Discussion
Data from the EGIR-RISC study did not confirm our hypothesis, that worsening markers of renal function (eGFR and UACR) precede declining insulin sensitivity in a healthy population, with renal markers within the normal range. In fact, we observed associations between a higher insulin sensitivity and markers of a worsening renal function.
In cross-sectional analyses, the insulin sensitivity indices were not related with markers of renal function in men, except for higher insulin sensitivity in those with higher UACR; this was no longer significant after adjustment for covariates. In women, clamp measured insulin sensitivity was higher in those with a lower eGFR, and this linear association remained after adjustment; clamp measured insulin sensitivity was also higher in women with detectable UACR, but this relation was attenuated after adjustment.
In longitudinal analyses, men with a higher and a lower eGFR had a higher insulin sensitivity at year-3 than the reference group (eGFR 90–105 ml/min/1.732). In agreement, larger increases in ΔISI and ΔHOMA-IS were also related with a low eGFR. These relations remained consistent after adjustment for covariates. The higher the baseline UACR, the higher the year-3 insulin sensitivity. For women, there were no such relations between baseline eGFR and insulin sensitivity indices, but as for men, the higher the baseline UACR the higher the 3-year insulin sensitivity.
In the literature, the relation between insulin resistance and renal function has mainly been studied in people with renal disease, who were compared to people without renal disease. Cross-sectional studies have reported that insulin resistance exists in people without diabetes but with chronic kidney disease. DeFronzo showed that peripheral insulin resistance is present in people with chronic renal failure, but hepatic insulin resistance may not be impaired [
1]. Peripheral insulin sensitivity is essentially insulin sensitivity in skeletal muscle [
28]. HOMA-IS is based on fasting glucose and fasting insulin, and reflects hepatic insulin sensitivity, but could also reflect insulin clearance. ISI uses insulin and glucose levels during the two-hour oral glucose tolerance test, providing a dynamic estimate, reflecting both hepatic and peripheral insulin sensitivity [
29]. The correlations between the clamp based insulin sensitivity measure and the surrogate indices were 0.50–0.60, similar to other studies.
The earliest publication in a population without diabetes or renal disease comes from Japan; a significant positive association was seen between insulin levels and serum creatinine [
3]. In the American NHANES III study, Chen et al. described a higher prevalence of chronic renal disease (eGFR< 60 ml/min/1.73m
2) in people without diabetes but with a low insulin sensitivity (odds ratio 2.6 for HOMA-IR above the upper quartile in comparison with below the lower quartile) [
4]. Another analysis from the same population showed that HOMA-IR was related with eGFR in men, but not in women [
5]. In older populations with lower eGFR, associations were seen with insulin resistance [
6,
8,
9], whereas no association was seen in a healthy population [
7]. In a study of a Korean population, Park et al. conclude that “there were no meaningful differences in HOMA-IR according to eGFR group” [
11]. The results from these cross-sectional studies are not consistent, and this may be due to the age of the study populations, the numbers with low eGFR, the population studied (with or without diabetes, the metabolic syndrome [
12], according to BMI [
10], the level of eGFR used to define chronic kidney disease) as well as the covariates used for adjustment. Our EGIR-RISC cohort is younger, only 15% had an eGFR < 90 ml/min/1.73m
2 and hypertension was an exclusion criterion.
Some studies have evaluated prospectively, whether a lowering of insulin sensitivity precedes a decline in renal function, but not the reverse relation, that renal decline comes first. Nerpin et al. investigated the association between insulin sensitivity measured by the hyperinsulinemic-euglycemic clamp and eGFR based on cystatin-C, in a cohort of Swedish men, average age 71 years [
16]. They show that a higher insulin sensitivity at baseline is associated with a lower risk of impaired renal dysfunction (eGFR< 50 ml/min/1.73m
2) over the 7 years of the study, independently of other aspects of glucose metabolism. In the EGIR-RISC cohort, we have shown that a low baseline clamp-based insulin sensitivity is associated with a higher UACR measured at year-3 [
19].
In the light of these publications, insulin sensitivity appears to be related with chronic renal disease in those with a compromised renal function. However, in our population of healthy people, this association was not apparent and none of the relations we observed were present in both sexes, and were not always concordant when the variables measuring renal function were analysed as continuous or as discrete variables. Sechi et al. showed that alterations of glucose metabolism in people with essential hypertension, are only evident for eGFR< 50 ml/min/1.73m
2 and this may be the reason why our results are not conclusive [
30].
Our results on UACR are unexpected, as a high baseline UACR, in comparison to an undetected level, was related with a higher insulin sensitivity three years later, and this was the case for men and women. UACR did increase over the three years of the study, as expected. While we have used UACR as a renal marker, it is also a marker of vascular function.
