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Body shape index and mortality in hemodialysis patients
Nutrition 29 (2013) 1214–1218
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Nutrition journal homepage: www.nutritionjrnl.com
Applied nutritional investigation
Body shape index and mortality in hemodialysis patients Baris Afsar M.D. a, *, Rengin Elsurer M.D. b, Alper Kirkpantur M.D. c a
Department of Medicine, Division of Nephrology, Konya Numune State Hospital, Konya, Turkey Department of Medicine, Division of Nephrology, Selcuk University, Faculty of Medicine, Konya, Turkey c Department of Medicine, Division of Nephrology, Diskapi Training and Research Hospital, Ankara, Turkey b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 12 December 2012 Accepted 8 March 2013
Objective: The relationship between various anthropometric parameters and mortality in hemodialysis (HD) patients is conflicting. Recently a new anthropometric parameter emerged, namely, body shape index (BSI). BSI is based on waist circumference (WC) but is independent of height, weight, and body mass index in predicting mortality in the general population. The aim of this study was to determine the relationship between BSI and mortality in HD patients. Methods: This retrospective study evaluated the demographic characteristics and anthropometric measures including BSI, laboratory parameters, and mortality data in HD patients in a single center. Results: There were 142 HD patients enrolled in the study. The median BSI was 0.0816. Because no normal value was defined for BSI, the patients were divided into two groups based on the median BSI: group 1 BSI < 0.0816 and group 2 BSI > 0.0816. During an average follow-up period of 40.1 19.2 mo (range 12–88 mo), 36 (25.4%) patients had died. The Cox regression analysis of independence showed that increased age (hazard ratio [HR], 1.077, 95% confidence interval [CI],1.031– 1.125; P ¼ 0.001), presence of diabetes (HR, 2.855, 95% CI, 1.258–6.481; P ¼ 0.012), hemoglobin (HR, 0.629, 95% CI, 0.452–0.875; P ¼ 0.006), and albumin (HR, 0.442, 95% CI, 0.204–0.955; P ¼ 0.038) were independently related with mortality. None of the anthropometric parameters including BSI were related with mortality. Kaplan-Meier analysis showed that there were no differences with respect to mortality among patients in group 1 and group 2 based on median BSI (P ¼ 0.332, logrank test). Conclusion: In conclusion, BSI is not independently associated with mortality in HD patients. Ó 2013 Elsevier Inc. All rights reserved.
Keywords: Body mass index Body shape index Dialysis Waist circumference Waist to hip ratio Mortality
Introduction In a general population without kidney disease, various studies have shown that body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) were all associated increased mortality [1–4]. In patients with chronic kidney disease (CKD), there are relatively few studies, with contrasting findings, that analyze the prognostic value of obesity measures including BMI, WC, and WHR with mortality. For example, some studies showed that there was an inverse relationship between BMI and all-cause mortality in hemodialysis (HD) (explained within the context of reverse epidemiology) [5–7]; others studies did not find any association between BMI and mortality [8]. There also are racial and ethnic differences regarding the effect of anthropometric measures. It was suggested that * Corresponding author. Tel.: þ(90) 332 235 45 00; fax: þ(90) 332 235 67 86. E-mail address: [email protected] (B. Afsar). 0899-9007/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.nut.2013.03.012
community racial/ethnic composition is an important correlate of risk for obesity, but the relationship differs greatly by individual race/ethnicity. To better understand the obesity epidemic and related racial/ethnic disparities, more must be learned about community-level risk factors, especially how environment and social norms operate within communities and across racial/ ethnic groups [9]. Indeed, various studies have shown that there are important ethnic group differences with regard to BMI and WC [10–12]. There also are studies showing that abdominal obesity as reflected by higher WC was associated with increased mortality in HD patients [6,13,14]. Indeed it was speculated that because BMI did not discriminate fat and muscle mass [15,16] and WC (which was closely related with mortality in HD patients) has been found to be more closely related with mortality compared with BMI [17–19], WC could be used as an alternative to BMI [20]. However abdominal fat distribution as reflected by WC is also sensitive to body size (height and weight) as well as to fat percentage and distribution [21]. Additionally, WC is highly correlated with BMI to the extent that it is difficult
B. Afsar et al. / Nutrition 29 (2013) 1214–1218
to differentiate the two as epidemiologic risk factors [22]. According to a consensus statement on the clinical usefulness of WC, further studies are needed to establish WC cut points that can assess cardiometabolic risk, independent of BMI in routine clinical assessments [23]. To avoid these drawbacks regarding the BMI and WC, a new anthropometric parameter, the body shape index (BSI), has emerged. The BSI is based on WC, but is independent of height, weight, and BMI in predicting mortality. The investigators speculated that the mortality risk for a given BMI also is affected by BSI particularly as a marker of abdominal fat deposits [21]. A recent study also demonstrated that BSI was better related with systolic and diastolic blood pressures than BMI and WC [24]. With this background in mind, BSI also may be related with mortality in HD patients. However, to the best of our knowledge, the specific relationship between BSI and mortality was not studied previously in HD patients. Thus, the current study evaluated the specific relationship between BSI and mortality apart from BMI, WC, and WHR in HD patients.
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Table 1 Etiologies of end-stage renal disease of 142 hemodialysis patients Etiology of renal failure
Patients
Diabetes mellitus Hypertension Glomerulonephritis Amyloidosis Vesicouretheral reflux Nephrolithiasis Polycystic kidney disease Ischemic nephropathy Contrast nephropathy Analgesic nephropathy Unknown
41 32 15 7 10 4 3 2 1 1 26
the comparison of categorical variables; c2 test or Fisher’s exact test was used as appropriate. For the correlation analysis, Pearson correlation coefficient r was used. The Cox proportional hazards model was used to identify independent factors predicting occurrence of mortality. Actuarial survival rates were determined by the Kaplan-Meier method. A log–rank test was used to compare the different survival curves.
