Predicting diabetes: clinical, biological, and genetic approaches: data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR)

Beverley Balkau, Céline Lange, Leopold Fezeu, Jean Tichet, Blandine de Lauzon-Guillain, Sebastien Czernichow, Frederic Fumeron, Philippe Froguel, Martine Vaxillaire, Stephane Cauchi, Pierre Ducimetière, Eveline Eschwège, Beverley Balkau, Céline Lange, Leopold Fezeu, Jean Tichet, Blandine de Lauzon-Guillain, Sebastien Czernichow, Frederic Fumeron, Philippe Froguel, Martine Vaxillaire, Stephane Cauchi, Pierre Ducimetière, Eveline Eschwège

Abstract

Objective: To provide a simple clinical diabetes risk score and to identify characteristics that predict later diabetes using variables available in the clinic setting as well as biological variables and polymorphisms.

Research design and methods: Incident diabetes was studied in 1,863 men and 1,954 women, 30-65 years of age at baseline, with diabetes defined by treatment or by fasting plasma glucose >or=7.0 mmol/l at 3-yearly examinations over 9 years. Sex-specific logistic regression equations were used to select variables for prediction.

Results: A total of 140 men and 63 women developed diabetes. The predictive clinical variables were waist circumference and hypertension in both sexes, smoking in men, and diabetes in the family in women. Discrimination, as measured by the area under the receiver operating curves (AROCs), were 0.713 for men and 0.827 for women, a little higher than for the Finish Diabetes Risk (FINDRISC) score, with fewer variables in the score. Combining clinical and biological variables, the predictive equation included fasting glucose, waist circumference, smoking, and gamma-glutamyltransferase for men and fasting glucose, BMI, triglycerides, and diabetes in family for women. The number of TCF7L2 and IL6 deleterious alleles was predictive in both sexes, but after including the above clinical and biological variables, this variable was only predictive in women (P < 0.03) and the AROC statistics increased only marginally.

Conclusions: The best clinical predictor of diabetes is adiposity, and baseline glucose is the best biological predictor. Clinical and biological predictors differed marginally between men and women. The genetic polymorphisms added little to the prediction of diabetes.

Figures

Figure 1
Figure 1
ROC curves and AROC statistics in men and women for the DESIR French clinical equation, the French clinical risk score, and the FINDRISC clinical score (3) (A) and for DESIR (clinical + biological) (B). French risk equation and Stern risk equation (9).

References

    1. Waugh N, Scotland G, McNamee P, Gillett M, Brennan A, Goyder E, Williams R, John A: Screening for type 2 diabetes: literature review and economic modelling. Health Technol Assess 11:1–125, 2007
    1. Balkau B, Sapinho D, Petrella A, Mhamdi L, Cailleau M, Arondel D, Charles MA; D.E.S.I.R. Study Group: Prescreening tools for diabetes and obesity-BMI, waist and waist hip ratio: the D.E.S.I.R. Study Eur J Clin Nutr 60:295–304, 2006
    1. Lindström J, Tuomilehto J: The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26:725–731, 2003
    1. Saaristo T, Peltonen M, Lindström J, Saarikoski L, Sundvall J, Eriksson JG, Tuomilehto J: Cross-sectional evaluation of the Finnish Diabetes Risk Score: a tool to identify undetected type 2 diabetes, abnormal glucose tolerance and metabolic syndrome. Diab Vasc Dis Res 2:67–72, 2005
    1. Schmidt MI, Duncan BB, Bang H, Pankow JS, Ballantyne CM, Golden SH, Folsom AR, Chambless LE; The Atherosclerosis Risk in Communities Investigators: Identifying individuals at high risk for diabetes: the Atherosclerosis Risk in Communities study. Diabetes Care 28:2013–2018, 2005
    1. Aekplakorn W, Bunnag P, Woodward M, Sritara P, Cheepudomwit S, Yamwong S, Yipintsoi T, Rajatanavin R: A risk score for predicting incident diabetes in the Thai population. Diabetes Care 29:1872–1877, 2006
    1. Simmons RK, Harding AH, Wareham NJ, Griffin SJ; EPIC-Norfolk Project Team: Do simple questions about diet and physical activity help to identify those at risk of type 2 diabetes? Diabet Med 24:830–835, 2007
    1. Schulze MB, Hoffmann K, Boeing H, Linseisen J, Rohrmann S, Mohlig M, Pfeiffer AF, Spranger J, Thamer C, Häring HU, Fritsche A, Joost HG: An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care 30:510–515, 2007
    1. Stern MP, Williams K, Haffner SM: Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med 136:575–581, 2002
    1. Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D'Agostino RB Sr: Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med 167:1068–1074, 2007
    1. Lecomte P, Vol S, Caces E, Born C, Chabrolle C, Lasfargues G, Halimi JM, Tichet J: Five-year predictive factors of type 2 diabetes in men with impaired fasting glucose. Diabete Metab 33:140–147, 2007
    1. Rathmann W, Martin S, Haastert B, Icks A, Holle R, Löwel H, Giani G; KORA Study Group: Performance of screening questionnaires and risk scores for undiagnosed diabetes: the KORA Survey 2000. Arch Intern Med 165:436–441, 2005
    1. Glümer C, Vistisen D, Borch-Johnsen K, Colagiuri S; DETECT-2 Collaboration: Risk scores for type 2 diabetes can be applied in some populations but not all. Diabetes Care 29:410–414, 2006
    1. Clavel-Chapelon F, van Liere MJ, Giubout C, Niravong MY, Goulard H, Le Corre C, Hoang LA, Amoyel J, Auquier A, Duauesnel E: E3N, a French cohort study on cancer risk factors: E3N Group: Etude Epidemiologique aupres de femmes de l'Education Nationale. Eur J Cancer Prev 6:473–478, 1997
    1. Czernichow S, Couthouis A, Bertrais S, Vergnaud AC, Dauchet L, Galan P, Hercberg S: Antioxidant supplementation does not affect fasting plasma glucose in the Supplementation with Antioxidant Vitamins and Minerals (SU.VI.MAX) study in France: association with dietary intake and plasma concentrations. Am J Clin Nutr 200684:395–399, 2006
    1. Vaxillaire M, Veslot J, Dina C, Proença C, Cauchi S, Charpentier G, Tichet J, Fumeron F, Marre M, Meyre D, Balkau B, Froguel P; for the DESIR Study Group: Impact of common type 2 diabetes risk polymorphisms in the DESIR prospective study. Diabetes 57:244–254, 2008
    1. Conen D, Ridker PM, Mora S, Buring JE, Glynn RJ: Blood pressure and risk of developing type 2 diabetes mellitus: the Women's Health Study. Eur Heart J 28:2937–2943, 2007
    1. Willi C, Bodenmann P, Ghali WA, Faris PD, Cornuz J: Active smoking and the risk of type 2 diabetes: a systematic review and meta-analysis. JAMA 298:2654–2664, 2007
    1. André P, Balkau B, Born C, Royer B, Wilpart E, Charles MA, Eschwège E: Hepatic markers and development of type 2 diabetes in middle aged men and women: a three-year follow-up study: the D.E.S.I.R. study (Data from an Epidemiological Study on the Insulin Resistance syndrome). Diabet Metab 31:542–550, 2005

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