TY - JOUR
T1 - Comparing analytical methods for the gut microbiome and aging
T2 - Gut microbial communities and body weight in the osteoporotic fractures in men (MrOS) study
AU - Osteoporotic Fractures in Men (MrOS) Study Group
AU - Shardell, Michelle
AU - Parimi, Neeta
AU - Langsetmo, Lisa
AU - Tanaka, Toshiko
AU - Jiang, Lingjing
AU - Orwoll, Eric
AU - Shikany, James M.
AU - Kado, Deborah M.
AU - Cawthon, Peggy M.
N1 - Funding Information:
This work was supported by the National Institutes of Health (grant numbers R01 AG048069, R01 AI116799). MrOS was supported by the National Institutes of Health (grant numbers U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128).
Funding Information:
M.S., E.O., J.M.S., D.M.K., and P.M.C. are supported by grants from the National Institutes of Health. L.L. received funding from Abbott Nutrition. Remaining authors have no conflicts of interest.
Publisher Copyright:
© The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Bayesian regression
KW - Compositional analysis
KW - Frailty
KW - Network analysis
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U2 - 10.1093/gerona/glaa034
DO - 10.1093/gerona/glaa034
M3 - Article
C2 - 32025711
AN - SCOPUS:85086747159
VL - 75
SP - 1267
EP - 1275
JO - Journals of Gerontology - Series A Biological Sciences and Medical Sciences
JF - Journals of Gerontology - Series A Biological Sciences and Medical Sciences
SN - 1079-5006
IS - 7
ER -