Materials and methods A retrospective cohort study was performed to investigate whether BSI was related with all-cause mortality in chronic HD patients who were treated at the dialysis unit of a state hospital. The dialysis prescription in our study included 4 to 5 h of HD three times a week for all patients with blood flow rates of 300 to 400 mL/min, using a standard bicarbonate dialysis solution. Urea kinetic modeling single-pool Kt/V (spKt/V) was performed to assess the delivered equilibrated dose of dialysis. Demographic data including age, sex, height, weight, BMI, WC, hip circumference, vascular access type, duration of dialysis, smoking status, etiology of kidney disease, history of cardiovascular and cerebrovascular disease, presence of hypertension and diabetes mellitus, and presence of previous transplantation history were collected from our hospital database and from outpatient dialysis unit records, when appropriate. The cohort study did not include any human or animal experimentation for which ethical approval was required. Because our analysis used existing and retrospective data, there were no risks posed to human life. The clinical status of each patient was evaluated by means of a routine clinical examination before each regular HD session. The dry weight of each patient was individually determined on the basis of post-HD cardiothoracic ratio without clinical symptoms of hypotension or muscle cramps. Blood pressure (BP) was measured with a mercury sphygmomanometer after the patient had rested in the supine position for 10 to 15 min, and the mean of the values were measured at each session used in the analysis. Coronary artery disease was defined as the presence of a previous myocardial infarction, angina pectoris, or coronary revascularization procedure. Cerebrovascular disease was defined as the presence of previous stroke, transient ischemic attack, and carotid revascularization procedure. Exclusion criteria included the presence of limb amputation, history of cancer, history of renal transplantation, transfer to other center, and lack of demographic and laboratory data. Laboratory parameters including serum glucose, hemoglobin, albumin, high-sensitive C-reactive protein (hs-CRP), predialysis calcium and phosphorus, predialysis blood urea nitrogen (BUN) and creatinine, total cholesterol, highdensity lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, and intact parathyroid hormone (PTH) were obtained from charts. Postdialysis serum urea nitrogen levels, used to calculate the urea reduction ratio, were also measured. BMI, WC, and hip circumference were also obtained from charts. WHR was determined by dividing WC (cm) by hip circumference (cm). The BSI was calculated by the following formula: BSI: WC/BMI2/3 height1/2 [21]. The participants were followed from 2005 to late December 2010 to determine the incidence of death. Participants who died were censored at the time of death, otherwise participants were censored as of their last follow-up visit. The causes of death also were reviewed from the medical records. Statistical analysis Statistical analysis was performed with SPSS software ver. 15.0 (SSPS, Chicago, IL, USA). Results were considered statistically significant if the twotailed P-value was < 0.05. The normality of the data was evaluated by the Kolmogorov-Smirnov test. Comparisons of the groups were assessed by means of the Student’s t test for normally distributed continuous variables and by the Mann–Whitney U-test for non-normally distributed continuous variables. For
Results Initially, 193 patients were enrolled. One patient had lung cancer, two patients had limb amputation, four received renal transplantation, and three transferred to other center; all were excluded. The demographic and laboratory data was absent in 41 patients. The final analysis was based on 142 HD patients. The etiologies of the renal failure are given in Table 1. The baseline demographic and clinical characteristics of the 142 HD patients were shown in Table 2. The laboratory parameters were shown in Table 3. For the entire group, Pearson correlation analysis showed that BSI was correlated with BMI (r, –0.442; P < 0.0001), WC (r, 0.406; P < 0.0001), hip circumference (r, 0.389; P < 0.0001), WHR (r, 0.221; P < 0.0001), calcium (r, –0.294; P < 0.0001), and hs-CRP (r, 0.214; P ¼ 0.012). The median BSI was 0.0816. Because no normal value was defined for BSI, the patients were divided into two groups based on median BSI: group 1 BSI < 0.0816 and group 2 Table 2 Baseline demographic and clinical characteristics of the 142 hemodialysis patients
Age (y)* Sex (M/F) Hemodialysis duration (mo)* Body mass index (kg/m2)* Waist circumference (cm)* Waist-to-hip ratio * Body shape index* Smoking status (M/F) Presence of diabetes mellitus (M/F) Presence of coronary artery disease (M/F) Presence of cerebrovascular disease (M/F) Hemodialysis access (fistula/graft/catheter) Presence of anti-HCV (M/F) Presence of HBsAg (M/F) Presence of hypertension (M/F) Presence of transplantation history (M/F) Predialysis systolic blood pressure (mm Hg)* Predialysis diastolic blood pressure (mm Hg)* SpKt/V*
53.1 76/66 86.4 24.4 88.3 0.893 0.0831 40/102 47/95 45/97 18/124 114/8/20 9/133 4/138 95/47 8/134 135.2 79.6 1.46
13.7 51.4 3.9 9.7 0.042 0.0084
21.0 9.8 0.26
HBsAg, hepatitis B surface antigen; HCV, hepatitis C virus; Sp, single pool * Mean SD.
