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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Arch Neurol. Author manuscript; available in PMC Apr 1, 2011.
Published in final edited form as:
PMCID: PMC2855150
NIHMSID: NIHMS146588

Lean Mass is Reduced in Early Alzheimer’s Disease and Associated with Brain Atrophy

Abstract

Objective

Alzheimer’s disease (AD) is associated with altered body composition with weight loss beginning years prior to the onset of dementia. We examined body composition in early AD and nondemented individuals and its relation to cognition and brain volume.

Design

Cross-sectional, case-control study

Setting

Alzheimer and Memory Program at the University of Kansas School of Medicine

Patients

Nondemented (Clinical Dementia Rating [CDR] 0, n=70) and early-stage AD (CDR 0.5 or 1, n=70) participants.

Main Outcome Measures

Participants were evaluated with brain magnetic resonance imaging (MRI), neuropsychological testing and dual energy x-ray absorptiometry (DEXA) to determine whole-body fat mass and lean mass. Body mass index (BMI) was determined from height and weight.

Results

Lean mass was reduced in early AD compared to nondemented controls (F=7.73, p=0.006) after controlling for sex. Whole-brain volume (beta=0.20, p<0.001), white matter volume (beta=0.19, p<0.001), and global cognitive performance (beta=0.12, p=0.007) were associated with lean mass (dependent variable) when controlling for age and sex. Total body fat and percent body fat were not different across groups or related to cognition and brain volume.

Conclusion

Loss of lean mass is accelerated in AD and associated with brain atrophy and cognitive performance perhaps as a direct or indirect consequence of AD pathophysiology or through shared mechanisms common to both AD and sarcopenia.

Keywords: Alzheimer’s disease, brain atrophy, whole brain volume, lean body mass, sarcopenia

Introduction

Alzheimer’s disease (AD) is associated with unintended weight loss1 beginning years prior to the recognition of AD-related clinical symptoms24 and may be a marker of preclinical AD.5 Weight loss in AD is associated with dementia severity and faster clinical progression.1 Epidemiological studies suggest a complex relationship between body composition and dementia that may be variable across the age-spectrum. Although obesity in midlife is a risk factor for developing dementia,6, 7 overweight and obesity in late-life is associated with lower dementia risk.8, 9 Most studies of body composition in dementia and AD are limited by nonspecific measures of body composition such as total body weight or body mass index (BMI) rather than specific measures of body fat and muscle mass. As normal aging is associated with increases in body fat and declines in lean mass without overall weight loss, nonspecific adiposity measures such as BMI have limited value in capturing these changes.

We used dual emission x-ray absorptiometry (DEXA) to quantify body composition in early-stage AD and nondemented subjects. We examined whether alterations in specific components of body composition (i.e., lean mass vs. fat mass) were apparent in individuals in the earliest clinical stages of AD. Given previous studies relating body composition with AD severity and progression we also examined the relationship of body composition with cognition. Additionally, AD-related brain changes including medial temporal lobe atrophy10 and neuropathological burden (plaques and tangles)11 are associated with reduced BMI suggesting that neurodegenerative processes may contribute to alterations in body composition. Thus, we further examined the relationship of components of body composition with imaging measures of neurodegeneration (i.e., brain atrophy). Since weight loss is associated with AD progression and altered body composition, we hypothesized body composition would be associated with brain volume and cognitive performance.

Methods

Sample and Recruitment

Participants were 60 years and older and either nondemented (Clinical Dementia Rating (CDR) 0 [n = 70]) or diagnosed with early-stage AD (CDR 0.5 [n=56] and 1 [n=14]) as detailed below. Nondemented participants and 63% of the AD participants were self-referred from the community (primarily through media coverage and word of mouth) while 37% of the AD participants were recruited from a referral-based memory clinic. Study exclusions included neurologic disease other than AD, diabetes mellitus, recent (<2 years) history of ischemic heart disease, clinically-significant depressive symptoms, use of antipsychotic and investigational medications, and significant sensory impairment or systemic illness that could impair completion of the study. All participants were required to be accompanied by a study partner who was knowledgeable about the participant’s daily activities. We have previously reported results on sub-samples of this cohort.1214

