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NDT Advance Access originally published online on February 21, 2007
Nephrology Dialysis Transplantation 2007 22(6):1587-1592; doi:10.1093/ndt/gfm037
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© The Author [2007]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Fasting insulin modifies the relation between age and renal function

Leendert H. Oterdoom1,2, Aiko P. J. de Vries1,2, Ron T. Gansevoort2, Paul E. de Jong2, Reinold O. B. Gans1, Stephan J. L. Bakker1 for the PREVEND Study Group

1Department of Internal Medicine and 2 Division of Nephrology, Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

Correspondence and offprint requests to: Stephan J. L. Bakker, MD, PhD, Department of Medicine, University Medical Center Groningen, Hanzeplein 1, Postbus 30.001, 9700 RB Groningen, The Netherlands. Email: s.j.l.bakker{at}int.umcg.nl



   Abstract
Top
 Abstract
Introduction
 Methods
 Results
 Discussion
 Acknowledgements
 References
 
Background. The worldwide increase in end-stage renal disease has been alleged to be associated with insulin resistance-related conditions. Insulin resistance and the concomitant compensatory hyperinsulinaemia may accelerate age-related decline in renal function through inducing glomerular hyperfiltration, even in non-diabetic subjects. This population-based study is the first to investigate whether fasting insulin concentrations modify the relationship between age and renal function in a general non-diabetic population.

Methods. Fasting insulin was measured in 3432 subjects, representing the general population. Cross-sectional analyses of the association between age, renal function and its modification by insulin were performed by means of linear regression. Renal function was assessed as 24-h creatinine clearance (CrCl) and 24-h urinary albumine excretion (UAE).

Results. Age was 48 ± 12 (range: 28–75) years, 44% were male, CrCl was 100 ± 26 ml/min and UAE was 7.0 (5.4–10.7)] mg/24 h. The results confirmed a parabolic relation between age and renal function. Fasting insulin modified these parabolic relationships of age with CrCl and UAE (P < 0.001 for both interaction terms), in such a way that hyperinsulinaemia is associated with a stronger inverse parabolic relation between age and CrCl, and stronger positive parabolic relation between age and UAE at an older age than lower insulin concentrations.

Conclusions. Our results are consistent with the hypothesis that insulin accelerates the age-related decline of renal function in the general non-diabetic population. This indicates that insulin resistance, and the concomitant compensatory hyperinsulinaemia could contribute to the increased incidence in end-stage renal disease.

Keywords: age; glomerular hyperfiltration; insulin resistance; renal function



   Introduction
Top
 Abstract
 Introduction
Methods
 Results
 Discussion
 Acknowledgements
 References
 
The incidence of end-stage renal disease is increasing worldwide, with the number of dialysis patients growing at an estimated annual rate of 7% [1]. In the Western world, this is mainly attributed to ageing of the population, and to higher prevalence of type 2 diabetes mellitus and renal vascular disease [2,3]. In addition, there is a steady increase in the proportion of subjects in whom no cause for end-stage renal disease can be identified [3]. Insulin resistance is alleged to be a common denominator of type 2 diabetes mellitus and vascular disease and has been argued to underlie many of the unexplained cases of end-stage renal disease as well [4].

Age-associated decline in renal function is thought to result from long-term presence of glomerular hyperfiltration and increased intra-glomerular pressure, leading to glomerulosclerosis, albuminuria and finally nephron loss [5,6]. Despite a diminishing number of functioning nephrons, kidneys can often maintain sufficient renal function through haemodynamic adaptation by hyperfiltration [7]. However, this adaptation may be expected to further increase glomerular pressure and accelerate the age-related decline in renal function. The notion of an accelerated age-related decline of renal function is supported by population-based studies that show a parabolic relationship between age and renal function [8].

Insulin resistance is accompanied by compensatory hyperinsulinaemia [9]. High insulin concentrations are known to induce glomerular hyperfiltration [10]. From these experimental data, it has been hypothesized that insulin resistance may contribute to accelerated age-related decline in renal function via compensatory hyperinsulinaemia [11]. However, it remains unknown whether compensatory hyperinsulinaemia modifies the relation between age and renal function in the general non-diabetic population. Therefore, we investigated the effect of insulin on the relation between age and renal function, as well as urinary albumine excretion (UAE), in a non-diabetic general population.



