Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study.

BACKGROUND Prediction rules for type 2 diabetes mellitus (T2DM) have been developed, but we lack consensus for the most effective approach. METHODS We estimated the 7-year risk of T2DM in middle-aged participants who had an oral glucose tolerance test at baseline. There were 160 cases of new T2DM, and regression models were used to predict new T2DM, starting with characteristics known to the subject (personal model, ie, age, sex, parental history of diabetes, and body mass index [calculated as the weight in kilograms divided by height in meters squared]), adding simple clinical measurements that included metabolic syndrome traits (simple clinical model), and, finally, assessing complex clinical models that included (1) 2-hour post-oral glucose tolerance test glucose, fasting insulin, and C-reactive protein levels; (2) the Gutt insulin sensitivity index; or (3) the homeostasis model insulin resistance and the homeostasis model insulin resistance beta-cell sensitivity indexes. Discrimination was assessed with area under the receiver operating characteristic curves (AROCs). RESULTS The personal model variables, except sex, were statistically significant predictors of T2DM (AROC, 0.72). In the simple clinical model, parental history of diabetes and obesity remained significant predictors, along with hypertension, low levels of high-density lipoprotein cholesterol, elevated triglyceride levels, and impaired fasting glucose findings but not a large waist circumference (AROC, 0.85). Complex clinical models showed no further improvement in model discriminations (AROC, 0.850-0.854) and were not superior to the simple clinical model. CONCLUSION Parental diabetes, obesity, and metabolic syndrome traits effectively predict T2DM risk in a middle-aged white population sample and were used to develop a simple T2DM prediction algorithm to estimate risk of new T2DM during a 7-year follow-up interval.

[1]  J. Shaw,et al.  Follow-up report on the diagnosis of diabetes mellitus. , 2003, Diabetes care.

[2]  E. Feskens,et al.  Performance of a predictive model to identify undiagnosed diabetes in a health care setting. , 1999, Diabetes care.

[3]  John Horgan,et al.  Principles for national and regional guidelines on cardiovascular disease prevention: a scientific statement from the World Heart and Stroke Forum. , 2004, Circulation.

[4]  B. Zinman,et al.  Identification of subjects with insulin resistance and beta-cell dysfunction using alternative definitions of the metabolic syndrome. , 2003, Diabetes.

[5]  N. Unwin,et al.  Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) Detection, Evaluation, and Treatment of High Blood Cholesterol Education Program (NCEP) Expert Panel on Executive Summary of the Third Report of the National , 2009 .

[6]  R. D'Agostino,et al.  Metabolic Syndrome as a Precursor of Cardiovascular Disease and Type 2 Diabetes Mellitus , 2005, Circulation.

[7]  D. Levy,et al.  Prediction of coronary heart disease using risk factor categories. , 1998, Circulation.

[8]  S. Haffner,et al.  Homeostasis model assessment of insulin resistance in relation to the incidence of cardiovascular disease: the San Antonio Heart Study. , 2002, Diabetes care.

[9]  S. Haffner,et al.  Identification of Persons at High Risk for Type 2 Diabetes Mellitus: Do We Need the Oral Glucose Tolerance Test? , 2002, Annals of Internal Medicine.

[10]  Bendix Carstensen,et al.  A Danish diabetes risk score for targeted screening: the Inter99 study. , 2004, Diabetes care.

[11]  Edward J Boyko,et al.  Comparison of a clinical model, the oral glucose tolerance test, and fasting glucose for prediction of type 2 diabetes risk in Japanese Americans. , 2003, Diabetes care.

[12]  Jaakko Tuomilehto,et al.  The diabetes risk score: a practical tool to predict type 2 diabetes risk. , 2003, Diabetes care.

[13]  N. Wareham,et al.  Diabetes risk score: towards earlier detection of Type 2 diabetes in general practice , 2000, Diabetes/metabolism research and reviews.

[14]  David M Eddy,et al.  Validation of the archimedes diabetes model. , 2003, Diabetes care.

[15]  M. Rutter,et al.  C-Reactive Protein, the Metabolic Syndrome, and Prediction of Cardiovascular Events in the Framingham Offspring Study , 2004, Circulation.

[16]  Neil R. Powe,et al.  The Atherosclerosis Risk in Communities Study , 2006 .

[17]  J. Murabito,et al.  Accuracy of Offspring Reports of Parental Cardiovascular Disease History: The Framingham Offspring Study , 2004, Annals of Internal Medicine.

[18]  Griffin,et al.  The performance of a risk score in predicting undiagnosed hyperglycemia. , 2002, Diabetes care.

[19]  R. D'Agostino,et al.  Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. , 2001, JAMA.

[20]  A. Karter,et al.  Prediction of Type 2 Diabetes Mellitus With Alternative Definitions of the Metabolic Syndrome: The Insulin Resistance Atherosclerosis Study , 2005, Circulation.

[21]  A. von Eckardstein,et al.  Risk for diabetes mellitus in middle-aged Caucasian male participants of the PROCAM study: implications for the definition of impaired fasting glucose by the American Diabetes Association. Prospective Cardiovascular Münster. , 2000, The Journal of clinical endocrinology and metabolism.

[22]  M. Engelgau,et al.  Diabetes trends in the U.S.: 1990-1998. , 2000, Diabetes care.

[23]  S. Haffner,et al.  The metabolic syndrome as predictor of type 2 diabetes: the San Antonio heart study. , 2003, Diabetes care.

[24]  Salvatore Panico,et al.  Prediction of coronary events in a low incidence population. Assessing accuracy of the CUORE Cohort Study prediction equation. , 2005, International journal of epidemiology.

[25]  David M Eddy,et al.  Archimedes: a trial-validated model of diabetes. , 2003, Diabetes care.

[26]  Dong Zhao,et al.  Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. , 2004, JAMA.

[27]  S. Grundy,et al.  The metabolic syndrome , 2003, The Lancet.

[28]  Heejung Bang,et al.  Identifying individuals at high risk for diabetes: The Atherosclerosis Risk in Communities study. , 2005, Diabetes care.

[29]  D. Singer,et al.  Hyperinsulinemia, hyperglycemia, and impaired hemostasis: the Framingham Offspring Study. , 2000, JAMA.

[30]  M. Engelgau,et al.  A New and Simple Questionnaire to Identify People at Increased Risk for Undiagnosed Diabetes , 1995, Diabetes Care.

[31]  Ralph B D'Agostino,et al.  Cardiovascular disease risk factors predict the development of type 2 diabetes: the insulin resistance atherosclerosis study. , 2004, Diabetes care.

[32]  Richard Kahn,et al.  The metabolic syndrome: time for a critical appraisal: joint statement from the American Diabetes Association and the European Association for the Study of Diabetes. , 2005, Diabetes care.

[33]  N. Schneiderman,et al.  Validation of the insulin sensitivity index (ISI(0,120)): comparison with other measures. , 2000, Diabetes research and clinical practice.

[34]  Tamara Harris,et al.  Predicting the development of diabetes in older adults: the derivation and validation of a prediction rule. , 2005, Diabetes care.

[35]  A. Stalenhoef,et al.  The Metabolic Syndrome: Targeting Dyslipidaemia to Reduce Coronary Risk , 2003, Journal of cardiovascular risk.