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Epidemiology/Health Services Research
Comparing different definitions of prediabetes with subsequent risk of diabetes: an individual participant data meta-analysis involving 76 513 individuals and 8208 cases of incident diabetes
  1. Crystal Man Ying Lee1,2,
  2. Stephen Colagiuri2,
  3. Mark Woodward3,4,5,
  4. Edward W Gregg6,
  5. Robert Adams7,8,9,
  6. Fereidoun Azizi10,
  7. Rafael Gabriel MD, PhD11,
  8. Tiffany K Gill9,
  9. Clicerio Gonzalez12,
  10. Allison Hodge13,14,
  11. David R Jacobs Jr Jr.15,
  12. Joshua J Joseph16,
  13. Davood Khalili17,18,
  14. Dianna J Magliano19,
  15. Kirsten Mehlig20,
  16. Roger Milne13,
  17. Gita Mishra21,
  18. Morgana Mongraw-Chaffin22,
  19. Julie A Pasco23,24,25,
  20. Masaru Sakurai26,
  21. Pamela J Schreiner15,
  22. Elizabeth Selvin27,
  23. Jonathan E Shaw28,
  24. Gary Wittert29,
  25. Hiroshi Yatsuya30,31,
  26. Rachel R Huxley3,32
  1. 1School of Psychology and Public Health, La Trobe University, Bundoora, Victoria, Australia
  2. 2Boden Collaboration for Obesity, Nutrition and Exercise & Eating Disorders, University of Sydney, Sydney, New South Wales, Australia
  3. 3The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia
  4. 4The George Institute for Global Health, University of Oxford, Oxford, UK
  5. 5Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, USA
  6. 6Department of Epidemiology and Statistics, School of Public Health, Imperial College London, London, UK
  7. 7Adelaide Institute for Sleep Health, College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia
  8. 8Respiratory and Sleep Service, Southern Adelaide Local Health Network, SA Health, Adelaide, South Australia, Australia
  9. 9Faculty of Health and Medical Sciences, Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia
  10. 10Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  11. 11National School of Public Health, National Institute of Health Carlos III, Madrid, Spain
  12. 12Unidad de Investigación en Diabetes y Riesgo Cardiovascular, Instituto Nacional de Salud Publica, Cuernavaca, Morelos, Mexico
  13. 13Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia
  14. 14Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
  15. 15Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
  16. 16Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Ohio State University Wexner Medical Center, Columbus, Ohio, USA
  17. 17Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  18. 18Department of Biostatistics and Epidemiology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  19. 19Diabetes and Population Health, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
  20. 20Department of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Goteborg, Sweden
  21. 21School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
  22. 22Department of Epidemiology & Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
  23. 23Department of Clinical and Biomedical Sciences, Barwon Health, The University of Melbourne, Geelong, Victoria, Australia
  24. 24School of Medicine, Faculty of Health, Deakin University, Geelong, Victoria, Australia
  25. 25Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
  26. 26Department of Social and Environmental Medicine, Kanazawa Medical University, Uchinada, Ishikawa, Japan
  27. 27Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
  28. 28Clinical Diabetes and Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
  29. 29Discipline of Medicine, Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, Australia
  30. 30Department of Public Health, School of Medicine, Fujita Health University, Toyoake, Aichi, Japan
  31. 31Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
  32. 32College of Science, Health and Engineering, La Trobe University, Bundoora, Victoria, Australia
  1. Correspondence to Dr Crystal Man Ying Lee; c.lee2{at}latrobe.edu.au

Abstract

Objective There are currently five widely used definition of prediabetes. We compared the ability of these to predict 5-year conversion to diabetes and investigated whether there were other cut-points identifying risk of progression to diabetes that may be more useful.

Research design and methods We conducted an individual participant meta-analysis using longitudinal data included in the Obesity, Diabetes and Cardiovascular Disease Collaboration. Cox regression models were used to obtain study-specific HRs for incident diabetes associated with each prediabetes definition. Harrell’s C-statistics were used to estimate how well each prediabetes definition discriminated 5-year risk of diabetes. Spline and receiver operating characteristic curve (ROC) analyses were used to identify alternative cut-points.