   Methods
Top
 Abstract
 Introduction
 Methods
Results
 Discussion
 Acknowledgements
 References
 
Study population and design
The present study population is part of the larger PREVEND study (Prevention of Renal and Vascular End-stage Disease). The protocol of the PREVEND study has been described elsewhere in more detail [12]. From this study, 3432 subjects were selected randomly to obtain a representative sample of the general population of the city of Groningen, The Netherlands. Non-fasting subjects (n = 336), subjects with known renal disease (n = 5), subjects with diabetes (n = 96) and subjects with missing values (n = 42) were excluded from the present analysis, leaving a total of 2953 non-diabetic subjects for analyses. Diabetes was defined as a fasting plasma glucose level of ≥7.0 mmol/l and/or use of anti-diabetic drugs or insulin according to the criteria of the American Diabetes Association [13]. Information on drug use was self report, and checked against information from community pharmacies in the city of Groningen as reported previously [14].

Study subjects completed two visits, which consisted of anthropometric measurements, fasting blood sampling (minimum of 8 h fasting), 10 supine blood pressure measurements taken at 1-min intervals (Dinamap XL Model 9300, Johnson-Johnson Medical, Tampa, FL), gathering of two 24-h urine collections, and a questionnaire about smoking habits. All participants signed informed consent. The PREVEND study was approved by the Institutional Review Board and was conducted in accordance with the guidelines of the declaration of Helsinki.

Measurements
Systolic and diastolic blood pressures were calculated from the final two recordings at both visits. Body mass index was calculated as weight divided by height squared (kg/m2). Body surface area was calculated according to DuBois and DuBois [15]. Waist-to-hip ratio (WHR) was calculated as waist circumference divided by hip circumference.

Laboratory methods
Plasma and urine creatinine were determined by Kodak Ektachem dry chemistry (Eastman Kodak, Rochester, NY, USA). Creatinine clearance (CrCl) was calculated as the mean of two 24-h urinary creatinine excretions divided by plasma creatinine. Urinary albumin concentration was determined by nephelometry (BNTMII Dade Behring Diagnostic, Marburg, Germany) with a threshold of 2.3 mg/l, and intra-assay and inter-assay coefficients of variation of 2.2% and 2.6%, respectively. UAE is given as the mean of two 24-h urinary excretions. Sodium and urea urinary excretion were determined on a MEGA clinical chemistry analyser (Merck, Darmstadt, Germany). Insulin was determined with an AxSym® auto-analyser (Abbott Diagnostics, Amstelveen, The Netherlands) with a threshold of 7 pmol/l (1.0 µU/ml) and intra-assay and inter-assay coefficients of variation of 2.6% and 4.3%, respectively. This assay shows no cross-reactivity with pro-insulin (0.016% at 106 g/ml). High sensitive CRP was determined by nephelometry (Dade Behring, Marburg, Germany). Serum concentrations of total cholesterol, HDL cholesterol and triglycerides were measured by standard methods. Glucose was determined in plasma using standard methods.

Statistical analysis
Analyses were performed using SPSS version 12.0 software (SPSS Inc., Chicago IL). Parametric values are presented as mean ± SD, and non-parametric variables as median (inter-quartile range). Characteristics of the subjects were analysed over tertiles of fasting insulin. Differences between tertiles were tested with one-way ANOVA for continuous variables with a normal distribution, Kruskal–Wallis for continuous variables with a skewed distribution and with Chi-square for nominal variables. A two-sided P-value <0.05 indicated statistical significance.

To investigate whether a parabolic association existed between age and renal function in the general population, we first we entered age and age2 as independent variables in a linear regression model with CrCl as the dependent variable (Model 1). Next, we additionally entered insulin, insulin x age and insulin x age2 as independent variables to the first model to investigate whether fasting insulin modifies the parabolic relation between age and CrCl (Model 2). Significance of the insulin x age2 interaction term indicates that insulin modifies the parabolic relationship between age and renal function.

A similar approach was undertaken with albuminuria as the dependent variable. UAE was logarithmically transformed twice to obtain a normal distribution.

