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 Table of Contents  
Year : 2020  |  Volume : 25  |  Issue : 2  |  Page : 103-106

The association between chronic kidney disease, waist circumference and body mass index: A case-control study from a tertiary hospital of West Bengal, India

Department of Medicine, College of Medicine and Jawaharlal Nehru Memorial Hospital, The West Bengal University of Health Sciences, Kalyani, West Bengal, India

Date of Submission01-Oct-2019
Date of Acceptance04-Feb-2020
Date of Web Publication15-Dec-2020

Correspondence Address:
Dr. Somak Kumar Das
23, Kalibari Lane, Millennium Apartment, Flat - A10, Jadavpur, Kolkata - 700 032, West Bengal
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jmgims.jmgims_64_19

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Introduction: In this hospital-based study, we investigated the association between chronic kidney disease (CKD) and two globally accepted obesity parameters – waist circumference (WC) and body mass index (BMI). Aims: To investigate the association between CKD with BMI and WC. Materials and Methods: We included 416 consecutive CKD patients (age >20 years, calculated glomerular filtration rate <60 ml/min/1.73 m2) as the case group and 408 consecutive age- and sex-matched non-CKD patients as the control group for our study. after obtaining consent, all groups were tested for WC and BMI. Results: Of 416 cases, the prevalence of high WC according to the International Diabetes Federation (IDF) criteria was 37.5% and of obesity according to the Indian BMI scale was 38.46%. When compared with the control group, we found that WC (p < 0.0001, odds ratio [OR] 2.182, 95% confidence interval [CI] 1.099–1.852) and BMI (p = 0.006, OR 3.125, 95% CI 1.625–3.125) were significantly high in the CKD group. Conclusion: The parameters of obesity were found to be significantly associated with the non-edematous CKD group.

Keywords: Body mass index, chronic kidney disease, waist circumference

How to cite this article:
Ghosh A, Das SK. The association between chronic kidney disease, waist circumference and body mass index: A case-control study from a tertiary hospital of West Bengal, India. J Mahatma Gandhi Inst Med Sci 2020;25:103-6

How to cite this URL:
Ghosh A, Das SK. The association between chronic kidney disease, waist circumference and body mass index: A case-control study from a tertiary hospital of West Bengal, India. J Mahatma Gandhi Inst Med Sci [serial online] 2020 [cited 2023 Jan 31];25:103-6. Available from: https://www.jmgims.co.in/text.asp?2020/25/2/103/303425

  Introduction Top

Chronic kidney disease (CKD) is one of the biggest public health problems globally. Patients with CKD are known to have poorer quality of daily life and decreased life-expectancy compared with individuals of the same age in the general population. CKD is defined as either structural and/or functional abnormality of the kidney or reduced glomerular filtration rate (GFR) to a level <60 ml/min/1.73 m2.[1] The causes of CKD include the following: diabetic kidney disease, hypertension, vascular disease such as renal artery stenosis and anti-cytoplasmic antibody (ANCA)-related vasculitis, glomerular disease (primary or secondary), cystic kidney diseases, tubulointerstitial disease, urinary tract obstruction or dysfunction, recurrent kidney stone disease, congenital (birth) defects of the kidney or bladder, and unrecovered acute kidney injury.

Previous studies have shown an independent association between obesity and CKD. Recent epidemiological studies have shown that, compared to normal BMI, overweight and obese BMI categories are independently related to CKD.[2],[3]

However, the role of obesity as a risk factor for myocardial infarction and mortality in patients with CKD is not well understood. Previous studies that have used body mass index (BMI) to evaluate obesity as a risk factor for adverse outcomes in CKD have shown conflicting results.[4],[5],[6]

Since waist circumference (WC) is a more sensitive marker for central obesity and potentially less influenced by muscle mass, WC may be better indicate risk associated with obesity in a population with a high prevalence of muscle loss and malnutrition, such as individuals with CKD.[7]

Although CKD and obesity are quite common in India, lack of detailed studies from India on their association prompted us to undertake this study. In this study, our purpose was to determine the association between BMI, WC, and CKD.

