1,721,185 research outputs found
Current Insights into the Joint Genetic Basis of Type 2 Diabetes and Coronary Heart Disease. Curr Cardiovasc Risk Rep. 2014 Jan 1;8(1):368.
No full textThe large-scale genome-wide association studies conducted so far identified numerous allelic variants associated with type 2 diabetes (T2D), coronary heart disease (CHD) and related cardiometabolic traits. Many T2D- and some CHD-risk loci are also linked with metabolic traits that are hallmarks of insulin resistance (lipid profile, abdominal adiposity). Chromosome 9p21.3 and 2q36.3 are the most consistently replicated loci appearing to share genetic risk for both T2D and CHD. Although many glucose- or insulin-related trait variants are also linked with T2D risk, none of them is associated with CHD. Hence, while T2D and CHD are strongly clinically linked together, further ongoing analyses are needed to clarify the existence of a shared underlying genetic signature of these complex traits. The present review summarizes an updated picture of T2D-CHD genetics as of 2013, aiming to provide a platform for targeted studies dissecting the contribution of genetics to the phenotypic heterogeneity of T2D and CHD
Response to comment on Vassy et al. polygenic type 2 diabetes prediction at the limit of common variant detection. Diabetes 2014;63:2172-2182.
Full text linkAbbasi et al. (1) raise excellent points about the current and future states of type 2 diabetes risk prediction. Two issues in particular are worth consideration.
First, our clinical and polygenic prediction models do not include time-varying assessments of known risk factors such as BMI and fasting glucose (2). Abbasi et al. are correct that doing so would likely improve the models’ predictive accuracy. Instead, we patterned our models on what is more common in clinical practice. In many ways, the Framingham Heart Study cardiovascular disease risk score defines the paradigm of using a “snapshot in time” approach to risk assessment. That is, what can the characteristics of a patient sitting in front of the clinician tell him or her about that patient’s risk of an outcome 10 years from now? The dynamic risk factors Abbasi et al. propose will be especially salient if clinicians increasingly incorporate risk factor trajectories into their clinical decision making.
Second, their tiered approach to risk stratification (i.e., obtaining more resource-intensive information only among those individuals whose history suggests higher risk) places an appropriate emphasis on the risks, benefits, and costs of screening. We agree with their call for an evaluation of such screening strategies, although we would argue that anthropometry and basic laboratory analyses are already routinely measured in the many clinical settings. An interesting question, then, is whether collection of genome-wide data will be increasingly routine in the clinical setting or even brought by the patients themselves after consulting genotyping services outside of the standard clinical setting. We think our analyses show that even if each individual had his or her genotype for common genetic variation stored in the electronic medical record, its marginal value in diabetes risk prediction would be small. Whether more sophisticated genetic information available soon from high-throughput whole-genome sequencing with detailed functional annotation will improve type 2 diabetes risk prediction, drug targeting, or patient care overall remains an important question for the future
Impact of Type 2 diabetes susceptibility loci on variation in multiple cardio-metabolic traits
No full textAims: Type 2 diabetes (T2D) is related to pathophysiological changes in metabolic and cardio-vascular traits. We aimed to uncover the mechanistic basis of T2D associations by exploring the pleiotropic effects of T2D risk variants on multiple cardio-metabolic traits.
Methods: We evaluated the effects of 65 established T2D-associated common genetic variants (Sept 2012) on 22 quantitative anthropometric, glycaemic, lipid, blood pressure, obesity, fat distribution and hypertension traits. We analysed the multi-trait effects using two cluster analysis methods, k-means clustering and complete hierarchical agglomerative clustering. Clustering identified groups of loci with shared genetic effects on cardio-metabolic traits. We compared genetic associations with known epidemiological correlations.
