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Genetic variants identified by genome-wide association studies explain only a modest proportion of heritability, suggesting that meaningful associations lie 'hidden' below current thresholds. Here, we integrate information from association studies with epigenomic maps to demonstrate that enhancers significantly overlap known loci associated with the cardiac QT interval and QRS duration. We apply functional criteria to identify loci associated with QT interval that do not meet genome-wide significance and are missed by existing studies. We demonstrate that these 'sub-threshold' signals represent novel loci, and that epigenomic maps are effective at discriminating true biological signals from noise. We experimentally validate the molecular, gene-regulatory, cellular and organismal phenotypes of these sub-threshold loci, demonstrating that most sub-threshold loci have regulatory consequences and that genetic perturbation of nearby genes causes cardiac phenotypes in mouse. Our work provides a general approach for improving the detection of novel loci associated with complex human traits. Most complex traits are governed by a large number of genetic contributors, each playing only a modest effect. This makes it difficult to identify the genetic variants that increase disease risk, hindering the discovery of new drug targets and the development of new therapeutics. To overcome this limitation in discovery power, the field of human genetics has traditionally sought increasingly large groups, or cohorts, of afflicted and non-afflicted individuals. Studies of large cohorts are a powerful approach for discovering new disease genes, but such groups are often impractical and sometimes impossible to obtain. However, it has become possible to complement the genetic evidence found in disease association studies with biological evidence of the effects of disease-associated genetic variants. Wang et al. focus specifically on genetic sites, or loci, that do not affect protein sequence but instead affect the non-coding control regions. These are known as enhancer elements, as they can enhance the expression of nearby genes. These loci constitute the majority of disease regions, and thus are extremely important, but their discovery has been hindered by our relatively poor understanding of the human genome. Chemical modifications known as epigenomic marks are indicative of enhancer regions. By studying the factors that affect heart rhythm, Wang et al. show that specific combinations of epigenomic marks are enriched in known trait-associated regions. This knowledge was then used to prioritize the further investigation of genetic regions that genome-wide association studies had only weakly linked to heart rhythm alterations. Wang et al. directly confirmed that genetic differences in “sub-threshold” regions indeed alter the activity of these regulatory regions in human heart cells. Furthermore, mutating or perturbing the predicted target genes of the sub-threshold enhancers caused heart defects in mouse and zebrafish. Wang et al. have demonstrated that epigenome maps can help to distinguish which sub-threshold regions from genome-wide association studies are more likely to contribute to a disease. This allows for the discovery of new disease genes with much smaller cohorts than would be needed otherwise, thus speeding up the development of new therapeutics by many years.

Leisure sedentary behaviours are associated with increased risk of cardiovascular disease, but whether this relationship is causal is unknown. The aim of this study is to identify genetic determinants associated with leisure sedentary behaviours and to estimate the potential causal effect on coronary artery disease (CAD). Genome wide association analyses of leisure television watching, leisure computer use and driving behaviour in the UK Biobank identify 145, 36 and 4 genetic loci ( P  < 1×10 −8 ), respectively. High genetic correlations are observed between sedentary behaviours and neurological traits, including education and body mass index (BMI). Two-sample Mendelian randomization (MR) analysis estimates a causal effect between 1.5 hour increase in television watching and CAD (OR 1.44, 95%CI 1.25–1.66, P  = 5.63 × 10 −07 ), that is partially independent of education and BMI in multivariable MR analyses. This study finds independent observational and genetic support for the hypothesis that increased sedentary behaviour by leisure television watching is a risk factor for CAD. Epidemiological studies have shown an association between sedentary behaviours and cardiovascular disease risk. Here, van de Vegte et al . perform GWAS for self-reported sedentary behaviours (TV watching, computer use, driving) and Mendelian randomization analyses to explore potential causal relationships with coronary artery disease.

