Skip to main content
Top
Published in: International Journal of Public Health 4/2010

01-08-2010 | Original Article

San Antonio heart study diabetes prediction model applicable to a Middle Eastern population? Tehran glucose and lipid study

Authors: Mohammadreza Bozorgmanesh, Farzad Hadaegh, Azadeh Zabetian, Fereidoun Azizi

Published in: International Journal of Public Health | Issue 4/2010

Login to get access

Abstract

Objectives

To assess the validity of the San Antonio heart study (SAHS) diabetes prediction model in a large representative Iranian population.

Methods

A risk function derived from data in the SAHS to predict the 7.5-year risk of diabetes, was tested for its ability to predict incident diabetes in 3,242 individuals aged ≥20 years. The performance or ability to accurately predict diabetes risk, of the SAHS function compared with the performance of risk functions developed specifically from the Tehran lipid and glucose study. Comparisons included goodness of fit, discrimination, and calibration.

Results

The participants were followed for 6.3 years. The area under the receiver operating characteristic curve (AROC) for diabetes of SAHS model was 0.83 (95% CI 0.80–0.86). The model overestimated the risk of diabetes in TLGS population with the overall bias of 111%. After the recalibration, the model-predicted probability agreed well with the actual observed 6-year risk of diabetes.

Discussion and conclusion

The American SAHS was a prediction model for diabetes with good discrimination in an Iranian target population after calibration.
Literature
go back to reference Azizi F, Ghanbarian A, Momenan AA, Hadaegh F, Mirmiran P, Hedayati M et al (2009) Prevention of non-communicable disease in a population in nutrition transition: Tehran lipid and glucose study phase II. Trials 10:5. doi:10.1186/1745-6215-10-5 PubMed Azizi F, Ghanbarian A, Momenan AA, Hadaegh F, Mirmiran P, Hedayati M et al (2009) Prevention of non-communicable disease in a population in nutrition transition: Tehran lipid and glucose study phase II. Trials 10:5. doi:10.​1186/​1745-6215-10-5 PubMed
go back to reference DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMed DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMed
go back to reference Friedwald WT, Levy RI, Fredrickson DS (1972) Estimation of the concentration of low density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18:419–503 Friedwald WT, Levy RI, Fredrickson DS (1972) Estimation of the concentration of low density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18:419–503
go back to reference Hadaegh F, Bozorgmanesh MR, Ghasemi A, Harati H, Saadat N, Azizi F (2008) High prevalence of undiagnosed diabetes and abnormal glucose tolerance in the Iranian urban population: Tehran Lipid and Glucose Study. BMC Public Health 8:176. doi:10.1186/1471-2458-8-176 CrossRefPubMed Hadaegh F, Bozorgmanesh MR, Ghasemi A, Harati H, Saadat N, Azizi F (2008) High prevalence of undiagnosed diabetes and abnormal glucose tolerance in the Iranian urban population: Tehran Lipid and Glucose Study. BMC Public Health 8:176. doi:10.​1186/​1471-2458-8-176 CrossRefPubMed
go back to reference Hadaegh F, Shafiee G, Azizi F (2009) Anthropometric predictors of incident type 2 diabetes mellitus in Iranian women. Ann Saudi Med 29:194–200PubMed Hadaegh F, Shafiee G, Azizi F (2009) Anthropometric predictors of incident type 2 diabetes mellitus in Iranian women. Ann Saudi Med 29:194–200PubMed
go back to reference Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36PubMed Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36PubMed
go back to reference Hosmer DW, Lemeshow S (2000). Applied logistic regression. Wiley-Interscience, New York Hosmer DW, Lemeshow S (2000). Applied logistic regression. Wiley-Interscience, New York
go back to reference Johnson SL, Tabaei BP, Herman WH (2005) The efficacy and cost of alternative strategies for systematic screening for type 2 diabetes in the US population 45–74 years of age. Diabetes Care 28:307–311CrossRefPubMed Johnson SL, Tabaei BP, Herman WH (2005) The efficacy and cost of alternative strategies for systematic screening for type 2 diabetes in the US population 45–74 years of age. Diabetes Care 28:307–311CrossRefPubMed
