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J. Willers1, A. Hahn1, T. Köbe2,3, S. Gellert1, V. Witte2,3,4, V. Tesky5, J. Pantel5, A. Flöel2,3,6, J.P. Schuchardt1


1. Institute of Food Science and Human Nutrition, Leibniz University Hannover, Germany; 2. Department of Neurology, Charité – University of Medicine Berlin, Germany; 3. NeuroCure Cluster of Excellence, Charité – University of Medicine Berlin, Germany; 4. Max Planck Institute of Human Cognitive and Brain Sciences, Department of Neurology, Leipzig, Germany and SFB 1052 Obesity Mechanism subproject A1, University of Leipzig, Germany; 5. Institute of General Practice, Goethe University, Frankfurt am Main, Germany; 6. Center for Stroke Research Berlin, Charité – University of Medicine Berlin, Germany

Corresponding Author: J. Willers, Institute of Food Science and Human Nutrition, Leibniz University Hannover, Am Kleinen Felde 30, 30167 Hannover, Germany, Tel: +49 (0)511 762 5755; Fax: +49 (0)511 762 5729; Email: willers@nutrition.uni-hannover.de

J Aging Res Clin Practice 2018;7:37-41
Published online March 1, 2018, http://dx.doi.org/10.14283/jarcp.2018.8



In this cross-sectional study, body composition of fifty-eight mild cognitive impairment (MCI) patients (single and multiple domain) and fifty healthy older control subjects by the use of bioelectrical impedance analysis (BIA) was assessed. Measurements were: height, weight, body mass index, BIA: phase angle (PA), total body water (TBW), lean body mass (LBM), body cell mass (BCM), extracellular mass (ECM), body fat mass (BFM), apolipoprotein E4, and physical activity level. Compared to BIA reference values and healthy subjects, MCI patients had significant differences in PA (only female), BCM and ECM/BCM index. Differences were more pronounced in females compared to males. The low levels of BCM and PA suggest that MCI patients, especially of female sex, have a poor nutritional status. BIA-derived PA might be a suitable indicator, that could enhance evaluation of nutritional status in patients with cognitive decline.

Key words: Mild cognitive impairment, bioelectrical impedance analysis, phase angle, body composition.

Abbreviations: APOE: apolipoprotein E; AVLT: auditory verbal learning test; BCM: body cell mass; BFM: body fat mass; BIA: bioelectrical impedance analysis; BMI: body mass index; CERAD: Consortium to establish a registry for Alzheimer’s disease; ECM: extracellular mass; LBM: lean body mass; MCI: mild cognitive impairment; MMSE: mini-mental status examination; PA: phase angle; PAL: physical activity level; R: resistance; TBW: total body water; Xc: capacitive reactance.



Different epidemiological studies observed a relationship between nutritional status and cognitive function in patients with cognitive impairment (1-3). There is also evidence that poor nutritional conditions are associated with cognitive decline (4-6) and may play an important role in progression of cognitive loss (7).
Techniques for measuring body composition include anthropometry and bioelectric impedance analysis (BIA). Especially the use of raw BIA data has become a standard procedure in the assessment and monitoring of the body composition and nutritional status of patients [8]. BIA measures the opposition to electrical flow arising from resistance and reactance. The BIA is a simple, inexpensive and non-invasive technique for assessing body composition, allowing conclusions on total body water (TBW), hydration status and body cell mass. This method is used for the determination of nutritional status and risk of morbidity in ambulatory and hospitalized patients (9). For the nutritional screening and clinical prognosis, the phase angle (PA) is the most established impedance parameter (10). The PA is calculated from resistive behaviour (R), which is mainly dependent on tissue hydration, and the capacitive behaviour of tissues (Xc) associated with cellularity, cell size, and integrity of the cell membrane (8). A low PA suggest cell death or decreased cell integrity and has strong predictive value according to morbidity and mortality in various disease conditions, e.g. HIV or cancer (11).
Due to a strong dependency of body composition on sex (e.g., females have less muscle and greater percentage of body fat than males), age (e.g., decreasing muscle mass and increasing fat mass with increasing age) and body mass index (BMI), reference values for PA and other BIA parameters are sex-, age-, and BMI-specific (8). A gender specific evaluation is therefore mandatory.
To our knowledge, only one recent study is published that investigated the nutritional status of mild cognitive impairment (MCI) patients in terms of the body composition via BIA (12). MCI is a frequent condition in the general aged population and is associated with an increased risk for the development of dementia. Therefore, body composition in MCI may be of relevance for prognosis of cognitive decline. Thus, the objective in this study was to compare BIA measurements in MCI patients with literature reference values and age-matched older adults without clinical dementia.



For this cross-sectional study, 58 patients with MCI were recruited consecutive between 2011 and 2014 in Berlin (memory clinic of the Department of Neurology of the Charité University Hospital and Neurology specialist practice) and Frankfurt am Main (Institute of General Practice), Germany. MCI patients (single and multiple domain) were diagnosed according to Mayo criteria based on subjective cognitive complaints and objective memory impairment in standardized tests (performing at least 1.5 SD below age- and education-specific norm in relevant subtests (Total Word List, Delayed Recall Word/Figures) of the CERAD-Plus test battery (13), relatively preserved general cognition, no impairment in activities of daily living, and no dementia (14). Fifty healthy older adults were recruited between 2010 and 2013 at the memory clinic of the Department of Neurology at the Charité Berlin, Germany. A detailed description of in- and exclusion criteria can be found in Köbe et al. (15) for MCI patients and Witte et al. (16) for healthy older adults.
MCI patients had a body mass index (BMI) range from 18 to 32 kg/m², whereas the healthy controls had a BMI between 24 kg/m² to 32 kg/m². Before comparing the two groups a BMI adjustment as well as an age-match was mandatory (Figure 1).