Results Sixteen studies, with 76 513 participants and 8208 incident diabetes cases, were available. Compared with normoglycemia, current prediabetes definitions were associated with four to eight times higher diabetes risk (HRs (95% CIs): 3.78 (3.11 to 4.60) to 8.36 (4.88 to 14.33)) and all definitions discriminated 5-year diabetes risk with good accuracy (C-statistics 0.79–0.81). Cut-points identified through spline analysis were fasting plasma glucose (FPG) 5.1 mmol/L and glycated hemoglobin (HbA1c) 5.0% (31 mmol/mol) and cut-points identified through ROC analysis were FPG 5.6 mmol/L, 2-hour postload glucose 7.0 mmol/L and HbA1c 5.6% (38 mmol/mol).

Conclusions In terms of identifying individuals at greatest risk of developing diabetes within 5 years, using prediabetes definitions that have lower values produced non-significant gain. Therefore, deciding which definition to use will ultimately depend on the goal for identifying individuals at risk of diabetes.

  • pre-diabetes
  • fasting blood glucose
  • glycated hemoglobin
  • incidence

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

https://doi.org/10.1136/bmjdrc-2019-000794

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    Footnotes

    • Contributors CMYL analyzed the data, co-drafted the manuscript and is the guarantor of this work. RRH conceived the design of the study and co-drafted the manuscript. MW provided statistical oversight. SC and EWG contributed to interpretation of the data. RA, FA, RG, TKG, CG, AH, DRJ, JJJ, DK, DJM, KM, RM, MM-C, JAP, MS, PJS, ES, JES, GW, HY provided study data. All authors contributed to the critical revision of the manuscript and approved the final version.

    • Funding This work was supported by the National Health and Medical Research Council of Australia (grant number 1103242). The Atherosclerosis Risk in Communities study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under contract nos. HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I, HHSN268201700004I. ES was supported by NIH/NIDDK grant K24DK106414. The Coronary Artery Risk Development in Young Adults Study (CARDIA) is supported by contracts HHSN2682018000031, HHSN2682018000041, HHSN2682018000051, HHSN2682018000061 and HHSN2682018000071 from the National Heart, Lung, and Blood Institute (NHLBI). The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute for Minority Health and Health Disparities (NIMHD). The Melbourne Collaborative Cohort Study (MCCS) recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further augmented by Australian National Health and Medical Research Council grants 209057, 396414 and 1074383 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database. The Multi-Ethnic Study of Atherosclerosis was supported by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 from the National Heart, Lung, and Blood Institute and by grants UL1-TR-000040 and UL1-TR-001079 from NCRR. The Population Study of Women in Gothenburg (PSWG) was financed in part by grants from the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement ALFGBG-720201. VIVA Study received grants 95/0029 and 06/90270 from the Instituto de Salud Carlos III, Spain.

    • Disclaimer The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health or the US Department of Health and Human Services.

    • Competing interests CMYL, JAP, GW and RRH received grants from the National Health and Medical Research Council of Australia (NHMRC) during the conduct of the study; MW received person fees from Amgen and Hyowa Hakko Kirin outside the submitted work; RA received grants from NHMRC and the Hospital Research Foundation during the conduct of the study; JJJ received grants from National Institute of Health (NIH), National Institute of Diabetes and Digestive and Kidney Diseases (K23DK117041) during the conduct of the study; DJM and JES received grants from Commonwealth Department of Health and Aged Care, Abbott Australasia Pty Ltd, Alphapharm Pty Ltd, AstraZeneca, Aventis Pharmaceutical, Bristol-Myers Squibb Pharmaceuticals, Eli Lilly (Aust) Pty Ltd, GlaxoSmithKline, Janssen-Cilag (Aust) Pty Ltd, Merck Lipha s.a., Merck Sharp & Dohme (Aust), Novartis Pharmaceutical (Aust) Pty Ltd, Novo Nordisk Pharmaceutical Pty Ltd, Pharmacia and Upjohn Pty Ltd, Pfizer Pty Ltd, Sanofi Synthelabo, Servier Laboratories (Aust) Pty Ltd, the Australian Kidney Foundation and Diabetes Australia during the conduct of the study, and personal fees from AstraZeneca, Mylan, Boehringer Ingelheim, Sanofi, Merck Sharp and Dohme, Novo Nordisk and Eli Lilly outside the submitted work; PJS received grants from National Heart, Lung and Blood Institute during the conduct of the study; ES received grants from NIH during the conduct of the study.

    • Patient consent for publication Not required.

    • Ethics approval This study was approved by the La Trobe University Human Ethics Committee (approval number: HEC18120).

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information.