10.1186/1471-2288-12-48BMC Medical Research Methodology12

Agus Salim

Calvin Woon-Loong Chin

CE Tan

Derrick Heng

DM Mann

DW Haslam

E Shyong Tai

Elian Hui San Chia

ER DeLong

ES Tai

Expert committee on the diagnostic and classification of diabetes mellitus

J Liu

J Marrugat

J Tuomilehto

Jeanette Lee

KJ Lee

L Chen

M Deurenberg-Yap

Maudrene Tan

MI Schmidt

MJ McNeely

MJ Pencina

MP Stern

O Vaccaro

P Deurenberg

PW Wilson

RB D'Agostino Sr

S van Buuren

Stefan Ma

W Aekplakorn

WC Knowler

WH Hernan

XR Pan

PubMed

Springer - Publisher Connector

The ARIC predictive model reliably predicted risk of type II diabetes in Asian populations

Abstract Background Identification of high-risk individuals is crucial for effective implementation of type 2 diabetes mellitus prevention programs. Several studies have shown that multivariable predictive functions perform as well as the 2-hour post-challenge glucose in identifying these high-risk individuals. The performance of these functions in Asian populations, where the rise in prevalence of type 2 diabetes mellitus is expected to be the greatest in the next several decades, is relatively unknown. Methods Using data from three Asian populations in Singapore, we compared the performance of three multivariate predictive models in terms of their discriminatory power and calibration quality: the San Antonio Health Study model, Atherosclerosis Risk in Communities model and the Framingham model. Results The San Antonio Health Study and Atherosclerosis Risk in Communities models had better discriminative powers than using only fasting plasma glucose or the 2-hour post-challenge glucose. However, the Framingham model did not perform significantly better than fasting glucose or the 2-hour post-challenge glucose. All published models suffered from poor calibration. After recalibration, the Atherosclerosis Risk in Communities model achieved good calibration, the San Antonio Health Study model showed a significant lack of fit in females and the Framingham model showed a significant lack of fit in both females and males. Conclusions We conclude that adoption of the ARIC model for Asian populations is feasible and highly recommended when local prospective data is unavailable.</p

Chin Calvin

Chia Elian

Ma Stefan

Heng Derrick

Tan Maudrene

Lee Jeanette

Tai E Shyong

Salim Agus

Directory of Open Access Journals

BMC Medical Research Methodology

BACKGROUND: Identification of high-risk individuals is crucial for effective implementation of type 2 diabetes mellitus prevention programs. Several studies have shown that multivariable predictive functions perform as well as the 2-hour post-challenge glucose in identifying these high-risk individuals. The performance of these functions in Asian populations, where the rise in prevalence of type 2 diabetes mellitus is expected to be the greatest in the next several decades, is relatively unknown. METHODS: Using data from three Asian populations in Singapore, we compared the performance of three multivariate predictive models in terms of their discriminatory power and calibration quality: the San Antonio Health Study model, Atherosclerosis Risk in Communities model and the Framingham model. RESULTS: The San Antonio Health Study and Atherosclerosis Risk in Communities models had better discriminative powers than using only fasting plasma glucose or the 2-hour post-challenge glucose. However, the Framingham model did not perform significantly better than fasting glucose or the 2-hour post-challenge glucose. All published models suffered from poor calibration. After recalibration, the Atherosclerosis Risk in Communities model achieved good calibration, the San Antonio Health Study model showed a significant lack of fit in females and the Framingham model showed a significant lack of fit in both females and males. CONCLUSIONS: We conclude that adoption of the ARIC model for Asian populations is feasible and highly recommended when local prospective data is unavailable

Chin, CWL

Chia, EHS

Ma, S

Heng, D

Tan, M

Lee, J

Tai, ES

Salim, A

University of Melbourne Institutional Repository

Abstract
          
            Background
            Identification of high-risk individuals is crucial for effective implementation of type 2 diabetes mellitus prevention programs. Several studies have shown that multivariable predictive functions perform as well as the 2-hour post-challenge glucose in identifying these high-risk individuals. The performance of these functions in Asian populations, where the rise in prevalence of type 2 diabetes mellitus is expected to be the greatest in the next several decades, is relatively unknown.
          
          
            Methods
            Using data from three Asian populations in Singapore, we compared the performance of three multivariate predictive models in terms of their discriminatory power and calibration quality: the San Antonio Health Study model, Atherosclerosis Risk in Communities model and the Framingham model.
          
          
            Results
            The San Antonio Health Study and Atherosclerosis Risk in Communities models had better discriminative powers than using only fasting plasma glucose or the 2-hour post-challenge glucose. However, the Framingham model did not perform significantly better than fasting glucose or the 2-hour post-challenge glucose. All 
                published
               models suffered from poor calibration. After recalibration, the Atherosclerosis Risk in Communities model achieved good calibration, the San Antonio Health Study model showed a significant lack of fit in females and the Framingham model showed a significant lack of fit in both females and males.
          
          
            Conclusions
            We conclude that adoption of the ARIC model for Asian populations is feasible and highly recommended when local prospective data is unavailable.
          </jats:sec

Crossref

English

Atherosclerosis Risk in Communities Investigators: Identifying individuals at high risk for diabetes: the Atherosclerosis risk in communities study. Diabetes Care

Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes Relat Metab Disord

BV: Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care

CE: Lowering the criterion for impaired fasting glucose: impact on disease prevalence and associated risk of diabetes and ischemic heart disease. Diabetes Care

CHD Risk Prediction Group: Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA

Clarke-Pearson DL: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics

Comparative validity of 3 diabetes mellitus risk prediction scoring models in a multiethnic US Cohort: the Multi-Ethnic Study of Atherosclerosis.

D’Agostino RB Sr: Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med

Diabetes Prevention Program Research Group: Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.

E: Prevalence of diabetes and ethnic differences in cardiovascular risk factors. The

Elevated body fat percentage and cardiovascular risks at low body mass index levels among Singaporean Chinese, Malays and Indians. Obes Rev

Engelgau MM, Diabetes Prevention Program Research Group: Costs associated with the primary prevention of type 2 diabetes mellitus in the diabetes prevention program. Diabetes Care

Expert committee on the diagnostic and classification of diabetes mellitus: Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care

Finnish Diabetes Prevention Study Group: Prevention of type 2 diabetes by changes in lifestyle among subjects with impaired glucose tolerance.

Fujimoto WY: Comparison of a clinical model, the oral glucose tolerance test, and fasting glucose for prediction of type 2 diabetes risk in Japanese Americans. Diabetes Care

JE: AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures.

Knook DL: Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med

Lancet

Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation.

Rajatanavin R: A risk score for predicting incident diabetes in the Thai population. Diabetes Care

Revision of Body Mass Index (BMI) cut off in Singapore

Risk of diabetes in the new diagnostic category of impaired fasting glucose: a prospective analysis. Diabetes Care

SM: Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med

Vasan RS: Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med

http://doaj.org/search?source=%7B%22query%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22term%22%3A%7B%22id%22%3A%22430bc1872cda4057bd89ef3cc982aa06%22%7D%7D%5D%7D%7D%7D

The ARIC predictive model reliably predicted risk of type II diabetes in Asian populations

Abstract

Similar works

Full text

Available Versions

Springer - Publisher Connector

Directory of Open Access Journals

University of Melbourne Institutional Repository

Springer - Publisher Connector

Crossref