Original Article | Published:

Protein, malnutrition and wasting diseases

Sarcopenia, sarcopenic obesity and mortality in older adults: results from the National Health and Nutrition Examination Survey III

European Journal of Clinical Nutrition volume 68, pages 10011007 (2014) | Download Citation

This work was presented in part at the American Geriatrics Society's Annual Meeting, May 2012, Seattle, WA, USA.




Sarcopenia is defined as the loss of skeletal muscle mass and quality, which accelerates with aging and is associated with functional decline. Rising obesity prevalence has led to a high-risk group with both disorders. We assessed mortality risk associated with sarcopenia and sarcopenic obesity in elders.


A subsample of 4652 subjects 60 years of age was identified from the National Health and Nutrition Examination Survey III (1988–1994), a cross-sectional survey of non-institutionalized adults. National Death Index data were linked to this data set. Sarcopenia was defined using a bioelectrical impedance formula validated using magnetic resonance imaging-measured skeletal mass by Janssen et al. Cutoffs for total skeletal muscle mass adjusted for height2 were sex-specific (men: 5.75 kg/m2; females 10.75 kg/m2). Obesity was based on % body fat (males: 27%, females: 38%). Modeling assessed mortality adjusting for age, sex, ethnicity (model 1), comorbidities (hypertension, diabetes, congestive heart failure, osteoporosis, cancer, coronary artery disease and arthritis), smoking, physical activity, self-reported health (model 2) and mobility limitations (model 3).


Mean age was 70.6±0.2 years and 57.2% were female. Median follow-up was 14.3 years (interquartile range: 12.5–16.1). Overall prevalence of sarcopenia was 35.4% in women and 75.5% in men, which increased with age. Prevalence of obesity was 60.8% in women and 54.4% in men. Sarcopenic obesity prevalence was 18.1% in women and 42.9% in men. There were 2782 (61.7%) deaths, of which 39.0% were cardiovascular. Women with sarcopenia and sarcopenic obesity had a higher mortality risk than those without sarcopenia or obesity after adjustment (model 2, hazard ratio (HR): 1.35 (1.05–1.74) and 1.29 (1.03–1.60)). After adjusting for mobility limitations (model 3), sarcopenia alone (HR: 1.32 ((1.04–1.69) but not sarcopenia with obesity (HR: 1.25 (0.99–1.58)) was associated with mortality. For men, the risk of death with sarcopenia and sarcopenic obesity was nonsignificant in both model-2 (HR: 0.98 (0.77–1.25), and HR: 0.99 (0.79–1.23)) and model 3 (HR: 0.98 (0.77–1.24) and HR: 0.98 (0.79–1.22)).


Older women with sarcopenia have an increased all-cause mortality risk independent of obesity.


Two distressing physical challenges of growing older are a progressive increase in body fat and a corresponding decrease in lean muscle mass and quality known as sarcopenia.1,2 Sarcopenia is a major risk factor for numerous adverse health outcomes associated with frailty, including weakness, falls, immobility, functional decline and institutionalization.3, 4, 5, 6, 7, 8, 9, 10 Despite the high prevalence of sarcopenia especially among the oldest old,11 current treatment options have been of limited value in attenuating this process.5 Better characterizing the predictors and long-term outcomes of sarcopenia is essential to developing targeted and effective interventions.

Recent population trends indicate an alarming rise in the prevalence of obesity among older adults,12,13 potentially adding a complementary condition that compounds the risk of poor health outcomes. Obesity increases the chance of numerous chronic health conditions14 and is also associated with an increased risk of mortality.6 Studies in older cohorts have highlighted that a body mass index 30 kg/m2 often impacts the quality of life,10 and it places subjects at a higher risk for disability8 and death.9

The interplay between sarcopenia and rising trends in obesity in an aging population is emerging as an important public health concern in geriatrics. Identifying whether these entities are associated with death and longevity is important. The purpose of this study was to examine the association of sarcopenia and obesity with mortality, in a representative cohort of United States subjects. We hypothesized that subjects with sarcopenia and obesity would have higher mortality risks than those with either condition alone.