To visualize the relation between insulin, age and renal function we stratified our population in tertiles of insulin. In each separate tertile of insulin we entered age and age2 in linear regression analyses with CrCl as dependent variable. We used the obtained regression formulas to calculate the estimated renal function at every age throughout the age range of our study population for the three separate tertiles. A similar method was used to visualize the relation between age and UAE, again according to tertiles of insulin. In subjects older then 65 years, we analysed whether there was a significant association between insulin and CrCl or UAE using linear regression.

Finally, we performed several sensitivity analyses to investigate the robustness of our findings. In the first sensitivity analysis, we adjusted for gender and adjusted CrCl for body surface area. Secondly, we excluded subjects with a more than 50% difference in urinary creatinine excretion between the two collections, because 24-h urine collections are prone to collection errors. This method has been used previously [8]. In the third sensitivity analysis, we excluded subjects with impaired fasting glucose (IFG) to exclude potentially under-diagnosed subjects with diabetes. IFG was defined by fasting glucose >5.6 mmol/l [16]. Thirdly, we excluded subjects using angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARB). This was done because ACEi and ARBs can effect insulin resistance as well as renal function, and therefore confound the associations under investigation [17]. We also performed a multivariate analysis in which we adjusted for gender, BMI, WHR, systolic blood pressure, smoking, fasting glucose, fasting triglycerides, total- and HDL-cholesterol, and CRP. Finally, we also used Cockroft–Gault and MDRD formulas instead of CrCl as measures of renal function.

All regression coefficients are reported as standardized regression coefficients (unless otherwise specified) to better compare the models amongst each other. Tolerance statistics were used to determine whether assumptions of co-linearity were not violated. Residuals were tested for the normality assumption.



   Results
Top
 Abstract
 Introduction
 Methods
 Results
Discussion
 Acknowledgements
 References
 
Table 1 shows the characteristics of the study population, according to tertiles of fasting insulin. Fasting insulin increased significantly with age, male gender and all other characteristics shown in Table 1. With increasing age, subjects had higher fasting insulin (R = 0.14, P < 0.001), lower CrCl (R = –0.28, P < 0.001) and more UAE (R = 0.11, P < 0.001).


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  		Table 1. Population characteristics according to tertiles of fasting insulin

 
In Model 1 (Table 2) we found age to have a parabolic relation with CrCl. This was indicated by the significance of the age2 term in the regression analysis (age2: ß = –0.62, P < 0.001, R2 = 0.09 for the overall model, Model 1, Table 2). The relation between age and CrCl was modified by fasting insulin concentrations, as indicated by significance of the interaction term of insulin x age2 (ß = –1.66, P < 0.001, R2 = 0.17 for the overall model, Model 2, Table 2).


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  		Table 2. Regression models of age and insulin with creatinine clearance and UAE

 
A similar statistical approach was undertaken for UAE as dependent variable (Table 2). Age and age2 showed a parabolic relation with double log-transformed UAE (age2: ß = 0.67, P < 0.001, R2 = 0.03 for the overall model, Model 1, Table 2). In this model, the interaction term insulin x age2 was also significant (ß = –1.51, P < 0.001, R2 = 0.04 for the overall model, Model 2, Table 2).

Figure 1A depicts graphically the linear regression analyses of CrCl and age in each tertile of fasting insulin. The figure indicates that middle-aged subjects with higher fasting insulin concentrations (hyperinsulinaemia) have higher CrCl than subjects with lower fasting insulin concentrations. Subjects with higher insulin concentrations have a stronger parabolic relation between age and CrCl than subjects with lower insulin concentrations. In subjects older than 65 years, there was no association between fasting insulin and CrCl (ß = 0.03, P = 0.60).


Figure 1
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  		Fig. 1. Linear regression analyses of age and age2 as independent variables in each tertile of fasting insulin with either creatinine clearance or UAE as dependent variable. Median insulin (95% CI) concentration for the 1st tertile is 33.0 pmol/l 95% CI (17.2–42.3) (n = 975), 2nd tertile 54.5 pmol/l, 95% CI (44.5–66.7) (n = 963), 3rd tertile 92.6 pmol/l, 95% CI (70.3–181.5) (n = 964). (A) Regression lines of age and creatinine clearance, stratified by tertiles of fasting insulin. (B) Regression lines of age and UAE, stratified by tertiles of fasting insulin.

