Abstract
Background and objectives
Muscle wasting is common among patients with ESRD, but little is known about differences in muscle mass in persons with CKD before the initiation of dialysis. If sarcopenia was common, it might affect the use of body mass index for diagnosing obesity in people with CKD. Because obesity may be protective in patients with CKD and ESRD, an accurate understanding of how sarcopenia affects its measurement is crucial.
Design, setting, participants, & measurements
Differences in body composition across eGFR categories in adult participants of the National Health and Nutrition Examination Survey 1999–2004 who underwent dual-energy x-ray absorptiometry were examined. Obesity defined by dual-energy x-ray absorptiometry versus body mass index and sarcopenia as a contributor to misclassification by body mass index were examined.
Results
Sarcopenia and sarcopenic obesity were more prevalent among persons with lower eGFR (P trend <0.01 and P trend <0.001, respectively). After multivariable adjustment, the association of sarcopenia with eGFR was U-shaped. Stage 4 CKD was independently associated with sarcopenia among participants ≥60 years old (adjusted odds ratio, 2.58; 95% confidence interval, 1.02 to 6.51 for eGFR=15–29 compared with 60–89 ml/min per 1.73 m2; P for interaction by age=0.02). Underestimation of obesity by body mass index compared with dual-energy x-ray absorptiometry increased with lower eGFR (P trend <0.001), was greatest among participants with eGFR=15–29 ml/min per 1.73 m2 (71% obese by dual-energy x-ray absorptiometry versus 41% obese by body mass index), and was highly likely among obese participants with sarcopenia (97.7% misclassified as not obese by body mass index).
Conclusions
Sarcopenia and sarcopenic obesity are highly prevalent among persons with CKD and contribute to poor classification of obesity by body mass index. Measurements of body composition beyond body mass index should be used whenever possible in the CKD population given this clear limitation.
Keywords: lean body mass, CKD, obesity
Introduction
Muscle wasting is common among patients with ESRD, and it is believed to be a significant contributor to the high morbidity and mortality that is observed in these patients (1–3). A number of studies of patients on dialysis have found associations of reduced lean body mass and other markers of protein-energy wasting with increased mortality (4–6). However, differences in muscle mass have been less well examined before the initiation of dialysis.
Obesity, defined as excess adiposity that is dangerous to health, is also highly prevalent in the CKD population and associates with the development of ESRD (7,8). Contrary to studies in the general population, obesity is associated with better clinical outcomes in patients with ESRD receiving dialysis (9,10) and a lower risk of death in patients with CKD (11). Given the high comorbidity burden and mortality risk among patients with advanced CKD and ESRD, it has been postulated that obesity is an indicator of better nutritional status (12). The use of body mass index (BMI) to define obesity is a possible explanation. BMI may misclassify patients with sarcopenia, because it does not differentiate between fat mass and muscle mass. Thus, obese individuals with significant loss of muscle mass may be wrongly classified as not obese. In analyses on the basis of BMI, higher BMI groups would then be enriched with obese individuals who have maintained muscle mass, which is likely a marker for better overall health. It is unclear to what extent this misclassification occurs in the CKD population, but it may contribute to the protective effect of obesity that has been observed in both patients with CKD and patients with ESRD.
We hypothesized that decreased muscle mass is common among people with predialysis CKD and that the use of BMI in diagnosing obesity would thus be limited in this population. We examined these questions using body composition data from participants of the National Health and Nutrition Examination Survey (NHANES) 1999–2004. We defined sarcopenia using appendicular skeletal muscle mass, because this is the most functionally relevant component of lean mass and not confounded by changes in visceral lean mass. We defined obesity using percent total body fat (%TBF), which is a direct measure of adiposity. We then examined the prevalence of sarcopenia, obesity, and their overlap within eGFR categories and compared BMI-defined obesity with our %TBF standard. We also examined the prevalence of sarcopenia using cystatin C to define eGFR, because cystatin C is less affected than creatinine by muscle mass (13).
Materials and Methods
Study Population
The NHANES 1999–2004 was a nationally representative survey of the noninstitutionalized civilian population in the United States (14). A stratified, multistage, probability sampling design was used to select participants. The NHANES protocol was approved by the National Center for Health Statistics ethics review board, and written informed consent was obtained from all participants. Overall, 12,732 adults ≥20 years of age completed the interview and examination components and had available body composition data. We excluded participants who were pregnant (n=0), had an eGFR<15 ml/min per 1.73 m2 (n=34), or were missing covariate data (n=1055). Thus, 11,643 participants were available for analysis.
