This event is hosted by the Department of Biostatistics.
Abstract: Osteoporosis represents the most prevalent metabolic bone disease, characterized by reduced bone mineral density (BMD) and increased susceptibility to low trauma fractures. Although the gut microbiome has been reported to play a role in bone metabolic processes, the individual species and underlying functional mechanisms have not yet been characterized. For the first time, to our knowledge, we conducted integrative analyses in a large cohort of peri- and early post-menopausal Chinese women with shotgun metagenomic sequencing and untargeted serum metabolomics profiles to identify the interactions between these biological factors which may be relevant for osteoporosis susceptibility. The novel systematic multi-omics analysis was divided into three main parts including single omics association analyses, exploration of the global inter-omics relationship, and integrative network analyses with respect to BMD. A supervised sparse canonical correlation analysis model was applied to select the features that are correlated across the paired microbiome and metabolite profiles with importance for the phenotype, and Bayesian network analysis was conducted to assess potential inter-omics causal relationships. Whole genome sequencing data from the same study subjects was then used to perform Mendelian randomization analyses to provide further support for the potential causal connections. In particular, we proposed a possible causal pathway for BMD regulation involving the bacteria species Lactobacillus delbrueckii and the gut metabolite 3-Phenylpropanoic acid, a phenolic acid compound produced by the microbial catabolism of dietary flavonoids. The findings reveal important insights into the pathophysiological mechanisms of osteoporosis and propose novel biomarkers/therapeutic targets for the treatment/prevention of disease.
Bio: Dr. Greenbaum is a research track faculty member in the Division of Biomedical Informatics and Genomics at Tulane University School of Medicine. He is a biostatistician with specialization in bioinformatics, and his long-term research goal is to develop and apply statistical methodology for multi-omics integration analysis in order to uncover the biological mechanisms contributing to complex human disease. The availability of statistical approaches that incorporate the biological interconnections between different omics is crucial for improving the accuracy of disease predication, understanding the functional mechanisms implicated in the disease pathophysiology, and developing novel therapeutic treatments.
Dr. Greenbaum pioneered an innovative multi-omics analysis which illuminated several potential functional mechanisms which may contribute to the associations between the gut microbiota, their metabolic byproducts, and osteoporosis susceptibility. His work represents some of the first steps towards developing a comprehensive understanding of the cross talk, interactions, and causal inference of the interactions between these biological factors with respect to bone health.