strategy.
This study also found that Hispanic respondents were more interested in participating in a prevention clinical trial than non-Hispanic White respondents. This contrasts with historical underrepresentation of Hispanic participants in AD/ADRD clinical trials and suggests that the problem is not a fear of, or a lack of interest in participation, but rather efforts of studies and trials to reach and provide access to diverse participants. Congruent with our finding is a national survey that found no differences in willingness to participate in a prevention trial among socioeconomic groups (26) and the recent report from the United States Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (US POINTER), which successfully enrolled 30.8% non-White participants (27).
Most clinical programs and trials are focused on older individuals. Here, we found interest in a dementia prevention program was higher among younger individuals ages 35-54 compared to those age 55 and older suggesting that inclusion of younger individuals in prevention clinics is feasible. There may be several reasons for this finding. Cognitive decline and dementia were considered to be a normal part of aging in previous decades. Concerted efforts have sought to dispel this notion, and younger individuals may acknowledge the fact that dementia is not a normal part of aging more than older individuals. They may also be aware of interventions to lower the risk of this outcome, whereas older individuals might view cognitive decline and dementia as an inevitable part of aging. Another possible explanation is in the changing attitudes regarding overall health among generations, with prioritization of “wellness,” (defined as “the active pursuit of activities, choices, and lifestyles that lead to a state of holistic health” (28)) being more prominent in younger generations. For example, with respect to diet, millennials (those born between 1981 and 1996) are much more likely (80% vs 64%) to prioritize health and wellness when making food choices as compared to baby boomers (those born between 1946 and 1964) (29). A similar phenomenon may be occurring in this study, with younger persons having a greater interest in proactively maintaining their health and wellness through lifestyle choices. Finally, there may be some degree of selection bias in the sample, with younger persons being more likely to participate in the survey if they had prior experience and knowledge of dementia and interest in ways to lower future risk.

Limitations

Study limitations include a reliance on self-reported data on education, income, and other demographic data. The study was conducted online and thus likely excluded some persons with no or little access to reliable internet. The sample was predominately female (63.3%). Next, respondents were not paid to complete the survey, and there may be some degree of selection bias of persons who have an interest in dementia prevention and modifiable risk factors as being more likely to offer their time and effort to complete the survey. This selection bias may have been introduced to a higher degree in younger persons who would presumably be more occupied with their careers and younger families, requiring a greater interest in the topic to motivate participation and completion. These limitations may have rendered the survey population less representative of the general population, reducing generalizability, and potentially skewing results to some degree. The sample was also intentionally representative of Arizona residents, and thus may not be generalizable beyond this geographic area. These limitations could be mitigated in future research by increasing geographic reach, obtaining more complete demographic information, offering monetary compensation for participation, and including non-web-based/electronic formats to promote participation among those with limited internet access.

Conclusions

Among a representative sample of Arizona residents, knowledge of modifiable risk factors for dementia was low, contrasting with the high degree of interest in a dementia prevention program addressing these risk factors. Interest in a clinic was higher among Hispanic compared to non-Hispanic White and younger compared to older respondents. Implementation of a cognitive health clinic aimed at dementia prevention inclusive of young and diverse participants is feasible in Arizona. Provided that access is equitable among ethnic groups, a dementia prevention program addressing modifiable risk factors may be one way to narrow AD/ADRD outcome disparities.

Funding: Women Investing in Science and Health of the Banner Health Foundation sponsored this study. 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.

Conflict of Interest Disclosure: Harshita Talkad, Yinghua Chen, and Adam Bress declare no conflicts of interest. Jessica Langbaum has received grant support from the NIH/NIA; State of Arizona, Arizona Alzheimer’s Consortium; and Eli Lilly; and has received consulting fees from Alector, Biogen, and Denovo Biopharma. Pierre Tariot has received grant support from the NIA and consulting fees from AbbVie, AC Immune, Acadia, Athira, Biogen, BioXcel, Bristol Myers Squibb, Cognition Therapeutics, Corium, Cortexyme, CuraSen, Eisai, Genentech, Immunobrain, Janssen, Lundbeck, MapLight, Merck & Co., Novartis, Novo Nordisk, Otsuka & Astex, Roche, Syneos, and T3D Therapeutics. He has received honoraria from Arizona Osteopathic Medical Association, Arbor Scientia, Clinical Care Operations, Health & Wellness Partners, Indiana University, Merck, Miller Medical Communications, River West Meeting Associates, Tucson Osteopathic Medical Foundation, and University of Cincinnati. He is a contributor to U.S. Patent #11/632, 747, “Biomarkers of Neurodegenerative Disease” and participates on a Data Safety Monitoring Board or Advisory Board for AbbVie, AC Immune, Acadia, Athira, Corium, Cortexyme, Eisai, Genentech, ImmunoBrain, Merck, Novo Nordisk, and Syneos Health. Jeremy Pruzin received support for this manuscript from Women Investing in Science and Health and the Banner Health Foundation. He has grant support from the NIA as a co-investigator, received honoraria from the American Academy of Neurology and consulting fees for an Eisai advisory board.

Ethical standards: Survey participants consented to participate in the survey through opting in to the web-based panel. All data was de-identified and remained so once collected. The study was determined to be Institutional Review Board exempt (Health and Human Resources §46.104 Exemption 3).

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

A.M.R. Hanna¹, A.-M.E. Hartford¹, S. Morassaei¹

1. Aging & Health Program, Department of Rehabilitation Science, Queen’s University, Kingston, ON, Canada

Corresponding Author: Andrew M. R. Hanna, Aging & Health Program, Department of Rehabilitation Science, Queen’s University, Louise D. Acton Building, 31 George St., Kingston, ON K7L 3N6 Canada, Institution Main Phone: 613-533-6000, Author Institutional Email: 19amrh@queensu.ca

J Aging Res & Lifestyle 2024;13:115-125
Published online December 12, 2024, http://dx.doi.org/10.14283/jarlife.2024.18

Abstract

Diet is a key modifiable risk factor in many chronic diseases, including age-related diseases. The Mediterranean diet (MedDiet) is an extensively studied dietary pattern which has been proposed as a lifestyle intervention to promote healthy aging in the general population, due to its numerous health benefits. Randomized controlled trials (RCTs) have attempted to explore the mechanism(s) by which the MedDiet exerts its beneficial effects on aging. One proposed mechanism is that the MedDiet helps to slow down a process called ‘inflamm-aging’, a type of chronic, low-grade inflammation which contributes to aging. To explore the evidence supporting this hypothesized mechanism, we conducted a scoping review of existing RCTs which used a MedDiet intervention and assessed at least one molecular outcome of potential relevance to inflamm-aging. We identified 14 papers representing 12 unique RCTs. Based on our findings, we present 10 recommendations for the next clinical trial of the MedDiet in inflamm-aging.

Key words: Mediterranean diet, clinical trial design, scoping review, inflammation, inflamm-aging, nutrition, molecular.

Part One: Executive Summary

Diet is a modifiable risk factor for many health conditions, including age-related diseases (ARDs) (1, 2), such as atherosclerosis, cardiovascular disease, certain cancers, type 2 diabetes, and hypertension. As such, healthcare professionals are interested in identifying healthy dietary patterns which can be promoted to the general population.
The Mediterranean Diet (MedDiet) is one of the most extensively studied diet patterns in the world. This centuries-old diet derives its name from the Mediterranean region where it is traditionally consumed (3). While exact definitions vary, it is generally characterized as involving a high intake of unrefined cereals (such as pasta and bread), fruits, vegetables, legumes, nuts, and extra virgin olive oil; moderate intake of poultry, dairy products (mostly light cheeses and yogurts), and alcohol (particularly, red wine); and low intake of red meat, sweets, and processed foods (3-5). Overall, the MedDiet is characterized by a low-glycemic index and an emphasis on plant-based sources of protein (6).
The MedDiet is being studied for its potential role in healthy aging. In fact, several benefits of the MedDiet in promoting healthy aging have been demonstrated in existing studies (5). For instance, adherence to the MedDiet has been shown to offer the following health benefits in regards to age-related decline: it may help in the prevention of muscle mass reduction, mineral bone density reduction, cognitive decline, immune system dysregulation, and cardiovascular diseases (4). Additionally, it can aid in preserving sexual capacity (4), reducing the onset of frailty (5), and increasing the lifespan (5). It seems that adherence to the MedDiet can confer these significant health benefits in aging if adopted by midlife (5, 7).
It would be of interest to understand why the MedDiet confers beneficial health effects to aging. Knowing this could provide new insights into developing new healthy diet interventions or optimizing existing ones. For instance, certain characteristics of the MedDiet, such as its high content of fibre, unsaturated fatty acids, antioxidants, vitamins, and phytochemicals, have been identified as specific components conferring health benefits (8). However, the exact mechanism(s) by which the MedDiet exerts its beneficial effects is unknown (9). Tosti et al. identified potential mechanisms, one of which is protection against inflammation, oxidative stress, and platelet aggregation (9). There is some evidence to support this suggestion; for instance, certain phytochemicals in the MedDiet are known to have anti-inflammatory effects (6, 8, 9). Also, the MedDiet has been shown to beneficially alter composition of the gut microbiome by favouring bacterial strains with anti-inflammatory properties (3).
Researchers are interested in the role of inflammation in aging. Specifically, chronic, sterile, low-grade inflammation is thought to accelerate the aging process through multiple mechanisms (4). It is proposed that this phenomenon, known as ‘inflamm-aging’, occurs due to an excessive immune response to normal stressors (10) due to immune system dysregulation with age (11). This contributes to the progression of ARDs (12) such as cardiovascular diseases, type 2 diabetes, cognitive decline, dementia, frailty, sarcopenia, and cancer (5). Relevant molecules in inflamm-aging include the transcription factor Nuclear Factor kappa B (NFκB) (12); inflammatory cytokines (12) such as interleukins, tumour necrosis factor alpha (TNFα), and interferon gamma (IFNγ); and C-reactive protein (CRP) (13).
Knowing that the MedDiet appears to have beneficial health effects regarding to aging and ARDs, and considering the emerging evidence of its anti-inflammatory effects, we decided to conduct a scoping review on the current state of evidence regarding the role of the MedDiet in reducing inflammation at the molecular level. We asked the question: “How is consumption of a MedDiet related to molecular changes of potential relevance to inflamm-aging?”
To conduct this scoping review, we searched four databases from inception to January 10, 2023, looking for articles that referred both to a MedDiet and to the inflammatory process at the molecular level. We included any randomized clinical trial (RCT) in healthy adult patients who were randomized to receive either a MedDiet or a control diet, and in which at least one molecular outcome related to the inflammatory process was measured. For instance, the study could look at the amount of transcription or translation of a protein involved in inflammation, such as a cytokine. Specifically, that molecule should be involved in a chronic, sterile inflammation i.e., inflamm-aging, regardless of whether the article specifically referred to the process as “inflamm-aging”. We decided to focus on RCTs which included a general population of adults, rather than a subset with a specific diagnosis, as the ultimate goal is to inform policy on whether or not the MedDiet is a useful lifestyle intervention for healthy aging for adults in general. Thus, we excluded RCTs for which a medical diagnosis, among other criteria, were required for enrolment. In all, we screened 4,614 articles and included 14 articles representing 12 RCTs. The included studies are summarized in Table 1. The inclusion and exclusion criteria are detailed in Table 2.

Table 1. Summary of Included Studies

*Note: The actual total sample size for the Jaacks et al., 2018 study is n=27 because there is a second intervention arm (“Supplements diet”). This is NR for the present scoping review as it was not treated as a control compared to the MedDiet; therefore, the results of this diet do not help to answer the research question. **Note: Some studies compared more than one MedDiet to more than one control. For those interested in the finer details beyond what is presented in the executive summary, please see Appendix 2B. NR: Not reported; CHO: Carbohydrate; CoQ: Coenzyme Q; EVOO: Extra virgin olive oil; HabDiet: Habitual diet; MUFA: Monounsaturated fatty acid; PUFA: Polyunsaturated fatty acid; RDA: Recommended daily allowance; SFA: Saturated fatty acid; USDA: United States Department of Agriculture; VOO: Virgin olive oil; WOO: Washed olive oil; PBMCs: Peripheral blood mononuclear cells; For full names of molecule acronyms, please see Appendix 2F.

Table 2. Inclusion & Exclusion Criteria

1. An experiment performed in vitro is acceptable if the sample originated from study participant; 2. Adjustments of MedDiet to local cultural or other preferences are acceptable.

Based on the results of our scoping review, we have developed the following list of 10 recommendations for designing a future clinical trial of the MedDiet in inflamm-aging.
1. Consider larger sample sizes to detect smaller effect sizes. The included studies varied in sample size from 20 (14, 15) to 166 participants (16). The sample size necessary for an adequately powered RCT looking at molecular changes relevant to inflamm-aging is most likely much higher. For instance, if we consider a continuous variable such as high sensitivity CRP (hs-CRP), if we would like to detect changes of at least 0.1 mg/dL (normal range is <0.3 mg/dL, with 0.3-1.0 mg/dL being considered “minor elevation” (17)), the estimated overall samples size for a two-arm study in which there is a 10 or 15% dropout rate are 872 to 922, respectively (if interested, please see Part Three: Detailed Methods and Results for the full calculation).
2. Collect data at multiple timepoints, for at least 1 year. The duration of time participants received a diet intervention or control varied from 4 weeks (14, 15, 18-20) to 1 year (21, 22). Most (n=5/9) short-term studies (4 weeks to 3 months) had statistically significant results (14, 15, 19, 20, 23), whereas most (n=4/5) long-term studies (4 months to 1 year) had statistically insignificant results (16, 22, 24, 25), regardless of the molecules, genes, or types of changes assessed. As we found that most significant results were observed in short-term studies, the MedDiet may have a transient effect on inflammation. Future studies should be long term (e.g., 1 year) and repeat measurements at multiple timepoints (e.g., 1, 3, 6, 9, 12 months), to evaluate the effect of the MedDiet over time.
3. Collect information on baseline diet. Only five studies had inclusion criteria for baseline diet (14, 15, 23, 24, 26, 27) and only three studies had a run-in or washout diet prior to randomization (25, 27, 28). Only one study reported actual baseline diet of participants (26). This is a potential confounding factor, as we cannot know whether the intervention and control groups had similar baseline diets. This also makes it difficult to compare between RCTs.
4. Collect information on patient socioeconomic status (SES). All three components of SES (occupation, education, and income level) are related to quality of diet (29). For instance, lower SES individuals tend to have higher intake of white bread and refined cereals, whereas higher SES individuals tend to consume more wholegrain products (29). Also, higher SES individuals tend to consume greater quantities of fruit and vegetables than lower and middle SES individuals (29). As the MedDiet places an emphasis on fruits, vegetables, and wholegrain, unrefined cereals, we would expect studies with a higher SES population to have a baseline diet more closely resembling the MedDiet. Despite this, only two studies reported on SES, of which the only factor considered was educational attainment, not income or occupation (24, 25). Furthermore, educational attainment information was incomplete for these studies, as they reported only on percentage of college graduates without providing the remaining breakdown (24, 25).
5. Collect information on race/ethnicity and include diverse populations. Similarly, only two studies reported race/ethnicity (the same two which reported on SES) (24, 25). Race/ethnicity could have implications for dietary studies (30) which could influence both participant baseline diet (especially for studies with HabDiet controls) and participant response to a MedDiet intervention. Including a diverse population in future RCTs would increase generalizability.
6. All study participants should follow an isocaloric diet, regardless of allocation. Most (n=11) studies did not report the caloric state of their experimental and control arms. The Western diet, which is calorically rich, has been shown to create a state of chronic inflammation (31). Conversely, caloric restriction has been shown to reduce systemic inflammation (11). Therefore, if studies had different caloric states between arms, this could create a confounding variable which could affect participant inflammatory status. Future studies should track caloric intake and compare it to expenditure to ensure that all trial arms have the same caloric excess or deficit. An isocaloric diet avoids this confounding and is an important control for both the intervention and control diets. The caloric intake of participants should be tracked over time to ensure it is both (i) isocaloric and (ii) comparable between arms.
7. Use a MedDiet intervention defined by flexible dietary patterns, not fixed macronutrient proportions, and assess adherence. Studies reported variety in the types of MedDiets used. The most common were self-described regular/traditional MedDiets (n=3) (18, 26-28) and MedDiet + virgin olive oil (VOO) (n=3) (19, 20, 23). Two RCTs used a MedDiet plus additional component, being coenzyme Q (CoQ) in one RCT (14, 15) and vitamin D in another (22). The remaining four articles described their intervention as some variation of the MedDiet such as a Med-inspired diet (n=1) (18), Med-style diet (n=1) (25), or modified MedDiet (n=2) (16, 24). At the surface, the most academically satisfying RCT might appear to be a precisely defined MedDiet with percentage macronutrient distributions (e.g., “5% of calories from monounsaturated fatty acids”), but on reflection this is unlikely to be a sustainable approach that most individuals would practice over the long term – they may not have the time, desire, knowledge, or resources to do so. Our ultimate goal should be to create a clinically relevant RCT that answers whether the intervention as it would be administered by a healthcare provider (e.g., a primary care provider recommending that a patient follow a MedDiet) is relevant to inflamm-aging. Therefore, we recommend that the RCT present the MedDiet intervention as a flexible dietary pattern; for instance, through one or both of the methods suggested by the Fundación Dieta Mediterránea (32): (i) basic recommendations (e.g., “eat plenty of fruits and vegetables”) and/or (ii) specific serving targets in the MedDiet pyramid (e.g., “1-2 servings of fruits per day, at least 2 servings of vegetables per day”). This would also allow people to more easily modify the MedDiet to suit their tastes and cultural background. Additionally, a MedDiet adherence score such as the 18-point score proposed by Sofi et al. (33) could be used to assess adherence and ensure it sufficiently differs from the control diet. Finally, it would be interesting to assess participant satisfaction with the MedDiet intervention and compare that to the control diet, to predict likelihood of long-term adherence. Ultimately, a healthcare provider can counsel a patient to adopt a MedDiet, but if the patient cannot implement it (e.g., it is too time-consuming, expensive, or complicated), then the intervention will not contribute to improving that patient’s health, and we would be performing an RCT which may be more focused on academic interests than pragmatic objectives.
8. Use a Habitual Diet (HabDiet) control, know what participants’ HabDiet is, and know if it is sufficiently different from MedDiet. The most common control diet was the Habitual diet (HabDiet), which was used in seven studies (16, 21-24, 26, 27). Of the seven studies which used HabDiet controls, only one reported baseline participant diet (26). Three other articles had inclusion criteria regarding participant baseline diet but did not report actual diet (23, 24, 27). Finally, another three articles had neither (16, 21, 22). While half (n=7) of studies used a HabDiet control, only one reported baseline participant diet (26). As mentioned earlier, this is an important limitation, since it means we have limited information as to what diet the intervention group was being compared to. If the HabDiet of participants in some studies already resembled a MedDiet, the comparison between two very similar diets could explain the non-significant results. Future studies using a HabDiet should have inclusion criteria regarding HabDiet characteristics, to ensure that any participants randomized to the HabDiet have a sufficiently different baseline diet from the MedDiet intervention to allow for meaningful comparison. As well, HabDiet participants should be required to complete a food diary.
9. Use caution regarding a Western diet control. The second most common control diet was the Western/SFA (Saturated Fatty Acid) diet which was used in four studies (14, 15, 20, 28). It is possible that most study participants’ HabDiet will already be a Western diet (that will be determined by food diaries). However, we do not recommend that RCTs randomize participants to a Western diet, for two reasons. Firstly, there is probably not clinical equipoise regarding the Western diet, which is known to be an unhealthy pro-inflammatory diet (34) which increases the likelihood of obesity (35). Not being in a state of clinical equipoise regarding the interventions being tested is one of the ‘transgressions of trialists’ identified by Meinert (36). Secondly, if participants’ HabDiet does not match a Western diet, especially depending on how that diet is defined, then the control diet could be considered an ‘exaggerated’ diet which may not be representative of the HabDiet of the average patient seeing their healthcare provider in that region. Again, our goal should be to create the most clinically relevant RCT possible.
10. Assess a wide variety of molecular changes, both upstream and downstream in inflammation. A total of 91 molecular changes were assessed across all 14 studies, with only 20 being statistically significant between MedDiet and control across six articles (14, 15, 19-21, 23). Overall, a total of 59 unique molecules and genes were assessed. The most common molecular change assessed was serum levels of molecules, in 11 studies (14, 16, 18-20, 23-28). Other common changes assessed included gene expression (19, 23), production by peripheral blood mononuclear cells (PBMCs) (21, 22), and mRNA levels (14, 15). The most commonly assessed molecules were cytokines (such as interleukins, tumour necrosis factor alpha (TNFα), and interferons) in nine studies (18-23, 25, 26, 28); CRP and hs-CRP collectively in six studies (16, 18, 23, 24, 27, 28); and nuclear factor kappa B (NFκB) and its associated molecules (i.e., p65 subunit, IKKβ, IκBα) in three studies (15, 19, 20). Despite the anti-inflammatory effect of the MedDiet on NFκB at multiple levels and for its related molecules, studies did not show significant effects for cytokines (15, 18-20, 23, 25, 26, 28) nor CRP and hs-CRP (16, 18, 23, 24, 26, 27). While consumption of a MedDiet may create molecular changes of potential relevance to inflamm-aging, based on existing studies these appear to be mostly limited to the NFκB signalling pathway. Interestingly, even though NFκB regulates production of pro-inflammatory cytokines (37), this did not correspond to significant changes in cytokine production. Furthermore, lower CRP or hs-CRP would be expected in individuals with lower inflamm-aging, as these molecules are among its key mediators (13), but this was not observed. Overall, this suggests that while a MedDiet creates a statistically significant reduction in NFκB signalling, this reduction may not be of a sufficient effect size to create a meaningful downstream reduction in pro-inflammatory cytokines or CRP and hs-CRP, based on existing studies. Thus, we recommend that future studies look at a wide variety of molecular changes, including NFκB and its associated molecules, a panel of inflammatory cytokines, and CRP or hs-CRP.

