Early-life microbiota in the LIMIT cohort: unveiling meconium microbiota types beyond maternal lifestyle

作者：El Masri D, Meriggi N, Loperfido F, Bianco I, Ferrara C, Maccarini B, Sottotetti F, De Filippo C, Loddo N, Cerbo RM, Ghirardello S, Garofoli F, Angelini M, Monti MC, Alemayohu MA, Lukassen MB, Cena H, De Giuseppe R · 发表于：Gut microbes · 年份：2026 · DOI：10.1080/19490976.2026.2712811 · 研究领域：16S rRNA sequencing、Early gut microbiota、gestational weight gain、lifestyle factors、meconium analysis、pre-pregnancy BMI、unsupervised analysis

INTRODUCTION: Early gut microbiota development plays an important role in lifelong human health, and meconium offers a unique matrix to understand prenatal microbial exposures. This study from the LIMIT cohort aimed at investigating associations between maternal pre-pregnancy body mass index (pre-BMI) and gestational weight gain (GWG), as well as other maternal lifestyle and environmental factors, with the structure of meconium microbiota at delivery. METHODS: Two hundred pregnant women were enrolled during the pre-hospital care before birth at Fondazione IRCCS Policlinico San Matteo (Pavia), according to the inclusion/exclusion criteria. Mothers were grouped based on their GWG gain category according to the IOM guidelines. 168 meconium samples were analyzed using a targeted long-reads sequence approach. Bacterial 16S rRNA (V1-V8) amplicons were sequenced using the Oxford Nanopore PromethION platform. Supervised analyses assessed associations between maternal variables and bacterial diversity, while an unsupervised learning approach based on the Partitioning Around Medoids (PAM) clustering algorithm was applied to identify specific microbial clusters unrelated to the cohort's metadata. RESULTS: No significant associations were observed between GWG, pre-BMI or other lifestyle factors and overall microbial diversity. Unsupervised learning revealed five distinct meconium microbial clusters, i.e. "microbiota-types", related to different taxonomic profiles, determined by the var...