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Body mass index and fat mass are the primary correlates of insulin resistance in nondiabetic stage 3–4 chronic kidney disease patients^1,2,3

¹ From the Division of Nephrology (MLT and TAI) and the Department of Biostatistics (AS), Vanderbilt University Medical Center, Nashville, TN, and the Division of Nephrology, Maine Medical Center, Portland, ME (JH)

² Supported by National Institutes of Health grants no. R01 DK45604 and K24 DK62849 and Diabetes Research and Training Center grant no. DK-20593 from the National Institute of Diabetes, Digestive and Kidney Diseases; grant no. R01 HL070938 from the National Heart, Lung, and Blood Institute; and grant no. M01 RR-00095 from the National Center for Research Resources (to the Vanderbilt General Clinical Research Center).

³ Reprints not available. Address correspondence to TA Ikizler, Division of Nephrology, Vanderbilt University School of Medicine, Medical Center North, S-3223, 1161 21st Avenue, Nashville, TN 37232-2372. E-mail: alp.ikizler{at}vanderbilt.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Background: Insulin resistance has been noted in patients with chronic kidney disease (CKD). The determinants of insulin resistance have not been well-studied in CKD patients.

Objective: The objective of this study was to examine the degree and determinants of insulin resistance in persons without diabetes but with stage 3–4 CKD.

Design: Demographic characteristics, metabolic hormones, and inflammatory markers were measured in 95 nonobese stage 3–4 CKD patients without prior diagnosis of diabetes mellitus and 36 control subjects without CKD. The estimated glomerular filtration rate (eGFR) was measured by using the Modification of Diet in Renal Disease study equation. Insulin resistance was measured with the use of the homeostasis model assessment of insulin resistance (HOMA-IR).

Results: After age and sex adjustments, HOMA-IR scores were significantly and positively correlated with body mass index (BMI) and percentage body fat. After control for age, race, adiponectin concentrations, sex, and eGFR in a multivariate regression model, BMI remained as the only significant predictor of insulin resistance (standardized regression coefficient = 0.55; P < 0.001). When substituted for BMI, percentage body fat also was an independent predictor of insulin resistance. The prevalence of abnormal HOMA did not differ significantly between CKD patients (98%) and BMI-matched control subjects (94%).

Conclusion: Whereas insulin resistance is highly prevalent in stage 3–4 CKD, the primary determinant of insulin resistance in this population is BMI, specifically, fat mass.

Key Words: Insulin resistance • chronic kidney disease • homeostasis model assessment • body mass index • adiposity

	INTRODUCTION

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Both the incidence and prevalence of chronic kidney disease (CKD) and end-stage renal disease (ESRD) continue to increase at an alarming rate in the United States. Much investigation has been focused on ESRD patients, but an increasing recognition of the high prevalence of moderate-to-severe CKD has redirected the attention to this patient population to identify risk factors associated with hospitalization, death, and progression to ESRD. Indeed, studies have shown that there is a greater risk of atherosclerotic events and a higher risk of death in patients with mild-to-moderate CKD than in those without kidney disease (1). Furthermore, CKD is accompanied by numerous metabolic derangements such as oxidative stress, chronic inflammation, and endothelial dysfunction (2).

Insulin resistance (IR) in advanced kidney disease has been well recognized since the seminal work by DeFronzo et al (3) using hyperinsulinemic euglycemic clamp techniques. IR was reported to be an independent risk factor for cardiovascular morbidity and mortality in patients with ESRD (4). IR associated with mild-to-moderate CKD has also been described, albeit in reports mainly from European and Japanese populations. To our knowledge, few studies have investigated IR in CKD patients in the United States, where 11% of the adult population is estimated to have CKD (5, 6), and potential determinants of IR in the US population have not been studied in detail. Greater attention is being focused on the role of inflammation, adiposity, and its associated adipokines such as adiponectin in the general and CKD population in the United States; however, their potential relation to IR and cardiovascular disease risk has yet to be clearly defined. The growing prevalence of obesity and metabolic syndrome in the United States, the complex relation of both conditions with CKD, and their association with cardiovascular disease risk underlie the importance of recognizing and defining the risk factors for IR in this patient population.

