A Ramachandran1, C Snehalatha, V Vijay, N J Wareham, S Colagiuri. 1. Diabetes Research Centre, M.V. Hospital for Diabetes, WHO Collaborating Centre for Research, Education and Training in Diabetes, Main Road, Royapuram, Chennai 600 013, Tamil Nadu, India. ramachandran@vsnl.com
Abstract
OBJECTIVE: Simple risk scores for identifying people with undiagnosed diabetes have been developed, mostly in Caucasian groups. This may not be suitable for Asian Indians, therefore this study was undertaken to develop and validate a simple diabetes risk score in an urban Asian Indian population with a high prevalence of diabetes. We also tested whether this score was applicable to South Asian migrants living in a different cultural context. RESEARCH DESIGN AND METHODS: A population based Cohort of 10,003 participants aged >or=20 years was divided into two equal halves (Cohorts 1 and 2), after excluding people with known diabetes. Cohort 1 (n=4993) was used to derive the risk score. Validation of the score was performed in the other half of the survey population (Cohort 2) (n=5010). The validation was also done in a separate survey population in Chennai, India (Cohort 3) (n=2002) (diagnosis of diabetes was based on OGTT) and in the South Asian Cohort of the 1999 Health Survey for England (n=676) (fasting glucose value>or=7 mmol/l and HbA1c>or=6.5% were used for diagnosis). A logistic regression model was used to compute the beta coefficients for risk factors. The risk score value was derived from a receiver operating characteristic curve. RESULTS: The significant risk factors included in the risk score were age, BMI, waist circumference, family history of diabetes and sedentary physical activity. A risk score value of >21 gave a sensitivity, specificity, positive predictive value and negative predictive value of 76.6%, 59.9%, 9.4% and 97.9% in Cohort 1, 72.4%, 59%, 8.3% and 97.6% in Cohort 2 and 73.7%, 61.0%, 12.2% and 96.9% in Cohort 3, respectively. The higher distribution of risk factors in the UK Cohort means that at the same cut point the score was much more sensitive but also less specific. (sensitivity 92.2%, specificity 25.7%, positive predictive value of 21.6% and negative predictive value of 93.7%). CONCLUSIONS: A diabetes risk score involving simple non-biochemical measurements was developed and validated in a native Asian Indian population. This easily applicable simple score could play an important role as the first step in the process of identifying individuals with an increased likelihood of having prevalent but undiagnosed diabetes. The different distribution of risk factors with the migrant Asian Indians living in England and the different relationship between sensitivity and specificity for the same score demonstrate that risk scores and cut-points developed and tested even within one ethnic group cannot be generalized to individuals of the same ethnic group living in a different cultural setting where the distribution of risk factors for diabetes is different.
OBJECTIVE: Simple risk scores for identifying people with undiagnosed diabetes have been developed, mostly in Caucasian groups. This may not be suitable for Asian Indians, therefore this study was undertaken to develop and validate a simple diabetes risk score in an urban Asian Indian population with a high prevalence of diabetes. We also tested whether this score was applicable to South Asian migrants living in a different cultural context. RESEARCH DESIGN AND METHODS: A population based Cohort of 10,003 participants aged >or=20 years was divided into two equal halves (Cohorts 1 and 2), after excluding people with known diabetes. Cohort 1 (n=4993) was used to derive the risk score. Validation of the score was performed in the other half of the survey population (Cohort 2) (n=5010). The validation was also done in a separate survey population in Chennai, India (Cohort 3) (n=2002) (diagnosis of diabetes was based on OGTT) and in the South Asian Cohort of the 1999 Health Survey for England (n=676) (fasting glucose value>or=7 mmol/l and HbA1c>or=6.5% were used for diagnosis). A logistic regression model was used to compute the beta coefficients for risk factors. The risk score value was derived from a receiver operating characteristic curve. RESULTS: The significant risk factors included in the risk score were age, BMI, waist circumference, family history of diabetes and sedentary physical activity. A risk score value of >21 gave a sensitivity, specificity, positive predictive value and negative predictive value of 76.6%, 59.9%, 9.4% and 97.9% in Cohort 1, 72.4%, 59%, 8.3% and 97.6% in Cohort 2 and 73.7%, 61.0%, 12.2% and 96.9% in Cohort 3, respectively. The higher distribution of risk factors in the UK Cohort means that at the same cut point the score was much more sensitive but also less specific. (sensitivity 92.2%, specificity 25.7%, positive predictive value of 21.6% and negative predictive value of 93.7%). CONCLUSIONS: A diabetes risk score involving simple non-biochemical measurements was developed and validated in a native Asian Indian population. This easily applicable simple score could play an important role as the first step in the process of identifying individuals with an increased likelihood of having prevalent but undiagnosed diabetes. The different distribution of risk factors with the migrant Asian Indians living in England and the different relationship between sensitivity and specificity for the same score demonstrate that risk scores and cut-points developed and tested even within one ethnic group cannot be generalized to individuals of the same ethnic group living in a different cultural setting where the distribution of risk factors for diabetes is different.
Authors: Meizi He; E T S Li; Stewart Harris; Murray W Huff; Chun Y Yau; G Harvey Anderson Journal: Can Fam Physician Date: 2010-05 Impact factor: 3.275