  Materials and Methods Top

Study design and variables studied

We conducted a hospital-based case-control study at a rural tertiary hospital, College of Medicine and Jawaharlal Nehru Memorial Hospital, Kalyani, Nadia, West Bengal, from March 2018 to April 2019 after getting clearance from the institutional ethics committee. We included 500 consecutive cases of CKD with any etiology diagnosed currently or previously, by applying the modification of diet in renal disease (MDRD) equation, where the calculated glomerular filtration rate (eGFR) was <60 ml/min/1.73 m2. The case group was defined as patients of both sexes, age >20 years, and with an eGFR <60 ml/min/1.73 m2. CKD patients who were edematous were excluded from our study. After application of the exclusion criteria and excluding the patients who opted out of our study, 416 patients of non-edematous patients of CKD were included in the case group. We also included 408 consecutive age- and sex- matched non-CKD patients as controls group.

After obtaining consent, participants from both the groups were thoroughly explored through structured history-taking, general physical examination (including measurement of BMI and WC), hematological examination (including complete blood counts), lipid profile, random blood glucose, hemoglobin A1c, renal function test, and urinalysis at the time of first contact.

Measurement of WC was done using the smallest circumference between the lower ribs and iliac crests. We defined increased WC when it was >90 cm in male and >80 cm in female (according to the International Diabetes Federation [IDF] criteria for central adiposity in Indian population). BMI was calculated by dividing the weight (kg) by height2 (m2). Obesity was defined according to Indian standards, i.e., BMI >25 kg/m2.

Statistical analysis

After collecting all the data, statistical analysis was performed using SPSS (IBM, Chicago, IL, USA) software version 20 for analysis of data. Mean and standard deviation (SD) were calculated separately for each group. Unpaired (two-tailed) Student's t-test was applied to determine differences in means of various parameters between the two groups. Pearson's correlation coefficient was calculated between obesity parameters and eGFR.p < 0.05 was taken as statistically significant.

  Results Top

Demographic data

In the present study, 416 cases and 408 control subjects participated. Among the cases, there were 256 males (61.53%) and 160 females 38.46%). The mean age was 53.27 years (SD: 14.36 years; range: 22–79 years). In the control arm, there were 232 men (56.86%) and 176 women (43.13%)). The mean age was 51.13 years (SD: 15.45 years, range: 21-77 years). There were no significant differences in the mean age and the distribution of age (p = 0.304). Majority of male cases were in the age group of 36–50 years, followed by those in the age group of >50 years. Most of the female cases belonged to the 51-65 years age group. In our study, we found that most of the cases were in CKD stage 4 (35.57%) followed by CKD stage 5 (32.69%). We found that hypertension and diabetes mellitus were present in 33.65% and 18.26% of the cases as compared to 20.58% and 7.84% of the controls, respectively. The other causes of CKD were lupus nephritis (2.65%), ANCA-associated vasculitis (1.44%), drug- and toxin-induced CKD (15.65%), primary glomerular disease (1.7%), cystic kidney diseases (0.98%), tubulointerstitial disease (5.79%), urinary tract obstruction or dysfunction (11.28%), recurrent kidney stone disease (6.98%), and unknown etiology (2.65%).

Obesity parameters

We analyzed obesity by measuring WC and BMI in the present study [Table 1 depicting independent sample t-test data of two groups]. Of 416 cases, the prevalence of high WC according to the IDF criteria was 37.5% and of obesity according to the Indian BMI scale was 38.46%.
Table 1: Independent sample t-test data of chronic kidney disease and non-chronic kidney disease group

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We found that the increased WC according to the IDF definition for Asian-Indians was strongly associated with CKD case group (p < 0.0001, odds ratio [OR] 2.182, 95% confidence interval [CI] 1.099–1.852). Females were more obese than males (19.23% vs. 18.26%) according to the WC definition. When compared to the control group, we found that BMI (P = 0.006, OR 3.125, 95% CI 1.625–3.125) was significantly high in the CKD group.

We also did Pearson's correlation study among obesity parameters and eGFR. We found that eGFR correlated significantly negatively with WC (r= −0.259,p< 0.001) and BMI (r= −0.201,p= 0.004). Age, height, weight, low-density lipoprotein (LDL), triglycerides (TG), fasting blood sugar, and postprandial sugar correlated strongly negatively, while high-density lipoprotein (HDL) correlated strongly positively [Table 2].
Table 2: Correlation between obesity parameters and eGFR

