Abstract
Background and objectives: Microalbuminuria increases cardiovascular risk and is considered a metabolic disorder. Low glomerular filtration rate is also associated with increased cardiovascular risk, but the relation of low glomerular filtration rate to metabolic disorders is not well understood.
Design, setting, participants, & measurements: Designed as a cross-sectional, epidemiologic study, the Insulin Resistance Atherosclerosis Study was conducted in four centers: San Antonio (Texas), San Luis Valley (Colorado), and Oakland and Los Angeles (California). The Modification of Diet in Renal Disease equation was used to classify individuals without diabetes and with normoalbuminuria (n = 856; age 40 to 69 yr) by the presence or absence of low glomerular filtration rate (<60 ml/min per 1.73 m2). A direct marker of insulin resistance, the insulin sensitivity index, was measured by the frequently sampled intravenous glucose tolerance test.
Results: Low glomerular filtration rate was related to hypertension and the metabolic syndrome. Low glomerular filtration rate was associated with fasting insulin concentration and insulin sensitivity index. Low glomerular filtration rate was also associated with insulin concentration after adjustment for potential determinants of glomerular filtration rate but was not associated with insulin sensitivity index.
Conclusions: Low glomerular filtration rate is associated with increased insulin concentration in individuals without diabetes and with normoalbuminuria. Longitudinal analyses are needed to determine whether insulin concentration (insulin resistance) precedes the deterioration of renal function.
Nineteen million adults have chronic kidney disease (CKD) in the United States (1). CKD is frequently undiagnosed (2) and leads more often to cardiovascular disease (CVD) events than to ESRD (3). CKD has been associated with several metabolic disorders (4–8), but the relation of CKD to these disorders is not well understood beyond the effects of diabetes and hypertension (9). A better understanding of this relationship may provide more opportunities for prevention and treatment of both CKD and CVD.
Microalbuminuria is an early marker of glomerular injury in patients with diabetes and may eventually progress to macroalbuminuria and a decrease in GFR in the absence of specific intervention (10). Microalbuminuria is considered a metabolic disorder because of its relation to insulin resistance (8,11,12), diabetes (13), and CVD (14,15). In participants without diabetes from the Insulin Resistance Atherosclerosis Study (IRAS), microalbuminuria had a more robust association with hypertension and insulin resistance than with fasting insulin concentration, obesity, and markers of chronic inflammation (11,12). Nevertheless, albuminuria is frequently absent in individuals with or without diabetes and with CKD for reasons that remain unknown (16–19).
In this study, we examined the relation of low GFR (defined as GFR <60 ml/min per 1.73 m2 body surface area) to insulin resistance and other metabolic disorders in IRAS participants who were free of diabetes and albuminuria. We hypothesized that low GFR might be similar to microalbuminuria in its relation to metabolic disorders. We used the Modification of Diet in Renal Disease (MDRD) equation to estimate GFR, because a direct measure of GFR was not available in the IRAS. The MDRD equation is considered appropriate for the identification of individuals with GFR <60 ml/min per 1.73 m2 (10).
Materials and Methods
Study Population
The IRAS is an epidemiologic study exploring the relationship between insulin resistance, CVD risk factors, and cardiometabolic outcomes in black, Hispanic, and non-Hispanic white individuals with a broad range of glucose tolerance. A detailed description of the IRAS has been already published (20). In brief, this study was conducted at four clinical centers (San Antonio, TX; San Luis Valley, CO; and Oakland and Los Angeles, CA) from 1992 to 1994. Using sampling strategies, recruitment was tailored to yield approximately equal numbers of participants by ethnicity, gender, and glucose tolerance categories (type 2 diabetes, impaired glucose tolerance, and normal glucose tolerance). A total of 1625 individuals aged 40 to 69 yr (overall enrollment rate of 48%) completed the baseline examination in two visits (approximately 1 wk apart [range 7 to 28 d]). Glucose tolerance status was determined by oral glucose tolerance test, and insulin sensitivity and secretion were assessed by the frequently sampled intravenous glucose tolerance test (FSIGTT) (21) during the first and second visits, respectively.
A total of 856 participants without diabetes and with normal urine excretion of albumin were eligible for analysis and used in this report. Study protocols met the standards of the Declaration of Helsinki and had the approval of local institutional review boards. All participants gave written informed consent.
