Prediction of Incident Diabetes Mellitus in Middle-aged Adults

Abstract

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 ␤-cell sensitivity indexes. Discrimina-tion 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 middleaged 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.

A prediction model for type 2 diabetes risk among Chinese people

“…The standard-error estimates and the confidence intervals were obtained based on 1,000 bootstrap samples. Finally, we compared the performance of the proposed prediction model with that of various prediction models derived from other populations, including Cambridge [8][9][10], Prospective Cardiovascular Münster (PROCAM) [30], San Antonia [13,14] and Framingham [5]. AUC was used to compare the discriminatory capabilities of these models and our simple points model.…”

“…Recent clinical trials demonstrated that lifestyle interventions in individuals with impaired glucose tolerance can substantially delay the development of diabetes [1,2], providing a rationale for the identification of high-risk individuals so as to implement early lifestyle intervention strategies to prevent diabetes. The prediction models for the risk of diabetes can help to guide screening and interventions and to predict diabetes occurrence [3][4][5]. Routinely available and easily collected clinical and lifestyle-related information has been found to be effective for identifying diabetes cases [6][7][8][9][10][11][12].…”

“…This San Antonia model included a variety of diabetes risk factors, including age, sex, ethnicity, fasting glucose, systolic blood pressure, HDL-cholesterol, body mass index and family history, to generate a prediction points system and appeared to have a predictive power similar to that of the diagnostic criteria for metabolic syndrome [13,15]. Several other prediction models have also been developed, primarily in white populations [3,5,16,17]. However, risk scores developed in white people may not apply to other ethnic groups [18].…”

Family history of diabetes modifies the effect of blood pressure for incident diabetes in Middle Eastern women: Tehran Lipid and Glucose Study

“…In the Framingham Offspring Study in the prediction model for incident diabetes, SBP was not associated with diabetes in the presence of parental history of diabetes mellitus. 19 Also, in an other prospective study of an Aboriginal community, with almost 35% positive family history of diabetes, after 10 years follow-up, SBP was only associated with an increased risk of diabetes in the age-and sex-adjusted model; however after adjustment with components of metabolic syndrome, SBP was not longer associated with diabetes. 20 There are some other studies that show SBP as an independent predictor of diabetes despite adjustment for parental history of diabetes in the sex-adjusted model but not in the sex-stratified analysis.…”

Comparison of indices of insulin resistance with metabolic syndrome classifications to predict the development of impaired fasting glucose in overweight and obese subjects: a 3-year prospective study

“…In general, these studies have shown that obesity, fasting plasma glucose and a variety of indices derived from fasting glucose, insulin and triglyceride concentrations are sensitive predictors of type 2 diabetes, and that composite models of these indices do not generally improve discriminatory power. [22][23][24] Our study differed from these earlier investigations by being a prospective analysis in overweight and obese subjects that compared the ability of several commonly used indices of IR with MetS classifications to predict the change from normoglycaemia to IFG, an important step in the development of diabetes. The predictive potential of the various indices was evaluated by ROC curve analysis and also by comparing the baseline values of subjects who were consistently normoglycaemic throughout the study with subjects who developed IFG.…”