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Table 3 Laboratory parameters of 142 patients
Fasting blood glucose (mmol/L)* Blood urea nitrogen(mmol/L) * Creatinine (mmol/L) * Hemoglobin (g/L)* Albumin (g/L)* Sodium (mmol/L)* Potassium (mmol/L)* Calcium (mmol/L)* Phosphorus (mmol/L)* Total cholesterol (mmol/L)* HDL-cholesterol(mmol/L)* LDL-cholesterol (mmol/L)* Trigylceride (mmol/L)* Intact parathyroid hormone (pg/ml)* Thyroid stimulating hormone (mU/L)* hs-Crp (mg/L)* Serum iron (mmol/L)* Ferritin (ng/mL)* Alkaline phosphatase (U/L)* Aspartate aminotransferase (mkat/L)* Alanine aminotransferase (mkat/L)*
6.88 23.6 690.4 115.6 36.9 137.3 5.18 2.21 1.65 4.46 0.95 2.63 1.94 357.5 2.63 6.13 12.41 585.7 130.1 0.28 0.29
2.81 6.5 219.2 28.7 5.7 2.7 0.67 0.23 0.50 1.0 0.42 0.82 0.81 387.6 1.35 17.2 5.76 430.8 89.4 0.12 0.20
HDL, high-density lipoprotein; hs-Crp, high-sensitivity C-reactive protein; LDL, low-density lipoprotein * Mean SD.
BSI > 0.0816. Comparison of demographic and laboratory values between these two groups are shown in Table 4. During an average follow-up period of 40.1 19.2 mo (range 12–88 mo), 36 (25.4%) patients had died. The deaths were due to myocardial infarction (9), decompensated heart failure (3), sudden cardiac arrest (6), septicemia (4), cerebrovascular disease (2), gastrointestinal system hemorrhage (2), pulmonary emboli (1), and unknown causes (9). The Cox regression analysis of independent factors including age, sex, BMI, WHR, BSI, presence of coronary artery disease, presence of diabetes mellitus, presence of cerebrovascular disease, smoking status, hemoglobin, albumin, BUN, creatinine, phosphorus, total cholesterol, triglyceride, intact PTH, and hsCrp related with mortality showed that increased age (hazard ratio [HR], 1.077, 95% confidence interval [CI],1.031–1.125; P ¼ 0.001), presence of diabetes (HR, 2.855, 95% CI, 1.258–6.481; P ¼ 0.012), hemoglobin (HR, 0.629, 95% CI, 0.452–0.875; P ¼ 0.006), and albumin (HR, 0.442, 95% CI, 0.204–0.955; P ¼ 0.038) were independently related with mortality. None of the anthropometric parameters including BSI was related with mortality. Kaplan-Meier analysis showed that there were no difference with respect to mortality among patients with group 1 and group 2 based on median of BSI (P ¼ 0.332, log-rank test) (Figure 1).
Table 4 Comparision of demographic and laboratory values between two groups based on median of body shape index
Age (y) Sex (M/F) Hemodialysis duration (mo)y Body mass index (kg/m2)y Waist circumference (cm)y Waist-to-hip ratioy Smoker/non-smoker (M/F) Presence of diabetes mellitus (M/F) Presence of coronary artery disease (M/F) Presence of cerebrovascular disease (M/F) Presence of anti-HCV (M/F) Presence of HBsAg (M/F) Presence of hypertension (M/F) Presence of transplantation history (M/F) Predialysis systolic blood pressure (mm Hg)y Predialysis diastolic blood pressure (mm Hg)y SpKt/Vy Fasting blood glucose (mmol/L)y Blood urea nitrogen (mmol/L)y Creatinine (mmol/L)y Hemoglobin (g/L)y Albumin (g/L)y Sodium (mmol/L)y Potassium (mmol/L)y Calcium (mmol/L)y Phosphorus (mmol/L)y Total cholesterol (mmol/L)y HDL-cholesterol (mmol/L)y LDL-cholesterol (mmol/L)y Trigylceride (mmol/L)y Intact parathyroid hormone (pg/ml)y Thyroid stimulating hormone (mU/L)y hs-Crp (mg/L)y Serum iron (mmol/L)y Ferritin (ng/mL)y Alkaline phosphatase (U/L)y Aspartate aminotransferase (mkat/L)y Alanine aminotransferase(mkat/L)y
Group 1 BSI < 0.0816 (n ¼ 71)
Group 2 BSI > 0.0816 (n ¼ 71)
P-value
52.0 39/32 81.8 25.6 84.5 0.88 19/52 25/46 20/51 4/67 3/68 3/68 51/20 3/68 133.6 79.2 1.45 6.65 24.1 722.2 114.6 37.2 137.4 5.12 2.26 1.69 4.52 1.01 2.61 1.99 438.1 2.95 6.10 12.4 548.9 143.6 0.26 0.27
54.3 37/34 90.9 23.2 92.0 0.90 21/50 22/49 25/46 14/57 6/65 1/70 44/27 5/66 136.8 79.9 1.48 7.12 23.2 658.6 117.0 36.6 137.2 5.25 2.17 1.60 4.40 0.89 2.64 1.88 279.2 2.36 6.14 12.5 622.4 116.5 0.29 0.29
0.337 0.736 0.332 <0.0001* <0.0001* 0.004* 0.709 0.593 0.367 0.012 0.493 0.620 0.212 0.719 0.569 0.996 0.536 0.754 0.249 0.089 0.691 0.646 0.669 0.273 0.011* 0.275 0.699 0.333 0.810 0.415 0.037* 0.168 0.164 0.846 0.895 0.621 0.293 0.577
13.8 46.3 4.0 8.9 0.048
21.1 10.1 0.24 2.30 6.39 204.2 26.5 5.5 2.49 0.68 0.21 0.48 1.07 0.53 0.87 0.86 453.0 1.48 10.44 5.3 348.7 110.4 0.09 0.22
13.6 56.1 3.6 8.8 0.031
21.0 9.6 0.27 3.23 6.64 230.7 30.8 5.8 3.0 0.66 0.24 0.52 0.95 0.26 0.78 0.79 293.8 1.20 21.8 6.4 499.5 59.7 0.13 0.19
HBsAg, hepatitis B surface antigen; HDL, high-density lipoprotein; hs-Crp, high-sensitivity C-reactive protein; LDL, low-density lipoprotein; M/F, male/female; Sp, single pool y Mean SD.
