Background/Objective: The gut microbiota is a crucial link between diet and cardiovascular health by producing beneficial metabolites, such as short-chain fatty acids (SCFAs), through fibre fermentation. However, most studies have focused on microbial DNA to infer microbiota function. Here, we employed metaproteomics, a novel method to capture gut human and microbiota protein expression profiles concurrently. We aimed to investigate the crosstalk between gut microbiota, diet, host gut health, and blood pressure (BP). Methods: Mass spectrometry-based metaproteomic analysis was performed on 61 faecal samples with paired ambulatory BP monitoring, food intake (food frequency questionnaire), and demographic data. Results: We identified 21,594 microbial and 1,072 human proteins. Traditional risk factors (age, BMI, sex, BP) explained only ∼10% of the proteome variance. However, unsupervised clustering analysis of both human and microbial proteome revealed two distinct clusters (low-risk and high-risk) with significantly different 24-hour systolic BP (mean±SD, low-risk 116±13mmHg vs high-risk: 126±15mmHg, P=0.016), women prevalence (72.4% vs 40.6%, P=0.025), and diet quality (assessed as Athlete Diet Index, 61.6±6.7 vs 57.8±7.6, P=0.044). In the human proteome, the low-risk group had lower expression of the VEGFA-VEGFR2 signalling pathway and tight junction proteins and higher SCFA production (particularly propionate), suggesting an anti-inflammatory gut environment. In the microbial proteome, the low-risk group had higher activity of inorganic ion transport and metabolism-related proteins, with increased expression of acetate kinase that produces both the SCFAs acetate and propionate, particularly in primary fibre-fermenting bacteria, including Phocaeicola dorei and Blautia wexlerae. Finally, via co-expression networks, we identified profilin-1 (PFN1), which contributes to hypertension and atherosclerosis, as a crucial human protein associated with several bacteria present in the high-risk group. Conclusions: Employing an unsupervised clustering based on human and microbial proteomics, we identified two groups of participants with varying traditional and emerging cardiovascular risk factors. This was explained by complex interactions among diet, gut microbiota, and BP. This underscores the need for sophisticated models to understand multifactorial risk factors for cardiovascular disease.
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