Heterogeneity in glucose response curves during an oral glucose tolerance test and associated cardiometabolic risk

We aimed to examine heterogeneity in glucose response curves during an oral glucose tolerance test with multiple measurements and to compare cardiometabolic risk profiles between identified glucose response curve groups. We analyzed data from 1,267 individuals without diabetes from five studies in Denmark, the Netherlands and the USA. Each study included between 5 and 11 measurements at different time points during a 2-h oral glucose tolerance test, resulting in 9,602 plasma glucose measurements. Latent class trajectories with a cubic specification for time were fitted to identify different patterns of plasma glucose change during the oral glucose tolerance test. Cardiometabolic risk factor profiles were compared between the identified groups. Using latent class trajectory analysis, five glucose response curves were identified. Despite similar fasting and 2-h values, glucose peaks and peak times varied greatly between groups, ranging from 7-12 mmol/L, and 35-70 min. The group with the lowest and earliest plasma glucose peak had the lowest estimated cardiovascular risk, while the group with the most delayed plasma glucose peak and the highest 2-h value had the highest estimated risk. One group, with normal fasting and 2-h values, exhibited an unusual profile, with the highest glucose peak and the highest proportion of smokers and men. The heterogeneity in glucose response curves and the distinct cardiometabolic risk profiles may reflect different underlying physiologies. Our results warrant more detailed studies to identify the source of the heterogeneity across the different phenotypes and whether these differences play a role in the development of type 2 diabetes and cardiovascular disease

Impact of isocaloric exchanges of carbohydrate for fat on postprandial glucose, insulin, triglycerides, and free fatty acid responses-a systematic review and meta-analysis

Varying the macronutrient composition of meals alters acute postprandial responses, but the effect sizes for specific macronutrient exchanges have not been quantified by systematic reviews. Therefore the aim is to quantify the effect size of exchanging fat for carbohydrates in mixed meals on postprandial glucose (PPG), insulin (PPI), triglycerides (PPTG), and free fatty acids (PPFFA) responses by performing a systematic review and meta-analysis of randomized controlled trials. A systematic literature search was undertaken on randomized controlled trials comparing isocaloric high fat with high carbohydrate meals, with comparable protein contents and at least one postprandial glycemic- and one lipid outcome. The outcome data were extracted and expressed as mean postprandial levels over 2 h. Ten studies involving 14 comparisons met the eligibility criteria. Data were available for meta-analysis from 347 participants, consuming mixed meals containing 250-1003 kcal, and total fat contents of 33.3-75.6 percentage of energy (en%) (intervention) versus 0-31.7 en% (control). Each 10en% increase in fat, replacing carbohydrates produced a mean reduction in PPG of 0.32 mmol/l (95% CI -0.64 to -0.00, p = 0.047), a reduction in PPI of 18.2 pmol/l (95% CI -24.86 to -11.54), an increase in PPTG of 0.06 mmol/l (95% CI 0.02 to 0.09, p = 0.004), with no statistically significant effect on PPFFA. Modest exchange of carbohydrates for fats in mixed meals significantly reduces PPG and PPI and increases PPTG responses. The quantitative relationships derived here may be applied to predict responses, and to design and optimize meal macronutrient compositions in dietary intervention studies

The Use and Effectiveness of Selected Alternative Markers for Insulin Sensitivity and Secretion Compared with Gold Standard Markers in Dietary Intervention Studies in Individuals without Diabetes: Results of a Systematic Review

Background: The gold‐standard techniques for measuring insulin sensitivity and secretion are well established. However, they may be perceived as invasive and expensive for use in dietary intervention studies. Thus, surrogate markers have been proposed as alternative markers for insulin sensitivity and secretion. This systematic review aimed to identify markers of insulin sensitivity and secretion in response to dietary intervention and assess their suitability as surrogates for the gold‐standard method-ology. Methods: Three databases, PubMed, Scopus, and Cochrane were searched, intervention studies and randomised controlled trials reporting data on dietary intake, a gold standard of analysis of insulin sensitivity (either euglycaemic‐hyperinsulinaemic clamp or intravenous glucose tolerance test and secretion (acute insulin response to glucose), as well as surrogate markers for insulin sensitivity (either fasting insulin, area under the curve oral glucose tolerance tests and HOMA‐IR) and insulin secretion (disposi-tion index), were selected. Results: We identified thirty‐five studies that were eligible for inclusion. We found insufficient evidence to predict insulin sensitivity and secretion with surrogate markers when compared to gold standards in nutritional intervention studies. Conclusions: Future research is needed to investigate if surrogate measures of insulin sensitivity and secretion can be repeatable and reproduci-ble in the same way as gold standards

Predicting glycated hemoglobin levels in the non-diabetic general population:Development and validation of the DIRECT-DETECT prediction model - a DIRECT study