B. Afsar et al. / Nutrition 29 (2013) 1214–1218
Figure 1. Probability of patient survival in hemodialysis patients with high and low body shape index groups based on body shape index mean.
Discussion In the current study, apart from other anthropometric measures, the specific relationship between BSI and mortality was studied in stable HD patients. We found that in addition to other anthropometric parameters such as BMI, WC, and WHR, BSI also was not related with mortality. To the best of our knowledge, the relationship between BSI and mortality has not been studied previously in HD patients. When compared with the general population, end-stage renal disease has been associated with a markedly increased risk for mortality, with cardiovascular disease being the most common cause of death [25,26]. In the general population without CKD, various studies have shown that BMI, WC, and WHR were all associated increased mortality [1–4]. In patients with CKD, there are relatively few studiesdwith contrasting findingsdanalyzing the prognostic value of obesity measures including BMI, WC, and WHR with mortality. For example, whereas some studies showed that there was an inverse relationship between BMI and all-cause mortality (explained within the context of reverse epidemiology) [5–7], others did not find any association between BMI and mortality [8]. It was concluded that abdominal obesity and visceral fat as measured by WC rather than subcutaneous fat was associated with increased mortality in HD patients [6,13,14]. However, WC also is closely related with BMI and is sensitive to height and weight. It also was suggested that further studies are needed to establish WC cut points that can assess cardiometabolic risk, not adequately captured by BMI and routine clinical assessments. Thus, to better understand the relationship between anthropometric measures, morbidity, and mortality in this specific group of patients, more must be learned and more validated and reliable anthropometric parameters are needed. Very recently, a new anthropometric parameter, the BSI, was developed. The BSI is based on WC, but it is independent of height, weight, and BMI in predicting mortality. The authors speculated that BSI is based on WC, weight, and height, where high BSI indicates that WC is higher than expected for a given height and weight and corresponds to a more central concentration of body volume [21].
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As explained previously, more validated and reliable anthropometric parameters are needed. Although the relationship between BSI and mortality has been shown in the general population there are no data regarding the relationship between BSI and HD patients. Thus, in the current study, to the best of our knowledge, we are the first to examine the specific relationship between BSI and mortality in HD patients. However, in contrast to the general population, we found no relationship between BSI and mortality. Currently, we do not know the cause of these discrete findings, however, some speculations can be made. First of all, BSI is not a measured parameter but it is calculated from the formula. The formula uses WC, BMI, and height. Because in the current study BMI and WC also did not influence mortality, it is not abnormal that BSI derived from BMI and WC also did not predict mortality. Second, because BSI is a calculated and numerical value, it is possible that it may not correlate with measured absolute fat mass. Third, dialysis patients are very different than the normal population. Traditional risk factors often are exaggerated in these patients. Additionally nontraditional risk factors such as malnutrition, inflammation, and oxidative stress are highly prevalent in HD patients. Thus, these risk factors may overwhelm the discriminative power of anthropometric measures including BSI for increased mortality. Fourth, the association between body fat and atherosclerosis may differ between sexes. Sezer et al found that atherosclerosis was correlated with high body fat for our female HD patients, but not for the males [27]. Because the number of deaths and the number of patients in each gender is relatively small, we believe that current study in not suitable to make differential analysis according to gender. Another factor for the lack of association may be the values of BMI and WC in the two groups based on BSI. The two study groups, although statistically different, were homogenous as reported by mean BMI (25.6 versus 23.2) and WC (84.5 versus 92). Thus, this issue may explain the lack of relationship between BSI and mortality. It could be studied whether more heterogeneous values with respect to BMI and WC would change the outcome regarding BSI and mortality. Finally, not all the studies found and association between WC (which is closely related with BSI) and cardiovascular mortality in HD patients [28,29]. The current study has some limitations that deserve mention. First, because the study is based on retrospective data, it is probable that not all the factors related to mortality are included in the analysis. Second, the number of patients is relatively small. Third, the patients included in the study were from a single center and the findings could not be generalized. In conclusion, BSI, a new anthropometric parameter, is not specifically associated with mortality in HD patients. Studies are needed to determine whether the findings of the current study can be generalized to other HD patients. References [1] Reis JP, Macera CA, Araneta MR, Lindsay SP, Marshall SJ, Wingard DL. Comparison of overall obesity and body fat distribution in predicting risk of mortality. Obesity 2009;17:1232–9. [2] Bajaj HS, Brennan DM, Hoogwerf BJ, Doshi KB, Kashyap SR. Clinical utility of waist circumference in predicting all-cause mortality in a preventive cardiology clinic population: a PreCIS database study. Obesity 2009;17:1615–20. [3] Boggs DA, Rosenberg L, Cozier YC, Wise LA, Coogan PF, Ruiz-Narvaez EA, et al. General and abdominal obesity and risk of death among black women. N Engl J Med 2011;365:901–8. [4] Li WC, Chen IC, Chang YC, Loke SS, Wang SH, Hsiao KY. Waist-to-height ratio, waist circumference, and body mass index as indices of cardiometabolic risk among 36,642 Taiwanese adults. Eur J Nutr 2013;52: 57–65.
