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Baichoo, M.

Publications and source records attributed to Baichoo, M..

2 recordsLinked to original sources

Dietary fiber intake associates with improved survival and microbiomecomposition in allogeneic hematopoietic cell transplantation

Diet is linked to changes in gut microbiota and metabolite production with clinical relevance in several disease settings, although these effects remain poorly defined. We performed prospective, real-time diet monitoring (37,929 food items, 3,837 patient days) and longitudinal microbiome and metabolite profiling (1,230 fecal samples) in a clinical cohort of 173 patients undergoing allogeneic hematopoietic cell transplantation. Patients with pre-transplant fiber intake above the cohort average had significantly improved overall survival (p=0.014) and reduced incidence of grades 2-4 acute graft-versus-host disease (GVHD) (p=0.032) post-transplant. Those consuming insoluble fiber had increased microbial diversity, enriched butyrate-producing taxa, and depleted Enterococcus. Those who developed lower gastrointestinal GVHD had reduced fecal butyrate levels. In a GVHD preclinical model, we confirmed that a fiber-enriched diet increased survival, cecal butyrate, and regulatory-to-conventional T cell ratio. Thus, we demonstrated that dietary fiber has clinical significance as a modifiable factor with microbiome-mediated effects.

cancer biology↗

Correlating High-dimensional longitudinal microbial features with time-varying outcomes with FLORAL

Correlating time-dependent patient characteristics and matched microbiome samples can identify biomarkers in longitudinal microbiome studies. Existing approaches typically repeat a pre-specified modeling approach for all taxonomic features, followed by a multiple testing adjustment step for false discovery rate (FDR) control. In this work, we develop an alternative strategy of using log-ratio penalized generalized estimating equations, which directly models the longitudinal patient characteristic of interest as the outcome variable and treats microbial features as high-dimensional compositional covariates. A cross validation procedure is developed for variable selection and model selection among different working correlation structures. In extensive simulations, the proposed method achieved superior sensitivity over the state-of-the-art methods with robustly controlled FDR. In the analyses of correlating longitudinal dietary intake, bloodstream infection status, and microbial features from matched samples of cancer patients, the proposed method effectively identified gut health indicators and clinically relevant microbial markers, showing robust utilities in real-world applications. The method is implemented under the open-source R package FLORAL, which is available at (https://vdblab.github.io/FLORAL/).

bioinformatics↗