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NDT Advance Access originally published online on December 21, 2007
Nephrology Dialysis Transplantation 2008 23(6):1910-1918; doi:10.1093/ndt/gfm878
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© The Author [2007]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org


Association between body mass index and chronic kidney disease in men and women: population-based study of Malay adults in Singapore

Anoop Shankar1,2, Chenlei Leng3, Kee Seng Chia1,2, David Koh1,2, E. Shyong Tai2,4, Seang Mei Saw1,2,5, Su Chi Lim2,6 and Tien Yin Wong5,7

1 Department of Community, Occupational and Family Medicine 2 Centre for Molecular Epidemiology 3 Department of Statistics and Applied Probability 4 Department of Endocrinology, Singapore General Hospital 5 Singapore Eye Research Institute 6 Department of Medicine, Alexandra Hospital, Singapore 7 Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia

Correspondence and offprint requests to: Anoop Shankar, Department of Community, Occupational, and Family Medicine, National University of Singapore, Singapore 117597. Tel: +65-6874-4968; Fax: +65-6779-1489; E-mail: ashankar{at}nus.edu.sg



   Abstract
Top
 Abstract
Short summary
 Introduction
 Methods
 Results
 Discussion
 References
 
Background. In contrast to previous studies from western populations, studies from Japan reported a positive association between body mass index (BMI) and chronic kidney disease (CKD) among men but not women. In this context, we examined the relationship between BMI and CKD, by gender, in a study of Malay adults from Singapore.

Methods. This was a population-based cross-sectional sample of adults (n = 2783, 53% women, aged 49–80 years), free of clinical cardiovascular disease. The outcome of interest was presence of CKD [estimated glomerular filtration rate (eGFR) <60 mL/min per 1.73 m2 (n = 517)]. The statistical methods used were logistic and nonparametric logistic regressions.

Results. Higher BMI levels were found to be positively associated with CKD among Malay men. Among men, compared to BMI quartile 1 (<23 kg/m2), the multivariable odds ratio (OR) [95% confidence intervals (CI)] of CKD was 3.12 (1.97–4.94) in quartile 2 (23–24.9 kg/m2), 2.49 (1.63–3.79) in quartile 3 (25–29.9 kg/m2) and 3.70 (2.13–6.42) in quartile 4 (≥30 kg/m2); P-trend < 0.0001. In contrast, among women BMI levels were not associated with CKD; P-trend = 0.32. In nonparametric models, among men, the observed positive association between BMI and CKD appeared to be present across the full range of BMI values, without any threshold. In contrast, among women, results from nonparametric models were consistent with the conclusion of a lack of association between BMI and CKD.

Conclusions. Higher BMI levels were positively associated with CKD among men but not women in a population-based study from Singapore. These results are consistent with the hypothesis of a male gender-specific association between BMI and CKD among Asians.

Keywords: BMI; CKD; kidney disease; obese; overweight



   Short summary
Top
 Abstract
 Short summary
Introduction
 Methods
 Results
 Discussion
 References
 
In contrast to previous studies from western populations, studies from Japan reported a positive association between body mass index (BMI) and chronic kidney disease (CKD) among men but not women. We therefore examined the relationship between BMI and CKD, by gender, in a population-based study of Malay adults (n = 2783) from Singapore. We found that higher BMI levels were positively associated with CKD among men but not women among Asian Malay adults. These results are consistent with the hypothesis of a male gender-specific association between BMI and CKD among Asians.



