BackgroundLittle is known about how the residential distance to the coast is associated with incident myocardial infarction (MI) and which mechanisms may explain the association. We aim to explore this association using data from a prospective, population-based cohort with unprecedented sample size and broad geographical coverage.MethodsThree hundred seventy-seven thousand three hundred forty participants from the UK Biobank were included. Results4,059 MI occurred during median 8.0 years follow-up. Using group (<1 km) as reference, group (20-50 km) was associated with a lower risk of MI (hazard ratio, HR 0.79, 95% CI 0.64-0.98) and a U-shaped relation between distance to the coast and MI was shown with the low-risk interval between 32 km and 64 km (Pnonlinear=0.0012). Using participants of the intermediate region (32-64 km) as a reference, participants of the offshore region (<32 km) and inland region (>64 km) were both associated with a higher risk of incident MI (HR 1.12, 95% CI 1.04-1.21 and HR 1.09, 95% CI 1.01-1.18, respectively). HR for offshore region (<32 km) was larger in subgroup with low total physical activity (<24 hours/week) (HR 1.24, 95% CI 1.09-1.42, Pinteraction= 0.043). HR for inland region (>64 km) was larger in subgroup in urban area (HR 1.12, 95% CI 1.03-1.22, Pinteraction=0.065) and in subgroup of high nitrogen dioxide air pollution (HR 1.29, 95% CI 1.11-1.50, Pinteraction=0.021).ConclusionWe found a U-shaped association between residential distance to the coast and incident MI, and the association was modified by physical activity, population density, and air pollution.

Background: Identifying unrecognized, potentially modifiable risk factors is essential for heart failure (HF) management.Methods: The Atherosclerosis Risk in Communities (ARIC) study was used for machine learning (ML) to establish the top 20 important variables as potential risk factors for HF. Multivariable Cox regression analysis was performed in an explorative manner to find independent factors for HF and Mendelian randomization (MR) analysis to address causality.Results: Of the 14,842 participants included in the ARIC analysis, 20.4% of participants (3,028) were identified as HF. The 20 variables with the highest importance selected by ML were creatinine, glucose, age, previous coronary artery disease (CAD), systolic blood pressure, fibrinogen, albumin, income, diabetes, magnesium, insulin, white blood cell, hemoglobin, sodium, education, phosphorus, diastolic blood pressure, protein-c, heart rate and body mass index (BMI). Cox regression analysis demonstrated 19 independently associated variables except sodium. MR analysis provided evidence supporting that genetically determined BMI, CAD, diabetes and education was causally associated with HF.Conclusions: The ML plus MR framework was useful in identifying important causal factors of HF. BMI, CAD, diabetes, and education not only served as excellent prognostic factors for HF, but therapeutics targeted at these factors were likely to prevent HF effectively.