Data Collection
Race/ethnicity was self-identified. Information on education, household income, physical activity, and comorbidities was obtained by self-report. Poverty was defined as <100% of the poverty index. Participants were asked about the frequency and duration of walking or bicycling, home or yard work, and moderate or vigorous leisure time physical activity performed within the past 30 days. These responses were used to calculate metabolic equivalents (METs min/wk) (15) and classify activity level as 0, <500, 500–2000, or >2000 MET min/wk. Activity level was also dichotomized as meeting or not meeting the recommended minimum activity levels on the basis of a cutoff corresponding to publicly available guidelines (500 MET min/wk) (16,17). Hypertension was defined as systolic BP≥140 mmHg, diastolic BP≥90 mmHg, physician diagnosis, and/or antihypertensive medication use (18). Diabetes mellitus was defined as a physician diagnosis while not pregnant, the current use of insulin or oral hypoglycemic medications, or a glycohemoglobin level ≥6.5%. Cardiovascular disease was defined by self-report of a physician diagnosis of congestive heart failure, coronary heart disease, angina, myocardial infarction, or stroke.
Serum chemistry values were measured using the Hitachi 917 multichannel analyzer (Roche Diagnostics, Indianapolis, IN) in 1999–2001 and the Beckman Synchron LX20 (Beckman Coulter Inc., Brea, CA) in 2002–2004. Serum albumin was measured by the bromocresol purple method. Serum bicarbonate was measured by the phosphoenolpyruvate carboxylase method from 1999 to 2001 and with a pH-sensitive electrode in from 2002 to 2004. C-reactive protein (CRP) was quantified by latex-enhanced nephelometry. Serum creatinine was measured by a modified kinetic Jaffé reaction. Values from 1999 to 2000 were calibrated to the Cleveland Clinic laboratory standard by multiplying by 1.013 and then adding 0.147. Correction of values from 2001 to 2004 was not necessary. eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration equation (19). Serum cystatin C was measured in a subsample of participants in 1999–2002 using a particle-enhanced nephelometric assay. Cystatin C-based eGFR was calculated using age, sex, race, and cystatin C (20). Appropriate sample weights were used to account for the smaller sample in which cystatin C levels were available (n=3963).
Outcome Variables
Body composition was assessed using whole-body dual-energy x-ray absorptiometry (DEXA). Because of the pattern of nonresponse, missing and invalid data were multiply imputed by the National Center for Health Statistics. Details of the DEXA protocol, quality control, data validation, and multiple imputation procedure are available at http://www.cdc.gov/nchs/nhanes/dxx/dxa.htm and elsewhere (21–23). We quantified muscle mass using the appendicular skeletal muscle mass index (ASMI), a measure of relative muscle mass that accounts for scaling of lean body mass with height (24). ASMI was calculated as the sum of lean mass for the arms and legs (kilograms)/height2 (meters2) (24). Sarcopenia was defined as ASMI<5.45 kg/m2 in women and <7.26 kg/m2 in men (24,25). These cutoffs correspond to 2 SDs below the sex-specific means for healthy adults ages 18–40 years (26). %TBF was calculated as 100×TBF/total mass. Participants were classified as obese by DEXA if their %TBF was above the sex-specific 60th percentile for the study sample, which corresponded to 42.1% in women and 29.6% in men (25). These cutpoints correspond to the current World Health Organization (WHO) guidelines for BMI-defined obesity (27). On the basis of the DEXA data, participants were then classified as nonsarcopenic, nonobese; sarcopenia only; obesity only; or sarcopenic obese. Sarcopenia and sarcopenic obesity, defined using these DEXA cutoffs, have each been associated with disability in community-dwelling elderly (24,25). Participants were classified as obese by BMI if they had BMI≥30 kg/m2 as per WHO guidelines.
Statistical Analyses
All analyses used NHANES-appropriate sampling weights and accounted for the complex multistage cluster design using the survey command in Stata 11.2 (Stata Corporation, College Station, TX). Age-standardized distributions of participant characteristics were examined by categories of creatinine-based eGFR using linear regression for continuous variables and chi-squared tests for categorical variables. Age standardization was performed using 2000 United States census data. The overall and age-standardized prevalence values of sarcopenia were calculated within eGFR categories. We then determined the prevalence of the four mutually exclusive body composition types within eGFR categories. Logistic regression models were used to examine the association of eGFR with the aforementioned body composition definitions and determine the association of sarcopenia with misclassification by BMI. Multivariable logistic regression models were also created to examine the association of prespecified covariates of interest with the likelihood of sarcopenia. Effect modification was tested by including multiplicative interaction terms in the multivariable model. Age-stratified models were then examined using 60 years old as a cutpoint, which we chose a priori as a reasonable age to compare older with younger participants given the sample size limitations of the NHANES data. A P value <0.05 was considered statistically significant.