Clinical Assessment

All participants were evaluated using a semi-structured interview of the participant and a study partner to determine the presence or absence of dementia, and its severity if present, using the Clinical Dementia Rating (CDR).15 Diagnostic criteria for AD require the gradual onset and progression of impairment in memory and in at least one other cognitive and functional domain.16 These diagnostic methods have an accuracy for AD of 93%17 and sensitively detect the earliest stages of AD by focusing on intra-individual change rather than comparison with group norms.18 Additionally, they accurately identify the subset of individuals meeting criteria for MCI who have early stage AD.19 A global CDR score is derived from individual ratings in each domain with CDR 0 indicating no dementia, CDR 0.5 very mild dementia, CDR 1 mild dementia. Subjects with moderate (CDR 2) or severe (CDR 3) dementia were not enrolled in the study.

A nurse clinician collected medications, medical history, education, and demographics from the study partner. A neurologist performed a standard physical and neurological examination. Functional activity level was estimated using the Mild Cognitive Impairment Activities of Daily Living Scale (MCI-ADL).20 The Geriatric Depression Scale21 was administered to the collateral source for all participants and used as a continuous variable for analyses. Apolipoprotein E genotyping was conducted using restriction enzyme isotyping.22

Neuropsychological Assessment

A trained psychometrician administered a standardized psychometric battery to all participants as previously described.1214 The battery included the standard measures of Logical Memory I and II, Free and Cued Selective Reminding Task, Boston Naming Test – 15 item, Verbal Fluency, Digit Span Forwards and Backwards, Letter-number sequencing, Trailmaking A and B, Stroop Color-Word Test and Block Design). Cognitive performance scores were converted to z-scores based on the mean and standard deviation of nondemented subjects. The mean of each participant’s z-scores served as an index of global cognitive performance. The mini-mental status examination (MMSE) was also administered as a measure of global cognition.

Body Composition

Dual energy x-ray absorptiometry (DEXA; Lunar Corp.) was used to determine total body measures of lean mass and fat mass. Percent body fat represents the percent of total body mass (determined by DEXA) composed of fat (i.e., total fatX100/total body mass). We used total body mass determined by DEXA. DEXA determined mass was highly correlated with our manually measured (by scale) body weight (r=0.99, p<0.001) and also minimized the influence of clothing. Height was measured using a standard stadiometer. Body mass index (BMI) was determined by dividing total body mass (in kg) by the square of height in meters.

Other Clinical Measures

We also examined potential covariates that may mediate brain and body composition relationships, including habitual level of physical activity, frailty, and laboratory assessments of insulin, lipids, inflammation and apolipoprotein E4 allele status as previously described.1214 Briefly, level of habitual physical activity was estimated using the Physical Activity Scale in the Elderly (PASE), a reliable and valid measure of physical activity developed specifically for older individuals.23 The PASE estimates an individual’s level of physical activity within the last seven days by assessing the frequency and duration of participation in a variety of activities. The PASE was administered to both the subject and the study partner and for individuals with AD, data collected from the study partner was used in the analyses. We assessed peripheral insulin levels by radioimmunoassay using a fasting 14-sample, three-hour intravenous glucose tolerance test as previously described.13 Total 3-hour area-under-the-curves (AUC) for glucose and insulin served as overall indices for glucose and insulin levels. The Physical Performance Test (PPT) was used as a measure of physical performance and frailty.24 Fasting venous blood samples were assessed using commercial enzymatic assays for total cholesterol (Diagnostic Chemicals, Ltd.). Highly-sensitive C-reactive protein (CRP) was determined in fasting blood by turbidimetric assay (Roche Diagnostics Systems).