 
Figure 1B depicts graphically the linear regression analyses of UAE and age in each tertile of fasting insulin. The figure indicates that UAE is similar at a young age for subjects with higher and lower insulin concentrations. With increasing age, however, UAE becomes higher in both groups, but more so in the high insulin group than in the low insulin group. In subjects older than 65 years, there was an association between fasting insulin and UAE (ß = –0.18, P = 0.018).

The notion of our findings did not change after adjustment for gender and adjustment of CrCl for body surface area. Furthermore, multivariate adjustment for atherosclerotic risk factors did not change the notion of our findings. Exclusion of urinary collection errors, subjects with IFG, or use of ACEi/ARB did not change our findings notably (sensitivity analyses, Table 2). Our findings were also not materially changed by using different measures of renal function, namely Cockroft–Gault (insulin x age2 term: P = 0.002) or MDRD (insulin x age2 term: P = 0.10).



   Discussion
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
Acknowledgements
 References
 
The present study is the first to show that fasting insulin concentrations modify the parabolic relation between age and renal function in the general non-diabetic population. The modification indicated that hyperinsulinaemia is associated with a stronger parabolic relation of age with CrCl and UAE, than lower insulin concentrations. The present population-based study is therefore consistent with the hypothesis that hyperinsulinaemia is associated with an accelerated age-related decline in renal function through induction of glomerular hyperfiltration [11].

We interpret these findings such that hyperinsulinaemia is associated with relative hyperfiltration at a younger age leading to accelerated renal function loss at an older age. The findings could also be interpreted in such a way that the capacity to hyperfiltrate is lost at an older age. However, the concomitant rise in UAE suggests the former: UAE is an accepted marker for renal damage, and higher UAE was already found in young subjects with relative hyperinsulinaemia. Furthermore, studies in type-1 and type-2 diabetes mellitus show that hyperfiltration precedes renal function decline and rise in UAE [18,19]. This makes it more plausible that hyperinsulinaemia is indeed associated with accelerated renal function loss rather than loss of existing hyperfiltration.

The relation between fasting insulin and renal function in a population-based study has been only investigated twice previously. In one study, the cross-sectional results indicated that fasting insulin was negatively associated with reciprocal of serum creatinine. However, there was no association between insulin concentrations and estimated GFR [20]. In the second cross-sectional study, higher insulin concentrations were associated with an increased risk of impaired renal function (estimated GFR below 60 ml/min/1.73 m2). This study also did not take into account a potential acceleration of the age-related decline of renal function [21]. The relation between insulin and the development of microalbuminuria has been studied more thoroughly [22–25]. One prospective study indicated that insulin resistance precedes the appearance of albuminuria in non-diabetic subjects [24].

There is ample evidence from experimental studies in humans to support that hyperinsulinaemia has a detrimental effect on renal function. In humans, the renal and haemodynamic effects of insulin have been studied in detail [26,27]. In healthy subjects, Stenvinkel et al. [26] demonstrated that hyperinsulinaemia resulted in time- and dose-dependent increases in renal plasma flow. Likewise, ter Maaten et al. [27] found that during insulin infusion, effective renal plasma flow and renal glomerular filtration rate increased with 23.4% (P < 0.001) and with 8.8% (P = 0.02), respectively. Besides insulin's haemodynamic effects, insulin resistance is associated with adverse changes in numerous cytokines, each of which is thought to contribute to progressive renal function decline as well [28]. These cytokines include plasminogen activator inhibitor-1 (PAI-1), tumour growth factor-ß (TGF-ß), and hepatocyte growth factor (HGF) [29–31]. Furthermore, insulin also has an affinity for the insulin growth factor-1 (IGF-1) receptor, enhancing proliferation of vascular smooth muscle cells [32].