Sensitivity Analyses
To examine the effect of choosing lower %TBF cutoffs to define obesity, we re-examined misclassification by BMI using TBF>25% for men and >30% for women as per the American Society of Bariatric Physicians (ASBP) guidelines (28). Associations of eGFR with sarcopenia were examined after adjustment for abdominal obesity and socioeconomic status (e.g., poverty and education) in participants for whom these data were available. Waist circumference (WC) was used to define abdominal obesity as WC≥88 cm for women and WC≥102 cm for men (29). Associations with CKD stages were examined after classifying participants by eGFR and albuminuria status (30). The association of eGFR, modeled as a continuous variable, with sarcopenia was examined using a two-slope linear model with separate slopes above and below eGFR=60 ml/min per 1.73 m2.
Results
Participant Characteristics
Participants with lower eGFR were older and after adjustment for age, had higher BMI, reported greater physical activity, and were more likely to have hypertension, diabetes, cardiovascular disease, and microalbuminuria (Table 1). They had lower levels of serum bicarbonate and albumin and higher levels of CRP.
Table 1.
Age-adjusted participant characteristics by eGFR in 11,643 participants of the National Health and Nutrition Examination Survey 1999–2004
| Characteristic | eGFR (ml/min per 1.73 m2) | P | |||||
|---|---|---|---|---|---|---|---|
| ≥120 | 90–119 | 60–89 | 45–59 | 30–44 | 15–29 | ||
| Number | 1303 | 5148 | 4086 | 775 | 254 | 77 | |
| Age (yr) | 27.7 (0.3) | 40.4 (0.3) | 54.6 (0.4) | 71.0 (0.6) | 74.5 (1.0) | 73.2 (1.4) | <0.001 |
| Women, % | 61.8 (2.0) | 51.0 (0.9) | 46.4 (1.3) | 57.2 (8.2) | 30.3 (4.6) | 15.4 (2.5) | <0.001 |
| Race/ethnicity, % | <0.001 | ||||||
| Non-Hispanic white | 22.5 (2.8) | 68.3 (2.0) | 81.8 (1.6) | 51.2 (5.2) | 47.6 (8.9) | 20.5 (2.2) | |
| Mexican American | 13.8 (1.8) | 8.1 (1.1) | 3.4 (0.5) | 1.2 (0.6) | 1.5 (0.9) | 5.5 (4.5) | |
| Non-Hispanic black | 56.5 (2.8) | 11.8 (1.2) | 6.6 (0.7) | 18.8 (7.8) | 22.7 (8.2) | 41.4 (4.6) | |
| Poverty (<100% poverty index), %a | 29.5 (1.6) | 14.8 (1.1) | 9.2 (1.1) | 18.1 (5.1) | 36.3 (5.1) | 2.1 (1.1) | <0.001 |
| Less than high school diploma, %a | 41.4 (2.1) | 22.6 (1.0) | 15.3 (1.0) | 19.8 (4.1) | 17.3 (3.6) | 16.6 (6.0) | <0.001 |
| Body mass index (kg/m2), % | <0.001 | ||||||
| <20 | 4.8 (0.7) | 6.1 (0.4) | 4.2 (0.6) | 1.4 (0.6) | 5.4 (3.5) | 0.5 (0.4) | |
| 20 to <25 | 34.0 (2.1) | 30.2 (1.2) | 28.6 (1.2) | 30.0 (3.0) | 17.8 (6.0) | 31.6 (2.4) | |
| 25 to <30 | 28.1 (2.6) | 33.2 (1.2) | 37.4 (1.2) | 33.3 (5.8) | 29.3 (5.9) | 19.7 (6.8) | |
| 30–35 | 17.6 (2.3) | 18.6 (0.9) | 19.4 (1.0) | 19.6 (5.9) | 14.4 (4.3) | 4.1 (1.2) | |
| >35 | 15.6 (1.7) | 11.9 (0.7) | 10.3 (0.9) | 15.7 (5.6) | 33.2 (3.8) | 44.1 (7.0) | |
| Abdominal obesity, %b | 51.4 (2.0) | 48.7 (1.3) | 49.3 (1.6) | 46.8 (5.6) | 64.0 (4.2) | 56.3 (6.7) | <0.001 |
| Waist circumference (cmb) | 94.8 (0.5) | 95.9 (0.4) | 96.4 (0.5) | 95.9 (2.2) | 107.8 (1.5) | 105.0 (1.9) | 0.004 |
| Activity level (MET min/wk), % | <0.001 | ||||||
| 0 | 35.7 (2.5) | 18.1 (1.0) | 13.3 (1.0) | 22.0 (5.4) | 27.8 (6.8) | 11.5 (2.2) | |
