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Nephrol Dial Transplant. Sep 2011; 26(9): 2814–2819.
Published online Jan 19, 2011. doi:  10.1093/ndt/gfq817
PMCID: PMC3203409
Correlates of insulin resistance in older individuals with and without kidney disease
Michael Landau,1 Manjula Kurella-Tamura,2 Michael G. Shlipak,3,4 Alka Kanaya,4 Elsa Strotmeyer,5 Annemarie Koster,6,7,8 Suzanne Satterfield,9 Eleanor M. Simsonick,10 Bret Goodpaster,11 Anne B. Newman,5,11 and Linda F. Friedcorresponding author5,11,12, for the Health, Aging and Body Composition Study
1Department of Pathology, Cleveland Clinic, Cleveland, OH, USA
2Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
3General Internal Medicine Section, San Francisco VA, San Francisco, CA, USA
4Departments of Medicine, Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
5Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
6Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, National Institutes of Health, Bethesda, MD USA
7School of Public Health and Primary Care (CAPHRI), Maastricht, The Netherlands
8Department of Internal Medicine, Maastricht University, Maastricht, The Netherlands
9Department of Preventive Medicine, University of Tennessee, Memphis, TN, USA
10National Institute of Aging, National Institute of Health, Baltimore, MD, USA
11Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
12Renal Section, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA
corresponding authorCorresponding author.
Correspondence and offprint requests to: Linda F. Fried; E-mail: linda.fried/at/va.gov
Received September 29, 2010; Accepted December 15, 2010.
Background. Chronic kidney disease (CKD) is associated with insulin resistance (IR). Prior studies have found that in individuals with CKD, leptin is associated with fat mass but resistin is not and the associations with adiponectin are conflicting. This suggests that the mechanism and factors associated with IR in CKD may differ.
Methods. Of the 2418 individuals without reported diabetes at baseline, participating in the Health, Aging and Body Composition study, a study in older individuals aged 70–79 years, 15.6% had CKD defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 based on cystatin C. IR was defined as the upper quartile of the homeostasis model assessment. The association of visceral and subcutaneous abdominal fat, percent body fat, muscle fat, lipids, inflammatory markers and adiponectin were tested with logistic regression. Interactions were checked to assess whether the factors associated with IR were different in those with and without CKD.
Results. Individuals with IR had a lower eGFR (80.7 ± 20.9 versus 75.6 ± 19.6, P < 0.001). After multivariable adjustment, eGFR (odds ratio per 10 mL/min/1.73m2 0.92, 95% confidence interval 0.87–0.98) and CKD (1.41, 1.04–1.92) remained independently associated with IR. In individuals with and without CKD, the significant predictors of IR were male sex, black race, higher visceral fat, abdominal subcutaneous fat and triglycerides. In individuals without CKD, IR was associated with lower high-density lipoprotein and current nonsmoking status in multivariate analysis. In contrast, among individuals with CKD, interleukin-6 (IL-6) was independently associated with IR. There was a significant interaction of eGFR with race and IL-6 with a trend for adionectin but no significant interactions with CKD (P > 0.1). In the fully adjusted model, there was a trend for an interaction with adiponectin for eGFR (P = 0.08) and significant for CKD (P = 0.04 ), where adiponectin was associated with IR in those without CKD but not in those with CKD.
Conclusions. In mainly Stage 3 CKD, kidney function is associated with IR; except for adiponectin, the correlates of IR are similar in those with and without CKD.
Keywords: chronic kidney disease, cystatin C, insulin resistance, subcutaneous fat
Insulin resistance (IR) is the central pathophysiologic process of the metabolic syndrome, an important risk factor for development of cardiovascular disease [1]. IR increases with age [2] and may be related to changes in body composition and fat distribution but could also follow a decline in kidney function. End-stage renal disease is characterized by IR [3], and nondiabetic individuals with mild-to-moderate kidney disease have lower insulin sensitivity [47]. In some studies [6, 8], but not all [7], the severity of IR was correlated with glomerular filtration rate (GFR). In a cross-sectional analysis of the National Health and Nutrition Examination Survey data, the prevalence of a low GFR increased with higher levels of IR [characterized by homeostasis model assessment (HOMA) or by insulin levels] [9].
The mechanism of IR in the general population that occurs as part of the metabolic syndrome is believed to be related to increased visceral fat deposits and altered adipose signaling [1]. However, the mechanism of IR in kidney disease, especially among nondiabetic patients, is more obscure. IR has been found to develop in animal models of kidney failure, suggesting that kidney disease causes IR independent of obesity [3]. In support of this hypothesis, one study demonstrated that resistin, an adipokine and proposed mediator of IR in the general population, was not associated with IR or fat mass in individuals with chronic kidney disease (CKD), despite higher levels in those with CKD [10]. The reported associations of adiponectin with obesity in CKD are conflicting. In some studies, the inverse relationship of adiponectin with IR or fat mass is similar to the general population [1113]. However, in other studies, no relationships were found after adjustment [1416].
