Determining new anthropometric markers for screening T2DM in Jaipur, India: A cross sectional study

Amruta Rajput; Upendra K Gupta; Rachna Rachna; Guri Tzivion; Ravindrasingh Rajput

doi:10.53730/ijhs.v6nS7.12299

Authors

Amruta Rajput
amrutawins@gmail.com
Assistant Professor, Department of Anatomy, American University of Antigua College of Medicine University Park, Coolidge, Antigua
Upendra K Gupta Professor and Head, Department of Anatomy, NIMS University, Jaipur, India
Rachna Assistant Professor, Department of Medical Laboratory Technology, Sharda School of Allied Health Sciences, Sharda University, Greater Noida, Uttar Pradesh, India
Guri Tzivion Professor and head, Department of Molecular Sciences, American University of Antigua College of Medicine University Park, Coolidge, Antigua
Ravindrasingh Rajput Department of Pathophysiology, American University of Antigua college of medicine, Antigua and Barbuda

Keywords:

Anthropometric measures, receiver operating characteristics, Arav body index, Height-to-waist ratio

Abstract

WHO has estimated expected cases of T2DM to rise to 57.2 million by the year 2025 and 80.9 million by the year 2030. Since past two decades there has been constant marked increase in the prevalence of diabetes in urban population of India with noticeable rise in southern part of India. The Prevalence of T2DM ranges from 10-16 % from region to region. The primary objective of the study was to determine the association between Indices using height, waist, hip, thigh, arm, and wrist circumference (cm) with development of T2DM and compare it to existing markers in test subjects and assessing their feasibility as predictive indicators for the development of T2DM. In a cross-sectional study, a total of 605 subjects were involved in the study which included health centers and health camps in Jaipur, India. The study duration was of 1 years (December 2019 to November 2020). Height-to-waist ratio (HtWR), ABI (Arav Body Index), Thigh to waist ratio (TWR) and Wrist to arm ratio (WAR) was compared to common existing markers such as WHtR, WHR and BMI.

Downloads

Download data is not yet available.

References

Zimmet KA. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO Consultation. Diabetic Medicine. 2004;

K Silventoinen H Tolonen D Monterde K Kuulasmaa H Kesteloot & J Tuomilehto SS. Trends in obesity and energy supply in the WHO MONICA Project. International Journal of Obesity volume. :pages710–718.

Mo-suwan L. Prevalence of Childhood and Adolescent Obesity. J Med Assoc Thai;

Yamborisut U, Mo-Suwan L. Prevalence of childhood and adolescent obesity in Thailand: a review. undefined. 2014;

M Woodward 1 A Martiniuk X Fang D F Gu Y Imai T H Lam W H Pan A Rodgers I Suh S H Jee H Ueshima R Huxley FB. Cohort profile: the Asia Pacific Cohort stidies collaboration. Int J Epidemiol. 2006;1412–6.

Adeboye B, Bermano G, Rolland C. Obesity and its health impact in Africa: A systematic review [Internet]. Vol. 23, Cardiovascular Journal of Africa. Clinics Cardive Publishing (Pty) Ltd.; 2012 [cited 2021 Jun 24]. p. 512–21. Available from: /pmc/articles/PMC3721807/

Berg C, Rosengren A, Aires N, Lappas G, Torén K, Thelle D, et al. Trends in overweight and obesity from 1985 to 2002 in Göteborg, West Sweden. International Journal of Obesity [Internet]. 2005 Aug [cited 2021 Jun 24];29(8):916–24. Available from: https://pubmed.ncbi.nlm.nih.gov/15852045/

Aranceta J, Moreno B, Moya M, Anadón A. Prevention of overweight and obesity from a public health perspective. In: Nutrition Reviews [Internet]. John Wiley & Sons, Ltd; 2009 [cited 2021 Jun 24]. p. S83–8. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1753-4887.2009.00166.x

Schokker DF, Visscher TLS, Nooyens ACJ, van Baak MA, Seidell JC. Prevalence of overweight and obesity in the Netherlands. Obesity Reviews [Internet]. 2007 Mar 1 [cited 2021 Jun 24];8(2):101–7. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1467-789X.2006.00273.x

Shields M, Tremblay MS, Laviolette M, Craig CL, Janssen I, Gorber SC. Fitness of Canadian adults: results from the 2007-2009 Canadian Health Measures Survey. Health reports / Statistics Canada, Canadian Centre for Health Information = Rapports sur la santé / Statistique Canada, Centre canadien d’information sur la santé [Internet]. 2010 Mar 1 [cited 2021 Jun 24];21(1):21–35. Available from: https://europepmc.org/article/med/20426224