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Contents lists available at ScienceDirect
Nutrition journal homepage: www.nutritionjrnl.com
Applied nutritional investigation
Body shape index and mortality in hemodialysis patients Baris Afsar M.D. a, *, Rengin Elsurer M.D. b, Alper Kirkpantur M.D. c a
Department of Medicine, Division of Nephrology, Konya Numune State Hospital, Konya, Turkey Department of Medicine, Division of Nephrology, Selcuk University, Faculty of Medicine, Konya, Turkey c Department of Medicine, Division of Nephrology, Diskapi Training and Research Hospital, Ankara, Turkey b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 12 December 2012 Accepted 8 March 2013
Objective: The relationship between various anthropometric parameters and mortality in hemodialysis (HD) patients is conflicting. Recently a new anthropometric parameter emerged, namely, body shape index (BSI). BSI is based on waist circumference (WC) but is independent of height, weight, and body mass index in predicting mortality in the general population. The aim of this study was to determine the relationship between BSI and mortality in HD patients. Methods: This retrospective study evaluated the demographic characteristics and anthropometric measures including BSI, laboratory parameters, and mortality data in HD patients in a single center. Results: There were 142 HD patients enrolled in the study. The median BSI was 0.0816. Because no normal value was defined for BSI, the patients were divided into two groups based on the median BSI: group 1 BSI < 0.0816 and group 2 BSI > 0.0816. During an average follow-up period of 40.1 19.2 mo (range 12–88 mo), 36 (25.4%) patients had died. The Cox regression analysis of independence showed that increased age (hazard ratio [HR], 1.077, 95% confidence interval [CI],1.031– 1.125; P ¼ 0.001), presence of diabetes (HR, 2.855, 95% CI, 1.258–6.481; P ¼ 0.012), hemoglobin (HR, 0.629, 95% CI, 0.452–0.875; P ¼ 0.006), and albumin (HR, 0.442, 95% CI, 0.204–0.955; P ¼ 0.038) were independently related with mortality. None of the anthropometric parameters including BSI were related with mortality. Kaplan-Meier analysis showed that there were no differences with respect to mortality among patients in group 1 and group 2 based on median BSI (P ¼ 0.332, logrank test). Conclusion: In conclusion, BSI is not independently associated with mortality in HD patients. Ó 2013 Elsevier Inc. All rights reserved.
Keywords: Body mass index Body shape index Dialysis Waist circumference Waist to hip ratio Mortality
Introduction In a general population without kidney disease, various studies have shown that body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) were all associated increased mortality [1–4]. In patients with chronic kidney disease (CKD), there are relatively few studies, with contrasting findings, that analyze the prognostic value of obesity measures including BMI, WC, and WHR with mortality. For example, some studies showed that there was an inverse relationship between BMI and all-cause mortality in hemodialysis (HD) (explained within the context of reverse epidemiology) [5–7]; others studies did not find any association between BMI and mortality [8]. There also are racial and ethnic differences regarding the effect of anthropometric measures. It was suggested that * Corresponding author. Tel.: þ(90) 332 235 45 00; fax: þ(90) 332 235 67 86. E-mail address: [email protected] (B. Afsar). 0899-9007/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.nut.2013.03.012
community racial/ethnic composition is an important correlate of risk for obesity, but the relationship differs greatly by individual race/ethnicity. To better understand the obesity epidemic and related racial/ethnic disparities, more must be learned about community-level risk factors, especially how environment and social norms operate within communities and across racial/ ethnic groups [9]. Indeed, various studies have shown that there are important ethnic group differences with regard to BMI and WC [10–12]. There also are studies showing that abdominal obesity as reflected by higher WC was associated with increased mortality in HD patients [6,13,14]. Indeed it was speculated that because BMI did not discriminate fat and muscle mass [15,16] and WC (which was closely related with mortality in HD patients) has been found to be more closely related with mortality compared with BMI [17–19], WC could be used as an alternative to BMI [20]. However abdominal fat distribution as reflected by WC is also sensitive to body size (height and weight) as well as to fat percentage and distribution [21]. Additionally, WC is highly correlated with BMI to the extent that it is difficult
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to differentiate the two as epidemiologic risk factors [22]. According to a consensus statement on the clinical usefulness of WC, further studies are needed to establish WC cut points that can assess cardiometabolic risk, independent of BMI in routine clinical assessments [23]. To avoid these drawbacks regarding the BMI and WC, a new anthropometric parameter, the body shape index (BSI), has emerged. The BSI is based on WC, but is independent of height, weight, and BMI in predicting mortality. The investigators speculated that the mortality risk for a given BMI also is affected by BSI particularly as a marker of abdominal fat deposits [21]. A recent study also demonstrated that BSI was better related with systolic and diastolic blood pressures than BMI and WC [24]. With this background in mind, BSI also may be related with mortality in HD patients. However, to the best of our knowledge, the specific relationship between BSI and mortality was not studied previously in HD patients. Thus, the current study evaluated the specific relationship between BSI and mortality apart from BMI, WC, and WHR in HD patients.
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Table 1 Etiologies of end-stage renal disease of 142 hemodialysis patients Etiology of renal failure
Patients
Diabetes mellitus Hypertension Glomerulonephritis Amyloidosis Vesicouretheral reflux Nephrolithiasis Polycystic kidney disease Ischemic nephropathy Contrast nephropathy Analgesic nephropathy Unknown
41 32 15 7 10 4 3 2 1 1 26
the comparison of categorical variables; c2 test or Fisher’s exact test was used as appropriate. For the correlation analysis, Pearson correlation coefficient r was used. The Cox proportional hazards model was used to identify independent factors predicting occurrence of mortality. Actuarial survival rates were determined by the Kaplan-Meier method. A log–rank test was used to compare the different survival curves.
