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
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...
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BMJ Publishing Group
2019-05-01
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| Series: | BMJ Open Diabetes Research & Care |
| Online Access: | https://drc.bmj.com/content/7/1/e000794.full |
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| author | Fereidoun Azizi Edward W Gregg Pamela J Schreiner Julie A Pasco David R Jacobs Jr Mark Woodward Gary Wittert Hiroshi Yatsuya Davood Khalili Gita Mishra Rachel R Huxley Crystal Man Ying Lee Elizabeth Selvin Tiffany K Gill Dianna J Magliano Jonathan E Shaw Stephen Colagiuri Robert Adams Rafael Gabriel Clicerio Gonzalez Allison Hodge Joshua J Joseph Kirsten Mehlig Roger Milne Morgana Mongraw-Chaffin Masaru Sakurai |
| author_facet | Fereidoun Azizi Edward W Gregg Pamela J Schreiner Julie A Pasco David R Jacobs Jr Mark Woodward Gary Wittert Hiroshi Yatsuya Davood Khalili Gita Mishra Rachel R Huxley Crystal Man Ying Lee Elizabeth Selvin Tiffany K Gill Dianna J Magliano Jonathan E Shaw Stephen Colagiuri Robert Adams Rafael Gabriel Clicerio Gonzalez Allison Hodge Joshua J Joseph Kirsten Mehlig Roger Milne Morgana Mongraw-Chaffin Masaru Sakurai |
| author_sort | Fereidoun Azizi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-61ef9a99e27b49db8b9dd98d50c3cfd5 |
| institution | Kabale University |
| issn | 2052-4897 |
| language | English |
| publishDate | 2019-05-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open Diabetes Research & Care |
| spelling | doaj-art-61ef9a99e27b49db8b9dd98d50c3cfd52024-12-15T16:35:08ZengBMJ Publishing GroupBMJ Open Diabetes Research & Care2052-48972019-05-017110.1136/bmjdrc-2019-000794Comparing different definitions of prediabetes with subsequent risk of diabetes: an individual participant data meta-analysis involving 76 513 individuals and 8208 cases of incident diabetesFereidoun Azizi0Edward W Gregg1Pamela J Schreiner2Julie A Pasco3David R Jacobs Jr4Mark Woodward5Gary Wittert6Hiroshi Yatsuya7Davood Khalili8Gita Mishra9Rachel R Huxley10Crystal Man Ying Lee11Elizabeth Selvin12Tiffany K Gill13Dianna J Magliano14Jonathan E Shaw15Stephen Colagiuri16Robert Adams17Rafael Gabriel18Clicerio Gonzalez19Allison Hodge20Joshua J Joseph21Kirsten Mehlig22Roger Milne23Morgana Mongraw-Chaffin24Masaru Sakurai25Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran , IranSchool of Population Health, RCSI University of Medicine and Health Sciences, Dublin, IrelandDivision of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, USAIMPACT – Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Victoria, AustraliaDivision of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USAThe George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, AustraliaDiscipline of Medicine, Adelaide Medical School, The University of Adelaide, Adelaide, South Australia, AustraliaPublic Health and Health Systems, Nagoya University Graduate School of Medicine Faculty of Medicine, Nagoya, Aichi, JapanPrevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranSchool of Public Health, The University of Queensland, Saint Lucia, Queensland, AustraliaFaculty of Health, Deakin University, Burwood, Victoria, AustraliaSchool of Population Health, Curtin University, Perth, Western Australia, Australia10 Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USAThe University of Adelaide Faculty of Health and Medical Sciences, Adelaide, South Australia, AustraliaDiabetes and Population Health, Baker Heart and Diabetes Institute, Melbourne, Victoria, AustraliaBaker Heart and Diabetes Institute, Melbourne, Victoria, Australia2 Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, AustraliaAdelaide Institute for Sleep Health (AISH), Flinders University, Adelaide, SA, AustraliaNational School of Public Health, National Institute of Health Carlos III, Madrid, SpainUnidad de Investigación en Diabetes y Riesgo Cardiovascular, Instituto Nacional de Salud Publica, Cuernavaca, Morelos, MexicoCancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, AustraliaDeparment of Medicine, Division of Endocrinology, Diabetes, and Metabolism, The Ohio State University, Columbus, Ohio, USA1 School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, SwedenCancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, AustraliaDepartment of Epidemiology & Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USADepartment of Social and Environmental Medicine, Kanazawa Medical University, Uchinada, JapanObjective 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.https://drc.bmj.com/content/7/1/e000794.full |
| spellingShingle | Fereidoun Azizi Edward W Gregg Pamela J Schreiner Julie A Pasco David R Jacobs Jr Mark Woodward Gary Wittert Hiroshi Yatsuya Davood Khalili Gita Mishra Rachel R Huxley Crystal Man Ying Lee Elizabeth Selvin Tiffany K Gill Dianna J Magliano Jonathan E Shaw Stephen Colagiuri Robert Adams Rafael Gabriel Clicerio Gonzalez Allison Hodge Joshua J Joseph Kirsten Mehlig Roger Milne Morgana Mongraw-Chaffin Masaru Sakurai 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 BMJ Open Diabetes Research & Care |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://drc.bmj.com/content/7/1/e000794.full |
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