Body mass index and heart failure risk: a cohort study in 1.5 million individuals and Mendelian randomisation analysis

Katsoulis, Michail; Lumbers, T.; Henry, Albert; Mordi, Ify; Lang, Chim C.; Hemingway, Harry; Langenberg, Claudia; Holmes, Michael V; Sattar, Naveed

doi:10.1101/2020.09.23.20200360

Cited by 5 publications

(3 citation statements)

References 49 publications

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“…Consistent with evidence from overexpression perturbation studies in animal models, Gal-3 48 and CSF-1 49 were positively associated with body mass index, a biomarker of adiposity and a known risk factor for HF. 50 CHI3L1 and CTSL1 were protective for CAD, consistent with reports of cardioprotective effects in animal models of cardiac ischemia. 51 , 52 A higher circulating CSF-1 level was associated with an increased risk of CAD, 53 whereas MMP-12 showed a protective effect, consistent with previous reports.…”

Section: Resultssupporting

confidence: 89%

Therapeutic Targets for Heart Failure Identified Using Proteomics and Mendelian Randomization

et al. 2022

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Background: Heart failure (HF) is a highly prevalent disorder for which disease mechanisms are incompletely understood. The discovery of disease-associated proteins with causal genetic evidence provides an opportunity to identify new therapeutic targets. Methods: We investigated the observational and causal associations of 90 cardiovascular proteins, which were measured using affinity-based proteomic assays. First, we estimated the associations of 90 cardiovascular proteins with incident heart failure by means of a fixed-effect meta-analysis of four population-based studies, comprising a total of 3,019 participants with 732 HF events. The causal effects of HF-associated proteins were then investigated by Mendelian randomization (MR), using cis -protein quantitative loci genetic instruments identified from genome-wide association studies (GWAS) in over 30,000 individuals. To improve the precision of causal estimates, we implemented an MR model that accounted for linkage disequilibrium between instruments and tested the robustness of causal estimates through a multiverse sensitivity analysis that included up to 120 combinations of instrument selection parameters and MR models per protein. The druggability of candidate proteins was surveyed, and mechanism of action and potential on-target side effects were explored with cross-trait MR analysis. Results: 44/90 proteins were positively associated with risk of incident HF (P < 6.0 x 10-4). Among these, eight proteins had evidence of a causal association with HF that was robust to multiverse sensitivity analysis: higher CSF-1 (macrophage colony-stimulating factor 1), Gal-3 (galectin-3) and KIM-1 (kidney injury molecule 1) were positively associated with risk of HF, whereas higher ADM (adrenomedullin), CHI3L1 (chitinase-3-like protein 1), CTSL1 (cathepsin L1), FGF-23 (fibroblast growth factor 23) and MMP-12 (Matrix metalloproteinase-12) were protective. Therapeutics targeting ADM and Gal-3 are currently under evaluation in clinical trials, and all the remaining proteins were considered druggable, except KIM-1. Conclusions: We identified 44 circulating proteins that were associated with incident HF, of which eight showed evidence of a causal relationship and seven were druggable, including adrenomedullin which represents a particularly promising drug target. Our approach demonstrates a tractable roadmap for the triangulation of population genomic and proteomic data for the prioritization of therapeutic targets for complex human diseases.

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Section: Resultssupporting

confidence: 89%

Therapeutic Targets for Heart Failure Identified Using Proteomics and Mendelian Randomization

et al. 2022

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“…Here we showcase an example in demonstrating the use of ASQ for researchers in triangulating evidence regarding epidemiological research questions. From the systematic dataset, ASQ extracted a claim triple “Obesity CAUSES Heart failure” from a preprint abstract regarding a Mendelian randomization analysis investigating causal relationships between body mass index and heart failure risk 15 , derived from the context “About 40% of the excess risk of HF due to adiposity is driven by SBP, AF, DM and CHD”, where “HF” is recognised as heart failure and “adiposity” as obesity. These results can be found on ASQ (https://asq.epigraphdb.org/triple?subject=Obesity&object=Heart%20failure&predicate=CAUSES&analysis).…”

Section: Resultsmentioning

confidence: 99%

Triangulating evidence in health sciences with Annotated Semantic Queries

Liu

Gaunt

2022

Preprint

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Integrating information from data sources representing different study designs has the potential to strengthen evidence in population health research. However, this concept of evidence "triangulation" presents a number of challenges for systematically identifying and integrating relevant information. We present ASQ (Annotated Semantic Queries), a natural language query interface to the integrated biomedical entities and epidemiological evidence in EpiGraphDB, which enables users to extract "claims" from a piece of unstructured text, and then investigate the evidence that could either support, contradict the claims, or offer additional information to the query. This approach has the potential to support the rapid review of pre-prints, grant applications, conference abstracts and articles submitted for peer review. ASQ implements strategies to harmonize biomedical entities in different taxonomies and evidence from different sources, to facilitate evidence triangulation and interpretation. ASQ is openly available at https://asq.epigraphdb.org.

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“…These findings highlight the importance of managing upstream risk factors for diabetes and CAD, for the prevention of HF. SGLT2 inhibitors may exert their beneficial effects on HF risk and progression through modification of upstream physiology for both T2D and HF 54 , such as higher body mass index; the effects of which are consistent across subtypes and may be independent of established risk factors 55 .…”

Section: Discussionmentioning

confidence: 99%

Mapping the aetiological foundations of the heart failure spectrum using human genetics

Henry,

Mo,

Finan

et al. 2023

Preprint

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Summary paragraphHeart failure (HF), a syndrome of symptomatic fluid overload due to cardiac dysfunction, is the most rapidly growing cardiovascular disorder. Despite recent advances, mortality and morbidity remain high and treatment innovation is challenged by limited understanding of aetiology in relation to disease subtypes. Here we harness the de-confounding properties of genetic variation to map causal biology underlying the HF phenotypic spectrum, to inform the development of more effective treatments. We report a genetic association analysis in 1.9 million ancestrally diverse individuals, including 153,174 cases of HF; 44,012 of non-ischaemic HF; 5,406 cases of non-ischaemic HF with reduced ejection fraction (HFrEF); and 3,841 cases of non-ischaemic HF with preserved ejection fraction (HFpEF). We identify 66 genetic susceptibility loci across HF subtypes, 37 of which have not previously been reported. We map the aetiologic contribution of risk factor traits and diseases as well as newly identified effector genes for HF, demonstrating differential risk factor effects on disease subtypes. Our findings highlight the importance of extra-cardiac tissues in HF, particularly the kidney and the vasculature in HFpEF. Pathways of cellular senescence and proteostasis are notably uncovered, includingIGFBP7as an effector gene for HFpEF. Using population approaches causally anchored in human genetics, we provide fundamental new insights into the aetiology of heart failure subtypes that may inform new approaches to prevention and treatment.

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Body mass index and heart failure risk: a cohort study in 1.5 million individuals and Mendelian randomisation analysis

Cited by 5 publications

References 49 publications

Therapeutic Targets for Heart Failure Identified Using Proteomics and Mendelian Randomization

Therapeutic Targets for Heart Failure Identified Using Proteomics and Mendelian Randomization

Triangulating evidence in health sciences with Annotated Semantic Queries

Mapping the aetiological foundations of the heart failure spectrum using human genetics

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