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American Journal of Clinical Nutrition, Vol. 83, No. 3, 543-549, March 2006
© 2006 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Association of body size with health status in patients beginning dialysis1,2,4

Kirsten L Johansen, Nancy G Kutner, Belinda Young and Glenn M Chertow

1 From the Division of Nephrology, San Francisco VA Medical Center, San Francisco, CA (KLJ); the Division of Nephrology, University of California, San Francisco Medical Center, San Francisco, CA (KLJ, GMC, and BY); the Departments of Medicine and of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA (KLJ and GMC); and the Department of Rehabilitation Medicine, Emory University, Atlanta, GA (NGK)

2 The authors are responsible for the content of this article. The article does not represent government policy.

3 Supported by contract N01-DK-1-2450 from the National Institutes of Health, National Institute of Diabetes, Digestive and Kidney Diseases.

4 Reprints not available. Address correspondence to KL Johansen, Division of Nephrology, 111J, San Francisco VA Medical Center, 4150 Clement Street, San Francisco, CA 94121. E-mail: johanse{at}itsa.ucsf.edu.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Greater weight-for-height has been associated with prolonged survival in patients with end-stage renal disease (ESRD) but not in the general population. The association between body size and health status has not been carefully evaluated.

Objectives: We compared the self-reported health status of 2467 participants in the Dialysis Morbidity and Mortality Study Wave 2 by using body mass index (BMI; in kg/m2) to approximate body size and composition.

Design: BMI was categorized into 4 groups (<19, 19 to <25, 25 to <30, and ≥30) corresponding to World Health Organization criteria for underweight, normal-weight, overweight, and obese status. We adjusted for demographic, clinical, and laboratory factors that may have confounded the association between body size and health status.

Results: Scores on the physical component summary and the physical functioning scale were significantly lower for obese subjects than for those with normal weight or moderately high BMI after adjustment for demographic factors, comorbidity, and laboratory markers of nutritional status. Mental component summary and symptom scores were unrelated to BMI. The underweight group scored lower on many Medical Outcomes Study 36-Item Short Form scales than did the normal-weight group.

Conclusions: Whereas higher BMI has consistently been associated with enhanced dialysis-related survival, health status—particularly physical function—may be impaired by obesity. Additional longitudinal studies of body weight and composition are needed for a better understanding of the complex effects of obesity and undernutrition in persons with ESRD and advanced chronic kidney disease.

Key Words: End-stage renal disease • dialysis • body mass index • quality of life • physical functioning • Medical Outcomes Study 36-Item Short Form • SF-36


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
It has been established through epidemiologic studies that obesity is associated with improved survival in patients with end-stage renal disease (ESRD; 1–7). This finding is distinctly different from the associations found in the general population, where being either overweight or underweight is associated with poorer survival (8–10). The apparent lack of a detrimental effect of excess adiposity is surprising because cardiovascular disease (CVD) is highly prevalent in dialysis patients and is the most common cause of death in that population. However, other traditional cardiac risk factors, such as hypertension (11) and hypercholesterolemia (12), are not associated with higher mortality in dialysis patients as they are in the general population.

Some have even suggested that patients with ESRD who are obese should not be counseled to lose weight or that attempts should be made for normal-weight patients to gain weight (13). However, it is important to consider that the paradoxical association of body mass index (BMI; in kg/m2) in dialysis patients has only been described for survival. High BMI is associated with poor health status in the general population (14, 15), and it is not known whether the association between BMI and quality of life follows the expected or the paradoxical pattern in dialysis patients. The limited survival of this population—>65% of incident patients die within 5 y (16)—makes quality-of-life considerations particularly pressing.

To investigate the association between BMI and health status, we used United States Renal Data System (USRDS) data from the Dialysis Morbidity and Mortality Study (DMMS) Wave 2 Special Study. In that study, incident dialysis patients were queried about their health status using the Kidney Disease Quality of Life (KDQOL) questionnaire. We hypothesized that high BMI would be associated with worse scores on the physical functioning (PF) scale and on the physical component summary (PCS) but not on the mental component summary (MCS) of the Medical Outcomes Study 36-Item Short Form (SF-36) or on kidney disease–specific symptoms.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Study population
Data were obtained from the standard analytic files of the USRDS. We used data from the DMMS Wave 2 file. The DMMS was an observational study in which data on demographics, comorbidity, laboratory values, treatment, socioeconomic factors, and insurance were collected from a random sample of US dialysis patients with the use of dialysis records. Wave 2 was a prospective study of incident hemodialysis and peritoneal dialysis patients in 1996 and of some incident patients entering the ESRD program in the first part of 1997 (17).