In the present study, we aimed to evaluate potential determinants of IR in a population of patients without diabetes but with stage 3–4 CKD. We hypothesized that the estimated glomerular filtration rate (eGFR) and body mass index (BMI; in kg/m²) would each be closely associated with levels of IR in persons with CKD. To test this hypothesis, we examined the relation among eGFR, BMI, and insulin resistance, as determined by using the homeostasis model assessment of IR (HOMA-IR) in 95 nondiabetic persons with moderate-to-severe (stage 3–4) CKD. We compared results in this group with those in a group of 36 subjects with normal kidney function who were frequency matched for race, sex, and BMI.

	SUBJECTS AND METHODS

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Patients
Subjects were recruited from among the patients attending the outpatient nephrology clinics at the Maine Medical Center (Portland, ME) and the Vanderbilt University Medical Center (Nashville, TN). Criteria for study participation included age > 18 y and CKD due to any cause, being followed in one of the above nephrology clinics, and stage 3–4 CKD as defined by an eGFR between 15 and 59 mL/min. The eGFR was calculated by using the abbreviated equation described in the Modification of Diet in Renal Disease (MDRD) study (7):

(1)

where SCr is serum creatinine. Patients with a prior diagnosis of diabetes mellitus, current use of oral hypoglycemics or insulin, or a fasting glucose concentration > 126 mg/dL (8) were excluded. Patients with acute inflammatory illnesses (eg, AIDS, active hepatitis B or C, malignancy, or systemic lupus erythematosus), hospitalization for cardiac or infection-related morbidity within the past 6 wk, severe comorbid complications, previous kidney transplantation, and current participation in experimental drug protocols; pregnant women; and prison inmates were also excluded from the study.

Control subjects between the ages of 45 and 80 y were frequency matched to the CKD group for BMI, race, and sex. Control subjects were recruited from the Vanderbilt University Medical Center vie E-mail communication; they had a normal eGFR and no prior diagnosis of diabetes mellitus. Demographic data, anthropometric measurements, nutritional and hormonal values, and total percentage body fat (%BF) were obtained.

All patients provided written informed consent before study enrollment. The study was approved by the institutional review board of each center.

Analytic procedures
Blood samples
All blood draws were performed either at the General Clinical Research Center (Vanderbilt) or the Research Core Laboratory (Maine Medical Center). Venous blood was drawn into Vacutainer tubes (Becton-Dickinson, Franklin Lakes, NJ) containing EDTA supplemented with 1000 U catalase/mL and into serum separator tubes containing clot activator for plasma and serum separation, respectively. Samples for plasma collection were transported on ice and immediately centrifuged at 4 °C and 1700 x g for 15 min; the samples for serum collection were allowed to clot at room temperature before centrifugation. Plasma and serum samples were thereafter stored at –70 °C until analysis.

Plasma glucose concentrations were measured by using the glucose oxidase method (Model II Glucose Analyzer; Beckman Instruments, Fullerton, CA). Plasma insulin was measured by using a double-antibody radioimmunoassay (Linco Research Inc, St Charles, MO). HOMA-IR was used as a measure of IR. This value was calculated from fasting concentrations of insulin and glucose by using the following equation (9-11):

(2)

Inflammatory biomarkers
Cytokine concentrations were measured in duplicate by using an enzyme-linked immunosorbent assay kit (ELISA; BioSource International, Camarillo, CA). Interleukin (IL)-1β, IL-6, and tumor necrosis factor- (TNF-) were measured in plasma, and IL-8 and IL-10 were measured in serum. The assay analytic sensitivity was 2.0 pg/mL for Il-1β and IL-6, 3.0 pg/mL for TNF-, 0.7 pg/mL for IL-8, and 1.0 pg/mL for IL-10. Interassay and intraassay variability for the cytokine measurements was as follows: 5% and 4% for Il-1β, 6% and 8% for IL-6, 10% and 5% for TNF-, 5% and 5% for IL-8, and 3% and 4% for IL-10, respectively. Serum C-reactive protein (CRP) concentrations were measured by using the high-sensitivity particle-enhanced immunoturbidimetric assay (Roche Modular System, Indianapolis, IN). Analytic sensitivity of the CRP assay was 0.003 mg/dL. Adiponectin and resistin analysis was evaluated by using the Human Serum Adipokine (Panel A) Lincoplex Kit [Linco Research (now Millipore), Billerica, MA]. The assay's analytic sensitivity was 6.7 pg/mL for resistin and 145.4 pg/mL for adiponectin. Intraassay and interassay variability was 1.4–7.9% and <21%, respectively.