Materials and methods A retrospective cohort study was performed to investigate whether BSI was related with all-cause mortality in chronic HD patients who were treated at the dialysis unit of a state hospital. The dialysis prescription in our study included 4 to 5 h of HD three times a week for all patients with blood flow rates of 300 to 400 mL/min, using a standard bicarbonate dialysis solution. Urea kinetic modeling single-pool Kt/V (spKt/V) was performed to assess the delivered equilibrated dose of dialysis. Demographic data including age, sex, height, weight, BMI, WC, hip circumference, vascular access type, duration of dialysis, smoking status, etiology of kidney disease, history of cardiovascular and cerebrovascular disease, presence of hypertension and diabetes mellitus, and presence of previous transplantation history were collected from our hospital database and from outpatient dialysis unit records, when appropriate. The cohort study did not include any human or animal experimentation for which ethical approval was required. Because our analysis used existing and retrospective data, there were no risks posed to human life. The clinical status of each patient was evaluated by means of a routine clinical examination before each regular HD session. The dry weight of each patient was individually determined on the basis of post-HD cardiothoracic ratio without clinical symptoms of hypotension or muscle cramps. Blood pressure (BP) was measured with a mercury sphygmomanometer after the patient had rested in the supine position for 10 to 15 min, and the mean of the values were measured at each session used in the analysis. Coronary artery disease was defined as the presence of a previous myocardial infarction, angina pectoris, or coronary revascularization procedure. Cerebrovascular disease was defined as the presence of previous stroke, transient ischemic attack, and carotid revascularization procedure. Exclusion criteria included the presence of limb amputation, history of cancer, history of renal transplantation, transfer to other center, and lack of demographic and laboratory data. Laboratory parameters including serum glucose, hemoglobin, albumin, high-sensitive C-reactive protein (hs-CRP), predialysis calcium and phosphorus, predialysis blood urea nitrogen (BUN) and creatinine, total cholesterol, highdensity lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, and intact parathyroid hormone (PTH) were obtained from charts. Postdialysis serum urea nitrogen levels, used to calculate the urea reduction ratio, were also measured. BMI, WC, and hip circumference were also obtained from charts. WHR was determined by dividing WC (cm) by hip circumference (cm). The BSI was calculated by the following formula: BSI: WC/BMI2/3 height1/2 [21]. The participants were followed from 2005 to late December 2010 to determine the incidence of death. Participants who died were censored at the time of death, otherwise participants were censored as of their last follow-up visit. The causes of death also were reviewed from the medical records. Statistical analysis Statistical analysis was performed with SPSS software ver. 15.0 (SSPS, Chicago, IL, USA). Results were considered statistically significant if the twotailed P-value was < 0.05. The normality of the data was evaluated by the Kolmogorov-Smirnov test. Comparisons of the groups were assessed by means of the Student’s t test for normally distributed continuous variables and by the Mann–Whitney U-test for non-normally distributed continuous variables. For
Results Initially, 193 patients were enrolled. One patient had lung cancer, two patients had limb amputation, four received renal transplantation, and three transferred to other center; all were excluded. The demographic and laboratory data was absent in 41 patients. The final analysis was based on 142 HD patients. The etiologies of the renal failure are given in Table 1. The baseline demographic and clinical characteristics of the 142 HD patients were shown in Table 2. The laboratory parameters were shown in Table 3. For the entire group, Pearson correlation analysis showed that BSI was correlated with BMI (r, –0.442; P < 0.0001), WC (r, 0.406; P < 0.0001), hip circumference (r, 0.389; P < 0.0001), WHR (r, 0.221; P < 0.0001), calcium (r, –0.294; P < 0.0001), and hs-CRP (r, 0.214; P ¼ 0.012). The median BSI was 0.0816. Because no normal value was defined for BSI, the patients were divided into two groups based on median BSI: group 1 BSI < 0.0816 and group 2 Table 2 Baseline demographic and clinical characteristics of the 142 hemodialysis patients
Age (y)* Sex (M/F) Hemodialysis duration (mo)* Body mass index (kg/m2)* Waist circumference (cm)* Waist-to-hip ratio * Body shape index* Smoking status (M/F) Presence of diabetes mellitus (M/F) Presence of coronary artery disease (M/F) Presence of cerebrovascular disease (M/F) Hemodialysis access (fistula/graft/catheter) Presence of anti-HCV (M/F) Presence of HBsAg (M/F) Presence of hypertension (M/F) Presence of transplantation history (M/F) Predialysis systolic blood pressure (mm Hg)* Predialysis diastolic blood pressure (mm Hg)* SpKt/V*
53.1 76/66 86.4 24.4 88.3 0.893 0.0831 40/102 47/95 45/97 18/124 114/8/20 9/133 4/138 95/47 8/134 135.2 79.6 1.46
13.7 51.4 3.9 9.7 0.042 0.0084
21.0 9.8 0.26
HBsAg, hepatitis B surface antigen; HCV, hepatitis C virus; Sp, single pool * Mean SD.