go back to reference Justice AC, Covinsky KE, Berlin JA (1999) Assessing the generalizability of prognostic information. Ann Intern Med 130:515–524PubMed Justice AC, Covinsky KE, Berlin JA (1999) Assessing the generalizability of prognostic information. Ann Intern Med 130:515–524PubMed
go back to reference Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW (2009) Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years. Ann Intern Med 150:741–751PubMed Kahn HS, Cheng YJ, Thompson TJ, Imperatore G, Gregg EW (2009) Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years. Ann Intern Med 150:741–751PubMed
go back to reference Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA et al (2002) Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346:393–403. doi:10.1056/NEJMoa012512 CrossRefPubMed Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA et al (2002) Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346:393–403. doi:10.​1056/​NEJMoa012512 CrossRefPubMed
go back to reference Kolberg JA, Jorgensen T, Gerwien RW, Hamren S, McKenna MP, Moler E et al (2009) Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care 32:1207–1212. doi:10.2337/dc08-1935 CrossRefPubMed Kolberg JA, Jorgensen T, Gerwien RW, Hamren S, McKenna MP, Moler E et al (2009) Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort. Diabetes Care 32:1207–1212. doi:10.​2337/​dc08-1935 CrossRefPubMed
go back to reference MacKay MF, Haffner SM, Wagenknecht LE, D’Agostino RB, Hanley AJG (2009) Prediction of type 2 diabetes using alternate anthropometric measures in a multi-ethnic cohort. Diabetes Care 32:956–958. doi:10.2337/dc08-1663 CrossRefPubMed MacKay MF, Haffner SM, Wagenknecht LE, D’Agostino RB, Hanley AJG (2009) Prediction of type 2 diabetes using alternate anthropometric measures in a multi-ethnic cohort. Diabetes Care 32:956–958. doi:10.​2337/​dc08-1663 CrossRefPubMed
go back to reference McNeely MJ, Boyko EJ, Leonetti DL, Kahn SE, Fujimoto WY (2003) 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 26:758–763CrossRefPubMed McNeely MJ, Boyko EJ, Leonetti DL, Kahn SE, Fujimoto WY (2003) 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 26:758–763CrossRefPubMed
go back to reference Nyamdorj R, Qiao Q, Soderberg S, Pitkaniemi JM, Zimmet PZ, Shaw JE et al (2009) BMI compared with central obesity indicators as a predictor of diabetes incidence in Mauritius. Obesity (Silver Spring) 17:342–348. doi:10.1038/oby.2008.503 Nyamdorj R, Qiao Q, Soderberg S, Pitkaniemi JM, Zimmet PZ, Shaw JE et al (2009) BMI compared with central obesity indicators as a predictor of diabetes incidence in Mauritius. Obesity (Silver Spring) 17:342–348. doi:10.​1038/​oby.​2008.​503
go back to reference Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX et al (1997) Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care 20:537–544CrossRefPubMed Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX et al (1997) Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care 20:537–544CrossRefPubMed
go back to reference Rahman M, Simmons RK, Harding AH, Wareham NJ, Griffin SJ (2008) A simple risk score identifies individuals at high risk of developing type 2 diabetes: a prospective cohort study. Fam Pract 25:191–196CrossRefPubMed Rahman M, Simmons RK, Harding AH, Wareham NJ, Griffin SJ (2008) A simple risk score identifies individuals at high risk of developing type 2 diabetes: a prospective cohort study. Fam Pract 25:191–196CrossRefPubMed
go back to reference Rathmann W, Martin S, Haastert B, Icks A, Holle R, Lowel H et al (2005) Performance of screening questionnaires and risk scores for undiagnosed diabetes: the KORA Survey 2000. Arch Intern Med 165:436–441CrossRefPubMed Rathmann W, Martin S, Haastert B, Icks A, Holle R, Lowel H et al (2005) Performance of screening questionnaires and risk scores for undiagnosed diabetes: the KORA Survey 2000. Arch Intern Med 165:436–441CrossRefPubMed