| <500 | 18.3 (2.1) | 21.8 (0.9) | 18.0 (1.1) | 15.0 (3.8) | 14.1 (4.1) | 16.0 (6.0) | |
| 500–2000 | 26.2 (2.5) | 33.9 (1.0) | 35.5 (1.0) | 47.2 (7.2) | 43.8 (6.2) | 43.2 (5.6) | |
| >2000 | 19.8 (1.9) | 26.2 (1.0) | 33.2 (1.4) | 15.7 (3.9) | 14.3 (7.5) | 29.3 (0.9) | |
| Met minimum activity level, %c | 46.0 (2.7) | 60.1 (1.3) | 68.7 (1.3) | 63.0 (5.8) | 58.1 (5.9) | 72.5 (5.7) | <0.001 |
| Hypertension, % | 35.1 (2.2) | 37.7 (1.2) | 40.9 (1.2) | 75.7 (5.4) | 98.6 (0.3) | 98.9 (0.6) | <0.001 |
| Diabetes mellitus, % | 12.3 (1.9) | 9.4 (0.6) | 7.3 (0.5) | 15.2 (3.6) | 17.3 (4.7) | 26.4 (5.8) | <0.001 |
| Cardiovascular disease, % | 4.8 (1.6) | 7.3 (0.9) | 7.8 (0.4) | 14.4 (2.5) | 39.3 (8.3) | 24.5 (4.7) | <0.001 |
| UACR>30 mg/g, % | 12.4 (2.3) | 9.3 (0.8) | 7.6 (0.6) | 30.9 (4.8) | 71.0 (4.5) | 90.3 (2.4) | <0.001 |
| Serum bicarbonate (mEq/L) | 24.1 (0.1) | 24.2 (0.1) | 24.1 (0.1) | 23.9 (0.3) | 22.1 (0.3) | 21.1 (0.3) | <0.001 |
| Serum albumin (g/dl) | 4.2 (0.02) | 4.3 (0.01) | 4.4 (0.01) | 4.3 (0.04) | 4.1 (0.1) | 4.1 (0.02) | <0.001 |
| Serum CRP (mg/dl), % | <0.001 | ||||||
| 0.22–0.99 | 33.6 (2.6) | 38.3 (1.1) | 36.7 (1.2) | 36.2 (7.2) | 41.8 (6.6) | 54.1 (6.1) | |
| ≥1.0 | 28.8 (2.4) | 10.0 (0.7) | 8.2 (0.6) | 9.4 (2.7) | 8.4 (3.2) | 10.0 (5.8) | |
| Serum creatinine (mg/dl) | 0.6 (0.006) | 0.8 (0.002) | 1.0 (0.004) | 1.4 (0.04) | 2.0 (0.06) | 3.4 (0.08) | <0.001 |
| Obese by DEXA, % | 38.2 (3.0) | 39.8 (1.0) | 36.3 (1.4) | 41.0 (6.0) | 55.0 (6.5) | 62.3 (6.2) | <0.001 |
| ASMI (kg/m2) | 7.5 (0.07) | 7.6 (0.03) | 7.8 (0.04) | 7.9 (0.3) | 8.8 (0.3) | 8.5 (0.2) | <0.001 |
| Total body fat, % | 35.1 (0.4) | 34.1 (0.2) | 33.0 (0.2) | 33.7 (1.8) | 34.2 (1.0) | 34.0 (0.5) | <0.001 |
Data are expressed as mean (SEM) or percentage (SEM). Values for all variables except age were calculated using age standardization with year 2000 United States census data. P values for differences across categories were calculated using linear regression for continuous variables and chi-squared tests for categorical variables. MET, metabolic equivalent; UACR, urine albumin-to-creatinine ratio; CRP, C-reactive protein; DEXA, dual-energy x-ray absorptiometry; ASMI, appendicular skeletal muscle mass index.
Data on socioeconomic status available for 10,662 participants.
Data on waist circumference and abdominal obesity available for 11,456 participants. Abdominal obesity was defined as waist circumference≥88 cm for women and ≥102 cm for men.
Minimum activity level defined as ≥500 MET min/wk.
Prevalence of Sarcopenia by eGFR Categories
In the unadjusted analyses, lower eGFR was associated with sarcopenia using both creatinine and cystatin C-based eGFR (P for trend <0.01 and P for trend=0.02, respectively) (Figure 1); in stage 4 CKD, defined using creatinine and cystatin C-based eGFR, the prevalence of sarcopenia was 34.1% and 20.8%, respectively. After age standardization, this association appeared U-shaped, and there was no longer a significant linear trend using either eGFR equation.
Figure 1.
Prevalence of sarcopenia by eGFR categories. Unadjusted and age-standardized prevalence of sarcopenia by (A) creatinine-based eGFR and (B) cystatin C-based eGFR. Error bars represent SEMs.
