{"doi":"10.1093/gerona/glaa034","title":"Comparing Analytical Methods for the Gut Microbiome and Aging: Gut Microbial Communities and Body Weight in the Osteoporotic Fractures in Men (MrOS) Study","abstract":"Determining the role of gut microbial communities in aging-related phenotypes, including weight loss, is an emerging gerontology research priority. Gut microbiome datasets comprise relative abundances of microbial taxa that necessarily sum to 1; analysis ignoring this feature may produce misleading results. Using data from the Osteoporotic Fractures in Men (MrOS) study (n = 530; mean [SD] age = 84.3 [4.1] years), we assessed 163 genera from stool samples and body weight. We compared conventional analysis, which does not address the sum-to-1 constraint, to compositional analysis, which does. Specifically, we compared elastic net regression (for variable selection) and conventional Bayesian linear regression (BLR) and network analysis to compositional BLR and network analysis; adjusting for past weight, height, and other covariates. Conventional BLR identified Roseburia and Dialister (higher weight) and Coprococcus-1 (lower weight) after multiple comparisons adjustment (p < .0125); plus Sutterella and Ruminococcus-1 (p < .05). No conventional network module was associated with weight. Using compositional BLR, Coprococcus-2 and Acidaminococcus were most strongly associated with higher adjusted weight; Coprococcus-1 and Ruminococcus-1 were most strongly associated with lower adjusted weight (p < .05), but nonsignificant after multiple comparisons adjustment. Two compositional network modules with respective hub taxa Blautia and Faecalibacterium were associated with adjusted weight (p < .01). Findings depended on analytical workflow. Compositional analysis is advocated to appropriately handle the sum-to-1 constraint.","journal":"The Journals of Gerontology Series A","year":2020,"id":79221,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":16,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.802,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":50682,"name":"Neeta Parimi","orcid":null,"position":1,"is_corresponding":false},{"id":361152,"name":"Lisa Langsetmo","orcid":"0000-0003-2245-1657","position":2,"is_corresponding":false},{"id":21720,"name":"Toshiko Tanaka","orcid":"0000-0002-4161-3829","position":3,"is_corresponding":false},{"id":225265,"name":"Lingjing Jiang","orcid":"0000-0001-8706-2850","position":4,"is_corresponding":false},{"id":250536,"name":"Eric Orwoll","orcid":"0000-0002-8520-7355","position":5,"is_corresponding":false},{"id":353721,"name":"James M. Shikany","orcid":"0000-0002-2424-9308","position":6,"is_corresponding":false},{"id":250537,"name":"Deborah M. Kado","orcid":"0000-0002-2582-5573","position":7,"is_corresponding":false},{"id":109435,"name":"Peggy M. Cawthon","orcid":"0000-0003-4938-9478","position":8,"is_corresponding":false},{"id":36350,"name":"Michelle Shardell","orcid":"0000-0002-5283-2082","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-18T21:50:33.748601Z","pmid":"32025711","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}