Adam Hulman1, Unjali P Gujral2, K M Venkat Narayan3, Rajendra Pradeepa4, Deepa Mohan5, Ranjit Mohan Anjana6, Viswanathan Mohan7, Kristine Færch8, Daniel R Witte9. 1. Department of Public Health, Aarhus University, Bartholins Allé 2, Building 1260, DK-8000, Aarhus C, Denmark; Danish Diabetes Academy, Odense University Hospital, Sdr Boulevard 29, DK-5000 Odense C, Denmark; Department of Medical Physics and Informatics, University of Szeged, Korányi fasor 9, H-6720 Szeged, Szeged, Hungary. Electronic address: adam.hulman@ph.au.dk. 2. Emory Global Diabetes Research Center, Hubert Department of Global Health, Rollins School of Public Health, 1518 Clifton Road NE. Room 7040 N Emory University, Atlanta, GA, USA. Electronic address: ugujral@emory.edu. 3. Nutrition and Health Sciences Program, Emory University, 1518 Clifton Road, Room 7000, Atlanta, GA, USA; Department of Medicine, School of Medicine, 201 Dowman Drive Emory University, Atlanta, GA, USA. Electronic address: knaraya@emory.edu. 4. Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, WHO Collaborating Centre for Non-communicable Diseases, Prevention & Control, IDF Centre of Education, Chennai, India. Electronic address: guhapradeepa@gmail.com. 5. Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, WHO Collaborating Centre for Non-communicable Diseases, Prevention & Control, IDF Centre of Education, Chennai, India. Electronic address: deepa.mohan1@gmail.com. 6. Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, WHO Collaborating Centre for Non-communicable Diseases, Prevention & Control, IDF Centre of Education, Chennai, India. Electronic address: dranjana@drmohans.com. 7. Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, WHO Collaborating Centre for Non-communicable Diseases, Prevention & Control, IDF Centre of Education, Chennai, India. Electronic address: drmohans@diabetes.ind.in. 8. Steno Diabetes Center Copenhagen, Niels Steensens Vej 2, DK-2820, Gentofte, Denmark. Electronic address: kristine.faerch@regionh.dk. 9. Department of Public Health, Aarhus University, Bartholins Allé 2, Building 1260, DK-8000, Aarhus C, Denmark; Danish Diabetes Academy, Odense University Hospital, Sdr Boulevard 29, DK-5000 Odense C, Denmark. Electronic address: daniel.witte@ph.au.dk.
Abstract
AIMS: Traditionally, fasting and 2-hour post challenge plasma glucose have been used to diagnose diabetes. However, evidence indicates that clinically relevant pathophysiological information can be obtained by adding intermediate time-points to a standard oral glucose tolerance test (OGTT). METHODS: We studied a population-based sample of 3666 Asian Indians without diabetes from the CARRS-Chennai Study, India. Participants underwent a three-point (fasting, 30-min, and 2-h) OGTT at baseline. Patterns of glycemic response during OGTT were identified using latent class mixed-effects models. After a median follow-up of two years, participants had a second OGTT. Logistic regression adjusted for diabetes risk factors was used to compare risk of incident diabetes among participants in different latent classes. RESULTS: We identified four latent classes with different glucose patterns (Classes 1-4). Glucose values for Classes 1, 2, and 4 ranked consistently at all three time-points, but at gradually higher levels. However, Class 3 represented a distinct pattern, characterized by high 30-min (30minPG), normal fasting (FPG) and 2-h (2hPG) plasma glucose, moderately high insulin sensitivity, and low acute insulin response. Approximately 22% of participants were categorized as Class 3, and had a 10-fold risk of diabetes compared to the group with the most favorable glucose response, despite 92.5% of Class 3 participants having normal glucose tolerance (NGT) at baseline. CONCLUSIONS: Elevated 30minPG is associated with high risk of incident diabetes, even in individuals classified as NGT by a traditional OGTT. Assessing 30minPG may identify a subgroup of high-risk individuals who remained unidentified by traditional measures.
AIMS: Traditionally, fasting and 2-hour post challenge plasma glucose have been used to diagnose diabetes. However, evidence indicates that clinically relevant pathophysiological information can be obtained by adding intermediate time-points to a standard oral glucose tolerance test (OGTT). METHODS: We studied a population-based sample of 3666 Asian Indians without diabetes from the CARRS-Chennai Study, India. Participants underwent a three-point (fasting, 30-min, and 2-h) OGTT at baseline. Patterns of glycemic response during OGTT were identified using latent class mixed-effects models. After a median follow-up of two years, participants had a second OGTT. Logistic regression adjusted for diabetes risk factors was used to compare risk of incident diabetes among participants in different latent classes. RESULTS: We identified four latent classes with different glucose patterns (Classes 1-4). Glucose values for Classes 1, 2, and 4 ranked consistently at all three time-points, but at gradually higher levels. However, Class 3 represented a distinct pattern, characterized by high 30-min (30minPG), normal fasting (FPG) and 2-h (2hPG) plasma glucose, moderately high insulin sensitivity, and low acute insulin response. Approximately 22% of participants were categorized as Class 3, and had a 10-fold risk of diabetes compared to the group with the most favorable glucose response, despite 92.5% of Class 3 participants having normal glucose tolerance (NGT) at baseline. CONCLUSIONS: Elevated 30minPG is associated with high risk of incident diabetes, even in individuals classified as NGT by a traditional OGTT. Assessing 30minPG may identify a subgroup of high-risk individuals who remained unidentified by traditional measures.
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