Dataset Information

Has Athletic Performance Reached its Peak?

ABSTRACT: Limits to athletic performance have long been a topic of myth and debate. However, sport performance appears to have reached a state of stagnation in recent years, suggesting that the physical capabilities of humans and other athletic species, such as greyhounds and thoroughbreds, cannot progress indefinitely. Although the ultimate capabilities may be predictable, the exact path for the absolute maximal performance values remains difficult to assess and relies on technical innovations, sport regulation, and other parameters that depend on current societal and economic conditions. The aim of this literature review was to assess the possible plateau of top physical capabilities in various events and detail the historical backgrounds and sociocultural, anthropometrical, and physiological factors influencing the progress and regression of athletic performance. Time series of performances in Olympic disciplines, such as track and field and swimming events, from 1896 to 2012 reveal a major decrease in performance development. Such a saturation effect is simultaneous in greyhound, thoroughbred, and frog performances. The genetic condition, exhaustion of phenotypic pools, economic context, and the depletion of optimal morphological traits contribute to the observed limitation of physical capabilities. Present conditions prevailing, we approach absolute physical limits and endure a continued period of world record scarcity. Optional scenarios for further improvements will mostly depend on sport technology and modification competition rules.

SUBMITTER: Berthelot G

PROVIDER: S-EPMC4536275 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Has Athletic Performance Reached its Peak?

Berthelot Geoffroy G Sedeaud Adrien A Marck Adrien A Antero-Jacquemin Juliana J Schipman Julien J Saulière Guillaume G Marc Andy A Desgorces François-Denis FD Toussaint Jean-François JF

Sports medicine (Auckland, N.Z.) 20150901 9

Limits to athletic performance have long been a topic of myth and debate. However, sport performance appears to have reached a state of stagnation in recent years, suggesting that the physical capabilities of humans and other athletic species, such as greyhounds and thoroughbreds, cannot progress indefinitely. Although the ultimate capabilities may be predictable, the exact path for the absolute maximal performance values remains difficult to assess and relies on technical innovations, sport reg ...[more]

PMID: 26094000

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Project description:BACKGROUND: The World Anti-Doping Agency (WADA) publishes the Prohibited List, a manually compiled international standard of substances and methods prohibited in-competition, out-of-competition and in particular sports. It would be ideal to be able to identify all substances that have one or more performance-enhancing pharmacological actions in an automated, fast and cost effective way. Here, we use experimental data derived from the ChEMBL database (~7,000,000 activity records for 1,300,000 compounds) to build a database model that takes into account both structure and experimental information, and use this database to predict both on-target and off-target interactions between these molecules and targets relevant to doping in sport. RESULTS: The ChEMBL database was screened and eight well populated categories of activities (Ki, Kd, EC50, ED50, activity, potency, inhibition and IC50) were used for a rule-based filtering process to define the labels "active" or "inactive". The "active" compounds for each of the ChEMBL families were thereby defined and these populated our bioactivity-based filtered families. A structure-based clustering step was subsequently performed in order to split families with more than one distinct chemical scaffold. This produced refined families, whose members share both a common chemical scaffold and bioactivity against a common target in ChEMBL. CONCLUSIONS: We have used the Parzen-Rosenblatt machine learning approach to test whether compounds in ChEMBL can be correctly predicted to belong to their appropriate refined families. Validation tests using the refined families gave a significant increase in predictivity compared with the filtered or with the original families. Out of 61,660 queries in our Monte Carlo cross-validation, belonging to 19,639 refined families, 41,300 (66.98%) had the parent family as the top prediction and 53,797 (87.25%) had the parent family in the top four hits. Having thus validated our approach, we used it to identify the protein targets associated with the WADA prohibited classes. For compounds where we do not have experimental data, we use their computed patterns of interaction with protein targets to make predictions of bioactivity. We hope that other groups will test these predictions experimentally in the future.

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Has Athletic Performance Reached its Peak?

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Has Athletic Performance Reached its Peak?

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