Acquisition of Measurements and Laboratory Data
Age, race/ethnicity, education, current cigarette smoking, alcohol consumption, diet, and medication were assessed by self-report. Height, weight, and waist circumference were measured following a standardized protocol. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters. Resting systolic BP (SBP) and diastolic BP (DBP) were measured three times, and the second and third measurements were averaged.
Glucose concentration was determined using standard methods (Yellow Springs Instruments, Yellow Springs, OH). Total and HDL cholesterol concentrations were measured in plasma by the β-quantification procedure. Triglyceride concentration was measured by enzymatic methods with the use of glycerol blanked assays on a Hitachi (Boehringer Mannheim, Indianapolis, IN) autoanalyzer. Serum creatinine concentration was determined by a modified kinetic Jaffe reaction (22) by standard clinical methods at the central IRAS laboratory with a Paramax PLA instrument (Baxter Diagnostics, Chicago, IL). Fasting insulin concentration was measured using the dextran-charcoal RIA (23), which had considerable cross-reactivity with proinsulin. Insulin intra-assay and interassay coefficients of variation (CV) were 10.6 and 19.3%, respectively. Fasting serum intact proinsulin concentration was determined using highly specific immunoradiometric assays with split-pair CV of 14% (24). C-reactive protein (CRP) concentration was measured using an ultrasensitive competitive immunoassay (Calbiochem, La Jolla, CA) with an interassay CV of 8.9% (25). Leptin concentration was measured using a RIA (Linco Research, St. Louis, MO) at the central IRAS laboratory at the University of Southern California, Los Angeles. Interassay CV for leptin was 7% in the IRAS.
Fasting insulin concentration and proinsulin-to-insulin ratio were used as surrogates of insulin sensitivity and secretion, respectively (26). Direct measures of insulin sensitivity and secretion were also measured by FSIGTT (21). Glucose in the form of 50% solution (0.3 g/kg) and regular human insulin (0.03 U/kg) were injected through an intravenous line at 0 and 20 min, respectively. Blood was collected at −5, 2, 4, 8, 19, 22, 30, 40, 50, 70, 100, and 180 min for plasma glucose and insulin concentrations. Insulin sensitivity, expressed as the insulin sensitivity index (Si), was calculated by mathematical modeling methods (MINMOD, version 3.0 [27]). FSIGTT has been compared with the hyperinsulinemic-euglycemic clamp and has been shown to be an adequate estimate of insulin resistance (28). Insulin secretion, expressed as the acute insulin response (AIR), was calculated as the mean of 2- and 4-min insulin concentrations after glucose administration. AIR correlates well with first-phase insulin response during the hyperglycemic clamp (29).
The MDRD equation was used to estimate GFR (10): GFR = 186 × creatinine−1.154× age−0.203× 0.742 [if female] × 1.21 [if black]. In this equation, GFR was expressed in ml/min per 1.73 m2, age in years, and serum creatinine in mg/dl. Participants with GFR <60 ml/min per 1.73 m2 were considered to have low GFR. None of them had a GFR <30 ml/min per 1.73 m2.
Participants with diabetes (fasting plasma glucose concentration ≥7.0 mmol/L [≥126 mg/dl], 2-h plasma glucose concentration ≥11.1 mmol/L [≥200 mg/dl], and/or treatment with hypoglycemic medications) or albuminuria (albumin-to-creatinine ratio ≥2 mg/mmol) (30) were excluded from analysis. Participants with three or more of the following five metabolic disorders were considered to have the metabolic syndrome (31): Increased waist circumference (≥102 cm in men, ≥88 cm in women), hypertriglyceridemia (≥1.7 mmol/L [≥150 mg/dl]), low HDL cholesterol concentration (<1.03 mmol/L [<40 mg/dl] in men, <1.3 mmol/L [<50 mg/dl] in women), elevated BP (SBP ≥130 mmHg and/or DBP ≥85 mmHg and/or pharmacologic treatment), and elevated fasting glucose (≥5.6 mmol/L [≥100 mg/dl]). Hypertension was defined as SBP ≥140 mmHg and/or DBP ≥90 mmHg and/or treatment with antihypertensive medications.
