Glycemic Index and Glycemic Load and Their Association with C-Reactive Protein and Incident Type 2 Diabetes

Objective. To investigate whether the Glycemic Index (GI) or Glycemic Load (GL) of a diet is associated with C-reactive Protein (CRP) and risk of type 2 diabetes in a prospective study. Materials and Methods. Our analysis included 4,366 participants who did not have diabetes at baseline. During follow-up 456 diabetes cases were confirmed. Dietary GI and GL were derived from a food-frequency questionnaire and its association with CRP was examined cross-sectionally using linear regression models. The association of GI and GL with diabetes incidence was examined using Cox proportional hazard models. Results. GL, but not GI, was associated with lnCRP at baseline (bGL = 0.11 per 50 units; P = .01). When comparing the highest to the lowest tertile of GI with respect to diabetes incidence, a Relative Risk (RR) of 0.95 [95%CI 0.75, 1.21] was found after adjustment for lifestyle and nutritional factors. For GL the RR for diabetes incidence was 1.00 [95%CI 0.74, 1.36]. Additional adjustment for CRP did not change RRs. Conclusion. Since GI was not associated with CRP and risk of type 2 diabetes, it is unlikely that a high GI diet induces the previously shown positive association between CRP and risk of type 2 diabetes by increasing CRP concentrations

A novel integrated QSP model of in vivo human glucose regulation to support the development of a glucagon/GLP-1 dual agonist

Glucagon‐like peptide‐1 (GLP‐1) receptor agonists (GLP‐1RAs) and dual GLP‐1/glucagon receptor agonists improve glycaemic control and cause significant weight loss in patients with type 2 diabetes.(1) These effects are driven in part by augmenting glucose‐stimulated insulin release (incretin effect), reducing caloric intake and delayed gastric emptying. We developed and externally validated a novel integrated quantitative systems pharmacology (QSP) model to gain quantitative insight into the relative contributions and mechanisms of drugs modulating glucose regulatory pathways. This model (4GI model) incorporates known feedback mechanisms among glucose, GLP‐1, glucagon, glucose‐dependent insulinotropic peptide (GIP), and insulin after glucose provocation (i.e., food intake) and drug intervention utilizing published nonpharmacological and pharmacological (liraglutide, a GLP‐1RA) data. The resulting model accurately describes the aforementioned mechanisms and independently predicts the effects of the GLP‐1RAs (dulaglutide and semaglutide) on system dynamics. Therefore, the validated 4GI model represents a quantitative decision‐making tool to support the advancement of novel therapeutics and combination strategies modulating these pathways

Heart Rate Variability and Incident Type 2 Diabetes in General Population

Context: Hyperglycemia and autonomic dysfunction are bidirectionally related. Objective: We investigated the association of longitudinal evolution of heart rate variability (HRV) with incident type 2 diabetes (T2D) among the general population. Methods: We included 7630 participants (mean age 63.7 years, 58% women) from the population-based Rotterdam Study who had no history of T2D and atrial fibrillation at baseline and had repeated HRV assessments at baseline and during follow-up. We used joint models to assess the association between longitudinal evolution of heart rate and different HRV metrics (including the heart rate-corrected SD of the normal-to-normal RR intervals [SDNNc], and root mean square of successive RR-interval differences [RMSSDc]) with incident T2D. Models were adjusted for cardiovascular risk factors. Bidirectional Mendelian randomization (MR) using summary-level data was also performed. Results: During a median follow-up of 8.6 years, 871 individuals developed incident T2D. One SD increase in heart rate (hazard ratio [HR] 1.20; 95% CI, 1.09-1.33), and log(RMSSDc) (HR 1.16; 95% CI, 1.01-1.33) were independently associated with incident T2D. The HRs were 1.54 (95% CI, 1.08-2.06) for participants younger than 62 years and 1.15 (95% CI, 1.01-1.31) for those older than 62 years for heart rate (P for interaction &lt;.001). Results from bidirectional MR analyses suggested that HRV and T2D were not significantly related to each other. Conclusion: Autonomic dysfunction precedes development of T2D, especially among younger individuals, while MR analysis suggests no causal relationship. More studies are needed to further validate our findings.</p