Figure 1 BMI adjustment and age-match for the group comparison

Figure 1
BMI adjustment and age-match for the group comparison


Anthropometric (e.g., body weight, height, BMI) and bioimpedance measurements (e.g., PA, TBW, lean body mass (LBM), body cell mass (BCM), extracellular mass (ECM), body fat mass (BFM)) were carried out. BIA was performed with B.I.A. 2000-M (Pöcking, Germany) and the software NutriPlus (Data Input GmbH, Darmstadt, Germany).
Fasting blood samples were obtained by venipuncture of an arm vein using sealed blood collection tubes and Monovettes® (Sarstedt, Nürnbrecht, Germany). Concentrations of cobalamine (vitamin B12) and folate were measured in serum samples at the IMD laboratory, Berlin, Germany.
For apolipoprotein E (ApoE) genotyping, DNA was extracted from whole blood using a blood mini-kit (Qiagen, Hilden, Germany) and stored at -80°C until analysis. Genotyping of apolipoprotein E4 (ApoE4) was performed on a Sequenom® MassARRAY iPLEX, TaqMan assay following procedures described previously (17).
Participants were tested on memory performance using the German version of the Rey Auditory Verbal Learning Test (AVLT) (18). Global cognitive dysfunction was estimated with the Mini Mental State Examination (MMSE) (19). Patients were asked to learn a list of 15 words within five immediate recall trials, followed by a 30 min delayed recall and delayed recognition test. Learning ability was defined as the sum of words learned in all five trials (maximum 75 words); delayed recall represented the total number of remembered words after 30 min (maximum 15 words). For delayed recognition (recognition memory), subjects were asked to recognize the 15 original words presented within 35 distractor words subsequent to the delayed recall tests (number of correctly recognized words minus false positives; maximum 15 words). All testing was conducted by trained staff members according to standard procedure.
Statistical analyses were processed with SPSS software version 24.0 (SPSS Inc., Chicago, IL, USA). Results are expressed as means ± SD unless otherwise specified. Differences between men and women were calculated by the non-parametric Mann-Whitney U test. Multiple linear regression models were used for group comparison (MCI vs. healthy controls). Initially, all analyses were conducted unadjusted. Subsequently, age, education, ApoE4 status, vitamin B12, folate status (factors that are known to be associated with cognition) (15), and physical activity level (a factor that influences body composition) were entered as covariates in multiple linear regression models to study whether potential group differences are independent. Patients reported their physical activity using the Freiburger physical activity questionnaire. Spearman’s rank correlation was used to test correlations between variables. P-values ≤ 0.05 were considered significant.


Results and discussion

Characterisation of the MCI group

Characterisation of the MCI patients according to anthropometric and BIA measurements is presented in Table 1. The total MCI collective includes fifty-eight MCI patients (28 women) with a mean age of 69.1 ± 7.8 years. As expected, gender differences were observed for body weight as well as all bioelectrical variables. Percentage of BCM was reduced in MCI women (44.3 ± 3.1%) vs. men (48.5 ± 3.9%), resulting in a lower ECM/BCM index in men (1.1 ± 0.3) vs. women (1.3 ± 0.2). Manufacturer reference values for this age group constitute ideal BCM values of 50 – 56% for women and 53 – 59% for men, while the ECM/BCM ratio should be < 1 for both sexes. Thus, our data suggest that MCI patients, especially female, are of poor body condition and exhibit a low muscle content and activity status (20).


Table 1 Anthropometric measures and bioelectrical impedance analysis of the MCI group (n = 58)

Table 1
Anthropometric measures and bioelectrical impedance analysis of the MCI group (n = 58)

* Mann-Whitney U test was performed for comparison between women and men; 1 n = 26


Additionally, male MCI patients had a significantly higher PA than female patients. The median PA of the total MCI collective was 4.9° (range 3.6° to 6.4°). About half of the MCI patients (48.3%, women: 67.9%, men: 30%) had a lower PA from 3.6° to 4.8°. There was a strong negative correlation between PA and age (r = -0.538; p < 0.001, Spearman’s rank correlation), which has been, likewise, reported in healthy populations (10, 21). Physiologically, increasing age is associated with decline in tissue mass, which results in decreasing PA (11). Simultaneously, hospitalized patients showed a significant lower LBM, higher BFM and lower PA in general (22). Nevertheless, the mean PA in MCI patients of this study was even lower than of MCI patients in a recently published cross-sectional study (women: 5.6 ± 0.6°, men: 6.4 ± 0.7°) (12) and lower compared to hospitalized patients (women: 5.0 ± 1.3°; men: 6.0 ± 1.4°) (22). Furthermore, various studies indicate that the PA can be considered as a marker of clinically relevant malnutrition caused by an increase of extracellular fluid and a decrease of BCM (11, 23).
Compared to reference BIA values of a large German database (214,732 adults) in corresponding gender, age and BMI groups (8), the body composition of the present MCI patients can be classified as worse. MCI patients of both sexes have lower PAs (MCI women: 4.6 ± 0.5°, MCI men: 5.4 ± 0.7°) according to their corresponding BMI and age classes (German reference sample: women: 5.1 ± 0.8°; men: 6.0 ± 0.8°).
Additionally, in female MCI patients (n = 28) learning ability was positively correlated with the PA (r = 0.433, p = 0.002, Spearman’s rank correlation) and negatively with the ECM/BCM index (r = -0.456, p = 0.001, Spearman’s rank correlation). In male MCI patients, no correlation was observed. Nevertheless, these data suggest an association between an impaired memory performance and a poor body composition, although it cannot be clarified whether a lower cognitive function is a cause or consequence of a poor nutritional status. Further longitudinal studies are necessary.

