Rong Liu1, Katherine Kaufer Christoffel2, Wendy J Brickman3, Xin Liu2, Meghana Gadgil4, Guoying Wang5, Donald Zimmerman4, Qi Chen2, Binyan Wang6, Zhiping Li6, Houxun Xing6, Xiping Xu7, Xiaobin Wang8. 1. Beijing An Zhen Hospital, Capital Medical University, The Key Laboratory of Remodeling-related Cardiovascular Diseases, Ministry of Education, Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing, China; Mary Ann and J. Milburn Smith Child Health Research Program, Department of Pediatrics, Northwestern University Feinberg School of Medicine and Ann & Robert H. Lurie Children's Hospital of Chicago and Children's Hospital of Chicago Research Center, Chicago, IL, USA. Electronic address: rongliu606@gmail.com. 2. Mary Ann and J. Milburn Smith Child Health Research Program, Department of Pediatrics, Northwestern University Feinberg School of Medicine and Ann & Robert H. Lurie Children's Hospital of Chicago and Children's Hospital of Chicago Research Center, Chicago, IL, USA. 3. Division of Endocrinology, Department of Pediatrics, Northwestern University Feinberg School of Medicine and Ann & Robert H. Lurie Children's Hospital of Chicago and Children's Hospital of Chicago Research Center, Chicago, IL, USA. 4. Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. 5. Mary Ann and J. Milburn Smith Child Health Research Program, Department of Pediatrics, Northwestern University Feinberg School of Medicine and Ann & Robert H. Lurie Children's Hospital of Chicago and Children's Hospital of Chicago Research Center, Chicago, IL, USA; Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA. 6. Institute of Biomedicine, Anhui Medical University, Hefei, China. 7. Division of Nephrology, Nanfang Hospital, Southern Medical University, National Clinical Research Center, Key Lab for Organ Failure Research, Ministry of Education, Guangzhou, China. 8. Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: xwang82@jhu.edu.
Abstract
AIMS: We designed a study to compare the predictive power of static and dynamic insulin resistance indices for categorized pre-diabetes (PDM)/type 2 diabetes (DM). METHODS: Participants included 1134 adults aged 18-60 years old with normal glucose at baseline who completed both baseline and 6-years later follow-up surveys. Insulin resistance indices from baseline data were used to predict risk of PDM or DM at follow-up. Two static indices and two dynamic indices were calculated from oral glucose tolerance test results (OGTT) at baseline. Area under the receiver operating characteristic curve (AROC) analysis was used to estimate the predictive ability of candidate indices to predict PDM/DM. A general estimation equation (GEE) model was applied to assess the magnitude of association of each index at baseline with the risk of PDM/DM at follow-up. RESULTS: The dynamic indices displayed the largest and statistically predictive AROC for PDM/DM diagnosed either by fasting glucose or by postprandial glucose. The bottom quartiles of the dynamic indices were associated with an elevated risk of PDM/DM vs. the top three quartiles. However, the static indices only performed significantly to PDM/DM diagnosed by fasting glucose. CONCLUSIONS: Dynamic insulin resistance indices are stronger predictors of future PDM/DM than static indices. This may be because dynamic indices better reflect the full range of physiologic disturbances in PDM/DM.
AIMS: We designed a study to compare the predictive power of static and dynamic insulin resistance indices for categorized pre-diabetes (PDM)/type 2 diabetes (DM). METHODS:Participants included 1134 adults aged 18-60 years old with normal glucose at baseline who completed both baseline and 6-years later follow-up surveys. Insulin resistance indices from baseline data were used to predict risk of PDM or DM at follow-up. Two static indices and two dynamic indices were calculated from oral glucose tolerance test results (OGTT) at baseline. Area under the receiver operating characteristic curve (AROC) analysis was used to estimate the predictive ability of candidate indices to predict PDM/DM. A general estimation equation (GEE) model was applied to assess the magnitude of association of each index at baseline with the risk of PDM/DM at follow-up. RESULTS: The dynamic indices displayed the largest and statistically predictive AROC for PDM/DM diagnosed either by fasting glucose or by postprandial glucose. The bottom quartiles of the dynamic indices were associated with an elevated risk of PDM/DM vs. the top three quartiles. However, the static indices only performed significantly to PDM/DM diagnosed by fasting glucose. CONCLUSIONS: Dynamic insulin resistance indices are stronger predictors of future PDM/DM than static indices. This may be because dynamic indices better reflect the full range of physiologic disturbances in PDM/DM.
Authors: R L Hanson; R E Pratley; C Bogardus; K M Narayan; J M Roumain; G Imperatore; A Fagot-Campagna; D J Pettitt; P H Bennett; W C Knowler Journal: Am J Epidemiol Date: 2000-01-15 Impact factor: 4.897
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Authors: Steffi Kopprasch; Srirangan Dheban; Kai Schuhmann; Aimin Xu; Klaus-Martin Schulte; Charmaine J Simeonovic; Peter E H Schwarz; Stefan R Bornstein; Andrej Shevchenko; Juergen Graessler Journal: PLoS One Date: 2016-10-13 Impact factor: 3.240