There is ongoing debate on the association between eosinophil count and diseases, as previous studies were inconsistent. We studied the relationship of eosinophil count with 22 complex metabolic, cardiac, and pulmonary traits and diseases. From the population-based LifeLines Cohort Study (N = 167,729), 13,301 individuals were included. We focused on relationship of eosinophil count with three classes of metabolic (7 traits, 2 diseases), cardiac (6 traits, 2 diseases), and pulmonary (2 traits, 2 diseases) outcomes. Regression analyses were applied in overall, women and men, while adjusted for age, sex, BMI and smoking. A p-value of <0.00076 was considered statistically significant. 58.2% of population were women (mean±SD 51.3±11.1 years old). In overall, one-SD higher of ln-eosinophil count was associated with a 0.04 (±SE ±0.002;p = 6.0×10-6) SD higher levels in ln-BMI, 0.06 (±0.007;p = 3.1×10-12) SD in ln-TG, 0.04 (±0.003;p = 7.0×10-6) SD in TC, 0.04 (±0.004;p = 6.3×10-7) SD in LDL, 0.04 (±0.006;p = 6.0×10-6) SD in HbA1c; and with a 0.05 (±0.004;p = 1.7×10-8) SD lower levels in HDL, 0.05 (±0.007;p = 3.4×10-23) SD in FEV1, and 0.09 (±0.001;p = 6.6×10-28) SD in FEV1/FVC. A higher ln-eosinophil count was associated with 1.18 (95%CI 1.09-1.28;p = 2.0×10-5) odds ratio of obesity, 1.29 (1.19-1.39;p = 1.1×10-10) of metabolic syndrome, 1.40 (1.25-1.56;p = 2.7×10-9) of COPD and 1.81 (1.61-2.03;p = 1.0×10-23) of asthma. Similar results were found in women. We found no association between ln-eosinophil count either with blood pressure indices in overall, women and men; or with BMI, LDL, HbA1c and obesity in men. In a large population based cohort, we confirmed eosinophil count as a potential factor implicated in metabolic and pulmonary outcomes.

ObjectivesTo determine normal pericoronary adipose tissue mean attenuation (PCATMA) values for left the anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) in patients without plaques on coronary CT angiography (cCTA), taking into account tube voltage influence.MethodsThis retrospective study included 192 patients (76 (39.6%) men; median age 49 years (range, 19–79)) who underwent cCTA with third-generation dual-source CT for the suspicion of CAD between 2015 and 2017. We selected patients without plaque on cCTA. PCATMA was measured semi-automatically on cCTA images in the proximal segment of the three main coronary arteries with 10 mm length. Paired t-testing was used to compare PCATMA between combinations of two coronary arteries within each patient, and one-way ANOVA testing was used to compare PCATMA in different kV groups.ResultsThe overall mean ± standard deviation (SD) PCATMA was − 90.3 ± 11.1 HU. PCATMA in men was higher than that in women: − 88.5 ± 10.5 HU versus − 91.5 ± 11.3 HU (p = 0.001). PCATMA of LAD, LCX, and RCA was − 92.4 ± 11.6 HU, − 88.4 ± 9.9 HU, and − 90.2 ± 11.4 HU, respectively. Pairwise comparison of the arteries showed significant difference in PCATMA: LAD and LCX (p < 0.001), LAD and RCA (p = 0.009), LCX and RCA (p = 0.033). PCATMA of the 70 kV, 80 kV, 90 kV, 100 kV, and 120 kV groups was − 95.6 ± 9.6 HU, − 90.2 ± 11.5 HU, − 87.3 ± 9.9 HU, − 82.7 ± 6.2 HU, and − 79.3 ± 6.8 HU, respectively (p < 0.001).ConclusionsIn patients without plaque on cCTA, PCATMA varied by tube voltage, with minor differences in PCATMA between coronary arteries (LAD, LCX, RCA). PCATMA values need to be interpreted taking into account tube voltage setting.Key Points• In patients without plaque on cCTA, PCATMAdiffers slightly by coronary artery (LAD, LCX, RCA).• Tube voltage of cCTA affects PCATMAmeasurement, with mean PCATMAincreasing linearly with increasing kV.• For longitudinal cCTA analysis of PCATMA, the use of equal kV setting is strongly recommended.

Atrial fibrillation (AF) patients have enlarged left atria (LA), but prior studies suggested enlarged atria as both cause and consequence of AF. The aim of this study is to investigate the causal association between AF and LA size and function. In the UK Biobank, all individuals with contoured cardiovascular magnetic resonance data were selected. LA maximal volume (LA max), LA minimal volume (LA min), LA stroke volume and LA ejection fraction were measured and indexed to body surface area (BSA). Two-sample Mendelian randomization analyses were performed using 84 of the known genetic variants associated with AF to assess the association with all LA size and function in individuals without prevalent AF. A total of 4274 individuals (mean age 62.0 ± 7.5 years, 53.2% women) were included. Mendelian randomization analyses estimated a causal effect between genetically determined AF and BSA-indexed LA max, LA min, and LA ejection fraction, but not between AF and LA stroke volume. Leave-one-out analyses showed that the causal associations were attenuated after exclusion of rs67249485, located near PITX2 gene. Our results suggest that AF causally increases LA size and decreases LA ejection fraction. The AF risk allele of rs67249485, located near the PITX2 gene, contributes strongly to these associations.

