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[...]in the Multi-Ethnic Study of Atherosclerosis, diabetes was among the risk factors that were associated with risk overestimation in a single-variable analysis.2 Since the early 2000s, the New Zealand Ministry of Health has recommended the use of cardiovascular disease risk prediction to inform preventive treatment decisions, and that recommendation resulted in an increase in screening for diabetes status. [...]in 2012, only 50% of eligible adults had been screened for diabetes. [...]New Zealand created a new national initiative to increase screening for diabetes in the eligible population, which replaced fasting blood glucose with non-fasting glycated haemoglobin (HbA1c) as the recommended screening test. Global diabetes prevalence is increasing from the rise in obesity, unhealthy diets, and widespread physical inactivity due in part to growing urbanisation and its impact on lifestyle factors.4 This increasing prevalence, in addition to the adoption of cardiovascular disease risk estimation by many countries that require screening for diabetes, will increase the heterogeneity of the cardiovascular disease risk profile among individuals with diabetes. Since many primary prevention guidelines from national organisations around the world consider patients with diabetes at high cardiovascular risk, these guidelines require re-examination when diabetes screening levels are high.

Nomograms to predict normal aortic root diameter for body surface area (BSA) in broad ranges of age have been widely used but are limited by lack of consideration of gender effects, jumps in upper limits of aortic diameter among age strata, and data from older teenagers. Sinus of Valsalva diameter was measured by American Society of Echocardiography convention in normal-weight, nonhypertensive, nondiabetic subjects ≥15 years old without aortic valve disease from clinical or population-based samples. Analyses of covariance and linear regression with assessment of residuals identified determinants and developed predictive models for normal aortic root diameter. In 1,207 apparently normal subjects ≥15 years old (54% women), aortic root diameter was 2.1 to 4.3 cm. Aortic root diameter was strongly related to BSA and height (r = 0.48 for the 2 comparisons), age (r = 0.36), and male gender (+2.7 mm adjusted for BSA and age, p <0.001 for all comparisons). Multivariable equations using age, gender, and BSA or height predicted aortic diameter strongly (R = 0.674 for the 2 comparisons, p <0.001) with minimal relation of residuals to age or body size: for BSA 2.423 + (age [years] × 0.009) + (BSA [square meters] × 0.461) − (gender [1 = man, 2 = woman] × 0.267), SEE 0.261 cm; for height 1.519 + (age [years] × 0.010) + (height [centimeters] × 0.010) − (gender [1 = man, 2 = woman] × 0.247), SEE 0.215 cm. In conclusion, aortic root diameter is larger in men and increases with body size and age. Regression models incorporating body size, age, and gender are applicable to adolescents and adults without limitations of previous nomograms.

The link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain. We conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination. We present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases.

There has been great interest in genetic risk prediction using risk scores in recent years, however, the utility of scores developed in European populations and later applied to non-European populations has not been successful. The goal of this study was to create a methylation risk score (MRS) for metabolic syndrome (MetS), demonstrating the utility of MRS across race groups using cross-sectional data from the Hypertension Genetic Epidemiology Network (HyperGEN, N = 614 African Americans (AA)) and the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN, N = 995 European Americans (EA)). To demonstrate this, we first selected cytosine-guanine dinucleotides (CpG) sites measured on Illumina Methyl450 arrays previously reported to be significantly associated with MetS and/or component conditions in more than one race/ethnic group ( CPT1A cg00574958, PHOSPHO1 cg02650017, ABCG1 cg06500161, SREBF1 cg11024682, SOCS3 cg18181703, TXNIP cg19693031). Second, we calculated the parameter estimates for the 6 CpGs in the HyperGEN data (AA) and used the beta estimates as weights to construct a MRS in HyperGEN (AA), which was validated in GOLDN (EA). We performed association analyses using logistic mixed models to test the association between the MRS and MetS, adjusting for covariates. Results showed the MRS was significantly associated with MetS in both populations. In summary, a MRS for MetS was a strong predictor for the condition across two race groups, suggesting MRS may be useful to examine metabolic disease risk or related complications across race/ethnic groups.