   Introduction
Top
 Abstract
 Short summary
 Introduction
Methods
 Results
 Discussion
 References
 
End-stage renal disease (ESRD) is an important public health problem. There are estimated to be ~470 000 patients with ESRD in the United States as of 2004 [1], and based on earlier data, an estimated additional 8 million US adults have chronic kidney disease (CKD) [2,3], defined as a glomerular filtration rate (GFR) of <60 ml/min per 1.73 m2, who are at risk of progression to ESRD and its attendant complications [4]. Similarly, in Asian countries such as Malaysia, the number of prevalent dialysis patients increased linearly from 3698 in 1996 to almost 15 000 at the end of the year 2006 [5]. Obesity is also a major worldwide public health problem [6]. Previous studies have shown an independent association between obesity and ESRD [4,7–9]; studies have also indicated that body mass index (BMI) levels below the obesity range, including overweight BMI range, are related to ESRD [10,11]. Recent epidemiological studies have shown that, compared to normal BMI, overweight and obese BMI categories are independently related to stages of kidney disease even earlier in the disease continuum, including CKD [8,12–20], where disease prevention/treatment efforts may be more effective. However, several questions regarding the putative association between BMI and kidney disease still remain unanswered. First, it is not entirely clear whether there is a continuous dose–response relationship between BMI and CKD or whether this association is present only beyond specific BMI cutoffs. Second, there were some differences among previous studies reporting a positive association between BMI and kidney disease; studies from Asian populations tended to report a gender-specific association confined to men but not women [8,10,20], while such gender differences were not reported in studies from some western populations [11,14,18]. However, all previous studies regarding BMI–CKD association from Asia were from Japan; there is little Asian data outside Japanese populations regarding this question. In this context, we specifically examined the association between BMI and CKD in a population-based study of Malay adults from Singapore, by gender. We also employed nonparametric analytical techniques to graphically examine the dose-response nature of the association between BMI and CKD.



   Methods
Top
 Abstract
 Short summary
 Introduction
 Methods
Results
 Discussion
 References
 
Study population
This population-based study of Malay adult persons aged 40–80 years in Singapore has been described in detail elsewhere [21]. The sampling frame consisted of 16 069 Malay individuals aged 40–80 years, drawn from the computer-generated random list provided by the Singapore Ministry of Home Affairs, living in 15 residential districts in Singapore. An age-stratified random sample comprising 5600 individuals (1400 people from each decade of 40–49, 50–59, 60–69 and 70–80 years) was drawn from this list. Out of the 5600 initial names, 4168 participants (74.4%) were determined to be eligible to participate based on pre-specified criteria. Of these, 3280 individuals participated in the study, giving an overall response rate of eligible participants of 78.7%.

In the current paper, we excluded subjects with missing data for essential variables (n = 155), including BMI and serum creatinine, subjects with clinical cardiovascular disease (n = 342), including self-reported myocardial infarction, angina or stroke, that could confound the association between BMI and kidney disease, resulting in 2783 eligible subjects who provided complete data for the analysis.

Measurements and definitions
Information on participants was collected by questionnaire, physical examination and laboratory measurement. The questionnaire collected data on age, gender, education, monthly income, cigarette smoking, alcohol consumption, personal and family health history and medication use. Smoking status was categorized into never smoker, former smoker and current smoker. Physical examination included anthropometry, blood pressure and pulse rate measurement apart from extensive eye examination. Height was measured in centimetres using a wall-mounted measuring tape and weight was measured in kilograms using a digital scale (SECA, model 782 2321009; Vogel & Halke, Germany). BMI was calculated as weight in kilograms divided by the height in metres squared (kg/m2).

Blood pressure was measured with a digital automatic blood pressure monitor (Dinamap model Pro Series DP110X-RW, 100V2; GE Medical Systems Information Technologies, Inc., USA) after the participants were seated for at least 5 min, consistent with current recommendations [22]. For each participant, the average of the last two out of a total of three measurements was used as the systolic and diastolic blood pressure value. Patients were considered hypertensive if they reported current blood pressure-reducing medication use and/or had systolic blood pressures ≥140 mmHg and/or diastolic blood pressures ≥90 mmHg [23]. We also defined a second blood pressure-related outcome, severe hypertension, defined as current blood pressure-reducing medication use and/or having systolic blood pressures ≥160 mmHg and/or diastolic blood pressures ≥100 mmHg, consistent with Stage 2 hypertension defined by the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) [23].