Part Two: Consolidated Methods and Results

We searched four databases (MEDLINE, Web of Science, EMBASE, CINAHL) for relevant articles on the MedDiet and inflamm-aging. Searches were conducted on January 10, 2023, and included articles in English since database inception.
Title and abstract and full-text screening were conducted by two reviewers (AMRH & AMEH) using the inclusion and exclusion criteria presented in Table 2. Key data extracted include individual study characteristics (including location, setting, and duration) baseline population characteristics (including age, sex, baseline diet, chronic disease, and social factors), dietary intervention details (description of experimental and control arms), and molecular changes (qualitative assessment of directional change for MedDiet intervention vs. control). Quality assessment was conducted using the Critical Appraisal Skills Programme (CASP) Randomized Controlled Trial (RCT) checklist, 2020 version. Article screening, data extraction, and quality assessment were conducted by two reviewers (AMRH & AMEH). Conflicts were resolved by consensus.
In total, 14 articles were included, representing 12 unique RCTs. Of these 12 RCTs, 4 were crossover studies (14, 15, 18-20). The RCTs were conducted in seven different countries, the majority (n=10) of which were in Europe. Three RCTs were in the USA (24-26) and one in Australia (16). All articles except one (24) reported on single-centre studies. Two articles (21, 22) reported on subsets of a larger multi-centre trial. Sample sizes ranges from 20 (14, 15) to 166 participants (16).
As per the inclusion criteria, we only included studies with adults. The mean age ranged from 29.7 years (25) to 71.0 years (16). Three articles did not report mean age (14, 15, 20). Of the 10 studies which reported mean body mass index (BMI), six included participants with a mean BMI considered overweight (15, 16, 18, 21, 22, 25). Most studies included both males and females, except two studies which included only females (24, 25) and one study which included only males (20).
Only five studies had inclusion criteria for baseline diet (14, 15, 23, 24, 26, 27) and only three studies had a run-in or washout diet prior to randomization (25, 27, 28). Only one study reported actual baseline diet of participants (26).
Only two studies reported on SES and race/ethnicity (24, 25). For SES, only education was listed (not income nor occupation). Educational attainment information was incomplete for both studies, reporting only on percentage of college graduates without providing the remaining breakdown (24, 25). Of these two studies, one provided only a partial breakdown of race/ethnicity with only the proportion of white participants reported (24).
Studies reported variety in the types of MedDiets used. The most common were self-described regular/traditional MedDiets (n=3) (18, 26-28) and MedDiet + virgin olive oil (VOO) (n=3) (19, 20, 23). Two RCTs used a MedDiet plus additional component, being coenzyme Q (CoQ) in one RCT (14, 15) and vitamin D in another (22). The remaining four articles described their intervention as some variation of the MedDiet such as a Med-inspired diet (n=1) (18), Med-style diet (n=1) (25), or modified MedDiet (n=2) (16, 24).
Half (n=7) of articles provided a breakdown of the proportion of macronutrients (protein, carbohydrates, and fat) as percent daily energy (14-16, 18-20, 22). The other half (n=7) provided either only a macronutrient partial breakdown (n=2) (26, 28), serving requirements/recommendations (n=3) (21, 25, 27), a combination of serving requirements/recommendations and macronutrient ratios (n=1) (24), or individualized advice to increase MedDiet score (n=1) (23).
The most common control diet was the Habitual diet (HabDiet), which was used in seven studies (16, 21-24, 26, 27), followed by a Western/SFA (Saturated Fatty Acid) diet which was used in four studies (14, 15, 20, 28). Of the seven studies which used HabDiet controls, only one reported baseline participant diet (26). Three other articles had inclusion criteria regarding participant baseline diet but did not report actual diet (23, 24, 27). Finally, another three articles had neither (16, 21, 22). Only three studies reported on the caloric state of their intervention and control arms (14-16).
A total of 91 molecular changes were assessed across all 14 studies, with only 20 being statistically significant between MedDiet and control across six articles (14, 15, 19-21, 23). Of those 20 changes, all were consistently anti-inflammatory (14, 15, 19-21, 23), meaning that a molecule with a pro-inflammatory role significantly decreased in the MedDiet group versus control, or significantly increased for a molecule with an anti-inflammatory role.
Overall, a total of 59 unique molecules and genes were assessed. The most common molecular change assessed was serum levels of molecules, in 11 studies (14, 16, 18-20, 23-28). Other common changes assessed included gene expression (19, 23), production by peripheral blood mononuclear cells (PBMCs) (21, 22), and mRNA levels (14, 15).
The most commonly assessed molecules were cytokines (such as interleukins, tumour necrosis factor alpha (TNFα), and interferons) in nine studies (18-23, 25, 26, 28); C-reactive protein (CRP) and high sensitivity CRP (hs-CRP) collectively in six studies (16, 18, 23, 24, 27, 28); and nuclear factor kappa B (NFκB) and its associated molecules (i.e., p65 subunit, IKKβ, IκBα) in three studies (15, 19, 20).
Some studies showed a significant effect on NFκB, in terms of its p65 subunit expression (19), its activation in PBMCs (20), and its mRNA levels and mRNA levels of related molecules in the signalling pathway (i.e., IKKβ and IκBα) (15). However, this effect was not consistently observed across all studies, and in addition, one study found a non-significant effect of the MedDiet on expression of NFκB and IκBα compared to two control diets (19). Despite the anti-inflammatory effect of the MedDiet on NFκB at multiple levels and for its related molecules, studies did not show significant effects for cytokines (15, 18-20, 23, 25, 26, 28) nor CRP and hs-CRP (16, 18, 23, 24, 26, 27).
Most (n=5/9) short-term studies (4 weeks to 3 months) had statistically significant results (14, 15, 19, 20, 23), whereas most (n=4/5) long-term studies (4 months to 1 year) had statistically insignificant results (16, 22, 24, 25), regardless of the molecules, genes, or types of changes assessed.

Part Three: Detailed Methods and Results

Detailed methods

Search strategy

We searched four databases (MEDLINE, Web of Science, EMBASE, CINAHL) for relevant articles focused on the MedDiet and inflamm-aging (see Appendix 1A for themes and keywords). The searches were conducted on January 10, 2023, and included all articles in English since database inception. Appendices 1B-E present the complete search strategies for each database. This review was registered with Open Science Framework: https://osf.io/cpgqw.

Screening & selection process

Title and abstract and full-text screening were conducted by two reviewers (AMRH & AMEH) using Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) using the inclusion and exclusion criteria as presented in Table 2. Duplicates were removed after importation of studies into Covidence. Conflicts were resolved by consensus. The PRISMA diagram is shown in Figure 1.

Figure 1. PRISMA Diagram

Data extraction

Data extraction was conducted by two reviewers (AMRH & AMEH) using Microsoft Excel (Microsoft Corporation, Redmond, US). Conflicts were resolved by consensus.
Key data extracted include individual study characteristics (including location, setting, and duration) baseline population characteristics (including age, sex, baseline diet, chronic disease, and social factors), dietary intervention details (description of experimental and control arms), and molecular changes (qualitative assessment of directional change for MedDiet intervention vs. control). Tables containing all extracted data can be found in Appendix 2.

Quality assessment

Quality assessment was conducted by two reviewers (AMRH & AMEH) using the Critical Appraisal Skills Programme (CASP) Randomized Controlled Trial (RCT) checklist, 2020 version. Conflicts were resolved by consensus. The quality assessment table is presented in Appendix 2E.
Scoring was conducted using the following principles, adapted from Arantzamendi et al. (38): Yes (Y) = 1 point, No (N) or Can’t tell (C) = 0 points. For question 4, which is split into three sub-questions, each Y = 1/3 point. Therefore, each study will receive a score out of 11. A score of 5 or less was considered “low” quality, 5 1/3 to 8 2/3 was considered “moderate” quality, and 9 or higher was considered “high” quality.

Synthesis

AMRH synthesized articles using the simplified thematic analysis approach outlined by Aveyard (39). To generate a theme, at least three articles were required, at least one of which had to be a high-quality study.

Limitations of methods

First, as it is not feasible to make a list of every single molecule potentially involved in inflamm-aging or inflammation, articles had to refer to one of the main molecules known to be involved in inflamm-aging or inflammation (see Table 2), or the molecule had to be explicitly stated as involved in inflamm-aging or inflammation to be included. Therefore, we cannot rule out the possibility that we have missed articles reporting on relevant molecules which were neither in this list nor explicitly stated by the authors as involved in inflamm-aging or inflammation.
Second, the search strategies (see Appendices 1B-E) exploded subject headings where possible, but not every minor subheading was included as a keyword for redundancy. For example, we included both the subject heading “Monokines” (under exploded “Cytokines”) and the keyword “monokine*” in MEDLINE but not the keyword “Oncostatin M”, which appears under the “Monokines” subject heading tree. Therefore, we may have missed articles reporting on a molecule such as Oncostatin M, if no other search terms appeared in the title or abstract.
Third, we did not include all alternate names of molecules as search terms. For example, while we included the exploded subject heading “Interleukins” (under exploded “Cytokines”) and the keyword “interleukin*” in MEDLINE, we did not include as keywords terms such as “epidermal cell derived thymocyte activating factor”, an alternate name for interleukin 1.
Fourth, we limited our search and selection to articles in English. It is possible that relevant articles in other languages were not included.

Detailed results: Descriptive overview

Study characteristics

In total, 14 articles were included, representing 12 unique RCTs. Of these 12 RCTs, 4 were crossover studies (14, 15, 18-20). The RCTs were conducted in seven different countries, the majority (n=10) of which were in Europe, of which nearly half (n=4) were conducted in Spain (14, 15, 19, 20, 23). Only four studies were conducted outside of Europe: three in the USA (24-26) and one in Australia (16).
All articles except one (24) reported on single-centre studies. Two articles (21, 22) reported on subsets of a larger multi-centre trial. Sample sizes ranged from 20 (14, 15) to 166 participants (16). Please see Appendix 2A for full details.

Baseline population characteristics

As per the inclusion criteria, we only included studies with adults. The mean age ranged from 29.7 years (25) to 71.0 years (16). Three articles did not report mean age (14, 15, 20). Of the 10 studies which reported mean body mass index (BMI), six included participants with a mean BMI considered overweight (15, 16, 18, 21, 22, 25). Most studies included both males and females, except two studies which included only females (24, 25) and one study which included only males (20).
All but two studies included only individuals without clinically diagnosed chronic diseases. One study had a total of 11 cases of chronic disease despite describing their study population (n=20 participants) as “healthy” at baseline (19). Another study had a sample exclusion criterion of a lengthy but not necessarily comprehensive list of chronic diseases (23).
Most studies included samples drawn from the general population; however, one study included only medical students (20), and another included only postpartum breastfeeding women (25).
Only five studies had inclusion criteria for baseline diet (14, 15, 23, 24, 26, 27) and only three studies had a run-in or washout diet prior to randomization (25, 27, 28). Only one study reported actual baseline diet of participants (26).
Only two studies reported on SES and race/ethnicity (24, 25). For SES, only education was listed (not income nor occupation). Educational attainment information was incomplete for both studies, reporting only on percentage of college graduates without providing the remaining breakdown (24, 25). Of these two studies, one provided only a partial breakdown of race/ethnicity with only the proportion of white participants reported (24). Please see Appendix 2C for full details.

Dietary intervention characteristics

Studies reported variety in the types of MedDiets used. The most common were self-described regular/traditional MedDiets (n=3) (18, 26-28) and MedDiet + virgin olive oil (VOO) (n=3) (19, 20, 23). Two RCTs used a MedDiet plus additional component, being coenzyme Q (CoQ) in one RCT (14, 15) and vitamin D in another (22). The remaining four articles described their intervention as some variation of the MedDiet such as a Med-inspired diet (n=1) (18), Med-style diet (n=1) (25), or modified MedDiet (n=2) (16, 24).
Half (n=7) of articles provided a breakdown of the proportion of macronutrients (protein, carbohydrates, and fat) as percent daily energy (14-16, 18-20, 22). Among these studies, the range was 15% to 20% protein, 47% to 55% carbohydrate, and 38% to ~40.5% fat. The other half (n=7) provided either only a macronutrient partial breakdown (n=2) (26, 28), serving requirements/recommendations (n=3) (21, 25, 27), a combination of serving requirements/recommendations and macronutrient ratios (n=1) (24), or individualized advice to increase MedDiet score (n=1) (23).
The most common control diet was the Habitual diet (HabDiet), which was used in seven studies (16, 21-24, 26, 27), followed by a Western/SFA (Saturated Fatty Acid) diet which was used in four studies (14, 15, 20, 28). Of the seven studies which used HabDiet controls, only one reported baseline participant diet (26). Three other articles had inclusion criteria regarding participant baseline diet but did not report actual diet (23, 24, 27). Finally, another three articles had neither (16, 21, 22).
Only three studies reported on the caloric state of their intervention and control arms (14-16). Only four studies specified the frequency of meals and/or snacks (15, 20, 25, 26). Please see Appendix 2D for full details.

Molecular changes

A total of 91 molecular changes were assessed across all 14 studies, with only 20 being statistically significant between MedDiet and control across six articles (14, 15, 19-21, 23). Overall, a total of 59 unique molecules and genes were assessed. The most common molecular change assessed was serum levels of molecules, in 11 studies (14, 16, 18-20, 23-28). Other common changes assessed included gene expression (19, 23), production by peripheral blood mononuclear cells (PBMCs) (21, 22), and mRNA levels (14, 15).
The most commonly assessed molecules were cytokines (such as interleukins, tumour necrosis factor alpha (TNFα), and interferons) in nine studies (18-23, 25, 26, 28); C-reactive protein (CRP) and high sensitivity CRP (hs-CRP) collectively in six studies (16, 18, 23, 24, 27, 28); and nuclear factor kappa B (NFκB) and its associated molecules (i.e., p65 subunit, IKKβ, IκBα) in three studies (15, 19, 20). Please see Appendix 2B for full details.

Quality assessment

Half (n=7) of the included articles were considered high quality and half (n=7) were considered moderate quality; none were low quality (Appendix 2E). The lowest score was 6 out of 11 and the highest was 10 2/3 out of 11. Of note, no studies received points for investigator blinding to dietary intervention, as it was either not performed or it was unclear whether it was performed for each study.

Detailed results: Major themes

MedDiet has anti-inflammatory effect in minority of molecules

As previously stated, only 20 of 91 molecular changes were statistically significant across studies. Of those 20 changes, all were consistently anti-inflammatory (14, 15, 19-21, 23), meaning that a molecule with a pro-inflammatory role significantly decreased in the MedDiet group versus control, or significantly increased for a molecule with an anti-inflammatory role.

Significant effect on NFκB

Some studies showed a significant effect on NFκB, in terms of its p65 subunit expression (19), its activation in PBMCs (20), and its mRNA levels and mRNA levels of related molecules in the signalling pathway (i.e., IKKβ and IκBα) (15). However, this effect was not consistently observed across all studies, and in addition, one study found a non-significant effect of the MedDiet on expression of NFκB and IκBα compared to two control diets (19).