Body Composition by Creatinine-Based eGFR Categories
Nonsarcopenic, nonobese body composition (defined by DEXA) was inversely associated with lower eGFR (P for trend <0.001) and only present among 12.9% (95% confidence interval [95% CI], 4.0 to 21.8) of individuals with eGFR=15–29 ml/min per 1.73 m2 (Figure 2). There was an increase in the prevalence of both obesity and sarcopenic obesity (defined by DEXA) with lower eGFR (P for trend <0.001 for each), with the latter present among 18.3% (95% CI, 7.0 to 29.5) of participants with eGFR=15–29 ml/min per 1.73 m2. These associations were attenuated and nonlinear after adjustment for age, such that 33.0% (95% CI, 20.9 to 45.1) of persons with eGFR=15–29 ml/min per 1.73 m2 were nonsarcopenic, nonobese and 11.2% (95% CI, 0.4 to 22.0) were sarcopenic obese (Supplemental Table 1). Although BMI-defined obesity was also more prevalent with lower eGFR (P for trend=0.05), BMI underestimated obesity compared with DEXA at all levels of eGFR (Figures 2 and 3A, obese by DEXA and obese by DEXA and BMI bars). Each 10 ml/min per 1.73 m2 higher eGFR was associated with 11% and 3% lower odds of obesity by DEXA and BMI, respectively (odds ratio [OR], 0.89; 95% CI, 0.87 to 0.91 for obese by DEXA; OR, 0.97; 95% CI, 0.95 to 0.99 for obese by BMI).
Figure 2.
Prevalence of body composition categories by creatinine-based eGFR. Body composition has been classified into nonsarcopenic, nonobese; sarcopenia; obese; and sarcopenic-obese by DEXA. Obese by BMI is included for comparison, and it represents the prevalence of BMI≥30 kg/m2 regardless of DEXA status. Error bars represent SEMs. BMI, body mass index; DEXA, dual-energy x-ray absorptiometry.
Figure 3.
Misclassification of obesity using BMI compared with DEXA and association with sarcopenia. A shows the prevalence of obesity by creatinine-based eGFR categories. DEXA-defined obesity on the basis of total body fat percentage was used as the standard for obesity (obese by DEXA). Among these participants, if they were also obese by BMI (BMI≥30 kg/m2), they were classified as obese by DEXA and BMI. If they were not obese by BMI (BMI<30 kg/m2; i.e., they were misclassified by BMI as not obese), they were classified as obese only by DEXA (not BMI). B shows the prevalence of sarcopenia by creatinine-based eGFR categories among participants who were obese by DEXA and BMI, were obese only by DEXA (not BMI), and had BMI≥30 kg/m2 (obese by BMI [regardless of DEXA]). Error bars represent SEMs. The denominator for all percentages is the total number of participants within each respective eGFR category.
Classification of Obesity Using BMI Versus DEXA
The proportion of individuals within an eGFR category who were obese by DEXA but misclassified as nonobese by BMI increased with lower eGFR (P for trend <0.001) (Figure 3A, obese only by DEXA [not BMI] bar and Supplemental Table 2). Among those with stage 4 CKD, 71% of participants were obese by DEXA, whereas only 41% of participants were classified as obese by BMI. Using the ASBP cutoffs, the proportion of participants misclassified as nonobese by BMI increased at all levels of eGFR (Supplemental Table 2).
The prevalence of sarcopenia was negligible among persons correctly classified as obese by BMI and among those with BMI≥30 kg/m2, regardless of their %TBF (Figure 3B). In contrast, among those misclassified as nonobese by BMI, sarcopenia was highly prevalent and associated with lower eGFR (P for trend=0.001). In particular, 61% of misclassified individuals with stage 4 CKD were sarcopenic.
Conversely, obese participants with sarcopenia were extremely unlikely to be correctly classified as obese by BMI. Only 2.3% had BMI≥30, whereas BMI=25 to <30 and BMI=20 to <25 were present in 47.7% and 49.8%, respectively. The proportion of obese participants (on the basis DEXA) who had BMI≥30 is shown in Table 2 broken down by eGFR category and sarcopenia status.
Table 2.
Likelihood (percentage) of having a body mass index ≥30 kg/m2 on the basis of sarcopenia status among participants of the National Health and Nutrition Examination Survey 1999–2004 who were obese by dual-energy x-ray absorptiometry
| eGFR Category | BMI≥30 kg/m2 | BMI<30 kg/m2 | Total |
|---|---|---|---|
| Overall (n=4972) | |||
| Sarcopenia | 0.2 (0.06)a | 8.5 (0.6) | 8.7 (0.6) |
| No sarcopenia | 63.7 (1.2) | 27.6 (1.0) | 91.3 (0.6) |
| Total | 63.9 (1.2) | 36.1 (1.2) | |
| eGFR≥90 ml/min per 1.73 m2 (n=2425) | |||
| Sarcopenia | 0.1 (0.06)a | 6.1 (0.6) | 6.2 (0.6) |
| No sarcopenia | 68.0 (1.5) | 25.8 (1.3) | 93.8 (0.6) |
| Total | 68.1 (1.5) | 31.9 (1.5) | |
| eGFR=60–89 ml/min per 1.73 m2 (n=1928) | |||
| Sarcopenia | 0.3 (0.1)a | 9.8 (0.9) | 10.1 (0.9) |
| No sarcopenia | 60.3 (1.6) | 29.6 (1.4) | 89.9 (0.9) |
| Total | 60.6 (1.6) | 39.4 (1.6) | |
| eGFR=15–59 ml/min per 1.73 m2 (n=619) | |||
| Sarcopenia | 0.5 (0.3)a | 16.8 (2.0) | 17.3 (2.0) |
| No sarcopenia | 53.5 (2.1) | 29.2 (2.0) | 82.7 (2.0) |
| Total | 54.0 (2.2) | 46.0 (2.1) |
Numbers in cells are percentages (SEMs) estimated using National Health and Nutrition Examination Survey sampling weights and accounting for the survey design. BMI, body mass index.
