Progress in genetic association studies of plasma lipids.

This review summarizes recently published large-scale efforts elucidating the genetic architecture of lipid levels. A supplemental file with all genetic loci is provided for research purposes and we performed bioinformatic analyses of the genetic variants to give an oversight of involved pathways

Life-long tailoring of management for patients with hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is the most common genetic heart disease, characterised by complex pathophysiology and extensive genetic and clinical heterogeneity. In most patients, HCM is caused by mutations in cardiac sarcomere protein genes and inherited as an autosomal dominant trait. The clinical phenotype ranges from severe presentations at a young age to lack of left ventricular hypertrophy in genotype-positive individuals. No preventative treatment is available as the sequence and causality of the pathomechanisms that initiate and exacerbate HCM are unknown. Sudden cardiac death and end-stage heart failure are devastating expressions of this disease. Contemporary management including surgical myectomy and implantable cardiac defibrillators has shown significant impact on long-term prognosis. However, timely recognition of specific scenarios – including transition to the end-stage phase – may be challenging due to limited awareness of the progression patterns of HCM. This in turn may lead to missed therapeutic opportunities. To illustrate these difficulties, we describe two HCM patients who progressed from the typical hyperdynamic stage of asymmetric septal thickening to end-stage heart failure with severely reduced ejection fraction. We highlight the different stages of this complex inherited cardiomyopathy based on the clinical staging pro-posed by Olivotto and colleagues. In this way, we aim to provide a practical guide for clinicians and hope to increase awareness for this common form of cardiac disease

Multimorbidity in Heart Failure: Leveraging Cluster Analysis to Guide Tailored Treatment Strategies

REVIEW PURPOSE: This review summarises key findings on treatment effects within phenotypical clusters of patients with heart failure (HF), making a distinction between patients with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF). FINDINGS: Treatment response differed among clusters; ACE inhibitors were beneficial in all HFrEF phenotypes, while only some studies show similar beneficial prognostic effects in HFpEF patients. Beta-blockers had favourable effects in all HFrEF patients but not in HFpEF phenotypes and tended to worsen prognosis in older, cardiorenal patients. Mineralocorticoid receptor antagonists had more favourable prognostic effects in young, obese males and metabolic HFpEF patients. While a phenotype-guided approach is a promising solution for individualised treatment strategies, there are several aspects that still require improvements before such an approach could be implemented in clinical practice. SUMMARY: Stronger evidence from clinical trials and real-world data may assist in establishing a phenotype-guided treatment approach for patient with HF in the future

Polygenic risk scores for coronary artery disease and subsequent event risk amongst established cases

BACKGROUND: There is growing evidence that polygenic risk scores (PRS) can identify individuals with elevated lifetime risk of coronary artery disease (CAD). Whether they can also be used to stratify risk of subsequent events among those surviving a first CAD event remains uncertain, with possible biological differences between CAD onset and progression, and the potential for index event bias. METHODS: Using two baseline subsamples of UK Biobank; prevalent CAD cases (N = 10 287) and individuals without CAD (N = 393 108), we evaluated associations between a CAD PRS and incident cardiovascular and fatal outcomes. RESULTS: A 1 S.D. higher PRS was associated with increased risk of incident MI in participants without CAD (OR 1.33; 95% C.I. 1.29, 1.38), but the effect estimate was markedly attenuated in those with prevalent CAD (OR 1.15; 95% C.I. 1.06, 1.25); heterogeneity P = 0.0012. Additionally, among prevalent CAD cases, we found evidence of an inverse association between the CAD PRS and risk of all-cause death (OR 0.91; 95% C.I. 0.85, 0.98) compared to those without CAD (OR 1.01; 95% C.I. 0.99, 1.03); heterogeneity P = 0.0041. A similar inverse association was found for ischaemic stroke (Prevalent CAD (OR 0.78; 95% C.I. 0.67, 0.90); without CAD (OR 1.09; 95% C.I. 1.04, 1.15), heterogeneity P < 0.001). CONCLUSIONS: Bias induced by case stratification and survival into UK Biobank may distort associations of polygenic risk scores derived from case-control studies or populations initially free of disease. Differentiating between effects of possible biases and genuine biological heterogeneity is a major challenge in disease progression research