Non-significant effect on cytokines and CRP

Despite the anti-inflammatory effect of the MedDiet on NFκB at multiple levels and for its related molecules, studies did not show significant effects for cytokines (15, 18-20, 23, 25, 26, 28) nor CRP and hs-CRP (16, 18, 23, 24, 26, 27).

Longer interventions correlated with non-significant results

Most (n=5/9) short-term studies (4 weeks to 3 months) had statistically significant results (14, 15, 19, 20, 23), whereas most long-term studies (n=4/5) (4 months to 1 year) had statistically insignificant results (16, 22, 24, 25), regardless of the molecules, genes, or types of changes assessed.

Example sample size calculation

Let us consider an outcome measure such as high sensitivity C-reactive protein (hs-CRP). The normal value for hs-CRP is < 0.3 mg/dL, with values 0.3-1.0 mg/dL being considered “minor elevation”, such as what might be seen in certain chronic diseases such as diabetes or being correlated with risk factors such as obesity or sedentary lifestyle (17).

Using the following equation (modified from Meinert (36)), we can estimate the necessary sample size:

Where:
– n = sample size, per arm.
– Z(α/2)= the Z-statistic corresponding to the significance level where α = probability of type I error, for a two-tailed test, i.e., in which we would like to detect changes in hs-CRP in both directions. Usually, α is set to 0.05.
– Zβ= the Z-statistic corresponding to the power, where β = probability of type II error; usually, β is set to 1- β = 0.90 or 0.80. For our purposes, we will use 0.80.
– σ2= standard deviation of hs-CRP in the hypothetical study population. As reported standard deviations in included studies differed in their reported standard deviations of hs-CRP will use a standard deviation of 0.5 mg/dL.
– δ2= the minimum effect size we wish to detect. In our case, we will use 0.1 mg/dL.

Using these values:

We calculate:

If we estimate a dropout rate of 10% or 15%, the estimated effect size increases to 436 (392/0.9) or 461 (392/0.85), respectively, per arm. Therefore, the entire study, considering a 1:1 allocation to intervention and control diet, in this scenario it is estimated that the RCT would require a sample size of between 872 (436 x 2) and 922 (361 x 2).

Funding: None.

Conflict of interest statement: The authors state that there is no conflict of interest.

Acknowledgements: We would like to thank Ms. Sarah Wickett (Queen’s University Library) for her valuable advice regarding the database search strategy for this scoping review.

CRediT author statement: AMRH: Conceptualization, Methodology, Formal analysis, Investigation, Writing – Original Draft, Writing – Review & Editing, Visualization. AMEH: Investigation, Writing – Review & Editing. SM: Conceptualization, Methodology, Resources, Writing – Review & Editing, Supervision.

Ethical Standards: N/A

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

A.S. Khachaturian

Corresponding Author: Ara Khachaturian, Editor-in-Chief, Journal of Aging Research and Lifestyle, Executive Vice-President, Campaign to Prevent Alzheimer’s Disease, USA, ara@pad2020.org

J Aging Res & Lifestyle 2024;13:113-114
Published online December 12, 2024, http://dx.doi.org/10.14283/jarlife.2024.17

Introduction

The Journal of Aging Research and Lifestyle is committed to advancing knowledge and inspiring new investigations into the intersections of lifestyle, health, and aging. Among the diverse papers published in our journal, the recent article by Andrew Hanna, «Ten Recommendations for the Next Clinical Trial of the Mediterranean Diet in Inflamm-Aging: Results & Insights from a Scoping Review,» exemplifies the quality and focus we aim to highlight. We believe this work offers valuable lessons not only in its findings but also in how scientific communication can be optimized for impact.

Introducing a New Layout

To better serve our readership—a broad spectrum of researchers, clinicians, and public health professionals—the Journal is exploring a new format for presenting scientific research. This alternative approach emphasizes simplicity, comprehensibility, and brevity without compromising scientific rigor. Borrowing insights from editorial standards in fields like Alzheimer’s research, we encourage authors to structure their work into three distinct parts:
1. Narrative Overview: A plain-language section designed for a wide audience, combining elements of the introduction, results, and discussion into a concise synopsis. This section focuses on answering the «so what?» question, highlighting the significance and implications of the research.
2. Consolidated Methods and Results: A middle-ground section that provides high-level details of the study’s design and key findings, helping readers grasp the essential elements quickly.
3. Detailed Methods and Data: For those seeking in-depth understanding, this section includes comprehensive methodological specifics, figures, and supplementary material to support reproducibility and further inquiry.

This format enhances accessibility while ensuring scientific depth, aligning with our goal of fostering multidisciplinary engagement.

About the Mediterranean Diet and Inflamm-Aging

Inflamm-aging—chronic, low-grade inflammation associated with aging—is increasingly recognized as a key factor in age-related diseases. The Mediterranean Diet (MedDiet) has long been heralded for its potential to mitigate such inflammation. Hanna’s article systematically reviewed 12 randomized controlled trials (RCTs) to examine the MedDiet’s molecular effects on inflamm-aging markers, such as NFκB signaling and cytokines. Despite promising findings, the review revealed inconsistencies and gaps in current research, leading to the formulation of ten actionable recommendations for future studies.

Ten Questions and Recommendations for Future Research

Building on Hanna’s work, the article addresses the following critical questions:
1. How can statistical power be increased? Larger sample sizes, with at least 900 participants per trial arm, are necessary to ensure robust conclusions.
2. What duration is needed for meaningful outcomes? Trials should extend beyond one year with periodic assessments.
3. How should baseline dietary habits be reported? Documenting participants’ habitual diets is essential for contextualizing findings.
4. What role does socioeconomic status (SES) play? Collecting SES data helps understand its impact on diet adherence and health outcomes.
5. Why prioritize racial and ethnic diversity? Diverse cohorts are critical for generalizable results.
6. How to standardize caloric intake? Controlling caloric consumption minimizes confounding variables.
7. Should interventions be flexible? Adaptable dietary patterns reflect real-world conditions better than rigid prescriptions.
8. What is the ideal control group? Habitual diets, rather than pro-inflammatory Western diets, provide an ethical and scientifically sound comparison.
9. Which biomarkers are most relevant? Comprehensive panels, including upstream (e.g., NFκB) and downstream (e.g., cytokines, CRP) markers, should be evaluated.
10. How to balance rigor with practicality? Studies must remain rigorous while reflecting real-world settings to inform public health strategies effectively.

The Road Ahead: Embracing Multidisciplinary Perspectives

Hanna’s recommendations lay a strong foundation for future research, but they also reflect a broader challenge: the need to integrate diverse methodologies and perspectives. Researchers must embrace multidisciplinary approaches, leveraging advancements in molecular biology, nutrition science, and public health to design trials that not only answer scientific questions but also translate findings into actionable health policies.

Call to Action

As the Journal of Aging Research and Lifestyle, we encourage authors to adopt this innovative format and consider the broader implications of their work. By doing so, we can collectively elevate the scientific discourse, addressing complex issues like inflamm-aging with clarity, precision, and actionable insights.
We invite submissions that mirror the qualities of Hanna’s work: rigorous, insightful, and impactful. Together, let us pave the way for a healthier, more informed aging population.

© Serdi 2024

M. Igase¹, K. Igase², S. Hino³, D. Uchida³, Y. Okada⁴, M. Ochi⁴, Y. Tabara⁵, Y. Ohyagi⁴

1. Department of Anti-Aging Medicine, Ehime University Graduate School of Medicine, Toon City, 791-0295, Japan; 2. Department of Advanced Neurosurgery Ehime University Graduate School of Medicine, Toon City, 791-0295, Japan; 3. Department of Oral and Maxillofacial Surgery, Ehime University Graduate School of Medicine, Toon City, 791-0295, Japan; 4. Department of Geriatric Medicine and Neurology, Ehime University Graduate School of Medicine, Toon City, 791-0295, Japan; 5. Graduate School of Public Health, Shizuoka Graduate University of Public Health, Kita-Ando 4-27-2, Aoi-ku, Shizuoka, 420-0881, Japan.

Corresponding Author: Michiya Igase MD, PhD, Department of Anti-Aging Medicine, Ehime University Graduate School of Medicine, Toon City, 791-0295, Japan, Tel: +81 89 960-5851Fax: +81 89 960-5852 E-mail: migase@m.ehime-u.ac.jp

J Aging Res & Lifestyle 2024;13:108-112
Published online December 4, 2024, http://dx.doi.org/10.14283/jarlife.2024.16

Abstract

BACKGROUND: Early detection of cognitive decline, including mild cognitive impairment, is expected to provide a better prognosis. Several studies have suggested an association between periodontitis and mild cognitive impairment.
OBJECTIVES/DESIGN: To test the hypothesis that there is an association between severe periodontitis and mild cognitive impairment in community residents who participated in a dental health check-up program.
PARTICIPANTS/SETTING: Community residents who participated in our dental health checkup program were enrolled (age=67.5±9.9, 62.9% female).
MEASUREMENTS: Mild cognitive impairment was tested using the MCI screening test. Periodontitis was diagnosed based on a widely used clinical periodontal parameter, the probing pocket depth. Statistical analysis was based on logistic regression models adjusted for potential confounders.
RESULTS: Among 321 subjects, mild cognitive impairment was detected in 41. Severe periodontitis (probing pocket depth > 6mm) was detected in 123 cases, with a higher prevalence of mild cognitive impairment in the severe periodontitis group (65.9%) than in the unimpaired group (34.3%). The inclusion of four variables (age, education, functional teeth, and presence of severe periodontitis) in a multivariate logistic regression model revealed a statistically significant difference in the association between severe periodontitis and mild cognitive impairment (odds ratio = 4.024, p < 0.001).
CONCLUSIONS: A strong association was seen between severe periodontitis and mild cognitive impairment. Severe periodontitis appears to be a risk factor for mild cognitive impairment, and patients with severe periodontitis should be assessed for mild cognitive impairment.

Key words: Periodontitis, mild cognitive impairment, MCI screen, probing pocket depth, multivariate logistic regression model.

Introduction

Dementia is a neurodegenerative disease syndrome of the central nervous system in the elderly (1). Alzheimer disease (AD) is the leading cause of age-related dementia, reported to affect approximately 57.4 million people worldwide in 2021 (2). AD is a disease with multiple etiologies and complex pathology. Effective therapy for AD is still in its early stages and usually demonstrates limited curative effect (3).
Early detection of AD in an earlier phase of cognitive decline is expected to provide a better prognosis (4). Recent attention has focused on patients with mild cognitive impairment (MCI), who are at high risk for developing dementias, including AD (5). MCI is representing the stage between the age dependent decline in memory and thinking and the more serious decline of dementia. In general, approximately 10% to 15% of MCI cases progress to AD each year (6).
Periodontitis is a chronic infectious disease of the oral cavity that manifests as the progressive destruction of the supporting tissues of the teeth (7). Also, periodontitis is a common source of systemic inflammation and especially neuroinflammation might be a result of this to accelerate progressive deterioration of neuronal functions during aging or exacerbate pre-existing neurodegenerative diseases, such as Alzheimer’s disease (AD).
Recent study showed that IL-1β and TNF-α were upregulated upon periodontitis and the systemic upregulation of these two cytokines may promoted a pro-inflammatory environment in the brain contributing to the development of AD (8). Given these results, it is considered that systemic inflammation serves as a connecting link between periodontitis and AD.
Probing pocket depth (PPD) is a dental procedure that measures the depth of the pocket between the tooth and the gum line and in general, the average healthy PPD is around 3 mm. Although with severe AD have been found to have significantly higher PPD values than those with mild AD (9).
In this study, we sought to establish an attributable risk of periodontitis as possible trigger for MCI using data from participants in our health checkup program and mitigate the possible routes of AD onset. In addition, several studies have suggested an association between periodontitis and MCI (10, 11). Although periodontitis might affect the development of cognitive impairment through different mechanisms, the question of whether periodontitis is a risk factor for mild cognitive impairment (MCI) in the general population remains uncertain.

Methods

Inclusion and exclusion criteria

The subjects were participants in a complete medical checkup program at the Ehime University Hospital Anti-aging Center (Ehime, Japan), which is specifically designed to evaluate for atherosclerosis, including cerebrovascular disease (12) and oral health status. This program is provided for community residents without specific requirements for participation and aims to evaluate factors relating to dementia. As for oral health status, applicants can select this. Participants who gave informed consent to the use of clinical information obtained at the health checkup program for a longitudinal study were enrolled in the Shimanami Health Promoting Program (the J-SHIPP study) conducted by the Ehime University Graduate School of Medicine. On behalf of all authors, the corresponding author states that there is no conflict of interest.
Of 324 participants who participated in the dental oral health checkup program between February 2016 and March 2023, we excluded one participant with no functional teeth and two participants those with a score of < 24 on the Mini-Mental State Examination (MMSE).

Assessment for MCI

To discriminate mild from severe cognitive impairment, we used the Mini-Mental State Examination (MMSE). This test is the most often-used short screening tool to obtain an overall measure of cognitive impairment in clinical, research and community settings and is the most commonly used cognitive screening test for MCI and dementia (13, 14). Maximum score is 30 points, and the 24-point cut-off score is considered to demonstrate good specificity and acceptable sensitivity (15).

MCI screening

MCI was assessed for using the Japanese version of the MCI screening test, a 10-min, computationally scored, staff-administered test (16). Cross-validation was performed using the Clinical Dementia Rating score as reference. Overall accuracy of this score in identifying subjects with MCI is 97% (17).

Education

All participants completed a questionnaire regarding the highest level of education achieved.

Blood sample collection

Blood samples were collected between 09.00 and 10.00 hours from the cubital vein after an overnight fast. HbA1c level was determined in fresh samples.

Periodontitis diagnosis

The diagnosis of periodontitis was based on a widely used clinical periodontal parameter, the PPD (18).

Statistical methodology

We performed a cross-sectional study looking for possible associations between periodontitis and MCI.
All continuous variables are expressed as mean ± SD, unless otherwise indicated. The normal distribution (Kolmogorov-Smirnov test) and the homoscedasticity (Levene test) of the data were verified. Comparisons between the MCI and normal groups were assessed using the unpaired t-test for parametric variables and the Mann-Whitney U-test for nonparametric variables. The chi-squared test was used to assess frequency differences between the number of MCI cases and number of male subjects. Covariate adjusted analysis was performed by multiple logistic regression analyses with possible independent parameters including age, Hba1c, education, functional teeth, PPD, and the presence of severe periodontitis. In all comparisons, a p value < 0.05 was considered statistically significant. Correlations between variables were evaluated using Pearson’s correlation coefficient. Analyses were performed using commercial software (SPSS software package for Windows version 17, SPSS, Chicago, IL, USA).

Results

Clinical characteristics of the MCI group and normal cognition group are summarized in Table 1. Among 321 cases, MCI was detected in 41 cases (12.8%), of which 18 (43.9%) were male. The MCI group was significantly older (74.8 ± 8.5 y) than the normal group (66.3 ± 9.5 year; p <0.001) and had significantly fewer years of education (12.0 ± 2.4 y) than the normal group (13.2 ± 2.1 y; p = 0.002). Other known risk factors for MCI, including sex, BMI, and HbA1c, did not significantly differ between the MCI and normal groups.

Table 1. Clinical characteristics of the MCI group and normal cognition group

MCI, Mild Cognitive Impairment; BMI, body mass index; HbA1c, glycosylated hemoglobin; MPI, Memory performance index; MMSE, Mini-Mental State Examination; PPD; probing pocket depth. *HbA1c: equivalent to the internationally used HbA1c defined by the National Glycohemoglobin Standardization Program.

There was a significant difference between the MCI and normal group in the number of functional teeth (26.0 ± 3.1 vs. 24.2 ± 3.4, p = 0.004). The incidence of MCI was not significantly higher in patients with mild to moderate periodontitis (odds ratio [OR] = 1.042; 95% confidence interval [CI]: 0.999–1.087 p = 0.054) (Table 2). In contrast, the percentage of teeth affected by periodontitis (PPD > 4 mm) was significantly higher in the MCI than in the normal group (p=0.001). In addition, severe periodontitis (PPD > 6 mm) was detected in 123 patients (38.3%) and the percentage of MCI was significantly higher in the severe periodontitis group than in the normal group (p < 0.001).
Four variables (age, education, number of functional teeth, and severe periodontitis) were entered into a multivariate logistic regression model. A statistically significant difference was seen in the association between severe periodontitis and MCI (OR = 4.024, 95% CI: 1.768–9.161, p < 0.001) (Table 2).

Table 2. Multiple logistic regression analysis to assess the presence of MCI

OR = odds ratio; CI = confidence interval; PPD = probing pocket depth

Discussion

In this study of subjects of a medical/dental checkup program, we identified severe periodontitis in 123 of 321 examinees. The prevalence of MCI was significantly higher in the severe periodontitis group than in the normal groups. Multivariate logistic regression analysis revealed a statistically significant difference in the association between severe periodontitis and MCI. These findings suggest that severe periodontitis may be a risk factor for MCI, and that affected patients should be assessed for mild cognitive impairment.
Because poor oral health is highly suspected to be a risk factor for dementia (19), the correlation between clinical oral condition, including the number of functional teeth and severity of periodontitis, and cognitive decline has now become a research concern. Our investigation of the effects of the number of functional teeth and severity of periodontitis on mild cognitive impairment (MCI) provides scientific evidence for further research.

Association between number of functional teeth, periodontitis and cognitive decline

In their cross-sectional study of the number of functional teeth in AD patients, Tsuneishi et al. reported that older people visiting dental offices with fewer teeth were more likely to have AD (20).
With regard to MCI, our study found a significant difference between MCI patients and normal patients in both the number of functional teeth and presence or absence of periodontitis (functional teeth: 24.2 ± 3.4 in the MCI group vs. 26.3 ± 3.0 in the normal group. p=0.004; presence of periodontitis: 12.1 ± 7.3 in the MCI group vs. 9.5 ± 7.5 in the normal group. p= 0.039). Luo H et al showed that the absence of ≥ 8 or more functional teeth was a significant risk factor for MCI, and suggested that better access to dental care, health education on risk factors of MCI, and promotion of good oral health may mitigate the burden of cognitive impairment (21). Their results are concordant with our present study.