Background: Whether sex differences exist in the inflammatory response after ST-elevation myocardial infarction (STEMI) remains to be elucidated. We studied leukocyte profiles and their prognostic value in men and women presenting with STEMI. Methods: From a total of 552 consecutive STEMI patients, blood samples were collected at hospital admission. Linear regression was used to assess the relationship between leukocyte profiles and enzymatic infarct size. Cox regression was used to assess the association between leukocyte profiles and one-year mortality. Results: Women presented with higher lymphocyte counts (2.3·10 9  cells/L (IQR 1.6–3.1) vs. 1.8·10 9  cells/L (IQR 1.4–2.5), p = 3.00 ∙ 10 −4 ) and percentages (21.1% (IQR 14.4–28.1) vs. 17.1% (IQR 12.3–24.3), p = 0.004). Lymphocyte to monocyte ratio (LMR) was also higher in women (3.25 (IQR 2.56–4.5) vs. 2.68 (IQR 2.08–3.59), p = 7.28 ∙ 10 −7 ). Higher LMR was associated with lower peak CK-MB (β = −0.27 (95% CI: −0.50, −0.03), p = 0.026), lower peak troponin T (β = −0.45 (95% CI: −0.77, −0.13), p = 0.006) and lower one-year mortality risk (HR 0.35 (95% CI: 0.13, 0.96), p = 0.042). Conclusion: At admission for STEMI, women present with higher lymphocyte count and LMR. Higher LMR is associated with smaller infarct size and decreased one-year mortality risk and could be used as a biomarker to predict outcome.

Background There is growing evidence to support the benefits of transcatheter aortic valve implantation (TAVI) over surgical aortic valve replacement (SAVR) in patients with symptomatic severe aortic stenosis (sSAS) who are at high- or intermediate-risk of surgical mortality. The PARTNER 3 trial showed clinical benefits with SAPIEN 3 TAVI compared with SAVR in patients at low risk of surgical mortality. Whether TAVI is also cost-effective compared with SAVR for low-risk patients in the Dutch healthcare system remains uncertain. This article presents an analysis using PARTNER 3 outcomes and costs data from the Netherlands to inform a cost-utility model and examine cost implications of TAVI over SAVR in a Dutch low-risk population. Methods A two-stage cost-utility analysis was performed using a published and validated health economic model based on adverse events with both TAVI and SAVR interventions from a published randomized low risk trial dataset, and a Markov model that captured lifetime healthcare costs and patient outcomes post-intervention. The model was adapted using Netherlands-specific cost data to assess the cost-effectiveness of TAVI and SAVR. Uncertainty was addressed using deterministic and probabilistic sensitivity analyses. Results TAVI generated 0.89 additional quality-adjusted life years (QALYs) at a €4742 increase in costs per patient compared with SAVR over a lifetime time horizon, representing an incremental cost-effectiveness ratio (ICER) of €5346 per QALY gained. Sensitivity analyses confirm robust results, with TAVI remaining cost-effective across several sensitivity analyses. Conclusions Based on the model results, compared with SAVR, TAVI with SAPIEN 3 appears cost-effective for the treatment of Dutch patients with sSAS who are at low risk of surgical mortality. Qualitative data suggest broader societal benefits are likely and these findings could be used to optimize appropriate intervention selection for this patient population.

The sensitivity of electrocardiogram (ECG) criteria to detect left ventricular hypertrophy (LVH) is low, especially in women. We determined sex-specific sensitivities of ECG-LVH criteria, and developed new criteria, using cardiovascular magnetic resonance imaging (CMR). Sensitivities of ECG-LVH criteria were determined in participants of the UK Biobank (N = 3632). LVH was defined when left ventricular mass was > 95% confidence interval (CI) according to age and sex. In a training cohort (75%, N = 2724), sex-specific ECG-LVH criteria were developed by investigating all possible sums of QRS-amplitudes in all 12 leads, and selecting the sum with the highest pseudo-R 2 and area under the curve to detect LVH. Performance was assessed in a validation cohort (25%, N = 908), and association with blood pressure change was investigated in an independent cohort. Sensitivities of ECG-LVH criteria were low, especially in women. Newly developed Groningen-LVH criterion for women (Q V2  + R I  + R V5  + R V6  + S V2  + S V4  + S V5  + S V6 ) outperformed all ECG-LVH criteria with a sensitivity of 42% (95% CI 35–49%). In men, newly developed criterion ((R I  + R V5  + S II  + S V2  + S V6 ) × QRS duration) was equally sensitive as 12-lead sum with a sensitivity of 44% (95% CI 37–51%) and outperformed the other criteria. In an independent cohort, the Groningen-LVH criteria were strongest associated with change in systolic blood pressure. Our proposed CMR sex-specific Groningen-LVH criteria improve the sensitivity to detect LVH, especially in women. Further validation and its association with clinical outcomes is warranted.