Comparison between MCI patients and healthy controls

Furthermore, we compared the MCI patients to healthy controls in terms of BIA measurements. As the participants were selected using different BMI ranges, an adjustment according to BMI and age was necessary (Figure 1). Finally, in each case, thirty-two MCI patients and healthy controls were compared with regard to anthropometric and BIA measures (Table 2 and 3).


Table 2 Comparison of anthropometric measures and bioelectrical impedance analysis between female MCI patients and healthy controls

Table 2
Comparison of anthropometric measures and bioelectrical impedance analysis between female MCI patients and healthy controls

Multiple linear regression models were used for group comparison, adjusting for potential confounders; p < 0.05; ß: unstandardized regression coefficient.

Table 3 Comparison of anthropometric measures and bioelectrical impedance analysis between male MCI patients and healthy controls

Table 3
Comparison of anthropometric measures and bioelectrical impedance analysis between male MCI patients and healthy controls

Multiple linear regression models were used for group comparison, adjusting for potential confounders; p < 0.05; ß: unstandardized regression coefficient.


Female healthy subjects had a higher BMI (28.0 ± 1.7 kg/m2) and were heavier (76.4 ± 6.4 kg) compared to female MCI patients (BMI: 26.8 ± 1.9 kg/m2; weight: 72.0 ± 10.1 kg). These data were not statistical significant. However, the parameter should not be underestimated as cognitive decline is faster and more severe with a low BMI (BMI cut-off 25 kg/m²) (24). Especially female MCI patients showed significantly lower values in basal metabolic rate, PA, BCM, ECM, and ECM/BCM index compared to healthy females (Table 2). The differences remained significant only for PA and BCM after adjustment for potential confounders such as age, education, ApoE4, vitamin B12, folate status, and physical activity. In male subjects, the differences were not significant (Table 3).
Limitations of this study relate to the small sample size and the BIA technique. Due to the necessary BMI adjustment, the study population was greatly reduced. Thus, the group comparison was comparatively weak and might limit the outcome. Additionally, the BIA measurement has technical and physiological limitations such as hydration status, body position during procedure, air and skin temperatures, recent physical activity. BIA is not traditionally used as a measure of malnutrition but it might improve assessment of nutritional status and prognosis among MCI patients.



Using BIA, we observed a poor body composition especially in female MCI patients indicating a poor general health condition. Considering the observed associations between the PA and memory functions, our data confirm earlier findings that determined a relationship between nutrition status and cognitive function in patients with cognitive impairment. However, based on our data we cannot clarify whether lower cognitive function is a cause or consequence of a poor nutritional status. Thus, further longitudinal studies may be undertaken to resolve this important question and eventually determine if the PA may serve as a potential prognostic factor for cognitive decline.


Acknowledgements: We would like to thank the participants who contributed their time to this project. The genotyping of ApoE4 in the laboratory of Prof. Dr. Dan Rujescu (University of Halle, Germany) is kindly acknowledged. Likewise, we thank Lucia Kerti from the Department of Neurology, Charité – University of Medicine Berlin for her involvement with examining healthy older subjects.

Funding: This research was funded by the German Federal Ministry of Education and Research (BMBF; FKZ 01EA1328D) and is registered under ClinicalTrials.gov identifier: NCT01219244.

Ethical statement: The study was conducted according to the German law and to good clinical practice and ethical principles of the Declaration of Helsinki. The Ethics Committee of the Charité – University of Medicine, Berlin, Germany, approved the study.

Consent statement: Written informed consent was obtained from all participants.

Dr Willers has nothing to disclose. Prof Hahn reports grants from Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research), during the conduct of the study. Theresa Köbe reports grants from German Federal Ministry of Education and Research, during the conduct of the study. Dr Gellert has nothing to disclose. Dr Witte has nothing to disclose. Dr Tesky has nothing to disclose. Prof Pantel has nothing to disclose. Prof Flöel reports grants from BMBF, during the conduct of the study. Dr Schuchardt reports grants from Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research), during the conduct of the study.



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M. Lombardo1, R. Magarotto1, F. Marinelli1, E. Padua1,3, M. Caprio1, G.Annino3, A. Bellia1,2, F. Iellamo3,4


1. Human Nutrition Unit, San Raffaele Rome Open University, Italy; 2. Department of Systems Medicine, Faculty of Medicine and Surgery, Tor Vergata University, Rome, Italy; 3. Dep. Clinical Science and Translational Medicine, University Tor Vergata; 4. IRCCS San Raffaele Pisana, Roma.

