| Literature DB >> 29363055 |
Candice Colbey1, Amanda J Cox1, David B Pyne1,2,3, Ping Zhang1, Allan W Cripps1, Nicholas P West4.
Abstract
Upper respiratory symptoms remain the most common illness in athletes. Upper respiratory symptoms during heavy training and competition may impair performance. Preventing illness is the primary reason for the use of supplements, such as probiotics and prebiotics, for maintaining or promoting gut health and immune function. While exercise-induced perturbations in the immune system may increase susceptibility to illness and infection, growing evidence indicates that upper respiratory symptoms are related to a breakdown in the homeostatic regulation of the mucosal immune system of the airways. Balancing protection of the respiratory tract with normal physiological functioning requires dynamic orchestration between a wide array of immune parameters. The intestinal microbiota regulates extra-intestinal immunity via the common mucosal immune system and new evidence implicates the microbiota of the nose, mouth and respiratory tract in upper respiratory symptoms. Omics' approaches now facilitate comprehensive profiling at the molecular and proteomic levels to reveal new pathways and molecules of immune regulation. New targets may provide for personalised nutritional and training interventions to maintain athlete health.Entities:
Mesh:
Year: 2018 PMID: 29363055 PMCID: PMC5790851 DOI: 10.1007/s40279-017-0846-4
Source DB: PubMed Journal: Sports Med ISSN: 0112-1642 Impact factor: 11.136
Fig. 1Factors contributing to upper respiratory symptoms (URS) in elite athletes include higher training load, sleep disruption, travel and jetlag and dietary alterations
Fig. 2Schematic of the mucosal immune system. Interaction with environmental antigens a the microbiota, microbial metabolites, antimicrobial proteins (AMPs) (c) and dendritic processes b provide the mucosal immune system with multiple transient activation signals. Antigen invasion is prevented by the mucus layer, its constituent components and ciliated airway cells. T- and B-cell subsets d provide multiple, but highly plastic cell differentiation programmes. CD cluster of differentiation cells, Th T-helper, IgA immunoglobulin A, Il interleukin, NF-kB nuclear factor-kappa B, TGF-β transforming growth factor beta, TLR toll-like receptor, T-regs regulatory T cells
Effect of probiotic supplementation on upper respiratory symptoms (URS) in athletic cohorts ranging from healthy active individuals through to elite athletes
| References | Study design and participants | Intervention | Impact on URS |
|---|---|---|---|
| Clancy et al. [ | Double-blind placebo-controlled trial of 18 healthy and nine fatigued recreational athletes over 4 weeks | Probiotic ( | Reversal of defect in IFN-γ secretion from T cells (viral control mechanism) |
| Cox et al. [ | Double-blind placebo-controlled trial of 20 healthy, elite male distance runners over 16 weeks | Probiotic ( | Reduced incidence of URS by 50% and reduced severity of symptoms and trend for higher IFN-γ secretion from T cells ( |
| Gleeson et al. [ | Double-blind placebo-controlled trial of 84 endurance athletes over 16 weeks | Probiotic ( | Reduced the number of URS episodes by ~ 50%; higher SIgA level in those taking probiotics |
| Haywood et al. [ | Single-blind, placebo-controlled, double-arm crossover trial of 30 rugby players, 4 weeks per treatment separated by a 4-week washout | Probiotic ( | No difference in the incidence of URS |
| West et al. [ | Double-blind placebo-controlled trial of 88 well-trained recreational cyclists over 11 weeks | Probiotic ( | No significant effects on URS; reduction of LRI in male cyclists by a factor of 0.31 but a 2.2-fold increase in LRI in female cyclists |
| Gleeson et al. [ | Double-blind placebo-controlled trial of 54 endurance athletes over 16 weeks | Probiotic ( | No difference in the incidence of URS |
| Kekkonen et al. [ | Double-blind placebo-controlled trial of 141 marathon runners over 3 months | Probiotic ( | No difference in the incidence of URS |
| West et al. [ | Double-blind placebo-controlled trial of 465 physically active individuals for 150 days | Probiotics ( | Bl-04 associated with a significant 27% reduction in the risk of URS compared with placebo |
IFN interferon, LRI lower respiratory illness, SIgA salivary immunoglobulin A
Fig. 3Analysing large data sets. Multi-parameter data sets require data visualisation and data reduction techniques to identify patterns between analytes of interest and that separate study groups under investigation. Cluster algorithms (left-hand figure) order cell types (rows) through NanoString immune gene expression by individuals (columns) to reveal shared and distinct patterns. In this case, the groups represent obese (1), endurance (2) and team sport (3) groups. Red is high expression and green is low expression. CITRUS (Cluster identification, characterisation and Regression; Cytobank, Santa Clara, CA, USA) (right-hand figures) for mass cytometry groups cells into nodes based on similarity of marker expression to reveal distinct patterns of cells/receptors between groups. Joined nodes represent phenotypic similarity and lineage relationships. In this diagram, the difference in expression of CD20 and CD4 between athletic groups is depicted. Red is high expression and blue is low expression. DC, NK natural killer, Th T-helper, Treg regulatory T cell