|Year : 2017 | Volume
| Issue : 1 | Page : 36-43
|Insulin resistance in predialytic, nondiabetic, chronic kidney disease patients: A hospital-based study in Eastern Uttar Pradesh, India
Neha Srivastava1, RG Singh1, Usha2, Alok Kumar3, Shivendra Singh1
1 Department of Nephrology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
2 Department of Pathology, Division of Immunopathology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
3 Division of Biostatistics and Community Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
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|Date of Web Publication||12-Jan-2017|
| Abstract|| |
Most investigations have focused on patients with end-stage renal disease (ESRD). More recently, due to increased recognition of the high prevalence of moderate-to-severe chronic kidney disease (CKD), attention has been redirected to this patient population to identify risk factors associated with hospitalization, death, and progression to ESRD. The objective of this study was to examine the degree and determinants of insulin resistance (IR) in predialytic, nondiabetic, CKD patients. Our study is a hospital-based cross-sectional study. The participants were aged 18 years and above with CKD due to any cause, were all nondiabetic patients, and the mean serum creatinine was 1.41-5 mg/dL. Anthropometric parameters included body weight, height, and skinfold thickness. Homeostasis model assessment of IR (HOMA-IR) score was 2.5 ± 1.2 in CKD patients and 1.9 ± 0.7 in controls. In the unadjusted analysis, there was a significant (P <0.05) correlation between HOMA-IR and body mass index (BMI), waist circumference, cholesterol, and triglyceride (TG) levels. Upon adjusting for age and sex, total body fat (BF), globulin, TG, and C-reactive protein were having positive, significant (P <0.05) correlation with HOMA-IR. In multivariate regression models, BMI and total BF% were significant (P <0.05) predictors of IR in patients with CKD but not in controls. BF% and BMI are indicators of IR in CKD as in non-CKD population.
|How to cite this article:|
Srivastava N, Singh R G, Usha, Kumar A, Singh S. Insulin resistance in predialytic, nondiabetic, chronic kidney disease patients: A hospital-based study in Eastern Uttar Pradesh, India. Saudi J Kidney Dis Transpl 2017;28:36-43
|How to cite this URL:|
Srivastava N, Singh R G, Usha, Kumar A, Singh S. Insulin resistance in predialytic, nondiabetic, chronic kidney disease patients: A hospital-based study in Eastern Uttar Pradesh, India. Saudi J Kidney Dis Transpl [serial online] 2017 [cited 2020 Jan 25];28:36-43. Available from: http://www.sjkdt.org/text.asp?2017/28/1/36/198114
| Introduction|| |
The incidence and prevalence of the chronic kidney disease (CKD) and end-stage renal disease (ESRD) are increasing at an alarming rate in India. Most investigations have focused on patients with ESRD, but an increasing recognition of the high prevalence of moderate- to-severe CKD has redirected the attention to this patient population to identify the risk factors associated with hospitalization, death, and progression to ESRD. Studies have shown that there is a greater risk of atherosclerotic events and death in patients with mild-to- moderate CKD than in those without kidney disease. Furthermore, CKD is accompanied by numerous metabolic derangements such as oxidative stress, chronic inflammation, and endothelial dysfunction. Insulin resistance (IR) has been well recognized in advanced kidney disease since the work of DeFronzo et al using hyperinsulinemic-euglycemic clamp techniques. IR has been reported to be an independent risk factor for cardiovascular morbidity and mortality in patients with ESRD. IR is also seen in mild-to-moderate CKD, as is evident in reports from European and Japanese populations. ,
It is estimated that there are 7.85 million patients with CKD in India. To the best of our knowledge, very few studies have been conducted to investigate IR in patients with CKD in India, and thus potential determinants of IR in the Indian population have not been studied in detail. The growing prevalence of obesity and metabolic syndrome in India and its complex relation with CKD and cardiovascular disease underscore the importance of recognizing and defining the risk factors for IR in the patient population.
In the present study, we aimed to evaluate potential determinants of IR in a population of patients without diabetes but with Stages 3, 4, and 5 CKD. We hypothesized that the estimated glomerular filtration rate (e-GFR) and body mass index (BMI) in kg/m2 would each be closely associated with levels of IR in person with CKD. To test this hypothesis, we examined the correlation among e-GFR, BMI, and IR, as determined using the homeostasis model assessment of IR (HOMA-IR) in 135 nondiabetic persons with moderate-to-severe (Stage 3, 4, and 5) CKD. We compared the results in this group with a group of 53 participants with normal kidney function.
| Objective|| |
The objective of this study was to examine the degree and determinants of IR in predialytic, nondiabetic, CKD patients.
| Materials and Methods|| |
Study design and population selection
This was a hospital-based cross-sectional study aimed at studying IR in a group of clinically stable CKD patients with Stages 3, 4, and 5 and under treatment in the Nephrology Department of Sir Sundarlal Hospital, Banaras Hindu University, Varanasi, during 2009-2010. The sample population consisted of 135 cases and 53 controls without CKD.
