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Background: The respiratory microbiome is dynamic, varying between anatomical niches, and it is affected by various host and environmental factors, one of which is lifestyle. Few studies have characterized the upper respiratory tract microbiome profile according to lifestyle. We explored the association between lifestyles and microbiota profiles in the upper respiratory tract of healthy adults. Methods: We analyzed nasal samples from 110 healthy adults who were living in Santiago, Chile, using 16S ribosomal RNA gene-sequencing methods. Volunteers completed a structured questionnaire about lifestyle. Results: The composition and abundance of taxonomic groups varied across lifestyle attributes. Additionally, multivariate models suggested that alpha diversity varied in the function of physical activity, nutritional status, smoking, and the interaction between nutritional status and smoking, although the significant impact of those variables varied between women and men. Although physical activity and nutritional status were significantly associated with all indexes of alpha diversity among women, the diversity of microbiota among men was associated with smoking and the interaction between nutritional status and smoking. Conclusions: The alpha diversity of nasal microbiota is associated with lifestyle attributes, but these associations depend on sex and nutritional status. Our results suggest that future studies of the airway microbiome may provide a better resolution if data are stratified for differences in sex and nutritional status. [ABSTRACT FROM AUTHOR]

Simple Summary: Ambient air contains a mixture of pollutants, the effects of which on animal and human health have been widely described. In contrast, the effects on physical performance are poorly understood, largely due to the difficulty of implementing an experimental model to study this problem. Thoroughbred horse racing involves many animal athletes, of similar genetics, environmental exposure, training, and diet, who participate by breathing varying mixtures of ambient air. This paper presents an analysis strategy based on the homogeneity of the races, the distance, and the design of the track. This paper presents a preliminary analysis in which we observe that the level of performance is decreased by concentrations of PM10, PM2.5, NO2, NO, and CO in the air. Thus, we note that this natural experiment may constitute a model of interest to advance the understanding of the problem. This study proposes the theoretical principles for the selection of a sample of horse races to study the relationship between air pollutants and performance. These criteria were then applied to an original dataset comparing the correlations between these variables obtained in "Handicap" versus "Conditional" type races. Methods: The mean concentration of pollutants during the six hours prior to the race and the speed of the test were determined in 441 official races at a racecourse in Santiago, Chile, during the summer and winter months of 2012. Using layout, track condition and distance (1000, 1100 and 1200 m) as criteria, a homogeneous group of races ("Handicap"; n = 214) versus a heterogeneous group ("Conditional"; n = 95) were compared using simple correlations (Spearman's test). Results: Race speed was related to greater levels of PM10, PM2.5, NO2, NO and SO2 and it was positively related to O3, a trend that was observed in the 1000, 1100 m races and in the total "Handicap" group. Similar results were observed only in 1000 m for the "Conditional" group with lower Rho, except for PM10 and PM2.5. The total races of the conditional group showed lower Rho values and significant associations of the same trend for CO, NO2, NO and SO2. Conclusions: Horse races between 1000 and 1200 m of the "Handicap" type appear to be an interesting group to study the relationship between air pollutants and the performance of racehorses. In the future, our observations should be expanded to other distances and other types of races. [ABSTRACT FROM AUTHOR]

Simple Summary: Thoroughbred horse racing is a natural experiment to study the effect of air pollutants on animal performance. In this activity, the animals are exposed to multiple mixtures of pollutants in the air and varying conditions of humidity and ambient temperature. Thus, in this work, in a homogeneous group of races, we used principal component analysis, which allowed us to gather information from all the environmental parameters measured, forming new variables called principal components. We found that the principal component is mainly determined as nitrogen oxides and carbon monoxide, while secondarily as particulate matter and sulfur oxides. Furthermore, this component is negatively related to the speed of the analyzed races. Thus, it is shown that air pollutants affect animal performance. The impact of some atmospheric pollutants (PM10, PM2.5, O3, NO2, NO, SO2, CO), humidity and temperature were studied on the performance of thoroughbred racehorses. The study included 162 official handicap races held in 2012 in Santiago de Chile, at distances of 1000, 1100 and 1200 m, on a track in good condition, with a layout that included a bend, during the summer and winter months. The environmental variables were measured at the time of the race and were obtained from a monitoring station located 470 m from the equestrian center. The environmental variables showed an autocorrelation of variables, so they were reduced using principal component analysis. Subsequently, the principal components were correlated with running speed using Pearson's method. Totals of 60.17 and 23.29% of the total variability of the data was explained by principal components 1 and 2 (PC1 and PC2), respectively. PC1 was mainly determined by NO, NO2, and CO (loadings~0.90) and secondarily by PM10, PM2.5, and SO2 (loadings~0.6), with which the data showed inverse associations, while with temperature and O3 it showed direct associations (loadings~0.7). In addition, this component correlated negatively with running speed (r = −0.50), while PC2 was not associated with this variable. In conclusion, using the principal component analysis strategy, it was determined that running speed is affected by air pollutants. [ABSTRACT FROM AUTHOR]