Furthermore, experimental animal studies support that hyperinsulinaemia has a detrimental effect on renal morphology over time. Studies in monkeys show that structural changes, especially glomerular hypertrophy are already present during hyperinsulinaemia, before the development of diabetes [33]. Also, obese Zucker rats that were fed ad libitum, developed hyperinsulinaemia with renal hypertrophy and glomerulosclerosis [34]. Interestingly, food restriction at an early age resulted in lower fasting insulin levels and less kidney disease. Further support for a role of insulin comes from the fact that reduction of insulin and triglyceride concentrations, by treatment with acarbose, resulted in less glomerulosclerosis and reduced incidence of renal insufficiency in obese Zucker rats [35].

A limitation of the study is its cross-sectional design, so that cause–effect relationships cannot be inferred. Prospective studies are required to investigate whether hyperinsulinaemia is predictive of accelerated deterioration of renal function. Another limitation of our study is that it may have underestimated the true decline in renal function due to healthy survivor bias. Bias may have been introduced because fasting insulin, renal function and UAE are independent predictors of cardiovascular disease morbidity and mortality [36]. Subjects with hyperinsulinaemia, poor renal function and UAE may have died preferentially compared to other groups before they could enter this study. It should furthermore be noted that declining renal function in association with increased UAE is a relatively late phenomenon. Inclusion bias and this pathophysiological phenomenon could be why insulin—in the elderly—was not associated with poor renal function, but only with increased UAE.

An important strength of this study is that we used 24-h urinary CrCl as a measure of renal function and not equations that estimate glomerular filtration rate such as the Cockroft–Gault or the MDRD [37,38]. This is important because age is included in these equations to correct for lower muscle mass and lower endogenous creatinine production at an older age. Because age is included in the formulas for the Cockroft–Gault and the MDRD, their use as independent variables in linear regression analysis, would introduce a statistical fallacy because age would be present in the analysis both as a dependent variable and as an independent variable (incorporated in the estimated GFR), which is statistically not permitted. Therefore, in our opinion, it is methodologically and statistically more correct to use 24-h CrCl for this study. The sensitivity analysis indicated that collection errors, which represent the main pitfall in the use of 24-h urine collections in analyses, did not alter our findings. Furthermore, we also obtained UAE as a marker of renal damage to corroborate our results of CrCl. Other strengths of the study are its large sample size and the availability of pharmacy data. Finally, we used a highly specific insulin assay, i.e. with very little cross-reactivity with pro-insulin, thereby attributing the current findings solely to insulin and not to pro-insulin or pro-insulin-like molecules.

An implication of our findings is that insulin resistance may predispose individuals to accelerated renal function decline even in non-diabetic subjects. Currently there are no diagnostic criteria to allow identification of subjects at risk of progressive renal function decline. One could argue whether this is clinically relevant as the average CrCl of even the relative hyperinsulinaemic elderly in this study was not below 70 ml/min. However, if one already has a diminished renal capacity, insulin resistance could possibly lead to clinically relevant renal function decline even before overt diabetes is present. Insulin sensitizers such as glitazones could be used in insulin-resistant individuals to halt hyperfiltration and decrease UAE, thereby protecting against further renal damage, even in absence of diabetes, defined according to present criteria. These implications need to be addressed in future studies.

In conclusion, our results show that fasting insulin concentrations modify the association between age and renal function in the general non-diabetic population. This association indicates that in non-diabetic subjects hyperinsulinaemia is associated with an increased age-related decline of renal function and with a concomitant rise in UAE with older age. Prospective studies are needed to give conclusive evidence that higher insulin concentrations indeed accelerate the age-related decline in renal function.



   Acknowledgements
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgements
References
 
We thank the Dutch Kidney Foundation for their financial support to make the PREVEND study possible (E 033). We also thank Dade Behring (Marbur, Germany) for supplying equipment (Behring Nephelometer II) and reagents for nephelometric measurement of urinary albumin concentration. Part of this work was presented at the 38th meeting of the ASN.

Conflict of interest statement. None declared.



