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BACKGROUND: The insulin-like growth factor (IGF) axis emerged as an important pathway in heart failure with preserved ejection (HFpEF). We aimed to identify IGF phenotypes associated with HFpEF in the context of high-dimensional proteomic profiling. METHODS: From the INtermountain Healthcare Biological Samples Collection Project and Investigational REgistry for the On-going Study of Disease Origin, Progression and Treatment (Intermountain INSPIRE Registry), we identified 96 patients with HFpEF and matched controls. We performed targeted proteomics, including IGF-1,2, IGF binding proteins (IGFBP) 1-7 and 111 other proteins (EMD Millipore and ELISA). We used partial least square discriminant analysis (PLS-DA) to identify a set of proteins associated with prevalent HFpEF, pulmonary hypertension and 5-year all-cause mortality. K-mean clustering was used to identify IGF phenotypes. RESULTS: Patients with HFpEF had a high prevalence of systemic hypertension (95%) and coronary artery disease (74%). Using PLS-DA, we identified a set of biomarkers, including IGF1,2 and IGFBP 1,2,7, that provided a strong discrimination of HFpEF, pulmonary hypertension and mortality with an area under the curve of 0.91, 0.77 and 0.83, respectively. Using K mean clustering, we identified 3 IGF phenotypes that were independently associated with all-cause 5-year mortality after adjustment for age, NT-proBNP and kidney disease (P = 0.004). Multivariable analysis validated the prognostic value of IGFBP-1 and 2 in the CATHeterization GENetics (CATHGEN) biorepository. CONCLUSION: IGF phenotypes were associated with pulmonary hypertension and mortality in HFpEF. ispartof: JOURNAL OF CARDIAC FAILURE vol:28 issue:6 pages:935-946 ispartof: location:United States status: published

AIMS: Mitral valve prolapse (MVP) is a common valvular heart disease with a prevalence of >2% in the general adult population. Despite this high incidence, there is a limited understanding of the molecular mechanism of this disease, and no medical therapy is available for this disease. We aimed to elucidate the genetic basis of MVP in order to better understand this complex disorder.METHODS AND RESULTS: We performed a meta-analysis of six genome-wide association studies that included 4884 cases and 434 649 controls. We identified 14 loci associated with MVP in our primary analysis and 2 additional loci associated with a subset of the samples that additionally underwent mitral valve surgery. Integration of epigenetic, transcriptional, and proteomic data identified candidate MVP genes including LMCD1, SPTBN1, LTBP2, TGFB2, NMB, and ALPK3. We created a polygenic risk score (PRS) for MVP and showed an improved MVP risk prediction beyond age, sex, and clinical risk factors.CONCLUSION: We identified 14 genetic loci that are associated with MVP. Multiple analyses identified candidate genes including two transforming growth factor-β signalling molecules and spectrin β. We present the first PRS for MVP that could eventually aid risk stratification of patients for MVP screening in a clinical setting. These findings advance our understanding of this common valvular heart disease and may reveal novel therapeutic targets for intervention.KEY QUESTION: Expand our understanding of the genetic basis for mitral valve prolapse (MVP). Uncover relevant pathways and target genes for MVP pathophysiology. Leverage genetic data for MVP risk prediction.KEY FINDING: Sixteen genetic loci were significantly associated with MVP, including 13 novel loci. Interesting target genes at these loci included LTBP2, TGFB2, ALKP3, BAG3, RBM20, and SPTBN1. A risk score including clinical factors and a polygenic risk score, performed best at predicting MVP, with an area under the receiver operating characteristics curve of 0.677.TAKE-HOME MESSAGE: Mitral valve prolapse has a polygenic basis: many genetic variants cumulatively influence pre-disposition for disease. Disease risk may be modulated via changes to transforming growth factor-β signalling, the cytoskeleton, as well as cardiomyopathy pathways. Polygenic risk scores could enhance the MVP risk prediction.

Additional file 1: Supplementary Figures. Figure S1 to S7. Study design, QQ plots, Manhattan plots, forest plots and eFORGE/FORGE2 analyses.

