Multi-Omics Analysis of Microbiome–Immune Interactions in the Lifelines-DEEP Population Cohort

This project will use the Lifelines-DEEP cohort to study microbiome–host molecular
relationships in a population-based multi-omics setting and to validate microbiome-derived bridge
signatures identified in related host–microbiome research. Our prior work identified stable crossmodal
bridge structure linking microbial features to host immune measurements in integrated multiomics
data. In the present project, we will test whether analogous microbial bridge signatures are
detectable in Lifelines-DEEP and whether they associate with immune-related protein and
metabolomic structure in consistent directions.
The requested data will be used for strictly non-commercial research into healthy ageing,
host–microbiome systems biology, and multi-omics statistical methodology. Analyses will integrate
metagenomics, 16S microbiome profiles, plasma metabolomics, circulating protein measurements,
and follow-up information where relevant. We will apply block-scaled multi-omics integration, latent
variable analysis, sparse statistical modeling, and network-based analyses to identify shared
microbiome–host structure and to evaluate cross-modal bridge and connector features spanning
microbial and immune-related layers.
This project is designed as a population-based validation and discovery study. No attempt
will be made to identify participants, and no linkage will be performed in a way that could enable reidentification.
All analyses will be performed on secure institutional infrastructure with access
restricted to approved users. Results will be reported only in aggregate form, including summary
statistics, latent features, module-level associations, and network-level findings. Individual-level data
will not be redistributed.
The scientific aim is to improve understanding of how gut microbial community structure
relates to host immune and metabolic organization in humans, and to evaluate the portability of
microbiome-derived molecular signatures across cohorts. This work will contribute to noncommercial
research on healthy ageing and to the development of robust statistical methods for
multi-omics integration.