The current study was approved by the Committee on Human Research of the University of California, San Francisco. The original study was a Centers for Medicare and Medicaid Services (formerly the Health Care Financing Administration)–mandated project that enrolled patients from dialysis units across the country. Return of patient material was considered to constitute consent. The current study did not involve any patient contact, and only de-identified data were used.

Predictor variable
Data abstractors were instructed to record patients' height from the medical record from any time during adult life. If height information was unavailable from the medical record, they were instructed to ask the patient or measure the patient's height. The prescribed dry weight was recorded. If no prescribed dry weight was available, the postdialysis weight nearest to the dialysis start date was used. BMI was calculated and divided into categories of <19, 19 to <25, 25 to <30, and ≥30, which were chosen to conform to the World Health Organization's classifications of underweight, normal-weight, overweight, and obesity, respectively (18).

Health status
The KDQOL instrument was administered to all patients in the DMMS Wave 2 (19). The questionnaire includes 36 items (RAND-36; Rand Corporation, Santa Monica, CA) that are identical to those included in the SF-36. These items were designed for use across diverse populations and health care settings, and they include 8 scales of self-reported health status: PF, role-physical (RP), bodily pain (BP), general health (GH), vitality, social functioning (SF), role-emotional (RE), and mental health (MH) (20). RP and RE refer to problems with work or other daily activities that result from physical health or emotional problems, respectively. These scales are scored from 0 to 100, and the higher scores indicate better function. Two normalized scores representing overall physical (PCS) and mental (MCS) functioning are calculated by using the dimensions related to physical and mental functioning (21). In addition, the KDQOL includes a symptom score that incorporates information related to several kidney disease–specific symptoms. The SF-36 and KDQOL have been used extensively in the dialysis population (22–31). We scored the instrument according to the recommendations of its developers (32, 33).

Statistical analysis
Baseline characteristics for those in the Wave 2 study with and without SF-36 data were compared with the use of unpaired t tests for continuous variables and chi-square tests for categorical variables. Baseline patient characteristics by BMI category were compared by using ANOVA for continuous variables and the Cochran-Armitage Trend test for dichotomous variables. SF-36 scores by BMI category were compared by using analysis of variance (ANOVA). When the overall ANOVA was statistically significant, pairwise comparisons to the referent group were conducted with Dunnett's test.

Multiple linear regression was used to ascertain whether categories of BMI were independently associated with health status and, if so, to explore the structure of that association. The reference BMI category for all analyses was 19–<25, which corresponds to normal body weight. The models were adjusted for demographic, clinical, and laboratory predictor variables. Demographic variables included age, sex, race (white, African American, Asian or Pacific Islander, or other), ethnicity (Hispanic or other), employment status, and marital status. Clinical variables included dialysis modality, tobacco use (yes or no), and the presence or absence of the following comorbidities: coronary artery disease, diabetes, cerebrovascular disease, peripheral vascular disease, amputation, and cancer. Laboratory variables measured before the initiation of dialysis included serum albumin and creatinine concentrations and hematocrit. Outcome variables included the PCS and MCS scores, the PF scale of the SF-36, and the symptom score (kidney disease–specific symptoms).

Missing data for serum creatinine (n = 175, or 4.7%) were replaced with the mean. Missing hematocrit values were replaced with 3 times the hemoglobin concentration if hemoglobin was available. The mean value of hematocrit was used for subjects whose data for both hemoglobin and hematocrit were missing (n = 100, or 2.7%). Because serum albumin concentrations were missing for a substantial number of patients (n = 353, or 9.5%), albumin was entered into the models as a categorical variable by using quartiles of serum albumin and by adding a category labeled "missing" (<3.1, 3.1–<3.5, 3.5–<3.8, ≥3.8, and missing).