Controls were aged between 35 and 60 years and were matched to the patients with CKD for BMI. Controls were recruited from the attendants of the patient and participants from Institute of Medical Sciences, Banaras Hindu University.
Controls had normal GFR and no prior diagnosis of diabetes mellitus. Demographic data, anthropometric measurements, and nutritional values and total body fat % (BF%) were recorded.
Patients aged 18 years and above, having CKD due to any cause, and not being diabetic, with serum creatinine of 1.41-5 mg/dL were included in the study.
Diabetes was excluded by the absence of past history of diabetes, normal glycosylated hemoglobin levels, and absence of diabetic retinopathy on fundus examination.
Patients with acute inflammatory illness such as acquired immunodeficiency syndrome, active hepatitis B or C, malignancy, previous kidney transplantation, current participation in a drug trial, age <18 years, presence of diabetes mellitus, and those on dialysis were excluded from the study.
The protocol of the study was approved by the Institutional Ethical Committee of Institute of Medical Sciences, Banaras Hindu University, Varanasi. Furthermore, all patients provided their consent before entering the study.
Clinical and anthropometric data were documented. All anthropometric measurements were performed by a trained dietician.
Anthropometric parameters included body weight, height, arm circumference, skinfold thickness (SKF) of triceps, biceps, subscapular, suprailiac, and waist circumference (WC). Body weight was measured using a personal weighing machine (Beam Balanced Scale). The weighing machine was placed on a leveled surface and set at 0 before taking the measurement. Participants were asked to stand straight, relaxed, and with minimum clothing to reduce any error. Height of the participants was measured in a standing position without any footwear and recorded to the nearest 0.5 cm.
BMI was calculated as body weight (kilograms)/height (meters). Standard ideal body weight was calculated using Broca's index (height in - 100 cm). SKF was measured on the right-hand side, using Harpenden SKF calipers, all measurements were taken to the nearest 0.50 mm. WC was measured at the umbilical level using a flexible plastic tape with a graduated scale to the nearest 0.1 cm. Since SKF cannot be accurately measured in overweight/obese patients, those with BMI ≥25 kg/m2 had their total BF assessed according to the validated equation of Weltman et al, , which uses WC, body weight, and height in the following equation: in men:
- BF (%) = [0.31457 × waist circumference (cm)] − [0.10969 × body weight (kg)] + 10.8336; in women
- BF (%) = [0.11077 × waist circumference (cm)] − [0.17666 × height (cm)] + [0.14354 × body weight (kg)] + 51.03301. For patients with BMI <25 kg/m2, BF% was estimated by the sum of SKF according to Durnin and Womersley, and then BF% was calculated by the Siri's equation.
Biochemical parameters such as serum creatinine (alkaline picrate method), fasting blood sugar (GOD - POD method), albumin (bromocresol Green, end-point method), total protein (biuret method, end-point method), globulin (total protein - albumin), cholesterol (enzymatic end-point method), triglyceride (TG) (GPO - PAP method), high-density lipoprotein (HDL) (direct method), low- density lipoprotein (LDL) (direct method), very LDL (TG/5) were performed; all the above tests were performed using a fully automated analyzer. C-reactive protein (CRP) was measured by nephelometry method (Siemens CardioPhase hsCRP, Newark, USA). Fasting insulin was measured by solid phase enzyme amplified sensitivity immunoassay (DIAsource INS-EASIA, Belgium). HOMA- IR was used as a measure of IR.
This value was calculated from fasting concentrations of insulin and glucose (participants were required to fast for 8 h) using the following equation: ,
HOMA-IR = [fasting serum insulin (U/mL) × fasting serum glucose (mg/dL)]/405.
Based on age, weight, and creatinine, e-GFR was calculated separately for men and women using the Cockroft-Gault equation. A GFR level ≥120 was considered as normal. GFR for men = [(140 − age) × weight]/[72 × creatinine (mg/dL)]; GFR for women = [(140 - age) × weight]/[72 × creatinine (mg/dL)] × 0.85.