Corresponding Author: Mauro Lombardo, MD, San Raffaele Rome Open University, Via di Val Cannuta, 247, 00166 Roma, Italy. E-mail: mauro.lombardo@unisanraffaele.gov.it


J Aging Res Clin Practice 2017;inpress
Published online January 5, 2017, http://dx.doi.org/10.14283/jarcp.2017.1



Objectives: This retrospective clinical study was intended to assess the ideal number of calories in the Mediterranean-style diet (MD) required for maximum weight reduction through a greater decrease in fat mass (FM) and maintenance of fat-free mass (FFM). Methods: We analysed the data of 90 non-smoking subjects (56 females, age = 32.5 ± 9.6 years, BMI = 28.3 ± 5.4 kg/m2, data as mean ± SD). The participants underwent two-month individualised MDs with similar macronutrient composition (55% carbohydrate, 30% fat, 15% protein and fibre > 30 g) but different amounts of energy, which varied daily from 374 kcal to 1305 kcal compared with the total energy expenditure measured by metabolic Holter. The sample was divided into nine groups of 10 subjects in order to establish the amount of energy restriction that was most effective in terms of achieving fat loss and maintaining muscle mass. Results: All subject groups had significant improvements in body composition parameters (weight loss = 2.7 ± 1.8 kg, FM loss = 2.2 ± 1.2 kg and FFM loss = 0.5 ± 1.3 kg). Differences between the nine groups were not significant but higher FM loss was observed in groups one, three, six and eight. Groups one and four had the highest FFM increase and groups two, three and eight had the highest FFM loss. Conclusions: These data suggest that increasing the amount of energy restriction in a low-calorie MD might be useless in terms of obtaining a higher FM loss but a lower restriction could be more effective for maintaining FFM.

Key words: Energy restriction, Mediterranean-style diet, body composition, obesity.

Abbreviations: MD: Mediterranean-style diet; FM: fat mass; FFM:f at-free mass; WL: weight loss; REE: resting energy expenditure; VLCD: very low calorie diet; %FML: % fat mass lost; %FFML: % fat-free mass lost; FFML: fat-free mass lost; FML: fat mass lost.



For successful long-term weight loss (WL) the focus should be on achieving the best body composition for maintaining health rather than merely the loss of body weight (BW) (1). Since FM is the most metabolically dangerous tissue type, it is a more meaningful measure of health risk (2). A decrease in weight during a diet should aim at the slightest reduction of FFM, trying to shift the deficit towards fat, particularly visceral fat (3). A variable amount of FFM is often lost in combination with fat loss, and the aim should be to keep this loss to a minimum and to preserve resting energy expenditure (REE). Weight loss-associated adaptations in REE may impair weight loss and contribute to weight regain. In contrast to weight-stable subjects, weight regainers showed a reduced REE adjusted for changes in organ and tissue masses after weight loss (4).
Promoting eating habits consistent with Mediterranean Diet (MD) patterns may be a useful factor in efforts to fight obesity. High MD adherence has been associated with a significantly lower chance of becoming obese among overweight subjects, with stronger associations after adjustment for underreporting of dietary data (5).
Reduced-calorie diets result in clinically meaningful weight loss regardless of which macronutrients they emphasise (6). In a recent trial (7) subjects lost more FM than FFM after consumption of different diets, with no differences in changes in body composition, abdominal fat, or hepatic fat between assigned macronutrient amounts. Thus a combination of diet and exercise provides greater improvement in physical function than either intervention alone (8).
The degree of caloric restriction, exercise and rate of weight loss influences the percentage of fat-free mass lost (%FFML). Comparison diets with similar nutrient composition show that the degree of caloric restriction impacts on %FFML, at least in the short term (9). A very low calorie diet (VLCD) is a diet with extremely low daily food energy consumption (800 kilocalories per day or fewer). VLCD provides quite rapid weight loss and higher loss of LBM. An accurate assessment of EE is necessary to determine caloric needs and to provide optimal nutrition support for in-patients, as well as nutrition counselling for outpatients (10).
This retrospective clinical study was intended to assess in the short term the ideal reduction of calories in subjects following an MD to obtain the maximum weight reduction through a greater decrease in FM and maintenance of FFM.


Subjects and methods

All 90 subjects were adult Caucasians and provided written informed consent to participate. The investigation was conducted in accordance with the Declaration of Helsinki. Exclusion criteria were as follows: age 55 years; pregnancy or nursing; any lifestyle treatment in the year before; alcoholism; diabetes mellitus; chronic kidney disease; glucocorticoids, oestrogens and anti-convulsant therapies; history of cardiovascular, neoplastic or other systemic diseases (both chronic and acute). All subjects underwent thorough medical examination and subsequent food history, physical examination and evaluation of body composition. Weight and height were measured after subjects fasted overnight and were wearing only underwear. Body composition: fat mass (FM), fat-free mass (FFM) and hydration status (TBW) were recorded by the BIA Tanita BC-420 MA, a validated instrument with DXA [11], which measures values from a standing position and without the use of electrodes to within 100 grams.
The same protocol with detection of the above information was carried out two months later. Patients were required to observe the following guidelines before body composition analysis: at least three hours after awakening and the beginning of the normal daily activities; three hours or more after meals and not eating or drinking too much the day before the measurement; 12 hours or more after a hard workout; urinating before the measurement; avoiding alcohol 12 hours before the visit and avoiding menstruation.
The total energy expenditure was measured by a multisensory armband (SenseWear Pro2 Armband, Bodymedia Inc., Pittsburgh, PA, USA) worn on the back of the upper right arm that recorded data for at least 48 hours in a free-living context. The measured values are represented by TEE (total energy expenditure), the steps taken, the energy expenditure during exercise and daily METs. All subjects were also asked to maintain the same lifestyle and sporting activity consistently for the duration of the study.
All participants were placed on a two-month hypocaloric nutritionally balanced MD tailored to the individual. The nutritional patterns of the MD and the distribution of the daily food ration are shown in Table 1. A diet with a higher number of calories in the first part of the day was elaborated in order to establish a greater reduction in fat mass as we demonstrated before (12). The main features of MD are as follows (13): eating primarily plant-based foods, such as fruits and vegetables, whole grains, legumes, and nuts; replacing butter with healthy fats such as olive oil; using herbs and spices instead of salt to flavour foods; limiting red meat to no more than a few times a month; and eating fish and poultry at least twice a week. Nutritional intakes were divided into three main meals and two or three snacks. Twice a month patients met a dietitian for a nutritional rehabilitation programmed designed to improve and promote change in eating habits and consisting of individual sessions (dietary assessment, evaluation of nutrient intake and adequacy, nutritional status, anthropometric data, eating patterns, readiness to adopt change). Patients were required to complete a three-day diet diary at the beginning of the study and then weekly throughout the follow-up. Diaries included one weekend day. In order to achieve a more favourable body composition regarding total fat and muscle mass, we recommended a combination diet with endurance and strength training tailored to the individual by a personal trainer (14). The sample was divided into nine homogeneous groups of 10 subjects in relation to the difference% (Δkcal%) between caloric intake and TEE.

