Sankara Nethralaya-Diabetic Retinopathy Epidemiology and Molecular Genetic Study (SN-DREAMS 1): study design and research methodology

Ophthalmic Epidemiol. 2005 Apr;12(2):143-53. doi: 10.1080/09286580590932734.

Abstract

Purpose: To describe the methodology of the Sankara Nethralaya-Diabetic Retinopathy Epidemiology and Molecular Genetic Study (SN-DREAMS 1), an ongoing population-based study to estimate the prevalence of diabetes and diabetic retinopathy in urban Chennai, Tamil Nadu, South India, and also to elucidate the clinical, anthropometric, biochemical and genetic risk factors associated with diabetic retinopathy.

Methods: In this ongoing study, we anticipate recruiting a total of 5830 participants. Eligible patients, over the age of 40 years, are enumerated using the multistage random sampling method. Demographic data, socioeconomic status, physical activity, risk of sleep apnea, dietary habits, and anthropometric measurements are collected. A detailed medical and ocular history and a comprehensive eye examination, including stereo fundus photographs, are taken at the base hospital. Biochemical investigations (total serum cholesterol, high-density lipoproteins, serum triglycerides, hemoglobin, glycosylated hemoglobin HbA1c) and genetic studies of eligible subjects are conducted. A computerized database is created for the records.

Conclusion: The study is expected to result in an estimate of the prevalence of diabetes and diabetic retinopathy and a better understanding of biochemical and genetic risk factors associated with diabetic retinopathy in an urban South Indian population. Worldwide, the prevalence of diabetes mellitus, in particular type II diabetes, is rising at an alarming rate. The World Health Organization (WHO) and International Diabetes Federation (IDF) have predicted that the number of cases of adult-onset diabetes would more than double by 2030 from the present level of 171 million to 366 million-an increase of 214%.1 In developed countries, this increase in diabetic population would be around 42% and in developing countries, particularly in India, it is even higher; i.e. 150%.1 In India, the prevalence of diabetes mellitus in the urban population is around 12.1%, as reported by the national urban diabetes study2 conducted in six major cities. Studies have shown the prevalence of diabetes to be higher among the high-income groups (25.5%) as compared to low-income groups (12.6%).3,4,5 The assessment of socioeconomic status was based on income,6, 7 education,2, 7 occupation2 or caste6-which are not representative of the actual socioeconomic status. In the present study, however, the sample was stratified on socioeconomic scoring. This scoring was calculated on the basis of several parameters such as the residence being rented or owned, the number of rooms in the house, the highest educational status, the highest salary, the highest occupation, material possessions (cycle, TV, audio, car, etc.) and house/land value. To the best of our knowledge, this kind of comprehensive socioeconomic scoring has not been done before for prevalence studies on diabetic retinopathy in the general population.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anthropometry
  • Apolipoproteins E / genetics
  • Cholesterol / blood
  • Cross-Sectional Studies
  • Diabetes Mellitus / epidemiology
  • Diabetic Retinopathy / epidemiology*
  • Diabetic Retinopathy / genetics
  • Epidemiologic Methods
  • Female
  • Glycated Hemoglobin / metabolism
  • Humans
  • India / epidemiology
  • Lipoproteins, HDL / blood
  • Male
  • Middle Aged
  • Molecular Biology
  • Pilot Projects
  • Prevalence
  • Protein Kinase C / genetics
  • Protein Kinase C beta
  • Receptor for Advanced Glycation End Products
  • Receptors, Immunologic / genetics
  • Research Design
  • Risk Factors
  • Triglycerides / blood
  • Vascular Endothelial Growth Factor A / genetics

Substances

  • Apolipoproteins E
  • Glycated Hemoglobin A
  • Lipoproteins, HDL
  • Receptor for Advanced Glycation End Products
  • Receptors, Immunologic
  • Triglycerides
  • VEGFA protein, human
  • Vascular Endothelial Growth Factor A
  • Cholesterol
  • Protein Kinase C
  • Protein Kinase C beta