CKD staging was done according to the Kidney Disease Outcomes Quality Initiative guidelines based on GFR.
| Statistical Analysis|| |
Statistical analysis was performed in two phases. In the first phase, baseline characteristics of cases according to the different stages of CKD were compared using analysis of variance, and baseline characteristics of cases and controls were compared using independent sample's t-test. Associations of HOMA-IR before and after adjusting for age and sex, with BMI, BF%, and biochemical parameters were assessed in study participants using Spearman's rank correlation coefficients. In the second phase, cases and controls were analyzed separately. Multivariate regression analysis was conducted to assess the independent effect of BMI and BF% on HOMA-IR, after adjusting for age, sex, and e-GFR. The interaction effect of BMI and BF% was seen on HOMA-IR using multivariate regression analysis. Statistical Package for the Social Sciences (SPSS) software version 16.0 for Windows (SPSS Inc., Chicago, IL, USA) was used for the analysis of the data.
| Results|| |
The baseline characteristics of the patients with CKD and controls are shown in [Table 1]; the patients with CKD were subdivided by the disease stage. The BMI differed significantly (P <0.05), whereas the BF% did not differ significantly between the patients of different CKD stages (Stage 3, 4, and 5); also, the BMI and BF% did not differ significantly between patients with CKD and the controls. The mean HOMA-IR score was 2.5 ± 1.2 in patients with CKD and 1.9 ± 0.7 in controls (P <0.01). As expected, baseline e-GFR was significantly (P <0.01) lower in the patients with CKD than the controls. HDL was significantly lower (P <0.01) and LDL was significantly higher (P <0.01) in controls. CRP was significantly (P <0.01) higher in cases than controls. Significant difference in patients with different stages of CKD was seen in the following: number of males (P <0.01), with maximum number of males as well as total cases being in Stage 4, BMI decreased significantly (P <0.05) as the stage of CKD increased, and expectedly GFR was significantly (P <0.01) lower in Stage 5.
Significant differences were noted by sex and age in BMI and BF%, and therefore, subsequent analyses were adjusted for age and sex. Significant differences in BMI and BF% were noted by age group too; however, it could not be considered as a definitive conclusion as the number of cases was small in each age group. The potential relation between IR and BMI, BF%, WC, e-GFR, CRP, albumin, globulin, cholesterol, and TG, in patients with CKD, is mentioned in [Table 2].
|Table 2. Association between HOMAs of IR, anthropometric and biochemical parameters in study participants: nondiabetic CKD patients (n=135).|
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In the unadjusted analysis, there was a significant (P <0.05) positive correlation between HOMA-IR and BMI, WC, cholesterol, and TG, whereas after adjustment with age and sex, only TG level showed positive significant (P <0.01) correlation with HOMA-IR and BMI.
BF% and HOMA-IR have an inverse significant (P <0.05) correlation upon adjustment for age and sex. Globulin had an inverse significant (P <0.05) correlation with HOMA- IR after adjustment for age and sex. CRP directly and significantly (P <0.05) correlated with HOMA-IR, after adjustment for age and sex.
Predictors of IR in patients with CKD were analyzed by multivariate analysis, after adjusting for age, sex, and e-GFR. BMI and BF% were independent and significant predictors in patients with CKD but not in controls. Combination of BMI and BF% significantly correlated with HOMA-IR in cases but not in controls ([Table 3]).
|Table 3. Multivariate regression models and significant predictors of IR1 with BMI, total BF (%), and combined BMI and total BF (%) among cases and control subjects.|
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| Discussion|| |
It is recognized from early times that a complex relation exists between uremia and insulin function. Several lines of evidence suggest a pathogenic role of IR on kidney dysfunction. Potential mechanisms are mostly due to the effect of single abnormalities related to IR and clustering into the metabolic syndrome. Hyperinsulinemia, which is inevitably associated with IR in nondiabetic states, also appears to play a role on kidney function by inducing glomerular hyperfiltration and increased vascular permeability. Early in 1951, alterations in insulin function were associated with CKD. , The effects of kidney disease on renal uptake and excretion of insulin were reported as early as 1970. Defronzo et al,, and Defronzo studied IR in patients with ESRD undergoing dialysis, using euglycemic insulin clamp techniques. Many studies have contributed to the study of IR in dialysis patients, but very few have been performed in predialysis cases. Recent studies have suggested that individuals with the metabolic syndrome are at increased risk for developing CKD. , , , The mechanism behind this increased risk may be aggregation of known risk factors for CKD in the diagnosis of metabolic syndrome. On the other hand, metabolic syndrome may indicate the presence of IR, which may directly increase the risk for CKD. Few studies have suggested a link between IR and CKD. , , Several small clinical studies have noted insulin resistance in nondiabetic patients with mild renal dysfunction. , , However, there are very fewer data on the relationship between insulin resistance, compensatory hyperinsulinemia, and the risk of CKD in nondiabetics.