Table 1 Composition and mean characteristics of the basal diet

Table 1
Composition and mean characteristics of the basal diet

SFA: saturated fatty acids. MUFA: monounsaturated fatty acids. PUFA: polyunsaturated fatty acids


Statistical analyses

The data were analysed with SOFA Statistics ver. 1.4.2 open source software. Results for descriptive statistics were expressed as mean ± standard deviation or median (range). Statistical comparisons of continuous variables among the groups were performed with one-way ANOVA. A P-value of <0.05 was considered statistically significant.



Baseline characteristics of the 90 (56 women, age 32 ± 10 years) participants recruited to the study are shown in Table 2. Subjects showed an average BMI of 28.3 ± 5.4 kg / m2 and FM of 25.3 ± 10.2 kg (38.3%), FFM 53.3 ± 12.6 Kg (61.7%) and TBW of 39.8 ± 9.2 kg (41.3%). The sample at the beginning of the study had a mean total energy expenditure of 2513 ± 541 kcal, an active energy expenditure of 330 ± 236 Kcal and daily average METs of 1.4 ± 0.2. The subjects wore the instrument metabolic Holter SenseWear® Armband for an average 2.13 ± 1.2 days continuously, performing a total number of steps of 7040 ± 3019. Correlations between %FML and calorie restriction (A), daily METs (B), diet protein (gr) for weight (kg) (C), and daily steps (D) are shown in Figure 1. Significant correlations were found for %FL and daily METs (p= 0.037) and diet protein (p= 0.017). Figure 2 shows the correlation between %FFML values and calorie restriction (A), daily METs (B), protein (gr) diet for weight (kg) (C), and daily steps (D). No statistical correlation was found between these values.

Figure 1 Correlation between fat mass loss (%FML) and calorie restriction (A), daily METs (B), diet protein (gr) for weight (kg) (C), and daily steps (D)

Figure 1
Correlation between fat mass loss (%FML) and calorie restriction (A), daily METs (B), diet protein (gr) for weight (kg) (C), and daily steps (D)


Figure 2 Correlation between fat-free mass loss (%FFML) values and calorie restriction (A), daily METs (B), diet protein (gr) for weight (kg) (C), and daily steps (D)

Figure 2
Correlation between fat-free mass loss (%FFML) values and calorie restriction (A), daily METs (B), diet protein (gr) for weight (kg) (C), and daily steps (D)


There were no significant nutritional differences between the nine groups except diet’s calories. Group nine had the highest amount of dietary restriction (1305kcal) and group one had the lowest (374kcal). Dietary features, energy expenditure and body composition of the nine groups are shown in Table 3. Groups 8 and 9 had the highest energy expenditure for sporting activity and higher daily average METs.


Table 2 Body composition, energy expenditure, and other characteristics of subjects

Table 2
Body composition, energy expenditure, and other characteristics of subjects

n: number of subjects ; BMI: body mass index; FM: fat mass; FFM: fat-free mass; TBW: Total body water; AEE (active energy expenditure); TEE: total energy expenditure; METs: Daily metabolic equivalent task


Table 3 Difference between diet features, body composition and energy expenditure parameters between the nine groups

Table 3
Difference between diet features, body composition and energy expenditure parameters between the nine groups

Data are mean values. Every group is formed by 10 subjects. TEE: total energy expenditure AEE (active energy expenditure); METs: daily metabolic equivalent task; STEPS: daily steps; BMI: body mass index; FM: fat mass; %FM: % fat mass, FFM: fat-free mass; TBW: total body water.


The differences for all subjects between the values at the beginning (T0) and end of the study (T1) are shown in Table 4. All subjects’ results returned the following changes (Δ): Δweight equal to 2.68 ± 1.79 kg, ΔBMI equal to 0.93 ± 0.6 kg / m2, %FML equal to 2.2 ± 1.2 kg, FFML equal to 0.46 ± 1.3 kg and TBWL 0.5 ± 0.9 kg. Table 5 shows differences in the average kg of fat mass between the start and end of the study sample for the nine groups.


Table 4 Body composition of all subjects before and after the two-month study

Table 4
Body composition of all subjects before and after the two-month study

Data are mean values. n: number of subjects; FM: fat mass; FFM: fat-free mass; TBW: total body water


Table 5 Difference in body composition parameters between the nine groups before and after study

Table 5
Difference in body composition parameters between the nine groups before and after study

Δkcal: mean difference in Kcal between caloric intake and energy expenditure; Δkcal%: % difference between caloric intake and energy expenditure (minimum and maximum value of the group); ΔWEIGHT: mean difference in weight in Kg between the start and end of study; ΔBMI: mean difference in BMI  between the start and end of study; %FML: mean difference of % fat mass between the start and end of study; ΔTBW:  mean difference in total body water (TBW); ΔFFM: mean FFM difference in Kg between the start and end of the study. There is no statistically significant difference between the groups.