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Table 3 Laboratory parameters of 142 patients
Fasting blood glucose (mmol/L)* Blood urea nitrogen(mmol/L) * Creatinine (mmol/L) * Hemoglobin (g/L)* Albumin (g/L)* Sodium (mmol/L)* Potassium (mmol/L)* Calcium (mmol/L)* Phosphorus (mmol/L)* Total cholesterol (mmol/L)* HDL-cholesterol(mmol/L)* LDL-cholesterol (mmol/L)* Trigylceride (mmol/L)* Intact parathyroid hormone (pg/ml)* Thyroid stimulating hormone (mU/L)* hs-Crp (mg/L)* Serum iron (mmol/L)* Ferritin (ng/mL)* Alkaline phosphatase (U/L)* Aspartate aminotransferase (mkat/L)* Alanine aminotransferase (mkat/L)*
6.88 23.6 690.4 115.6 36.9 137.3 5.18 2.21 1.65 4.46 0.95 2.63 1.94 357.5 2.63 6.13 12.41 585.7 130.1 0.28 0.29
2.81 6.5 219.2 28.7 5.7 2.7 0.67 0.23 0.50 1.0 0.42 0.82 0.81 387.6 1.35 17.2 5.76 430.8 89.4 0.12 0.20
HDL, high-density lipoprotein; hs-Crp, high-sensitivity C-reactive protein; LDL, low-density lipoprotein * Mean SD.
BSI > 0.0816. Comparison of demographic and laboratory values between these two groups are shown in Table 4. During an average follow-up period of 40.1 19.2 mo (range 12–88 mo), 36 (25.4%) patients had died. The deaths were due to myocardial infarction (9), decompensated heart failure (3), sudden cardiac arrest (6), septicemia (4), cerebrovascular disease (2), gastrointestinal system hemorrhage (2), pulmonary emboli (1), and unknown causes (9). The Cox regression analysis of independent factors including age, sex, BMI, WHR, BSI, presence of coronary artery disease, presence of diabetes mellitus, presence of cerebrovascular disease, smoking status, hemoglobin, albumin, BUN, creatinine, phosphorus, total cholesterol, triglyceride, intact PTH, and hsCrp related with mortality showed that increased age (hazard ratio [HR], 1.077, 95% confidence interval [CI],1.031–1.125; P ¼ 0.001), presence of diabetes (HR, 2.855, 95% CI, 1.258–6.481; P ¼ 0.012), hemoglobin (HR, 0.629, 95% CI, 0.452–0.875; P ¼ 0.006), and albumin (HR, 0.442, 95% CI, 0.204–0.955; P ¼ 0.038) were independently related with mortality. None of the anthropometric parameters including BSI was related with mortality. Kaplan-Meier analysis showed that there were no difference with respect to mortality among patients with group 1 and group 2 based on median of BSI (P ¼ 0.332, log-rank test) (Figure 1).
Table 4 Comparision of demographic and laboratory values between two groups based on median of body shape index
Age (y) Sex (M/F) Hemodialysis duration (mo)y Body mass index (kg/m2)y Waist circumference (cm)y Waist-to-hip ratioy Smoker/non-smoker (M/F) Presence of diabetes mellitus (M/F) Presence of coronary artery disease (M/F) Presence of cerebrovascular disease (M/F) Presence of anti-HCV (M/F) Presence of HBsAg (M/F) Presence of hypertension (M/F) Presence of transplantation history (M/F) Predialysis systolic blood pressure (mm Hg)y Predialysis diastolic blood pressure (mm Hg)y SpKt/Vy Fasting blood glucose (mmol/L)y Blood urea nitrogen (mmol/L)y Creatinine (mmol/L)y Hemoglobin (g/L)y Albumin (g/L)y Sodium (mmol/L)y Potassium (mmol/L)y Calcium (mmol/L)y Phosphorus (mmol/L)y Total cholesterol (mmol/L)y HDL-cholesterol (mmol/L)y LDL-cholesterol (mmol/L)y Trigylceride (mmol/L)y Intact parathyroid hormone (pg/ml)y Thyroid stimulating hormone (mU/L)y hs-Crp (mg/L)y Serum iron (mmol/L)y Ferritin (ng/mL)y Alkaline phosphatase (U/L)y Aspartate aminotransferase (mkat/L)y Alanine aminotransferase(mkat/L)y
Group 1 BSI < 0.0816 (n ¼ 71)
Group 2 BSI > 0.0816 (n ¼ 71)
P-value
52.0 39/32 81.8 25.6 84.5 0.88 19/52 25/46 20/51 4/67 3/68 3/68 51/20 3/68 133.6 79.2 1.45 6.65 24.1 722.2 114.6 37.2 137.4 5.12 2.26 1.69 4.52 1.01 2.61 1.99 438.1 2.95 6.10 12.4 548.9 143.6 0.26 0.27
54.3 37/34 90.9 23.2 92.0 0.90 21/50 22/49 25/46 14/57 6/65 1/70 44/27 5/66 136.8 79.9 1.48 7.12 23.2 658.6 117.0 36.6 137.2 5.25 2.17 1.60 4.40 0.89 2.64 1.88 279.2 2.36 6.14 12.5 622.4 116.5 0.29 0.29
0.337 0.736 0.332 <0.0001* <0.0001* 0.004* 0.709 0.593 0.367 0.012 0.493 0.620 0.212 0.719 0.569 0.996 0.536 0.754 0.249 0.089 0.691 0.646 0.669 0.273 0.011* 0.275 0.699 0.333 0.810 0.415 0.037* 0.168 0.164 0.846 0.895 0.621 0.293 0.577
13.8 46.3 4.0 8.9 0.048
21.1 10.1 0.24 2.30 6.39 204.2 26.5 5.5 2.49 0.68 0.21 0.48 1.07 0.53 0.87 0.86 453.0 1.48 10.44 5.3 348.7 110.4 0.09 0.22
13.6 56.1 3.6 8.8 0.031
21.0 9.6 0.27 3.23 6.64 230.7 30.8 5.8 3.0 0.66 0.24 0.52 0.95 0.26 0.78 0.79 293.8 1.20 21.8 6.4 499.5 59.7 0.13 0.19
HBsAg, hepatitis B surface antigen; HDL, high-density lipoprotein; hs-Crp, high-sensitivity C-reactive protein; LDL, low-density lipoprotein; M/F, male/female; Sp, single pool y Mean SD.