Mortality of Inherited Arrhythmia Syndromes Insight Into Their Natural History

Background-For most arrhythmia syndromes, the risk of sudden cardiac death for asymptomatic mutation carriers is ill defined. Data on the natural history of these diseases, therefore, are essential. The family tree mortality ratio method offers the unique possibility to study the natural history at a time when the disease was not known and patients received no treatment. Methods and Results-In 6 inherited arrhythmia syndromes caused by specific mutations, we analyzed all-cause mortality with the family tree mortality ratio method (main outcome measure, standardized mortality ratio [SMR]). In long-QT syndrome (LQTS) type 1, severely increased mortality risk during all years of childhood was observed (1-19 years), in particular during the first 10 years of life (SMR, 2.9; 95% CI, 1.5-5.1). In LQTS type 2, we observed increasing SMRs starting from age 15 years, which just reached significance between age 30 and 39 (SMR, 4.0; 95% CI, 1.1-10.0). In LQTS type 3, the SMR was increased between age 15 and 19 years (SMR, 5.8; 95% CI, 1.2-16.9). In the SCN5A overlap syndrome, excess mortality was observed between age 10 and 59 years, with a peak between 20 and 39 years (SMR, 3.8; 95% CI, 2.5-5.7). In catecholaminergic polymorphic ventricular tachycardia, excess mortality was restricted to ages 20 to 39 years (SMR, 3.0; 95% CI, 1.3-6.0). In Brugada syndrome, excess mortality was observed between age 40 and 59 (SMR, 1.79; 95% CI, 1.2-2.4), particularly in men. Conclusions-We identified age ranges during which the mortality risk manifests in an unselected and untreated population, which can guide screening in these families. (Circ Cardiovasc Genet. 2012;5:183-189.

Associations of baseline glycemic status and its transitions with cognitive and physical functioning decline

OBJECTIVE: Evidence about the decline of cognition and physical function across glycemic status (normoglycemia, prediabetes, and diabetes) is inconsistent. We evaluated longitudinal changes in cognition and physical function according to glycemic status and also different glycemic transitions. STUDY DESIGN: Population-based cohort study. METHODS: 9307 participants (mean age 59.7 years, 53.7 % women) were included from the China Health and Retirement Longitudinal Study (2011-2018). Global cognition (assessed by orientation, memory, and executive function) and physical function (calculated as the sum of impaired basic and instrumental activities of daily living) were assessed in each wave. The glycemic status was assessed in waves 2011 and 2015. Diabetes was defined as fasting blood glucose ≥7.0 mmol/L, HbA1c ≥6.5 %, self-reported diabetes, or glucose-lowering medication use. Prediabetes was defined as fasting blood glucose 5.6-6.9 mmol/L or an HbA1c of 5.7-6.4 %. RESULTS: Compared with normoglycemia, baseline diabetes was associated with a faster decline in orientation (-0.018 SD/year, 95%CI -0.032, -0.004) and a faster increase in physical function score (0.082 /year, 95%CI 0.038, 0.126). We did not observe any effect of prediabetes on the changing rate of cognition and physical function. Progression from normoglycemia to diabetes between waves 2011 and 2015 was associated with a significantly faster decline in global cognition, memory, executive function, and physical function compared with stable normoglycemia. CONCLUSIONS: Baseline diabetes was associated with accelerated decline of cognition and physical function. Associations with prediabetes were not observed, suggesting an important short diagnostic window when diabetes presents de novo

Efficacy of statins in familial hypercholesterolaemia: a long term cohort study

Objective To determine the efficacy of statin treatment on risk of coronary heart disease in patients with familial hypercholesterolaemia

Genomic prediction of coronary heart disease

Aims Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores. Methods and results We generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61-1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18-1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5-1.6%, P = 60 years old (meta-analysis C-index: +4.6-5.1%, P <0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12-18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking. Conclusions A GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores.Peer reviewe

Mortality Risk Prediction by an Insurance Company and Long-Term Follow-Up of 62,000 Men

Background: Insurance companies use medical information to classify the mortality risk of applicants. Adding genetic tests to this assessment is currently being debated. This debate would be more meaningful, if results of present-day risk prediction were known. Therefore, we compared the predicted with the observed mortality of men who applied for life insurance, and determined the prognostic value of the risk assessment. Methods: Long-term follow-up was available for 62,334 male applicants whose mortality risk was predicted with medical evaluation and they were assigned to five groups with increasing risk from 1 to 5. We calculated all cause standardized mortality ratios relative to the Dutch population and compared groups with Cox's regression. We compared the discriminative ability of risk assessments as indicated by a concordance index (c). Results: In 844,815 person years we observed 3,433 deaths. The standardized mortality relative to the Dutch male population was 0.76 (95 percent confidence interval, 0.73 to 0.78). The standardized mortality ratios ranged from 0.54 in risk group 1 to 2.37 in group 5. A large number of risk factors and diseases were significantly associated with increased mortality. The algorithm of prediction was significantly, but only slightly better than summation of the number of disorders and risk factors (c-index, 0.64 versus 0.60, P,0.001). Conclusions: Men applying for insurance clearly had better survival relative to the general population. Readily available medical evaluation enabled accurate prediction of the mortality risk of large groups, but the deceased men could not have been identified with the applied prediction method