Subjects and methods

Study design and population

We used data from the National Health and Nutrition Examination Surveys (NHANES), a series of epidemiological cross-sectional surveys of non-institutionalized US adults. We used NHANES III (1988–1994), which oversampled minorities and older adults. All data, study design and procedures, including questionnaires, examination and laboratory components, are publically available online at http://www.cdc.gov/nchs/nhanes.htm. The institutional review board exempted this study protocol from formal review owing to the de-identified nature of the data.

All data were downloaded in February 2011. NHANES III consisted of 39 695 participants. We included those participants who were 60 years or older (n=5724) and had complete anthropometric and bioelectrical impedance (BIA) data to compute skeletal muscle index and % body fat (n=4652), respectively. Participants were interviewed and examined in a mobile examination unit by a physician following standard protocols and procedures to obtain data as outlined. All interviews were conducted by trained field staff in English or Spanish, with automated data collection. Respondents were asked questions directly, and if they were unable to answer or if the questions were irrelevant they were asked to answer by proxy. We categorized respondents as nonHispanic white, nonHispanic black and Hispanic American, with the latter including both Mexican American and other Hispanics, based self report. Those not fitting one of these ethnicity categories were designated as ‘other’. Date of birth was ascertained by self report at the initial screening and verified against an age verification chart to determine subject age and was considered baseline age. Procedures were available to reconcile differences.

Mortality data

Mortality data were obtained from the National Death Index data set and linked to the NHANES III data set using a unique identifier. The National Death Index is available publically and contains de-identified death certificate data, updated through 31 December 2006. Death was based upon a probabilistic match between NHANES and the National Death Index. Mortality source and cause of death were determined using death certificates. Cause of death followed the International Statistical Classification of Disease, Injuries and Causes of Death guidelines with the 9th revision used for those dying before 1999 and the 10th revision for all others. NHANES procedures harmonized differences in definitions and causes of death, all of which have been demonstrated to be comparable. We grouped causes of death as follows: malignant neoplasms, cardiovascular, respiratory, gastrointestinal, renal disease, neurological (including stroke), infectious and other causes of death. Time of follow up was calculated in months from date of interview to date of death or most recent vital status record. Mortality data were complete in over 99% of our sample.

Demographic and functional variables

Self-report history was obtained for the following variables by asking the question “Has a doctor ever told you have (disease state): hypertension, osteoporosis, congestive heart failure, non skin cancer, coronary artery disease and arthritis”. Diabetes was characterized as having a self-reported diagnosis, fasting sugar of 126 mg/dl15 or subjects on insulin or oral diabetes medications. Participants were classified as ‘smokers’ if they had smoked at least 100 cigarettes in their lifetime. Self-reported health was determined using Likert scale scores: ‘Is your health, in general, excellent/very good/ good/fair or poor?’. We characterized subjects’ physical activity with the question, ‘Are you active compared with men/women your age?’ (more active, less active, about the same and do not know). The Modification of Diet in Renal Disease formula was used to calculate glomerular filtration rate <60 ml/min/m2.16

Mobility limitation was a dichotomous variable defined by self-reported difficulty in any of the following: walking ¼ mile, walking 10 stairs without resting, lifting or carrying 10 pounds, stooping/crouching/kneeling or standing up from a chair. Subjects were defined as disabled if they answered yes to either of the following questions: ‘Do you need help with personal care needs? Do you need help handling routine needs?’ Gait speed (in meters/second) was assessed as the best of two timed trials walking 8 feet at usual walking speed.17


All procedures and measurements were performed on the right side of the body, except in subjects with casts, amputations or other reasons. Body weight was measured on an electronic digital scale, calibrated in kilograms. The subject’s height was measured standing on a vertical backboard of a stadiometer, with their weight evenly distributed on both feet after deep inhalation. For quality assurance purposes, replicates and data review were performed. Body mass index was calculated as weight (in kilograms) divided by height (in meters) squared. Waist circumference was measured in the standing position, with the examiner palpating at the area of the right iliac crest, crossing the mid-axillary line and placing the measuring tape around the trunk of the body at minimal exhalation.