What are the possible mechanisms for an association between insulin sensitivity and chronic kidney disease? Low insulin sensitivity (as measured by the minimal model technique) has been described in people with renal disease but a normal eGFR (evaluated by inulin clearance); insulin sensitivity was similar across the range of eGFR [
2]. These results imply that renal dysfunction, could precede the onset of declining insulin sensitivity. A rhesus monkey model provides additional arguments [
31]. Recent studies have identified specific uremic toxins that could mediate an association between chronic renal disease and insulin sensitivity, toxins such as p-cresyl sulfate a protein in the intestinal microbiota [
32].
In our healthy cohort, we showed that a higher filtration: eGFR (≥105 ml/min/1.73m
2) was associated, cross-sectionally, with lower insulin sensitivity in women. This result is not so surprising as insulin resistance precedes the development of diabetes, which in turn is associated with a higher glomerular filtration rate [
33]. However, the reverse was the case in men for our prospective study, as those with a higher eGFR were more likely to have a higher 3-year insulin sensitivity and a more pronounced increase in insulin sensitivity than the reference group, even if in the whole population both eGFR and insulin sensitivity decreased over time.
The multicentre aspect of this study is one of its strengths, as the study population covered a range of European lifestyles and diets. Differences between centres were accounted for in analyses by a random effect. At inclusion, insulin sensitivity was measured by the hyperinsulinemic-euglycemic clamp, a procedure that was carefully standardised across the European centres, for this large cohort study, with more than 1300 participants. All biological assays in the EGIR-RISC study are from central laboratories. At baseline the UACR was measured on two occasions and the mean used, leading to a more precise estimate. Another force is that there is little missing data in this study, and for the few variables where data were missing, we imputed with the sex-specific median value.
Our study differs from other studies in that all analyses have been done for men and women separately. This was justified by their differences in characteristics, and the study of interactions with sex. Other EGIR-RISC analyses have shown differences between men and women [
20‐
22]. It is also unique in that we studied a cohort of healthy individuals, without chronic renal disease, diabetes, hypertension or dyslipidaemia.
The EGIR-RISC study has a number of limitations. The study population consists of healthy volunteers, and thus is not representative of the general healthy population of the same age. Further, as we excluded people who were not present at the three-year examination, we have selected an even healthier population, according to their characteristics at baseline. With the surrogate measures of insulin sensitivity, we were not able to precisely evaluate insulin sensitivity and its change over the three year follow-up period, even if the correlations with clamp based insulin sensitivity were of the order of 0.6. One of the major limitations of the EGIR-RISC study is that it is a very healthy population with only 15% of our population having an eGFR< 90 ml/min/1.73m2. There are likely to be only very small changes in parameters over three years in such a population, so a much longer follow-up would be required to show associations.
Acknowledgements
EGIR-RISC Investigators.
EGIR-RISC recruiting centres.
Amsterdam, The Netherlands: RJ Heine, J Dekker, S de Rooij, G Nijpels, W Boorsma.
Athens, Greece: A Mitrakou, S Tournis, K Kyriakopoulou, P Thomakos.
Belgrade, Serbia: N Lalic, K Lalic, A Jotic, L Lukic, M Civcic.
Dublin, Ireland: J Nolan, TP Yeow, M Murphy, C DeLong, G Neary, MP Colgan, M Hatunic.
Frankfurt, Germany: T Konrad, H Böhles, S Fuellert, F Baer, H Zuchhold.
Geneva, Switzerland: A Golay, E Harsch Bobbioni,V. Barthassat, V. Makoundou, TNO Lehmann, T Merminod.
Glasgow, Scotland: JR Petrie, C Perry, F Neary, C MacDougall, K Shields, L Malcolm.
Kuopio, Finland: M Laakso, U Salmenniemi, A Aura, R Raisanen, U Ruotsalainen, T Sistonen, M Laitinen, H Saloranta.
London, England: SW Coppack, N McIntosh, J Ross, L Pettersson, P Khadobaksh.
Lyon, France: M Laville, F. Bonnet (now Rennes), A Brac de la Perriere, C Louche-Pelissier, C Maitrepierre, J Peyrat, S Beltran, A Serusclat.
Madrid, Spain: R. Gabriel, EM Sánchez, R. Carraro, A Friera, B. Novella.
Malmö, Sweden (1): P Nilsson, M Persson, G Östling, (2): O Melander, P Burri.
Milan, Italy: PM Piatti, LD Monti, E Setola, E Galluccio, F Minicucci, A Colleluori.