Association between periodontitis and cognitive decline

Tiisanoja et al. demonstrated that when periodontitis was defined as more than 1 tooth with a PPD > 4 mm, the relative risks of periodontitis and dementia did not significantly differ (22).
A recent meta-analysis also showed no statistical significance in the effect of mild periodontitis on dementia (OR = 1.59; 95%CI, 0.92–2.76). However, subgroup analysis revealed that moderate or severe periodontitis was significantly associated with dementia (OR = 2.13; 95%CI, 1.25–3.64) (23). Consistent with these findings, we also identified a slight but significant difference between the MCI group and normal group regarding the number of periodontitis-affected teeth, but did not find a significantly higher incidence of MCI in patients with mild to moderate periodontitis. However, the percentage of MCI was significantly higher in the severe periodontitis group than in the normal groups. Finally, we demonstrated using multivariate logistic regression analysis that severe periodontitis was an independent risk factor for the presence of MCI, with a greater than fourfold higher OR of MCI in those with severe periodontitis than in those without this condition.
The search for effective predictors of dementia in people with MCI has attracted strong interest. Our present and previous study results indicate that patients with severe periodontitis are at higher risk of developing MCI, followed by dementia. Periodontitis can be alleviated and improved through self-management and medical examination. Considering the treatability of periodontitis, prospective analysis of the association between periodontitis treatment and MCI may be useful.
Several limitations of this study are noteworthy. First, routine screening programs in most countries do not include a medical/dental checkup program. In Japan, in contrast, medical/dental checkup programs are widely available to the general public. Our institution’s medical/dental checkup program provides extensive discretionary screening. However, it is needed to recognize that there was high selection bias in our study. Because the body size of Asians, including Japanese, is smaller than that of Westerners (24), our findings may not be entirely generalizable to other populations. Second, although we used the MCI screen in this study, the Montreal Cognitive Assessment (MoCA) test is reported to better meet the criteria for screening for detection of MCI among patients aged over 60 years (25). If we had also used the MoCA test, the accuracy of our results may have improved. Third, with regard to MCI assessment, our analyses were retrospective, and a degree of selection bias may have been introduced. Study participants were recruited from antiaging checkup examinees, and accordingly the participants may not necessarily have represented the general population. In particular, our program participants were fitness-conscious, and heavy smokers and alcohol drinkers may have been underrepresented. Fourth, the cross-sectional nature of the study prevents any assignation of causality in the association between periodontitis and mild cognitive impairment. Moreover, we were unable to assess the association of periodontitis with dementia as no program participant had dementia. Allowing for these limitations, we found a strong association between severe periodontitis and MCI.

Acknowledgments: We thank Guy Harris, PhD, from Dmed (https: www.dmed.co.jp) for editing a draft of this manuscript.

Funding: None.

Conflict of interest: None.

Ethical Standards: All study procedures were approved by the Ethics Committee of Ehime University Graduate School of Medicine (30-K6). Written informed consent was obtained from all participants.

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 2024

K. Belqaid¹, G.F. Irving², N. Waldréus³

1. Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden, and Medical Unit Health Professionals, Karolinska University Hospital, Stockholm, Sweden; 2. Department of Neurobiology, Care sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm Sweden; 3. Department of Neurobiology, Care sciences and Society, Division of Nursing, Karolinska Institutet, Stockholm Sweden

Corresponding Author: Kerstin Belqaid, Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden, and Medical Unit Health Professionals, Karolinska University Hospital, Stockholm, Sweden, kerstin.belqaid@ki.se

J Aging Res & Lifestyle 2024;13:99-107
Published online November 5, 2024, http://dx.doi.org/10.14283/jarlife.2024.15

Abstract

Frailty is a clinical condition common among older persons with heart failure (HF) and has been associated with an increased risk of adverse outcomes such as falls, disability, long-term care, and death. Malnutrition in terms of weight loss and sarcopenia is closely related to frailty. This review summarises nutritional interventions to improve components of frailty in older persons with HF. The online databases of Medline, Embase, Web of Science and Cinahl were searched in 2022 to identify studies of nutritional interventions among older persons with HF with outcomes related to frailty (e.g., body composition or functional measures). The records were screened, and eligible articles identified. In addition, reference lists of eligible articles and of four previously published reviews regarding HF and nutrition were screened. Eight articles were included in the review, of which seven were controlled trials and one was a feasibility study. Nutritional interventions included: vitamin D supplementation (n =2), protein supplementation (n =3), enteral nutrition (EN) or oral nutritional supplements (ONS) (n =2), or a low carbohydrate diet (n =1). The studies using protein supplementation, ONS or EN reported improvements on functional measures or body composition. Furthermore, the results from this review add to the evidence of the importance of combining nutritional support with physical activity to improve muscle mass and functional outcomes among older persons with HF.

Key words: Heart failure, frailty, nutrition therapy, malnutrition.

Introduction

Heart failure (HF) is a clinical syndrome that implies elevated intracardiac pressures and/or inadequate cardiac output as a result of structural and/or functional abnormality of the heart (1). The prevalence of HF increases with age, with an incidence rate of 1% for persons aged <55 years to >10% among those aged ≥70 years (2). There are several known causes of HF, with coronary artery disease and hypertension being the most common in Western and developed countries (3). The prognosis for HF has improved over the last decades, with a mortality rate reported of 67% within five years after diagnosis (4). Quality of life among persons with HF is often reduced, as symptoms such as shortness of breath and fatigue impair physical function and the capability to carry out social activities, with the risk of social isolation as a consequence (5).
Frailty is common among persons with HF, with an estimated prevalence of around 45% (6). Frailty is a clinical condition that increases with older age and is characterized by increased vulnerability to stress (7). Frail, older persons have an increased risk of adverse outcomes such as falls, disability, long-term care, and death (8). One important component in the frailty phenotype, as described by Fried et al. (9), is poor nutrition in terms of weight loss and sarcopenia, e.g., low muscle mass and decreased strength. In a recent review and meta-analysis, the prevalence of malnutrition among persons with HF was found to be 46%, and the all-cause mortality of those individuals with malnutrition was almost double compared with those without malnutrition (10). Another significant component of frailty is reduced functional capacity, which may impair quality of life. Munk et al. (11) reported that a nutritional intervention for older persons who had been discharged from the hospital improved both physical function and quality of life. Thus, effective interventions with the potential to treat malnutrition and improve frailty are important for persons with HF, as they may reduce mortality and increase functional capacity and quality of life.
Several underlying mechanisms for malnutrition, sarcopenia, and frailty among persons with HF have been reported. Reduced nutrient intake may be attributed to loss of appetite and lack of energy to prepare food (12). Furthermore, several metabolic abnormalities have been found in skeletal muscles in persons with HF, which may contribute to sarcopenia and loss of muscle mass. For example, compared to healthy persons, those with HF have a shortage of energy in muscle (13), reduced muscle blood flow and early anaerobic metabolism during exercise (14), as well as intrinsic changes in skeletal muscle fibres/histology and biochemistry (15). Older persons in general have been found to require higher doses of protein or amino acids to achieve muscle protein synthesis in comparison to younger persons (16). Overall, vitamin D deficiency has been associated with muscle weakness and sarcopenia among older persons (17, 18). Additionally, impaired bowel perfusion may lead to malabsorption and protein loss from the gut (19).
Previous research on nutritional interventions in HF has been performed with diverse outcomes and is summarised in a review by Billingsley et al (20). Sodium restriction and fluid restriction have been studied to reduce fluid retention, thereby achieving a reduction in HF signs and symptoms. The results conflicted with some studies reporting that sodium restriction improved the amount of extracellular fluid, fatigue, and quality of life. On the contrary, other studies found increased readmissions and hospitalizations among persons on sodium restriction and decreased energy intake. Dietary patterns such as the Mediterranean diet and Dietary Approaches to Stop Hypertension (DASH) have been reported to prevent the onset of HF and possibly reduce HF signs and symptoms and quality of life. Caloric restriction has been studied in overweight persons with HF (BMI ≥30), that has resulted in reduced body weight and improved glucose control and cardiac function (21). On the other hand, unintentional weight loss is a risk factor for malnutrition, which in turn is associated with increased mortality. A common strategy to increase weight and muscle mass in malnutrition is energy and/or protein supplementation (20).
The overall purpose of this review was to provide an overview of nutrition-related interventions to improve the components of frailty in older persons with HF by 1) identifying and describing nutrition interventions evaluated to improve nutritional and functional outcomes in older persons with HF and 2) summarising and describing the effects of these nutrition interventions.

Methods

Search strategies

To identify potentially relevant articles, the online databases Medline, Embase, Web of Science, and Cinahl were searched in June 2022. No time limit was applied. The final search strategies were drafted by an experienced librarian in collaboration with two of the authors (KB, NW) and included the search terms ‘heart failure’, ‘malnutrition’, and ‘frailty’. The complete search strategy is presented as a supplementary file.
Each record title and abstract were screened by two out of a total of three reviewers (KB, NW, GF) working independently in a blinded process facilitated by the Rayyan free tool for systematic reviews (22). After the screening, all three reviewers met to discuss inconsistencies in their judgment of records.
To find more relevant articles, the reference lists of articles identified through the database search mentioned above were screened, as were the reference lists of four previously published literature reviews regarding HF and nutrition (12, 20, 21, 23).

Inclusion criteria

The search strategies were based on the following PICO:
• Population: Older persons (mean age ≥65 years) with chronic HF with no further specification.
• Intervention: Any nutritional intervention
• Control: No restrictions regarding the control group.
• Outcome: Measures related to frailty as defined by Fried et al (9), such as nutritional status (e.g., body composition, weight change) or functional measures (e.g., hand grip strength, timed get up and go test (TGUG), 6-minute walk test (6MWT)).

Original articles in the English language reporting any nutritional intervention evaluated in a group of older persons (mean age ≥65 years) with HF and outcomes related to the concept of frailty (9)—i.e., body composition/sarcopenia and functional capability—were included in the review. We included randomised controlled trials, controlled trials, and feasibility studies.

Quality assessment

The Revised Cochrane risk-of-bias tool for randomised trials (24) was used to assess the methodological quality of the studies. The risk of bias was assessed by the first author (KB).

Data synthesis and presentation

There were large variations among the articles regarding interventions and methodology, so a narrative review approach was chosen to present the results (25). The results are presented according to the nature of the intervention studied. Data were extracted by the first author (KB). The PRISMA checklist (26) was followed and is available as a supplementary file. No review protocol was prepared for this study.

Results

Selection process

After removing duplicates from the database search, the remaining 1083 records were screened, resulting in seven articles eligible for more thorough reading. Of these, only two articles were found to meet inclusion criteria for the review.
Screening the reference lists of the seven articles mentioned above resulted in an additional ten articles. Screening the reference lists of the four previously published literature reviews regarding HF and nutrition resulted in an additional 20 articles. These were retrieved and read thoroughly, of which six were found eligible for inclusion in the review. Thus, a total of eight articles were included in the final review.
See Figure 1 for an overview of the process of identifying eligible articles.

Figure 1. Prisma 2020 flow diagram of literature search

Study characteristics

Among the eight included studies, six were randomised controlled trials (RCT’s), one was a controlled trial, and one was a feasibility study. Table 1 shows the characteristics of the included studies. Year of publication varied between 1994 to 2017. The number of participants included in the RCTs fell between 22 (27) and 105 (28). Countries of origin included Italy (29), the United States (30, 31), Sweden (27), Mexico (32, 33), the United Kingdom (28), and China (34). Risk of bias was assessed in the controlled trials, and of these (n =7) two were judged to have low risk of bias, five had some concerns of bias, and one was assessed to be at high risk of bias. Figure 2 summarise the dimensions of the risk of bias assessment.

Table 1. Data extraction from included studies

Table 1 (continued). Data extraction from included studies

6MWT six-minute walk test, AMC arm muscle circumference, BCAA branched chain amino acids, BMI body mass index, CG control group, HF heart failure, IG intervention group, MAC mid-arm circumference, ONS oral nutritional supplement, NRS nutritional risk screening, NYHA New York Heart Association Classification, NS not statistically significant, ONS oral nutritional supplement, TGUG timed get up and go, TSF triceps skin fold

Participants and interventions

The severity of chronic HF among participants in the included studies varied regarding NYHA score (see Table 1). The mean age of participants ranged from 65 (30) to 84 years old (31). Depending on intervention and outcome, some studies had additional inclusion criteria, such as low muscle mass (29), low levels of serum 25-hydroxyvitamin D (28, 30), BMI within a specific range (31), or being at nutritional risk (34).

Figure 2. Risk of bias assessment using the Risk of Bias Assessment tool RoB2

D1: Bias arising from the randomization process, D2: Bias due to deviations from intended interventions, D3: Bias due to missing outcome data, D4: bias in measurement of the outcome, D5: bias in selection of the reported result

The interventions all aimed at improving components of frailty, such as body composition or nutritional or functional performance, but took different approaches. Thus, in the presentation of the results, studies were grouped according to the type of intervention used. Three studies hypothesised that supplementing protein or amino acids would improve nutritional status and protein synthesis (29, 31, 33). Two studies used supplements, including energy, protein, and other nutrients, in order to improve nutritional status (27, 34). Two studies investigated the effects of vitamin D on functional performance (28, 30). One study investigated the effects of a low carbohydrate diet on body composition (32).

Summary of interventions and results

Supplementation of protein or amino acids

Three studies investigated the supplementation of protein or amino acids on improving nutritional status through increased weight and muscle mass (29, 33) or functional performance (31). The rationale for this among all three studies was to improve muscle synthesis by addressing anabolic resistance. The studies using amino acids instead of whole protein (29, 33) referred to amino acids as being more effective in the aforementioned goals by enhancing protein turnover and muscle metabolism. George et al. (31) and Pineda Suarez et al. (33) combined protein supplementation or branched-chain amino acids (BCAA) with exercise, whereas Aquilani et al. (29) did not combine supplementation of essential amino acids with any physical activity intervention; however, one inclusion criteria was that study participants already had adequate daily physical activity.
The amount of protein supplementation was either flexible, to reach a general goal of total protein intake (1.5 grams protein per kilogram body weight/day in George et al. (31)) or as a supplement of 8–10 grams of amino acids a day to the usual diet. However, Pineda Suarez et al. subtracted the 10 grams of (BCAA) from the total protein intake in order to reach a total protein intake of 20% in relation to fats and carbohydrates.
The duration of the interventions varied by two months (29), 12 weeks (33), and six months (31). Control groups received exercise programs without protein supplementation (33), usual care (31), or no supplementation (29).
The three papers had conflicting results. The study by Aquilani et al. (29) reported that participants in the intervention group who received essential amino acids improved 6MWT and had a larger increase in weight compared to the control group. This study was judged to have a low risk of bias.
In contrast, Pineda Suarez et al. (33) reported improvements in hand grip strength in both intervention and control groups and therefore concluded that these improvements resulted from the exercise intervention independent of the BCAA supplementation. This study was found to have some concerns regarding the methodology, which included uncertainty of concealment of allocation sequence at randomisation, and it was unclear whether outcome assessors were aware of whether the participant had been in the control or the intervention group.
George et al. (31) was a feasibility study with only 11 participants included (of whom 6 completed the study). They found no statistically significant difference regarding functional performance measured by TGUG, hand grip strength, and 6MWT.

Supplementation of energy, protein, and other nutrients

Two studies focused on nutritional support through liquid supplementation, including energy, protein, and other nutrients in order to improve nutritional status (27, 34). Broqvist et al. (27) used oral nutritional supplements, providing a daily intake of 750kcal and 30g of protein in addition to the usual dietary intake. Zhou et al. (34) provided 500mL supplementation of enteral nutrition (EN) through nasogastric tube feeding, containing 450kcal and 17g protein (product name: RuiDai; Fresenius Kabi Deutschland GmbH, 500 ml/bottle). The interventions lasted eight weeks (27) and either one or three months, as Zhou et al. (34) had two intervention groups with different intervention times. The control group in Broqvist et al. (27) received a diluted version of the oral nutritional supplement, whereas Zhou et al. (34) had a control group with no nutritional support.
Both studies found improvements in nutritional status after the intervention in the form of increased muscle and fat mass, measured through arm muscle circumference and triceps skin fold. However, it is important to note that these results should be interpreted with caution, as the study by Zhou had several methodological concerns, including no randomisation and a lack of blinding.

Supplementation of Vitamin D

Two studies investigated vitamin D supplementation to improve functional performance (28, 30). The rationale behind this approach was that low levels of 25-hydroxyvitamin D is common among older persons with HF. In addition, vitamin D deficiency also has been associated with low functional capacity and skeletal myopathy. Thus, the authors hypothesised that improving vitamin D status among those with vitamin D deficiency would, in turn, improve physical capacity. None of the studies combined the vitamin D supplement with any exercise intervention. Control groups received a placebo.
The studies lasted 20 weeks (28) and six months (30). The administration of the vitamin D supplement equaled a daily vitamin D intake of 35µg/1428 IU (28) or 179µg/7143 IU (30).
Both studies reported good compliance with vitamin D supplementation and were judged to be of good quality in the risk of bias assessment. None of the studies found any improvement in the functional measures of TGUG and 6MWT.

Diet approach—low carbohydrate diet

One study investigated the effect of a low-carbohydrate diet on oxygen saturation, body composition, and clinical variables in persons with HF (32). The rationale behind the design was that HF compromises respiratory efficiency through impaired oxygen consumption, and in other populations, a diet with limited energy contribution from carbohydrates and an increase in fat has been associated with better respiratory efficiency.
The study diet consisted of 40% carbohydrates, 20% protein, and 40% fats (12% saturated fats). The control group was recommended a standard diet according to American Heart Association Dietary guidelines (50% carbohydrates and 30% fats). Both the intervention and control diet were normocaloric. The duration of the study was two months. By the end of the study, no statistically significant differences regarding body composition measured with bioimpedance analysis and hand grip strength were found. However, the authors reported a statistically significant increase in oxygen saturation in the intervention group. There were some concerns regarding risk of bias in this study, as participants and personnel delivering the intervention were naturally not blinded and drop-outs in the study were younger than those who completed.

Discussion

Here, we have summarised current evidence on nutritional interventions to improve the frailty of older persons with heart failure. As described in the introduction, previous research has focused on sodium restriction, dietary patterns, or caloric restriction. However, these kinds of interventions are often not appropriate in persons with frailty. Because frailty often includes malnutrition and/or sarcopenia, there is a risk of reduced energy and nutrient intake with restrictive diets. Therefore, it is unsurprising that most of the included studies had interventions that added to the study participants’ diets rather than restricted them, with one exception in the study by Gonzalez-Islas (32). In contrast, five of the eight papers studied macronutrients such as protein supplementation with or without extra energy. Two papers investigated the effect of high vitamin D supplementation.