Фінанси санаторно-курортних підприємств України

Метою дослідження є аналіз стану санаторно-курортної сфери України як на рівні галузі, так і на рівні окремого санаторно-курортного підприємства

Unraveling the relationships between alpha- and beta-adrenergic modulation and the risk of heart failure

Background: The effects of α and ß adrenergic receptor modulation on the risk of developing heart failure (HF) remains uncertain due to a lack of randomized controlled trials. This study aimed to estimate the effects of α and ß adrenergic receptors modulation on the risk of HF and to provide proof of principle for genetic target validation studies in HF. Methods: Genetic variants within the cis regions encoding the adrenergic receptors α1A, α2B, ß1, and ß2 associated with blood pressure in a 757,601-participant genome-wide association study (GWAS) were selected as instruments to perform a drug target Mendelian randomization study. Effects of these variants on HF risk were derived from the HERMES GWAS (542,362 controls; 40,805 HF cases). Results: Lower α1A or ß1 activity was associated with reduced HF risk: odds ratio (OR) 0.83 (95% CI 0.74–0.93, P = 0.001) and 0.95 (95% CI 0.93–0.97, P = 8 × 10−6). Conversely, lower α2B activity was associated with increased HF risk: OR 1.09 (95% CI 1.05–1.12, P = 3 × 10−7). No evidence of an effect of lower ß2 activity on HF risk was found: OR 0.99 (95% CI 0.92–1.07, P = 0.95). Complementary analyses showed that these effects were consistent with those on left ventricular dimensions and acted independently of any potential effect on coronary artery disease. Conclusions: This study provides genetic evidence that α1A or ß1 receptor inhibition will likely decrease HF risk, while lower α2B activity may increase this risk. Genetic variant analysis can assist with drug development for HF prevention

Automatic Identification of Patients With Unexplained Left Ventricular Hypertrophy in Electronic Health Record Data to Improve Targeted Treatment and Family Screening

Background: Unexplained Left Ventricular Hypertrophy (ULVH) may be caused by genetic and non-genetic etiologies (e.g., sarcomere variants, cardiac amyloid, or Anderson-Fabry's disease). Identification of ULVH patients allows for early targeted treatment and family screening. Aim: To automatically identify patients with ULVH in electronic health record (EHR) data using two computer methods: text-mining and machine learning (ML). Methods: Adults with echocardiographic measurement of interventricular septum thickness (IVSt) were included. A text-mining algorithm was developed to identify patients with ULVH. An ML algorithm including a variety of clinical, ECG and echocardiographic data was trained and tested in an 80/20% split. Clinical diagnosis of ULVH was considered the gold standard. Misclassifications were reviewed by an experienced cardiologist. Sensitivity, specificity, positive, and negative likelihood ratios (LHR+ and LHR-) of both text-mining and ML were reported. Results: In total, 26,954 subjects (median age 61 years, 55% male) were included. ULVH was diagnosed in 204/26,954 (0.8%) patients, of which 56 had amyloidosis and two Anderson-Fabry Disease. Text-mining flagged 8,192 patients with possible ULVH, of whom 159 were true positives (sensitivity, specificity, LHR+, and LHR- of 0.78, 0.67, 2.36, and 0.33). Machine learning resulted in a sensitivity, specificity, LHR+, and LHR- of 0.32, 0.99, 32, and 0.68, respectively. Pivotal variables included IVSt, systolic blood pressure, and age. Conclusions: Automatic identification of patients with ULVH is possible with both Text-mining and ML. Text-mining may be a comprehensive scaffold but can be less specific than machine learning. Deployment of either method depends on existing infrastructures and clinical applications