# Integrating multi-omics data: Modeling biomarkers and mechanisms to reduce bacterial vaginosis recurrence

> **NIH NIH K01** · UNIVERSITY OF MARYLAND BALTIMORE · 2021 · $92,865

## Abstract

Bacterial vaginosis (BV) is characterized by a vaginal microbiota with a low abundance of Lactobacillus spp. and
higher abundances of anaerobic bacteria and affects nearly 30% of reproductive-age North American women
and closer to 50% of sub-Saharan African women. BV is diagnosed by clinical observation of Amsel’s
criteria (Amsel-BV), but treatment is only recommended when symptoms are reported, leaving a significant
proportion of women untreated. Even with treatment, BV recurrence rates range from 50-70% within 6 months,
increasing a woman’s risk of negative sequelae. Regardless of symptoms, BV is associated with serious adverse
health outcomes, including preterm birth and HIV, and can seriously impact a woman’s quality of life. Ideal BV
treatment would eliminate recurrence. The vaginal microbiome and microenvironment together provide
a detailed evaluation of BV states, and hold key functional insights to predict and understand Amsel-BV
recurrence. The goal of this proposal is to integrate and operationalize microbiome (metagenomes) and
microenvironment (metabolomes and immune markers) data to develop a prognostic indicator of recurrent BV,
and identify candidate biomarkers and causal mechanisms which reduce recurrence. Recent work by the PI
functionally categorized the vaginal microbiome for use in large clinical research studies (vaginal metagenomic
community state types, mgCSTs). The broad hypothesis in this proposal is that not all microbiomes
associated with bacterial vaginosis have the same potential for recurrence. Preliminary data suggest that
BV recurrence is more frequently observed in only two of the nine mgCSTs containing BV-associated bacteria.
This study proposes to utilize archived cervicovaginal samples from the NIH 1999 Longitudinal Study of Vaginal
Flora in which participants were followed quarterly for one year. Multi-omic analyses of baseline samples will be
assessed to identify microbial (metagenomic and metabolomic) and host (metabolomic and targeted immune
markers) signatures of susceptibility to recurrent BV. Specific aims of this proposal are to: (1) conduct an
epidemiological analysis to evaluate the demographic and lifestyle correlates of mgCSTs, and (2) employ
supervised machine learning and causal inference modeling to identify prognostic factors and drivers
of the vaginal microbiome and microenvironment which lead to recurrent BV. Cases are defined as women
with Amsel-BV at baseline, then clearance 3 months later, followed by recurrence at six months. Controls are
women with Amsel-BV that do not experience recurrence within 9 months. This grant will support the PI’s training
in epidemiology and biostatistics with the completion of a Certificate Program in Clinical Research. The PI’s long-
term goal is to create an independent research program translating the basic science of the vaginal microbiome
to improve women’s reproductive health outcomes. The Institute for Genome Sciences and the University of
Maryland School...

## Key facts

- **NIH application ID:** 10282850
- **Project number:** 1K01AI163413-01
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** Johanna B. Holm
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $92,865
- **Award type:** 1
- **Project period:** 2021-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10282850

## Citation

> US National Institutes of Health, RePORTER application 10282850, Integrating multi-omics data: Modeling biomarkers and mechanisms to reduce bacterial vaginosis recurrence (1K01AI163413-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10282850. Licensed CC0.

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