Venous blood (40 ml) was collected from participants to measure serum lipid levels, serum creatinine and plasma glucose. All serum biochemistry tests were carried out at the National University Hospital Reference Laboratory. Diabetes was defined consistent with the American Diabetes Association criteria as a casual serum glucose ≥200 mg/dL or self-reported current use of oral hypoglycaemic medication or insulin.

Estimated GFR (eGFR) was the preferred measure of kidney function in the current study. GFR was indirectly estimated using the four-variable Modification of Diet in Renal Disease Study (MDRD) equation [24]. The main outcome of interest was CKD, defined as eGFR of <60 mL/min per 1.73 m2, consistent with National Kidney Foundation Kidney Disease Outcomes Quality Initiative (KDOQI) > Stage 2 CKD [2].

Statistical analysis
Consistent with the study hypothesis, all analyses were stratified by gender. There was a statistically significant interaction by gender when we formally examined multiplicative interaction by creating a cross-product interaction term between BMI quartiles x gender in the multivariable model (P-interaction = 0.02). We examined BMI as quartiles: <23 kg/m2, 23–24.9 kg/m2, 25–29.9 kg/m2 and ≥30 kg/m2. We also examined BMI as the following categories: normal (<25 kg/m2), overweight (25–29.9 kg/m2) and obese (≥30 kg/m2). The odds ratio (OR) [95% confidence interval (CI)] of CKD was calculated for each BMI category, with the lowest quartile as the reference, using multivariable logistic regression models. We used two models: the age (years)-adjusted model and the multivariable model additionally adjusted for education (below primary school education, primary school education, high school education, college/university education), monthly income (unemployed or <$1000, $1000–2000, $2001–3000, >$3000, retirees), smoking (never, former, current), ever drinker (no, yes), physical activity (yes, no), diabetes mellitus (absent, present), hypertension (absent, present), mean arterial pressure (mmHg) and serum high-density lipoprotein cholesterol (mmol/L) and serum triglyceride (mmol/L). Trends in the OR of CKD across increasing BMI categories were determined by modelling the BMI category as an ordinal variable. As the observed association between BMI and CKD may be explained by the presence of diabetes mellitus or hypertension, we performed a subgroup analysis among relatively ‘healthy’ study subjects, without diabetes mellitus or severe/JNC7 Stage 2 hypertension. We chose subjects without severe/JNC7 Stage 2 hypertension for the hypertension subgroup analysis as we did not have enough subjects free of hypertension and therefore statistical power in this subgroup; ~67% of the study population was hypertensive. Finally, to examine the dose–response relationship in the observed association between BMI and CKD without linearity assumptions, we used flexible nonparametric logistic regression employing the generalized additive modelling approach [R system for statistical computing, available from Comprehensive R Archive Network (http://www.CRAN.R-project.org) and S-PLUS 2000 (Mathsoft, Seattle, Washington, USA)] to calculate odds of CKD adjusting for all covariates in the multivariable model; the odds of CKD were then plotted against increasing BMI levels [25,26].



   Results
Top
 Abstract
 Short summary
 Introduction
 Methods
 Results
Discussion
 References
 
Among 2783 Malay adults ≥40 years of age and without clinical cardiovascular disease included in the current analysis there were 1308 men and 1475 women. Overall, 570 subjects had CKD, including 245 men and 272 women. Table 1 presents the characteristics of men in the study population by BMI quartiles. Men with higher BMI levels were more likely to be younger, college/university educated, never smoker; to have higher income categories, diabetes mellitus, hypertension, higher blood pressure levels, higher total cholesterol and triglyceride levels and lower high-density lipoprotein cholesterol levels; less likely to be uneducated, unemployed or a former smoker. Among men, eGFR significantly decreased with increasing BMI quartiles. Table 2 presents the characteristics of women in the study population by BMI quartiles. Women with higher BMI levels were more likely to have diabetes mellitus, hypertension, higher blood pressure levels and lower high-density lipoprotein cholesterol levels; less likely to be uneducated and an ever drinker. Among women, there was no overall difference in eGFR with increasing BMI quartiles in contrast to men.