Body composition

Body composition data were measured using a Valhalla 1990B Bio-Resistance Body Composition Analyzer (Valhalla Scientific, San Diego, CA, USA). All values were independently certified in NHANES III. Subjects were asked to avoid eating or drinking anything but water during an overnight fast. A single tetrapolar measurement of resistance at 50 kHz was taken between the right wrist and ankle while supine. All resistance data were converted to RJL resistance values developed on a separate independent sample using sex-specific predictive equations to calculate total body water and fat-free mass.18 Each equation was applied to each individual participant. Total body fat was calculated as the difference between weight (kg) and fat-free (lean) mass, and the quotient of total body fat by weight multiplied by 100 was defined as % body fat. Subjects were classified as fulfilling criteria for obesity based on Baumgartner’s criteria of % body fat 27% in men and 38% in women.

A number of sarcopenia definitions exist;11 hence, we based our definitions on BIA-derived formulas, suggested by consensus.5 As NHANES had BIA data, we used a similar approach19 to calculate skeletal mass using the following formula,20 which had been validated using magnetic resonance imaging-measured skeletal muscle mass: skeletal mass (kg)=((height)2/resistance (R) × 0.401)+(sex × 3.825)+( age in years × −0.071))+5.102, and adjusted for height (in meters) squared. Muscle mass was normalized by height to provide the skeletal muscle mass index (kg/m2). We used the sex-specific cutoffs proposed by Janssen et al.21 for sarcopenia (men, normal: 10.76 kg/m2; class I sarcopenia: 8.51–10.75 kg/m2; class II sarcopenia: 8.50 kg/m2; females, normal: 6.76 kg/m2; class I sarcopenia: 5.76–6.75 kg/m2; class II sarcopenia: 5.75 kg/m2). These cutoffs are based on the risk of physical disability. Overall sarcopenia included subjects fulfilling either class I or class II sarcopenia. Participants were classified as having sarcopenia if they fulfilled the criteria for sarcopenia with or without comorbid obesity. Subjects fulfilling criteria for both sarcopenia and obesity were classified as having sarcopenic obesity.

Statistical analysis

Data were merged into a single data set and analyzed. Statistical methods for multistage stratified clustered weighted random samples were used. Specifically, the analyses used weights and the primary sampling unit and strata supplied by the NHANES. All continuous data are presented as means±standard errors, and categorical data as count (%). Prevalence of sarcopenia and sarcopenic obesity was ascertained using the definitions and equations outlined above. The primary outcome was all-cause mortality. Our primary aim was to determine the risk of death in subjects with sarcopenia or obesity, or both, compared with subjects without either among individuals over the age of 60 years. Cox proportional hazards model was used to estimate mortality ratios. The mortality ratios were calculated overall and by subgroups based on gender and age group: 60–69.9 years; 70–79.9 years; and 80 years. We anticipated that there are a number of confounders and a priori adjusted for several models. Our modeling was additive in nature by considering demographic characteristics (model 1: age, gender, ethnicity), then adding medical comorbidity and subjective health status (model 2: hypertension, diabetes mellitus, osteoporosis, congestive heart failure, coronary artery disease, arthritis, non skin cancer, physical activity, self-reported health and smoking status), and finally a functional variable (model 3: mobility limitation). The proportionality of hazard assumption was confirmed by examining the hazard ratio over a time partition. All analyses were conducted using R (version 2.10.1), including the survey library (http://cran.r-project.org/web/packages/survey/index.html). A P-value<0.05 was considered statistically significant.


Baseline characteristics of the 4652 subjects are shown in Table 1. Mean age was 71.1 years in women and 70.0 years in men, and 2283 subjects (49.1%) were male. The majority were nonHispanic white in both sexes. Body mass index, waist circumference and % body fat decreased with increasing age, as did skeletal muscle mass. Table 2 presents prevalence of sarcopenia, obesity and sarcopenic obesity by age group and sex. The prevalence of sarcopenia increased with age in both men and women, whereas the prevalence of obesity dropped with age. Sarcopenia prevalence in women and men was 35.4% and 75.5%, respectively, whereas obesity prevalence was 60.8% and 54.4%, respectively. Sarcopenic obesity increased with age in both sexes, whereas the prevalence of sarcopenic obesity dropped with increasing age in men 80 years of age. Overall prevalence of sarcopenic obesity was 18.1% and 42.9% in women and men, respectively. There were 2782 deaths in the cohort aged >60 years, of which cardiovascular causes and non skin cancer deaths accounted for 39.0% and 21%, respectively. There were 34 subjects without causes of death. Mean follow-up time was 14.3 years (interquartile range 12.5–16.1) in the overall cohort.