The proportion of obese participants (obese by dual-energy x-ray absorptiometry) who were both sarcopenic and classified as obese by BMI (i.e., BMI≥30 kg/m2).
Factors Associated with Sarcopenia
Because sarcopenia had an important effect on the accuracy of BMI-defined obesity, we sought to determine clinical and laboratory factors that were associated with sarcopenia. The association of eGFR with sarcopenia was modified by age (P for interaction=0.01). We, therefore, fit models separately in participants <60 and ≥60 years of age. Factors associated with sarcopenia were largely similar in both age groups (Table 3). In the younger and older age groups, the lowest likelihood of sarcopenia after multivariable adjustment was present in participants with eGFR=60–89 and 30–59 ml/min per 1.73 m2, respectively (Table 3). Compared with participants ≥60 years of age who had eGFR=60–89 ml/min per 1.73 m2, those with eGFR=15–29 had an OR for sarcopenia of 2.58 (95% CI, 1.02 to 6.51). In both age groups, participants in the highest respective eGFR category had a significantly greater likelihood of being sarcopenic. These results were unchanged after adjustment for abdominal obesity and socioeconomic status or using the Kidney Disease Outcomes Quality Initiative CKD stages (Supplemental Table 3). Modeling eGFR as a continuous variable demonstrated a significant association of both low and high eGFR with greater likelihood of sarcopenia among those <60 years of age (Supplemental Table 1).
Table 3.
Adjusted odds ratios for sarcopenia in 11,643 participants of the National Health and Nutrition Examination Survey 1999–2004
| Characteristic | Odds Ratio (95% Confidence Interval) | ||||
|---|---|---|---|---|---|
| Age<60 yr (n=7507) | P | Age≥60 yr (n=4136) | P | ||
| Age (per 10 yr) | 1.95 (1.76 to 2.17) | <0.001 | 1.61 (1.33 to 1.96) | <0.001 | |
| Men | 2.65 (2.07 to 3.40) | <0.001 | 3.13 (2.23 to 4.39) | <0.001 | |
| Race/ethnicity | |||||
| Mexican American | 1.51 (1.08 to 2.13) | 0.02 | 1.06 (0.72 to 1.55) | 0.78 | |
| Non-Hispanic black | 0.14 (0.09 to 0.22) | <0.001 | 0.07 (0.04 to 0.12) | <0.001 | |
| Other | 1.23 (0.84 to 1.80) | 0.28 | 0.93 (0.49 to 1.77) | 0.83 | |
| Body mass index (per 1 kg/m2) | 0.48 (0.45 to 0.51) | <0.001 | 0.55 (0.53 to 0.58) | <0.001 | |
| Activity level (MET min/wk) | |||||
| <500 | 1.10 (0.81 to 1.48) | 0.54 | 0.87 (0.56 to 1.36) | 0.54 | |
| 500–2000 | 0.74 (0.53 to 1.03) | 0.07 | 0.72 (0.53 to 0.99) | 0.04 | |
| >2000 | 0.53 (0.39 to 0.72) | <0.001 | 0.44 (0.31 to 0.64) | <0.001 | |
| Diabetes mellitus | 1.28 (0.68 to 2.42) | 0.44 | 0.85 (0.58 to 1.24) | 0.39 | |
| Hypertension | 1.20 (0.88 to 1.65) | 0.24 | 1.40 (1.04 to 1.88) | 0.03 | |
| Cardiovascular disease | 2.66 (1.15 to 6.17) | 0.03 | 1.01 (0.73 to 1.40) | 0.96 | |
| eGFR (ml/min per 1.73 m2) | |||||
| Age<60 yr | Age≥60 yr | ||||
| ≥120 | — | 4.23 (2.92 to 6.12) | <0.001 | — | |
| 90–119 | ≥90 | 1.82 (1.41 to 2.36) | <0.001 | 2.66 (1.90 to 3.72) | <0.001 |
| 60–89 | 60–89 | Reference | Reference | ||
| 15–59 | 45–59 | 1.43 (0.25 to 8.31) | 0.68 | 0.79 (0.57 to 1.09) | 0.14 |
| — | 30–44 | — | 0.67 (0.41 to 1.12) | 0.12 | |
| — | 15–29 | — | 2.58 (1.02 to 6.51) | 0.05 | |
| UACR>30 mg/g, % | 1.14 (0.62 to 2.09) | 0.67 | 1.47 (1.08 to 2.00) | 0.02 | |
| Serum albumin (g/dl) | 0.72 (0.46 to 1.12) | 0.14 | 0.99 (0.67 to 1.47) | 0.97 | |
| Serum bicarbonate (mmol/L) | 1.02 (0.96 to 1.08) | 0.59 | 0.97 (0.90 to 1.04) | 0.41 | |
| C-reactive protein (mg/dl) | |||||
| 0.22–0.99 | 2.02 (1.57 to 2.59) | <0.001 | 1.63 (1.18 to 2.24) | 0.005 | |
| ≥1.0 | 2.28 (1.39 to 3.74) | 0.002 | 2.52 (1.50 to 4.25) | 0.001 | |
Models included all variables listed: age, sex, race/ethnicity, body mass index, activity-level categories, diagnosis of hypertension, diagnosis of diabetes mellitus, cardiovascular disease by self-report, eGFR categories, albuminuria, serum albumin, serum bicarbonate, and C-reactive protein categories. Reference categories are women, non-Hispanic white for race/ethnicity, 0 MET min/wk for activity level, UACR≤30 mg/g, and C-reactive protein<0.22 mg/dl. UACR, urinary albumin-to-creatinine ratio.
