Aims: Despite treatment advancements, cardiovascular disease remains a leading cause of death worldwide. Identifying new targets is crucial for enhancing preventive and therapeutic strategies. The gut microbiome has been associated with coronary artery disease (CAD), however our understanding of specific changes during CAD development remains limited. We aimed to investigate microbiome changes in participants without clinically manifest CAD with different cardiovascular risk levels and in patients with ST-elevation myocardial infarction (STEMI). Methods and results: In this cross-sectional study, we characterized the gut microbiome using metagenomics of 411 faecal samples from individuals with low (n = 130), intermediate (n = 130), and high (n = 125) cardiovascular risk based on the Framingham score, and STEMI patients (n = 26). We analysed diversity, and differential abundance of species and functional pathways while accounting for confounders including medication and technical covariates. Collinsella stercoris, Flavonifractor plautii, and Ruthenibacterium lactatiformans showed increased abundances with cardiovascular risk, while Streptococcus thermophilus was negatively associated. Differential abundance analysis revealed eight species and 49 predicted metabolic pathways that were differently abundant among the groups. In the gut microbiome of STEMI patients, there was a depletion of pathways linked to vitamin, lipid, and amino acid biosynthesis. Conclusion: We identified four microbial species showing a gradual trend in abundance from low-risk individuals to those with STEMI, and observed differential abundant species and pathways in STEMI patients compared to those without clinically manifest CAD. Further investigation is warranted to gain deeper understanding of their precise role in CAD progression and potential implications, with the ultimate goal of identifying novel therapeutic targets.

Background: Inflammatory bowel diseases (IBDs) pose a significant challenge due to their diverse, often debilitating, and unpredictable clinical manifestations. The absence of prognostic tools to anticipate the future complications that require therapy intensification presents a substantial burden to patient private life and health. We aimed to explore whether the gut microbiome is a potential biomarker for future therapy intensification in a cohort of 90 IBD patients. Methods: We conducted whole-genome metagenomics sequencing on fecal samples from these patients, allowing us to profile the taxonomic and functional composition of their gut microbiomes. Additionally, we conducted a retrospective analysis of patients’ electronic records over a period of 10 years following the sample collection and classified patients into (1) those requiring and (2) not requiring therapy intensification. Therapy intensification included medication escalation, intestinal resections, or a loss of response to a biological treatment. We applied gut microbiome diversity analysis, dissimilarity assessment, differential abundance analysis, and random forest modeling to establish associations between baseline microbiome profiles and future therapy intensification. Results: We identified 12 microbial species (eg, Roseburia hominis and Dialister invisus) and 16 functional pathways (eg, biosynthesis of L-citrulline and L-threonine) with significant correlations to future therapy intensifications. Random forest models using microbial species and pathways achieved areas under the curve of 0.75 and 0.72 for predicting therapy intensification. Conclusions: The gut microbiome is a potential biomarker for therapy intensification in IBD patients and personalized management strategies. Further research should validate our findings in other cohorts to enhance the generalizability of these results.

Emerging evidence suggests the gut microbiome's potential in predicting response to biologic treatments in patients with inflammatory bowel disease (IBD). In this prospective study, we aimed to predict treatment response to vedolizumab and ustekinumab, integrating clinical data, gut microbiome profiles based on metagenomic sequencing, and untargeted fecal metabolomics. We aimed to identify predictive biomarkers and attempted to replicate microbiome-based signals from previous studies. We found that the predictive utility of the gut microbiome and fecal metabolites for treatment response was marginal compared to clinical features alone. Testing our identified microbial ratios in an external cohort reinforced the lack of predictive power of the microbiome. Additionally, we could not confirm previously published predictive signals observed in similar sized cohorts. Overall, these findings highlight the importance of external validation and larger sample sizes, to better understand the microbiome's impact on therapy outcomes in the setting of biologicals in IBD before potential clinical implementation.