Therefore, we postulated that the pathways to IR, and therefore the correlates of IR among nondiabetic individuals with and without CKD, would differ. In particular, we hypothesized that in individuals with CKD, IR would not be correlated with measures of subcutaneous, visceral or muscle fat.
Study population
The Health, Aging and Body Composition (Health ABC) study is a longitudinal study of changes in body composition, clinical conditions affecting these changes and their impact on functional status and development of disability in 3075 older individuals. Participants were recruited from Medicare eligibility lists in Pittsburgh, PA and Memphis, TN between March 1997 and July 1998. Whites were recruited from a random sample of the lists; blacks were recruited from all age-eligible persons residing in the respective communities. Eligibility criteria were age 70–79 years, no reported difficulty in performing activities of daily living, walking one-quarter of a mile or walking up 10 steps without resting, no reported need of assistive devices to ambulate (e.g. cane, walker), no history of active treatment for cancer in the prior 3 years and no plan to move out of the area in the next 3 years. All participants gave informed consent. For this analysis, those who reported a history of diabetes, used a diabetic medication at baseline or had a fasting glucose = 126 mg/dL were excluded (n = 576). Baseline data were used for this analysis.
Main outcomes/predictors
IR was assessed using the HOMA: HOMA-IR = fasting glucose (mmol/L) × fasting insulin (μU/mL)/22.5 [17] and defined as the upper quartile of the HOMA score in individuals without diabetes [1]. Kidney function was assessed by using serum cystatin C values. CKD was characterized as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 using a formula based on serum cystatin C derived from iothalamate clearances eGFRcys = 76.7×cysC−1.18 [18]. Secondary analyses classified CKD using the CKD-EPI formula [14], which has been shown to be more accurate than the MDRD formula for classifying individuals as having CKD (eGFR = 141 × min(Scr/κ,1)α × max(Scr/κ, 1)−1.209 × 0.993age × 1.018 [if female] × 1.159 [if black], where κ = 0.7 for women and 0.9 for men, α = −0.329 for women and −0.411 for men and min indicates minimum of Scr/κ or 1 and max indicates the maximum of Scr/κ or 1) [19].
Fasting insulin was measured using a microparticle enzyme immunoassay on the Abbott IMx (Abbott Laboratories Diagnostic Division, South Pasadena, CA) and had a coefficient of variation (CV) of 4.4%. Fasting serum glucose and creatinine were measured using the Vitros 950 (Johnson & Johnson, Raritan, NJ). Cystatin C was measured using a particle-enhanced immunonephelometric assay with a nephelometer, CV 2.0–2.8% (BNII; Dade Behring, Newark, DE).
Covariates
Covariates were selected a priori based on their proposed biological relationship with IR or CKD and included age, sex, race by self-report, systolic blood pressure (BP), diastolic BP, triglycerides, cholesterol, high-density lipoprotein (HDL), C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis alpha (TNF-α), adiponectin, fat mass, lean mass, percent fat, waist circumference, body mass index (BMI), visceral abdominal fat, subcutaneous abdominal fat and thigh muscle fat. Smoking status was defined as current smoking.
Lean body mass and fat mass were estimated using dual-energy X-ray absorptiometry using a Hologic QDR4500A Scanner with Software Version 8.21 [20]. The scans were read centrally at the University of California, San Francisco reading center. Abdominal (visceral and subcutaneous fat) and intermuscular thigh fat were measured using computed tomography (CT) [9800 Advantage scanner (GE, Milwaukee, WI), in Pittsburgh and either Picker PQ 2000S (Marconi Medical Systems, Cleveland, OH) or Somatom Plus 4 (Siemens, Erlangen, Germany) at Memphis]. Scans of the abdomen were taken between the fourth and fifth lumbar vertebrae. Visceral fat was distinguished from subcutaneous fat by drawing a line along the internal abdominal wall fascial plane. In the thighs, intermuscular and visible intramuscular fat tissues were separated from subcutaneous adipose tissue by drawing a line along the deep fascial plane surrounding the muscles. Areas were calculated by multiplying the number of pixels by the pixel area for each type of fat using ILD development software (RSI Systems, Boulder, CO).
IL-6 was measured using a commercial ELISA High Sensitivity HS600 Quantikine kit, CV 10.3% (R&D Systems Inc., Minneapolis, MN); TNF-α was measured using a commercial ELISA High Sensitivity HSTA50 kit, CV 15.8% (R&D Systems Inc.); CRP was measured by ELISA, CV 8.0% (Calbiochem, San Diego, CA) and total adiponectin was measured in duplicate by radioimmunoassay, CV 1.8–3.6% (Linco Research, St Charles, MO).