Zaninotto P, Falaschetti E, Sacker A. Age trajectories of quality of life among older adults: Results from the English longitudinal study of Ageing. Quality of Life Research [Internet]. 2009 Dec [cited 2021 Jun 24];18(10):1301–9. Available from: https://pubmed.ncbi.nlm.nih.gov/19806468/

Berghöfer A, Pischon T, Reinhold T, Apovian CM, Sharma AM, Willich SN. Obesity prevalence from a European perspective: A systematic review [Internet]. Vol. 8, BMC Public Health. BMC Public Health; 2008 [cited 2021 Jun 24]. Available from: https://pubmed.ncbi.nlm.nih.gov/18533989/

Malina RM, Peña Reyes ME, Tan SK, Buschang PH, Little BB. Overweight and obesity in a rural amerindian population in Oaxaca, Southern Mexico, 1968-2000. American Journal of Human Biology [Internet]. 2007 Sep 1 [cited 2021 Jun 24];19(5):711–21. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/ajhb.20622

King H, Zimmet P. Trends in the prevalence and incidence of diabetes : non-insulin-dependent diabetes mellitus / Hilary King & Paul Zimmet. World health statistics quarterly 1988 ; 41(3/4) : 190-196 [Internet]. 1988 [cited 2021 Jun 25]; Available from: https://apps.who.int/iris/handle/10665/51278

King H, Aubert RE, Herman WH. Global burden of diabetes, 1995-2025: Prevalence, numerical estimates, and projections. Diabetes Care [Internet]. 1998 [cited 2021 Jun 25];21(9):1414–31. Available from: https://pubmed.ncbi.nlm.nih.gov/9727886/

Mohan V& PRGuha. Epidemilogy of Diabetes in Different Regions of India. . J Health Adm Educ. 2009;22.

Gujral UP, Pradeepa R, Weber MB, Narayan KMV, Mohan V. Type 2 diabetes in South Asians: Similarities and differences with white Caucasian and other populations. Annals of the New York Academy of Sciences [Internet]. 2013 [cited 2021 Jun 25];1281(1):51–63. Available from: /pmc/articles/PMC3715105/

Yajnik CS. The insulin resistance epidemic in India: Fetal origins later lifestyle, or both? [Internet]. Vol. 59, Nutrition Reviews. International Life Sciences Institute; 2001 [cited 2021 Jun 25]. p. 1–9. Available from: https://pubmed.ncbi.nlm.nih.gov/11281246/

Yajnik CS, Fall CHD, Vaidya U, Pandit AN, Bavdekar A, Bhat DS, et al. Fetal Growth and Glucose and Insulin Metabolism in Four‐year‐old Indian Children. Diabetic Medicine [Internet]. 1995 [cited 2021 Jun 25];12(4):330–6. Available from: https://pubmed.ncbi.nlm.nih.gov/7600749/

Deepa R SCPGSNM v. Prevalence of insulin resistance syndrome in a selected south Indian population--the Chennai urban population study-7 [CUPS-7]. Indian J Med Res . 2002;115:118–27.

Viswanathan M, Mohan V, Snehalatha C, Ramachandran A. High prevalence of Type 2 (non-insulin-dependent) diabetes among the offspring of conjugal Type 2 diabetic parents in India. Diabetologia [Internet]. 1985 Dec [cited 2021 Jun 25];28(12):907–10. Available from: https://link.springer.com/article/10.1007/BF00703134

Davey G, Ramachandran A, Snehalatha C, Hitman GA, McKeigue PM. Familial aggregation of central obesity in Southern Indians. International Journal of Obesity. 2000;24(11):1523–7.

Silverman BL RTCNMBE. Long-term effects of the intrauterine environment. The Northwestern University Diabetes in Pregnancy Center. Diabetes Care. 1998;21(2):142–9.