To ascertain whether the association between BMI and health status was reverse J-shaped and to test for possible interactions, a linear regression model was performed with BMI and BMI2 as linear predictor variables and with multiplicative interaction terms for sex, race, and dialysis modality with BMI. To confirm associations described in larger datasets (1–7, 22, 31, 34), we used logistic regression analysis to ascertain the odds ratio (OR) of death and 95% CIs associated with categories of BMI and with PCS and MCS scores in the Wave 2 cohort. All statistical analyses were performed with the use of SAS software (version 8.2; SAS Institute, Cary, NC).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
A total of 4024 patients were enrolled in the DMMS Wave 2 study. Of these, 3731 were aged > 18 y, and their data for height and weight were neither missing nor more extreme than <1st percentile for Asian Americans or >99th percentile for white Americans. Health status data were available for 2467 of these patients. The average age and BMI of patients with and without health status data did not differ significantly, but the racial distribution did: significantly more whites (64.7% and 60.0%, respectively; P = 0.004) and significantly fewer Asians or Pacific Islanders (2.2% and 4.1%, respectively; P = 0.004) than African Americans had available health status data. Patients who completed the SF-36 questionnaire also were significantly less likely to have diabetes than not to have diabetes (47.7% and 51.4%, respectively; P = 0.03) and significantly less likely to be on peritoneal dialysis than on hemodialysis (47.4% and 52.7%, respectively; P = 0.002).

The characteristics of the group with health status data by BMI category are shown in Table 1Go. Compared with those in the normal-weight category, obese patients were younger and more likely to be female, African American, and employed and to have diabetes. Obese patients tended to be less likely to have a diagnosis of coronary artery or cerebrovascular disease or to smoke (P for trend = 0.10).


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  		TABLE 1 Patient characteristics by BMI category1

 
Unadjusted KDQOL scores are shown in Table 2Go. As expected, dialysis patients had much lower PCS scores than did the general population, but the former were much less impaired on measures related to mental health than on measures related to physical health (26, 28, 35). The underweight group scored lower on all scales than did the normal-weight group, although the difference was not always statistically significant. The scores on the KDQOL scales did not differ significantly between overweight and normal-weight subjects. However, there was a trend for obese subjects (BMI ≥ 30) to score lower than normal-weight subjects on the PCS scale, the PF scale, and the BP scale. Obese subjects did not score significantly lower on the scales related to MH, including the MCS, or on the symptom scale.


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  		TABLE 2 Kidney Disease Quality of Life instrument scores by BMI category1

 
Linear regression analyses showed that obesity was related to lower quality of life on the PCS but not the MCS scale after adjustment for demographic, clinical, and laboratory measures (Table 3Go, Figure 1Go, and Figure 2Go). As expected, the lower score on the PCS was mainly driven by limitations in self-reported physical functioning, which was 4.06 ± 1.45 points lower in the obese group than in the normal-weight group (Table 3Go). BMI was not associated with kidney disease–specific symptoms (Table 3Go). In general, women reported poorer physical functioning than did men.


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  		TABLE 3 Multiple regression model for Kidney Disease Quality of Life instrument scores1

 

Figure 1
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  		FIGURE 1. Unadjusted and adjusted physical component summary (PCS) scores by BMI category (in kg/m2; n = 2316). PCS scores were compared across BMI categories by using ANOVA with and without adjustment for covariates. {circ}, Unadjusted mean PCS scores; {blacksquare}, adjusted means. Error bars represent 95% CIs for the adjusted means.

 

Figure 2
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  		FIGURE 2. Unadjusted and adjusted mental component summary (MCS) scores by BMI category (in kg/m2; n = 2316). MCS scores were compared across BMI categories by using ANOVA with and without adjustment for covariates. {circ}, Unadjusted mean MCS scores; {blacksquare}, adjusted means. Error bars represent 95% CIs for the adjusted means.

 
To confirm the visual impression that PCS scores follow a curvilinear distribution across BMI categories (Figure 1Go) and to test whether the association of BMI with PCS was similar across sex and race categories, we performed a linear regression analysis with BMI and BMI2 as linear predictor variables and with interaction terms for sex, race, diabetes, and dialysis modality. In this model, BMI and BMI2 terms were statistically significant (P < 0.0001), which confirmed the reverse J-shaped relation. There were no significant interactions between BMI and sex (P = 0.68), race (African American compared with non-African American; P = 0.89), diabetes (P = 0.80), or dialysis modality (P = 0.10).