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  Discussion Top

Excessive abdominal fat (central obesity) has caused more health hazards than deposition of fat on the bottom and limbs. Now, it is generally accepted that the WC is the most simple and practical indicator to measure the abdominal fat. Several mechanisms exist to explain an observed association between BMI and CKD and between obesity and renal injury, including hemodynamic effects, inflammation, and renal lipotoxicity.[8] Excess adipose tissue can lead to the activation of the sympathetic nervous system and renin angiotensin systems. Angiotensin II is elevated in the blood of obese individuals, leading to vasoconstriction of efferent arterioles. Thereby, plasma aldosterone is also slightly elevated in obese individuals, causing salt and water retention, and ultimately proteinuria.[8],[9] Obesity is also seen in the development focal segmental glomerulosclerosis and glomerulomegaly.[10] Obesity is regarded as a chronic low-grade inflammation, and cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) play a major role in CKD development. IL-6 activates the adipose tissue and helps release transforming growth factor-beta 1 receptor trafficking, leading to renal fibrosis.[11] TNF-α inhibits the activity of the nephron gene promoter in cultured podocytes, resulting in podocyte dysfunction in the obese population.[12],[13]

2013, the Hallym Aging Study conducted by Oh et al on Korean population found an association between central obesity and renal function deterioration.[14] However, Lu et al., in 2014 analyzed the data from a nationally representative cohort of 453,946 United States veterans with eGFR <60 ml/min per 1.73 m2 and found a very complex relationship between obesity and CKD.[15] He et al. conducted a cross-sectional survey on 123,629 Chinese urban adults and found that the higher levels of BMI and WC were each positively associated with increased odds for CKD among men (OR 1.19 [95% CI 1.13–1.25] for BMI and OR 1.12 [95% CI 1.08–1.16] for WC).[16]

However, in a systematic review and meta-analysis published by the United States-based researchers, Ahmadi et al. in 2016 observed differential association indicating obesity paradox in CKD patients.[17] This is different from that of observed in southeastern countries. It may be due to different ethnicity.

Dong et al. observed that the adiposity parameters such as WC and BMI were significantly negatively correlated with eGFR (BMI in males r= −0.051 [<0.0001], BMI in females r= −0.014 [<0.0001] and WC in males r= −0.061 [<0.0001], WC in female r= −0.070 [<0.0001]).[1] Another meta-analysis published in 2019 by Chang et al. noticed that the elevated BMI, WC, and waist-to-height ratio were the independent risk factors for GFR decline.[18]

Our observations in the present study are similar to the data published from South Asian countries. We found a statistically significant association between CKD patients and obesity parameters such as BMI (p = 0.0006, OR 3.125, 95% CI 1.625–3.125) and WC (P = 0.0147, OR 2.182, 95% CI 1.099–1.852). Our data completely support the data published by Dong et al. We observed that WC, BMI, height, weight, TG, LDL, TC, and fasting blood sugar were strongly negatively correlated with eGFR decline, whereas HDL was not.

We extensively searched literature in PubMed for Indian data. However, we found only few studies done by Indian medical scientists. Of them, Singh et al., a US-based (Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA) researcher gathered Indian data from Western, Northern, and few South Indian medical institutions in 2005–2007.[19] Another study by Trivedi et al. from Western India in 2016 showed similar association between obesity and CKD as we found in our study.[20] However, there is a paucity of studies from Eastern India. Our study is probably the first of its kind which has gathered data from Eastern India. Although the sample size was not so large in this study, it showed similar relationship between CKD and obesity as found in previous studies from Northwest India and Southeast Asia. As our sample size was not very large, we could not make concrete comments on the relationship between obesity and CKD. Inclusion of different etiologies of CKD might have confounded our study finding. Larger studies are needed to draw better conclusions.

