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

Characteristics associated with polypharmacy in people with type 2 diabetes:the Dutch Diabetes Pearl cohort

Contains fulltext : 232027.pdf (Publisher’s version ) (Open Access)AIM: To describe the prevalence and characteristics of polypharmacy in a Dutch cohort of individuals with type 2 diabetes. METHODS: We included people with type 2 diabetes from the Diabetes Pearl cohort, of whom 3886 were treated in primary care and 2873 in academic care (secondary/tertiary). With multivariable multinomial logistic regression analyses stratified for line of care, we assessed which sociodemographic, lifestyle and cardiometabolic characteristics were associated with moderate (5-9 medications) and severe polypharmacy (≥10 medications) compared with no polypharmacy (0-4 medications). RESULTS: Mean age was 63 ± 10 years, and 40% were women. The median number of daily medications was 5 (IQR 3-7) in primary care and 7 (IQR 5-10) in academic care. The prevalence of moderate and severe polypharmacy was 44% and 10% in primary care, and 53% and 29% in academic care respectively. Glucose-lowering and lipid-modifying medications were most prevalent. People with severe polypharmacy used a relatively large amount of other (i.e. non-cardiovascular and non-glucose-lowering) medication. Moderate and severe polypharmacy across all lines of care were associated with higher age, low educational level, more smoking, longer diabetes duration, higher BMI and more cardiovascular disease. CONCLUSIONS: Severe and moderate polypharmacy are prevalent in over half of people with type 2 diabetes in primary care, and even more in academic care. People with polypharmacy are characterized by poorer cardiometabolic status. These results highlight the significance of polypharmacy in type 2 diabetes

Predicting Type 2 Diabetes Based on Polymorphisms From Genome-Wide Association Studies : A Population-Based Study

OBJECTIVE—Prediction of type 2 diabetes based on genetic testing might improve identification of high-risk subjects. Genome-wide association (GWA) studies identified multiple new genetic variants that associate with type 2 diabetes. The predictive value of genetic testing for prediction of type 2 diabetes in the general population is unclear

An RBP4 promoter polymorphism increases risk of type 2 diabetes

Aims/hypothesis: Retinol-binding protein 4 (RBP4), originally known for retinol transport, was recently identified as an adipokine affecting insulin resistance. The RBP4 -803GA promoter polymorphism influences binding of hepatic nuclear factor 1α and is associated with type 2 diabetes in case-control studies. We hypothesised that the RBP4 -803GA polymorphism increases type 2 diabetes risk at a population-based level. In addition, information on retinol intake and plasma vitamin A levels enabled us to explore the possible underlying mechanism. Methods: In the Rotterdam Study, a prospective, population-based, follow-up study, the -803GA polymorphism was genotyped. In Cox proportional hazards models, associations of the -803GA polymorphism and retinol intake with type 2 diabetes risk were examined. Moreover, the interaction of the polymorphism with retinol intake on type 2 diabetes risk was assessed. In a subgroup of participants the association of the polymorphism and vitamin A plasma levels was investigated. Results: Homozygous carriers of the -803A allele had increased risk of type 2 diabetes (HR 1.83; 95% CI 1.26-2.66). Retinol intake was not associated with type 2 diabetes risk and showed no interaction with the RBP4 -803GA polymorphism. Furthermore, there was no significant association of the polymorphism with plasma vitamin A levels. Conclusions/interpretation: Our results provide evidence that homozygosity for the RBP4 -803A allele is associated with increased risk of type 2 diabetes in the Rotterdam population. This relationship was not clearly explained by retinol intake and vitamin A plasma levels. Therefore, we cannot differentiate between a retinol-dependent or -independent mechanism of this RBP4 variant

New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

A Methodological Perspective on Genetic Risk Prediction Studies in Type 2 Diabetes: Recommendations for Future Research

Fueled by the successes of genome-wide association studies, numerous studies have investigated the predictive ability of genetic risk models in type 2 diabetes. In this paper, we review these studies from a methodological perspective, focusing on the variables included in the risk models as well as the study designs and populations investigated. We argue and show that differences in study design and characteristics of the study population have an impact on the observed predictive ability of risk models. This observation emphasizes that genetic risk prediction studies should be conducted in those populations in which the prediction models will ultimately be applied, if proven useful. Of all genetic risk prediction studies to date, only a few were conducted in populations that might be relevant for targeting preventive interventions

Cerebral Accumulation of Dietary Derivable Plant Sterols does not Interfere with Memory and Anxiety Related Behavior in Abcg5−/− Mice

