Yang, ChaoranCamargo Tavares, LeticiaLee, Han ChungSteele, Joel R.Ribeiro, Rosilene V.Beale, Anna L.Yiallourou, StephanieCarrington, Melinda J.Kaye, David M.Head, Geoffrey A.Schittenhelm, Ralf B.Marques, Francine Z.2026-06-112026-06-111949-0976PubMed:39709554WOS:001381828100001ORCID:/0000-0001-7278-0999/work/219172344https://hdl.handle.net/1885/733810236The gut microbiota is a crucial link between diet and cardiovascular disease (CVD). Using fecal metaproteomics, a method that concurrently captures human gut and microbiome proteins, we determined the crosstalk between gut microbiome, diet, gut health, and CVD. Traditional CVD risk factors (age, BMI, sex, blood pressure) explained < 10% of the proteome variance. However, unsupervised human protein-based clustering analysis revealed two distinct CVD risk clusters (low-risk and high-risk) with different blood pressure (by 9 mmHg) and sex-dependent dietary potassium and fiber intake. In the human proteome, the low-risk group had lower angiotensin-converting enzymes, inflammatory proteins associated with neutrophil extracellular trap formation and auto-immune diseases. In the microbial proteome, the low-risk group had higher expression of phosphate acetyltransferase that produces SCFAs, particularly in fiber-fermenting bacteria. This model identified severity across phenotypes in heart failure patients and long-term risk of cardiovascular events in a large population-based cohort. These findings underscore multifactorial gut-to-host mechanisms that may underlie risk factors for CVD.F.Z.M. is supported by a Senior Medical Research Fellowship from the Sylvia and Charles Viertel Charitable Foundation, a National Heart Foundation Future Leader Fellowship (105663), and a National Health & Medical Research Council (NHMRC) Emerging Leader Fellowship (GNT2017382). C.Y. is supported by Monash Graduate Scholarship (MGS) and Monash International Tuition Scholarship (MITS). M.J.C. receives an endowed fellowship in the Cardiology Centre of Excellence from Filippo and Maria Casella. D.M.K. is supported by an NHMRC Investigator Grant (GNT2008017). We thank Yao Chen, Ghent University, for giving general advice on machine learning modeling. We are grateful for the support of the Monash Bioinformatics Platform and the access to the M3 server, the research nurses at the Alfred Hospital and the MODERN clinic who helped us with sample collection, and the community members who volunteered for this research. This study used BPA-enabled (Bioplatforms Australia)/NCRIS-enabled (National Collaborative Research Infrastructure Strategy) infrastructure located at the Monash Proteomics and Metabolomics Platform. This research was conducted using the UK Biobank Resource under Application Number 86879. This work uses data provided by patients and collected by the NHS as part of their care and support. We also acknowledge the help of the VicGut participants.20en© 2024 The Author(s).disease riskmachine learningMetaproteomeshort-chain fatty acids202510.1080/19490976.2024.244135685214189261