  Conclusion Top

We found that the prevalence of high WC according to the IDF criteria was 37.5% and of obesity according to the Indian BMI scale was 38.46%. Non-edematous CKD showed a strong association with obesity parameters in our study. This corroborated with previous studies from western and northern parts of India and also with the data from South Asian countries. We hope that, in the future, more researchers will come forward to perform population-based study in the eastern part of the country so that we will be able to get a better picture of this association.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Dong Y, Wang Z, Chen Z, Wang X, Zhang L, Nie J, et al. Comparison of visceral, body fat indices and anthropometric measures in relation to chronic kidney disease among Chinese adults from a large scale cross-sectional study. BMC Nephrol 2018;19:40.  Back to cited text no. 1
Stengel B, Tarver-Carr ME, Powe NR, Eberhardt MS, Brancati FL. Lifestyle factors, obesity and the risk of chronic kidney disease. Epidemiology 2003;14:479-87.  Back to cited text no. 2
Iseki K, Ikemiya Y, Kinjo K, Inoue T, Iseki C, Takishita S. Body mass index and the risk of development of end-stage renal disease in a screened cohort. Kidney Int 2004;65:1870-6.  Back to cited text no. 3
Kalantar-Zadeh K, Abbott KC, Salahudeen AK, Kilpatrick RD, Horwich TB. Survival advantages of obesity in dialysis patients. Am J Clin Nutr 2005;81:543-54.  Back to cited text no. 4
Kovesdy CP, Anderson JE, Kalantar-Zadeh K. Paradoxical association between body mass index and mortality in men with CKD not yet on dialysis. Am J Kidney Dis 2007;49:581-91.  Back to cited text no. 5
Beddhu S, Pappas LM, Ramkumar N, Samore M. Effects of body size and body composition on survival in hemodialysis patients. J Am Soc Nephrol 2003;14:2366-72.  Back to cited text no. 6
Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr 2004;79:379-84.  Back to cited text no. 7
Soltani Z, Washco V, Morse S, Reisin E. The impacts of obesity on the cardiovascular and renal systems: Cascade of events and therapeutic approaches. Curr Hypertens Rep 2015;17:7.  Back to cited text no. 8
Snyder S, Turner GA, Turner A. Obesity-related kidney disease. Prim Care 2014;41:875-93.  Back to cited text no. 9
Verani RR. Obesity-associated focal segmental glomerulosclerosis: Pathological features of the lesion and relationship with cardiomegaly and hyperlipidemia. Am J Kidney Dis 1992;20:629-34.  Back to cited text no. 10
Zhang XL, Topley N, Ito T, Phillips A. Interleukin-6 regulation of transforming growth factor (TGF)-beta receptor compartmentalization and turnover enhances TGF-beta1 signaling. J Biol Chem 2005;280:12239-45.  Back to cited text no. 11
Takano Y, Yamauchi K, Hayakawa K, Hiramatsu N, Kasai A, Okamura M, et al. Transcriptional suppression of nephrin in podocytes by macrophages: Roles of inflammatory cytokines and involvement of the PI3K/Akt pathway. FEBS Lett 2007;581:421-6.  Back to cited text no. 12
Ikezumi Y, Suzuki T, Karasawa T, Kawachi H, Nikolic-Paterson DJ, Uchiyama M. Activated macrophages down-regulate podocyte nephrin and podocin expression via stress-activated protein kinases. Biochem Biophys Res Commun 2008;376:706-11.  Back to cited text no. 13
Oh H, Quan SA, Jeong JY, Jang SN, Lee JE, Kim DH. Waist circumference, not body mass index, is associated with renal function decline in Korean population: Hallym aging study. PLoS One 2013;8:e59071.  Back to cited text no. 14
Lu JL, Kalantar-Zadeh K, Ma JZ, Quarles LD, Kovesdy CP. Association of body mass index with outcomes in patients with CKD. J Am Soc Nephrol 2014;25:2088-96.  Back to cited text no. 15
He Y, Li F, Wang F, Ma X, Zhao X, Zeng Q. The association of chronic kidney disease and waist circumference and waist-to-height ratio in Chinese urban adults. Medicine (Baltimore) 2016;95:e3769.  Back to cited text no. 16
Ahmadi SF, Zahmatkesh G, Ahmadi E, Streja E, Rhee CM, Gillen DL, et al. Association of body mass index with clinical outcomes in non-dialysis-dependent chronic kidney disease: A systematic review and meta-analysis. Cardiorenal Med 2015;6:37-49.  Back to cited text no. 17
Chang AR, Grams ME, Ballew SH, Bilo H, Correa A, Evans M, et al. Adiposity and risk of decline in glomerular filtration rate: Meta-analysis of individual participant data in a global consortium. BMJ 2019;364:k5301.  Back to cited text no. 18
Singh AK, Farag YM, Mittal BV, Subramanian KK, Reddy SR, Acharya VN, et al. Epidemiology and risk factors of chronic kidney disease in India - Results from the SEEK (Screening and Early Evaluation of Kidney Disease) study. BMC Nephrol 2013;14:114.  Back to cited text no. 19
Trivedi H, Vanikar A, Patel H, Kanodia K, Kute V, Nigam L, et al. High prevalence of chronic kidney disease in a semi-urban population of Western India. Clin Kidney J 2016;9:438-43.  Back to cited text no. 20


  [Table 1], [Table 2]


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