Results discussion

Supplementation with protein or amino acids

This review supports the evidence that protein and physical exercise are needed to build muscle mass. The studies using protein supplementation found improvements in outcomes. One of the studies (33) combined protein supplementation (BCAA) with exercise, compared with exercise alone in the control group. As both groups improved muscle strength, the authors referred the positive result to the exercise part of the intervention. On the other hand, the study by Aquilani et al. (29) reported that protein supplementation (essential amino acids) among persons with adequate physical activity improved nutritional status. However, it should be noted that the amount of protein supplementation was rather modest (8–10 grams) while it has been suggested that older persons would require up to 40 grams of extra protein to stimulate protein synthesis (35). Previous research has shown that protein supplementation alone may not be sufficient to achieve a change in muscle strength, but that exercise is necessary (36).

Vitamin D

Previous research has established the association between vitamin D and lower muscle function—e.g., hand grip strength (37). Despite this, none of the studies included here found any positive effects on functional measures, which is in line with a systematic review of two large interventions on vitamin D supplementation for older persons (38).

Combination of energy, protein, and other nutrients

One of the two included studies used oral nutritional supplements (ONS) and the other used enteral nutrition (EN) through a nasogastric tube. The use of ONS in older persons with malnutrition or risk of malnutrition is a Grade A recommendation by the European Society of Enteral and Parenteral Nutrition (ESPEN) (7). Several studies have shown that ONS, as a supplement to ordinary food intake, improves dietary intake and body weight and lowers the risk of complications (39) during hospital care and functional decline after discharge (40). This is in line with the result from the study included in this review, where the authors found increased weight and triceps skin fold measurements (27). Zhou et al. (34) provided the EN supplement through a nasogastric tube; however, the rationale behind this administration method is not specified in the article. The ESPEN recommendation is to initiate EN when oral intake is expected to be impossible for more than three days or <50% of estimated energy needs more than one week despite interventions to optimise oral intake (7). Such a situation could be dysphagia or acute illness. Thus, the practical implementation of the intervention described is probably not feasible in clinical practice. On the other hand, Zhou et al. (34) demonstrate that the addition of supplemental energy and nutrients may improve nutritional status and that this improvement also increases over time.

Low carbohydrate diet

Finally, the study that used a low carbohydrate diet reported no statistically significant differences in hand grip strength after two months (32). However, they did report a statistically significant improvement in oxygen saturation in the intervention group. The association between a diet low in carbohydrates and higher in fat and reduced respiratory effort has been studied in persons with obstructive pulmonary disease and was recently summarised in a systematic review by Guerra et al. (41). They did report an association between ventilatory parameters and a higher intake of fat relative to carbohydrates. Still, they reported that the possible clinical advantages for the individual had not been investigated. Thus, they concluded that the evidence grade was not strong enough to come to any recommendations. The low-carbohydrate diet investigated by Gonzalez-Islas et al. (32) was rather similar to the Mediterranean diet, defined as a distribution of macronutrients equating to 37% total fat (9% saturated fat), 15% protein and 43% carbohydrates (42). The Mediterranean diet has been extensively studied with coronary heart disease and specifically in preventing or improving clinical outcomes in HF (43). The results have been conflicting, and current HF recommendations from the European Society of Cardiology do not specify dietary advice other than striving for a healthy diet and preventing malnutrition (1).

Methodological considerations

One of this study’s strengths is the focus on nutritional and functional outcomes related to frailty, which may be more relevant for the individual’s everyday life and function than clinically oriented outcomes such as mortality. Another strength is the systematic methodology for identifying articles and the risk of bias assessment.
This study also has limitations. There is a large variation among interventions and outcomes in the included studies, which does not allow for meta-analyses or pooling of data. Several studies are carried out in small populations, and 4 out of 8 were published ≥10 years ago. Not all studies had frailty as inclusion criteria, and thus, the generalisability of the results to this population must be made with caution.

Conclusion and further research

The results from this review suggest that components of frailty, such as muscle mass and functional capacity, can be improved with energy and protein supplementation, especially in combination with physical exercise. This highlights the importance of nutritional and exercise interventions as an integrated part of clinical care of heart failure in older persons. Further research is needed to find out which combination of energy, protein, or amino acids is the most efficient and how this should be combined with physical exercise.

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

Funding: This study was supported by funding from the Center for Innovative Medicine and King Gustaf V’s, and Queen Victoria’s Freemasons’ Foundation.

Ethical standards: No ethics is required.

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

SUPPLEMENTARY MATERIAL2

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© The Authors 2024

M. Gallibois^1,2, C. Hennah³, M. Sénéchal^1,2, M.F. Fuentes Diaz^1,2, B. Leadbetter^1,2, D.R. Bouchard^1,2

1. Cardiometabolic Exercise and Lifestyle Laboratory, Faculty of Kinesiology, University of New Brunswick, Canada; 2. Faculty of Kinesiology, University of New Brunswick, Canada; 3. School of Psychology, Queen’s University Belfast, Belfast, United Kingdom

Corresponding Author: Danielle R. Bouchard, Ph.D., CSEP-CEP, Faculty of Kinesiology, University of New Brunswick, Cardiometabolic Exercise & Lifestyle Laboratory (CELLAB), 90 Mackay Drive, (Room 322), Fredericton, New Brunswick, Canada, E3B 5A3, Phone, Office: (506) 443-3908, Fax: (506) 453-3511, Email: Danielle.bouchard@unb.ca

J Aging Res & Lifestyle 2024;13:93-98
Published online July 17, 2024, http://dx.doi.org/10.14283/jarlife.2024.14

Abstract

BACKGROUND: Falls, and more specifically, fall-related injuries, are costly to the healthcare system and can harm one’s autonomy.
OBJECTIVES: To study the impact of sedentary behaviour associated with fall-related injuries and how a change in sedentary behaviour may impact the risk of a fall-related injury.
DESIGN: From baseline to the first follow-up, cross-sectional and longitudinal data analysis from the Canadian Longitudinal Study of Aging (CLSA) cohort.
PARTICIPANTS: CLSA data from 43,558 Canadians aged 45-85 were included in this study.
MEASUREMENTS: At baseline and follow-up, sedentary behaviour time was categorized as low (<1,080 minutes/week), moderate (1,080-1,440), or high (>1,440). Sedentary behaviour was estimated via the Physical Activity Scale for the Elderly (PASE). At follow-up, participants were dichotomized as either increased or decreased/no change in sedentary behaviour according to their categorical change between time points.
RESULTS: Sedentary behaviour was associated with fall-related injuries independently of age, sex, number of chronic conditions, and total physical activity levels OR (95%CI) 1.10 (1.05-1.15). In contrast, a change in sedentary behaviour was not associated with the risk of fall-related injury 1.00 (0.92-1.01).
CONCLUSION: A higher level of sedentary behaviour is associated with injurious falls for people between 40 and 80 years old. However, a short-term change in sedentary behaviour does not influence the risk of injury-related falls. Despite the results, a more precise measure of sedentary behaviour is needed for epidemiology studies to capture changes over time better.

Key words: Sedentary behaviour, Fall-related injuries, CLSA.

Introduction

Falls are the second leading cause of injury deaths worldwide (1), contributing to 80% of all injury-related hospitalizations (2). Fall-related injuries are estimated to cost ~$10,000 per person (3), largely due to lengthy hospital stays. They are complex and lead to long-term health consequences such as chronic pain, social isolation, and disability (4). An estimated 30% of community-dwelling older adults (65+) fall annually, from which one in three are injured (5). Most fall research has focused on older adults, but middle-aged adults also experience a high incidence rate of falls and fall-related injuries (6). Including middle-aged adults in fall prevention, research offers insight into early risk factor identification and reduction. The Baltimore Longitudinal Study of Aging found that 21% of middle-aged adults reported falling in the past two years (7). Middle-aged adults may experience high fall rates simply because they are more active than older adults (8) and as biological and behavioural risk factors increase, including sedentary behaviour (7). Sedentary behaviour includes any waking behaviour characterized by low energy expenditure while sitting, reclining, or lying (9).
A common misconception is that sedentary behaviour and physical inactivity can be used interchangeably. However, physical inactivity is used for a person who does not meet the recommended physical activity guidelines of 150 minutes of moderate to vigorous physical activity per week (9). The average adult in North America spends 9-10 hours of their day engaging sedentary behaviour (10) and many do not reach the physical activity guidelines. Less known is that even if one is active, the risk factor related to sitting too much is not cancelled (11). As a result, there is a growing interest in the health benefits of simply sitting less (i.e., reducing sedentary behaviour).
In fact, independent of physical activity, sedentary behaviour is associated with many negative health outcomes, such as chronic conditions and death (12–14). Sedentary behaviour exacerbates the risk of falls with aging because of its association with reduced bone mass (15), muscle mass and strength (16), which may increase the fall risk as one ages. However, the relationship between sedentary behaviour and fall-related injuries is inconsistent. Multiple studies have found that sedentary time is associated with increased falls among older adults (17, 18). It is suggested that sedentary behaviour can reduce physical function components which may lead to an increase in falls. On the contrary, increased sedentary behaviour may be protective as it can reduce the exposure and opportunity for falls to occur. A study by Bea et al. (2013) found that people displaying a highly sedentary lifestyle were less likely to experience a fall (19), Which could be due to the tool used to capture the exposure. Sedentary behaviour can be measured through wearable devices such as accelerometers and inclinometers—the latter is the gold standard. However, these tools are costly, making large-scale studies difficult. Sedentary Behaviour can also be measured subjectively through self-reported questionnaires. While questionnaires tend to have lower validity, they are cost-effective tools more commonly used in large cohort studies (20).
As such, more research is needed to identify the relationship between sedentary behaviour and falls. Provided that sedentary behaviour is potentially a modifiable risk factor for fall related injuries, there is a need to explore this relationship further. This study aimed to investigate the association between sedentary behaviour and fall-related injuries and explore whether changes over 18 months are associated with fall-related injuries.

Methods

Study Design and Participants

The participants were drawn from the Canadian Longitudinal Study on Aging (CLSA) (22). This Canada-wide study includes a representative sample of 51,338 community-dwelling adults aged between 45 and 85 when recruited. The objective of the CLSA is to perform tests on the cohort every three years for 20 years. The CLSA comprises a comprehensive (n=~30,000) and tracking cohort (n=~20,000). Data collection for participants in the tracking cohort is conducted through telephone interviews only, while participants in the comprehensive cohort undergo additional physical assessments at data collection sites (23). Excluded from CLSA are residents in the three territories and some remote regions, persons living on federal First Nations reserves and other First Nations settlements in the provinces, full-time members of the Canadian Armed Forces, and individuals living in institutions. Data from baseline and follow-up collection points were included. Of the 51,338 participants in the CLSA, 7,780 were excluded due to missing data related to falls or sedentary behaviour levels at baseline and/or follow-up. Therefore, 43,558 participants were included in the study for the cross-sectional and longitudinal analyses.
All participants provided written consent, and this study was approved by the Research Ethical Board Review Committee (REB approval # 2020/050).

Measurements

Main outcome: Fall-related Injuries

Fall-related injuries were self-reported at baseline and follow-up via structured interview questionnaires. At baseline, participants were asked to respond (yes or no) to if they had a fall-related injury in the past 12 months. At follow-up, a 2-step procedure was used to derive fall-related injuries. First, participants were asked, “In the last 12 months, have you had any injuries that limited some of your normal activities? For example, a broken bone, a bad cut or burn, a sprain, or a poisoning”. Second, if the participant answered “Yes” to the question, they were then prompted, “Was this injury (were any of these injuries) caused by a fall?” At follow-up, three years after, incidences of fall-related injury in the past 12 months were dichotomized into a yes/no response as the outcome variable.

Exposure: Sedentary Behaviour

Sedentary behaviour is operationalized as self-reported sitting time. In the CLSA, the modified Physical Activity Scale for the Elderly (PASE) questionnaire was used to measure sedentary behaviour after 18 months from baseline and at the 3-year visit. Participants were asked to report their frequency and duration of sitting time over the past seven days. Because of the nature of the questionnaire, it is impossible to treat the exposure as a continuous variable. In addition, the distribution was skewed. To capture the weekly frequency of sedentary behaviour, participants were asked, “Over the past seven days, how often did you participate in sitting activities such as reading, watching TV, computer activities, or doing handcrafts?”. Possible responses were: Never (0); seldom (1 to 2 days); sometimes (3 to 4 days); and often (5 to 7 days)”. To measure daily duration, they were asked: “On average, how many minutes per day did you engage in these sitting activities?”. Possible responses to this question were: less than 30 minutes; more than 30 minutes but less than 1 hour; more than 1 hour but less than 2 hours; more than 2 hours but less than 4 hours and 4 hours or more. The mid-point of each frequency and duration category (except for the “four hours or more” duration category, where four was used) was used to estimate weekly sedentary time by their product. Due to the high prevalence of sedentary behaviour and the nature of the questions, the distribution was skewed, with 38.0 % of the sample maximizing the score at baseline and 47.7% at follow-up. As a result, according to total sedentary time, participants were categorized into three weekly levels of sedentary behaviour:
– Low: Less than 1080 minutes with different combinations of frequencies/times
– Medium: 1080 minutes (3-4 days per week for 4 hours or more)
– High: 1440 minutes (5-7 days per week for 4 hours or more).

Change in sedentary behaviour was categorized into two different categories:
– Increased sedentary behaviour: Greater category at follow-up compared with baseline (low to medium; medium to high).
– Decreased or no change: Lower category at follow-up compared with baseline or no change in sedentary behaviour.

Covariates

All variables were collected for both cohorts except medication and measured with the tracking cohort only. Age, sex, ethnicity, educational level, and marital status were self-reported via a questionnaire at baseline. The number of chronic conditions was quantified by asking participants if they had chronic conditions diagnosed by a healthcare provider. The chronic conditions included in our analysis were osteoarthritis of the knee, osteoarthritis of the hip, rheumatoid arthritis, osteoporosis, chronic obstructive pulmonary diseases, cardiovascular disease, Parkinson’s, multiple sclerosis, dementia (including memory problems), kidney disease, diabetes, incontinence (bowel and urinary), and mood disorder. These conditions are associated with falls (24–27).
Participants were asked to recall how often and how many prescription medications they took in the past month. The product of frequency and number of medications was computed into the average number of medications per week. Methods for collecting height and weight to calculate body mass index (BMI) were different between cohorts. Participants in the tracking cohort were asked to self-report their height and weight, which were objectively measured by trained professionals in the comprehensive cohort.
The PASE was used to estimate moderate to vigorous physical activity. Aerobic physical activity was defined as self-reported moderate and strenuous activities in the past seven days. Participants were asked to estimate the frequency and duration of engagement in each moderate and strenuous training activity. Possible responses for frequencies were Never (0 days), Seldom (1 to 2 days), Sometimes (3 to 4 days), and Often (5 to 7 days). Possible answers for the duration were: Less than 30 minutes; More than 30 minutes but less than 1 hour; More than 1 hour but less than 2 hours; More than 2 hours but less than 4 hours; 4 hours or more. The midpoint of each frequency and duration category (except for the 4 hours or more duration category) was multiplied to estimate weekly totals. The weekly totals for moderate and strenuous time were summed together in minutes for aerobic physical activity. To be used in the study’s models, aerobic physical activity was categorized according to whether participants reported spending zero minutes or greater than zero minutes engaging in aerobic activity.
The last steps were repeated for resistance training. The frequency and duration of resistance training were computed into weekly total time and then dichotomized as doing some resistance training in the past week (1) or none (0).

Statistical Analysis

Differences between sedentary behaviour groups (low, medium, high) were investigated using analysis of variance (ANOVA). Logistic regression models were developed to investigate the odds of a fall-related injury based on sedentary behaviour categorization at baseline and changes in sedentary behaviour categorization between baseline and follow-up while adjusting for covariates using the ‘low sedentary group’ as the control group. Three sets of analyses were performed. First, the covariates used in the models were: Model 1 = Unadjusted; Model 2: Adjusted for age; Model 3: adjusted for age and sex; Model 4: adjusted for age, sex, chronic conditions, moderate to vigorous physical activity, and resistance training. The 4-step model was repeated while adjusting for the same potential confounders to investigate the association of fall-related injuries with increased sedentary behaviour. In addition, participants who fell at baseline were removed from the model. Subgroup analyses were performed by separating middle-aged and older aged adults. All data were analyzed using IBM SPSS Statistics (Version 27) and SAS.

Results

A total of 43,558 participants from the CLSA were included in our analysis (Table 1). Sixty percent of participants were middle aged (45-65 years), 48.8% were male, and more than 95% were White Caucasian. The average BMI was classified as overweight (BMI between 25 and 29.9), with 1/3 of participants reporting at least one chronic condition. The participants classified as having high sedentary behaviour (n= 16,555, 38.0%) were significantly older, heavier, reported more chronic conditions, did less exercise, and reported more fall-related injuries than those classified as low sedentary behaviour. At the baseline, 4813 (11%) participants reported a fall-related injury in the past 12 months, while only 2953 (6.8%) participants reported a fall-related injury at follow-up.

Table 1. Descriptive Characteristics of Participants (n=43,558)

Data presented as mean + SD or N (%); BMI = Body mass index; † = Only data from Tracking cohort (n=21,000).

At baseline, 22.3% of participants were classified as having a ‘low level’ of sedentary behaviour, with that number decreasing to 17.3% at follow-up (Figure 1). Inversely, 9.7% of participants classified as ‘high level’ of sedentary behaviour went from 38.0% to 47.7%.

Figure 1. Distribution of participants within sedentary behaviour groups (Low-Medium-High) n=43,558

Logistic regression investigated the association between injurious falls and sedentary behaviour at baseline. When the model was fully adjusted (age, sex, chronic conditions, moderate-to-vigorous physical activity, and resistance training), the odds of injurious falls increased by 1.10 (1.05 – 1.15) for each categorical increase in sedentary behaviour (Figure 2).

Figure 2. Association between injurious falls and sedentary behaviour at follow-up

Data are presented as RR (95% CI). Model 1 = Unadjusted; Model 2: Adjusted for age; Model 3: Adjusted for age and sex; Model 4: Adjusted for age, sex, chronic conditions, moderate to vigorous physical activity, and resistance training.

Findings from the logistic regression model investigating the association between injurious falls and an increase in sedentary behaviour between baseline and follow-up showed that the odds of injurious falls were not significant,1.00 (0.92-1.01). When the model was fully what? Adjusted? (age, sex, chronic conditions, moderate-to-vigorous physical activity, and resistance training) (Table 2). Additionally, when participants who reported injurious falls at baseline were removed, the odds of fall-related injuries were 1.05 (0.93-1.12) when adjusting for the same confounders.

Table 2. Association between change in sedentary behaviour and fall-related injuries

Model does not included people who reported a fall with injury at baseline

A subgroup analysis of middle-aged and older adults yielded similar results. Cross-sectionally, the logistic regression model showed a significant association between falls and sedentary behaviour for middle-aged 1.11 (1.05-1.17) and older adults 1.09 (1.02-1.17). However, a change in sedentary behaviour was not significantly associated with falls in any of he groups 1.02 (0.91-1.15) for middle-aged and 0.95 (0.84-1.09) for older adults.