To confirm associations described in larger datasets, we ascertained the RR of death associated with categories of BMI in the Wave 2 cohort. When the BMI 19 to <25 category was used as the referent group, the OR of death was 1.15 for the group with BMI <19 group (95% CI: 0.72, 1.84), 0.46 for the group with BMI 25–<30 (0.30, 0.69), and 0.41 for the group with BMI >30 (0.25, 0.68) (P for trend < 0.001). The associations between PCS and MCS scores and survival were also examined. After adjustment for BMI category and demographic and clinical variables, a 1.0-point increase in PCS score was associated with a 4% decrease in the odds of death (OR: 0.96; 95% CI: 0.94, 0.98) and a 1.0-point increase in MCS score was associated with a 3% decrease in the odds of death (OR: 0.97; 95% CI: 0.96, 0.99).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
In this national sample of incident dialysis patients, obese patients reported significantly lower PF than did their normal-weight counterparts after adjustment for demographic, clinical, and laboratory variables associated with physical functioning. This result is similar to findings for the general population (14, 15, 36). Whereas the differences in health status between groups categorized by BMI were relatively small, the magnitude of the difference in mean PCS score associated with obesity in this cohort was similar to the difference associated with a 5-y increase in age in the general population, and the difference in the PF scale was comparable to the difference associated with a 10-y increase in age (37). Thus, the association between BMI and health status (particularly physical functioning) does not show a paradoxical association within the dialysis population, as does the association of obesity and survival. Obesity was not associated with significantly poorer self-reported mental health or with significantly greater kidney disease–specific symptoms.

Although it seems logical that obesity was associated with poorer physical functioning, this finding must be reconciled with the improved survival associated with both higher BMI (1, 3, 5–7, 13) and higher PCS scores (22, 31, 34) in dialysis patients. Specifically, obesity was associated with a 59% reduction in mortality, and a 1.0-point increase in PCS was associated with a 4% increase in survival. Adiposity may be the mediator of this paradox. Our analysis of a large cohort of incident dialysis patients suggests that higher body fat per se is associated with improved survival (1). On the other hand, increased fat mass would not be expected to improve physical functioning and may in fact have a direct negative effect on functioning. It is possible that additional fat stores are protective against the catabolism and inflammation that have been associated with ESRD (1, 13). When persons are subjected to the specific stressors associated with ESRD, the protective effect of fat stores may outweigh the other effects of high BMI on survival, including worse physical functioning and a higher incidence of diabetes.

Whereas available evidence suggests that greater adiposity and greater lean body mass (LBM) are both associated with improved survival in dialysis patients (1, 38), it is likely that these components of body composition exert the opposite effect on physical functioning. Greater LBM is expected to accompany greater adiposity in obese persons (39), and the higher serum creatinine in the absence of higher BUN observed in the obese patients in this cohort is consistent with greater LBM. In turn, greater LBM could be expected to be associated with better physical functioning. Our analysis showed a trend toward association between higher serum creatinine and higher PCS and PF scores (Table 3Go), and adjustment for factors including serum creatinine strengthened the association between obesity and poor physical functioning. Thus, our data are consistent with a beneficial effect of greater LBM and a detrimental effect of greater fat mass on physical functioning in patients on dialysis.

We observed that women reported poorer physical functioning than did men, but the 2 groups had similar mental functioning. In addition, we observed some associations between ethnicity and health status, most notably that Hispanic patients had higher PCS scores but lower MCS scores than did other racial-ethnic groups. These findings are similar to recently published reports of health status in other dialysis populations. For example, Chiang et al (29) recently reported on the health status of hemodialysis patients in Taiwan. Overall, this population had a slightly higher PCS than did the US population we studied, and the influence of body size was not investigated. However, Chiang et al reported lower PCS but not lower MCS scores in women than in men, which is similar to our findings. Analysis of health status in participants in the Hemodialysis (HEMO) Study also showed significantly lower PCS but not significantly lower MCS in women than in men (28). The finding of lower PCS in women than in men also appears to be true for the general population (14). Han et al (14) investigated the relation between BMI and PCS and found that, although women had significantly lower PCS scores than did men, the influence of high BMI was similar in the sexes. Finally, Lopes et al (27) investigated the influence of ethnicity on the health status of US participants in the Dialysis Outcomes and Practice Patterns Study (DOPPS). They reported that MCS and PCS scores were significantly higher in African Americans than in whites. In addition, Hispanic patients had significantly higher PCS scores but lower MCS scores than did whites. Whereas we did not observe significantly higher PCS scores in African Americans than in other racial groups, these results are otherwise similar to our findings.