   References
Top
 Abstract
 Introduction
 Methods
 Results
 Discussion
 Acknowledgements
 References
 

  1. Lysaght MJ. Maintenance dialysis population dynamics: current trends and long-term implications. J Am Soc Nephrol (2002) 13(Suppl 1):S37–S40.[Abstract/Free Full Text]
  2. Fatica RA, Port FK, Young EW. Incidence trends and mortality in end-stage renal disease attributed to renovascular disease in the United States. Am J Kidney Dis (2001) 37:1184–1190.[Web of Science][Medline]
  3. Gansevoort RT, van der Heij B, Stegeman CA, et al. Trends in the incidence of treated end-stage renal failure in The Netherlands: hope for the future? Kidney Int Suppl (2004) 92:S7–S10.[Medline]
  4. El Atat FA, Stas SN, McFarlane SI, Sowers JR. The relationship between hyperinsulinemia, hypertension and progressive renal disease. J Am Soc Nephrol (2004) 15:2816–2827.[Abstract/Free Full Text]
  5. Brenner BM, Lawler EV, Mackenzie HS. The hyperfiltration theory: a paradigm shift in nephrology. Kidney Int (1996) 49:1774–1777.[Web of Science][Medline]
  6. Bruzzi I, Benigni A, Remuzzi G. Role of increased glomerular protein traffic in the progression of renal failure. Kidney Int Suppl (1997) 62:S29–S31.[Medline]
  7. Fliser D, Franek E, Joest M, et al. Renal function in the elderly: impact of hypertension and cardiac function. Kidney Int (1997) 51:1196–1204.[Web of Science][Medline]
  8. Stuveling EM, Hillege HL, Bakker SJ, et al. C-reactive protein is associated with renal function abnormalities in a non-diabetic population. Kidney Int (2003) 63:654–661.[CrossRef][Web of Science][Medline]
  9. Laakso M. How good a marker is insulin level for insulin resistance? Am J Epidemiol (1993) 137:959–965.[Abstract/Free Full Text]
  10. ter Maaten JC, Bakker SJ, Serne EH, et al. Insulin's acute effects on glomerular filtration rate correlate with insulin sensitivity whereas insulin's acute effects on proximal tubular sodium reabsorption correlation with salt sensitivity in normal subjects. Nephrol Dial Transplant (1999) 14:2357–2363.[Abstract/Free Full Text]
  11. Kincaid-Smith P. Hypothesis: obesity and the insulin resistance syndrome play a major role in end-stage renal failure attributed to hypertension and labelled ‘hypertensive nephrosclerosis’. J Hypertens (2004) 22:1051–1055.[CrossRef][Web of Science][Medline]
  12. Pinto-Sietsma SJ, Janssen WM, Hillege HL, et al. Urinary albumin excretion is associated with renal functional abnormalities in a nondiabetic population. J Am Soc Nephrol (2000) 11:1882–1888.[Abstract/Free Full Text]
  13. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care (1997) 20:1183–1197.[Web of Science][Medline]
  14. Atthobari J, Monster TB, de Jong PE, de Jong-van den Berg LT. The effect of hypertension and hypercholesterolemia screening with subsequent intervention letter on the use of blood pressure and lipid lowering drugs. Br J Clin Pharmacol (2004) 57:328–336.[CrossRef][Web of Science][Medline]
  15. Dubois D, Dubois E. A formula to estimate the approximate surface area if height and weight be known. Arch Intern Med (1916) 17:863–871.[Web of Science]
  16. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care (1997) 20:1183–1197.[Web of Science][Medline]
  17. Bernobich E, de Angelis L, Lerin C, Bellini G. The role of the angiotensin system in cardiac glucose homeostasis: therapeutic implications. Drugs (2002) 62:1295–1314.[CrossRef][Web of Science][Medline]
  18. Rudberg S, Persson B, Dahlquist G. Increased glomerular filtration rate as a predictor of diabetic nephropathy–an 8-year prospective study. Kidney Int (1992) 41:822–828.[Web of Science][Medline]
  19. Silveiro SP, Friedman R, de Azevedo MJ, Canani LH, Gross JL. Five-year prospective study of glomerular filtration rate and albumin excretion rate in normofiltering and hyperfiltering normoalbuminuric NIDDM patients. Diabetes Care (1996) 19:171–174.[Abstract]
