Abstract

Despite the recognized gut-brain axis link, natural variations in microbial profiles between patients hinder definition of normal abundance ranges, confounding the impact of dysbiosis on infant neurodevelopment. We infer a digital twin of the infant microbiome, forecasting ecosystem trajectories from a few initial observations. Using 16S ribosomal RNA profiles from 88 preterm infants (398 fecal samples and 32,942 abundance estimates for 91 microbial classes), the model (Q-net) predicts abundance dynamics with R2 = 0.69. Contrasting the fit to Q-nets of typical versus suboptimal development, we can reliably estimate individual deficit risk (Mδ) and identify infants achieving poor future head circumference growth with ≈76% area under the receiver operator characteristic curve, 95% ± 1.8% positive predictive value at 98% specificity at 30 weeks postmenstrual age. We find that early transplantation might mitigate risk for ≈45.2% of the cohort, with potentially negative effects from incorrect supplementation. Q-nets are generative artificial intelligence models for ecosystem dynamics, with broad potential applications.

Document Type

Article

Publication Date

4-12-2024

Notes/Citation Information

Copyright © 2024 the Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. no claim to original U.S. Government Works. distributed under a creative commons Attribution license 4.0 (cc BY).

Digital Object Identifier (DOI)

https://doi.org/10.1126/sciadv.adj0400

Funding Information

This study was partially supported by NIH grants P30dK042086 Ccenter for Interdisciplinary Study of Inflammatory Intestinal Disorders), R01HD105234 (e.c.c.), institutional support from the Biological Sciences Division, and the resources provided by the Research Computing Center (RCC) of the University of Chicago.

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