Statistical Analyses
The analysis was performed using the SAS statistical software (SAS Institute, Cary, NC). One-way analysis of covariance was used to compare continuous variables between GFR categories to account for the effect of age, gender, race/ethnicity, and clinic location. Similarly, logistic regression analysis was used to compare rates. Multiple logistic regression analysis was also used to determine the odds for hypertension (other individual metabolic disorders or antihypertensive treatment) in individuals with low GFR relative to the odds in individuals with normal/near-to-normal GFR after adjustment for potentially confounding variables. We entered in separate models interaction terms for low GFR × gender and low GFR × metabolic syndrome to examine differences in the relation of low GFR to metabolic disorders between men and women and between race/ethnic groups. The amount of change in relevant metabolic variables associated with low GFR relative to normal/near-to-normal GFR was assessed by multiple linear regression analysis to account for the effect of potential determinants of GFR. Natural logarithmic (ln) transformation of AIR; albumin-to-creatinine ratio; and levels of triglycerides, insulin, proinsulin, and CRP were used to correct for skewness and kurtosis. Given that some participants had Si = 0 and leptin concentration = 0, we used the natural log transformation of (Si + 1) and (leptin + 1), respectively. These variables were then back-transformed to their natural units for presentation in tables.
Results
Low GFR (<60 ml/min per 1.73 m2) rate was 7.0% (95% confidence interval [CI] 5.5 to 8.9). Low GFR rate in women was higher than that in men (9.3% [95% CI 7.0 to 12.3] versus 4.2% [95% CI 2.6 to 6.7]; P = 0.004). This difference remained significant (5.2% [95% CI 3.4 to 8.0] versus 2.1% [95% CI 1.2 to 3.9]; P = 0.003) after adjustment for age, ethnic origin, or clinic location. Interaction terms for gender × ethnic origin was tested in a logistic regression model that included low GFR as the dependent variable and age, gender, ethnic origin, and clinic location as covariates. Interaction terms for gender × ethnic origin was not statistically significant (P = 0.992); however, rates of low GFR were not equally distributed among ethnic groups: Black 2.7% (95% CI 1.0 to 6.7); Hispanic 2.3% (95% CI 1.2 to 4.5); non-Hispanic white 7.1% (95% CI 4.6 to 10.9). Relative to non-Hispanic white individuals, the odds of having low GFR were lower in black (odds ratio [OR] 0.36; 95% CI 0.13 to 0.99) and Hispanic individuals (OR 0.31; 95% CI 0.15 to 0.63).
Metabolic syndrome, hypertension, antihypertensive treatment, hypertriglyceridemia, elevated fasting glucose, and urinary albumin-to-creatinine ratio were more frequent in individuals with low GFR than in those with normal/near-to-normal GFR (Table 1). Individuals with low GFR also had higher levels of triglycerides, fasting insulin, proinsulin, Si, and leptin.
Table 1.
Sociodemographic and clinical variables by GFR categories after adjustment for age, gender, race/ethnicity, and clinic locationa
| Variable | GFR
|
P | |
|---|---|---|---|
| ≥60 ml/min | <60 ml/min | ||
| n | 796 | 60 | – |