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Figure 1. Probability of patient survival in hemodialysis patients with high and low body shape index groups based on body shape index mean.
Discussion In the current study, apart from other anthropometric measures, the specific relationship between BSI and mortality was studied in stable HD patients. We found that in addition to other anthropometric parameters such as BMI, WC, and WHR, BSI also was not related with mortality. To the best of our knowledge, the relationship between BSI and mortality has not been studied previously in HD patients. When compared with the general population, end-stage renal disease has been associated with a markedly increased risk for mortality, with cardiovascular disease being the most common cause of death [25,26]. In the general population without CKD, various studies have shown that BMI, WC, and WHR were all associated increased mortality [1–4]. In patients with CKD, there are relatively few studiesdwith contrasting findingsdanalyzing the prognostic value of obesity measures including BMI, WC, and WHR with mortality. For example, whereas some studies showed that there was an inverse relationship between BMI and all-cause mortality (explained within the context of reverse epidemiology) [5–7], others did not find any association between BMI and mortality [8]. It was concluded that abdominal obesity and visceral fat as measured by WC rather than subcutaneous fat was associated with increased mortality in HD patients [6,13,14]. However, WC also is closely related with BMI and is sensitive to height and weight. It also was suggested that further studies are needed to establish WC cut points that can assess cardiometabolic risk, not adequately captured by BMI and routine clinical assessments. Thus, to better understand the relationship between anthropometric measures, morbidity, and mortality in this specific group of patients, more must be learned and more validated and reliable anthropometric parameters are needed. Very recently, a new anthropometric parameter, the BSI, was developed. The BSI is based on WC, but it is independent of height, weight, and BMI in predicting mortality. The authors speculated that BSI is based on WC, weight, and height, where high BSI indicates that WC is higher than expected for a given height and weight and corresponds to a more central concentration of body volume [21].
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As explained previously, more validated and reliable anthropometric parameters are needed. Although the relationship between BSI and mortality has been shown in the general population there are no data regarding the relationship between BSI and HD patients. Thus, in the current study, to the best of our knowledge, we are the first to examine the specific relationship between BSI and mortality in HD patients. However, in contrast to the general population, we found no relationship between BSI and mortality. Currently, we do not know the cause of these discrete findings, however, some speculations can be made. First of all, BSI is not a measured parameter but it is calculated from the formula. The formula uses WC, BMI, and height. Because in the current study BMI and WC also did not influence mortality, it is not abnormal that BSI derived from BMI and WC also did not predict mortality. Second, because BSI is a calculated and numerical value, it is possible that it may not correlate with measured absolute fat mass. Third, dialysis patients are very different than the normal population. Traditional risk factors often are exaggerated in these patients. Additionally nontraditional risk factors such as malnutrition, inflammation, and oxidative stress are highly prevalent in HD patients. Thus, these risk factors may overwhelm the discriminative power of anthropometric measures including BSI for increased mortality. Fourth, the association between body fat and atherosclerosis may differ between sexes. Sezer et al found that atherosclerosis was correlated with high body fat for our female HD patients, but not for the males [27]. Because the number of deaths and the number of patients in each gender is relatively small, we believe that current study in not suitable to make differential analysis according to gender. Another factor for the lack of association may be the values of BMI and WC in the two groups based on BSI. The two study groups, although statistically different, were homogenous as reported by mean BMI (25.6 versus 23.2) and WC (84.5 versus 92). Thus, this issue may explain the lack of relationship between BSI and mortality. It could be studied whether more heterogeneous values with respect to BMI and WC would change the outcome regarding BSI and mortality. Finally, not all the studies found and association between WC (which is closely related with BSI) and cardiovascular mortality in HD patients [28,29]. The current study has some limitations that deserve mention. First, because the study is based on retrospective data, it is probable that not all the factors related to mortality are included in the analysis. Second, the number of patients is relatively small. Third, the patients included in the study were from a single center and the findings could not be generalized. In conclusion, BSI, a new anthropometric parameter, is not specifically associated with mortality in HD patients. Studies are needed to determine whether the findings of the current study can be generalized to other HD patients. References [1] Reis JP, Macera CA, Araneta MR, Lindsay SP, Marshall SJ, Wingard DL. Comparison of overall obesity and body fat distribution in predicting risk of mortality. Obesity 2009;17:1232–9. [2] Bajaj HS, Brennan DM, Hoogwerf BJ, Doshi KB, Kashyap SR. Clinical utility of waist circumference in predicting all-cause mortality in a preventive cardiology clinic population: a PreCIS database study. Obesity 2009;17:1615–20. [3] Boggs DA, Rosenberg L, Cozier YC, Wise LA, Coogan PF, Ruiz-Narvaez EA, et al. General and abdominal obesity and risk of death among black women. N Engl J Med 2011;365:901–8. [4] Li WC, Chen IC, Chang YC, Loke SS, Wang SH, Hsiao KY. Waist-to-height ratio, waist circumference, and body mass index as indices of cardiometabolic risk among 36,642 Taiwanese adults. Eur J Nutr 2013;52: 57–65.
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