Results: Complete hierarchical cluster analysis grouped 65 T2D loci into five major clusters based on patterns of their associations with 24 cardio-metabolic traits. T2D risk-variants near GCKR and CILP2 were associated with lower LDL-cholesterol, total cholesterol and triglycerides, whilst those at FTO and MC4R were correlated with obesity-related traits. The group including ARAP1, GCK and MTNR1B were related to hyperglycaemia and decreased beta-cell function. K-means clustering distinguished two additional sub-groups of loci: (I) GRB14, IRS1, PPARG1, KLF14 and ADAMTS9; and (II) CDKAL1, ADCY5 and SLC30A8. In both sub-groups, the T2D risk-alleles were associated with “leanness” via an impaired metabolic profile. However, only in the second sub-group were T2D risk-alleles also associated with decreased beta-cell function. All other loci showed no clear-cut cardio-metabolic trait associations.
Conclusions: Our findings indicate that T2D susceptibility variants exert their effects on multiple cardio-metabolic traits through a variety of mechanisms
Prevalence and correlates of post-prandial hyperglycaemia in a large sample of patients with type 2 diabetes mellitus.
No full textAims/hypothesis: Post-prandial glucose may be a risk factor for cardiovascular disease and chronic diabetic complications. We tested the hypothesis that post-prandial hyperglycaemia is common in type 2 diabetes, even among patients in apparently good glycaemic control, and that simple clinical characteristics identify subsets of diabetic patients with frequent post-prandial hyperglycaemia.
Subjects and methods: Three self-assessed daily blood glucose profiles over a 1-week period, including 18 glucose readings before and 2 h after meals, were obtained from 3,284 unselected outpatients (men 51%; age 63±10 years) with non-insulin-treated type 2 diabetes mellitus attending 500 different diabetes clinics operating throughout Italy.
Results: A post-prandial blood glucose value >8.89 mmol/l
(160 mg/dl) was recorded at least once in 84% of patients,
and 81% of patients had at least one Δglucose ≥2.22 mmol/l
(40 mg/dl). Among patients with apparently good metabolic
control, 38% had >40% of post-prandial blood glucose readings >8.89 mmol/l (≥4 of 9 meals in total), and 36% had >40% Δglucose ≥2.22 mmol/l. In multivariate analysis adjusted for pre-prandial glucose levels, older age, longer duration of diabetes, absence of obesity, hyperlipidaemia and hypertension, as well as treatment with sulfonylureas, were significantly associated with greater glucose excursions after meals.
Conclusions/interpretation: These results indicate that post-prandial hyperglycaemia is a very frequent phenomenon in patients with type 2 diabetes mellitus on active treatment; can occur even when metabolic control is apparently good; and can be predicted by simple clinical features
Dissecting the pleiotropic effects of established type 2 diabetes and other cardiometabolic trait loci to define pathways and gene networks involved in type 2 diabetes pathogenesis
No full textBackground and aims: Recent genome-wide association studies (GWAS) for human complex phenotypes have identified hundreds of genetic variants for cardio-metabolic traits and risk of disease. At many loci or specific variants associations are observed with multiple epidemiologically correlated traits. We formed the Cross-Consortia Pleiotropy Group to investigate the patterns of multi-cardio-metabolic trait associations across the genome. We aimed (a) to examine the associations of cardio-metabolic trait loci with epidemiologically correlated traits by grouping shared patterns of individual trait effects; (b) to define pathway and gene networks involved in the trait variability within the association pattern groups.
Materials and methods: We evaluated the genetic effects of 544 independent variants (r2<0.8) from a total of 687 SNPs from published GWAS meta-analyses (thru Sep 2012) of 20 quantitative cardio-metabolic traits, including systolic/diastolic blood pressure, 8 glycaemic, 6 obesity/anthropometric, 4 lipid traits, and 2 diseases (Type 2 Diabetes (T2D), hypertension). We applied a complete hierarchical cluster analysis, which grouped variants according to their impact on the cardio-metabolic traits. We combined these data with annotated pathways, protein-protein interactions and semantic relationships from the published literature using GRAIL and DAPPLE software tools, which estimated the significance of connections between putative genes
Impact of Reference Category and Number of Traits in the Cluster on Risk of Coronary Heart Disease in Metabolic Syndrome: Prospective Data from the Bruneck Study.