| Age (yr; mean ± SE)b | 54.4 ± 0.3 | 60.8 ± 1.1 | <0.001 |
| Female gender (%; 95% CI)b | 53.9 (50.4 to 57.3) | 73.3 (60.8 to 83.0) | 0.004 |
| Ethnicity (%; 95% CI)b | |||
| black | 26.5 (23.6 to 29.7) | 10.0 (4.6 to 20.5) | 0.007 |
| Hispanic | 36.3 (33.0 to 39.7) | 23.3 (14.3 to 35.6) | 0.046 |
| non-Hispanic white | 37.2 (33.9 to 39.7) | 66.7 (53.9 to 77.4) | <0.001 |
| Hypertension (%; 95% CI) | 26.8 (23.6 to 30.2) | 43.6 (30.8 to 57.4) | 0.012 |
| Metabolic syndrome (%; 95% CI) | 31.8 (28.6 to 35.3) | 49.0 (35.9 to 62.3) | 0.012 |
| Components of the metabolic syndrome (%; 95% CI) | |||
| elevated waist circumference | 28.3 (25.1 to 31.7) | 26.1 (16.4 to 39.0) | 0.724 |
| hypertriglyceridemia | 28.9 (25.7 to 32.3) | 45.8 (33.0 to 59.2) | 0.011 |
| low HDL cholesterol | 50.5 (46.8 to 54.1) | 52.8 (39.3 to 66.1) | 0.743 |
| elevated BP | 42.6 (38.9 to 46.3) | 55.4 (41.5 to 68.5) | 0.082 |
| elevated fasting glucose | 39.2 (35.7 to 42.8) | 55.5 (41.8 to 68.4) | 0.025 |
| BMI (kg/m2; mean ± SE) | 28.30 ± 0.20 | 28.40 ± 0.70 | 0.861 |
| BMI range (kg/m2) | 15.7 to 63.3 | 18.8 to 44.3 | – |
| Waist circumference (cm; mean ± SE) | 90.00 ± 0.40 | 90.90 ± 1.50 | 0.590 |
| Waist circumference range (cm) | 58.8 to 140.0 | 66.9 to 116.7 | – |
| Fasting glucose (mmol/L; mean ± SE) | 5.43 ± 0.02 | 5.58 ± 0.07 | 0.057 |
| 2-h glucose (mmol/L; mean ± SE) | 6.78 ± 0.06 | 7.15 ± 0.24 | 0.293 |
| Triglycerides (mmol/L; mean ± SE)c | 1.28 ± 0.03 | 1.63 ± 0.12 | <0.001 |
| HDL cholesterol (mmol/L; mean ± SE) | 1.21 ± 0.01 | 1.18 ± 0.05 | 0.592 |
| SBP (mmHg; mean ± SE) | 120.80 ± 0.50 | 121.00 ± 2.00 | 0.915 |
| DBP (mmHg; mean ± SE) | 77.20 ± 0.30 | 76.80 ± 1.20 | 0.713 |
| Fasting insulin (pmol/L; mean ± SE)c | 73.70 ± 1.50 | 100.50 ± 9.50 | <0.001 |
| Fasting leptin (ng/ml; mean ± SE)c | 12.20 ± 0.30 | 16.10 ± 1.60 | 0.003 |
| Insulin sensitivity index (Si) (×10−4 min−1 · μU−1 · mL−1 | 1.83 ± 0.06 | 1.41 ± 0.17 | 0.038 |
| AIR (μU/ml; mean ± SE)c | 49.40 ± 1.50 | 53.00 ± 5.60 | 0.521 |
| Proinsulin (pmol/L; mean ± SE)c | 27.40 ± 0.60 | 34.90 ± 3.30 | 0.015 |
| Proinsulin-to-insulin ratio (mean ± SE)c | 0.37 ± 0.01 | 0.34 ± 0.03 | 0.254 |
| CRP (μg/ml; mean ± SE)c | 1.72 ± 0.07 | 2.18 ± 0.35 | 0.118 |
| Urinary albumin-to-creatinine ratio (mg/mmol; mean ± SE)c | 0.66 ± 0.01 | 0.79 ± 0.06 | 0.014 |
| Non-steroidal anti-inflammatory drugs (%; 95% CI) | 8.9 (7.1 to 11.2) | 13.2 (6.6 to 24.5) | 0.285 |
| Antihypertensive medications (%; 95% CI) | |||
| all | 16.9 (14.3 to 19.9) | 35.5 (23.7 to 49.3) | 0.001 |
| ACE inhibitors | 5.0 (3.7 to 6.8) | 11.3 (4.6 to 22.5) | 0.049 |
| β blockers and diuretics | 10.8 (8.7 to 13.4) | 20.2 (11.9 to 32.1) | 0.025 |
ACE, angiotensin-converting enzyme; AIR, acute insulin response; BMI, body mass index; CI, confidence interval; CRP, C-reactive protein; DBP, diastolic BP: SBP, systolic BP; Si, insulin sensitivity index.
Nonadjusted values.
Natural log transformation of Si, AIR, albumin-to-creatinine and proinsulin-to-insulin ratios, and levels of triglycerides, insulin, proinsulin, leptin, and CRP were used to correct for skewness and kurtosis. These variables were then back-transformed to their natural units for presentation here.