Table 1: Baseline characteristics of 4652 subjects aged 60 in the NHANES III cohort
Table 2: Baseline prevalence of sarcopenia, obesity and sarcopenic obesity—NHANES III

Primary outcomes are demonstrated in Tables 3 and 4. Overall risk of death is increased in sarcopenic obesity after adjusting for demographic characteristics (model 1) and after adjusting for medical comorbidities and subjective health status (model 2) observed in women but not in men. However, risk of death was not increased for sarcopenic obesity after adjusting for mobility limitation (model 3) or disability in either sex (data not shown). Women with sarcopenia had a higher mortality risk compared with men, regardless of the presence or absence of obesity. There were no statistically significant differences in mortality risks by age group, although they were slightly higher in the 80 years of age range. Overall risk of death in class II sarcopenic obesity is higher than those with obesity alone. No differences were observed by sex.

Table 3: Multivariable mortality models for sarcopenia, obesity and sarcopenic obesity, aged 60 years by sex
Table 4: Multivariable mortality models for sarcopenic obesity aged 60 years


Both sarcopenia and sarcopenic obesity have been shown to be related to incident functional decline and disability in cross-sectional and longitudinal studies.4,22, 23, 24, 25, 26 Disability may increase the risk of death, and hence there is a natural supposition that subjects with sarcopenia or sarcopenic obesity are at a higher risk of death. Our study demonstrates mixed results in that sex-specific differences in mortality risk may exist, but that obesity may blunt the mortality estimates observed in sarcopenia alone.

The most exciting finding in our study is that although women had a lower prevalence of sarcopenia and sarcopenic obesity their mortality risk was ultimately higher than that in men. Our sex-specific results of higher mortality risk in a female population are similar to what we have previously published27 in a normal-weight obesity population. Women have more fat and lower absolute muscle mass than men, and hence may be at a greater risk of developing obesity and lower muscle strength with aging.28,29 The impact of obesity on women may be exaggerated owing to the greater loss of existing lower muscle stores reaching a threshold for sarcopenia in advance of that in men.28 The interplay at a cellular/muscle level with regard to fat infiltration into muscle also remains unclear. Further, as cardiovascular causes of death predominated in this cohort, we can speculate that other reasons may explain our findings include the modulating effect of estrogen on cardiovascular risk factors. Gender-specific biologic differences of lesser body fat and the role of the gynecoid profile of fat deposition on time-dependent mortality are unclear. A greater fat mass in women may be related to higher levels of proinflammatory cytokines,25 and whether the duration of exposure to persistent inflammation modulated by other mechanisms of homeostasis, may also explain our results.

Our results may in fact have been somewhat expected. Accurate case identification of sarcopenic obesity remains controversial and ill-defined. Although we used the cutoffs proposed by Janssen et al.21 and recommended by the European Working Group for Sarcopenia,5 slight alterations to their thresholds leads to changes in prevalence rates, which in turn impacts mortality estimates. An alternative approach, in line with previous definitions of sarcopenia, would be to base cutoffs on Gaussian distributions or quintiles.11 In fact, our group demonstrated 10 to 15-fold variability in prevalence estimates depending on the definition used.11 Many definitions are based on referent cohorts that differ in both physical and functional characteristics than our own. While both appendicular skeletal muscle mass and total body skeletal muscle mass are associated with disability,4,19,23 our results provide some additional credence that perhaps the latter should not be considered in mortality estimations.

We relied on muscle mass and not muscle quality in our definition of sarcopenia. Consistently, evidence suggests that ‘dynapenia’ or low strength or function as characterized by gait speed, grip strength or other measures of muscle quality, are inversely associated with disability30,31 and mortality32, 33, 34, 35, 36, 37 and may be of more significance than muscle mass alone. Although our analytical modeling incorporated muscle mass, we only partially accounted for self-reported function and not muscle quality, which may explain our findings, highlighting the importance of incorporating a measure of strength in the definition of sarcopenia, as suggested by the European working group.5 We attempted to account for mobility limitations as a surrogate for muscle quality in model 3; however, the data available in NHANES may be construed as more of an outcome of sarcopenia or obesity, rather than as a confounder and should be interpreted with caution. Future studies need to highlight the impact of either gait speed and/or grip strength with appendicular skeletal muscle to estimate mortality.