Statistical analysis
Differences between groups were compared using t-test or Wilcoxon test for continuous variables and chi-square test for categorical variables. Characteristics of individuals with and without IR were compared in the overall group and then after stratification by CKD status.
Logistic regression was performed to evaluate characteristics associated with IR. Due to their skewed distribution, CRP, IL-6, TNF-α, adiponectin, visceral abdominal fat, subcutaneous abdominal fat and thigh muscle fat were log transformed. The initial model included eGFR or CKD, age, sex, race, systolic BP, diastolic BP, smoking, triglycerides, cholesterol, HDL, CRP, IL-6, TNF, visceral abdominal fat, subcutaneous abdominal fat and thigh muscle fat. As the variables assessing fat (fat mass, percent fat, waist circumference and BMI) were highly correlated with subcutaneous abdominal fat and visceral fat (>0.6), they were excluded from the models. Backwards step-wise regression, forcing age, race, sex and CKD/eGFR into the model were performed. We tested for potential interactions of variables associated with IR with CKD or eGFR. In the primary analysis, the regression was performed using the upper quartile of HOMA for IR (>2.33). Sensitivity analysis was performed using linear regression with log (HOMA) as a continuous variable. We also evaluated the association of CKD and other covariates with IR in individuals of normal weight (BMI < 25 kg/m2).
Statistical analyses were performed using SAS, version 9.1 (SAS Institute, Cary, NC).
Of the 3075 individuals evaluated at the baseline examination, we excluded 576 because of diabetes and 81 because of missing values for fasting glucose, fasting insulin or cystatin C.
Mean age of the overall group was 73.6 years ± 2.8, 53% were female and 37% were black. The overall eGFR was 67.9 ± 14.4, with a mean eGFR of 50.8 ± 8.9 in those with CKD. Of the individuals with CKD, 293 had Stage 3A, 68 had Stage 3B, 10 had Stage 4 and 6 had Stage 5 CKD. Individuals with IR were more likely to be black, to have hypertension, CKD, a higher total weight, percent fat, waist circumference, BMI, visceral fat, subcutaneous fat and thigh muscle fat. They also had higher triglycerides, CRP, IL-6 and TNF-α and lower HDL and eGFR (Table 1). Figure 1 shows the relationships between measures of body fat stratified by CKD. The relationship of BMI and percent body fat were similar (Figure 1A). Individuals with CKD had higher visceral fat (median 145.1 versus 124.3 cm2, P < 0.001) and intermuscular fat (18.7 versus 17.7 cm2, P < 0.001) but similar abdominal subcutaneous fat (261.3 versus 264.0 cm2, P = 0.44) to individuals without CKD (Figure 1A and B). However, the relationships with IR were similar, though there was a trend toward a statistically significant interaction for intermuscular thigh fat (P = 0.06) (Figure 1A). When stratified by CKD status, individuals with CKD had higher inflammatory marker levels and lower HDL than those without CKD. However, the relationships between those with and without IR were similar and none of the P-values for interaction were significant in unadjusted analyses (Table 2). In unadjusted analysis, there were significant interactions of eGFR with race (P = 0.03), IL-6 (P = 0.02) and a trend for log(adiponectin) (P = 0.07).
Table 1.
Table 1.
Participant characteristics by IR status (IR defined as upper quartile of HOMA >2.33)a
Fig. 1.
Fig. 1.
(A) Measures of fat mass stratified by IR and kidney disease in older individuals without diabetes. IR was defined as the upper quartile of HOMA (>2.33). CKD was defined as eGFR <60 mL/min/1.73m2. BMI units: kg/m2, intermuscular fat units: (more ...)
Table 2.
Table 2.
Participant characteristics by IR status, stratified by kidney diseasea
The multivariable associations with IR are shown in Table 3. eGFR and CKD were independently associated with IR. The other independent factors associated with IR were race, sex, measures of fat distribution and dyslipidemia (higher triglycerides, lower HDL) and in the model with eGFR, muscle fat. Current smokers were less likely to be insulin resistant. The only significant interaction in the multivariate analysis was CKD with adiponectin (P = 0.04). There was a trend for an interaction of adiponectin with eGFR (P = 0.07). When stratified by CKD, the associations and the magnitude of the associations with race, triglycerides and fat mass were similar. However, adiponectin, HDL and current smoking were not independently associated and IL-6 was significantly associated with IR in those with CKD. We also evaluated the associated factors with IR in those with normal body weight; CKD remained a significant predictor of IR (odds ratio 2.61, 95% confidence interval 1.27–5.36). Other significant predictors were female sex, higher triglycerides and visceral fat and lower adiponectin.
Table 3.
Table 3.
