The ongoing epidemic of diabetes mellitus in India: genetics or lifestyle?
India is estimated to have the second highest number of cases of diabetes mellitus in the world after China. Recent epidemiological evidence indicates that people of lower socioeconomic group in India are equally or even more susceptible to diabetes. Family history is a very strong risk factor for developing type 2 diabetes mellitus; the lifetime risk is nearly 40% for individuals who have one parent affected and approaches 70% if both parents are affected. Genome-wide association studies identified more than 50 genetic variants associated with type 2 diabetes mellitus, but these risk alleles identified to date could only explain less than 10% of the observed heritability. Acquisition of the same unhealthy lifestyle from the parents could be the major reason for the observed heritability that genetics could not explain. The global age-standardised prevalence of diabetes has nearly doubled since 1980, rising from 4.7 to 8.5% in the adult population. If genes are responsible for this doubling of prevalence, the responsible gene pool should also amplify to the same extent in the population. The Hardy–Weinberg law states that allele and genotype frequencies in a population will remain constant from generation to generation in the absence of other evolutionary influences, making genetics as the etiology for this ongoing epidemic less likely. Indians have a tendency to become metabolically obese and develop type 2 diabetes mellitus with normal weight; thus, body mass index cut-off for overweight and obesity is kept lower in Indians. Primary and secondary prevention strategies should be more emphasised at the community level. Physical activity recommended is at least 150 min/week. All adults should decrease the amount of time spent in daily sedentary behaviour. Dietary modifications by reducing carbohydrate intake and increasing the intake of proteins, green leafy vegetables, fruits, and nuts should be promoted.
KeywordsDiabetes mellitus Heritability of diabetes Lifestyle Thin fat Indian Central obesity
All authors contributed equally to the work, participating in collection of the data and writing the manuscript and approving the final version of it.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflicts of interest.
This article does not contain any studies with human participants performed by any of the authors. Informed consent was obtained from all individual participants included in the survey.
- 1.International Diabetes Federation. IDF Diabetes Atlas 7th Edition, International Diabetes Federation; 2015. [E-book] Available: www.diabetesatlas.org. [Internet].
- 4.Kobberling J, Tillil H. Empirical risk figures for first-degree relatives of non-insulin-dependent diabetics. In: Kobberling J, editor. Genetics of diabetes mellitus. London: Academic; 1982. p. 201–9.Google Scholar
- 14.Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. The Lancet 2016;387:1513–30.Google Scholar
- 17.Chambers JC, Loh M, Lehne B, Drong A, Kriebel J, Motta V, et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol. 2015;3(7):526–34.CrossRefPubMedPubMedCentralGoogle Scholar
- 29.Misra A, Chowbey P, Makkar BM, Vikram NK, Wasir JS, Chadha D, et al. Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. J Assoc Physicians India. 2009;57:163–70.PubMedGoogle Scholar
- 30.Waist circumference and waist–hip ratio [Internet]. WHO. World Health Organization; [cited 2017Aug29]. Available from: http://www.who.int/nutrition/publications/obesity/WHO_report_waistcircumference_and_waisthip_ratio/en/
- 31.Premanath M, Basavanagowdappa H, Mahesh M, Suresh M. Correlation of abdominal adiposity with components of metabolic syndrome, anthropometric parameters and insulin resistance, in obese and non obese, diabetics and non diabetics: a cross sectional observational study. (Mysore visceral adiposity in diabetes study). Indian J Endocrinol Metab. 2014;18(5):676–82.PubMedPubMedCentralGoogle Scholar
- 42.Revival of Public Distribution System in Kerala [Internet]. Economic and Political Weekly; 2017 [cited 2017Aug23]. Available from: http://www.epw.in/journal/2017/25-26/notes/revival-public-distribution-system-kerala.html
- 43.Food & Civil Supplies Page [Internet]. Food & Civil Supplies - Government of Kerala, India. [cited 2017Aug23]. Available from: https://kerala.gov.in/food-civil-supplies
- 45.Rayappa PH, Raju KN, Kapur A, Bjork S, Sylvest C, Kumar KM. The impact of socio-economic factors on diabetes care. Int J Diab Dev Coun. 1999;19:8–16.Google Scholar
- 46.Shobhana R, Begum R, Snehalatha C, Vijay V, Ramachandran A. Patients’ adherence to diabetes treatment. J Assoc Phys India. 1999;47:1173–5.Google Scholar
- 53.Colberg SR. Key points from the updated guidelines on exercise and diabetes. Front Endocrinol (Lausanne). 2017;8:33.Google Scholar
- 55.Deepa M, Bhansali A, Anjana RM, Pradeepa R, Joshi SR, Joshi PP, et al. Knowledge and awareness of diabetes in urban and rural India: the Indian Council of Medical Research India Diabetes Study (phase I): Indian Council of Medical Research India Diabetes 4. Indian J Endocrinol Metab. 2014;18(3):379–85.CrossRefPubMedPubMedCentralGoogle Scholar
- 57.Jablonski KA, McAteer JB, de Bakker PI, Franks PW, Pollin TI, Hanson RL, et al. Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the Diabetes Prevention Program. Diabetes. 2010;59(10):2672–81.CrossRefPubMedPubMedCentralGoogle Scholar
- 58.Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group; Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group. Recommendations from the EGAPP Working Group: does genomic profiling to assess type 2 diabetes risk improve health outcomes? Genet Med. 2013;15(8):612–7.CrossRefGoogle Scholar