A significant decrease of FM was observed in all groups with different numbers of calories and energy expenditure reduction; the groups with greater restriction, such as eight and nine, had decreases comparable to groups with less restriction, such as one and three. Furthermore, the analysis of variance between groups (ANOVA) did not find any significant difference.



Our findings suggest that different rates of energy restriction result in similar body composition variation after two months of MD in overweight or obese individuals (15). Common findings suggest the necessity for acute energetic imbalance; in our results there appears to be no relationship between an higher energy deprivation and better body composition results after MD. Our data confirm the idea of a more complex network of factors that influence overall body composition and health issues for adults (16).
Typical weight loss rules assert that a energy deficit of 7700 kcal is required to lose 1 kg of body weight, or equivalently 32.2 MJ per kg. However, it has been pointed out that FFM is lost in concert with FM during WL and thus it is now generally accepted that these rules overestimates FML%. A more recent rule on expected fat-free mass (FFM) states that approximately one-quarter of weight loss will be FFM (i.e. ΔFFM/ΔWeight = ~0.25), with the remaining three-quarters being FM (17). The ideal number of calories for maximising FM loss while preserving FFM has not been established in previous study (5).
All nine groups achieved weight reductions between 2 and 3.6 kg in the observation period or in two months; parameters are in line with the recommendations set by the National Institute of Health of between 250 and 1000 grams per week. Most patients trying to lose weight do not employ the recommended combination of reducing calorie intake and engaging in leisure-time physical activity of 150 minutes or more per week. The degree of caloric restriction, exercise and rate of weight loss influence the proportion of weight lost. Comparison of different diets gives clear evidence that the degree of caloric restriction affects %FFML. Previous studies demonstrated that the increased initial rate of weight loss achieved using VLCDs compared with LCDs may be the cause of the greater FFM loss on these diets, at least in the short term (9). VLCDs provide quite rapid weight loss and substantial loss of FFM, but stable weight may be more important in terms of the long-term benefits of living a healthier lifestyle.
Exercise training is associated with an increase in energy expenditure, thus promoting changes in body composition and bodyweight while keeping dietary intake constant. The advantages of strength training may have greater implications than initially proposed with respect to decreasing percentage body fat and sustaining FFM (18).
Group one, which had the smaller caloric restriction, obtained one of the best results in terms of fat mass loss and muscle maintenance. We may therefore conclude that excessive caloric restriction is not required to achieve the best results. A less restrained diet would be easier to maintain in the long term because it requires fewer sacrifices (19).
These results also show that as regards the effectiveness of the diet correct directions by the dietitian on nutritional choices and physical activity are more important than the degree of caloric restriction. Perhaps we simply need to tell our patients which food they need to avoid and what sort of physical activity they should do without stringent and often unrealistic calorie restrictions.
Our retrospective study found useful conclusions in relation to the various degrees of calorie restriction for proper weight loss. It could be argued that a smaller restriction of daily calorie intake results in the short term in a significant decrease in weight in terms of fat mass and stabilisation of lean body mass. On the other hand, it is clear that a higher caloric restriction did not lead to a more significant FM decrease. The study could open up new areas of inquiry in relation to healthy lifestyle and physical activity prescriptions for ideal weight loss.



Patients usually lose weight while on a programme but regain weight after they revert to their former lifestyle. We demonstrated that a lower energy restriction is enough to obtain similar %FML and preserve FFM than more unrealistic weight loss programmes not suitable for long-term lifestyle results.


Conflict of interest: The authors declares that there is no conflict of interest regarding the publication of this paper.

Ethical standard: All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008. Informed consent was obtained from all patients for being included in the study.



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A.S. Diab, L.A. Hale, M.A. Skinner, G. Hammond-Tooke, A.L. Ward, D.L. Waters


Centre for Health, Activity, and Rehabilitation Research, School of Physiotherapy, University of Otago, Dunedin, New Zealand. 

Corresponding Author: Professor Leigh A. Hale, Centre for Health, Activity, and Rehabilitation Research, School of Physiotherapy, University of Otago, PO Box 56, Dunedin 9054, New Zealand, Tel:+64 3 479 5425, Email: leigh.hale@otago.ac.nz 


Abstract: Objectives: Idopathic Parkinson’s disease (PD) is the second most common neurodegenerative disorder. Our objective was to investigate the relationship between body composition and postural instability in people with PD, and age- and sex-matched controls. Design: Cross-sectional study among PD sufferers and age- and sex-matched controls. Setting: University of Otago’s Balance Clinic, School of Physiotherapy. Participants: Forty-seven people with PD and 58 age- and sex-matched controls. Measurements: Postural stability was assessed with the Sensory Organization Test, Motor Control Test, Timed Up and Go Test, and Step Test. Body composition was measured by dual energy x-ray absorptiometry (DXA). Movement Disorders Society-Unified Parkinson’s Disease Rating Scale was applied to assess PD severity. Results: Mean group differences between PD and controls for the equilibrium composite score, Timed Up and Go Tests, and Step Test were statistically significant (p<0.05); strategy and latency composite scores and body composition variables were not (p>0.05). Three PD participants were sarcopenic; 15 PD and 24 controls were obese. In PD participants, total body lean mass and age predicted latency composite scores. Disease, age, and leg fat mass predicted the Timed Up and Go Test results (p<0.05). Sex and disease predicted the equilibrium composite score (p<0.01). Conclusion: The prevalence of obesity was high and sarcopenia low in the PD group, which is a novel finding. Not surprisingly, participants with PD had reduced postural stability compared to controls. Disease status, age and sex were influential factors in the weak relationships found between postural stability and body composition. These findings may have clinical relevance for the treatment of the physical symptoms of those suffering from PD.