Metabolic health state is associated with major adverse cardiovascular events (MACE). However, whether metabolic health states differ in coronary artery disease (CAD) phenotype and whether this association is modified by obesity is unknown. We included 4,381 stable chest pain patients, who

DNA methylation age (DNAm age) has become a widely utilized epigenetic biomarker for the aging process. The Horvath method for determining DNAm age is perhaps the most widely utilized and validated DNA methylation age assessment measure. Horvath DNAm age is calculated based on methylation measurements at 353 loci which were present on Illumina’s 450k and 27k DNA methylation microarrays. With the increasing use of the more recently developed Illumina MethylationEPIC (850k) microarray, it is worth revisiting this widely used aging measure to evaluate differences in DNA methylation age estimation based on array design. Of the requisite 353 loci, 17 are missing from the current 850k microarray. Using 17 datasets with 27k, 450k, and/or 850k methylation data, we calculated and compared each sample’s epigenetic age estimated from all 353 loci required from the Horvath DNAm age calculator (full), and using only the 336 loci present on the 27k, 450k, and 850k arrays (reduced). In 450k/27k data, missing loci caused underestimation of epigenetic age when compared with the full clock. Underestimation of full epigenetic age grew from ages 0 to ~20, remaining stable thereafter (mean= −3.46 y, SD=1.13) years for individuals ≥20 years. Underestimation of DNAm age by the reduced 450k/27k data was similar to the underestimation observed in the 850k data indicating that array differences in DNAm age estimation are primarily driven by missing probes. Correlations between age and DNAm age were not dependent on missing probes or on array designs and consequently associations between DNAm age and outcomes such as sex remained the same independent of missing probes and probe design. In conclusion, DNAm age estimations are array dependent driven by missing probes between arrays. Though correlations and associations with DNAm age may remain the same, researchers should exercise caution when interpreting results based on absolute differences in DNAm age or when mixing samples assayed on different arrays.DisclaimerThis paper has been reviewed by the National Health and Environmental Effects Research Laboratory, U.S. EPA, and is approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. The authors declare they have no competing financial interests.

Objective: Two alleles in the apoliprotein L1 ( APOL1 ) gene confer increased risk of chronic kidney disease (CKD) in African Americans. However, harboring these alleles is not sufficient to cause CKD. This study sought to examine clinical modifiers of kidney disease progression in APOL1 homozygote African Americans in a large cardiovascular cohort. Methods: We sequenced APOL1 in 1733 African Americans (by self-report) enrolled in the CATHGEN biorepository, which consists of sequential individuals referred for cardiac catheterization at Duke University Hospital from 2001-2010. The genotype distribution was: n = 1493 non-carriers and heterozygotes, and n = 240 homozygotes. Analysis, with a multivariable logistic regression model, was restricted to homozygotes with an eGFR ≥60 mL/min at baseline (64.6%, n = 155). The main outcome measure was development of incident CKD, defined as at least two subsequent eGFR < 60 separated by at least 3 months. Potential clinical modifiers examined included age, sex, hypertension, type 2 diabetes status, smoking, HDL-C, LDL-C, ejection fraction and number of diseased coronary arteries. Results: The median age for homozygotes was 55.6 years (25th/75th: 47, 64) and 56% (n = 135) were male. During follow-up, 45.3% of homozygotes with an eGFR ≥60 at baseline developed incident CKD. For individuals without type 2 diabetes on baseline, 38.8% (n = 26) developed incident CKD; 56.1% (n = 23) individuals with baseline type 2 diabetes developed incident CKD (p = 0.08). Of clinical variables examined, only presence of type 2 diabetes at baseline was a significant predictor of developing CKD (univariate odds ratio 1.7, 95% CI 1.3-2.3, p = 0.0002), and remained significant after adjusting for the other covariables (OR 2.2, 95% CI 1.4 - 3.7, p=0.001). Conclusions: A large proportion of APOL1 homozygote African Americans with preserved kidney function at baseline enrollment in a large cardiovascular study developed incident CKD. Type 2 diabetes status was the only significant independent predictor for incident CKD in these individuals. These results highlight the potential need for greater diabetes-related efforts to prevent CKD progression in African Americans already at high-risk for adverse cardiovascular outcomes.

Although the presentation and pathobiology of acute coronary syndromes differ significantly by sex, there have been few studies of sex differences in stable symptomatic outpatients with suspected coronary artery disease (CAD). We compared demographics, risk profiles, presentation and provider’s

Additional file 2: Supplementary Methods. Description of different constituent cohorts and studies, in addition to power analyses.

Additional file 2: Supplementary Methods. Description of different constituent cohorts and studies, in addition to power analyses.