  20. Kubo M, Kiyohara Y, Kato I, et al. Effect of hyperinsulinemia on renal function in a general Japanese population: the Hisayama study. Kidney Int (1999) 55:2450–2456.[CrossRef][Web of Science][Medline]
  21. Chen J, Muntner P, Hamm LL, et al. Insulin resistance and risk of chronic kidney disease in nondiabetic US adults. J Am Soc Nephrol (2003) 14:469–477.[Abstract/Free Full Text]
  22. Kim CH, Kim HK, Park JY, et al. Association of microalbuminuria and atherosclerotic risk factors in non-diabetic subjects in Korea. Diabetes Res Clin Pract (1998) 40:191–199.[CrossRef][Web of Science][Medline]
  23. Forsblom CM, Eriksson JG, Ekstrand AV, et al. Insulin resistance and abnormal albumin excretion in non-diabetic first-degree relatives of patients with NIDDM. Diabetologia (1995) 38:363–369.[Web of Science][Medline]
  24. Fujikawa R, Okubo M, Egusa G, Kohno N. Insulin resistance precedes the appearance of albuminuria in non-diabetic subjects: 6 years follow up study. Diabetes Res Clin Pract (2001) 53:99–106.[CrossRef][Web of Science][Medline]
  25. Mykkanen L, Zaccaro DJ, Wagenknecht LE, et al. Microalbuminuria is associated with insulin resistance in nondiabetic subjects: the insulin resistance atherosclerosis study. Diabetes (1998) 47:793–800.[Abstract]
  26. Stenvinkel P, Bolinder J, Alvestrand A. Effects of insulin on renal haemodynamics and the proximal and distal tubular sodium handling in healthy subjects. Diabetologia (1992) 35:1042–1048.[CrossRef][Web of Science][Medline]
  27. ter Maaten JC, Serne EH, van Eps WS, et al. Effects of insulin and atrial natriuretic peptide on renal tubular sodium handling in sickle cell disease. Am J Physiol Renal Physiol (2000) 278:F499–F505.[Abstract/Free Full Text]
  28. Klahr S, Morrissey J. Progression of chronic renal disease. Am J Kidney Dis (2003) 41:S3–S7.[Web of Science][Medline]
  29. Lahlou A, Peraldi MN, Thervet E, et al. Chronic graft dysfunction in renal transplant patients: potential role of plasminogen activator inhibitor type 1. Transplantation (2002) 73:1290–1295.[CrossRef][Web of Science][Medline]
  30. Sharma K, Ziyadeh FN, Alzahabi B, et al. Increased renal production of transforming growth factor-beta1 in patients with type II diabetes. Diabetes (1997) 46:854–859.[Abstract]
  31. Wang SN, LaPage J, Hirschberg R. Role of glomerular ultrafiltration of growth factors in progressive interstitial fibrosis in diabetic nephropathy. Kidney Int (2000) 57:1002–1014.[CrossRef][Web of Science][Medline]
  32. Sarafidis PA, Ruilope LM. Insulin resistance, hyperinsulinemia, and renal injury: mechanisms and implications. Am J Nephrol (2006) 26:232–244.[CrossRef][Web of Science][Medline]
  33. Cusumano AM, Bodkin NL, Hansen BC, et al. Glomerular hypertrophy is associated with hyperinsulinemia and precedes overt diabetes in aging rhesus monkeys. Am J Kidney Dis (2002) 40:1075–1085.[CrossRef][Web of Science][Medline]
  34. Maddox DA, Alavi FK, Santella RN, Zawada ET Jr. Prevention of obesity-linked renal disease: age-dependent effects of dietary food restriction. Kidney Int (2002) 62:208–219.[CrossRef][Web of Science][Medline]
  35. Michel O, Heudes D, Lamarre I, et al. Reduction of insulin and triglycerides delays glomerulosclerosis in obese Zucker rats. Kidney Int (1997) 52:1532–1542.[Web of Science][Medline]
  36. Ruige JB, Assendelft WJJ, Dekker JM, et al. Insulin and risk of cardiovascular disease: a meta-analysis. Circulation (1998) 97:996–1001.[Abstract/Free Full Text]
  37. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron (1976) 16:31–41.[Web of Science][Medline]
  38. Levey AS, Bosch JP, Lewis JB, et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med (1999) 130:461–470.[Abstract/Free Full Text]
Received for publication: 10.10.06
Accepted in revised form: 11. 1.07


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