Key words: Body composition, postural instability, Parkinson’s disease. 



Idiopathic Parkinson’s disease (PD) is a common neurodegenerative disorder, which presents with a variety of motor and non-motor manifestations (1). Postural instability is considered a cardinal sign of PD, impacting independence and increasing falls risk (2). Postural instability typically presents late in PD, (3) although mechanisms for this timing are not fully understood. A number of factors have been suggested which include deficits in anticipatory and reactive responses to perturbations (4), visual and vestibular systems (5), sensory-motor integration (6), muscle tone (7), cognition (8), and muscle power (9). A recent review identified six primary factors contributing to postural instability in PD, dysfunction in sensory reorganization, bradykinesia, abnormal postural response patterns, L-dopa induced dyskinesia, hypotension, and cognitive impairment (10).

Postural instability is also prevalent in older adults without PD, resulting in increased risk of falling (11), and the associated factors are numerous (12). One possible factor identified in older adults is abnormal body composition phenotype, which is associated with losses in lean body mass and bone mineral density, and increases in fat mass and a sarcopenic-obese phenotype (13, 14). The association between reduced physical function and the sarcopenic obese phenotype has been widely reported (15, 16).

Body composition in PD has been investigated (17-20), especially in those with advanced stages of the disease (18), and it has been reported that people with PD are disproportionately sarcopenic (21).  To our knowledge, no research has focused on the association between body composition phenotype and postural instability in PD. The aim of this study was to investigate this relationship. 



Study design

A cross-sectional case-matched study compared people with PD to an age- and sex-matched control group. The Lower South Regional Ethics Committee, New Zealand approved the study.  

Participant recruitment

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Participants with PD were recruited through the Dunedin Hospital Neurology Department, the Parkinson Society, and via community advertising. Control participants were recruited via community advertising. Volunteers were screened for eligibility by telephone using a standardised checklist. Those meeting eligibility requirements were sent a study information sheet and assigned an appointment time, at which time eligibility and consent was confirmed.  The diagnosis of PD was confirmed by the study neurologist. Inclusion criteria were age over 40 years, and the ability to perform the measurement tests independently with or without assistive mobility devices. Participants with other types of Parkinsonism, the inability to undergo the measurement tests safely due to cognitive or physical disability, or significant co-morbidity were excluded. Inclusion criteria for controls were age and sex-matched to the participants with PD, with no known neurological disorder, and self-reported to be sedentary. 

Study procedures

All participants attended two appointments. Degrees of PD severity were assessed with the Movement Disorders Society-Unified Parkinson’s Disease Rating Scale, and disease stage was defined by Hoehn and Yahr (22). 

Postural instability was tested using both computerised posturography and clinical measures. Computerised posturography included the Sensory Organization Test (SOT) and the Motor Control Test (MCT) performed on the NeuroCom® Smart EquiTest® version 8.4.0. The clinical measures were the Timed Up and Go Test used without (TUG B) and with (TUG H) a concomitant cognitive task, which was naming the days of the week backwards, and the Step Test. The retest and inter-rate reliability of the TUG among people with PD has been found to be high during both L-dopa “off” and “on” states. The Step Test is a simple, reliable test commonly used for investigating dynamic stability and postural responses in older adults (23) and reliability has been reported to be good for those with PD and comparison groups (24).

Dual-energy X-ray Absorptiometry (DXA; Lunar DPX-L scanner GE LUNAR Corp; Madison, WI) was used to measure whole body and regional body composition. Prior to testing the scanner was calibrated per manufacturer protocol. Height and weight were measured using a standardized protocol. The appendicular skeletal muscle (ASM) was calculated by summing lean mass in the arms and legs. Phenotypes were determined by validated criteria. The sarcopenic phenotype was defined as ASM Index (ASM/height2) of < 7.26 and <5.4 for males and females, respectively, and obese as an ASM Index of ≥ 27% and ≥38% for males and females, respectively (15). 

Data Analysis

Data were entered into the SPSS statistics computer programme. Data from the postural stability tests and body composition were described in terms of mean and 95% CIs. The Student’s T-test was used to compare the mean of the study and control groups for each test variable. Pearson’s correlation coefficients (r) tests were used to test the strength and direction of association between the postural stability and body composition variables for the stepwise regression analysis. The selection criterion for choosing variables for regression was based on the results of the correlation analysis and informed by variables used in previously published research (25). Eight predictor variables (sex, age, disease, total body lean, total body legs lean, total body fat, percent body fat, and total body legs fat) used in the stepwise regression model. 



Table 1 shows participant characteristics from 47 participants with PD (male 57%), and 58 control participants (male 34%). Twenty-seven participants were in a moderate state (stage II) of PD, seven were in an advanced state (stage III-IV), and thirteen were in a mild state (stage I). Forty-one PD participants were on anti-Parkinsonian medications. No participants with PD had received deep-brain stimulation prior to the study. Dyskinesia was reported in those in stage II, and one participant in stage III. Dyskinesia was slight to mild and was self-reported not to impact on activities of daily living. Participants with PD who were on medication were categorized as in a state of “on” drug therapy.