Sex differences in cardiometabolic risk factors, pharmacological treatment and risk factor control in type 2 diabetes:findings from the Dutch Diabetes Pearl cohort

Introduction Sex differences in cardiometabolic risk factors and their management in type 2 diabetes (T2D) have not been fully identified. Therefore, we aimed to examine differences in cardiometabolic risk factor levels, pharmacological treatment and achievement of risk factor control between women and men with T2D. Research design and methods Cross-sectional data from the Dutch Diabetes Pearl cohort were used (n=6637, 40% women). Linear and Poisson regression analyses were used to examine sex differences in cardiometabolic risk factor levels, treatment, and control. Results Compared with men, women had a significantly higher body mass index (BMI) (mean difference 1.79 kg/m 2 (95% CI 1.49 to 2.08)), while no differences were found in hemoglobin A 1c (HbA 1c) and systolic blood pressure (SBP). Women had lower diastolic blood pressure (-1.94 mm Hg (95% CI -2.44 to -1.43)), higher total cholesterol (TC) (0.44 mmol/L (95% CI 0.38 to 0.51)), low-density lipoprotein cholesterol (LDL-c) (0.26 mmol/L (95% CI 0.22 to 0.31)), and high-density lipoprotein cholesterol (HDL-c) sex-standardized (0.02 mmol/L (95% CI 0.00 to 0.04)), and lower TC:HDL ratio (-0.29 (95% CI -0.36 to -0.23)) and triglycerides (geometric mean ratio 0.91 (95% CI 0.85 to 0.98)). Women had a 16% higher probability of being treated with antihypertensive medication in the presence of high cardiovascular disease (CVD) risk and elevated SBP than men (relative risk 0.84 (95% CI 0.73 to 0.98)), whereas no sex differences were found for glucose-lowering medication and lipid-modifying medication. Among those treated, women were less likely to achieve treatment targets of HbA 1c (0.92 (95% CI 0.87 to 0.98)) and LDL-c (0.89 (95% CI 0.85 to 0.92)) than men, while no differences for SBP were found. Conclusions In this Dutch T2D population, women had a slightly different cardiometabolic risk profile compared with men and a substantially higher BMI. Women had a higher probability of being treated with antihypertensive medication in the presence of high CVD risk and elevated SBP than men, and were less likely than men to achieve treatment targets for HbA 1c and LDL levels

Eating Fish and Risk of Type 2 Diabetes: A population-based, prospective follow-up study

Objective: To investigate the relation between total fish, type of fish (lean and fatty), and EPA&DHA intake and risk of type 2 diabetes in a population-based cohort. Research design and methods: The analysis included 4,472 Dutch participants aged =55 years without diabetes at baseline. Dietary intake was assessed with a semi-quantitative food frequency questionnaire. Hazard ratios (RR) with 95% confidence intervals (95% CI) were used to examine risk associations adjusted for age, sex, lifestyle, and nutritional factors. Results: After 15 years of follow-up, 463 participants developed type 2 diabetes. Median fish intake, mainly lean fish (81% ), was 10 g/d. Total fish intake was associated positively with risk of type 2 diabetes; the RR was 1.32 (95% CI 1.02, 1.70) in the highest total fish group (=28 g/d) compared with non-fish eaters (p for trend= 0.04). Correspondingly, lean fish intake tended to be associated positively with type 2 diabetes (RR highest group (=23 g/d): 1.30 (95% CI 1.01, 1.68), p for trend= 0.06), but fatty fish was not. No association was observed between EPA&DHA intake and type 2 diabetes (RR highest group (=149.4 mg/d): 1.22 (95% CI 0.97, 1.53)). When additionally adjusted for intake of selenium, cholesterol, and vitamin D this RR decreased to 1.05 (95% CI 0.80, 1.38) (p for trend= 0.77). Conclusion: The findings do not support a beneficial effect of total fish, type of fish, or EPA&DHA intake on the risk of type 2 diabetes. Alternatively, other dietary components, like selenium, and unmeasured contaminants present in fish might explain our result