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      Overtraining Syndrome as a Complex Systems Phenomenon

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          Abstract

          The phenomenon of reduced athletic performance following sustained, intense training (Overtraining Syndrome, and OTS) was first recognized more than 90 years ago. Although hundreds of scientific publications have focused on OTS, a definitive diagnosis, reliable biomarkers, and effective treatments remain unknown. The present review considers existing models of OTS, acknowledges the individualized and sport-specific nature of signs/symptoms, describes potential interacting predisposing factors, and proposes that OTS will be most effectively characterized and evaluated via the underlying complex biological systems. Complex systems in nature are not aptly characterized or successfully analyzed using the classic scientific method (i.e., simplifying complex problems into single variables in a search for cause-and-effect) because they result from myriad (often non-linear) concomitant interactions of multiple determinants. Thus, this review 1) proposes that OTS be viewed from the perspectives of complex systems and network physiology, 2) advocates for and recommends that techniques such as trans-omic analyses and machine learning be widely employed, and 3) proposes evidence-based areas for future OTS investigations, including concomitant multi-domain analyses incorporating brain neural networks, dysfunction of hypothalamic-pituitary-adrenal responses to training stress, the intestinal microbiota, immune factors, and low energy availability. Such an inclusive and modern approach will measurably help in prevention and management of OTS.

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          Diet rapidly and reproducibly alters the human gut microbiome

          Long-term diet influences the structure and activity of the trillions of microorganisms residing in the human gut 1–5 , but it remains unclear how rapidly and reproducibly the human gut microbiome responds to short-term macronutrient change. Here, we show that the short-term consumption of diets composed entirely of animal or plant products alters microbial community structure and overwhelms inter-individual differences in microbial gene expression. The animal-based diet increased the abundance of bile-tolerant microorganisms (Alistipes, Bilophila, and Bacteroides) and decreased the levels of Firmicutes that metabolize dietary plant polysaccharides (Roseburia, Eubacterium rectale, and Ruminococcus bromii). Microbial activity mirrored differences between herbivorous and carnivorous mammals 2 , reflecting trade-offs between carbohydrate and protein fermentation. Foodborne microbes from both diets transiently colonized the gut, including bacteria, fungi, and even viruses. Finally, increases in the abundance and activity of Bilophila wadsworthia on the animal-based diet support a link between dietary fat, bile acids, and the outgrowth of microorganisms capable of triggering inflammatory bowel disease 6 . In concert, these results demonstrate that the gut microbiome can rapidly respond to altered diet, potentially facilitating the diversity of human dietary lifestyles.
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            Linking long-term dietary patterns with gut microbial enterotypes.

            Diet strongly affects human health, partly by modulating gut microbiome composition. We used diet inventories and 16S rDNA sequencing to characterize fecal samples from 98 individuals. Fecal communities clustered into enterotypes distinguished primarily by levels of Bacteroides and Prevotella. Enterotypes were strongly associated with long-term diets, particularly protein and animal fat (Bacteroides) versus carbohydrates (Prevotella). A controlled-feeding study of 10 subjects showed that microbiome composition changed detectably within 24 hours of initiating a high-fat/low-fiber or low-fat/high-fiber diet, but that enterotype identity remained stable during the 10-day study. Thus, alternative enterotype states are associated with long-term diet.
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              Richness of human gut microbiome correlates with metabolic markers.

              We are facing a global metabolic health crisis provoked by an obesity epidemic. Here we report the human gut microbial composition in a population sample of 123 non-obese and 169 obese Danish individuals. We find two groups of individuals that differ by the number of gut microbial genes and thus gut bacterial richness. They contain known and previously unknown bacterial species at different proportions; individuals with a low bacterial richness (23% of the population) are characterized by more marked overall adiposity, insulin resistance and dyslipidaemia and a more pronounced inflammatory phenotype when compared with high bacterial richness individuals. The obese individuals among the lower bacterial richness group also gain more weight over time. Only a few bacterial species are sufficient to distinguish between individuals with high and low bacterial richness, and even between lean and obese participants. Our classifications based on variation in the gut microbiome identify subsets of individuals in the general white adult population who may be at increased risk of progressing to adiposity-associated co-morbidities.
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                Author and article information

                Contributors
                Journal
                Front Netw Physiol
                Front Netw Physiol
                Front. Netw. Physiol.
                Frontiers in Network Physiology
                Frontiers Media S.A.
                2674-0109
                18 January 2022
                2021
                : 1
                : 794392
                Affiliations
                [ 1 ] Human Performance Laboratory , University of Connecticut , Storrs, CT, United States
                [ 2 ] Sport Sciences and Medicine and Performance Health , WTA Women’s Tennis Association , St. Petersburg, FL, United States
                [ 3 ] Department of Energy and Renewables , Heriot-Watt University , Stromness, United Kingdom
                [ 4 ] Riverside Behavioral Health Center , Hampton, VA, United States
                Author notes

                Edited by: Natàlia Balagué, University of Barcelona, Spain

                Reviewed by: Andreas Venhorst, Universität des Saarlandes, Germany

                Rocío Cupeiro, Universidad Politécnica de Madrid, Spain

                *Correspondence: Lawrence E. Armstrong, lawrence.armstrong@ 123456uconn.edu

                This article was submitted to Network Physiology of Exercise, a section of the journal Frontiers in Network Physiology

                Article
                794392
                10.3389/fnetp.2021.794392
                10013019
                36925581
                e0af0f46-c01b-4460-9549-9e8af004fc60
                Copyright © 2022 Armstrong, Bergeron, Lee, Mershon and Armstrong.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 13 October 2021
                : 13 December 2021
                Categories
                Network Physiology
                Review

                stress,exercise,hypothalamic-pituitary-adrenal axis,network,overreaching,metabolism,genome

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