Discussion

This study investigated the association between sedentary behaviour and fall-related injuries and whether sedentary behaviour changes impacted the risk of injurious falls. Our results revealed that sedentary behaviour was associated with fall-related injuries in the CLSA cohort of middle-aged and older adults independent of age, number of medical conditions, and physical activity. However, changes in sedentary behaviour between baseline and follow-up testing did not lead to a change in relative injury fall risk.
Considering the primary aim of this study, these results were consistent with those found in the systematic review by Semanik et al. (28), where sedentary behaviour was associated with increased falls. This study shows that this relationship exists independently of physical activity in both men and women. This could have significant implications for maintaining the health and well-being of middle-aged and older adults, as this study highlights the potential harms of sustained sedentary behaviour. High sedentary behaviour could also identify those most at risk of fall-related injuries in middle-aged and older adults.
Interestingly, our results are inconsistent with a similar study performed by Lustosa et al. (14), who found no association between sedentary time and fall-related injuries despite an association between sedentary time and self-reported falls. However, their study used a median split method, which only allowed for sedentary time to be classified as one of two (low or high) sedentary time categories. In contrast, the present study used a three-category model where participants were classified as having either low, moderate, or high levels of sedentary time. These findings reinforce the dose-response relationship between sedentary behaviour and health outcomes and highlight the necessity for a continuous measure of sedentary time rather than a categorical approach.
While the results of this paper confirmed the main hypothesis, exploring our secondary aim yielded surprising results, as categorical changes in sedentary behaviour (between baseline and follow-up) were not associated with a change in relative fall risk. This is incongruent with our primary finding that sedentary behaviour is associated with falls. In addition, our results challenge the work of Bea et al. (2017), who found that fall risk did increase with a reduction in sedentary behaviour in a female sample over six years (19). A longer period of sustained reduction in sedentary behaviour may be required to see the benefits. From this perspective, it is reasonable to conclude that short-term increases or decreases in sedentary behaviour are unlikely to harm or benefit this population’s risk of falls or fall-related injuries. It could be helpful to revisit this question with the CLSA data in the future to assess the impact of sedentary behaviour change over a longer duration.
This study has some limitations that must be noted. Firstly, in the PASE questionnaire, daily sedentary behaviour was capped at four hours. This creates an unbalanced weighting of participants in the ‘High’ sedentary condition and may have adversely affected the reporting of sedentary behaviour. The categories created to answer the research question are arbitrary and were directed by the tool and the exposure distribution. Future research would benefit from a more precise method of reporting sedentary behaviour (i.e., wearable device), particularly in aging adults, where it is more common (30). Secondly, the number of medical conditions and medications was reported and controlled. However, some medications will more substantially affect fall risk and fall-related injury than others. Without specific details of these medications, we cannot accurately determine their impact on the relationship between falls and sedentary behaviour. In addition, Fall-related injuries were collected through different questionnaires at baseline and follow-up. This may explain why fewer people recorded falling at follow-up.
Based on the results of this study, health practitioners should recommend limiting sedentary behaviour as young as possible to reduce the relative risk of falls and fall-related injuries. Future research in this area should focus on determining how much sedentary behaviour needs to be reduced and over what length to decrease the relative risk of injurious falls in middle—and older-aged adults.

Conclusion

In conclusion, higher levels of sedentary behaviour are associated with a higher risk of fall-related injury independent of confounders in people aged 45 to 85. Considering this, older middle and older adults or those at risk of falling may benefit from less sedentary time. However, we found that changes in sedentary behaviour were not associated with injurious falls. Despite the results, a more precise measure of sedentary behaviour is needed for epidemiology studies to capture changes over time better.

Acknowledgments: This research was made possible using the data/biospecimens collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation. This research has been conducted using the CLSA dataset Baseline and Follow-Up 1 under Application Number 2002007. The CLSA is led by Drs. Parminder Raina, Christina Wolfson and Susan Kirkland. The opinions expressed in this manuscript are the author’s own and do not reflect the views of the Canadian Longitudinal Study on Aging.

Conflict of Interest Statement: All authors declare that they have no conflicts of interest.

Ethical standards: All subjects gave informed consent for inclusion before participating in the study. The study was conducted following the Declaration of Helsinki, and the protocol was approved by the Institution Ethics Committee of UNB 2023-019.

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 2024

W.-H. Lu^1,2

1. IHU HealthAge, Toulouse, France; 2. Institute on Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France.

Corresponding Author: Wan-Hsuan Lu, Postal address: Gérontopôle de Toulouse, Institut du Vieillissement, 37 Allée Jules Guesde, 31000 Toulouse, France, E-mail address: wanhsuanlu@gmail.com, Phone number: + 33- 561-145-691

J Aging Res & Lifestyle 2024;13:88-92
Published online June 5, 2024, http://dx.doi.org/10.14283/jarlife.2024.13

Abstract

Biological age is a concept that uses bio-physiological parameters to account for individual heterogeneity in the biological processes driving aging and aims to enhance the prediction of age-related clinical conditions compared to chronological age. Although engaging in healthy lifestyle behaviors has been linked to a lower mortality risk and a reduced incidence of chronic diseases, it remains unclear to what extent these health benefits result from slowing the pace of the biological aging process. This short review summarized how modifiable lifestyle factors — including diet, physical activity, smoking, alcohol consumption, and the aggregate of multiple healthy behaviors — were associated with established estimates of biological age based on clinical or cellular/molecular markers, including Klemera-Doubal Method biological age, homeostatic dysregulation, phenotypic age, DNA methylation age, and telomere length. In brief, the available studies tend to show a consistent association of lifestyle factors with physiological measures of biological age, while findings regarding molecular-based metrics vary. The limited evidence highlights the need for further research in this field, particularly with a life-course approach.

Key words: Healthy aging, healthspan, biomarker of aging, epigenetic age, age acceleration.

Aging is the time-related deterioration that occurs in an organism at all levels, from the molecular and cellular to the physiological and functional, ultimately increasing vulnerability to death (1). For decades, scientists and clinicians have observed that chronological age, representing the time since birth, is a significant predictor of various age-related health conditions; however, it may not accurately describe how an organism functions, especially in the later life stages (2, 3). Biological age seeks to quantify the bio-physiological processes driving aging. Generally, biomarkers or clinical metrics designed to forecast the remaining lifespan and healthspan (the absence of disability) are considered indicators of biological age (4, 5). The disparity between predicted biological and chronological age is defined as age acceleration, and the positive age acceleration implies that individuals may undergo age-related decline faster than their peers (6, 7).
Engaging in healthy lifestyle behaviors has been linked to a lower mortality risk (8) and decreased incidence of a myriad of medical conditions, including cardiovascular diseases (9), metabolic syndrome (10), cancer (11), neurodegenerative and psychiatric disorders (12), and geriatric syndromes (13, 14). Although the mechanisms connecting lifestyle factors to extended lifespan/ healthspan are not fully understood, it is plausible that the health benefits result, at least partly, from slowing down the biological aging process. This short review explored how modifiable lifestyle factors, such as diet, physical activity, smoking, and alcohol consumption, are associated with biological aging.

Biological age estimation

Articles published in English were searched from the Pubmed database for this review. Several papers investigating the relationship between modifiable lifestyle factors and biological age using various measures from physiological to molecular scales were identified. Only research that quantified biological age using validated algorithms, epigenetic clocks, or telomere length were included (see Table 1).

Table 1. Biological age estimation methods used in prior studies on modifiable lifestyle factors

Diet and biological aging

An unhealthy diet may accelerate biological aging due to its inflammatory and oxidative stress potentials. The cross-sectional study conducted by Wang and colleagues, which involved 8,839 participants from the National Health and Nutrition Examination Survey (NHANES) of the United States, showed a consistent association of consuming foods with higher Dietary Inflammatory Index (DII) and Dietary Oxidative Balance Score (DOBS) with accelerated biological aging. In this work, biological age was assessed through clinical biomarkers using established algorithms, including Klemera-Doubal Method biological age (KDM-BA), homeostatic dysregulation (HD), and phenotypic age (PA) (25). Another study of 10,191 Taiwanese aged ≥50 revealed that adopting a diet rich in plant foods was associated with a reduced likelihood of experiencing an acceleration in the multidimensional aging measure (MDAge) over 8 years, composed of selected clinical chemistry biomarkers (26). Kresovich et al.’s cross-sectional study demonstrated the beneficial impact of healthy eating approaches, including the diet designed for hypertension management and the Mediterranean diet (MED), on DNA methylation age (DNAmAge) acceleration among non-Hispanic white women (the Sister study); the most significant associations were observed in acceleration in PhenoAge and GrimAge (27). Conversely, an 18-month randomized controlled trial (RCT) in 294 adults with obesity or dyslipidemia observed no significant differences in the change of epigenetic ages between three dietary interventions, which included providing guidelines to promote a healthy diet and implementing a calorie-restricted MED and a plant-rich MED, respectively (28). In summary, the observational studies suggest that a healthy diet may decelerate biological aging, while further evidence is required to determine whether different dietary strategies are superior.

Physical activity and biological aging

Several non-interventional studies had reported that higher physical activity levels or lower sedentary time were associated with slower epigenetic aging (29–31). However, the association may be partially attributed to body mass index (BMI) and other confounders, with the associations tending to attenuate or disappear after adjusting for those confounders (29, 30). Further insights from Fox and colleagues revealed that cardiovascular health and immune function mediated the effect of physical activity on DNAm GrimAge acceleration (31). Physical activity also showed a favorable impact on telomere attrition. In their study recruiting 284,479 participants from the UK Biobank, Zhu et al. discovered that physical activities during leisure time, housework, and public transportation were associated with reduced leukocyte telomere length (LTL) deviation, which referred to the difference between genetically determined and observed LTL. Notably, engaging in job-related activities was linked to a greater LTL deviation (32). In short, engaging in physical activities outside of work could slow down the rate of biological age acceleration, as measured by cellular markers.

Smoking, alcohol consumption, and biological aging

As calculated by KDM-BA and PA, individuals who smoked and drank alcohol had a greater age acceleration than those who reported as non-smokers/non-drinkers, with evidence from 94,433 adults aged 30 to 70 in Taiwan (33). This finding is supported by a study investigating epigenetic age among 2,316 women from the Sister study, which indicated that both lifetime and recent alcohol consumption were associated with DNAm GrimAge acceleration (34). Furthermore, smoking and alcohol consumption were cross-sectionally associated with acceleration in several DNAmAge clocks in the GENOA study composed of 1,100 African Americans; however, only current smokers showed a significant association with increased PhenoAge acceleration over time (35). To summarize, tobacco and alcohol consumption have been correlated with accelerated biological aging, as demonstrated by cross-sectional studies, but longitudinal evidence supporting these associations remains insufficient.

Multiple lifestyle factors and biological aging

The effect of engaging in multiple healthy behaviors on deceleration in biological aging had also been evaluated, including nonsmoking, less alcohol consumption, daily fruit and vegetable intake, being physically active or regular exercise, good sleep habits, and maintaining normal BMI and waist-to-hip ratio (36–38). Overall, adherence to more health-promoting factors was associated with slower biological aging, either assessed via the phenotypic measure (frailty index) (36) or clinical biomarkers (KDM-BA and PA) (37, 38).
Despite limited sample sizes, data from RCTs suggested that lifestyle interventions may modify biological age. The pilot trial of Fitzgerald et al. performed an 8-week treatment program about diet, dietary supplements, sleep, exercise, and stress management for 43 men aged 50 to 72 without chronic diseases. Compared to the controls, the lifestyle intervention was associated with a decrease in Horvath DNAmAge of 3.23 years. Moreover, in the intervention group, Horvath DNAmAge decreased by an average of 1.96 years by the end of the program (not reaching statistical significance) (39). In a secondary analysis of a 24-month RCT that enrolled 219 healthy post-menopausal women, participants who received the healthy-dietary intervention had a lower GrimAge acceleration than their no-intervention counterparts. On the other hand, the physical activity intervention reduced the epigenetic mutation load (40), which reflects the age-related dysfunction of the epigenetic maintenance system (41). Finally, a secondary analysis of an RCT involving 93 obese older adults observed that a 12-month calorie-restricted diet, whether combined with exercise or not, was associated with decreased biological age as per three different algorithms. In contrast, the exercise intervention alone did not significantly alter biological age over time and showed no difference from controls (42). To sum up, multiple healthy behaviors may collectively slow biological aging.

Perspectives on the way forward

Individuals who engage in a healthy lifestyle may exhibit a slower pace of biological aging, as their DNA methylation profile and physiological biomarkers are in a healthier state that typically indicates lower risks of mortality and age-related diseases (Figure 1). However, most studies linking lifestyle factors and biological aging are cross-sectional designs, making it difficult to establish causation. Furthermore, it is worth noting that previous research investigating lifestyle factors and biological aging was commonly obtained from specific US cohorts, such as NHANES and the Sister study, probably due to the difficulty of having both biological age measures and comprehensive lifestyle data in other large cohorts. More evidence derived from diverse populations needs to be included. The impact of lifestyle factors on biological aging warrants investigation using a life-course approach. It is possible that alterations in biological mechanisms become evident only if these behaviors start at a younger age or are consistently adopted in the long term. Due to the difficulty of following individuals throughout their lifespans, initiatives such as the INSPIRE project (43) are crucial for contributing to this topic, as they enable the following of a large age range over a relatively long period. Finally, larger-sample RCTs are needed to validate observed effects.

Figure 1. Lifestyle factors showed an association with the deceleration of biological age suggested in prior studies

Note: This figure was created with BioRender.com.

Several measurement issues of biological age also remain in the field. For example, even if the same biological age algorithm is used, employing different biomarker selection strategies may result in the diverse compositions of the biomarkers and, thus, different performance of estimated biological age (44). Similarly, the lack of standardization in biomarker formulations and study design/performing procedures can lead to heterogeneous results when examining aging biomarkers across cohorts (45). Measuring and parameterizing biological age will continue to pose challenges in future observational studies or RCTs on lifestyle factors. Notably, recently proposed guidelines for validating biomarkers of aging offer a solution to harmonize future cross-population studies, which provide several recommendations for investigating omics-based aging biomarkers at different stages, from data maintenance and biomarker development to external validation (45). Lastly, using digital markers collected by wearable sensors for measuring biological age is a promising field that requires further exploration. A previous study showed that biological age acceleration estimated from step count data could distinguish morbidity and smoking status as effectively as blood-based markers (46). However, it remains to be investigated how this digital biomarker-based measure of biological age can help reveal the impact of modifiable lifestyle factors.
Some interventions demonstrated symptom-relieved effects without significantly altering the underlying pathology (47); the same question could be posed regarding the biological influence of lifestyle behaviors discussed in this article. The available evidence tends to show a consistent association of lifestyle factors with physiological measures of biological age, while findings regarding molecular-based metrics (especially epigenetic clocks) vary. This suggests that lifestyle factors have a greater impact on physiological health, reflecting the overall accumulation of cellular and molecular damage, rather than targeting a specific aging mechanism. Future research comparing multiple biological aging measures derived from different levels of organization within the body (physiological, cellular, and molecular) can provide insight into the topic. In addition, given that health-promoting factors have been shown to modify the association between disease pathologies and phenotypic outcomes — such as the role of physical activity in neurodegenerative diseases (48) — it is important to investigate whether these modifying effects result from the decelerated biological aging and the enhanced biological resilience.

Conflict of interest: The author has no conflicts of interest.

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 2024

M.C.B. de Souza¹, G. da Silva Rocha², E. de Souza Sampaio³, P.C. de Oliveira Garcia Rodrigues⁴, R.A. Vieira⁵, A.F. Souza Gomes⁶, T.R. Pereira de Brito⁷

1. Institute of Motricity Sciences, Federal University of Alfenas, Alfenas, Brazil; 2. Health and Sport Sciences Center, Federal University of Acre, Rio Branco, Brazil; 3. Faculty of Nutrition, Federal University of Alfenas, Alfenas, Brazil; 4. Faculty of Nutrition, Federal University of Alfenas, Alfenas, Brazil; 5. Nursing school, Federal University of Alfenas, Alfenas, Brazil; 6. Postgraduate Program in Health and Nutrition, Nutrition School, Federal University of Ouro Preto; Ouro Preto, MG, Brazil 7. Faculty of Nutrition, Federal University of Alfenas, Alfenas, Brazil

Corresponding Author: Tábatta Renata Pereira de Brito, PhD, Faculty of Nutrition, Federal University of Alfenas, Alfenas, Brazil, Rua Gabriel Monteiro da Silva, 700, Centro, Alfenas, Minas Gerais, CEP: 37130-000, Faculty of Nutrition. Telephone Number: +55 35 3701 9742. E-mail address: tabatta.brito@unifal-mg.edu.br, ORCID: https://orcid.org/0000-0001-9466-2993

J Aging Res & Lifestyle 2024;13:82-87
Published online May 27, 2024, http://dx.doi.org/10.14283/jarlife.2024.12

Abstract

BACKGROUND: Metabolic Syndrome is a set of disorders that characterized by the association of three or more risk factors, like the obesity central, dyslipidemia, borderline blood pressure, hyperglycemia, and the increase of triglycerides. However, these factors also can be associated with pathophysiology of frailty.
OBJECTIVES: verifying whether the metabolic syndrome is associated to the positive frailty screening in the older people.
DESIGN: Cross-sectional study. Participants: 443 older people living in Rio Branco, Brazil.
SETTING: Data collection was carried out in two stages: a personal interview and blood collection.
MEASUREMENTS: The diagnosis of metabolic syndrome was based on the criteria of the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults. The frailty screening was performed using subjective questions validated in a previous study. Descriptive statistics and multinomial logistic regression were used for data analyses.
RESULTS: There was a predominance of female older people (69.07%), aged between 60 and 79 years (87.13%), with an income greater than or equal to one minimum wage (72.09%), no cognitive decline (75.94%) and depressive symptoms (63.31%), independent for BADL (86.46%) and dependent for IADL (51.69%). From the total sample, 56.88% of the older people were identified as frail, 34.09% pre-frail and 9.03% non frail. The prevalence of metabolic syndrome was 51.69%. After adjusting by the independent variables, an association between metabolic syndrome and pre-frailty was observed, and older people with metabolic syndrome were more likely to be prefrail (RRR=2.36; 95%CI=1.08-5.18).
CONCLUSION: The metabolic syndrome was associated to the increase chance of screening for prefrailty in the older people evaluated, which reinforces the needy to establish preventive measures in relation to the metabolic syndrome to avoid frailty in the older people.

