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Rhinitis is one of the most common respiratory diseases, influenced by various environmental factors such as green space, air pollution and indoor microbiomes. However, their interactions and combined effects have not been reported. We recruited 1121 preschool children from day care centers in a northern city of China. Health and demographic data were collected through questionnaires answered by the children’s parents. Surrounding green space was assessed by Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and land cover data of grassland proportion within 1500/3000 m. Ambient air pollution was estimated using the inverse distance weighted (IDW), and the indoor microbiome in classroom vacuum dust was profiled by bacterial 16S rRNA and fungal ITS amplicon sequencing. Mixed-effect logistic regression revealed the proportion of natural grassland, grassland leaf-off and total grassland was negatively associated with current rhinitis. Stratified analysis indicated that greater green space exposure was associated with a reduced current rhinitis in children at high levels of air pollution. Additionally, grassland also protects children against environmental tobacco smoke at home. Indoor microbiome analysis showed Haemophilus and Dolosigranulum were enriched in low-rhinitis day care centers, while Amaricoccus, Blautia and Mycosphaerella were enriched in high-rhinitis day care centers. Mediation analysis indicated that the indoor microbiome did not have significant mediating effects on the relationship between green space and children’s current rhinitis. This is the first study to reveal interactions of green space, air pollution and indoor microbiome on rhinitis, providing new insights into how environmental factors collectively influence respiratory health in children.

Background/Objectives: The integration of microbiome and metabolome data could unveil profound insights into biological processes. However, widely used multi-omic data analyses often employ a stepwise mining approach, failing to harness the full potential of multi-omic datasets and leading to reduced detection accuracy. Synergistic analysis incorporating microbiome/metabolome data are essential for deeper understanding. Method: This study introduces a Coupled Matrix and Tensor Factorization (CMTF) framework for the joint analysis of microbiome and metabolome data, overcoming these limitations. Two CMTF frameworks were developed to factorize microbial taxa, functional pathways, and metabolites into latent factors, facilitating dimension reduction and biomarker identification. Validation was conducted using three diverse microbiome/metabolome datasets, including built environments and human gut samples from inflammatory bowel disease (IBD) and COVID-19 studies. Results: Our results revealed biologically meaningful biomarkers, such as Bacteroides vulgatus and acylcarnitines associated with IBD and pyroglutamic acid and p-cresol associated with COVID-19 outcomes, which provide new avenues for research. The CMTF framework consistently outperformed traditional methods in both dimension reduction and biomarker detection, offering a robust tool for uncovering biologically relevant insights. Conclusions: Despite its stringent data requirements, including the reliance on stratified microbial-based pathway abundances and taxa-level contributions, this approach provides a significant step forward in multi-omics integration and analysis, with potential applications across biomedical, environmental, and agricultural research.