1 Department of Medicine, 3 Department of Psychiatry and Behavioral Sciences and 4 School of Public Health, George Washington University Medical Center, 2 Department of Psychology, George Washington University, 5 Department of Human Development and Psychoeducational Studies and 6 Department of Medicine, Howard University Medical Center and 7 Department of Medicine, Washington Hospital Center, Washington, DC, USA
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Abstract |
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Methods. At the outset of the study, patients were assessed using demographic and anthropometric indices including arm fat area (AFA), arm muscle area (AMA), BMI, per cent ideal weight (PIW), pre-dialysis sAlb, and circulating levels of tumour necrosis factor- (TNF-
), IL-1 and IL-6. A severity index, previously demonstrated to be a mortality marker, was used to grade medical co-morbidity.
Results. Two-hundred and forty patients entered the study. The mean age was 55.1±14.3 years, mean sAlb 3.76±0.60 mg/dl, mean AFA 1742±1225 mm2, mean AMA 5464±1817 mm2, mean PIW 101.0±21.3% and mean BMI 24.9±5.6 kg/m2. PIW, BMI, AFA and AMA were, as expected, all highly correlated with one another. SAlb correlated with serum transferrin; however, neither sAlb nor serum transferrin concentration correlated with circulating cytokine levels. Circulating cytokines and sAlb did not correlate with PIW, BMI, AFA or AMA. In Cox regression analyses using multiple control variables, IL-6 predicted survival, while the anthropometric measures did not.
Conclusions. Pro-inflammatory cytokines and sAlb are robust predictors of death in patients treated with HD. PIW and BMI correlate well with other anthropometric measures in patients treated with HD, but these measures do not correlate with markers of inflammation. Anthropometric measures are poor predictors of survival compared with measures linked to the acute-phase response.
Keywords: anthropometric indices; cytokines; demographic indices; interleukins; serum albumin; survival
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Introduction |
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Nutritional factors are thought to be associated with HD patient outcome, but the best method of assessing nutrition is unclear in such patients. Pre-dialysis serum albumin concentration (sAlb) is a widely used surrogate for nutritional status [2], and predicts survival in HD patients [14]. However, pre-dialysis sAlb is influenced by factors other than nutritional status. Pre-dialysis sAlb may also be affected by interdialytic weight gain (IWG), and has been shown to be associated with levels of circulating pro-inflammatory cytokines [35]. Increased levels of circulating pro-inflammatory cytokines are associated with increased HD patient mortality [3,5]. In addition to albumin, serum transferrin concentration (sTrans) has been used as a biochemical marker of nutritional status [6,7]. Transferrin, however like albumin, is affected by the acute-phase response [7].
Other objective markers of nutrition include anthropometric measures. Anthropometry has been used since the early 1970s as a marker of nutritional status [8]. Anthropometry is an applicable method to assess nutrition, but it is time consuming and difficult to perform, and hence expensive. In addition, it is unclear whether measures of anthropometry also predict HD patient survival. Specifically, we were interested in determining whether measures of body fat and muscle mass as estimated by anthropometric assessments would predict survival in an HD patient population.
In order to determine whether body fat and muscle mass are associated with inflammation and survival in patients with ESRD treated with HD, we prospectively examined the predictive power of arm muscle area (AMA), arm fat area (AFA), body mass index (BMI) and per cent ideal weight (PIW), after controlling for multiple medical and dialytic risk factors in patients with ESRD treated with HD, with and without diabetes mellitus. We hypothesized that higher levels of muscle mass, as estimated by AMA, would predict survival independent of sAlb and levels of circulating pro-inflammatory cytokines. In contrast, we hypothesized that total body fat as measured by AFA would not be related to patient survival.
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Subjects and methods |
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Measures
Medical risk factors. Disease severity was determined by an ESRD severity coefficient that had been previously validated in a large sample of ESRD patients and in our earlier studies [3,4]. The product of the patients' age and the relative risk of additional medical illness, such as cardiovascular and cerebrovascular disease, diabetes mellitus, collagen vascular disease and malignancy, was used to derive the coefficient, an overall measure of the level of severity of the patient's renal and co-morbid chronic illnesses. The theoretical range of the severity coefficient is 05.92, which includes children with very low scores. Correlations between the severity coefficient and demographic, biochemical, treatment and nutritional parameters in our patient population have been published previously [3,4].
