M R P Markus1, T Ittermann2, S E Baumeister3, C Huth4, B Thorand4, C Herder5, M Roden6, U Siewert-Markus7, W Rathmann8, W Koenig9, M Dörr10, H Völzke2, S Schipf11, C Meisinger12. 1. Department of Study of Health in Pomerania/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany; German Center for Diabetes Research (DZD), Partner Site Greifswald, Greifswald, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany. Electronic address: marcello.markus@uni-greifswald.de. 2. Department of Study of Health in Pomerania/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; German Center for Diabetes Research (DZD), Partner Site Greifswald, Greifswald, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany. 3. Department of Study of Health in Pomerania/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; Department of Epidemiology and Preventive Medicine, University of Regensburg, Regensburg, Germany; Chair of Epidemiology, Ludwig-Maximilians-University Munich, UNIKA-T Augsburg, Augsburg, Germany. 4. German Center for Diabetes Research (DZD), München-Neuherberg, Germany; Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany. 5. German Center for Diabetes Research (DZD), München-Neuherberg, Germany; Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany. 6. German Center for Diabetes Research (DZD), München-Neuherberg, Germany; Institute for Medical Psychology, University Medicine Greifswald, Greifswald, Germany; Department of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. 7. Institute for Psychology, Ernst-Moritz-Arndt-Universität Greifswald, Greifswald, Germany; Institute for Medical Psychology, University Medicine Greifswald, Greifswald, Germany. 8. German Center for Diabetes Research (DZD), München-Neuherberg, Germany; Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany. 9. Deutsches Herzzentrum München, Technical University of Munich, Munich, Germany; German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany. 10. Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany; German Centre for Cardiovascular Research (DZHK), Partner Site Greifswald, Greifswald, Germany. 11. Department of Study of Health in Pomerania/Clinical-Epidemiological Research, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany; German Center for Diabetes Research (DZD), Partner Site Greifswald, Greifswald, Germany. 12. Chair of Epidemiology, Ludwig-Maximilians-University Munich, UNIKA-T Augsburg, Augsburg, Germany; German Center for Diabetes Research (DZD), München-Neuherberg, Germany.
Abstract
BACKGROUND AND AIMS: We investigated the associations of serum fasting (FG) and 2-h postload (2HG) glucose from an oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c), fasting insulin and the homeostasis model assessment-insulin resistance index (HOMA-IR) with urinary albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR). METHODS AND RESULTS: We performed cross-sectional analyses of 2713 subjects (1429 women; 52.7%) without known type 2 diabetes, aged 31-82 years, from the KORA (Cooperative Health Research in the Augsburg Region) F4-Study. FG, 2HG, HbA1c, fasting insulin, HOMA-IR and glucose tolerance categories were analyzed for association with ACR and eGFR in multivariable adjusted linear and median regression models, and with isolated microalbuminuria (i-MA), isolated reduced kidney function (i-RKF) and chronic kidney disease (CKD, defined as MA and/or RKF) in multivariable adjusted logistic regression models. Among the 2713 study participants, 28% revealed prediabetes (isolated impaired fasting glucose [i-IFG], isolated glucose tolerance [i-IGT] or both by American Diabetes Association definition), 4.2% had unknown type 2 diabetes, 6.5% had i-MA, 3.1% i-RKF and 10.9% CKD. In multivariable adjusted analysis, all continuous variables (FG, 2HG, HbA1c, fasting insulin and HOMA-IR) were associated with i-MA, i-RKF and CKD. The odds ratios (ORs) for i-MA and CKD were 1.54 (95% confidence interval: 1.02-2.33) and 1.58 (1.10-2.25) for individuals with i-IFG. Moreover, the OR for i-RKF was 2.57 (1.31-5.06) for individuals with IFG + IGT. CONCLUSION: Our findings suggest that prediabetes might have harmful effects on the kidney.
BACKGROUND AND AIMS: We investigated the associations of serum fasting (FG) and 2-h postload (2HG) glucose from an oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c), fasting insulin and the homeostasis model assessment-insulin resistance index (HOMA-IR) with urinary albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR). METHODS AND RESULTS: We performed cross-sectional analyses of 2713 subjects (1429 women; 52.7%) without known type 2 diabetes, aged 31-82 years, from the KORA (Cooperative Health Research in the Augsburg Region) F4-Study. FG, 2HG, HbA1c, fasting insulin, HOMA-IR and glucose tolerance categories were analyzed for association with ACR and eGFR in multivariable adjusted linear and median regression models, and with isolated microalbuminuria (i-MA), isolated reduced kidney function (i-RKF) and chronic kidney disease (CKD, defined as MA and/or RKF) in multivariable adjusted logistic regression models. Among the 2713 study participants, 28% revealed prediabetes (isolated impaired fasting glucose [i-IFG], isolated glucose tolerance [i-IGT] or both by American Diabetes Association definition), 4.2% had unknown type 2 diabetes, 6.5% had i-MA, 3.1% i-RKF and 10.9% CKD. In multivariable adjusted analysis, all continuous variables (FG, 2HG, HbA1c, fasting insulin and HOMA-IR) were associated with i-MA, i-RKF and CKD. The odds ratios (ORs) for i-MA and CKD were 1.54 (95% confidence interval: 1.02-2.33) and 1.58 (1.10-2.25) for individuals with i-IFG. Moreover, the OR for i-RKF was 2.57 (1.31-5.06) for individuals with IFG + IGT. CONCLUSION: Our findings suggest that prediabetes might have harmful effects on the kidney.
Authors: Wei Li; Anping Wang; Jiajia Jiang; Guangxu Liu; Meiping Wang; Dongxue Li; Jing Wen; Yiming Mu; Xiaoyan Du; Herbert Gaisano; Jingtao Dou; Yan He Journal: BMJ Open Diabetes Res Care Date: 2020-04
Authors: Sara Nunes; André Alves; Inês Preguiça; Adelaide Barbosa; Pedro Vieira; Fernando Mendes; Diana Martins; Sofia D Viana; Flávio Reis Journal: Nutrients Date: 2020-03-25 Impact factor: 5.717