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  		Table 1 Characteristics of the Singapore Malay study participants by quartile of body mass index in men (n = 1308)

 

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  		Table 2 Characteristics of the Singapore Malay study participants by quartile of body mass index in women (n = 1475)

 
Table 3 presents the ORs of CKD by increasing BMI levels in men. Among men, increasing BMI levels were positively associated with CKD in both the age-adjusted and the multivariable-adjusted model; models evaluating the trend in this association were also statistically significant. Further, in the analysis involving BMI quartiles, compared to the lowest BMI quartile (<23 kg/m2), we observed a statistically significant association even in the second BMI quartile (23–24.9 kg/m2), whose BMI range is below the overweight BMI cutoff (≥25 kg/m2).


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  		Table 3 Association between body mass index (BMI) and chronic kidney disease (CKD) in mena

 
Table 4 presents the ORs of CKD by increasing BMI levels in women. Among women, there was no clear association between increasing BMI levels and CKD either in the age-adjusted or the multivariable-adjusted model; models evaluating the trend in this association were also not statistically significant.


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  		Table 4 Association between body mass index (BMI) and chronic kidney disease (CKD) in womena

 
Table 5 presents the ORs of CKD by increasing BMI quartiles among subjects without diabetes mellitus or severe/JNC7 Stage 2 hypertension. Consistent with the results for the whole cohort, we found that there was a clear positive association between increasing BMI quartiles and CKD among men but not among women within these apparently ‘healthy’ subgroups also.


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  		Table 5 Association between body mass index (BMI) and chronic kidney disease (CKD) among subjects without diabetes or severe hypertensiona, by gender

 
We then employed nonparametric models to graphically examine if the observed positive association between BMI and CKD was present across the full range of BMI levels available in the study (Figure 1 and Figure 2 among men and women, respectively). Among the men in our study, overall, there appeared to be a continuous positive association between BMI levels and CKD with increasing BMI levels; there was no evidence of any threshold effect (Figure 1). In contrast, among women the nonparametric dose–response curve appeared to be horizontal and running flat with increasing BMI levels (Figure 2).


Figure 1
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  		Fig. 1 Multivariable-adjusted odds of chronic kidney disease (CKD) according to body mass index (kg/m2) in men. Solid thick line represents the predicted odds of CKD from nonparametric logistic regression; dashed lines, 95% confidence limits for the nonparametric logistic regression estimates. The nonparametric logistic regression was adjusted for age (years), education (below primary school education, primary school education, high school education, college/ university education), monthly income (unemployed or <$1000, $1000–2000, $2001–3000, >$3000, retirees), smoking (never, former, current), ever drinker (no, yes), physical activity (yes, no), diabetes mellitus (absent, present), hypertension (absent, present), mean arterial pressure (mmHg), serum high-density lipoprotein cholesterol (mmol/L) and serum triglyceride (mmol/L). X-axis: BMI (kg/m2) level, Y1-axis: predicted odds of CKD plotted in log scale, Y2-axis: participant number (men) for each BMI level.

 

Figure 2
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  		Fig. 2 Multivariable-adjusted odds of chronic kidney disease (CKD) according to body mass index (kg/m2) in women. Solid thick line represents the predicted odds of CKD from nonparametric logistic regression; dashed lines, 95% confidence limits for the nonparametric logistic regression estimates. The nonparametric logistic regression was adjusted for age (years), education (below primary school education, primary school education, high school education, college/ university education), monthly income (unemployed or <$1000, $1000–2000, $2001–3000, >$3000, retirees), smoking (never, former, current), ever drinker (no, yes), physical activity (yes, no), diabetes mellitus (absent, present), hypertension (absent, present), mean arterial pressure (mmHg), serum high-density lipoprotein cholesterol (mmol/L) and serum triglyceride (mmol/L). X-axis: BMI (kg/m2) level, Y1-axis: predicted odds of CKD plotted in log scale, Y2-axis: participant number (women) for each BMI level.