Nutritional and anthropometric indices. PCR, pre-dialysis sAlb and sTrans after enrolment were determined as baseline values. TSF, MAC and AMC measurements were obtained by dieticians trained and certified in the Modification by Diet of Renal Disease (MDRD) Study at George Washington University Medical Center at study entry, as described previously [3,4], using the methods outlined by Durnin and Womersley [8] and Heymsfield et al. [9]. The percentage of ideal weight (PIW) was calculated according to the reference standards obtained from healthy adults during the National Health and Nutrition Examination Surveys (NHANES II), as described previously [4]. In brief, all measurements were made after dialysis when the patient was at dry weight. The right upper arm was used whenever possible, with exceptions made for patients when dialysis access placement, injury or stroke precluded measurement. Each measurement was taken to the nearest 0.1 cm with a flexible steel tape [8]. Patients' heights and weights were determined at study entry, after an HD treatment, at dry weight. BMI, AMA and AFA were calculated from TSF, AMC, MAC and height and weight according to standard equations [10].
Circulating cytokine concentrations
Immunological variables at baseline were assessed in patients in the three dialysis units at the time of their regular monthly laboratory evaluations at study entry. Blood samples obtained before initiating HD were immediately processed for recovery of plasma. Briefly, plasma was removed, aliquoted and stored at -70°C, as described previously [3]. Also, as described previously [3], circulating cytokine concentrations [IL-1, IL-6 and tumour necrosis factor alpha (TNF-)] were detected and measured by a chemiluminescence-enhanced capture ELISA technique using specific antibodies directed against each cytokine of interest (R & D Systems, Minneapolis, MN and Bachem Biosciences, Inc., King of Prussia, PA, USA).
Dialytic treatment parameters
IWG was calculated as the patients' weight at the beginning of each HD session (pre-weight) minus the weight after (post-weight) the previous HD session, divided by the nephrologists' determined dry weight, divided by the interdialytic period in days, expressed as per cent change per day (%/d) [4]. IWG was calculated on the basis of the average of all measurements over a 3-month period, beginning with the date of study entry, accounting for 2- and 3-day interdialytic intervals. The value for dry weight was continuously updated according to nephrologists' changes in orders throughout the study, over the entire period, so the mean could be used as a single final baseline variable.
Kt/V and PCR were assessed monthly at GWUMC and HUMC, and quarterly at VAMC using the per cent urea reduction (URR), as described previously [3,4]. The dialyser used in each patient's treatment at study entry was noted and categorized as unmodified cellulose, modified cellulose or synthetic [3,4].
Statistics
Correlations between nutritional and anthropometric variables, dialytic parameters and pro-inflammatory cytokines were assessed by Pearson correlation coefficients or Spearman rank order correlation coefficients, in the cases of skewed distribution of data, as described previously [3,4]. Alternatively, when distributions of variables were skewed, log transformation was undertaken before assessment with Pearson correlation coefficients. Intra-assay and inter-assay coefficients of variation for circulating cytokine measurements (15 repetitive samples on five separate days) were <5%. Patients' individual cytokine values at first assessment and at a second evaluation 13 months later correlated highly (range 0.650.90, all P-values <0.001).
Differences between groups were assessed by unpaired t-tests, the Wilcoxon test, 2-square analysis and analysis of variance as appropriate.