go back to reference Schulze M, Joost H (2009) Rapid responses to: Henry S. Kahn, Yiling J. Cheng, Theodore J. Thompson, Giuseppina Imperatore, and Edward W. Gregg. Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years in Ann Intern Med 2009; 150:741–751: Integer points based scoring systems for diabetes prediction: The German Diabetes Risk Score (18 June). Retrieved 15 Jul 2009, from http://www.annals.org/cgi/eletters/150/11/741#114512 Schulze M, Joost H (2009) Rapid responses to: Henry S. Kahn, Yiling J. Cheng, Theodore J. Thompson, Giuseppina Imperatore, and Edward W. Gregg. Two risk-scoring systems for predicting incident diabetes mellitus in US adults age 45 to 64 years in Ann Intern Med 2009; 150:741–751: Integer points based scoring systems for diabetes prediction: The German Diabetes Risk Score (18 June). Retrieved 15 Jul 2009, from http://​www.​annals.​org/​cgi/​eletters/​150/​11/​741#114512
go back to reference Schulze MB, Hoffmann K, Boeing H, Linseisen J, Rohrmann S, Mohlig M et al (2007) An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care 30:510–515CrossRefPubMed Schulze MB, Hoffmann K, Boeing H, Linseisen J, Rohrmann S, Mohlig M et al (2007) An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care 30:510–515CrossRefPubMed
go back to reference Schulze MB, Weikert C, Pischon T, Bergmann MM, Al-Hasani H, Schleicher E et al (2009) Use of multiple metabolic and genetic markers to improve the prediction of type 2 diabetes: the EPIC-Potsdam Study. Diabetes Care 32:2116–2119. doi:10.2337/dc09-0197 CrossRefPubMed Schulze MB, Weikert C, Pischon T, Bergmann MM, Al-Hasani H, Schleicher E et al (2009) Use of multiple metabolic and genetic markers to improve the prediction of type 2 diabetes: the EPIC-Potsdam Study. Diabetes Care 32:2116–2119. doi:10.​2337/​dc09-0197 CrossRefPubMed
go back to reference Stern MP, Williams K, Haffner SM (2002) Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med 136:575–581PubMed Stern MP, Williams K, Haffner SM (2002) Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med 136:575–581PubMed
go back to reference Talmud PJ Hingorani AD Cooper JA, Marmot MG, Brunner EJ, Kumari M et al (2010) Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ 340:b4838. doi:10.1136/bmj.b4838 Talmud PJ Hingorani AD Cooper JA, Marmot MG, Brunner EJ, Kumari M et al (2010) Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ 340:b4838. doi:10.​1136/​bmj.​b4838
go back to reference Tulloch-Reid MK, Williams DE, Looker HC, Hanson RL, Knowler WC (2003) Do measures of body fat distribution provide information on the risk of type 2 diabetes in addition to measures of general Obesity? Diabetes Care 26:2556–2561. doi:10.2337/diacare.26.9.2556 CrossRefPubMed Tulloch-Reid MK, Williams DE, Looker HC, Hanson RL, Knowler WC (2003) Do measures of body fat distribution provide information on the risk of type 2 diabetes in addition to measures of general Obesity? Diabetes Care 26:2556–2561. doi:10.​2337/​diacare.​26.​9.​2556 CrossRefPubMed
go back to reference Wang Y, Rimm EB, Stampfer MJ, Willett WC, Hu FB (2005) Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr 81:555–563PubMed Wang Y, Rimm EB, Stampfer MJ, Willett WC, Hu FB (2005) Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr 81:555–563PubMed
go back to reference Wild S, Roglic G, Green A, Sicree R, King H (2004) Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27:1047–1053CrossRefPubMed Wild S, Roglic G, Green A, Sicree R, King H (2004) Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27:1047–1053CrossRefPubMed
Metadata
Title
San Antonio heart study diabetes prediction model applicable to a Middle Eastern population? Tehran glucose and lipid study
Authors
Mohammadreza Bozorgmanesh
Farzad Hadaegh
Azadeh Zabetian
Fereidoun Azizi
Publication date
01-08-2010
Publisher
SP Birkhäuser Verlag Basel
Published in
International Journal of Public Health / Issue 4/2010
Print ISSN: 1661-8556
Electronic ISSN: 1661-8564
DOI
https://doi.org/10.1007/s00038-010-0130-y

Other articles of this Issue 4/2010

International Journal of Public Health 4/2010 Go to the issue