Examining 105 reproductive-aged women, researchers analyzed how vaginal microbiota community state types and metabolic profiles align with psychological profiles. The study utilized 16S rRNA gene sequencing and 1H-NMR spectroscopy to uncover metabolic links to motivational and inhibitory traits.
Examining 105 reproductive-aged women, researchers investigated the connections between vaginal microbiota, community state types, and self-reported emotional and motivational traits, moving beyond simple taxonomic descriptions to examine underlying biochemical environments.
Microbial Community State Types Align With Behavioral Traits
Different microbial profiles showed specific patterns of alignment with motivational and inhibitory systems. Women characterized by CST IV—the polymicrobial profile—exhibited lower BAS-Reward Responsiveness and BAS-Drive scores.
Direct correlations at the individual taxon level were limited. Instead, the vaginal metabolome demonstrated wider connections with psychological measures. CST IV proved metabolically distinct, showing lower lactate alongside higher glucose and short-chain fatty acids compared to lactobacilli-dominated profiles.
Metabolomic Profiling Identifies Candidate Mediators
To characterize the vaginal metabolome, investigators used 1H-NMR spectroscopy alongside 16S rRNA gene sequencing across the V3-V4 regions, with reads classified using SILVA and VALENCIA. Structural equation modeling subsequently tested whether specific metabolites mediated the relationship between community state types and psychological dimensions.
Aminobutyrate, valine, and phenylalanine emerged as candidate mediators in the structural equation models. Aminobutyrate partially mediated lower BAS-Reward Responsiveness in CST IV profiles, while valine fully mediated lower BAS-Drive. Phenylalanine partially mediated the relationship between CST IV and BIS inhibition, with a reported indirect effect that was significant.
Authors Report Funding Sources
Financial support for the research and publication of the Nature study involved multiple grants. Paola Castellano received support from a doctoral fellowship of the National Operation Program Research and Innovation 2014–2020 (CCI. 2014IT16M2OP005), European Social Fund REACT-EU resources, and Action IV.5 Doctorates on Green themes
under CUP code J35F21003350006. Silvia Conti received funding from the European Union-funded grant NextGenerationEU (Mission 4, Component 2, Investment 1.1, 2023-PRIN-VS_001 – PRIN 2022 – CUP F53D23007190006) and the INVAT project, CNR FOE 2022, under CUP B53C22010140001.
Additional support came via Camilla Ceccarani’s research grant, funded by the National Recovery and Resilience Plan, Mission 4 Component 2 Investment 1.4, through the Italian Ministry of University and Research and NextGenerationEU under Project Code CN_00000033 and CUP B83C22002930006 for the National Biodiversity Future Center – NBFC
. Marielle Ezekielle Djusse was supported by the European Union-funded grant NextGenerationEU from the National Recovery and Resilience Plan Mission 4, Component 1, Investment 3.4 (DM 118/2023) for Digital and Environmental Transitions under CUP J33C23002390002. The study funders had no involvement in study design, data collection, analysis, interpretation, or manuscript preparation.