Bioelectrical impedance analysis
Lean body mass and total %BF was determined by using a bioelectrical impedance analyzer (RJL Systems, Clinton Township, MI). The subjects were placed in a supine position with their arms at, but not touching, their sides and with their legs apart. Disposable impedance plethysmography source electrodes were positioned on the dorsal surface of the wrist on the right side and the anterior surface of the ipsilateral ankle. The proximal detector electrodes were placed between the distal prominences of the radius and ulna and between the malleoli of the ankle. A current of 800 µA at 50 kHz was applied to the subject at the distal electrodes. A voltage drop detected through the proximal electrodes records impedance. Resistance and reactance are measured as current flows through the compartments of the body and are used to determine overall %BF (12).

Statistical analysis
Statistical analyses were performed in 2 phases. In the first phase, baseline characteristics, the prevalence of IR and the presence of the potential determinants of IR were compared in CKD patients and control subjects by using the Mann-Whitney U test for continuous variables and the chi-square or Fisher's exact test for categorical variables.

Associations between HOMA-IR and the inflammatory cytokines, adipokines, hormonal markers, BMI, and %BF were assessed in study participants by using Spearman's correlation coefficients (r_s). To adjust for age and sex, multiple linear regression models were used with HOMA-IR as the outcome variable. BMI was further categorized into tertiles (BMI: <24.9, 25–30, or >30), and mean HOMA-IR values within each BMI category were graphically presented separately for CKD patients and control subjects. We conducted a test for linear trend to assess the equivalence in HOMA-IR scores among BMI categories.

In the second phase, CKD patients and control subjects were analyzed separately. Multivariate linear regression was conducted to assess the independent effect of BMI on HOMA-IR after adjustment for covariates including race, sex, age, adiponectin, and eGFR. Covariates were chosen a priori within an allowable number to prevent the model from over-fitting, because they were considered to be associated with the outcome variable, HOMA-IR. To assess whether the observed effect of BMI and %BF is caused by a factor that was correlated to both variables, we further combined the 2 variables into a factor by using principal components analysis. This combined variable was then assessed in a similar multivariable regression.

Regression residuals were verified for normality. If residual analysis did not fulfill the assumptions of normality, sensitivity analysis was performed with natural log transformation of the dependent variable, which did not affect the overall significance. We used SPSS software (version 13.0; SPSS Inc, Chicago, IL) for analyses and a 2-sided 5% significance level for all statistical inferences.

	RESULTS

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Baseline characteristics
The baseline characteristics of the CKD patients and the matched control subjects are shown in Table 1; the CKD patients were subdivided by disease stage. Race, sex, and BMI did not differ significantly between the groups. CKD patients were significantly older than the control subjects. Both the CKD and the control groups were composed of overweight and obese persons (BMI range: 25–32 and 26–30, respectively). Mean %BF for the CKD and control groups also did not differ significantly. In this nondiabetic population, the mean HOMA-IR score was 3.7 ± 3.2 in CKD patients and 3.1 ± 1.8 in control subjects (P = 0.73). As expected, baseline eGFR was significantly (P < 0.001) lower in the CKD patients than in the control subjects. The only significant (P < 0.001) difference between patients with stage 3 CKD and those with stage 4 CDK was in baseline eGFR. Significant differences were noted by sex in baseline weight, %BF, and adiponectin concentrations; therefore, subsequent analyses were adjusted for age and sex.

Table 2

Figure 1

View larger version (10K): [in this window] [in a new window]   FIGURE 1.. Homeostasis model assessment of insulin resistance (HOMA-IR) versus BMI ranges in patients with chronic kidney disease (CKD) and control subjects. Mean HOMA-IR scores in CKD patients and control subjects by BMI ranges of normal (<24.9), overweight (25–30), and obese (>30) differed significantly between the 3 ranges (P < 0.001, linear trend test). Error bars represent 95% CIs. BMI <24.9 data from control subjects were omitted because n = 3.

Table 3

	DISCUSSION

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In the present study, we examined the determinants of IR in nondiabetic patients with stage 3–4 CKD on the basis of the hypothesis that worsening kidney function would be associated with increasing IR. To our surprise, our results show that IR in this CKD patient cohort is primarily determined by BMI and not by eGFR. Furthermore, %BF, when substituted for BMI, was also predictive of IR in CKD patients. Further analysis indicated that the %BF of BMI is the relevant component predicting IR (ie, HOMA-IR); this relation was maintained in multivariate regression analysis. Adjustment for IL-6 and TNF- did not change these results. Whereas a significant association between BMI and IR is well recognized in the general population, the relation between body composition (in particular, %BF and IR) in stage 3–4 CKD patients has been less well studied, and it constitutes a novel aspect of the present study. To our knowledge, this study is one of the first to describe BMI as the primary determinant of IR in CKD patients, and it is the first to evaluate the relative contribution of fat mass in this relation.