Survival time for each individual patient was determined by the number of days between date of initial study evaluation and the end of the study observation period or date of death. As a further check for the validity of the survival analyses, since a substantial proportion of the patients in the study were prevalent at entry, additional analyses were performed using survival times calculated from the start of renal replacement therapy for ESRD, as described previously [3,4]. Survival status was confirmed using the Health Care Finance Administration database, obtained through ESRD Network 5 (Richmond, VA) for all patients enrolled in the study. Cox proportional hazards regression was used to predict mortality hazard. Following the results of initial bivariate Cox regressions with selected demographic and dialytic indices, regression analyses were performed in the whole HD population, in the groups of diabetic and non-diabetic HD patients, and in incident and prevalent populations with and without diabetes mellitus. The relationship between anthropometric indices and survival was examined in these groups while simultaneously controlling for the effect of variation in several predictor medical risk factors (patients' age, severity coefficient, level of sAlb, dialyser type and dialysis site) in different survival models, as in our previously reported studies. Gender, PCR and Kt/V were not included as covariates because they did not predict mortality in this population in univariate analyses [3,4]. Relative risks (RR) or hazards as outlined in text and tables represent the expected change in mortality risk associated with a 1-unit increase in parameters except for age, where 1-decade increments were used. Analyses were performed using PROC PHREG in SAS 6.12 (SAS Institute Inc., Cary, NC) using the exact method for ties. The alpha level of tests of survival and group differences was 0.05. Data are presented as mean±SD.
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Results |
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The patients' mean sAlb was 3.76±0.60 g/dl (Table 2). Mean AFA and AMA were 1742±1225 mm2 and 5464±1817 mm2, respectively. Mean PIW was 101±21.3% and mean BMI was 24.9±5.6 kg/m2. These findings suggest the patients had comparable nutritional status to a normative cohort of patients with ESRD treated with HD [11]. The patients' baseline mean PCR was 1.07±0.27 g/kg/day and mean Kt/V was 1.23±0.31. These values are also comparable to those delineated in the ESRD Core Indicator Project from the period 1992 to 1995 [4]. Of the patients, 42.4% were treated with unmodified cellulose and 57.6% with modified cellulose or with synthetic dialysers. The mean value for the severity coefficient was 2.37±1.0 (median 2.3), with a range of 0.615.92.
There were no differences between the mean age, sTrans, sAlb, Kt/V, PCR, IWG or circulating levels of cytokines of the men and women in the study (data not shown). As expected, there were differences between the genders in mean anthropometric measures. Women had greater mean PIW, BMI and AFA than men, while men had greater mean AMA (Table 3).
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AMA was correlated with age, but AFA, BMI, PIW, sAlb and sTrans did not. While severity coefficient correlated with age, there was no correlation of the severity coefficient with anthropometric indices, Kt/V, PCR, sAlb or sTrans levels (data not shown). AMA, BMI, PIW and sAlb correlated with Kt/V but AFA did not. AMA, AFA, BMI and PIW did not correlate with PCR, but sAlb did. AMA, AFA, BMI and PIW did not correlate with log TNF-, log IL-1 or log IL-6.
There was no correlation of the severity coefficient with any of the cytokines. sAlb and sTrans did not correlate with log TNF-, log IL-1 or log IL-6.
Associations with duration of ESRD therapy
Prevalent patients had higher mean sAlb than incident patients (3.92±0.46 vs 3.47±0.54 g/dl, P=0.0001). There were no significant differences in the mean measurements of AMA or AFA, between prevalent and incident patients (data not shown).
Relationships between variables were similar in the subset of incident patients, with the exception of correlations with Kt/V. Among incident patients, Kt/V correlated with sAlb (r=0.37, P=0.001), but not with AMA. In contrast, among prevalent patients, Kt/V correlated with AMA (r=-0.24, P=0.004), but not with sAlb.
Associations with gender
Correlations between parameters were similar in men and women with regard to most variables. Anthropometric parameters correlated with Kt/V in the smaller group of women, but not in the men, except for PIW and BMI, which correlated with Kt/V in the subset of men, women and the entire sample (Table 4). Notably, there were differences between the correlations of levels of circulating cytokines in the groups of men and women. AFA correlated with log IL-1 (r=0.20, P=0.01) and log IL-6 (r=0.21, P=0.01), but not with log TNF-
in the larger group of men. AMA and PIW did correlate with circulating cytokines in males. In contrast, there was no correlation of cytokines and anthropometric parameters in the smaller group of women.