Neuroimaging

Structural MRI was obtained on all participants using a Siemens 3.0 Tesla Allegra MRI scanner (Siemens Medical Solutions, Erlangan, Germany). High-resolution T1-weighted anatomic images were acquired to provide detailed gross anatomy with high gray-white matter contrast (MP-RAGE; 1×1×1mm3 voxels; TR=2500ms, TE=4.38ms, TI=1100ms, FOV 256×256mm2 with 18% oversample, flip angle=8 degrees). Normalized Whole Brain Volume (WBV) was computed for each image session using validated imaging tools from the FMRIB Software Library (FSL; www.fmrib.ox.ac.uk/fsl) as previously described12 using the Laboratory of Neuroimaging Pipeline (University of California Los Angeles, www.pipeline.loni.ucla.edu). Briefly, the images were pre-processed and skull-stripped using Brain Extraction Tool. Skull-stripped images were then segmented into white matter, gray matter, and cerebrospinal fluid using FMRIB’s Automated Segmentation Tool (FAST) by registering them to the Montreal Neuroimaging Institute avg152 template. Normalized volumes for white matter (WhMV), gray matter (GMV), and whole brain (WBV; sum of white and gray matter) were calculated by dividing each by the total intracranial volume (the sum of white, gray, and cerebrospinal fluid volumes) and expressed as the percent of total intracranial volume. Normalized brain volumes minimize sex differences and produce an estimate of brain atrophy. Imaging data was unavailable on three nondemented and one early AD participant.

Statistical Analyses

Analyses were conducted using SPSS 16.0. Continuous variables were summarized by means and standard deviations and categorical variables were summarized by frequency and percent. The Student’s t-test was used to compare continuous demographic and imaging variables in early AD and nondemented groups. A chi square test was used to compare categorical variables. Pearson’s correlation coefficients were calculated to assess simple relationships between variables.

A multistep hierarchical linear regression was conducted to examine the relationship between clinical predictors (i.e., brain volume, cognition) with body composition (i.e., BMI, lean mass, and fat mass as dependent variables). All analyses controlled for age and sex. Variables correlating with body composition (i.e., dementia status, physical activity, and insulin [3-hour AUC]) were used as covariates and the increment in R2 assessed the additional variance predicted from each new variable entered into the model. A final exploratory regression model examined the influence of all variables of interest and covariates on body composition. Age and sex were forced into the model in step 1 with covariates later entered in a stepwise fashion.

Results

Descriptive Statistics (Table 1)

Table 1
Characteristics of Study Population

Non-demented and early AD groups were well-matched with respect to age (mean 74.1, SD=6.8) and sex (58% female). Participants with early AD were slightly less educated than controls (15.2 years versus 16.5 years; t=2.60, p=0.01). As expected they also demonstrated mild global cognitive dysfunction. On average, they scored 3.4 points lower on the MMSE (out of 30 points) and 1.7 (z-scored) standard deviations lower on the global cognitive composite index than nondemented controls. More early AD participants were carriers of the apolipoprotein E4 allele. The groups did not differ on clinical indices of metabolic function (total cholesterol, CRP, Insulin AUC, and Glucose AUC).

Brain volumetrics demonstrated evidence of whole brain and gray matter atrophy in early AD with no difference in WhMV across groups, suggesting differences in WBV were driven by reduced GMV in the AD participants.

Indices of physical function were significantly lower in early-AD. Individuals with early AD had impairments in activities of daily living (MCI-ADL) and physical function (PPT) and lower levels of habitual physical activity (PASE).

Body Composition in AD and Nondemented Subjects

BMI, body weight and body fat measures were not different across nondemented and early AD groups (Table 2). Total lean mass was reduced in individuals with early AD compared to nondemented controls after controlling for known sex differences in lean mass (F=7.73, p=0.006). There were no dementia group × sex interactions, suggesting AD-related differences in lean mass were not different in men and women.

Table 2
Body Composition Means (SD) in Early AD and Nondemented Controls

To identify potential mediators of reduced lean mass in AD, we performed a series of linear regressions that examined the relationship of individual covariates with lean mass (dependent variable). Because age and sex influence lean mass and all of the clinical covariates tested (all β>0.10, p<0.01), we included age and sex as the first-step in all regression analyses and report resultant partial correlations as standardized beta values (Table 3).