Very few studies have been conducted in India on IR in nondiabetic patients with CKD and those conducted are on diabetic CKD population. According to one such study in India on IR performed on patients with diabetic kidney disease, there was a positive correlation between increasing IR and worsening GFR. Hence, there is a need to study IR in nondiabetic CKD population, and its determinants to look for modifiable factors as have been performed in the healthy population. Studies in the general population have revealed that abdominal fat is associated with IR, hyperinsulinemia and, dyslipidemia, in Asian Indians. ,
In the present study, we observed that in our CKD patient group, e-GFR did not correlate with the degree of IR. Other investigators who evaluated the presence of IR in CKD , concluded similar observations, irrelevant to the method by which IR or GFR was measured.
In the present study, it was found that IR is primarily determined by BMI and BF%. Further analysis (age, sex, and e-GFR adjusted) indicated that the BF% and BMI are the relevant components indicating IR (HOMA- IR); similar results have been reported in other studies conducted in the US population. Thus, BF% and BMI are indicators of IR in CKD population as in the non-CKD population.
To the best of our knowledge, this study is one of the first to describe BMI as the primary determinant and to evaluate the contribution of total BF in IR, in the nondiabetic CKD population of Eastern Uttar Pradesh, India.
Furthermore, no studies have mentioned HOMA-IR value in nondiabetic CKD population in India; however, there are several studies which describe HOMA-IR in diabetic CKD population in India (HOMA-IR: 2.4). In our study, the mean HOMA-IR reported is
2.5 in cases and 1.9 in controls, the mean fasting insulin level is 11.34 in cases and 8.15 in controls, which is significantly high.
| Limitations of the Study|| |
This study has several limitations: the size of the study population is small, and study is of cross-sectional nature. Controls were younger than the cases although they were age and sex matched. Although the study showed a correlation between IR and BMI and BF%, there is no information regarding the causal relationships. There are several more advanced methods for measuring BF% such as bioelectric impedance analysis, and HOMA-IR could be measured using hyperinsulinemic euglycemic clamp technique, which is the best measure for IR, but its use is laborious and time consuming. HOMA-IR is inferior to the clamp technique in terms of accuracy, but using HOMA-IR makes it possible to study a large number of participants and with a single glucose and insulin measurement in the fasting state.
| Conclusion|| |
In summary, our data show that the BMI and BF% are major determinants of IR in nondiabetic Stages 3, 4, and 5 CKD patients in this population. Body composition likely plays a more significant role in the development of IR in patients with less severe renal disease. Prospective studies are needed to more clearly define this relation and to determine whether interventions targeting IR in this patient population can decrease cardiovascular morbidity and mortality, as well as progression to ESRD.
Conflict of interest: None declared.
| References|| |
Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med 2004;351:1296-305.
Himmelfarb J, Stenvinkel P, Ikizler TA, Hakim RM. The elephant in uremia: oxidant stress as a unifying concept of cardiovascular disease in uremia. Kidney Int 2002;62:1524- 38.
DeFronzo RA, Alvestrand A, Smith D, Hendler R, Hendler E, Wahren J. Insulin resistance in uremia. J Clin Invest 1981;67:563-8.
Shinohara K, Shoji T, Emoto M, et al. Insulin resistance as an independent predictor of cardiovascular mortality in patients with endstage renal disease. J Am Soc Nephrol 2002;13:1894-900.
Otero A, Gayoso P, Garcia F, de Francisco AL; EPIRCE Study Group. Epidemiology of chronic renal disease in the Galician population: results of the pilot Spanish EPIRCE study. Kidney Int Suppl 2005;99:S16-9.
Ninomiya T, Kiyohara Y, Kubo M, et al. Metabolic syndrome and CKD in a general Japanese population: the Hisayama Study. Am J Kidney Dis 2006;48:383-91.
Dash SC, Agarwal SK. Incidence of chronic kidney disease in India. Nephrol Dial Transplant 2006;21:232-3.
Weltman A, Seip RL, Tran ZV. Practical assessment of body composition. Am J Clin Nutr 1988;48:1179-83.
Weltman A, Levine S, Seip RL, Tran ZV. Accurate assessment of body composition in obese females. Am J Clin Nutr 1988;48:1179- 83.
Durnin JV, Womersley J. Body fat assessed from total body density and its estimation from skinfold thickness: measurements on 481 men and women aged from 16 to 72 years. Br J Nutr 1974;32:77-97.
