OBJECTIVE To develop and validate a new diabetes risk algorithm (the QDScore) for estimating 10 year risk of acquiring diagnosed type 2 diabetes over a 10 year time period in an ethnically and socioeconomically diverse population. DESIGN Prospective open cohort study using routinely collected data from 355 general practices in England and Wales to develop the score and from 176 separate pract...

AIM The aim of this study was to investigate whether the Indian Diabetes Risk Score (IDRS) could assist in classifying type 2 diabetes mellitus (T2DM) and non-T2DM among patients attending clinics in India. METHODS Patient records from 2006 through 2009 were taken from the clinical database of a tertiary care diabetes hospital in Chennai, Southern India. A total of 8747 patients with diabetes...

india is on the verge of becoming the diabetic capital of the world. the fact is that even in ancient india diabetes was prevalent and reference of diabetic-like condition is there in atharvaveda, known as one of the oldest literature in the world. a search through ancient indian religious, political and medical scripture shows various references of diabetes, better known aspramehaor madhumehat...

Globally the increasing prevalence of diabetes mellitus (DM) is major public health concern. The Indian Diabetes Risk Score (IDRS) was developed by Madras Diabetes Research Foundation (MDRF) as a simple tool to help detect undiagnosed type 2 DM (T2DM) in the community. We conducted a study among 911 adults of Jabalpur District to validate the IDRS score against increased fasting blood sugar lev...

Diabetes mellitus is a very common and serious disease in many American Indian tribes, Indians, and many other populations in the world. Several well-known risk factors such as parental diabetes, genetic markers, obesity, diet are considered as the main risk factors for diabetes mellitus, while the precise nature of the gene or genes remains unknown. Objectives: The Pimas, Indians, and many oth...

Background: The predictive power of obesity measures varies according to the presence of coexistent measures. The present study aimed to determine the predictive power of combinations of obesity measures for diabetes by calculation of a linear risk score. Methods: Data from a population-based cross-sectional study of 994 representative samples of Iranian adults in Babol, Iran were analyzed. Me...