Table 3
Standardized coefficients (β) predicting lean mass after controlling for Age and Sex

The strongest predictor of lean mass was WBV (figure 1), largely driven by a relationship between WhMV and lean mass. GMV was not related to lean mass. Additional singificant correlates related to lean mass included cognitive indices (global cognitive performance and MMSE), insulin levels (3-hour AUC), and habitual physical activity levels. Glucose, CRP, ADLs, physical performance, depressive symptoms, and Apolipoprotein E4 status were not related to lean mass. Thus, additional analyses included further examination of the clinical and functional significance of body composition in AD in relation to brain volume, cognition, and the covariates of insulin and physical activity level.

Figure 1
Relationship between Lean Mass and Whole Brain Volume

Body Composition and Cognitive Performance

Both indices of cognitive performance (global cognitive performance and MMSE) were related to lean mass (Table 3). Cognitive performance was not related to other measures of body composition (BMI, total body fat, and percent body fat). As our global cognitive performance measure is a composite measure of a variety of cognitive tests, we next assessed the relationship of lean mass with performance on component cognitive subtests. Lean mass was associated with performance on Logical Memory I (β=0.12, p=0.007), Logical Memory II (β=0.09, p=0.04), Trailmaking A (β=−0.09, p=0.05), Trailmaking B (β=−0.11, p=0.01), Category Fluency (β=0.09, p=0.05), Block Design (β=0.13, p=0.005), and Digit Span Forward (β=0.10, p=0.03). There were no dementia status × cognitive performance interactions in predicting lean mass suggesting that the relationship between lean mass and cognition is similar in AD and nondemented participants. Controlling for dementia status, however, resulted in attenuation of these results, suggesting that group differences in both cognition and lean mass may be responsible for the results.

Body Composition and Brain Structure

Regression analyses identified a relationship between WBV and lean mass (figure 1). This relationship was driven primarily by a relationship between WhMV and lean mass (β=0.19, p<0.001). In contrast, GMV was unrelated to lean mass. The association of whole brain and WhMV with lean mass was unchanged after controlling for additional covariates of dementia status, physical activity, global cognition, and insulin AUC. There were no dementia status × WBV or sex × WBV interactions, suggesting that the positive relationship between WBV and body composition was similar in AD vs. nondemented participants and men vs. women.

WBV was not related to total body fat (β=0.14, p=0.21) or percent body fat (β=0.01, p=0.89) but was modestly associated with BMI (β=0.20, p=0.05), with higher BMI associated with higher brain volume (i.e., less brain atrophy). Although BMI is a proxy measure for adiposity (r=0.55, p<0.001) it also reflects lean mass (r=0.28, p=0.001) and thus, the modest relationship between BMI and WBV appears largely driven by the observed lean mass – WBV relationship.

Overall Model

We next examined an overall model that included all variables of interest (WBV, global cognition) and covariates (age, sex, dementia status, physical activity, and insulin levels) to assess which variables were most strongly related to lean mass. Age, sex, and dementia status (AD vs control) were forced into the model (step 1) with all covariates assessed in step 2 using a stepwise model of inclusion (F=3.94; p<0.05 to retain). In this model, WBV (β=0.12, p=0.04), insulin (3-hour AUC; β=0.10, p=0.02), and physical activity level (β=0.11, p=0.02) were each independently associated with lean mass. When white matter and GMV were used rather than WBV, WhMV, but not GMV, was retained (β=0.17, p<0.001) with insulin (β=0.09, p=0.05) and physical activity (β=0.12, p=0.009).

Comment

Our findings are consistent with prior reports that alterations in body composition are apparent in the earliest clinical stages of AD24 and extends these reports by suggesting that AD-related alterations in body composition may be predominantly related to loss of lean mass (i.e., sarcopenia). This is consistent with at least one large, epidemiological study that found an association between cognitive impairment and reduced muscle mass in non-demented women.25 Although the cross-sectional, case-control study design limits our ability to infer causal relationships, our data suggest that sarcopenia may be accelerated in the earliest stages of AD.