Key words: Cross-sectional studies, frail older people, frailty, metabolic syndrome, older adults.

Introduction

According to the World Health Organization, the combination between common aspects of modernity, such as globalization, urbanization, and changes in the lifestyle, makes chronic non-communicable diseases one of the main causes of mortality (1). In this context, metabolic syndrome stands out, as it is a health condition strongly associated with behavioral issues, increasing the incidence of cardiovascular diseases and mortality (2).
Metabolic Syndrome (MetS) is a set of disorders that affect the cardiovascular system, characterized, according to the National Cholesterol Education Program’s Adult Treatment Panel III (NCEP-ATP III), by the association of three or more risk factors, like the obesity central, dyslipidemia, borderline blood pressure, hyperglycemia, and the increase of triglycerides (3, 4). Once the circulatory system is affected in the metabolic syndrome, some chronic diseases are commonly associated, such as atherosclerosis and coronary artery disease (5).
The prevalence of metabolic syndrome varies according to ethnic differences, sex and the criteria used to define the syndrome (4). For example, the World Health Organization (WHO) criteria consider the presence of type 2 Diabetes to be necessary, so when these criteria are used, the prevalence tends to be lower compared to the criteria of the International Diabetes Federation (IDF) or the NCEP-ATPIII (6).
However, the associated factors with MetsS are not limited only to cardiometabolic complications, and frailty may occur, for example, since characteristics of the metabolic syndrome, such as obesity and insulin resistance, may be present in the pathophysiology of frailty (7). Frailty is a geriatric syndrome with multiple causes, characterized by the decrease of strength, of the resistance and the physiological function, leading to increased vulnerability, functional loss, institutionalization, falls, and high risk for mortality (8, 9).
Due to the different definitions of frailty proposed, there are several ways to identify the syndrome, and the identification of phenotypes that require objective measures, such as grip strength, making this difficult to apply in clinical practice, especially in developing countries, where human resources may be scarce. Thus, alternative frailty assessment strategies have been elaborated to be applied in the clinical context, basing mainly on self-reported measures. These alternatives aim to capture the central aspects of frailty, maintaining predictive validity for adverse results, and may be useful for frailty screening (10, 11).
Despite the high prevalence of MetS and frailty, and the vast literature about these conditions, studies about the association between the two syndromes, especially among older people, are still low. Studies utilizing self-reported measures for frailty screening can be valuable for initiating early preventive actions targeting metabolic syndrome-associated frailty. Especially in developing countries, human resources for conducting assessments of older individuals, including physical measurements, may be scarce. Therefore, this study aims to investigate the association between frailty, measured through self-reported screening instruments, and metabolic syndrome in older individuals.

Materials and Methods

Study design and participants

This is a cross-sectional study, carried out in the municipality from Rio Branco, Acre, Brazil. Rio Branco is the capital of Acre, situated in the northern region of Brazil. It stands as the most populous and developed city in the state. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cross-sectional studies (12).
The calculation of the sample size was obtained considering the estimation of proportions in the order of 0.50, a confidence interval of 95%, a design effect (deff) of 1.17, and a population of 24.043 older people, resulting in a sample of 443 older people. The design effect is determined as the ratio between the variance of an estimate obtained through a specific sampling strategy and the variance of the identical estimate derived from a simple random sample comprising the same number of observational units (13). The deff of 1.17 was adopted based on previous study (14) (Figure 1).

Figure 1. Sample definition

The random selection of the sample was carried out from the records of the older people in the G-MUS System – Municipal Health Management, using Microsoft Office Excel. To perform the draw, the list of older people registered in November 2018 was used, which added up to 22.370 older people, that is, 89.3% of the population aged 60 years or older used to the sample size calculation.
Inclusion criteria were being aged 60 years or older, presenting neurological and/or cognitive conditions that enabled answering of the questionnaire (perceived by the interviewer during the presentation of the research and invitation to participate), and the absence of permanent or temporary inability to walk, except with the use of a walking aid device. Exclusion criteria were refusing to donate biological material (blood).
Data were collected from July to December 2019 at two different times. First, a home interview was carried out and, up to seven days after the interview, blood was collected at the older people’s home.

Ethical aspects

All procedures were performed in accordance with the ethical standards of the Helsinki Declaration (as revised in Brazil 2013) and based on resolution 466/2012 of the National Research Ethics Committee in Brazil. This research was submitted to the Research Ethics Committee of Federal University of Acre, being approved in October 2017 under opinion No. 2.319.053. All participants signed an informed consent form and were able to clarify any possible doubts regarding their participation in this study.

Study variables

Dependent variable

The dependent variable of this study was the self-reported frailty obtained through questions related to the components of this syndrome: unintentional weight loss, strength reduction, slowness (walking speed reduction), low physical activity and fatigue. The questions used were validated in a study carried out in Brazil (15). They were considered “frail” older people who scored for three or more components, “prefrail” those who scored positively for one or two, and “non frail” those who didn’t score in any of the components described.

Independent variable of interest

The independent variable of interest in the study was the metabolic syndrome identified as recommended by the NCEP ATPIII, which considers the combination of at least three components: abdominal obesity (measured by waist circumference, >102cm for men and >88cm for women), triglycerides ≥150mg/dL or the use of lipid-lowering drugs, HDL cholesterol <40mg/dL for men and <50mg/dL for women, blood pressure ≥130mmHg or ≥85mmHg or the use of antihypertensive drugs, fasting glucose ≥100mg/dL or previous diagnosis of Type 2 diabetes mellitus or the use of hypoglycemic agents (16).

Descriptive and adjustment variables

Descriptive and adjustment variables were sex (male; female); age group (60 – 79 years; 80 years and over); cognitive decline (no decline; with decline); depressive symptoms (no; yes); performance in Basic Activities of Daily Living (BADLs) (independent; dependent); and performance in Instrumental Activities of Daily Living (IADLs) (independent; dependent).

Instruments used to collect information

To evaluate cognitive decline, depressive symptoms, and performance in BADLs and IADLs, the validated instruments described below were used.
Cognitive Abilities Screening Instrument – Short Form (CASI-S): an instrument designed to identify cognitive alterations in older people. The maximum score is 33 points and the cut-off point adopted for screening for cognitive decline is 23 (17, 18).
Geriatric Depression Scale (GDS): identifies the presence of depressive symptoms in older adults through 15 questions with yes/no answers. Positive screening for depressive symptoms is considered a score ≥ 6 (19, 20).
Katz scale: evaluates performance in BADLs. The BADLs consist of self-care tasks, including six functions: going to the bathroom, dressing, taking a shower, moving around, being continent (keeping control over eliminations), and eating (21). Older adults who performed all BADLs without assistance were considered independent.
Lawton & Brody scale: the scale evaluates the performance of the older adults in IADLs (22), which are adaptive tasks developed together with the community in an independent life and which include tasks such as using transport, doing household chores (taking care of the house and preparing meals), shopping, making phone calls, managing their own finances, and taking medication. Older people who performed all IADLs without assistance were considered independent.

Data treatment and statistical analysis

The database was built in Microsoft Office Excel, version 2019 (16.0), with double data entry being performed in order to correct possible typing errors. Statistical analyses were performed using Stata software, version 13.0. In the descriptive analysis of the data, the proportions were estimated and the differences between the groups were identified using the Pearson’s χ2 test. For the association analysis, multinomial logistic regression was used. All independent variables were kept in the final model for adjustment. In all analyses, a significance index of 5% was used.

Results

There was a predominance of female older people (69.07%), aged between 60 and 79 years (87.13%), with an income greater than or equal to one minimum wage (72.09%), with no cognitive decline (75.94%) and depressive symptoms (63.31%), independent for BADL (86.46%) and dependent for IADL (51.69%) (Table 1).

Table 1. Percentage distribution of the older people according to the socioeconomic, health and metabolic syndrome characteristics. Rio Branco, Acre, Brazil, 2019. (n=443)

a. BADL (Basic Activities of Daily Living); b. IADL (Instrumental Activities of Daily Living).

Table 2. Association between metabolic syndrome and frailty syndrome in the older people. Rio Branco, Acre, Brazil, 2019. (n=443)

a. BADL (Basic Activities of Daily Living); b. IADL (Instrumental Activities of Daily Living).

Positive frailty screening was identified in 56.88% of the older people, prefrail represented 34.09% and only 9.03% were classified as non-frail. The prevalence of metabolic syndrome was 51.69%.
Despite the proportion of older people with metabolic syndrome being higher among prefrail and frail, the test of difference in proportions wasn’t statistically significant. In the univariate multinomial regression analysis, it was observed that the older people with metabolic syndrome were more likely to be prefrail and frail (Table 2).
After adjusting by the independent variables, there was an association between metabolic syndrome and prefrailty, and the older people with metabolic syndrome were more likely to be prefrail (RRR=2.36; 95%CI=1.08-5.18). The dependence for IADL was associated with both prefrailty condition (RRR=2.52; 95%CI=1.09-5.81) and frailty (RRR=2.29; 95%CI=1.02-5.14). Depressive symptoms were associated only with the frailty condition (RRR=4.69; 95%CI=1.72-12.79) (Table 2).

Discussion

This study aimed to investigate the association between frailty, measured through a self-reported screening tool, and metabolic syndrome in older people. The results indicated that metabolic syndrome was associated with an increased likelihood of pre-frailty in the evaluated older population, consistent with other studies that used objective measures to identify the frailty phenotype (7, 23, 24). Considering that the association between the two conditions was identified using a self-reported frailty screening tool, these findings are important in demonstrating that a simple and easily applicable tool can be used to screen frail older people in Brazil, which may help prevent metabolic syndrome.
A meta-analysis determined in the adult population in Brazil estimated, a prevalence of 42% when using the NCEP-ATPIII among the oldest (age ≥ 45 years) (25). As in Brazil, studies carried out in other countries with samples only of the older people are low. Similar results of prevalence were found in a study with 1099 Australians aged between 50 and 80 years, which estimated the prevalence of metabolic syndrome at 32% using the NCEP ATPIII criteria (26). On another hand, the prevalence of frailty in community-dwelling older people in Latin America and the Caribbean was 19.6% (95% CI: 15.4–24.3%) with a range of 7.7% to 42.6% in the studies reviewed, depending on the definition adopted (27).
A divergent result was found in a study that used data from the US National Health and Nutrition Examination Survey (NHANES) which found that younger people had a higher prevalence of metabolic syndrome and higher frailty index compared with the older people. This divergence of results can be explained by the fact that in the study with data from NHANES, different criteria were used to define MetS and frailty, and that the correlation analyzes were not adjusted for other variables (28).
Metabolic syndrome and frailty may share common pathophysiological mechanisms that put the older people at risk due to cardiovascular risk factors, coagulopathies, and metabolic deregulation (29). It is observed that the increase in blood pressure, a risk factor in MetS, can be correlated with a sedentary lifestyle, which in turn is associated with a decrease in functional capacity, and so can lead to a decrease in walking performance, making it slower. This decline in walking speed is present in the cycle of clinical manifestations of frailty (30).
Metabolic, immunological, and endocrine changes characteristic of MetS in older people may be related to the mechanisms of the frailty syndrome, since the chances of being prefrail or frail increased by about 50% with the presence of the MetS (7). The literature presents limited studies regarding the relationship between frailty and metabolic syndrome. The absence of similar research conducted with older people Brazilians complicates result comparisons. Nevertheless, it is noteworthy to mention a study conducted in Spain, revealing that. older people with MetS and compared with individuals without MetS presents an increase in the risk of frailty over a period from 3.5 years (23).
Regard to a possible explanation for the effect of metabolic syndrome about the frailty, didn’t find any association between the isolated components of MetS and frailty, suggesting that MetS as a set of disorders, rather than the sum of its parts, can increase the chances of frailty (31). The presence of hyperglycemia and hypercholesterolemia, as well as the use of drugs to treat these conditions, chronic inflammatory processes, and the catabolic state in an individual’s body, when associated, can cause clinical manifestations (31, 32).
These changes can lead to a loss of weight, muscle mass, energy, and walking speed, which are components of the Frailty Phenotype (9, 33). In addition, MetS is followed by peripheral insulin resistance, chronic microinflammation, activation of oxidative and prothrombotic mechanisms, and deregulation of the renin-angiotensin axis (31). All these mechanisms can have a detrimental effect on nutrition, as well as on the neuromuscular system and cognition of the older people. Furthermore, MetS has been associated to the higher occurrence and severity of microvascular brain damage, a condition that can accelerate cognitive and functional decline, leading individuals to the frailty (24).
The limitations of this study include the cross-sectional outline, which makes impossible the accomplishing causal inference and the difficulty in generalizing the results, since it is a sample of older people living in the Amazon region of Brazil. Furthermore, unfortunately, even with so much information available on the main physiopathological mechanisms of both syndromes, it was not possible to analyze, for example, blood biomarkers such as pro-inflammatory cytokines, immunological or hormonal profiles that could add more information to our study.
A strong point of this study is using a frailty tracking instrument that can be easily used by any professional in different health services. In addition to easy application and accessibility due to low cost, some evidence already indicates that the use of these tools can reliably contribute to frailty screening, which will make their use more common in the field of research and clinical practice (10, 11). Future studies may benefit from the use of these instruments and thus add to the body of existing evidence for their improvement. In the long term, this may enable the creation of guidelines that will add new elements to objective diagnostic methods.

Conclusion

In conclusion, the metabolic syndrome was associated to the increase chance of screening for prefrailty in the older people evaluated. The results found from the use of this instrument in the present study indicate that frailty screening can be crucial among the older people with MetS, since frailty is a reversible process and that early interventions can prevent adverse outcomes potentiated by MetS. In addition, the diagnosis of MetS is easy and accessible, so that the identification of this condition in the older people can alert health professionals to the need for a more specific investigation of the health status of the older people, using instruments to identify frailty. Thus, the diagnosis of MetS in the older people can lead to a treatment that already considers the prevention of frailty.

Acknowledgements: The present study had resources obtained through research funding by the Research Program for SUS: Shared Health Management – PPSUS/AC. Call FAPAC-SESACRE-Decit/SCTIE/MS-CNPq under process No. 33376.512.21332.21092017. The authors wish to thank all participants for being involved in this study.

Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.

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

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 2024

J. Raffin^1,2

1. Institut Hospitalo-Universitaire (IHU) HealthAge, Toulouse, France; 2. Institut du Vieillissement, Gérontopôle de Toulouse, Centre Hospitalo-Universitaire de Toulouse, 37 allées Jules Guesde, 31000 Toulouse, France

Corresponding Author: Jérémy Raffin, PhD, Gérontopôle de Toulouse, Institut du Vieillissement, Bâtiment B, 37 Allées Jules Guesde, 31000, Toulouse, France,+ 33 5 61 14 56 28, E-mail: jeremy.raffin@live.fr

J Aging Res & Lifestyle 2024;13:77-81
Published online May 22, 2024, http://dx.doi.org/10.14283/jarlife.2024.11

Abstract

Physical exercise is well known for its benefits on brain health. However, the mechanisms through which these benefits occur remain discussed, especially in the context of cognitive conditions such as Alzheimer’s disease. The present short review summarizes the findings of interventional studies that examined the effects of exercise training on the specific and non-specific biomarkers of Alzheimer’s disease. Controlled exercise intervention studies published in the English language were selected if they assessed the effects of a physical exercise intervention of at least 2 weeks in middle-aged or older adults on one of the following biomarkers measured either in the brain, the cerebrospinal fluid or the blood: beta-amyloid, tau, neurofilament light chain, and glial fibrillary acidic protein. Overall, there was no strong evidence of significant effects of exercise interventions on any of the selected biomarkers. However, in specific populations, such as women with obesity, pre-diabetes, or depression, favorable changes in blood beta-amyloid concentrations were reported. Further benefits on cerebrospinal fluid beta-amyloid were also demonstrated in APOE-ε4 allele carriers with Alzheimer’s disease. In conclusion, the current evidence suggests that physical exercise does not modulate the pathophysiology of Alzheimer’s disease in the overall population of middle-aged and older adults. Nonetheless, some specific populations, such as women with metabolic disorders and Alzheimer’s disease patients with APOE-ε4 genotype, seem to be favorably affected. Further studies, including long follow-ups, large sample sizes, and concomitantly assessing the effects of other factors such as sedentary behavior and diet, are required to bring further evidence to the field.

Key words: Alzheimer’s disease, physical exercise, biomarkers, amyloid, tau.

Introduction

Cognitive impairment and dementia are major causes of disability during aging (1). The current number of people living with dementia has been estimated at about 50 million, and this number will triple by 2050 (1, 2). Hence, preventing cognitive decline and dementia represents a major goal in aging societies, given the economic and social impact they induce (3). Alzheimer’s disease (AD) is the leading cause of dementia (4) and is notably characterized by an abnormal accumulation of dysfunctional beta-amyloid (Aβ) and phosphorylated tau proteins in the brain (5). In addition, the pathophysiology of AD also involves neuronal damages and neuro-inflammation that are respectively mirrored by an increased production of neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP) (6). Hence, all together, Aβ, phosphorylated tau, NFL and GFAP have been defined as main biomarkers for AD, the former two being core biomarkers and the latter two being non-specific biomarkers (6).
Various strategies to prevent AD have been developed, including drug therapy trials (7) as well as lifestyle interventions comprising cognitive stimulation, diet regulation, and physical exercise (8). While the positive effects of physical exercise on cognitive function are well demonstrated (9, 10), the pathway through which physical exercise induces beneficial effects remains unclear (11, 12). More specifically, the question of whether chronic physical exercise modulates the physiopathology of AD has not been established, and only a few studies have been conducted in humans (11). The present narrative review addresses this question by summarizing the effects of the published controlled interventions conducted in middle-aged and older adults that investigated the effect of regular physical exercise on the main biomarkers of AD (13).

Methods

The present work is a short non-systematic narrative review on the effects of regular exercise on the main biomarkers of AD. We selected the interventional controlled studies that examined whether physical exercise intervention of any type (eg, aerobic, resistance, balance exercises, or multicomponent exercise training), conducted for at least 2 weeks, had an effect on AD biomarkers. AD biomarkers may have been measured either in the brain, the cerebrospinal fluid (CSF) or in the blood. Four biomarkers of AD were selected based on the latest version of the Revised Criteria for Diagnosis and Staging of Alzheimer’s Disease (6): Aβ (including Aβ38, Aβ40, Aβ42, and Aβ42/40 species), tau proteins (including total and phosphorylated species), NFL and GFAP. Only human studies conducted in adults were chosen with no restriction regarding age, sex, chronic diseases or cognitive status. Studies not published in the English language were not included.