Zhiwu Zhang and colleagues report a mixed linear model approach for correcting for population structure and family relatedness in genome-wide association studies. Mixed linear model (MLM) methods have proven useful in controlling for population structure and relatedness within genome-wide association studies. However, MLM-based methods can be computationally challenging for large datasets. We report a compression approach, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups. We also present a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components. We applied these two methods both independently and combined in selected genetic association datasets from human, dog and maize. The joint implementation of these two methods markedly reduced computing time and either maintained or improved statistical power. We used simulations to demonstrate the usefulness in controlling for substructure in genetic association datasets for a range of species and genetic architectures. We have made these methods available within an implementation of the software program TASSEL.

Background Developing an understanding of the biochemistry of aging in both sexes is critical for managing disease throughout the lifespan. Lipidomic associations with age and sex have been reported, but prior studies are limited by measurements in serum rather than plasma or by participants taking lipid-lowering medications. Methods Our study included lipidomic data from 980 participants aged 18–87 years old from the Genetics of Lipid-Lowering Drugs and Diet Network (GOLDN). Participants were off lipid-lowering medications for at least 4 weeks, and signal intensities of 413 known lipid species were measured in plasma. We examined linear age and sex associations with signal intensity of (a) 413 lipid species; (b) 6 lipid classes (glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, fatty acids, and acylcarnitines); and (c) 15 lipid subclasses; as well as with the particle sizes of three lipoproteins. Results Significant age associations were identified in 4 classes, 11 subclasses, 147 species, and particle size of one lipoprotein while significant sex differences were identified in 5 classes, 12 subclasses, 248 species, and particle sizes of two lipoproteins. For many lipid species ( n  = 97), age-related associations were significantly different between males and females. Age*sex interaction effects were most prevalent among phosphatidylcholines, sphingomyelins, and triglycerides. Conclusion We identified several lipid species, subclasses, and classes that differ by age and sex; these lipid phenotypes may serve as useful biomarkers for lipid changes and associated cardiovascular risk with aging in the future. Future studies of age-related changes throughout the adult lifespan of both sexes are warranted. Trial registration ClinicalTrials.gov NCT00083369 ; May 21, 2004.

Background Whole transcriptome sequencing (RNA-seq) represents a powerful approach for whole transcriptome gene expression analysis. However, RNA-seq carries a few limitations, e.g., the requirement of a significant amount of input RNA and complications led by non-specific mapping of short reads. The Ion AmpliSeq™ Transcriptome Human Gene Expression Kit (AmpliSeq) was recently introduced by Life Technologies as a whole-transcriptome, targeted gene quantification kit to overcome these limitations of RNA-seq. To assess the performance of this new methodology, we performed a comprehensive comparison of AmpliSeq with RNA-seq using two well-established next-generation sequencing platforms (Illumina HiSeq and Ion Torrent Proton). We analyzed standard reference RNA samples and RNA samples obtained from human induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs). Results Using published data from two standard RNA reference samples, we observed a strong concordance of log2 fold change for all genes when comparing AmpliSeq to Illumina HiSeq (Pearson’s r  = 0.92) and Ion Torrent Proton (Pearson’s r  = 0.92). We used ROC, Matthew’s correlation coefficient and RMSD to determine the overall performance characteristics. All three statistical methods demonstrate AmpliSeq as a highly accurate method for differential gene expression analysis. Additionally, for genes with high abundance, AmpliSeq outperforms the two RNA-seq methods. When analyzing four closely related hiPSC-CM lines, we show that both AmpliSeq and RNA-seq capture similar global gene expression patterns consistent with known sources of variations. Conclusions Our study indicates that AmpliSeq excels in the limiting areas of RNA-seq for gene expression quantification analysis. Thus, AmpliSeq stands as a very sensitive and cost-effective approach for very large scale gene expression analysis and mRNA marker screening with high accuracy.

