Abstract Body (Do not enter title and authors here): Background
Recent studies have reported associations between alterations in gut microbiome composition (GMC) and cardiovascular disease risk factors (CVR). However, data regarding the use of GMC as a biomarker of CVR is scarce.
Aims
The current study was designed to assess the distinct patterns in GMC among patients with varying degrees of CVR and/or atherosclerotic cardiovascular disease (ASCVD).
Methods
Patients with a range of CVR including hypertension (HTN), hyperlipidemia (HLD), diabetes (DM), and/or ASCVD referring to Mayo Clinic from 2013 to 2018 were prospectively enrolled. DNA extracted from stool samples was analyzed using the V3-V5 region of the 16s data. Microbial α-diversity was assessed by the observed taxonomic units, Shannon, and Chao1 indices. β-diversity was assessed using Bray-Curtis dissimilarity and plotted using principal coordinates analysis. Hierarchical clustering was used to identify patterns in the GMC samples. Random Forest analysis was used to identify the most important clinical factors differentiating the clusters.
Results
A total of 211 patients with a median age of 60 [IQR: 50,70] years and with 90 (42.7%) males were included. Two clusters of GMC were identified (Figure 1A). Cluster 1 and 2 had 104 (49.3%) and 107 (50.7%) patients, respectively. Among CVRs, age and body mass index were the most prominent factors contributing to the difference in GMC among clusters (Figure 1B). Cluster 2 had a better α diversity profile than Cluster 1 (Figure 1C-E). There was no significant difference in TMAO between clusters (P=0.6). Cluster 2 patients were younger (P<0.001), leaner (P=0.007), more physically active (P<0.001), less male (P=0.009), and had a lower prevalence of ASCVD (P=0.003), HTN (P=0.010), and HLD (P=0.005). There was no significant difference in the prevalence of DM (P=0.063), smoking (P=0.446), and alcohol intake (P=0.134) between the clusters.
Conclusion
This study suggests the potential of GMC profiling as a valuable biomarker for assessing CVRs, with age and BMI as the most prominent factors associated with GMC clustering.