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

Adult Diabetes Mellitus, Type 2 / diagnosis Female Glucose Tolerance Test Humans Male Middle Aged Obesity / diagnosis ROC Curve Sensitivity and Specificity Waist Circumference Waist-Height Ratio Waist-Hip Ratio
Normal-weight obesity Obesity indicators ROC, receiver operating characteristics T2DM, type 2 diabetes mellitus Thin–fat phenotype Type 2 diabetes mellitus Visceral adiposity WC, waist circumference WHR, waist:hip ratio WHtR, waist:height ratio

Journal

Journal of nutritional science
ISSN: 2048-6790
Titre abrégé: J Nutr Sci
Pays: England
ID NLM: 101590587

Informations de publication

Date de publication:
2020
Historique:
received: 30 10 2019
accepted: 13 03 2020
entrez: 25 4 2020
pubmed: 25 4 2020
medline: 5 6 2021
Statut: epublish

Résumé

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 (

Identifiants

DOI: 10.1017/jns.2020.8 PMID: 32328239 PMC: PMC7163399
pubmed: 32328239
doi: 10.1017/jns.2020.8
pmc: PMC7163399
doi:

Types de publication

Journal Article Pragmatic Clinical Trial Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e15

Subventions

Organisme : FIC NIH HHS
ID : D43 TW008332
Pays : United States

Informations de copyright

© The Author(s) 2020.

Références

Metabolism. 2019 Mar;92:61-70
pubmed: 30586573
Int J Epidemiol. 2003 Feb;32(1):1-22
pubmed: 12689998
Clin Ther. 2019 Aug;41(8):1638-1642
pubmed: 31270012
Nutrients. 2018 Oct 10;10(10):
pubmed: 30308974
Lancet Diabetes Endocrinol. 2019 Mar;7(3):169-172
pubmed: 30581081
AIDS. 2019 Jun 1;33(7):1105-1115
pubmed: 31045941
Diabetes Care. 2007 Jun;30(6):1647-52
pubmed: 17360974
Curr Diabetes Rev. 2006 Nov;2(4):367-73
pubmed: 18220642
Int J Obes Relat Metab Disord. 1998 Dec;22(12):1164-71
pubmed: 9877251
Diabetes Care. 2013 Apr;36(4):1047-55
pubmed: 23520370
Prim Care Diabetes. 2018 Oct;12(5):383-392
pubmed: 29752222
Lancet. 2004 Jan 10;363(9403):163
pubmed: 14726172
Diabetes Care. 2017 Jan;40(Suppl 1):S11-S24
pubmed: 27979889
Circulation. 2017 Jun 13;135(24):2373-2388
pubmed: 28500271
Lancet. 2018 Feb 10;391(10120):541-551
pubmed: 29221645
Expert Rev Endocrinol Metab. 2016 Nov;11(6):521-528
pubmed: 30058916
Eur J Clin Invest. 2019 May;49(5):e13079
pubmed: 30734926
Prev Chronic Dis. 2013;10:E37
pubmed: 23517580
Acad Radiol. 2004 Nov;11(11):1260-73
pubmed: 15561573
PLoS One. 2011;6(7):e22112
pubmed: 21829446
Diabet Med. 2013 Mar;30(3):267-75
pubmed: 23075228
N Engl J Med. 2017 Jul 6;377(1):13-27
pubmed: 28604169
BMC Public Health. 2013 Nov 04;13:1035
pubmed: 24180316
Obes Res. 2001 Nov;9(11):696-705
pubmed: 11707536
J Biosoc Sci. 2019 Jul;51(4):624-626
pubmed: 30944046
Int J Body Compos Res. 2010;8(2):45-50
pubmed: 21318088
Lancet. 2006 Nov 11;368(9548):1681-8
pubmed: 17098087
J Biosoc Sci. 2019 Jul;51(4):619-621
pubmed: 30944050
Diabetologia. 2010 May;53(5):890-8
pubmed: 20146052
Nephrol Dial Transplant. 2008 Jan;23(1):47-51
pubmed: 17890752
Obesity (Silver Spring). 2006 Nov;14(11):2107-17
pubmed: 17135629
J Diabetes. 2012 Mar;4(1):47-54
pubmed: 22018101
Lancet Glob Health. 2018 Dec;6(12):e1352-e1362
pubmed: 30219315
BMC Public Health. 2012 Sep 01;12:732
pubmed: 22937748
Obes Res. 1998 Nov;6(6):461-2
pubmed: 9845237
Curr Opin Clin Nutr Metab Care. 2011 Nov;14(6):542-7
pubmed: 21892076
Obes Res. 1998 Sep;6 Suppl 2:51S-209S
pubmed: 9813653
Indian J Endocrinol Metab. 2014 May;18(3):394-9
pubmed: 24944937
Medicine (Baltimore). 2017 Sep;96(39):e8126
pubmed: 28953643
BMJ. 2017 Sep 13;358:j4030
pubmed: 28903916
Lancet. 2004 Sep 11-17;364(9438):937-52
pubmed: 15364185

Auteurs

Nitin Kapoor (N)

Department of Endocrinology, Diabetes and Metabolism, Christian Medical College & Hospital, Vellore, Tamil Nadu, India.
Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Science, The University of Melbourne, Melbourne, VIC, Australia.
ORCID: 0000-0002-9520-2072

Mojtaba Lotfaliany (M)

Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Science, The University of Melbourne, Melbourne, VIC, Australia.

Thirunavukkarasu Sathish (T)

Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Science, The University of Melbourne, Melbourne, VIC, Australia.
Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada.
Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

K R Thankappan (KR)

Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India.
Department of Public Health and Community Medicine, Central University, Kasaragod, Kerala, India.

Nihal Thomas (N)

Department of Endocrinology, Diabetes and Metabolism, Christian Medical College & Hospital, Vellore, Tamil Nadu, India.

John Furler (J)

Department of General Practice, Faculty of Medicine, Dentistry and Health Science, The University of Melbourne, Melbourne, VIC, Australia.

Brian Oldenburg (B)

Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Science, The University of Melbourne, Melbourne, VIC, Australia.

Robyn J Tapp (RJ)

Melbourne School of Population and Global Health, Faculty of Medicine, Dentistry and Health Science, The University of Melbourne, Melbourne, VIC, Australia.
School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

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questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Obesity indicators that best predict type 2...
questionsmedicales.fr › Maladies endocriniennes › Diabète › Diabète de type 2 › Obesity indicators that best predict type 2...
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