Table 1 Descriptive characteristics of the PD and Control groups

*MDS-UPDRS: Movement Disorders Society –Unified Parkinson’s Disease Rating Scale results; **Hoehn and Yahr; † «On” /“Off»: state of medical therapy


Table 2 shows the mean and 95% confidence intervals for the postural stability tests and the body composition variables. To maintain balance both groups were primarily using the ankle strategy (79%) and without a delay, as the differences were non-significant for the strategy composite and latency composite scores. The mean Body Mass Index (BMI) was not significantly different between the PD and control groups. 


Table 2 Results for postural stability tests and body composition

*Control; **Not significant; †millisecond; ‡seconds; §number of steps in 15 seconds; ||TUG B: Timed Up and Go Basic test; TUG H: ¶Timed-Up and Go High cognition test


There were no significant differences in the body composition variables. Males in the PD group had slightly less total fat mass than control males. The mean total fat mass in the PD female group was higher than the control females; however, these differences were not significant (Table 2). The number of females with obesity in both the PD and the control group were nearly double that of the males (9%, 26%, respectively).

The three participants with PD in the sarcopenic phenotype were of relatively advanced age (≥75.6 years) but varied in disease stage and severity of symptoms. Neither group presented as the sarcopenic obese phenotype. In fact, most presented as obese. There was a small positive relationship between total leg fat mass and the TUG B and TUG H scores. Disease, sex, and age were each found to be predictive of body composition in relation to postural stability. Disease predicted the equilibrium composite score, the TUG B and the TUG H scores, and age predicted the latency composite score and the TUG B and TUG H. 

Of the many variables considered for the prediction equation, only a small subset of variables was selected to obtain good predictive results to fit the model. Results revealed a very low to moderate level of multicollinearity between the predictors. The equilibrium composite variable was not significantly correlated in either the PD or the control groups, but was deemed necessary to keep in the regression model, as it is widely used clinically to assess postural stability (25). Table 3 summarises the results of the stepwise regression. In the group with PD, total body lean mass, leg fat mass, sex, age, and disease stage significantly predicted postural instability. 

Table 3 Summary of the stepwise regression results

*SOT: Sensory Organization Test, †=p<0.01, ‡= p<0.001



We found a significant difference in postural stability between those with PD and an age- and sex-matched control group as measured by the SOT equilibrium composite score, the MCT, the TUG Tests and the Step Test. The SOT strategy composite score and the MCT latency composite scores were not significantly different. There was a significant difference between groups for both the TUG B (p<.001) and the high cognition TUG H (p<.001) tests. 

We established a tenuous association between postural stability and two body composition variables (total lean body mass and leg fat mass); postural stability was similarly associated with sex, age and disease status. Of the body composition variables, total body lean mass, appendicular lean mass and leg fat mass most strongly predicted variations in postural stability. For the posturography measures of postural stability, only the latency composite score showed a relationship with body composition variables, which was a positive association with the total body lean mass variable.

Female sex was a negative predictor for the equilibrium composite score. This agrees with previous studies showing female gender to be a significant contributor to variance in balance control in PD (26, 27). Age and disease were found to be positive predictive factors for postural stability as reported by previous studies (11). There were no significant differences in BMI, body composition variables or body composition phenotype, which is not consistent with previous studies. For example, in a meta-analysis of seven studies (28), people with PD had a significantly lower BMI than the comparison group, correlated with disease staging. Also, DXA results in the present study showed no participants with sarcopenic obesity, which is consistent with the findings of Toth et al (29), but inconsistent with other studies reporting the presence of sarcopenic obesity in those with PD (18-21). 

Notably, only three of our participants with PD were sarcopenic. This is an unusual finding, as PD is often linked with sarcopenia (18-21). Obesity is not a body composition phenotype normally associated with PD, yet one-third of our participants with PD were found to be obese.  Revilla et al showed total fat and percentage of fat were higher, and the total lean mass was lower, in males with PD when compared with male controls, suggesting a sarcopenic phenotype; females with PD and female controls had similar values (19). Thus, our findings with regard to obesity in individuals with PD are unique and may reflect rising levels of obesity in the general older adult population (15).

Potential study limitations include the cross sectional design, the fact that participants with PD were primarily in a mild to moderate staging of their disease, which may account for some differences in our results and those of previous studies, and the criteria used to define the body composition phenotypes. Although there are now several published operational criteria, in the current study we used criteria recommended by Baumgartner et al (15) and not the criteria set which includes muscle function/strength, as this is still widely debated in the literature. The main study strengths were the relatively large sample size, and case matching to a control group. 

Although postural instability is a frequent manifestation of PD, the reasons are not fully understood and most likely a complex interplay of numerous factors is responsible. Our study investigated body composition as one such contributing factor, and a small association was identified. While the group with PD were significantly different with regard to measures of postural stability, body composition variables were not significantly different. Unexpected findings were that only three participants were found to be sarcopenic, and a large number of participants with PD were obese, in contrast with findings in the current literature. The findings of the current study could have significant implications for the clinical treatment of the physical symptoms of those suffering from PD.


Funding: This work was supported by a grant from The Physiotherapy New Zealand Scholarship Trust Fund. The first author received financial assistance from The Iraqi Ministry of Higher Education and Scientific Research/Mission Office and the School of Physiotherapy, College of Health and Medical Technology, Baghdad, Iraq. 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: There are no conflicts of interest.

Ethical Standards: Ethical approval was granted by the Lower South Regional Ethics Committee, New Zealand (Ref: LRS/10/10/047).



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