Obesity indicators that best predict type 2 diabetes in an Indian population : insights from the Kerala Diabetes Prevention Program

Obesity indicators are known to predict the presence of type 2 diabetes mellitus (T2DM); however, evidence for which indicator best identifies undiagnosed T2DM in the Indian population is still very limited. In the present study we examined the utility of different obesity indicators to identify the...

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Main Authors: Kapoor, Nitin, Lotfaliany, Mojtaba, Sathish, Thirunavukkarasu, Thankappan, K. R., Thomas, Nihal, Furler, John, Oldenburg, Brian, Tapp, Robyn J.
Other Authors: Lee Kong Chian School of Medicine (LKCMedicine)
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Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/148627
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spelling sg-ntu-dr.10356-1486272023-03-05T16:50:23Z Obesity indicators that best predict type 2 diabetes in an Indian population : insights from the Kerala Diabetes Prevention Program Kapoor, Nitin Lotfaliany, Mojtaba Sathish, Thirunavukkarasu Thankappan, K. R. Thomas, Nihal Furler, John Oldenburg, Brian Tapp, Robyn J. Lee Kong Chian School of Medicine (LKCMedicine) Centre for Population Health Sciences Science::Medicine Obesity Indicators Type 2 Diabetes Mellitus Obesity indicators are known to predict the presence of type 2 diabetes mellitus (T2DM); however, evidence for which indicator best identifies undiagnosed T2DM in the Indian population is still very limited. In the present study we examined the utility of different obesity indicators to identify the presence of undiagnosed T2DM and determined their appropriate cut point for each obesity measure. Individuals were recruited from the large-scale population-based Kerala Diabetes Prevention Program. Oral glucose tolerance tests was performed to diagnose T2DM. Receiver operating characteristic (ROC) curve analyses were used to compare the association of different obesity indicators with T2DM and to determine the optimal cut points for identifying T2DM. A total of 357 new cases of T2DM and 1352 individuals without diabetes were identified. The mean age of the study participants was 46⋅4 (sd 7⋅4) years and 62 % were men. Waist circumference (WC), waist:hip ratio (WHR), waist:height ratio (WHtR), BMI, body fat percentage and fat per square of height were found to be significantly higher (P < 0⋅001) among those with diabetes compared with individuals without diabetes. In addition, ROC for WHR (0⋅67; 95 % 0⋅59, 0⋅75), WHtR (0⋅66; 95 % 0⋅57, 0⋅75) and WC (0⋅64; 95 % 0⋅55, 0⋅73) were shown to better identify patients with T2DM. The proposed cut points with an optimal sensitivity and specificity for WHR, WHtR and WC were 0⋅96, 0⋅56 and 86 cm for men and 0⋅88, 0⋅54 and 83 cm for women, respectively. The present study has shown that WHR, WHtR and WC are better than other anthropometric measures for detecting T2DM in the Indian population. Their utility in clinical practice may better stratify at-risk patients in this population than BMI, which is widely used at present. Published version 2021-05-19T01:39:42Z 2021-05-19T01:39:42Z 2020 Journal Article Kapoor, N., Lotfaliany, M., Sathish, T., Thankappan, K. R., Thomas, N., Furler, J., Oldenburg, B. & Tapp, R. J. (2020). Obesity indicators that best predict type 2 diabetes in an Indian population : insights from the Kerala Diabetes Prevention Program. Journal of Nutritional Science, 9. https://dx.doi.org/10.1017/jns.2020.8 2048-6790 https://hdl.handle.net/10356/148627 10.1017/jns.2020.8 32328239 2-s2.0-85083188031 9 en Journal of Nutritional Science © 2020 The Author(s). This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creative-commons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Medicine
Obesity Indicators
Type 2 Diabetes Mellitus
spellingShingle Science::Medicine
Obesity Indicators
Type 2 Diabetes Mellitus
Kapoor, Nitin
Lotfaliany, Mojtaba
Sathish, Thirunavukkarasu
Thankappan, K. R.
Thomas, Nihal
Furler, John
Oldenburg, Brian
Tapp, Robyn J.
Obesity indicators that best predict type 2 diabetes in an Indian population : insights from the Kerala Diabetes Prevention Program
description Obesity indicators are known to predict the presence of type 2 diabetes mellitus (T2DM); however, evidence for which indicator best identifies undiagnosed T2DM in the Indian population is still very limited. In the present study we examined the utility of different obesity indicators to identify the presence of undiagnosed T2DM and determined their appropriate cut point for each obesity measure. Individuals were recruited from the large-scale population-based Kerala Diabetes Prevention Program. Oral glucose tolerance tests was performed to diagnose T2DM. Receiver operating characteristic (ROC) curve analyses were used to compare the association of different obesity indicators with T2DM and to determine the optimal cut points for identifying T2DM. A total of 357 new cases of T2DM and 1352 individuals without diabetes were identified. The mean age of the study participants was 46⋅4 (sd 7⋅4) years and 62 % were men. Waist circumference (WC), waist:hip ratio (WHR), waist:height ratio (WHtR), BMI, body fat percentage and fat per square of height were found to be significantly higher (P < 0⋅001) among those with diabetes compared with individuals without diabetes. In addition, ROC for WHR (0⋅67; 95 % 0⋅59, 0⋅75), WHtR (0⋅66; 95 % 0⋅57, 0⋅75) and WC (0⋅64; 95 % 0⋅55, 0⋅73) were shown to better identify patients with T2DM. The proposed cut points with an optimal sensitivity and specificity for WHR, WHtR and WC were 0⋅96, 0⋅56 and 86 cm for men and 0⋅88, 0⋅54 and 83 cm for women, respectively. The present study has shown that WHR, WHtR and WC are better than other anthropometric measures for detecting T2DM in the Indian population. Their utility in clinical practice may better stratify at-risk patients in this population than BMI, which is widely used at present.
author2 Lee Kong Chian School of Medicine (LKCMedicine)
author_facet Lee Kong Chian School of Medicine (LKCMedicine)
Kapoor, Nitin
Lotfaliany, Mojtaba
Sathish, Thirunavukkarasu
Thankappan, K. R.
Thomas, Nihal
Furler, John
Oldenburg, Brian
Tapp, Robyn J.
format Article
author Kapoor, Nitin
Lotfaliany, Mojtaba
Sathish, Thirunavukkarasu
Thankappan, K. R.
Thomas, Nihal
Furler, John
Oldenburg, Brian
Tapp, Robyn J.
author_sort Kapoor, Nitin
title Obesity indicators that best predict type 2 diabetes in an Indian population : insights from the Kerala Diabetes Prevention Program
title_short Obesity indicators that best predict type 2 diabetes in an Indian population : insights from the Kerala Diabetes Prevention Program
title_full Obesity indicators that best predict type 2 diabetes in an Indian population : insights from the Kerala Diabetes Prevention Program
title_fullStr Obesity indicators that best predict type 2 diabetes in an Indian population : insights from the Kerala Diabetes Prevention Program
title_full_unstemmed Obesity indicators that best predict type 2 diabetes in an Indian population : insights from the Kerala Diabetes Prevention Program
title_sort obesity indicators that best predict type 2 diabetes in an indian population : insights from the kerala diabetes prevention program
publishDate 2021
url https://hdl.handle.net/10356/148627
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