Narayan, K.M. Venkat

Background The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the ‘mass’ approach), the ‘high risk’ approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke Riskometer™, has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. Methods 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke Riskometer™) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R2 statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. Results The Stroke Riskometer™ performed well against the FSRS five-year AUROC for both males (FSRS = 75·0% (95% CI 72·3%–77·6%), Stroke Riskometer™ = 74·0(95% CI 71·3%–76·7%) and females [FSRS = 70·3% (95% CI 67·9%–72·8%, Stroke Riskometer™ = 71·5% (95% CI 69·0%–73·9%)], and better than QStroke [males–59·7% (95% CI 57·3%–62·0%) and comparable to females = 71·1% (95% CI 69·0%–73·1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0·51–0·56, D-statistic ranging from 0·01–0·12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data ( P < 0·006). Conclusions The Stroke Riskometer™ is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke Riskometer™ will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors.

OBJECTIVE—To examine the influence of obesity and prevention of weight gain on the incidence of type 2 diabetes. RESEARCH DESIGN AND METHODS—We examined participants in the San Antonio Heart Study, a prospective population-based study of Mexican Americans and non-Hispanic whites residing in San Antonio, Texas. BMI was stratified into four categories: normal (&lt;25 kg/m2), overweight (≥25 kg/m2 and &lt;30 kg/m2), obese (≥30 kg/m2 and &lt;35 kg/m2), and very obese (≥35 kg/m2). The number and proportion of incident cases prevented by targeting each BMI category were estimated. In addition, we calculated the decrease in risk of developing type 2 diabetes associated with weight gain prevention across both the BMI and age spectra. RESULTS—Preventing normal individuals from becoming overweight would result in the greatest reduction in incidence of type 2 diabetes. This would result in a 62 and 74% reduction in the incidence of type 2 diabetes in Mexican Americans and non-Hispanic whites, respectively. Preventing the entire population from gaining, on average, 1 BMI unit would result in a reduction in incidence of type 2 diabetes of 12.4 and 13.0% in Mexican Americans and non-Hispanic whites, respectively. CONCLUSIONS—The majority of cases of type 2 diabetes were in individuals who were overweight or mildly obese with a family history of type 2 diabetes. Public health resources should be directed toward the prevention of weight gain among normal and overweight individuals in order to prevent the maximum number of cases of type 2 diabetes.

OBJECTIVE—Increasing obesity within the general population has been accompanied by rising rates of diabetes. The extent to which obesity has increased among people with diabetes is unknown, as are the potential consequences for diabetes outcomes. RESEARCH DESIGN AND METHODS—Community medical records (hospital and ambulatory) of all Rochester, Minnesota, residents aged ≥30 years who first met standardized research criteria for diabetes from 1970 to 1989 (n = 1,306) were reviewed to obtain data on BMI and related characteristics as of the diabetes identification date (±3 months). Vital status as of 31 December 1999 and date of death for those who died were obtained from medical records, State of Minnesota death tapes, and active follow-up. RESULTS—As of the identification date, data on BMI were available for 1,290 cases. Of the 272 who first met diabetes criteria in 1970–1974, 33% were obese (BMI ≥30), including 5% who were extremely obese (BMI ≥40). These proportions increased to 49% (P &lt; 0.001) and 9% (P = 0.012), respectively, for the 426 residents who first met diabetes criteria in 1985–1989. BMI increased significantly with increasing calendar year of diabetes identification in multivariable regression analysis. Analysis of survival revealed an increased hazard of mortality for BMI ≥41, relative to BMI of 23–25 (hazard ratio 1.60, 95% CI 1.09–2.34, P = 0.016). CONCLUSIONS—The prevalence of obesity and extreme obesity among individuals at the time they first met criteria for diabetes has increased over time. This is disturbing in light of the finding that diabetic individuals who are extremely obese are at increased risk of mortality compared with their nonobese diabetic counterparts.