Our findings also suggest that lean mass may be a more sensitive measure relating body composition with cognitive outcomes and dementia than measures of adiposity. Lean mass was reduced in individuals with AD compared to nondemented controls and was associated with brain volume and cognition; total body fat, however, was not related to dementia status, brain volume, or cognition. Although we observed a modest relationship between the nonspecific adiposity measure BMI and brain volume, this relationship was primarily driven by lean mass as only lean mass and not fat mass was associated with WBV. Thus, our data highlight the importance of assessing specific measures of body composition and suggest the hypothesis that loss of lean mass may underlie previously reported relationships of nonspecific measures of body composition (i.e. BMI) with cognitive decline and dementia.9, 26, 27

We observed a direct correlation between WBV (an estimate of brain atrophy) and lean mass suggesting that brain atrophy and loss of muscle mass may co-occur. Brain atrophy is considered a neuroimaging measure reflective of AD pathology.28 Thus, our data are consistent with other studies suggesting that brain pathology may contribute to decline in body composition11 perhaps by disrupting CNS regulation of energy metabolism and food intake.29 While AD and neurodegeneration predominantly affect gray matter, we find it particularly interesting that we observed a strong relationship between lean mass and WhMV, rather than GMV, and this relationship was similar in nondemented and AD participants, suggesting that mechanisms other than AD processes may underlie these relationships.

Sarcopenia in normal aging is most strongly associated with age-related reductions in physical activity.30 In our cohort, individuals with early AD had reduced physical activity levels compared to the nondemented cohort. Additionally, lower physical activity was associated with less lean mass suggesting that behavioral changes associated with AD may result in loss of lean mass. Alternatively, physical activity itself may attenuate the structural and functional brain and body changes associated with AD and aging. This is biologically plausible given accumulating animal and human evidence linking exercise and physical fitness with brain health.12, 3133 Even after controlling for physical activity levels, however, lean mass remained independently associated with brain volume, suggesting that decline in physical activity observed in aging and AD is unlikely to fully explain our results.

An alternative explanation for our observations is that AD and sarcopenia share common underlying mechanisms. AD is associated with systemic anabolic and inflammatory abnormalities that are also implicated in sarcopenia.3437 Although our measures of anabolic and inflammatory processes are limited in this study, we observed an independent relationship between lean mass and insulin, a well-known anabolic hormone38 that may have neurotrophic39 and neuroprotective40 properties. We previously reported that insulin levels are associated with cognition and brain volume in early AD and, as in this study, the association was stronger for white matter than gray.13 Interestingly, insulin signaling preferentially affects the development of white matter structures,41 which, taken with our prior report, suggests insulin signaling may play a role in maintaining cerebral white matter. Thus, our observation that WhMV, lean mass, and insulin levels are inter-related suggests that reduced anabolic support to both muscle and brain may be a potential mechanism underlying the observed relationships.

The current study is limited by its cross-sectional design and further longitudinal and interventional studies will be necessary to more precisely define the nature and mechanisms of body composition changes in AD. Although clinical methods are imperfect in predicting AD pathology, we used sensitive18 and validated17 methods for diagnosing the earliest stages of AD. Additionally, participants were a convenience sample which limits generalizability and may have introduced sampling bias that could impact the results. The relatively small sample size (n=140) could limit the power to resolve group differences or important interactions for marginal effects and thus increase the chance of type II error. Additionally, potentially important dietary factors were not measured. Nevertheless, our data suggest loss of lean mass may be accelerated in AD perhaps as a direct or indirect consequence of AD pathophysiology or through shared mechanisms common to both AD and sarcopenia.

Acknowledgments

This study was supported by grants AG026374 and AG029615 from the National Institute of Aging, K23NS058252 from the National Institute on Neurological Disorders and Stroke and by generous support from the University of Kansas Endowment Association. The University of Kansas General Clinical Research Center (M01RR023940) provided essential space, expertise, and nursing support and the Hoglund Brain Imaging Center (C76HF00201) provided imaging support. Dr. Brooks was also supported by NS039123 and AG026482, HD050534, DK080090, and RR015563 and Dr. Swerdlow by AG022407. The Hoglund Brain Imaging Center was supported by a gift from Forrest and Sally Hoglund. This study used the LONI Pipeline environment (http://pipeline.loni.ucla.edu), which was developed by the Laboratory of Neuro Imaging and partially funded by NIH grants P41RR013642, R01MH71940 and U54RR021813. We also acknowledge and thank Hal Oppenheimer and the Oppenheimer Foundation for financial support.

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