No full textInsulin resistance as estimated by homeostasis model assessment predicts incident symptomatic cardiovascular disease in caucasian subjects from the general population.
No full textOBJECTIVE— The purpose of this study was to evaluate whether insulin resistance is associated to cardiovascular disease (CVD) and to understand whether this association can be
explained by traditional and novel CVD risk factors associated with this metabolic disorder.
RESEARCH DESIGN AND METHODS— We examined a sample representative of the population of Bruneck, Italy (n=919; aged 40–79 years). Insulin-resistant subjects were those with a score in the top quartile of the homeostasis model assessment (HOMA) for insulin resistance (HOMA-IR). Risk factors correlated with insulin resistance included BMI, A1C, HDL cholesterol, triglycerides, blood pressure, high-sensitivity C-reactive protein (hsCRP), fibrinogen, oxidized LDL, vascular cell adhesion molecule-1 (VCAM-1), and adiponectin. Subjects without CVD at baseline were followed up for 15 years for incident CVD, a composite end point including fatal and nonfatal myocardial infarction and stroke, transient ischemic attack, and any
revascularization procedure.
RESULTS— During follow-up, 118 subjects experienced a first symptomatic CVD event. Levels of HOMA-IR were higher at baseline among subjects who developed CVD (2.8) compared
with those remaining free of CVD (2.5) (P < 0.05). Levels of HOMA-IR also were significantly correlated (P< 0.05) with most CVD risk factors we evaluated. In Cox proportional hazard
models, insulin-resistant subjects had an age-, sex-, and smoking-adjusted 2.1-fold increased risk (95% CI 1.3–3.1) of incident symptomatic CVD relative to non–insulin-resistant subjects. After sequential adjustment for physical activity and classic risk factors (A1C, LDL cholesterol, and hypertension) as well as BMI, HDL cholesterol, triglycerides, and novel risk factors, including fibrinogen, oxidized LDL, hsCRP, VCAM-1, and adiponectin, the association between HOMA-IR and incident CVD remained significant and virtually unchanged (hazard ratio 2.2
[95% CI 1.4 –3.6], P < 0.001).
CONCLUSIONS— HOMA-estimated insulin resistance is associated with subsequent symptomatic CVD in the general population independently of all classic and several nontraditional
risk factors. These data suggest that insulin resistance may be an important target to reduce CVD risk
Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes.
No full textOBJECTIVE:
A genetic risk score (GRS) comprised of single nucleotide polymorphisms (SNPs) and metabolite biomarkers have each been shown, separately, to predict incident type 2 diabetes. We tested whether genetic and metabolite markers provide complementary information for type 2 diabetes prediction and, together, improve the accuracy of prediction models containing clinical traits.
RESEARCH DESIGN AND METHODS:
Diabetes risk was modeled with a 62-SNP GRS, nine metabolites, and clinical traits. We fit age- and sex-adjusted logistic regression models to test the association of these sources of information, separately and jointly, with incident type 2 diabetes among 1,622 initially nondiabetic participants from the Framingham Offspring Study. The predictive capacity of each model was assessed by area under the curve (AUC).
RESULTS:
Two hundred and six new diabetes cases were observed during 13.5 years of follow-up. The AUC was greater for the model containing the GRS and metabolite measurements together versus GRS or metabolites alone (0.820 vs. 0.641, P < 0.0001, or 0.820 vs. 0.803, P = 0.01, respectively). Odds ratios for association of GRS or metabolites with type 2 diabetes were not attenuated in the combined model. The AUC was greater for the model containing the GRS, metabolites, and clinical traits versus clinical traits only (0.880 vs. 0.856, P = 0.002).
CONCLUSIONS:
Metabolite and genetic traits provide complementary information to each other for the prediction of future type 2 diabetes. These novel markers of diabetes risk modestly improve the predictive accuracy of incident type 2 diabetes based only on traditional clinical risk factors
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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