Another limitation of our findings was that skeletal mass was ascertained using BIA. Although BIA is easily implemented in large-scale population-based studies, it may overestimate or underestimate prevalence rates in that it cannot reasonably distinguish between appendicular and nonappendicular fat and nonfat mass. Other studies that targeted appendicular skeletal mass using dual-energy absorptiometry or CT imaging noted significant associations between sarcopenia and mortality.38, 39, 40 The formula derived by Janssen et al.20 was developed and cross-validated using magnetic resonance imaging, a gold-standard approach in body composition, with correlation measures of 0.93. However, this may underpredict skeletal mass, particularly in ethnicities with differences in body build,20,41, 42, 43 in turn impacting our estimates. Others have considered body mass index44 and waist circumference,45 neither of which precisely identified BIA-identified body fat. Whether incremental prediction of % body fat using skinfolds remains a possibility.

Our study’s methodological limitations reflect those of the NHANES epidemiological survey, including its cross-sectional nature, the sampling approach and the selected variables, leading to data at one time point, which may overestimate or underestimate one’s true values. Standard procedures can partially account and minimize this variability. Sarcopenia and sarcopenic obesity are known to be associated with functional decline and institutionalization,5 and NHANES is unable to capture the data on such non-institutionalized subjects, including nursing home residents. As such, our mortality estimates may be conservative in nature. Incorporating longitudinal nursing home level data is needed to accurately reflect the degree of mortality risk of this population. This may also explain the reduced prevalence, particularly with advanced age, observed in this study. Our data should be extrapolated only to the population of the United States that is non-institutionalized. Although vital status is clear, cause of death should be interpreted with caution, as death certificates are known to have inaccuracies. Last, our prevalence estimates were higher in men than in women, which may have either overexaggerated or underexaggerated mortality estimations because of a number of reasons. First, this may be simply because of power issues. Second, prevalence rates are highly dependent on cutoffs, definition of sarcopenia used and the referent population they are based on.11 We additionally did not incorporate a measure of function, as NHANES III did not have the one recommended by the working group.5 Third, we used validated prediction BIA equations as recommended by other authors.18 Fourth, we included subjects labeled on ‘other’ ethnicity. Fifth, we elected to use cutoffs linked to physical impairment and did not exclude subjects with missing physical function data.19 Importantly, definitions should be based on distal functional outcomes.11 Last, we used cutoffs recommended by the European Working Group for the Study of Sarcopenia.5 Further limitations in our analysis were that our models were parsimonious and hence we likely omitted known comorbidity, social or pharmacological confounders. Additionally, we relied on self-reported variables, which may bias our estimates. The definitions of smoking may also be too conservative and may not account for the biologic effects of total cigarette consumption. Our other multivariable analyses, particularly parsing out class I and II sarcopenia, were purely exploratory and should be considered as such. In fact, one would expect that the prevalence estimates be higher in men than in women in both sarcopenia and obesity.


Our results suggest that there may be sex-specific differences in mortality risk in those with sarcopenia and sarcopenic obesity. Future studies should focus on a standardized approach in identifying sarcopenia and obesity by incorporating functional measures, and using appendicular skeletal mass to determine whether sex-specific differences in fact exist. Providers and researchers alike should concentrate on preventing the development of these conditions. The goal is to further explore these epidemiologic associations in efforts to develop clinical trials to limit one’s long-term functional decline and mortality risk.


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This project was funded by the Centers for Aging, The Dartmouth Institute and the Department of Medicine, Dartmouth-Hitchcock Medical Center.

Author information


  1. Section of General Internal Medicine, Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA

    • J A Batsis
  2. Geisel School of Medicine at Dartmouth, Hanover, NH, USA

    • J A Batsis
    • , T A Mackenzie
    • , L K Barre
    •  & S J Bartels
  3. Centers for Aging and Aging Research, Dartmouth College, Hanover, NH, USA

    • J A Batsis
    • , L K Barre
    •  & S J Bartels
  4. Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic, Rochester, MN, USA

    • F Lopez-Jimenez


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The authors declare no conflict of interest.

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Correspondence to J A Batsis.

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