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 Ն7.0 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 ␥-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 Ͻ 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.

The Paris Prospective Study is a long-term, large-scale study of the factors predicting coronary heart disease. The first follow-up examination included, for subjects not known as having diabetes mellitus, a 75 g oral glucose tolerance test with measurement of plasma insulin and glucose levels, fasting and 2 h post-load. Between 1968 and 1973, 6903 men aged 43-54 years were thus examined. Causes of death were ascertained within this group after 15 years of mean follow-up. The baseline variables were tested as predictors of death from coronary heart disease by a Cox regression analysis. Significant independent predictors of coronary heart disease death were: systolic blood pressure, number of cigarettes per day, plasma cholesterol level, and 2 h post-load plasma insulin level when entered as a categorical variable (below or above 452 pmol/l. i.e. the lower limit of the fifth quintile of the distribution). This dichotomization was performed to account for the non-linear univariate distribution of deaths with increasing post-load insulin values. Fasting plasma insulin level was not an independent predictor of death by coronary heart disease over this long-term follow-up. Levels of blood glucose were not significant independent predictors of death by coronary heart disease when plasma insulin levels were included in the model. The same applied to abnormalities of glucose tolerance when the 125 men with known non-insulin-treated diabetes at baseline were added to the group. Under the assumption that hyperinsulinaemia is a marker of insulin resistance, the results are consistent with the hypothesis that insulin resistance is associated with a higher risk of coronary heart disease mortality. However, it is doubtful that circulating insulin per se is a direct cause of arterial complications.

SummaryBackgroundRestless legs syndrome is a prevalent chronic neurological disorder with potentially severe mental and physical health consequences. Clearer understanding of the underlying pathophysiology is needed to improve treatment options. We did a meta-analysis of genome-wide association studies (GWASs) to identify potential molecular targets.MethodsIn the discovery stage, we combined three GWAS datasets (EU-RLS GENE, INTERVAL, and 23andMe) with diagnosis data collected from 2003 to 2017, in face-to-face interviews or via questionnaires, and involving 15 126 cases and 95 725 controls of European ancestry. We identified common variants by fixed-effect inverse-variance meta-analysis. Significant genome-wide signals (p≤5 × 10−8) were tested for replication in an independent GWAS of 30 770 cases and 286 913 controls, followed by a joint analysis of the discovery and replication stages. We did gene annotation, pathway, and gene-set-enrichment analyses and studied the genetic correlations between restless legs syndrome and traits of interest.FindingsWe identified and replicated 13 new risk loci for restless legs syndrome and confirmed the previously identified six risk loci. MEIS1 was confirmed as the strongest genetic risk factor for restless legs syndrome (odds ratio 1·92, 95% CI 1·85–1·99). Gene prioritisation, enrichment, and genetic correlation analyses showed that identified pathways were related to neurodevelopment and highlighted genes linked to axon guidance (associated with SEMA6D), synapse formation (NTNG1), and neuronal specification (HOXB cluster family and MYT1).InterpretationIdentification of new candidate genes and associated pathways will inform future functional research. Advances in understanding of the molecular mechanisms that underlie restless legs syndrome could lead to new treatment options. We focused on common variants; thus, additional studies are needed to dissect the roles of rare and structural variations.FundingDeutsche Forschungsgemeinschaft, Helmholtz Zentrum München–Deutsches Forschungszentrum für Gesundheit und Umwelt, National Research Institutions, NHS Blood and Transplant, National Institute for Health Research, British Heart Foundation, European Commission, European Research Council, National Institutes of Health, National Institute of Neurological Disorders and Stroke, NIH Research Cambridge Biomedical Research Centre, and UK Medical Research Council.

OBJECTIVE -Early identification of subjects at high risk for diabetes is essential, and random HbA 1c (A1C) may be more practical than fasting plasma glucose (FPG). The predictive value of A1C, in comparison to FPG, is evaluated for 6-year incident diabetes. RESEARCH DESIGN AND METHODS -From the French cohort study Data from anEpidemiological Study on the Insulin Resistance Syndrome (DESIR), 1,383 men and 1,437 women, aged 30 -65 years, were volunteers for a routine health check-up. Incident diabetes was defined by FPG Ն7.0 mmol/l or treatment by antidiabetic drugs. Multivariate logistic regression models were used to predict diabetes at 6 years. Receiver operating characteristic curves compared the predictive values of A1C and FPG.RESULTS -At 6 years, 30 women (2.1%) and 60 men (4.3%) had developed diabetes. Diabetes risk increased exponentially with A1C in both sexes (P Ͻ 0.001). After stratifying on FPG, A1C predicted diabetes only in subjects with impaired fasting glucose (IFG) (FPG Ն6.10 mmol/l): the odds ratio (95% CI) for a 1% increase in A1C was 7.20 (3.00 -17.00). In these subjects, an A1C of 5.9% gave an optimal sensitivity of 64% and specificity of 77% to predict diabetes.CONCLUSIONS -A1C predicted diabetes, even though the diagnosis of diabetes was based on FPG, but it was less sensitive and specific than FPG. It could be used as a test if fasting blood sampling was not available or in association with FPG. In subjects with IFG, A1C is better than glucose to evaluate diabetes risk, and it could be used to select subjects for intensive early intervention. Diabetes Care 29:1619 -1625, 2006T he prevalence of type 2 diabetes is increasing worldwide, and it is projected that the number of adults with diabetes will double between 2000 and 2030 (1). This means a large burden for the health care system. Recent clinical trials have demonstrated that lifestyle (2-4) or pharmaceutical (4 -6) interventions in individuals with impaired glucose tolerance (IGT) can delay or prevent diabetes; thus high-risk subjects should be identified for early intensive lifestyle counseling or even pharmaceutical treatment (7).Fasting and 2-h plasma glucose after an oral glucose tolerance test (OGTT) are currently used to identify subjects at high risk of diabetes (8): those with impaired fasting glucose (IFG) and IGT. However, the OGTT is not common in clinical practice, because it is time consuming, costly, and less reproducible (9) than measurement of fasting plasma glucose (FPG).HbA 1c (A1C), an indirect measure of mean blood glucose over the previous 2-3 months, is correlated with FPG and 2-h plasma glucose (10 -12). A1C is more reproducible than FPG (13) and withinsubject coefficients of variation are 1.7 and 5.7%, respectively (14). Moreover, measurement of A1C does not require that the subject is fasting. The use of A1C could better integrate chronic hyperglycemia than FPG.Few studies have investigated predicting diabetes using A1C and none in a Caucasian population. Moreover, previous investigations were in populations a...