Associations with diabetes mellitus
Diabetic patients were older than non-diabetic patients (mean age 59.2±11.7 vs 51.3±11.4 years, P=0.0001, data not shown). Diabetic patients had a higher PIW than non-diabetic patients (106.3±19.6 vs 97.2±21.7%, P=0.002), as well as higher BMI (data not shown). There were no significant differences in mean Kt/V or PCR between diabetic and non-diabetic patients (data not shown). The difference between mean sAlb in diabetic and non-diabetic patients reached the level of statistical significance (3.65±0.60 vs 3.84±0.46 g/dl, P=0.03). There was no significant difference between mean AMA in patients with and without diabetes (5660±1484 vs 5316±2025 mm2, respectively). AFA differed between non-diabetic and diabetic patients (1477±1163 vs 2097±1223 mm2, P=0.0001).
Survival analyses
Mean and median follow-up times were 39.3±17.4 and 44 months, respectively. During the observation period, 38.8% of the study population died. The effects of variation in AMA, AFA, BMI and PIW were tested in various models. Patients' age, severity coefficient, level of sAlb and dialyser type and site were first entered in Cox regression equations in a series of different combinations, and thus controlled for, prior to analysis (Table 5). In the whole HD population and in subsets of diabetic and non-diabetic and incident and prevalent patients (the latter not shown), variation in AMA, AFA, BMI and PIW were not associated with a significant increase in relative mortality risk, when variations in age, severity of illness, sAlb and dialyser type and site were controlled (Table 5
). The confidence intervals for the relationships between the anthropometric variables and survival suggest that study size was not critical in our inability to link these parameters with differential mortality in the patient population.
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Discussion |
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The signs of malnutrition in HD patients include reduced energy stores (subcutaneous fat stores) and lower total body lean-muscle mass as estimated by anthropometric methods, low total body nitrogen determined by in vivo neutron activation analysis, and low serum concentrations of albumin, transferrin and other visceral proteins [6]. Because serum pre-albumin, serum albumin and serum transferrin concentrations are acutely affected by inflammation and nitrogen balance, physical examination and anthropometric measures are types of non-biochemical objective measurements that are utilized to evaluate nutrition [2,6]. Composite measures like the Subjective Global Nutritional Assessment (SGA), a technique incorporating medical history and physical examination, have been employed as nutritional measurement tools [12]. In 1998, Qureshi and colleagues [13] showed that HD patients with low SGA values had biochemical and anthropometric indices of malnutrition. In other small studies, malnutrition, assessed by lower anthropometry scores in conjunction with diminished levels of circulatory biochemical markers of nutrition, predicted mortality [14]. However, the role of anthropometry in predicting survival independently has not been extensively studied. We selected the anthropometric measures AMA and AFA because of their frequent use as surrogates for, and correlations with, total body skeletal muscle and total body fat mass, respectively [9,10]. Marcén and colleagues [15] evaluated prevalent patients treated by HD with anthropometric measures and biochemical markers of nutrition. In that study, the investigators found, as we did, that anthropometric measures and BMI did not predict survival. Similarly, in the present study, anthropometric measures, which correlated well with PIW and BMI, did not predict survival.
The role of inflammation in patients treated with HD has also been studied extensively. Several groups [5,16,17] have shown a correlation of lower sAlb and biochemical markers associated with inflammation. We were unable to show an association between lower sAlb and the pro-inflammatory cytokines. Like albumin, elevated levels of circulating pro-inflammatory cytokines have also been shown to predict outcomes [3,5]. In the present study we have shown that IL-6 levels independently predict survival when the variation in other mortality markers is controlled. Since individual cytokines with multiple synergistic and antagonistic actions affect different cellular targets, particular cytokine combinations may have unpredictable effects on various cellular and organ systems, leading to differential outcomes. Contrary to our hypothesis, our study showed that although IL-6 predicted survival, anthropometric variables did not. Differences between our findings and those of others must be assessed by studies of larger and more diverse samples, recruited under standardized conditions.