Siri WE. Body composition from fluids spaces and density: Analysis of methods. In: Brozek J, Henschel A, eds. Techiniques for Measuring Body Composition. Washington, DC: National Research Council; 1961. p. 234-44.
Shoji T, Emoto M, Nishizawa Y. HOMA index to assess insulin resistance in renal failure patients. Nephron 2001;89:348-9.
Runyan JW Jr., Hurwitz D, Robbins SL. Effect of Kimmelstiel-Wilson syndrome on insulin requirements in diabetes. N Engl J Med 1955;252:388-91.
De Cosmo S, Menzaghi C, Prudente S, Trischitta V. Role of insulin resistance in kidney dysfunction: insights into the mechanism and epidemiological evidence. Nephrol Dial Transplant 2013;28:29-36.
Zubrod CG, Eversole SL, Dana GW. Amelioration of diabetes and striking rarity of acidosis in patients with Kimmelstiel-Wilson lesions. N Engl J Med 1951;245:518-28.
Rabkin R, Simon NM, Steiner S, Colwell JA. Effect of renal disease on renal uptake and excretion of insulin in man. N Engl J Med 1970;282:182-7.
DeFronzo RA, Smith D, Alvestrand A. Insulin action in uremia. Kidney Int Suppl 1983;16: S102-14.
DeFronzo RA. Pathogenesis of glucose intolerance in uremia. Metabolism 1978;27 12 Suppl 2:1866-80.
Chen J, Muntner P, Hamm LL, et al. The metabolic syndrome and chronic kidney disease in U.S. adults. Ann Intern Med 2004;140:167-74.
Kurella M, Lo JC, Chertow GM. Metabolic syndrome and the risk for chronic kidney disease among non diabetic adults. J Am Soc Nephrol 2005;16:2134-40.
Chen J, Gu D, Chen CS, et al. Association between the metabolic syndrome and chronic kidney disease in Chinese adults. Nephrol Dial Transplant 2007;22:1100-6.
Luk AO, So WY, Ma RC, et al. Metabolic syndrome predicts new onset of chronic kidney disease in 5,829 patients with type 2 diabetes: a 5-year prospective analysis of the Hong Kong Diabetes Registry. Diabetes Care 2008;31:2357-61.
Chen J, Muntner P, Hamm LL, et al. Insulin resistance and risk of chronic kidney disease in non diabetic US adults. J Am Soc Nephrol 2003;14:469-77.
Landau M, Kurella-Tamura M, Shlipak MG, et al. Correlates of insulin resistance in older individuals with and without kidney disease. Nephrol Dial Transplant 2011;26:2814-9.
Vareesangthip K, Tong P, Wilkinson R, Thomas TH. Insulin resistance in adult polycystic kidney disease. Kidney Int 1997;52: 503-8.
Fliser D, Pacini G, Engelleiter R, et al. Insulin resistance and hyperinsulinemia are already present in patients with incipient renal disease. Kidney Int 1998;53:1343-7.
Hjelmesaeth J, Hartmann A. Insulin resistance in patients with adult polycystic kidney disease. Nephrol Dial Transplant 1999;14: 2521-2.
Viswanathan V, Tilak P, Meerza R, Kumpatla S. Insulin resistance at different stages of diabetic kidney disease in India. J Assoc Physicians India 2010;58:612-5.
Raji A, Seely EW, Arky RA, Simonson DC. Body fat distribution and insulin resistance in healthy Asian Indians and Caucasians. J Clin Endocrinol Metab 2001;86:5366-71.
Chandalia M, Abate N, Garg A, Stray Gundersen J, Grundy SM. Relationship between generalized and upper body obesity to insulin resistance in Asian Indian men. J Clin Endocrinol Metab 1999;84:2329-35.
Becker B, Kronenberg F, Kielstein JT, et al. Renal insulin resistance syndrome, adiponectin and cardiovascular events in patients with kidney disease: the mild and moderate kidney disease study. J Am Soc Nephrol 2005;16: 1091-8.
Trirogoff ML, Shintani A, Himmelfarb J, Ikizler TA. Body mass index and fat mass are the primary correlates of insulin resistance in nondiabetic stage 3-4 chronic kidney disease patients. Am J Clin Nutr 2007;86:1642-8.
Dwimartutie N, Setiati S, Oemardi M. The correlation between body fat distribution and insulin resistance in elderly. Acta Med Indones 2010;42:66-73.
Bonora E, Targher G, Alberiche M, et al. Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care 2000;23:57- 63.
Department of Nephrology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh
[Table 1], [Table 2], [Table 3]
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