Structural and functional profiles of the gut microbial community in polycystic ovary syndrome with insulin resistance (IR-PCOS): a pilot study Original paper
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Dr. Umar
Read MoreClinical Pharmacist and Clinical Pharmacy Master’s candidate focused on antibiotic stewardship, AI-driven pharmacy practice, and research that strengthens safe and effective medication use. Experience spans digital health research with Bloomsbury Health (London), pharmacovigilance in patient support programs, and behavioral approaches to mental health care. Published work includes studies on antibiotic use and awareness, AI applications in medicine, postpartum depression management, and patient safety reporting. Developer of an AI-based clinical decision support system designed to enhance antimicrobial stewardship and optimize therapeutic outcomes.
Microbiome Signatures identifies and validates condition-specific microbiome shifts and interventions to accelerate clinical translation. Our multidisciplinary team supports clinicians, researchers, and innovators in turning microbiome science into actionable medicine.
Karen Pendergrass is a microbiome researcher specializing in microbiome-targeted interventions (MBTIs). She systematically analyzes scientific literature to identify microbial patterns, develop hypotheses, and validate interventions. As the founder of the Microbiome Signatures Database, she bridges microbiome research with clinical practice. In 2012, based on her own investigative research, she became the first documented case of FMT for Celiac Disease—four years before the first published case study.
What was studied?
This study investigated the IR-PCOS gut microbiome, comparing intestinal microbial structure and predicted metabolic functions in women with insulin-resistant polycystic ovary syndrome (IR-PCOS), non-insulin-resistant PCOS (NIR-PCOS), and healthy controls. Using 16S rRNA sequencing of stool samples, the researchers sought to determine whether insulin resistance is linked to distinct microbial signatures beyond those attributable to PCOS alone. The analysis included microbial diversity measures, taxonomic shifts at phylum-to-genus levels, and microbiome–clinical parameter correlations. Functional predictions using PICRUSt were used to infer potential metabolic consequences of these compositional shifts, emphasizing pathways relevant to steroidogenesis, inflammation, and carbohydrate, amino acid, and lipid metabolism.
Who was studied?
Twenty-nine premenopausal women were initially recruited, and after exclusions for obesity, smoking, antibiotic use, and abnormal glucose response, 25 participants remained: eight healthy controls, eight NIR-PCOS patients, and nine IR-PCOS patients. All participants were within a similar age and BMI range, reducing confounding by adiposity. Dietary patterns were also balanced across groups. PCOS diagnoses followed Rotterdam criteria, and insulin resistance was defined via HOMA-IR thresholds and oral glucose tolerance testing. This carefully selected cohort enabled assessment of microbiome differences attributable specifically to PCOS phenotypes and insulin resistance.
Most important findings
The IR-PCOS gut microbiome exhibited the greatest dysbiosis. Alpha diversity (observed OTUs and Shannon index) was lowest in IR-PCOS patients, and beta-diversity plots showed clear separation between IR-PCOS and healthy controls. Across samples, Bacteroidetes and Firmicutes dominated, but their balance shifted significantly: NIR-PCOS women showed increased Firmicutes: Bacteroidetes ratios, whereas IR-PCOS patients had the opposite trend. A pronounced and clinically relevant pattern emerged at the family level. Bacteroidaceae abundance increased progressively from healthy controls to NIR-PCOS to IR-PCOS, while Prevotellaceae were sharply reduced, nearly absent in most PCOS participants. Additional reductions in Ruminococcaceae and Lachnospiraceae were observed in IR-PCOS, taxa typically associated with butyrate production and mucosal health. Correlation analyses showed Bacteroidaceae were positively associated with insulin resistance, testosterone, lipids, and inflammatory cytokines, while Prevotellaceae demonstrated inverse correlations and a positive association with estradiol. Functional predictions illustrated 73 altered metabolic pathways in PCOS patients, with IR-PCOS showing distinct enrichment in lipopolysaccharide biosynthesis and steroid hormone biosynthesis—mechanisms likely contributing to androgen excess and chronic inflammation.
Key implications
The findings suggest that insulin resistance in PCOS is associated with a more severe gut microbiome disturbance than PCOS alone. Elevated Bacteroidaceae and depleted Prevotellaceae may contribute to metabolic dysfunction by promoting low-grade inflammation, impairing gut barrier integrity, and influencing hormone regulation. The distinct microbial and functional patterns in IR-PCOS indicate that microbiome-targeted interventions may need to differentiate between PCOS phenotypes. These results support the potential for precision microbiome therapeutics, including restoration of SCFA-producing taxa, reduction of pro-inflammatory bacteria, and modulation of microbial metabolic pathways relevant to steroidogenesis.
Citation
Zeng B, Lai Z, Sun L, et al. Structural and functional profiles of the gut microbial community in polycystic ovary syndrome with insulin resistance (IR-PCOS): a pilot study. Research in Microbiology. 2019;170(1):43-52. doi:10.1016/j.resmic.2018.09.002
Polycystic ovary syndrome (PCOS) is a common endocrine disorder that affects women of reproductive age, characterized by irregular menstrual cycles, hyperandrogenism, and insulin resistance. It is often associated with metabolic dysfunctions and inflammation, leading to fertility issues and increased risk of type 2 diabetes and cardiovascular disease.
Short-chain fatty acids are microbially derived metabolites that regulate epithelial integrity, immune signaling, and microbial ecology. Their production patterns and mechanistic roles provide essential functional markers within microbiome signatures and support the interpretation of MBTIs, MMAs, and systems-level microbial shifts across clinical conditions.