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. 2019;23(6):538-546.
doi: 10.1007/s12603-019-1194-x.

The Association between Objectively Measured Physical Activity and the Gut Microbiome among Older Community Dwelling Men

Affiliations

The Association between Objectively Measured Physical Activity and the Gut Microbiome among Older Community Dwelling Men

L Langsetmo et al. J Nutr Health Aging. 2019.

Abstract

Objectives: To determine the relationship between objectively measured physical activity (PA) and the gut microbiome among community-dwelling older men.

Design: Cross-sectional study.

Setting: Osteoporotic Fractures in Men (MrOS) cohort participants at Visit 4 (2014-16).

Participants: Eligible men (n=373, mean age 84 y) included participants with 5-day activity assessment with at least 90% wear time and analyzed stool samples.

Measurements: PA was measured with the SenseWear Pro3 Armband and stool samples analyzed for 16S v4 rRNA marker genes using Illumina MiSeq technology. Armband data together with sex, height, and weight were used to estimate total steps, total energy expenditure, and level of activity. 16S data was analyzed using standard UPARSE workflow. Shannon and Inverse Simpson indices were measures of (within-participant) α-diversity. Weighted and unweighted Unifrac were measures of (between-participant) β-diversity. We used linear regression analysis, principal coordinate analysis, zero-inflated Gaussian models to assess association between PA and α-diversity, β-diversity, and specific taxa, respectively, with adjustments for age, race, BMI, clinical center, library size, and number of chronic conditions.

Results: PA was not associated with α-diversity. There was a slight association between PA and β-diversity (in particular the second principal coordinate). Compared to those who were less active, those who had higher step counts had higher relative abundance of Cetobacterium and lower relative abundance of taxa from the genera Coprobacillus, Adlercreutzia, Erysipelotrichaceae CC-115 after multivariable adjustment including age, BMI, and chronic conditions. There was no consistent pattern by phylum.

Conclusion: There was a modest association between levels of PA and specific gut microbes among community-dwelling older men. The observed associations are consistent with the hypothesis that underlying health status and composition of the host microbiome are related.

Keywords: Physical activity; activity monitor; gut microbiome; older men; step count.

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Conflict of interest statement

Drs. Langsetmo, Orwoll, Ensrud, Cauley, and Shikany have received NIH funding as noted above. Dr. Langsetmo reports grants from Abbott outside the submitted work. Dr. Johnson reports personal fees from Nestlé Health Science, grants from General Mills Inc., outside the submitted work. Dr. Orwoll reports grants from Lilly, grants from Nordic Biosciences, grants from Mereo Biopharma, other from Bayer, outside the submitted work. Drs. Ryan Demmer, Fino, Hoffman, Shmagel, Meyer have no disclosures.

Figures

Figure 1
Figure 1
Study Flow Diagram
Figure 2
Figure 2
Distributions and Predictors of α-Diversity Measures
Figure 3
Figure 3
The Associations between Quartiles of Steps and Quartiles of Self-reported PA (PASE score) and β-Diversity Principal Coordinate Analysis
Figure 4
Figure 4
The Association between PA variables and Taxa (Genus-level agglomeration)

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References

    1. Zhernakova A, Kurilshikov A, Bonder MJ, Tigchelaar EF, Schirmer M, Vatanen T, Mujagic Z, Vila AV, Falony G, Vieira-Silva S, Wang J, Imhann F, Brandsma E, Jankipersadsing SA, Joossens M, Cenit MC, Deelen P, Swertz MA, Weersma RK, Feskens EJ, Netea MG, Gevers D, Jonkers D, Franke L, Aulchenko YS, Huttenhower C, Raes J, Hofker MH, Xavier RJ, Wijmenga C, Fu J. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science. 2016;352:565–569. 10.1126/science.aad3369 PubMed PMID: 27126040, PMCID 5240844. - DOI - PMC - PubMed
    1. Xu Z, Knight R. Dietary effects on human gut microbiome diversity. Br J Nutr. 2015;113:S1–S5. 10.1017/S0007114514004127 PubMed PMID: 25498959. - DOI - PMC - PubMed
    1. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, Fischbach MA, Biddinger SB, Dutton RJ, Turnbaugh PJ. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–563. 10.1038/nature12820 PubMed PMID: 24336217. - DOI - PMC - PubMed
    1. Claesson MJ, Jeffery IB, Conde S, Power SE, O’Connor EM, Cusack S, Harris HM, Coakley M, Lakshminarayanan B, O’Sullivan O, Fitzgerald GF, Deane J, O’Connor M, Harnedy N, O’Connor K, O’Mahony D, van SD, Wallace M, Brennan L, Stanton C, Marchesi JR, Fitzgerald AP, Shanahan F, Hill C, Ross RP, O’Toole PW. Gut microbiota composition correlates with diet and health in the elderly. Nature. 2012;488:178–184. 10.1038/nature11319 PubMed PMID: 22797518. - DOI - PubMed
    1. Weir T. Exercise: The Next Frontier in Microbiota Research. Exerc Sport Sci Rev. 2017;45:4–5. 10.1249/JES.0000000000000097 PubMed PMID: 27801725. - DOI - PubMed

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