Using cystatin C to define eGFR, there was no evidence of effect modification by age (P for interaction=0.34). Because of the smaller sample size, we created fewer eGFR categories for our analysis and did not examine age-stratified models. Compared with participants with eGFR=60–89 ml/min per 1.73 m2 (n=1795), the multivariable-adjusted OR for sarcopenia was 0.52 (95% CI, 0.31 to 0.88), 0.86 (95% CI, 0.52 to 1.41), and 0.91 (95% CI, 0.43 to 1.92) for eGFR≥90 (n=1409), 45–59 (n=513), and 15–44 ml/min per 1.73 m2 (n=245), respectively.
Discussion
Using nationally representative data, we found that BMI commonly misclassifies obese individuals with CKD as nonobese, and this seems to be explained by loss of muscle mass. Obese people who are sarcopenic have a >97% likelihood of being misclassified by BMI as nonobese. In fact, nearly one half have a normal BMI. This is particularly important in the CKD population, because as we have shown, sarcopenia and its co-occurrence with obesity occur substantially more often among people with CKD than in those without CKD. These data have important ramifications for the assessment of body composition in clinical practice and future research in the CKD population.
Among patients with ESRD receiving dialysis, loss of lean mass is common and an integral component of protein-energy wasting. In contrast, there are limited data on alterations in lean mass in patients with CKD before the initiation of dialysis. We found that sarcopenia was highly prevalent among persons with advanced predialysis CKD using either cystatin-based or creatinine-based eGFR. Sarcopenic obesity specifically was highly prevalent among persons with CKD and not those without CKD. This finding has two important implications. Individuals who are both obese and sarcopenic may have a particularly poor prognosis, because sarcopenic obesity has been associated with an increased risk of disability in the elderly (25) and greater inflammation and an increased risk of death in patients with ESRD (31). The constellation of low muscle mass and obesity also has implications for the use of BMI in the CKD population. A small study of 77 patients with CKD stages 2–4 found that lean mass was lower in obese patients with normal BMI compared with those with high BMI (32). Our study confirms this finding in a representative sample of the general United States population. Individuals with advanced CKD were the most likely to be misclassified by BMI as nonobese, and this was largely explained by decreased muscle mass; sarcopenia was highly prevalent in misclassified individuals but nearly absent in those correctly classified.
Furthermore, we may have underestimated the problem of misclassification. We used a conservative definition of DEXA-defined obesity. The %TBF criteria that we selected have a functionally relevant correlation and correspond to WHO guidelines (25). However, others have defined obesity using lower %TBF thresholds (33). For example, the most recent ASBP guidelines classify men as obese when TBF is >25% and women as obese when TBF is >30% (28). As such, our estimates of the prevalence of obesity among people with kidney disease would have been higher had we chosen a lower cutoff for %TBF. Similarly, the frequency of misclassification by BMI would have been greater with such a cutoff. This report should, therefore, be viewed as the smallest estimate of the magnitude of this problem.
Our findings have important implications for studies examining obesity in patients with kidney disease. Multiple studies in patients with CKD and patients with ESRD receiving hemodialysis have found a lower risk of death among patients with higher BMI (6,9–11,34). This has led to speculation about the possible protective effects of greater adiposity in these populations. Studies have also suggested that this result could be related to higher muscle mass among people with higher BMI (10,35,36). Our findings illustrate the importance of muscle mass in determining BMI among persons with CKD. Whether muscle itself is protective or simply a reflection of less severe comorbidity and better preserved nutrition is unknown and a subject for future investigations. Regardless, we should be cautious about using BMI to draw conclusions about the effects of obesity in the CKD population.