 
We performed several supplementary analyses. First, we calculated eGFR using the Cockcroft–Gault equation corrected by BSA and repeated the main analyses; the results were essentially similar to the main findings presented in Tables 3–5 using the MDRD equation. Second, as the effect of oestrogen may disappear after 60 years in women, we performed a subgroup analysis examining the association between BMI quartiles and CKD among elderly subjects (>60 years of age); the results were materially similar here also. For example, among elderly men, compared to the lowest BMI quartile (referent) the OR (95% CI) of CKD was 4.10 (2.34–6.87) in quartile 2, 2.01 (1.23–3.28) in quartile 3 and 4.10 (2.09–8.05) in quartile 4; P-trend < 0.0001. Among elderly women, compared to the lowest BMI quartile (referent), the OR (95% CI) was 1.24 (0.75–2.05) in quartile 2, 1.16 (0.70–1.90) in quartile 3 and 1.23 (0.79–1.92); P-trend = 0.41. Third, we repeated the main analyses using sex-specific BMI quartiles; the results were essentially similar here also. Fourth, we performed a supplementary analysis after excluding n = 6 persons with renal failure (eGFR <15); the results were similar here also. Finally, when we repeated the graphical nonparametric models restricting the sample to ‘healthy’ participants (those without diabetes and severe hypertension), a similar pattern of association was observed.



   Discussion
Top
 Abstract
 Short summary
 Introduction
 Methods
 Results
 Discussion
References
 
Higher BMI levels were found to be positively associated with CKD among Asian men in a population-based sample of Malay adults from Singapore. In contrast, there was no clear association between BMI and CKD among women. This observed positive association among men persisted after adjusting for age, smoking, alcohol intake, diabetes mellitus, hypertension, blood pressure and serum lipid levels and was consistently present in subgroup analysis among subjects without diabetes mellitus or severe/JNC7 Stage 2 hypertension. Among men, the OR of CKD increased in a dose-dependent manner with increasing quartiles of BMI. In a subsequent analysis employing nonparametric models, the observed positive association between BMI quartiles and CKD among men was present continuously across the full range of BMI. In contrast, among women, results from nonparametric models were consistent with the conclusion of an overall lack of association between BMI and CKD. Our results are consistent with previous reports from Japan [8,10,20] on a male gender-specific association between BMI and kidney disease and contribute to the current literature by demonstrating this gender-specific association in an ethnically and culturally different Asian population.

Several plausible mechanisms exist to explain an observed association between BMI and CKD including an increase in single nephron perfusion and increased intracapillary perfusion pressure with BMI that may in turn lead to glomerulosclerosis and loss of GFR over time [27–29], an increase in the activity of sympathetic nervous system and the rennin–angiotensin system (RAS) [30,31], the reported relation of BMI with markers of inflammation [32–34], endothelial function [33,34], coagulability and impaired fibrinolysis [33] and the close relationship between BMI and factors known to be related to CKD, including hypertension [9,12,16,35], diabetes mellitus [9,12,16,35], components of the metabolic syndrome [35] and to insulin resistance and compensatory hyperinsulinaemia [33,36].

The current study results from a population-based sample of Malay adults in Singapore, when considered together with previous Japanese studies [8,10,20], appear to suggest that among Asians, the BMI–kidney disease association is mainly present among men. Among women this association either appeared to be absent or if present, of very low magnitude. In the current study, we had >95% statistical power to detect an OR of 1.3 among women in each of the higher BMI quartiles; lack of power among women is therefore an unlikely explanation of the observed gender difference.