In logistic regression analysis, low GFR was associated with increased odds for hypertension, antihypertensive treatment, metabolic syndrome, hypertriglyceridemia, and elevated fasting glucose after adjustment for age, gender, race/ethnicity, clinic location, education, diet, smoking, and treatment with nonsteroidal anti-inflammatory drugs (Table 2). In models that had metabolic syndrome or elevated fasting glucose as the dependent variable, interaction terms of low GFR × gender were significant (P < 0.1). In men, low GFR was associated with increased odds for metabolic syndrome (OR 6.89; 95% CI 2.03 to 23.4) and elevated fasting glucose (OR 6.32; 95% CI 1.62 to 24.8); in women, however, it was not (OR 1.25 [95% CI 0.63 to 2.49] and 1.44 [95% CI 0.71 to 2.91], respectively). In all models, interaction terms of low GFR × ethnicity were not statistically significant. Low GFR was associated with none of the metabolic disorders after the additional adjustment for Si, but the association of low GFR with antihypertensive treatment retained its statistical significance.
Table 2.
Adjusted OR of low GFR (low versus normal/near-normal GFR) for hypertension, other metabolic disorders, and antihypertensive treatmenta
| Metabolic Disorder | Model A (OR [95% CI])b | Model B (OR [95% CI])c |
|---|---|---|
| Hypertension | 2.15 (1.19 to 3.88) | 1.71 (0.93 to 3.16) |
| Antihypertensive treatment | 2.72 (1.48 to 5.00) | 2.12 (1.12 to 4.04) |
| Metabolic syndrome | 1.93 (1.09 to 3.41) | 1.54 (0.82 to 2.91) |
| Components of the metabolic syndrome definition | ||
| elevated waist circumference | 0.89 (0.48 to 1.66) | 0.53 (0.25 to 1.11) |
| hypertriglyceridemia | 1.96 (1.10 to 3.46) | 1.72 (0.94 to 3.14) |
| low HDL cholesterol | 1.02 (0.57 to 1.83) | 0.89 (0.48 to 1.66) |
| elevated BP | 1.59 (0.88 to 2.87) | 1.26 (0.68 to 2.31) |
| elevated fasting glucose | 1.99 (1.11 to 3.56) | 1.76 (0.95 to 3.24) |
OR, odds ratio.
Included the following covariates: Age, gender, race/ethnicity, clinic location, education, diet, smoking, and treatment with nonsteroidal anti-inflammatory drugs.
Included covariates in model A plus Si.
In linear regression analysis, low GFR was associated with fasting insulin and leptin concentrations independent of confounding variables, including Si and both measures of obesity, BMI, and waist circumference (Table 3). Low GFR was not related to markers of insulin secretion (AIR and proinsulin-to-insulin ratio) or chronic inflammation (CRP). Low GFR was also associated with Si, but this association was not statistically significant after adjustment for other metabolic abnormalities. In all of these models, interaction terms for low GFR × gender and low GFR × ethnicity were tested, and none of them was statistically significant.
Table 3.
Change in individual metabolic variables associated with low GFR relative to normal/near-normal GFRa
| Metabolic Variable | Model (β [95% CI])b
|
||||
|---|---|---|---|---|---|
| Ac | Bd | Ce | Df | Eg | |
| Waist circumference | 0.87 (−2.29 to 4.02) | – | – | – | – |
| SBP | 0.22 (−3.83 to 4.27) | – | – | – | – |
| DBP | −0.44 (−2.81 to 1.93) | – | – | – | – |
| Fasting insulin concentrationh | 0.32 (0.15 to 0.48) | 0.30 (0.13 to 0.47) | 0.22 (0.08 to 0.36) | 0.17 (0.04 to 0.30) | 0.17 (0.04 to 0.30) |
| Insulin sensitivity index (Si)h | −0.15 (−0.30 to −0.01) | −0.15 (−0.29 to 0.00) | −0.10 (−0.21 to 0.02) | – | – |
| AIRh | 0.07 (−0.14 to 0.27) | – | – | – | – |
| Proinsulin-to-insulin ratioh | 0.11 (−0.07 to 0.29) | – | – | – | – |
| Fasting leptin concentrationh | 0.27 (0.09 to 0.44) | 0.26 (0.09 to 0.44) | 0.20 (0.07 to 0.33) | 0.17 (0.04 to 0.30) | 0.18 (0.05 to 0.30) |
| CRP concentrationh | 0.24 (−0.06 to 0.54) | – | – | – | – |
Covariates as shown were fit into the respective models in addition to all covariates in the previous models in order to examine to what degree the relation of low GFR to individual metabolic variables was explained by the respective covariates. Additional models were not tested when the regression coefficient was not statistically significant.