Results

Studies conducted on Aβ species, including middle-aged and older men and women with normal cognitive status, mild cognitive impairment or AD indicated no significant effect of exercise interventions, lasting from 8 weeks to 1 year and including 3 to 5 sessions of 45 to 60 min weekly, on either blood (14–18), CSF(19) or brain (20–22) amyloid levels, compared to control groups. However, subgroup analyses demonstrated that in APOE-ε4 allele carriers with AD, an increase in CSF Aβ40 was observed in inactive controls after 16 weeks of follow-up while the carriers from the exercise group maintained their baseline concentrations (19). Other interventions specifically conducted in older women reported that 12 to 16 weeks of resistance exercise training induced a significant reduction in blood Aβ42 concentrations in obese (23) or pre-diabetic (24) individuals, compared to inactive groups. Such changes were accompanied by significant reductions in glycated haemoglobin (24). Likewise, 12 weeks of Taekwondo exercise administrated in older women with depression significantly reduced the blood levels of Aβ42 (25) compared to no exercise. Exercise interventions performed in older women with no specific health condition reported mixed findings with both significant changes and no change reported after 12 (26) and 16 weeks of aerobic training (27).
Regarding the effects of exercise on tau proteins, studies are scarce but it has been shown that in non-demented middle-aged individuals, 2 weeks of resistance exercise concomitant to a bed-rest intervention did not modulate the blood levels of total tau compared to bed rest alone (28). Likewise, 6 months of cycling exercise had no impact on the blood levels of phosphorylated tau 181 in cognitively healthy older adults (18). Furthermore, in older adults with AD, neither 16 (19) or 24 weeks (17) of aerobic exercise impacted the concentrations of total and phosphorylated tau proteins measured in the CSF or in the blood.
Similar negative findings have been reported on the non-specific markers of AD. In non-demented middle-aged and older adults, 2 weeks of resistance exercise conducted in parallel to a bed rest protocol did not impact NFL and GFAP blood concentrations compared to bed rest without exercise (28). Similarly, the blood concentrations in GFAP and NFL were not affected by 6 months of aerobic training in older adults with no cognitive impairment (18). A long-term intervention of 2 years of combined resistance and aerobic exercise reported no effects on blood concentrations in NFL in comparison to a control group (29). In patients with AD, it was reported that, compared to a no-exercise group, 16 weeks of aerobic training did not produce any significant changes in CSF NFL concentrations (30), even in subjects that were classified as amyloid positive (CSF concentrations Aβ42 < 550 pg/ml) (19).

Discussion

Overall, the studies included in this short review demonstrated no effect of exercise interventions on the main biomarkers of AD, which is in line with previous reviews on the topic (31, 32). Nonetheless, some factors such as sex, APOE genotype or health status, seem to modify the effect of exercise training, as favourable and significant findings regarding Aβ levels were reported in women with obesity (23), pre-diabetes (24), or depression (25) and in APOE-ε4 carriers individuals with AD (19). Hence, it is possible that exercise still has an effect on specific populations that display risks factors for AD such as women (33), APOE-ε4 genotype carriers (34), individuals with metabolic disorders (35) or depression (36). Notably, the favourable effects found in AD patients with APOE-ε4 genotype is also in accordance with previous work demonstrating that APOE-ε4 carriers display greater improvements in cognitive functions in response to exercise compared to non-carriers (37). Regarding the lack of effects in the overall population, because most of trials were performed in older adults, it is possible that an advanced age might counteract the benefits of physical training, although some authors have reported greater effects of exercise on cognition in healthy adults older than 60 (38) compared to younger counterparts. Our findings also diverge from the observational studies and meta-analyses that demonstrated beneficial associations between PA and amyloid (39, 40), NFL (41–43) and GFAP (43) levels, although the relationships with tau remain contrasted (39, 44–48).
Even though interventional studies reported little effects on the specific biomarkers, PA may still improve cognition through other pathways. Recent reviews published on this topic have reported that chronic exercise has a positive effect on brain glucose metabolism, vascular function, and BDNF concentrations, along with providing benefits on cognition (11, 32, 38). This indicates a pleiotropic effect of exercise that is consistent with its positive impact on the biological hallmarks of aging (49), which are thought to be the common roots of most of age-related diseases (50). Future studies should thus focus on long term interventions not only in middle-aged and older adults, but also in young adults as abnormal proteins deposition may start decades before the disease onset (51, 52). Studies examining the factors that may moderate the effect of exercise, such as sex, genotype, or health status are also required, as well as studies with large sample sizes, given that the subject samples of the studies selected for this review were relatively small, ranging from 156 to 14 individuals. We also recommend that cognition should be assessed along with the measurement of the neurodegeneration biomarkers in order to determine whether changes in the latter could mediate changes in the former. In addition, most of the studies included herein focused on tau, amyloid, and NFL proteins, but there is a lack of evidence regarding the impact of exercise on GFAP. Yet, the research on the non-specific markers of AD, namely NFL and GFAP, remains important. Indeed, GFAP is an indicator of astrocyte activation (6) and has been shown to be an early predictor of Aβ production (53) while NFL reflect axonal damages (6). Yet, higher blood levels of both NFL and GFAP have been associated with reduced cognitive capacity (54, 55) and greater prospective cognitive decline (54). Importantly also, the biomarkers examined herein may interact with each other, such that the benefits of exercise on one biomarker may depend on the levels of other biomarkers. Interventional studies simultaneously measuring the effects of several biomarkers and examining their interactions may thus provide significant contributions to the field.
In conclusion, physical exercise interventions do not demonstrate favorable AD-modifying effects, except in women with impaired metabolic health or depression and APOE-ε4 carriers patients with AD. While more studies are needed given the paucity of available evidence, other important factors such as diet (56, 57), cognitive stimulation (58), or sleep quality (59) may also modulate AD pathophysiology and should be explored collectively. Importantly, sedentary behaviour, which demonstrated significant association with incident dementia (60), may have deleterious independent and/or exercise-counteracting effects on brain physiopathology. All of these factors may act synergistically and potentiate the single effects of each individually, emphasizing the need for holistic approach interventions to prevent dementia (61).

Conflict of interest: The author declares no conflict of interest.

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 2024

K.V. Giudici¹

1. Institute of Aging, Gerontopole of Toulouse, Toulouse University Hospital, Université Toulouse III Paul Sabatier, Toulouse, France.

Corresponding Author: Kelly Virecoulon Giudici, Institute of Aging, Gérontopôle of Toulouse, Toulouse University Hospital, Université Toulouse III Paul Sabatier, 37 Allée Jules Guesde, 31000 Toulouse, France, E-mail: kellygiudici@gmail.com

J Aging Res & Lifestyle 2024;13:73-76
Published online May 22, 2024, http://dx.doi.org/10.14283/jarlife.2024.10

Abstract

Because nutrition is one of the main factors related to Alzheimer’s disease (AD), questions arise about how taking nutrients as supplements can affect its pathophysiological process. In the present study, an overview of the potential effects of nutritional supplementation on the main biomarkers related to the AD pathophysiology (i.e., amyloid-β and tau) is explored. Trials testing the supplementation of single or combined nutrients versus placebo identified effects on some AD biomarkers, but changes were not always accompanied by positive effects on cognitive function. Differences in characteristics of studied populations (cognitive status, age, educational level), choice of nutrient combinations and doses, duration of intervention, and adjustments for potential confounders are some factors that may explain discrepancies in findings.

Key words: Alzheimer’s, supplementation, amyloid, tau, cognitive decline, aging.

Abbreviations: Aβ: amyloid-β; AD: Alzheimer’s disease; ADAS-cog: Alzheimer disease assessment scale-cognitive subscale; ALA: α-lipoic acid; APOE: apolipoprotein E; CSF: cerebrospinal fluid; DHA: docosahexaenoic acid; EPA: eicosapentaenoic acid; IQ: intelligence quotient; MAPT: Multidomain Alzheimer Preventive Trial; MCI: mild cognitive impairment; MMSE: Mini Mental State Examination; PET: positron emission tomography; p-tau181: phosphorylated tau at threonine 181; PUFA: polyunsaturated fatty acids; RCT: randomized controlled trial; t-tau: total tau; VISP: Vitamin Intervention for Stroke Prevention.

Introduction

The pathophysiological process leading to the characterization of Alzheimer’s disease (AD) as a unique neurodegenerative disorder, among other types of dementia, consists of the accumulation of amyloid-β (Aβ) plaques and pathologic tau deposits in the brain (1). The neurodegeneration influenced by these processes, coupled with dementia, results in gradual cognitive decline that may reach advanced stages in which quality of life is severely affected (2).
Although there is a strong genetic risk factor for the development of AD (the presence of the APOE ε4 allele) (3), many other factors such as diet, physical activity level, stress management and sleep quality are known to affect the probability of accumulating Aβ and contributing to tau phosphorylation and aggregation in the brain, and consequently increasing the risk of AD (4).
The detection and diagnosis of AD has been classically based on the evaluation of Aβ and tau biomarkers in the brain (by positron emission tomography – PET) or in the cerebrospinal fluid (CSF) (1, 2), which are either expensive or invasive methods. More recently, blood-based biomarkers have emerged as less complex alternatives, but with compatible reliability (5, 6). These measures have been used as outcomes in trials testing what can be done, in terms of lifestyle changes, to prevent or fight this neurodegenerative disease. Since nutrition is one of the main factors related to AD (7), questions arise if taking nutrients as supplements is able to affect its pathophysiological process. In the present study, an overview on the potential effects of nutritional supplementation on the main biomarkers related to the AD pathophysiology (i.e., Aβ and tau) is explored.

Effects of nutritional supplementation on Aβ and tau biomarkers

The main omega-3 polyunsaturated fatty acids (PUFA) (eicosapentaenoic acid – EPA and docosahexaenoic acid – DHA) are known by their substantial anti-inflammatory and antioxidant properties (8). DHA is especially important to brain function: besides its essential structural properties, it regulates synaptic function, modulates gene expression, acts as an indirect antioxidant and contributes to neuroprotection (9). The Multidomain Alzheimer Preventive Trial (MAPT) explored the effects of a 3-year supplementation with omega-3 PUFA (800mg DHA and 225mg EPA/day), alone or combined to physical activity and cognitive training, on AD biomarkers and clinical tests among 1,680 community-dwelling older adults living in France and Monaco (10, 11). At the end of the 3-year follow-up, no effects of interventions were observed in cognitive function (evaluated with a composite cognitive score) (11), nor in plasma phosphorylated tau at threonine 181 (p-tau181) when a subsample of 527 participants with this measure was analyzed (12). Interestingly, in another secondary analysis of MAPT among a subsample of 483 participants with plasma Aβ42/40 ratio assessments, the combined intervention showed a positive effect on cognitive function in the per-protocol positive amyloid group (i.e., Aβ42/40≤0.0107; n=154), after 1 year and 3 years. However, no differences were found between intervention and placebo groups after two additional years of observational follow-up (13).
In the OmegAD Study, a 6-month omega-3 PUFA supplementation (2.3g/day) or placebo was offered to 35 patients diagnosed with AD. Compared to placebo, intervention did not affect CSF Aβ38, Aβ40, Aβ42, total tau (t-tau) and p-tau (14, 15). A secondary analysis with 33 participants revealed that changes in CSF levels of DHA due to supplementation were inversely correlated with CSF levels of t-tau and p-tau, indicating that the more DHA increased in CSF, greater was the change in CSF tau biomarkers (16). Another trial offered 2g/day of DHA or placebo for 240 individuals with mild cognitive impairment (MCI) living in China over 2 years, and found decreases in blood Aβ42 levels and expression of Aβ protein precursor mRNA, which were accompanied by increases in scores of full-scale intelligence quotient (IQ), verbal IQ and subdomains of information and digit span, among those taking the DHA supplement (17).
Vitamin D is another nutrient believed to contribute to the development of cognition and its maintenance over time (18). Acting as a hormone with multiple actions in metabolism, it impacts neurocognition by inducing neuroprotection, modulating oxidative stress, regulating calcium homeostasis and inhibition inflammation (19). A trial with 210 participants testing a 1-year vitamin D supplementation (800IU/day) in older adults with AD observed a decrease in plasma Aβ42 and improvements in cognitive assessments (information, arithmetic, digit span, vocabulary, block design and picture arrange scores) among the intervention group (20). Another trial testing the effect of a high-dose short-term vitamin D supplementation (50,000IU/week for 8 weeks) versus placebo on plasma Aβ40 of 24 vitamin D insufficient adults observed a greater plasma Aβ40 increase among the intervention group, what authors suggested to be an indicative of decreased brain Aβ (21).
Excessive homocysteine (Hcy) has direct neurotoxic effects, due to inducing oxidative stress, causing DNA damage and apoptosis, and stimulating Aβ deposition in the brain (22). Some B-vitamins are known by their role in Hcy remethylation, thus contributing to decrease Hcy levels and to potentially avoid its neurotoxicity (23). Chen et al. (24) explored the effects of a 6-month folate supplementation (1.25mg/day) on inflammatory biomarkers and cognitive function among patients with AD. They found that plasma Aβ40 levels were lower, Aβ42/40 ratio was higher and mean Mini Mental State Examination (MMSE) score was slightly increased in the intervention group compared to the control group at the end of follow-up. In the Vitamin Intervention for Stroke Prevention (VISP) trial, 300 adults with ischemic stroke and high total Hcy (tHcy) levels (a risk factor for AD) were treated with either a high-dose supplement (composed of 25mg of pyridoxine, 0.4mg of cobalamin, and 2.5mg of folic acid) or a low-dose supplement (200mcg of pyridoxine, 6mcg of cobalamin, and 20mcg of folic acid) for 2 years (25). At the end of follow-up, no alterations were observed for Aβ40, Aβ42 or Aβ42/40 ratio. On the other hand, tHcy levels significantly decreased in both groups (more among participants taking the high dose) and were strongly correlated with plasma Aβ40, but not with Aβ42 concentrations (25).
Important copper concentrations and localization changes have been identified in AD cerebral regions, raising questions whether therapeutic approaches for regulating its levels could affect AD pathophysiology (26). Kessler et al. (27) offered a 12-month supplementation of 8mg/day of this nutrient or placebo to a sample of patients with mild AD. They found no effect on the progression of CSF tau and p-tau levels nor in MMSE and Alzheimer disease assessment scale-cognitive subscale (ADAS-cog) scores. Still, a lower decline in CSF Aβ42 was observed in the intervention group (a positive finding, since decreased CSF Aβ42 is a diagnostic marker for AD).
The Nolan Study, in turn, tested the effect of a 1-year multi-nutrient supplementation (including omega-3 PUFA, vitamin C, vitamin D, vitamin E, thiamin, riboflavin, niacin, pantothenic acid, pyridoxine, folic acid, biotin, cobalamin, selenium, choline and citrulline) on clinical tests, imaging and blood biomarkers related to the AD pathology among a sample of 362 community-dwelling older adults living in France (28, 29). At the end of the follow-up, supplementation could not postpone the increase in plasma p-tau181 (observed in both intervention and placebo groups) (29), and neither showed an effect on cognitive function (28). Another randomized controlled trial (RCT) found no benefits of a 16-week supplementation of combined vitamin E (800IU/day), vitamin C (500mg/day) and α-lipoic acid (ALA) (900mg/day), or coenzyme Q alone (1200mg/day) on CSF Aβ42, t-tau or p-tau181 in a sample of 66 subjects with mild to moderate AD (30). Surprisingly, a faster decline in MMSE score was identified among the group receiving vitamin E, vitamin C and ALA (30).

Conclusions and perspectives

Despite the well-established influence of diet in the development of the AD neurodegenerative process (7), it is still not clear how nutritional supplementation may contribute to preventing or postponing it and, consequently, to protect cognitive function. Trials currently show that some biomarkers related to the AD development can be modified with supplementation protocols varying from months to years. Still, changes are not always accompanied by positive effects on cognitive function. Differences in characteristics of studied populations (cognitively normal participants or subjects with MCI or AD, age ranges, educational level), choice of nutrients’ combinations and doses, duration of interventions and adjustments for potential confounders (such as APOE ε4 status) are some factors that may explain discrepancies in findings.
It is known that AD slowly develops for decades before cognitive decline is perceptible and starts negatively affecting a person’s life (2). It is thus comprehensible that nutritional supplementation alone in advanced age seem to be not able to neutralize the decades of metabolic processes that have been slowly acting on a person’s body and brain (and triggered not only by inadequate diet, but also by other lifestyle factors as stress, bad sleep quality and sedentary behavior (31), leading to the development of AD.
In spite of increasing costs and complexity, future research on the topic might benefit of enhanced sample sizes and/or duration of interventions (covering a higher percentage of average lifespan) in study protocols. Another point to consider is that not every person may benefit from supplementation. In this sense, scanning for nutritional deficiencies related to cognitive performance may help identify individuals for whom supplementation would be more probably effective. Moreover, genetic variants are able to affect the way nutrients act on metabolism. In AD, the APOE ε4 allele is recognized as the major genetic risk factor in late onset Alzheimer’s (3), partly due to impairing lipid transport from neurons to astrocytes (32), altering microglia function (33), impairing neuronal insulin signaling (34), favoring blood-brain barrier dysfunction (35) and increasing DHA β-oxidation (36). Thus, studies taking into account major polymorphisms related to AD physiopathology take a step forward in this investigation.
Current and growing knowledge on the theme must be used to support a careful choice of nutrients in future studies. Since evidence points towards oxidative stress as an early event leading to Aβ deposition and dimerization of tau protein and its subsequent hyperphosphorylation (37, 38), therapeutic approaches focusing on antioxidants (as vitamin C, vitamin E and selenium) might be considered. Additionally, inflammation (39) and impaired glucose metabolism (40) are both related to the development of AD, and might be the target of nutritional interventions as well – by offering, for example, nutrients with anti-inflammatory abilities (as omega-3 PUFA (8) and folate (24)), and nutrients known to improve glucose homeostasis (as vitamin D (41)).
Finally, it should be kept in mind that nutritional supplementation does not aim to overlap the importance of following balanced dietary patterns over the life course in order to prevent AD and other diseases – even because, to date, nutrients synergy as experienced with food intake cannot be replicated with supplements. Notwithstanding, identifying specific nutrients or bioactive compounds for which a high dose (incompatible with usual food intake) would be effective in fighting the AD pathophysiological process is another factor that may justify supplementation. Altogether, the state of art points towards this as a hot topic in research, for which further important discoveries are yet to be achieved.

Conflict of interest: None.

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