Significant associations with IR, stratified by CKD among Health ABC participants
We repeated the analyses using HOMA as a continuous variable and the results were similar. The results were also similar if CKD was defined using the CKD-EPI formula (not shown).
In a nondiabetic sample of adults aged 70–79 years, CKD was independently associated with a 40% increased odds of IR and was associated with higher visceral fat mass. However, the correlates of IR, including multiple measures of fat mass, were largely similar in those with and without CKD. Only the association of adiponectin with IR varied according to CKD status; specifically, adiponectin was associated with a reduced odds of IR among those without CKD but not among those with CKD. These findings suggest that the mechanisms of IR in CKD are similar to those without CKD.
It has long been known that CKD is associated with IR even in the absence of diabetes [21]. Several large prospective studies have demonstrated that IR is associated with an increased risk for developing CKD [2224]. CKD may also contribute to IR. The evidence that kidney disease directly causes IR comes mainly from animal models. In experimental models, kidney failure led to hyperglycemia and hyperinsulinemia with glucose loads and fasting hyperinsulinemia [3, 25, 26]. We were not able to find any prospective studies that evaluated whether kidney disease predicted the development or worsening of IR. IR is present in mild kidney dysfunction and progresses with decreasing GFR [6, 8, 27] and improves after beginning hemodialysis [28].
In the general population, IR is associated with central adiposity [29]. The accumulation of abdominal fat can occur in either the subcutaneous or visceral compartments and there is controversy as to which type of fat accumulation is more closely associated with IR. Some studies have shown that IR is more closely associated with visceral fat [30, 31], while others have shown the association to be stronger with subcutaneous fat [32, 33]. A recent longitudinal study showed that visceral but not subcutaneous adiposity in Japanese Americans predicts future IR [34]. The mechanism of IR in obesity is thought to involve the disruption of insulin signaling via increases in free fatty acids, adipokines and inflammatory markers, together with reduced adiponectin secretion [35, 36].
The mechanism of IR in nondiabetic CKD is not fully understood. Many different mechanisms have been proposed including uremic carbamylation of signaling proteins, acidosis, changes in the apolipoprotein profile, oxidative stress, accumulation of free fatty acids and inflammation [27, 35, 3740]. CKD is associated with a loss of lean mass and a small increase in fat mass [41] thus BMI and abdominal circumference may be poor surrogate markers for fat mass. In addition, accumulation of adipokines, such as leptin, resistin and visfatin, as GFR declines may also contribute to IR [40]. Although resistin has been shown to mediate obesity-induced IR in mice, studies do not support a similar role for resistin in humans [10]. Adiponectin is an adipokine that is made exclusively in adipocytes; it is anti-inflammatory and insulin sensitizing [42, 43]. In the general population, adiponectin is inversely associated with IR and fat mass. Despite higher levels of IR in end-stage renal disease, adiponectin levels are higher than in healthy controls, which may represent a counter-regulatory response to inflammation or reduced clearance [44]. Within persons with kidney disease, some studies [1113], but not all, have found adiponectin inversely associated with fat mass or visceral fat [1416]. Thus, there may be more than one opposing determinant for adiponectin levels in CKD (e.g. inflammation leading to higher levels and visceral fat leading to lower levels). Whether this explains the significant interaction seen in this study is not clear.
A strength of the study is that the data include CT-based fat measurements allowing separation of abdominal fat into subcutaneous and visceral fat and measurement of muscle fat and measurement of several cytokines associated with IR. Weaknesses of the study include the lack of actual measurement of GFR as well as the limitations of using HOMA to assess IR. Although the euglycemic clamping method is the gold standard for measuring IR, this technique is not feasible in a large epidemiologic study. Shoji et al. [45] did find a strong relationship of HOMA with insulin clamp studies in kidney failure (r = −0.668). Few Health ABC participants had advanced CKD, thus we were unable to determine whether these associations differed by severity of CKD. It is possible that CKD may contribute to IR only when eGFR is severely reduced. We also did not have information on microalbuminuria, which is commonly associated with IR. The age range is restricted to those aged 70–79 years, who were well functioning, which limits generalizability. Finally, as this was a cross-sectional study, we could not determine whether the observed associations were causal.
This study demonstrates that the correlates of IR, including CT measures of greater fat mass, are similar among elderly individuals with and without CKD, suggesting that the mechanisms of IR may be similar in the CKD population. The similar associations with IR in CKD imply that interventions directed at obesity (e.g. exercise) would have similar benefit as in the general population.
Acknowledgments
The Health, Aging and Body Composition Study is supported by NIA contract numbers of all participating centers (N01-AG-6-2101; N01-AG-6-2103; N01-AG-6-2106). This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging. The contents do not represent the views of the Department of Veterans Affairs or the United States Government. The results presented in this paper have not been published previously in whole or part.
Conflict of interest statement. None declared.
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