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Purpose: Reducing postprandial hyperglycemia has beneficial effects on diabetes-related risk factors, but the magnitude of the reduction needed to achieve such an effect is unknown. The purpose of the study was to quantify the relationship of acute glucose and insulin postprandial responses with longer-term effects on diabetes-related risk factors by performing a systematic review and meta-analysis of dietary intervention studies. Methods: We systematically searched EMBASE and MEDLINE. Dietary intervention studies among any human population aiming to reduce postprandial glycemia, with actual measures of postprandial glucose (PPG) and/or insulin (PPI) as acute exposures (incremental area under the curve, iAUC) as well as markers of glucose metabolism (fasting glucose, HbA1c) and insulin sensitivity (fasting insulin, HOMA-IR) after at least 4 weeks of diet intervention as outcomes were included. Meta-analyses were performed for the effects on acute exposures and on diabetes-related risk factors. The relationship between changes in acute exposures and changes in risk factor outcomes was estimated by meta-regression analyses. Results: Out of the 13,004 screened papers, 13 papers with 14 comparisons were included in the quantitative analysis. The dietary interventions acutely reduced mean PPG [mean difference (MD), − 0.27 mmol/l; 95% CI − 0.41 to − 0.14], but not mean PPI (MD − 7.47 pmol/l; 95% CI − 16.79 to 1.86). There were no significant overall effects on fasting glucose and insulin. HbA1c was reduced by − 0.20% (95% CI − 0.35 to − 0.05). Changes in acute PPG were significantly associated with changes in fasting plasma glucose (FPG) [per 10% change in PPG: β = 0.085 (95% CI 0.003, 0.167), k = 14], but not with fasting insulin [β = 1.20 (95% CI − 0.32, 2.71), k = 12]. Changes in acute PPI were not associated with changes in FPG [per 10% change in PPI: β = − 0.017 (95% CI − 0.056, 0.022), k = 11]. Conclusions: Only a limited number of postprandial glucose-lowering dietary intervention studies measured acute postprandial exposures to PPG/PPI during the interventions. In this small heterogeneous set of studies, an association was found between the magnitude of the acute postprandial responses and the change in fasting glucose, but no other outcomes. More studies are needed to quantify the relationship between acute postprandial changes and long-term effects on risk factors. [ABSTRACT FROM AUTHOR]

Background: Chronic cardiometabolic diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD), share many modifiable risk factors and can be prevented using combined prevention programs. Valid risk prediction tools are needed to accurately identify individuals at risk.Objective: We aimed to validate a previously developed non-invasive risk prediction tool for predicting the combined 7-year-risk for chronic cardiometabolic diseases.Design: The previously developed tool is stratified for sex and contains the predictors age, BMI, waist circumference, use of antihypertensives, smoking, family history of myocardial infarction/stroke, and family history of diabetes. This tool was externally validated, evaluating model performance using area under the receiver operating characteristic curve (AUC)-assessing discrimination-and Hosmer-Lemeshow goodness-of-fit (HL) statistics-assessing calibration. The intercept was recalibrated to improve calibration performance.Participants: The risk prediction tool was validated in 3544 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab).Key Results: Discrimination was acceptable, with an AUC of 0.78 (95% CI 0.75-0.81) in men and 0.78 (95% CI 0.74-0.81) in women. Calibration was poor (HL statistic: p < 0.001), but improved considerably after intercept recalibration. Examination of individual outcomes showed that in men, AUC was highest for CKD (0.85 [95% CI 0.78-0.91]) and lowest for T2D (0.69 [95% CI 0.65-0.74]). In women, AUC was highest for CVD (0.88 [95% CI 0.83-0.94)]) and lowest for T2D (0.71 [95% CI 0.66-0.75]).Conclusions: Validation of our previously developed tool showed robust discriminative performance across populations. Model recalibration is recommended to account for different disease rates. Our risk prediction tool can be useful in large-scale prevention programs for identifying those in need of further risk profiling because of their increased risk for chronic cardiometabolic diseases. [ABSTRACT FROM AUTHOR]