AIMS/HYPOTHESIS: To develop a prediction model that can predict HbA1c levels after six years in the non-diabetic general population, including previously used readily available predictors. METHODS: Data from 5,762 initially non-diabetic subjects from three population-based cohorts (Hoorn Study, Inter99, KORA S4/F4) were combined to predict HbA1c levels at six year follow-up. Using backward selection, age, BMI, waist circumference, use of anti-hypertensive medication, current smoking and parental history of diabetes remained in sex-specific linear regression models. To minimize overfitting of coefficients, we performed internal validation using bootstrapping techniques. Explained variance, discrimination and calibration were assessed using R2, classification tables (comparing highest/lowest 50% HbA1c levels) and calibration graphs. The model was externally validated in 2,765 non-diabetic subjects of the population-based cohort METSIM. RESULTS: At baseline, mean HbA1c level was 5.6% (38 mmol/mol). After a mean follow-up of six years, mean HbA1c level was 5.7% (39 mmol/mol). Calibration graphs showed that predicted HbA1c levels were somewhat underestimated in the Inter99 cohort and overestimated in the Hoorn and KORA cohorts, indicating that the model's intercept should be adjusted for each cohort to improve predictions. Sensitivity and specificity (95% CI) were 55.7% (53.9, 57.5) and 56.9% (55.1, 58.7) respectively, for women, and 54.6% (52.7, 56.5) and 54.3% (52.4, 56.2) for men. External validation showed similar performance in the METSIM cohort. CONCLUSIONS/INTERPRETATION: In the non-diabetic population, our DIRECT-DETECT prediction model, including readily available predictors, has a relatively low explained variance and moderate discriminative performance, but can help to distinguish between future highest and lowest HbA1c levels. Absolute HbA1c values are cohort-dependent

Progression and Regression: Distinct Developmental Patterns of Diabetic Retinopathy in Patients With Type 2 Diabetes Treated in the Diabetes Care System West-Friesland, the Netherlands

OBJECTIVE: To identify distinct developmental patterns of diabetic retinopathy (DR) and assess the risk factor levels of patients in these clusters. RESEARCH DESIGN AND METHODS: A cohort of 3,343 patients with type 2 diabetes mellitus (T2DM) monitored and treated in the Diabetes Care System West-Friesland, the Netherlands, was followed from 2 to 6 years. Risk factors were measured, and two-field fundus photographs were taken annually and graded according to the EURODIAB study group. Latent class growth modeling was used to identify distinct developmental patterns of DR over time. RESULTS: Five clusters of patients with distinct developmental patterns of DR were identified: A, patients without any signs of DR (88.9%); B, patients with a slow regression from minimal background to no DR (4.9%); C, patients with a slow progression from minimal background to moderate nonproliferative DR (4.0%); D, patients with a fast progression from minimal or moderate nonproliferative to (pre)proliferative or treated DR (1.4%); and E, patients with persistent proliferative DR (0.8%). Patients in clusters A and B were characterized by lower risk factor levels, such as diabetes duration, HbA(1c), and systolic blood pressure compared with patients in progressive clusters (C-E). CONCLUSIONS: Clusters of patients with T2DM with markedly different patterns of DR development were identified, including a cluster with regression of DR. These clusters enable a more detailed examination of the influence of various risk factors on DR

HbA1c levels in non-diabetic older adults - No J-shaped associations with primary cardiovascular events, cardiovascular and all-cause mortality after adjustment for confoundersin a meta-analysis of individual participant data from six cohort studies

Background: To determine the shape of the associations of HbA1c with mortality and cardiovascular outcomes in non-diabetic individuals and explore potential explanations. Methods: The associations of HbA1c with all-cause mortality, cardiovascular mortality and primary cardiovascular events (myocardial infarction or stroke) were assessed in non-diabetic subjects >50years from six population-based cohort studies from Europe and the USA and meta-analyzed. Very low, low, intermediate and increased HbA1c were defined as <5.0, 5.0 to <5.5, 5.5 to <6.0 and 6.0 to <6.5% (equals <31, 31 to <37, 37 to <42 and 42 to <48mmol/mol), respectively, and low HbA1c was used as reference in Cox proportional hazards models. Results: Overall, 6,769 of 28,681 study participants died during a mean follow-up of 10.7years, of whom 2,648 died of cardiovascular disease. Furthermore, 2,493 experienced a primary cardiovascular event. A linear association with primary cardiovascular events was observed. Adjustment for cardiovascular risk factors explained about 50% of the excess risk and attenuated hazard ratios (95% confidence interval) for increased HbA1c to 1.14 (1.03-1.27), 1.17 (1.00-1.37) and 1.19 (1.04-1.37) for all-cause mortality, cardiovascular mortality and cardiovascular events, respectively. The six cohorts yielded inconsistent results for the association of very low HbA1c levels with the mortality outcomes and the pooled effect estimates were not statistically significant. In one cohort with a pronounced J-shaped association of HbA1c levels with all-cause and cardiovascular mortality (NHANES), the following confounders of the association of very low HbA1c levels with mortality outcomes were identified: race/ethnicity; alcohol consumption; BMI; as well as biomarkers of iron deficiency anemia and liver function. Associations for very low HbA1c levels lost statistical significance in this cohort after adjusting for these confounders. Conclusions: A linear association of HbA1c levels with primary cardiovascular events was observed. For cardiovascular and all-cause mortality, the observed small effect sizes at both the lower and upper end of HbA1c distribution do not support the notion of a J-shaped association of HbA1c levels because a certain degree of residual confounding needs to be considered in the interpretation of the results. \ua9 2016 Sch\uf6ttker et al