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Nutritional Epidemiology (Poster Session)

(P13-073-24) Network Analysis Reveals Multi-omic Associations With Hyperandrogenism and Hepatic Fat in Individuals With PCOS

Sunday, June 30, 2024
12:45 PM – 1:45 PM CT
Location: Poster Board 461

    Presenting Author(s)

  • AW

    Anthony A. Wang, MD PhD (he/him/his)

    Instructor/Fellow
    University of Colorado Anschutz Medical Campus
    Aurora, Colorado, United States

    • Co-Author(s)

  • JL

    Jaclyn Lo

    University of Colorado Anschutz Medical Campus, Colorado, United States

  • SB

    Samantha Bothwell

    University of Colorado Anschutz Medical Campus, Colorado, United States

  • LP

    Laura Pyle

    University of Colorado Anschutz Medical Campus, Colorado School of Public Health, Colorado, United States

  • JS

    Janet Siebert

    CytoAnalytics, Colorado, United States

  • Sarah Borengasser, PhD, MS (she/her/hers)

    Associate Professor
    University of Oklahoma Health Sciences Center
    Oklahoma City, Oklahoma, United States

  • MC

    Melanie Cree

    University of Colorado Anschutz, Children’s Hospital Colorado, Colorado, United States

Disclosure(s):
Anthony A. Wang, MD PhD: No relevant financial relationship(s) with ineligible companies to disclose.


Objectives: While PCOS is characterized by hyperandrogenism, insulin resistance, and hepatic steatosis, its pathogenesis is likely multifactorial. We sought to better understand the interrelationships between several -omic datasets (stool microbiome, stool metabolomics, serum metabolomics) and indicators of metabolic dysfunction in PCOS.

Methods: Study participants included young females (n=21) aged 12-21 with BMI percentile >90% who met international criteria for the diagnosis of PCOS. Fasting and 2-hour OGTT blood samples were obtained for measurement of serum cardiometabolic markers and metabolomic analyses. Stool samples were processed for metabolomic analyses and 16S sequencing of bacteria. Hepatic fat was measured by MRI. Associations between -omic datasets and primary outcomes of hepatic fat and free androgen index (FAI) were determined using linear regression, with the strongest associations summarized in a multi-omic network and in predictive models.

Results: Our network analysis revealed 153 pairwise associations among the -omic data and primary outcomes (FDR adjusted p< 0.20). For example, ALT (r=0.68, p=0.03), waist circumference (r=0.61, p=0.049), fasting serum C-peptide (r=0.59, p=0.049), and BMI percentile (r=0.50, p=0.11) were positively associated with hepatic fat. OGTT 2-hour serum insulin (r=0.61, p=0.049), ALT (r=0.59, p=0.049), and C-peptide (r=0.60, p=0.049) were positively associated with FAI. Next, we used network neighborhoods to build predictive models for primary outcomes. The model for hepatic fat included BMI percentile, fasting serum C-peptide, and serum 1,3 bisphosphoglycerate (adj. R2=0.86, p< 0.001). The model for FAI had 7 predictors including OGTT 2-hour serum insulin, prostaglandin, fasting C-peptide, S24-7 (Muribaculaceae), fasting glucagon, caloric intake, and free testosterone (adj. R2=0.90, p< 0.001).

Conclusions: The integrative approach herein demonstrated both known and novel associations between several multi-omic features and the primary outcomes. This provides insight into the complexities of metabolic and hormonal changes in individuals with PCOS and identifies avenues for further exploration.

Funding Sources: Research was supported by NIDDK T32, Doris Duke Foundation CSDA, Children’s Hospital of Colorado, and NIH/NCATS Colorado CTSA.