In this study, we conducted an epigenome-wide association study of metabolic syndrome (MetS) among 846 participants of European descent in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN). DNA was isolated from CD4+ T cells and methylation at ~470,000 cytosine-phosphate-guanine dinucleotide (CpG) pairs was assayed using the Illumina Infinium HumanMethylation450 BeadChip. We modeled the percentage methylation at individual CpGs as a function of MetS using linear mixed models. A Bonferroni-corrected P-value of 1.1 x 10(-7) was considered significant. Methylation at two CpG sites in CPT1A on chromosome 11 was significantly associated with MetS (P for cg00574958 = 2.6x10(-14) and P for cg17058475 = 1.2x10(-9)). Significant associations were replicated in both European and African ancestry participants of the Bogalusa Heart Study. Our findings suggest that methylation in CPT1A is a promising epigenetic marker for MetS risk which could become useful as a treatment target in the future.

Cardiac hypertrophy is an independent risk factor for cardiovascular disease and heart failure. There is increasing evidence that microRNAs (miRNAs) play an important role in the regulation of messenger RNA (mRNA) and the pathogenesis of various cardiovascular diseases. However, the ability to comprehensively study cardiac hypertrophy on a gene regulatory level is impacted by the limited availability of human cardiomyocytes. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) offer the opportunity for disease modeling. Here we utilize a previously established in vitro model of cardiac hypertrophy to interrogate the regulatory mechanism associated with the cardiac disease process. We perform miRNA sequencing and mRNA expression analysis on endothelin 1 (ET-1) stimulated hiPSC-CMs to describe associated RNA expression profiles. MicroRNA sequencing revealed over 250 known and 34 predicted novel miRNAs to be differentially expressed between ET-1 stimulated and unstimulated control hiPSC-CMs. Messenger RNA expression analysis identified 731 probe sets with significant differential expression. Computational target prediction on significant differentially expressed miRNAs and mRNAs identified nearly 2000 target pairs. A principal component analysis approach comparing the in vitro data with human myocardial biopsies detected overlapping expression changes between the in vitro samples and myocardial biopsies with Left Ventricular Hypertrophy. These results provide further insights into the complex RNA regulatory mechanism associated with cardiac hypertrophy.

Long-chain n-3 polyunsaturated fatty acids (PUFAs) can derive from diet or from α-linolenic acid (ALA) by elongation and desaturation. We investigated the association of common genetic variation with plasma phospholipid levels of the four major n-3 PUFAs by performing genome-wide association studies in five population-based cohorts comprising 8,866 subjects of European ancestry. Minor alleles of SNPs in FADS1 and FADS2 (desaturases) were associated with higher levels of ALA (p = 3 x 10⁻⁶⁴) and lower levels of eicosapentaenoic acid (EPA, p = 5 x 10⁻⁵⁸) and docosapentaenoic acid (DPA, p = 4 x 10⁻¹⁵⁴). Minor alleles of SNPs in ELOVL2 (elongase) were associated with higher EPA (p = 2 x 10⁻¹²) and DPA (p = 1 x 10⁻⁴³) and lower docosahexaenoic acid (DHA, p = 1 x 10⁻¹⁵). In addition to genes in the n-3 pathway, we identified a novel association of DPA with several SNPs in GCKR (glucokinase regulator, p = 1 x 10⁻⁸). We observed a weaker association between ALA and EPA among carriers of the minor allele of a representative SNP in FADS2 (rs1535), suggesting a lower rate of ALA-to-EPA conversion in these subjects. In samples of African, Chinese, and Hispanic ancestry, associations of n-3 PUFAs were similar with a representative SNP in FADS1 but less consistent with a representative SNP in ELOVL2. Our findings show that common variation in n-3 metabolic pathway genes and in GCKR influences plasma phospholipid levels of n-3 PUFAs in populations of European ancestry and, for FADS1, in other ancestries.