Newcastle-upon-Tyne, England: M Walker, IM Ibrahim, M Jayapaul, D Carman, C Ryan, K Short, Y McGrady, D Richardson.
Odense, Denmark: H Beck-Nielsen, P Staehr, K Højlund, V Vestergaard, C Olsen, L Hansen.
Perugia, Italy: GB Bolli, F Porcellati, C Fanelli, P Lucidi, F Calcinaro, A Saturni.
Pisa, Italy: E Ferrannini, A Natali, E Muscelli, S Pinnola, M Kozakova, A Casolaro, BD Astiarraga.
Rome, Italy: G Mingrone, C Guidone, A Favuzzi. P Di Rocco.
Vienna, Austria: C Anderwald, M Bischof, M Promintzer, M Krebs, M Mandl, A Hofer, A Luger, W Waldhäusl, M Roden.
Project Management Board: B Balkau (Villejuif, France), F Bonnet (Rennes, France), SW Coppack (London, England), JM Dekker (Amsterdam, The Netherlands), E Ferrannini (Pisa, Italy), A Mari (Padova, Italy), A Natali (Pisa, Italy), J Petrie (Glasgow, Scotland), M Walker (Newcastle, England).
Core laboratories and reading centres.
Lipids Dublin, Ireland: P Gaffney, J Nolan, G Boran.
Hormones Odense, Denmark: C Olsen, L Hansen, H Beck-Nielsen.
Albumin:creatinine Amsterdam, The Netherlands: A Kok, J Dekker.
Genetics Newcastle-upon-Tyne, England: S Patel, M Walker.
Stable isotope laboratory Pisa, Italy: A Gastaldelli, D Ciociaro.
Ultrasound reading centre Pisa, Italy: M Kozakova.
ECG reading, Villejuif, France: MT Guillanneuf.
Actigraph, Villejuif, France: B Balkau, L Mhamdi.
Data Management Villejuif, France, Padova, and Pisa, Italy: B Balkau, A Mari, L Mhamdi, L Landucci, S Hills, L Mota.
Mathematical modelling and website management Padova, Italy: A Mari, G Pacini, C Cavaggion, A Tura.
Coordinating office: Pisa, Italy: SA Hills, L Landucci. L Mota.
Further information on the EGIR-RISC Study and participating centres can be found on
www.egir.org.
Ethics approval and consent to participate
The study was approved by ethics committees in each recruitment centre:
1. University of Pisa Ethics Committee, Pisa, ITALY;
2. East London and The City Research Ethics Committee 1 then East London REC 1, Whitechapel, London, UNITED KINGDOM;
3. Ethics Committee, VU university medical center – VUMC, Amsterdam, THE NETHERLANDS;
4. North East – Newcastle and North Tyneside 1 Ethics Committee, Newcastle, UNITED KINDGOM;
5. Comité Consultative de Protection des Personnes dans la Recherche Biomedicale De Lyon A, Lyon, FRANCE;
6. Scientific Ethical Committee of the Counties of Vejle and Funen, Odense, DENMARK;
7. The Adelaide & Meath Hospital Ethics Committee, Dublin, IRELAND;
8. Ceas Umbria Comitato Etico Aziende Sanitarie, Perugia, ITALY;
9. Hopitaux Universitaire de Genève, Comité d’éthique, Geneva, SWITZERLAND;
10. Ethics Committee of the Landesarztekammer Hesse Im Vogelsgesang 3, Frankfurt am Main, GERMANY;
11. Lunds Universite Medicinska Fakulteten Forksningsetikkommitten, Lund, SWEDEN;
12. Università Cattolica Sacro Cuore Facolta di Medicina e Chirurgia “Agostino Gemelli” Comitato Etico, Rome, ITALY;
13. North Glasgow University Hospital West Ethics Committee West Infirmary, Glasgow, UNITED KINGDOM;
14. Ethik Kommission der Medizinischen Universitat Weibn und des Allgemeinen Krankenhauses der Stadt Wein Akh, Vienne, AUSTRIA;
15. Hospital Universitario de La Princesa Comitato Etico de Investigacion Clinica, Madrid, SPAIN;
16. Research Committee of Araiteion Hospital, Athens, GREECE;
17. Comitato Etico della Fondazione Centro San Raffaele del Monte Tabor, Milan, ITALY;
18. Klinicki Centar Srbje Eticki Komiter, Belgrade, SERBIA;
19. Tutkimuseettinen Toimikunta Kuopio Yliopistollinen Sairaala, Kuopio, FINLAND.
The declaration of Helsinki was adhered to and participants gave written informed consent to participate.