The regression coefficient (β) quantified the amount of change in individual metabolic variables associated with low GFR relative to normal/near-normal GFR. Coefficients with 95% CI crossing 0 were considered not statistically significant.
Included the following covariates: Age, gender, race/ethnicity, and clinic location.
Included covariates in model A plus education, diet, smoking, and treatment with nonsteroidal anti-inflammatory drugs.
Included covariates in models A and B plus waist circumference, BP, antihypertensive treatment, and levels of triglycerides, HDL cholesterol, fasting glucose, and 2-h glucose.
Included covariates in models A, B, and C plus Si.
Included covariates in models A, B, C, and D plus BMI.
Natural log transformation of Si, AIR, proinsulin-to-insulin ratio, and levels of insulin, leptin, and CRP were used to correct for skewness and kurtosis.
Discussion
In individuals without diabetes and with normoalbuminuria, low GFR was associated with higher fasting insulin and leptin concentrations independent of the effect of potential confounding determinants of GFR. Although low GFR was associated with hypertension, hypertriglyceridemia, impaired fasting glucose, metabolic syndrome, and insulin resistance in univariate analysis, none of these relations remained statistically significant after adjustment for potentially confounding variables.
Insulin may contribute to the development of the glomerular changes that precede the onset of microalbuminuria. Insulin infusion increases GFR in healthy individuals without diabetes, and insulin resistance correlates with the increment in GFR during insulin infusion (32). Thus, individuals with insulin resistance may be at higher risk for developing structural changes in the kidney and decrease in GFR. Several potential mechanisms may be responsible for the effect of insulin on GFR. Insulin exerts a direct effect on glomerular cells (33,34). Insulin enhances matrix production by mesangial cells, which may result in decrease in GFR by decreasing surface area available for filtration (34). Insulin may also contribute to interstitial fibrosis by enhancing collagen production by proximal tubular epithelial cells (35). Nevertheless, the clinical significance of the nonhemodynamic effects of chronic hyperinsulinemia is not known.
Low GFR (<60 ml/min per 1.73 m2) is associated with insulin resistance (measured by the Homeostasis Model of Assessment) and fasting insulin concentration in participants without diabetes in the Third National Health and Nutrition Examination Survey (NHANES III) (36). In that study, however, Chen et al. acknowledged the following potential limitations: (1) The analysis is cross-sectional and cannot assess causality or temporal relationship; (2) the kidney is an important organ in insulin metabolism, and insulin levels may depend on renal function (37); and (3) insulin resistance and CKD are not measured by precise methods. Because microalbuminuria is associated with insulin resistance in IRAS (11,12), our objective has been to examine the relation of low GFR to fasting insulin concentration and insulin resistance in individuals without diabetes and with normoalbuminuria. Low GFR is associated with fasting insulin concentration independent of potential determinants of GFR but is not associated with insulin resistance. Although a precise measure of insulin resistance is available in IRAS, our analysis has the limitations enunciated by Chen et al. (36). Thus, a longitudinal study is needed to confirm the hypothesis that high insulin may cause impairment of renal function independent of ongoing changes in albumin excretion rate.
Leptin is a cytokine-like molecule that plays a major role in regulating appetite and energy homeostasis. The main role of leptin is signaling the central nervous system that caloric intake and fat deposits are adequate (38). Leptin is secreted in proportion to body fat mass (the most important determinant of leptin concentration), hyperinsulinemia, and chronic inflammation also in individuals with CKD (39,40); however, GFR decline increases serum leptin concentration (40). The significance of leptin in CKD remains unclear. It has been hypothesized that leptin promotes anorexia and poor nutrition in patients with CKD (38), adrenergic activation and increase in BP (41), and glomerulosclerosis (42,43). In animal models, leptin induced proliferation of glomerular endothelial cells (42) and glucose uptake and type I collagen production in mesangial cells (43). In humans, however, leptin concentration has not been linked to progression of CKD (44). Our results indicate that low GFR is associated with higher serum leptin concentration independent of the effect of obesity and insulin resistance. Our analysis cannot establish causality or temporal relationship, but worsening plasma clearance may, at least in part, explain the increase in leptin levels in individuals with low GFR.