The gut metabolite trimethylamine N-oxide (TMAO) at admission has a prognostic value in ST-elevation myocardial infarction (STEMI) patients. However, its sequential changes and relationship with long-term infarct-related outcomes after primary percutaneous coronary intervention (PCI) remain elusive. We delineated the temporal course of TMAO and its relationship with infarct size and left ventricular ejection fraction (LVEF) post-PCI, adjusting for the estimated glomerular filtration rate (eGFR). We measured TMAO levels at admission, 24 h and 4 months post-PCI in 379 STEMI patients. Infarct size and LVEF were determined by cardiac magnetic resonance 4 months after PCI. TMAO levels decreased from admission (4.13 ± 4.37 μM) to 24 h (3.41 ± 5.84 μM, p = 0.001) and increased from 24 h to 4 months (3.70 ± 3.86 μM, p = 0.026). Higher TMAO values at 24 h were correlated to smaller infarct sizes (rho = −0.16, p = 0.024). Larger declines between admission and 4 months suggestively correlated with smaller infarct size, and larger TMAO increases between 24 h and 4 months were associated with larger infarct size (rho = −0.19, p = 0.008 and rho = −0.18, p = 0.019, respectively). Upon eGFR stratification using 90 mL/min/1.73 m² as a cut-off, significant associations between TMAO and infarct size were only noted in subjects with impaired renal function. In conclusion, TMAO levels in post-PCI STEMI patients are prone to fluctuations, and these fluctuations could be prognostic for infarct size, particularly in patients with impaired renal function.

Lower levels of physical activity (PA) have been associated with increased risk of cardiovascular disease. Worldwide, there is a shift towards a lifestyle with less PA, posing a serious threat to public health. One of the suggested mechanisms behind the association between PA and disease development is through systemic inflammation, in which circulating blood cells play a pivotal role. In this study we investigated the relationship between genetically determined PA and circulating blood cells. We used 68 single nucleotide polymorphisms associated with objectively measured PA levels to perform a Mendelian randomization analysis on circulating blood cells in 222,645 participants of the UK Biobank. For inverse variance fixed effects Mendelian randomization analyses, p < 1.85 × 10⁻³ (Bonferroni-adjusted p-value of 0.05/27 tests) was considered statistically significant. Genetically determined increased PA was associated with decreased lymphocytes (β = –0.03, SE = 0.008, p = 1.35 × 10⁻³) and decreased eosinophils (β = –0.008, SE = 0.002, p = 1.36 × 10⁻³). Although further mechanistic studies are warranted, these findings suggest increased physical activity is associated with an improved inflammatory state with fewer lymphocytes and eosinophils.

Background and Aims: Microbiome studies reveal distinct microbial differences in inflammatory bowel disease (IBD), indicating its potential role in pathophysiology and as a noninvasive diagnostic biomarker. This study aims to profile the gut microbiome in children with IBD, compared to both healthy controls (HC) and controls with gastrointestinal symptoms (CGI), and to assess the potential of microbiome profiles as noninvasive diagnostic markers for de novo treatment-naïve pediatric IBD, and as early predictive markers for therapy response. Methods: We analyzed baseline fecal samples and clinical data from 103 therapy-naïve children with IBD, 75 CGI, and 356 age and sex matched HC. Metagenomic sequencing was performed, and prediction models assessed diagnostic potential and prediction of induction therapy response at 3 months. Results: Alpha diversity progressively decreased from HC to CGI (P < .001) and decreased even further in IBD patients (P = .0056). Beta diversity analysis showed significant clustering differences (P < .001, R2 = 0.045). Differential abundance analysis revealed 116 species differing between HC and IBD, and 30 species between CGI and IBD. Prediction models based on microbiome features accurately distinguished IBD from HC (area under the curve [AUC] = 0.96) and from CGI (AUC = 0.71). However, these models were outperformed by clinical features, such as fecal calprotectin. Microbiome-based prediction of response to induction therapy in general showed limited accuracy (AUC = 0.63), as well as for response to nutritional induction therapy (AUC = 0.67). Conclusions: We observed profound gut microbiome differences between de novo, therapy-naïve pediatric IBD patients and controls. While microbiome profiles hold promise for improving diagnostic precision, their predictive value for therapy response seems limited.