Large-scale interventional trials of dialysis dose, such as the HEMO Study, have failed to improve survival of hemodialysis patients, despite good separation of the dosage groups (40). It seems unlikely, therefore, that a weight-gain intervention would be a success in this population, because it would be much more difficult to implement such an intervention than to raise the dose of dialysis. On the other hand, given that the median survival of patients beginning dialysis is low—on the order of 2.5 y (16)—it is important to consider quality of life in this group. The association of obesity with poorer health status, and the possibility of worse transplant outcomes in this group (41–45), is a potential reason to counsel those dialysis patients who are obese to reduce body fat. Dialysis patients who are overweight but not obese may be better off not losing weight because survival may be compromised by such a strategy, and our results suggest that quality of life is not adversely affected by modestly excessive body weight.

Although the focus of our hypotheses was the association of obesity with health status, it is worth noting the findings for other categories of BMI. Specifically, both extremes of body size were associated with poorer quality of life, and overweight patients did not have worse quality of life than did normal-weight subjects. Underweight patients scored significantly worse than did obese patients on almost all of the KDQOL scales in unadjusted analysis and on the PCS and PF scores in multivariate analyses. Thus, whereas underweight patients had a significantly higher incidence of some comorbid conditions that could be associated with lower quality of life, there was a persistent association between reduced adiposity and poorer health status after adjustment for comorbid conditions. Furthermore, the effect of underweight on physical functioning was greater than that of obesity. Overweight had no apparent detrimental effect on health status in this cohort. In fact, the trend was toward better scores on all scales within the BMI 25 to <30 category. This is somewhat different from results in the Nurses' Health Study, which found a dose-response relation with progressively lower PF scores with increasing BMI above the normal range (46). Thus, it is possible that ESRD modifies the relation between physical functioning to some extent but that a full paradoxical association does not exist.

This study was limited by the quality of the height and weight data used to calculate BMI. The methods of measuring height (relying on the medical record or patient recollection) and weight (prescribed dry weight) were imprecise, and it is unclear whether the dry weight of peritoneal dialysis patients included or excluded dialysate. Furthermore, conditions such as amputation and polycystic kidney disease can affect the interpretation of BMI. However, this imprecision would generally serve to minimize any differences in health status related to BMI. Furthermore, exclusion of patients with amputation did not alter the results (data not shown). Because the DMMS Wave 2 database contains information only about the presence or absence of comorbid conditions, the results of the current study could be affected by residual confounding that is due to the severity of comorbid conditions or to undiagnosed disease. Thus, it is possible that obese patients had worse PF scores because they had more severe coronary artery disease or a higher proportion of undiagnosed coronary artery disease, for example, than did normal-weight and overweight patients. Such residual confounding, if present, would not negate the general conclusion that physical functioning is worse in obese patients. However, this possibility highlights our inability to conclude that adiposity per se is the cause of the association of obesity with poorer physical functioning.

In conclusion, we showed that, whereas high BMI is associated with improved survival in patients on dialysis, patients with BMI in the range considered obese by the World Health Organization have poorer health status within domains related to physical functioning, as is true for the general population. Thus, it is inappropriate to assert that BMI exhibits a full paradox in dialysis patients. Additional longitudinal studies of body weight and composition are required to better understand the complex effects of obesity and undernutrition in persons with ESRD and advanced chronic kidney disease. For now, recommendations regarding dietary intake and weight gain or loss after starting dialysis should be individualized.


   ACKNOWLEDGMENTS
 
KLJ contributed to the design of the study, analysis of data, and writing of the manuscript. NGK contributed to the design of the study and provided advice and consultation on the writing of the manuscript as well as final review and approval. BY contributed to the analysis of the data and reviewed and approved the final manuscript. GMC contributed to the design of the study, data analysis, and writing of the manuscript. None of the authors had a personal or financial conflict of interest.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication August 18, 2005. Accepted for publication November 22, 2005.




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