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

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) ß-diversity. We used linear regression analysis, principal coordinate analysis, zero-inflated Gaussian models to assess association between PA and α-diversity, ß-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 ß-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.

Inflection points in longitudinal models: Tracking recovery and return to play following concussion.

The return to play (RTP) process may occur during longitudinal studies tracking recovery after concussion. This factor, which is often omitted within statistical designs, could affect the fit and overall interpretation of the statistical model. This article demonstrates the difference in results and interpretation between 2 linear mixed-model designs: (1) a between-group longitudinal (GROUP) analysis and (2) a between-group longitudinal model that used an inflection point to account for changes around the time of RTP (RTP analysis). These analyses were conducted on instrumented balance data collected on 23 concussed athletes and 25 controls over 8 weeks following concussion. Total sway area and the range of mediolateral acceleration were used as outcome measures. No significant findings were found in the GROUP design for either outcome measure. In contrast, the RTP analysis revealed significant effects of time (P = .007) and RTP change (P = .007), and group*time (P = .028) and group*RTP change (P = .022) interactions for total sway area, and effects of group (P = .011), time (P = .010), and RTP change (P = .014), and group*time (P = .013) and group*RTP change interactions (P = .013) for range of mediolateral acceleration. For both outcomes, the RTP model fit the data significantly better on comparison of likelihood ratios (P ≤ .027). These results suggest that allowing for an inflection point in the statistical design may assist understanding of what happens around clinically meaningful time points. The choice of statistical model had a considerable effect on the interpretation of findings, and provokes discussion around the best method for analyzing longitudinal datasets when important clinical time points like RTP exist.