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Introduction
Heart failure (HF) and atrial fibrillation (AF) are major cardiac diseases that cause a considerable burden in terms of health and economic costs, as well as mortality1, 2–3. HF is a clinical diagnosis secondary to dysfunction of the right ventricle (RV) or left ventricle (LV), while AF is defined by uncoordinated electrical activation and consequently ineffective contraction of the atria. Both diseases are intricately related, and while the causative relationship between the two conditions has not been fully determined, it is clear these two diseases frequently co-occur4.
Despite recent advances in medicines, for example, offered by sodium-glucose co-transporter-2 inhibitors, drug development for cardiac disease suffers from high failure rates, often occurring during costly late-stage clinical testing5, 6–7. Unlike the cholesterol content on low-density lipoprotein particles for coronary heart disease, drug development for AF and HF is impeded by a lack of robust early-stage surrogates (or intermediates) for cardiac disease.
Cardiac magnetic resonance (CMR) imaging is the gold standard for the quantification of atrial and ventricular function and morphology and has become an integral diagnostic modality for cardiac diseases. It is, however, unclear to what extent CMR measurements act as surrogates for the development of cardiac disease in otherwise healthy individuals.
Both HF and AF are associated with multimorbidity, including non-cardiac diseases, such as stroke, chronic kidney disease (CKD), diabetes mellitus (T2DM), and neurological diseases, such as Alzheimer’s disease (AD). Because HF and AF are clinical manifestations of underlying changes in cardiac function and structure, patients with similar diagnoses may vary considerably in underlying pathophysiology and disease progression. Unlike HF or AF diagnoses, CMR measurements directly reflect cardiac physiology and therefore, provide an opportunity to explore the effects changes in cardiac function and structure may elicit in other organs.
Recently, CMR measurements of thousands of subjects have been linked to genetic data and analysed through genome-wide association studies (GWAS). Aggregate data from GWAS, consisting of variant-specific point estimates and standard errors, can be used in Mendelian randomization analyses to ascertain the causal effects a CMR trait may have on disease. In the current manuscript, we leveraged data from two recent GWAS of CMR measurements of cardiac structure and function8, and left atrial (LA) volume9, jointly...