In IRAS participants who were free of diabetes, BP values and insulin resistance were the strongest correlates of microalbuminuria (11,12). This is not surprising because of the hemodynamic and structural effects of BP and insulin resistance on the glomeruli (32–34). Obesity is a risk factor for microalbuminuria (12) and CKD (5,7,8). Consequently, it is intriguing to find no independent relationship between low GFR and some relevant metabolic disorders (particularly insulin resistance, hypertension, and obesity) in individuals without diabetes and with normoalbuminuria. To explain these results, we advance three possible explanations. First, GFR estimates are based on a single measure of creatinine. Moreover, the number of individuals with low GFR may be relatively small (60 of 856 participants [7.0%]) and none of them have a GFR <30 ml/min per 1.73 m2. Second, participants with impaired glucose tolerance are overrepresented in the IRAS cohort (20). Impaired glucose tolerance is an insulin resistance–related metabolic abnormality (26) that has been associated with increased GFR (45). Third, low GFR and microalbuminuria differ in their relation to obesity. The absence of relationship between obesity and low GFR may be due to the exclusion of participants with albuminuria. Microalbuminuria and increased GFR may be early manifestations in most individuals with obesity-related glomerulopathy (41).
Our study has two other unexpected results: Lower rates of low GFR in men relative to women and in black and Hispanic individuals relative to non-Hispanic white individuals. Nevertheless, similar findings have been described in the NHANES III (1,2,46) and NHANES 1999 to 2000 (2). Three potential explanations have been suggested for the gender differences in low GFR (46): (1) the MDRD equation has been developed using data from patients with chronic renal insufficiency and has not been validated in the general population, (2) interlaboratory variation and calibration of creatinine measurements, and (3) biased estimation of clearance based on a single random creatinine measurement without reference to hydration or diet. Likewise, these explanations may also account for the ethnic differences in low GFR, but others may apply, such as a faster progression to ESRD in black individuals (1,2) and lower serum creatinine concentration in Hispanic individuals (47).
Our study has several limitations. First, only one measure of serum creatinine concentration was available for IRAS participants to estimate GFR by the MDRD equation. The diagnosis of stage 3 CKD requires a GFR <60 ml/min per 1.73 m2 of at least 3 mo duration (10). Second, the MDRD equation may underestimate GFR measurement in individuals with normal serum creatinine levels, particularly in those who are younger than 65 yr across all BMI levels (48). This equation has not been validated in the general population even though it is considered useful for the identification of individuals with GFR <60 ml/min per 1.73 m2 (10). Third, creatinine concentration is not calibrated for the MDRD equation in the IRAS; however, creatinine concentration has been measured by the same laboratory and method for all participants. Finally, our analysis is cross-sectional; therefore, it cannot establish cause–effect or temporal relationships. Despite these limitations, our results indicate that the relation of low GFR to metabolic disorders among individuals without diabetes and with normoalbuminuria is relevant in men and women and in all three ethnic groups.
Conclusions
The relation of low GFR to fasting insulin and leptin concentration is independent of the effect of potential determinants of GFR in individuals without diabetes and with normoalbuminuria. Because this is a cross-sectional study, we do not know the temporal relation between low GFR and insulin (or leptin) concentration. Moreover, we cannot rule out the possibility of worsening plasma clearance as a cause for elevated insulin and leptin levels in individuals with low GFR. Prospective data are needed to confirm the hypothesis that high insulin may cause impairment of renal function independent of ongoing changes in albumin excretion rate.
Disclosures
None.
Acknowledgments
This study was supported by National Heart, Lung, and Blood Institute grants (HL-47887, HL-47889, HL-47890, HL-47892, and HL-47902) and the General Clinical Research Centers Program (NCRR GCRC, M01 RR431 and M01 RR01346).
This study was presented as an abstract at the 67th annual meeting of American Diabetes Association; June 22 to 26, 2007; Chicago, IL.
Published online ahead of print. Publication date available at www.cjasn.org.
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