Plant sterols such as sitosterol and campesterol are frequently applied as functional food in the prevention of atherosclerosis. Recently, it became clear that plasma derived plant sterols accumulate in murine brains. We questioned whether plant sterols in the brain are associated with alterations in brain cholesterol homeostasis and subsequently with brain functions. ATP binding cassette (Abc)g5−/− mice, a phytosterolemia model, were compared to Abcg5+/+ mice for serum and brain plant sterol accumulation and behavioral and cognitive performance. Serum and brain plant sterol concentrations were respectively 35–70-fold and 5–12-fold increased in Abcg5−/− mice (P < 0.001). Plant sterol accumulation resulted in decreased levels of desmosterol (P < 0.01) and 24(S)-hydroxycholesterol (P < 0.01) in the hippocampus, the brain region important for learning and memory functions, and increased lanosterol levels (P < 0.01) in the cortex. However, Abcg5−/− and Abcg5+/+ displayed no differences in memory functions or in anxiety and mood related behavior. The swimming speed of the Abcg5−/− mice was slightly higher compared to Abcg5+/+ mice (P < 0.001). In conclusion, plant sterols in the brains of Abcg5−/− mice did have consequences for brain cholesterol metabolism, but did not lead to an overt phenotype of memory or anxiety related behavior. Thus, our data provide no contra-indication for nutritional intake of plant sterol enriched nutrition

Statin treatment increases lipoprotein(a) levels in subjects with low molecular weight apolipoprotein(a) phenotype

Background and aims: We aimed to evaluate the effect of statin treatment initiation on lipoprotein(a) [Lp(a)] levels in patients with dyslipidemia, and the interactions with the apolipoprotein(a) [apo(a)] phenotype, LPA single nucleotide polymorphisms (SNPs) and change in LDL cholesterol. Methods: The study population consisted of patients with dyslipidemia, predominantly familial hypercholesterolemia, who first initiated statin treatment (initiation group; n = 39) or were already on stable statin treatment for at least 4 months (control group; n = 42). Plasma Lp(a) levels were determined with a particle-enhanced immunoturbidimetric assay before and at least 2 months after start of statin treatment in individuals of the initiation group, and at two time points with an interval of at least 2 months in the control group. High and low molecular weight (HMW and LMW, respectively) apo(a) phenotype was determined by immunoblotting, and the common LPA SNPs rs10455872, rs3798220 and rs41272110 by Taqman assay. Results: Plasma Lp(a) levels did not increase significantly in the initiation group (median 20.5 (IQR 10.9–80.7) to 23.3 (10.8–71.8) mg/dL; p = 0.09) nor in the control group (30.9 (IQR 9.2–147.0) to 31.7 (IQR 10.9–164.0) mg/dL; p = 0.61). In patients with the LMW apo(a) phenotype, Lp(a) levels increased significantly from 66.4 (IQR 23.5–148.3) to 97.4 (IQR 24.9–160.4) mg/dL (p = 0.026) in the initiation group, but not in the control group and not in patients characterized by the HMW apo(a) phenotype. Interactions with common LPA SNPs and change in LDL cholesterol were not significant. Conclusions: Statins affect Lp(a) levels differently in patients with dyslipidemia depending on the apo(a) phenotype. Statins increase Lp(a) levels exclusively in patients with the LMW apo(a) phenotype

Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies.

OBJECTIVE: Whole-grain foods are touted for multiple health benefits, including enhancing insulin sensitivity and reducing type 2 diabetes risk. Recent genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with fasting glucose and insulin concentrations in individuals free of diabetes. We tested the hypothesis that whole-grain food intake and genetic variation interact to influence concentrations of fasting glucose and insulin. RESEARCH DESIGN AND METHODS: Via meta-analysis of data from 14 cohorts comprising ∼ 48,000 participants of European descent, we studied interactions of whole-grain intake with loci previously associated in GWAS with fasting glucose (16 loci) and/or insulin (2 loci) concentrations. For tests of interaction, we considered a P value <0.0028 (0.05 of 18 tests) as statistically significant. RESULTS: Greater whole-grain food intake was associated with lower fasting glucose and insulin concentrations independent of demographics, other dietary and lifestyle factors, and BMI (β [95% CI] per 1-serving-greater whole-grain intake: -0.009 mmol/l glucose [-0.013 to -0.005], P < 0.0001 and -0.011 pmol/l [ln] insulin [-0.015 to -0.007], P = 0.0003). No interactions met our multiple testing-adjusted statistical significance threshold. The strongest SNP interaction with whole-grain intake was rs780094 (GCKR) for fasting insulin (P = 0.006), where greater whole-grain intake was associated with a smaller reduction in fasting insulin concentrations in those with the insulin-raising allele. CONCLUSIONS: Our results support the favorable association of whole-grain intake with fasting glucose and insulin and suggest a potential interaction between variation in GCKR and whole-grain intake in influencing fasting insulin concentrations