The exact reason for the observed gender difference in BMI–kidney disease association in the current study is not clear. However, it is widely known that men are at higher risk of developing kidney disease, and tend to develop kidney disease earlier in life than women [37–41]. Such gender difference in kidney disease development has also been reported in experimental animal models [42–44]. It is possible that several of the biological mechanisms involved in the putative BMI–kidney disease pathway may also be affected by sex hormones. For example, in some animal studies, oestrogens have been shown to reduce mesangial proliferation and synthesis of types I and IV collagen, in murine cell cultures [45,46]. Oestrogens have also been shown to stimulate renal nitric oxide generation and to have antioxidant properties [47]. In contrast, androgens may increase arterial pressure by causing a hypertensive shift in the pressure–natriuresis relationship, either by having a direct effect to increase proximal tubular reabsorption or by activation of the RAS [48,49]. Also, in vitro studies indicate that androgens prime a receptor-linked apoptotic pathway that has been shown to interact with the mitochondrial pathway, which may be activated by other mechanisms such as ischaemia and toxins [46,50]. This suggests that mechanisms to cell death that are primed by androgens may interact with others occurring in several conditions, including higher BMI/overweight leading to the loss of renal cells [46]. Finally, there are several studies in the general population that support the hypothesis that BMI reflects visceral fat (a critical source of cytokines) better in males than in females [51,52]. Therefore, this may be an alternate explanation to the observed gender differences in BMI–kidney disease association in the current study.

However, when taken together, the results from Asian populations on a male gender-specific association between BMI and CKD appear to be in contrast to some reports from western populations that found a positive association among both men and women [11,14,18]. The correct reason for this difference in the BMI–CKD association between Asian and western studies, if a true observation, is not known, though it has been shown that the same BMI level is associated with significantly higher total and subcutaneous body fat and different fat distribution and lower insulin sensitivity among Asians compared to whites [53,54].

We believe that further investigations regarding this topic are important in both western and Asian populations until a lack/presence of gender difference in the BMI–kidney disease association is concluded.

The main strengths of the current study include its population-based nature, adequate sample size to perform gender-specific analysis, standardized methods of data collection and the uniqueness of studying an Asian population outside Japan to test our specific hypothesis. The main study limitation is the cross-sectional nature of our study that precludes conclusions regarding the temporal nature of the observed association between BMI and CKD. Second, since the MDRD equation was developed in US white and black participants, the validity of this equation in Asian Malay race ethnicity is known. Third, intra-individual variation of the single serum creatinine measurement may have contributed to misclassification of CKD status. Fourth, as BMI does not differentiate between muscle and fat, persons with low or high muscle mass may be incorrectly classified as over/under weight. Also, muscle mass may also effect serum creatinine, resulting in potential misclassification of CKD. Finally, we did not have data on other measures of obesity such as waist circumference or waist-to-hip ratio or data on markers of inflammation to examine their role in CKD.

In conclusion, higher BMI levels were found to be positively associated with CKD among Asian men in a population-based sample of Malay adults from Singapore. In contrast, there was no clear association between BMI and CKD among women. As previously reported by Ramirez et al., among the different race ethnicities in Southeast Asia, Malays appear to have higher odds of kidney disease [55]. In light of our findings, a corollary observation is that strategies for weight reduction [56] for preventing/treating CKD [57] may be more effective in Asian Malay men than women.



   Acknowledgments
 
All authors contributed to the intellectual development of this paper. A.S. had the original idea for the study, wrote the first draft paper and is the guarantor. L.C., A.S. analysed the data. C.K.S., D.K., T.E.S., S.S.M., L.S.C. and T.Y.W. provided critical corrections to the manuscript. T.Y.W. supervised data collection. This study followed the recommendations of Declaration of Helsinki and was approved by the Singapore Eye Research Institute institutional review board. Written, informed consent was obtained from all participants. This study was supported by the Biomedical Research Council (BMRC) grants, 05/1/21/19/387 and 501/1/25-5, the National Medical Research Council (NMRC) grant, 0796/2003 and with support from the Singapore Prospective Study Program and the Singapore Tissue Network, A*STAR.

Conflict of interest statement. None declared.



   References
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 Abstract
 Short summary
 Introduction
 Methods
 Results
 Discussion
 References
 

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Received for publication: 1. 6.07
Accepted in revised form: 19.11.07


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