There are clinical implications as well. Although nutrition and lifestyle recommendations might not differ for individuals misclassified as overweight instead of obese, indications for other current or future weight loss treatments could be affected. Insurance coverage for certain interventions could also hinge on a BMI-based diagnosis of obesity. For the nearly 50% of obese individuals with sarcopenia and BMI=20–25, however, even nutrition and lifestyle recommendations would change with better awareness by clinicians of their body composition.
Much of the high prevalence of sarcopenia in persons with CKD was explained by the presence of aging, inflammation, and comorbidity, which are particularly relevant, because they characterize an increasing proportion of the CKD population. However, stage 4 CKD was independently associated with an increased likelihood of sarcopenia in participants ≥60 years old, and our analyses may have been limited by the confounding effect of muscle mass on GFR estimation. The U-shaped association that we noted after multivariable adjustment supports this possibility. Participants with eGFR≥120 ml/min per 1.73 m2 had the highest CRP levels and lowest physical activity after age adjustment and a similar or even greater likelihood of sarcopenia than those with stage 4 CKD. It seems likely that muscle wasting related to inflammation and inactivity result in overestimation of GFR in such individuals. The use of cystatin C-based eGFR might be preferable when studying muscle wasting, and indeed, lower odds of sarcopenia were seen with high cystatin C-based eGFR in the fully adjusted model. However, we did not find a stronger association of low eGFR with sarcopenia using cystatin C compared with creatinine. These analyses were limited by the smaller sample size available, but other confounding factors could play a role, such as the effects of aging and inflammation on cystatin C production.
DEXA is a highly accurate method for assessing %TBF, because it directly measures body composition using a three-compartment model. The NHANES measurements were validated by comparison with criterion methods, including isotope dilution, underwater weighing, and air-displacement plethysmography (37). Precision is excellent, with coefficients of variation <2% reported in multiple studies (38). However, DEXA has certain limitations. Its measurements may be confounded by the presence of edema. This would have resulted in overestimation of lean mass among individuals with CKD, because participants with lower eGFR would be the most likely to have significant edema and inappropriately high estimates of ASMI. Comparisons between different machines are problematic, and serial measurements of an individual should be performed on the same scanner (39). It is relatively costly and involves minimal radiation exposure. If detailed measurements of specific body compartments are desired, computed tomography or magnetic resonance imaging may be preferable (39). Less precise methods, such as anthropometric measurements and bioimpedance analysis, are less costly and easier to implement for nonresearch purposes, and they likely assess body fat more accurately in patients with CKD than BMI (40). Measures of abdominal obesity, such as waist circumference, may also provide better prognostic information than BMI, and they are easily obtainable in clinical practice (40).
Several other limitations of our study should be noted. We used a stringent definition of sarcopenia. This emphasizes the most severe manifestation of muscle wasting but may obscure the importance of lesser but still clinically relevant loss of muscle mass among people with CKD. Had we derived cutoffs from older adults, our prevalence estimates would likely have been lower. However, the use of healthy young adults as the reference mirrors the definition of osteoporosis, another common complication of aging. Also, obesity has become increasingly common since the collection of these NHANES data. Our prevalence estimates should be viewed with this caveat. We were unable to examine misclassification of overweight individuals by BMI, because there is no well defined %TBF threshold. Finally, because this was a cross-sectional analysis, our data do not define longitudinal changes in skeletal muscle mass in the CKD population, and we cannot comment on causality between loss of kidney function and changes in body composition.
In conclusion, sarcopenia and sarcopenic obesity are highly prevalent among persons with CKD stages 3 and 4. This adversely affects the classification of obesity by BMI. Therefore, the protective effects of obesity have potentially been overstated in some studies examining the spectrum of patients from CKD to ESRD. Given this clear limitation of BMI, analyses of obesity in the CKD population should use measurements of body composition beyond BMI whenever possible.
Disclosures
None.
Supplementary Material
Acknowledgments
This research was supported by an American Society of Nephrology Carl W. Gottschalk Research Scholar Grant and Clinical and Translational Science Award Grants 1 UL1-TR001073-01, 1 TL1-TR001072-01, and 1 KL2-TR001071-01 from the National Center for Advancing Translational Sciences, a component of the National Institutes of Health (NIH).
Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Footnotes
Published online ahead of print. Publication date available at www.cjasn.org.
This article contains supplemental material online at http://cjasn.asnjournals.org/lookup/suppl/doi:10.2215/CJN.02140214/-/DCSupplemental.
See related editorial, “Misclassification of Obesity in CKD: Appearances Are Deceptive,” on pages 2025–2027.
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