Hyperlipidemia is a major, modifiable driver of global cardiovascular risk. The intestinal microbiota, comprising bacteria, archaea, fungi, and viruses, modulates lipid metabolism through bile acid transformation, energy harvest, and inflammatory signaling. This study profiled the gut microbiota of 15 adults with type 2 diabetes mellitus (T2DM) and explored associations with fasting lipid measures using 16S rRNA gene sequencing (V3–V4 region) on the Illumina MiSeq platform and PICRUSt2 functional prediction. Overall α‐diversity was reduced, and community composition was dominated by Firmicutes and Actinobacteria with relative depletion of Bacteroidetes. At lower taxonomic ranks, enrichment of Prevotella copri , Collinsella spp., Ruminococcus spp., and selected Bifidobacterium spp. was observed, alongside depletion of short‐chain fatty acid (SCFA)‐linked taxa, including Akkermansia muciniphila , Lactobacillus plantarum , and members of the Bacteroides and Parabacteroides lineages. Exploratory within‐cohort trends indicated that higher triglycerides (TGs) and lower HDL‐C tended to co‐occur with increased Collinsella and clostridial signals and reduced SCFA‐associated taxa. Predicted Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog functions suggested shifts in lipid, carbohydrate, and secondary bile acid metabolism, consistent with a metabolically activated and proinflammatory intestinal milieu. In this single‐arm cohort of adults with T2DM, a low‐diversity, Firmicutes/Actinobacteria‐weighted microbiome with depletion of SCFA‐linked taxa paralleled an atherogenic lipid profile, supporting an association between gut microbial dysbiosis and lipid abnormalities in adults with T2DM. These findings suggest the potential of microbiota‐informed adjuncts, including dietary fermentable fiber, targeted probiotics and next‐generation biotherapeutics, and bile‐acid‐modulating strategies as supportive approaches to lipid management in T2DM. This was a pilot, single‐arm, exploratory study without a nondiabetic control group, and findings should be interpreted as hypothesis‐generating. Nevertheless, the cross‐sectional design, small sample size, and 16S‐based taxonomic resolution limit causal interpretation. Larger, longitudinal studies integrating shotgun metagenomics and metabolomics are needed to confirm these associations, validate biomarkers, and elucidate mechanistic pathways that could guide precision interventions for diabetic dyslipidemia.
Menezes et al. (Thu,) studied this question.