There is evidence that higher BMI confers a survival advantage [18,19]. Fleischmann et al. [18] showed a relationship between BMI and survival in a primarily AfricanAmerican population. In that study, however, there were fewer diabetic and female patients than in our population. Kopple et al. [19] showed that higher levels of weight normalized for height ratios were associated with survival in a population with significantly fewer African-Americans. In comparison to the USRDS data [1], our population is slightly younger, but the mean weights in the two populations are comparable. However, in contrast to these aforementioned studies, our population of primarily African-American subjects had an overrepresentation of men. In an attempt to assess the differences between outcomes in all these populations, we evaluated the relationship between PIW and BMI and survival in subsets of incident and prevalent patients with and without diabetes mellitus, a strategy that has successfully delineated groups at differential risk [4]. There was, however, with one exception, no relationship between PIW or BMI and survival in any of the models we analysed using various co-variates. PIW predicted survival in prevalent non-diabetic females. There may be different relationships between pro-inflammatory factors and anthropometric measures in different populations, perhaps related to dialyser membrane type, medications used, treatment times, water delivery and treatment systems, or patient-specific factors such as diet or psychosocial status. In fact, we were able to show in this population that in the smaller subset of women, there were no correlations of cytokines and anthropometric measures, while there were significant correlations of these values in men. Such potential differences between the genders deserve further study.
It is not clear why PIW or BMI did not predict outcomes more generally in this study. There are several characteristics of our work that should be considered in interpreting the results. Although the number of patients in our study is low for a clinical trial, it is large for an observational study with specialized measurements. However, there are possible survivor biases in our study, since not all patients were recruited at inception of ESRD therapy. We have attempted to deal with this by performing all Cox regressions in incident and prevalent populations, and by performing analyses in patients with survival time starting at study entry, as well as starting from the time of entry into ESRD programmes. There were no differences in the conclusions to be made from any of these supplementary analyses. In addition, there was no difference between the proportion of race, gender or patients with diabetes in the refuser population compared with those who entered the study. The mean age and proportion of patients with diabetes in our study is comparable to that of the US ESRD programme [1]. Our sample size has, however, been adequate to show associations with differential mortality and age, severity of illness, levels of pro-inflammatory and T-cell regulatory cytokines, compliance with the dialysis prescription, psychosocial parameters and dialyser type, but not gender, PCR or Kt/V. It is, however, possible that limitations in sample size prevented us from delineating associations of anthropometric measures and survival. We were unfortunately not able to assess associations between anthropometric measures and hospitalizations in this study.
Interestingly, in our population of African-American patients, there was no relationship of PIW or BMI and levels of pro-inflammatory cytokines, which may explain our findings relative to those in other studies. Inclusion of a Veterans Administration site hampered our recruitment of women. We could not show differences in levels of cytokines between men and women in this study. In analyses, the mean levels in men and women were almost identical (data not shown). Yet we were able to show expected differences in anthropometric measures between men and women (Table 3). Differences in the relationships between cytokines and anthropometric parameters between the genders may be an area of future study.
In the entire population, we showed, as expected, that anthropometric measures are negatively correlated with Kt/V. The relationship between Kt/V and PIW or BMI may be altered at extremes of patient weight. Kt/V, however, was not a criterion value in this study, since we were unable to demonstrate associations with survival, as shown in other studies.
Unfortunately we did not assess SGA in this study. Future studies might assess SGA and its components in relation to specific cytokines. Currently, there is no technique to assess clinically the net contribution of inflammation and nutritional states to sAlb, sTrans and other biochemical markers that are associated with both inflammation and nutrition. In addition, sAlb has been shown to correlate poorly with anthropometric measures in chronic HD patients [20].
These findings must be confirmed in a larger sample of patients, with more representative distribution of subjects of different ethnic backgrounds and gender. We conclude that anthropometric indices do not correlate with markers of inflammation, and therefore are poorer survival predictors. Anthropometric measures may not be as reliable a measure of nutritional status in patients treated with HD as in the general population. Specifically, patients treated with HD are routinely subjected to large fluid shifts, and have metabolic derangements stemming from the uraemic milieu and from the presence of inflammation. Perhaps other objective measures of nutritional status such as bioimpedance, dual-energy X-ray absorptiometry or neutron activation analysis may provide better and more consistent assessments of nutrition, and therefore outcomes, in patients treated with HD. Alternatively, longitudinal assessments of anthropometric measures in ESRD patients treated with HD may provide more predictive power related to clinically important outcomes.
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Notes |
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References |
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