Metcalfe C. Biostatistics: A Foundation for Analysis in the Health Sciences. 7th edn. Wayne W. Daniel, Wiley, 1999. No. of. pages: xiv+755+appendices. Price: £28.95. ISBN 0-471-16386-4. Statistics in Medicine [Internet]. 2001 Jan 30;20(2):324–6. Available from: https://doi.org/10.1002/1097-0258(20010130)20:2<324::AID-SIM635>3.0.CO

Nishida C, Barba C, Cavalli-Sforza T, Cutter J, Deurenberg P, Darnton-Hill I, et al. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. The Lancet [Internet]. 2004 Jan 10 [cited 2021 Jun 23];363(9403):157–63. Available from: https://pubmed.ncbi.nlm.nih.gov/14726171/

Rajput A, Tzivion G, Rajput R, Gupta UK. Determining new anthropometric markers for screening type 2 DM in a Caribbean region. IJSR [Internet]. 2021 [cited 2021 Jun 23];10(4):58–61. Available from: https://www.worldwidejournals.com/international-journal-of-scientific-research-(IJSR)/article/determining-new-anthropometric-markers-for-screening-type-2-dm-in-a-caribbean-region/MzQzNDA=/?is=1&b1=161&k=41

Waist circumference and waist-hip ratio: report of a WHO expert consultation [Internet]. [cited 2021 Jun 29]. Available from: https://www.who.int/publications/i/item/9789241501491

Łopatyński J Szcześniak G. MG. A comparative evaluation of waist circumference, waist-to-hip ratio, waist-to-height ratio and body mass index as indicators of impaired glucose tolerance and as risk factors for type-2 diabetes mellitus. Ann Univ Mariae Curie Sklodowska Med. 2003;58(1):413–9.

Łopatyński J MGSG. A comparative evaluation of waist circumference, waist-to-hip ratio, waist-to-height ratio and body mass index as indicators of impaired glucose tolerance and as risk factors for type-2 diabetes mellitus. Ann Univ Mariae Curie Sklodowska Med. 2003;58(1):413–9.

Mansour AA, Al-Jazairi MI. Cut-off Values for Anthropometric Variables That Confer Increased Risk of Type 2 Diabetes Mellitus and Hypertension in Iraq. Archives of Medical Research. 2007 Feb 1;38(2):253–8.

Khader Y, Batieha A, Jaddou H, El-Khateeb M, Ajlouni K. The performance of anthropometric measures to predict diabetes mellitus and hypertension among adults in Jordan. BMC Public Health [Internet]. 2019 Oct 29 [cited 2021 Jun 23];19(1). Available from: https://pubmed.ncbi.nlm.nih.gov/31664979/

Zafari N Mansournia MA Khalili D Azizi F Hadaegh F. LM. Optimal cut-points of different anthropometric indices and their joint effect in prediction of type 2 diabetes: results of a cohort study. BMC Public Health. 2018;18(1).

Qiao Q NR. The optimal cutoff values and their performance of waist circumference and waist-to-hip ratio for diagnosing type II diabetes. Eur J Clin Nutr. 2010;64(1):23–9.

Mirzaei M KM. Comparison of anthropometric indices (body mass index, waist circumference, waist to hip ratio and waist to height ratio) in predicting risk of type II diabetes in the population of Yazd, Iran. Diabetes Metab Syndr. 2018;12(5):677–82.

VA Diaz AMRBMCAM. How does ethnicity affect the association between obesity and diabetes? Diabet Med. 2007 Nov;24(11):1199–204.

v Mohan MDSFKNMDRD. Anthropometric cut points for identification of cardiometabolic risk factors in an urban Asian Indian population. Metabolism. 2007 Jul;56(7):961–8.

Gómez-García A, Nieto-Alcantar E, Gómez-Alonso C, Figueroa-Nuñez B, Álvarez-Aguilar C. Parámetros antropométricos como predictores de resistencia a la insulina en adultos con sobrepeso y obesidad. Atencion Primaria [Internet]. 2010 Jul [cited 2021 Jun 23];42(7):364–71. Available from: https://pubmed.ncbi.nlm.nih.gov/20116888/

Suryasa, I. W., Rodríguez-Gámez, M., & Koldoris, T. (2021). Get vaccinated when it is your turn and follow the local guidelines. International Journal of Health Sciences, 5(3), x-xv. https://doi.org/10.53730/ijhs.v5n3.2938

Kwon Han K. A. Ku Y. H. Ahn H. J. Koo B. K. Kim H. C. & Min K. W. HR. The effects of resistance training on muscle and body fat mass and muscle strength in type 2 diabetic women. Korean diabetes journal. 2010;34(2):101–10.

Jung Kimm H. Yun J. E. & Jee S. H. KJ. Thigh circumference and diabetes: obesity as a potential effect modifier. Journal of epidemiology. 2013;23(5):329–36.