ObjectivesEarly synthetic gadolinium enhancement (ESGE) imaging from post-contrast T1 mapping after adenosine stress-perfusion cardiac magnetic resonance (CMR) was compared to conventional late gadolinium enhancement (LGE) imaging for assessing myocardial scar.MethodsTwo hundred fourteen consecutive patients suspected of myocardial ischaemia were referred for stress-perfusion CMR. Myocardial infarct volume was quantified on a per-subsegment basis in both synthetic (2–3 min post-gadolinium) and conventional (9 min post-gadolinium) images by two independent observers. Sensitivity, specificity, PPV and NPV were calculated on a per-patient and per-subsegment basis.ResultsBoth techniques detected 39 gadolinium enhancement areas in 23 patients. The median amount of scar was 2.0 (1.0–3.1) g in ESGE imaging and 2.2 (1.1–3.1) g in LGE imaging (p=0.39). Excellent correlation (r=0.997) and agreement (mean absolute difference: -0.028±0.289 ml) were found between ESGE and LGE images. Sensitivity, specificity, PPV and NPV of ESGE imaging were 96 (78.9–99.9), 99 (97.1–100.0)%, 96 (76.5–99.4) and 99.5 (96.6–99.9) in patient-based and 99 (94.5–100.0), 100 (99.9–100.0)%, 97.0 (91.3–99.0) and 100.0 (99.8–100.0) in subsegment-based analysis.ConclusionESGE based on post-contrast T1 mapping after adenosine stress-perfusion CMR imaging shows excellent agreement with conventional LGE imaging for assessing myocardial scar, and can substantially shorten clinical acquisition time.Key Points• Synthetic gadolinium enhancement images can be used for detection of myocardial scar.• Early synthetic gadolinium enhancement images can substantially shorten clinical acquisition time.• ESGE has high diagnostic accuracy as compared to conventional late gadolinium enhancement.• Quantification of myocardial scar with ESGE closely correlates with conventional LGE.• ESGE after stress perfusion CMR avoids need for additional gadolinium administration.

AbstractObjectiveTo assess age differences in risk factors for incident heart failure in the general population.DesignPooled population based cohort study.SettingFramingham Heart Study, Prevention of Renal and Vascular End-stage Disease Study, and Multi-Ethnic Study of Atherosclerosis.Participants24 675 participants without a history of heart failure stratified by age into young (<55 years; n=11 599), middle aged (55-64 years; n=5587), old (65-74 years; n=5190), and elderly (≥75 years; n=2299) individuals.Main outcome measureIncident heart failure.ResultsOver a median follow-up of 12.7 years, 138/11 599 (1%), 293/5587 (5%), 538/5190 (10%), and 412/2299 (18%) of young, middle aged, old, and elderly participants, respectively, developed heart failure. In young participants, 32% (n=44) of heart failure cases were classified as heart failure with preserved ejection fraction compared with 43% (n=179) in elderly participants. Risk factors including hypertension, diabetes, current smoking history, and previous myocardial infarction conferred greater relative risk in younger compared with older participants (P for interaction <0.05 for all). For example, hypertension was associated with a threefold increase in risk of future heart failure in young participants (hazard ratio 3.02, 95% confidence interval 2.10 to 4.34; P<0.001) compared with a 1.4-fold risk in elderly participants (1.43, 1.13 to 1.81; P=0.003). The absolute risk for developing heart failure was lower in younger than in older participants with and without risk factors. Importantly, known risk factors explained a greater proportion of overall population attributable risk for heart failure in young participants (75% v 53% in elderly participants), with better model performance (C index 0.79 v 0.64). Similarly, the population attributable risks of obesity (21% v 13%), hypertension (35% v 23%), diabetes (14% v 7%), and current smoking (32% v 1%) were higher in young compared with elderly participants.ConclusionsDespite a lower incidence and absolute risk of heart failure among younger compared with older people, the stronger association and greater attributable risk of modifiable risk factors among young participants highlight the importance of preventive efforts across the adult life course.