Recent studies have suggested a complex relation between IR and CKD. A cross-sectional study utilizing participants from the third National Health and Nutrition Examination Survey (NHANES III) examined associations between metabolic syndrome and CKD and found that a person's odds of having kidney disease increased as the number of metabolic syndrome components possessed by him or her increased. This association remained significant after adjustment for the presence of hypertension and diabetes, 2 well-known causes of CKD (5). Kurella et al (18) conducted a prospective study using the Atheroslcerosis Risk in Communities study cohort to establish the metabolic syndrome as an independent risk factor for CKD in nondiabetic adults. Their data indicated that obesity and other components of the metabolic syndrome may contribute to the development or progression of CKD, but the data did not indicate whether the development of CKD also contributes to IR.

We observed that, in our stage 3–4 CKD patient group, eGFR did not correlate with the degree of IR. This has also been noted by other investigators who evaluated the presence of IR in CKD (19, 20), regardless of the method by which IR or GFR was measured. Previous studies that examined IR in kidney disease patients are summarized in Table 4. Kobayashi et al (22) described a relation between eGFR and IR that was calculated with the use of the hyperinsulinemic euglycemic clamp technique, but that relation was not maintained in multivariate analysis. Thus, whereas IR is present in these patients, the severity of underlying CKD does not seem to be the principal cause of the metabolic derangement—at least in our study population.

Visceral fat contains greater amounts of inflammatory mediators—including CRP, IL-6, and TNF-—than does subcutaneous fat, and these mediators are thought to contribute to the development of IR (29). In the ESRD population, Axelsson et al (30) found an association between the inflammatory biomarkers and regional fat distribution, in which greater truncal fat mass correlated with higher concentrations of IL-6 and CRP. It is interesting that the data in the present study showed a negative correlation between HOMA-IR and the concentrations of individual cytokines. The cause of this counterintuitive relation is not clear, and that lack of clarity calls for further studies examining the mechanisms underlying these observations.

There are several limitations to our study, in particular the relatively small size of the study population. In addition, the cross-sectional nature of this study, although showing an association between BMI and IR, does not provide information regarding causal relations. Moreover, the control subjects were significantly younger than the CKD patients, which may have accounted for the differences noted. However, subsequent analysis was adjusted for age and sex. Rather than direct measurement, the GFR in both groups was estimated by the use of the abbreviated equation described in the MDRD study (7). This equation has been found to be an adequate predictor of GFR when 24-h creatinine clearance or inulin clearance is not available (31). Ideally, the use of the hyperinsulinemic euglycemic clamp would have provided the best measure of IR in this study, but its use is laborious and time-consuming for a study of this size. Shoji et al (9) showed that HOMA-IR scores correlate well with the hyperinsulinemic euglycemic clamp as a measure of IR in individuals with a wide range of GFRs. Whereas we provide intriguing data regarding body composition and IR, the %BF measured in the CKD patients and in the control subjects in the current study did not differentiate between truncal and nontruncal fat, which may have resulted in an underestimation of the relation of %BF, adiponectin concentrations, and IR. Finally, limiting our study population to persons with stage 3 or 4 CKD limited the extrapolation of our findings to other stages of kidney disease and hindered the detection of a correlation between GFR and IR over a wider range of kidney functions.

In summary, our data show that BMI measures, particularly %BF, are the major determinant of IR in nondiabetic stage 3–4 CKD patients. Whereas the IR of uremia may be seen in the population of ESRD patients undergoing dialysis, who experience greater metabolic stress (3), 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.

	ACKNOWLEDGMENTS

 
The authors thank Karen Majchrzak, Cindy Booker, Andrew Vincz, and the Vanderbilt General Clinical Research Center nursing staff and Jane Kane at Maine Medical Center for their excellent technical assistance.

The authors’ responsibilities were as follows—TAI and JH: contributed equally to designing the experiment, collecting and analyzing the data, and writing the manuscript; MLT: analyzed the data and wrote the manuscript; and AS: performed data analyses. None of the authors had a personal or financial conflict of interest.

	REFERENCES

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