Approved Research

Interactive effects of ageing, lifestyle factors, metabolism, immunity and genotypes on different aetiologies and outcomes of heart failure with preserved or reduced left ventricular ejection fraction

Lay summary

Heart failure (HF) is an intractable clinical syndrome that affects >30 million individuals worldwide. As a multifactorial and multisystemic syndrome with diverse pathophysiologies and phenotypes, HF presents a significant challenge and burden to clinical diagnosis and treatment. HF has complex aetiologies and unclear pathophysiology, with poor outcomes determined by environment × gene interactions. Despite considerable advances in HF management, nearly 50% of affected individuals die within 5 years of their initial diagnosis. HF with preserved ejection fraction (HFpEF) is one of the most significant subtypes of HF and is a leading cause of hospitalization and mortality worldwide. Further extensive studies on the interactive effects of ageing, lifestyle factors, metabolism, immunity, and genotypes on different aetiologies and outcomes of HF may improve the pathophysiological and phenotypic understanding of HF and identify preventive, diagnostic and therapeutic targets of HF. The current study is designed to explore the interactive effects of ageing, lifestyle factors, metabolism, immunity and genotypes on different aetiologies and outcomes of HF with preserved or reduced left ventricular ejection fraction (LVEF) using artificial intelligence and machine-learning methods over the next few years. In the current study, the aetiologies of HF are determined, and the genotypes are refined based on medical diagnoses and heart magnetic resonance imaging (MRI). Participants in different groups are compared based on lifestyle factors, family history, sociodemographic and mental conditions, physical measures (grip strength, bone density, arterial stiffness and carotid structures), metabolic syndrome, genetic information (genotypes, genomics, whole-genome sequencing, exome sequencing and telomeres), medical diseases, immune inflammation (human leukocyte antigen), and cognitive function. Based on the follow-up data, participants without HF and patients with different aetiologies and genotypes of HF have their outcomes determined by the effects of ageing, lifestyle factors, metabolism, immunity, genotypes, and aetiologies. Survival analyses of participants without HF and patients with different aetiologies and genotypes of HF are performed to observe significant differences and main causes of their survival time. Moreover, interactive effects of ageing, lifestyle factors, metabolism, immunity and genotypes on different aetiologies and outcomes of HF with preserved or reduced LVEF are analysed using artificial intelligence and machine-learning methods. The interactive effects of ageing, lifestyle factors, metabolism, immunity, and genotypes on different aetiologies and outcomes of HF may improve the pathophysiological and phenotypic understanding of HF and identify preventive, diagnostic and therapeutic targets of HF and HFpEF, further significantly improving the clinical outcomes and life expectancy of patients with HF.

Scope extension:

Heart failure (HF) is a multifactorial and multisystemic syndrome with diverse pathophysiologies and phenotypes and presents a significant challenge and burden to clinical diagnosis and treatment. HF with preserved ejection fraction (HFpEF) is one of the most important subtypes of HF and is a leading cause of hospitalization and mortality worldwide. Further extensive studies on the interactive effects of ageing, lifestyle factors, metabolism, immunity, and genotypes on different aetiologies and outcomes of HF may improve the pathophysiological and phenotypic understanding of HF and identify preventive, diagnostic and therapeutic targets of HF. The current study is designed to explore the interactive effects of ageing, lifestyle factors, metabolism, immunity and genotypes on different aetiologies and outcomes of HF with preserved or reduced left ventricular ejection fraction (LVEF) using artificial intelligence and machine-learning methods.

Extending the scope to inflammaging-related diseases (including cardiovascular disease, diabetes, obesity, dementia, musculoskeletal health, and more).