Literature DB >> 23264537

Six weeks of a polarized training-intensity distribution leads to greater physiological and performance adaptations than a threshold model in trained cyclists.

Craig M Neal1, Angus M Hunter, Lorraine Brennan, Aifric O'Sullivan, D Lee Hamilton, Giuseppe De Vito, Stuart D R Galloway.   

Abstract

This study was undertaken to investigate physiological adaptation with two endurance-training periods differing in intensity distribution. In a randomized crossover fashion, separated by 4 wk of detraining, 12 male cyclists completed two 6-wk training periods: 1) a polarized model [6.4 (±1.4 SD) h/wk; 80%, 0%, and 20% of training time in low-, moderate-, and high-intensity zones, respectively]; and 2) a threshold model [7.5 (±2.0 SD) h/wk; 57%, 43%, and 0% training-intensity distribution]. Before and after each training period, following 2 days of diet and exercise control, fasted skeletal muscle biopsies were obtained for mitochondrial enzyme activity and monocarboxylate transporter (MCT) 1 and 4 expression, and morning first-void urine samples were collected for NMR spectroscopy-based metabolomics analysis. Endurance performance (40-km time trial), incremental exercise, peak power output (PPO), and high-intensity exercise capacity (95% maximal work rate to exhaustion) were also assessed. Endurance performance, PPOs, lactate threshold (LT), MCT4, and high-intensity exercise capacity all increased over both training periods. Improvements were greater following polarized rather than threshold for PPO [mean (±SE) change of 8 (±2)% vs. 3 (±1)%, P < 0.05], LT [9 (±3)% vs. 2 (±4)%, P < 0.05], and high-intensity exercise capacity [85 (±14)% vs. 37 (±14)%, P < 0.05]. No changes in mitochondrial enzyme activities or MCT1 were observed following training. A significant multilevel, partial least squares-discriminant analysis model was obtained for the threshold model but not the polarized model in the metabolomics analysis. A polarized training distribution results in greater systemic adaptation over 6 wk in already well-trained cyclists. Markers of muscle metabolic adaptation are largely unchanged, but metabolomics markers suggest different cellular metabolic stress that requires further investigation.

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Year:  2012        PMID: 23264537     DOI: 10.1152/japplphysiol.00652.2012

Source DB:  PubMed          Journal:  J Appl Physiol (1985)        ISSN: 0161-7567


  29 in total

1.  Urine metabolomic analysis for monitoring internal load in professional football players.

Authors:  Guillermo Quintas; Xavier Reche; Juan Daniel Sanjuan-Herráez; Helena Martínez; Marta Herrero; Xavier Valle; Marc Masa; Gil Rodas
Journal:  Metabolomics       Date:  2020-03-28       Impact factor: 4.290

Review 2.  Metabolomics, physical activity, exercise and health: A review of the current evidence.

Authors:  Rachel S Kelly; Michael P Kelly; Paul Kelly
Journal:  Biochim Biophys Acta Mol Basis Dis       Date:  2020-08-19       Impact factor: 5.187

3.  Polarized and Pyramidal Training Intensity Distribution: Relationship with a Half-Ironman Distance Triathlon Competition.

Authors:  Sergio Selles-Perez; José Fernández-Sáez; Roberto Cejuela
Journal:  J Sports Sci Med       Date:  2019-11-19       Impact factor: 2.988

4.  Do olympic athletes train as in the Paleolithic era?

Authors:  Daniel A Boullosa; Laurinda Abreu; Adrián Varela-Sanz; Iñigo Mujika
Journal:  Sports Med       Date:  2013-10       Impact factor: 11.136

5.  Metabolomics in Exercise and Sports: A Systematic Review.

Authors:  Kayvan Khoramipour; Øyvind Sandbakk; Ammar Hassanzadeh Keshteli; Abbas Ali Gaeini; David S Wishart; Karim Chamari
Journal:  Sports Med       Date:  2021-10-30       Impact factor: 11.136

6.  The rating of perceived exertion is able to differentiate the post-matches metabolomic profile of elite U-20 soccer players.

Authors:  Alisson Henrique Marinho; Filipe Antonio de Barros Sousa; Rubens de Alcântara Moura Pimentel Vilela; Pedro Balikian; Edson de Souza Bento; Thiago de Mendonça Aquino; Alessandre Crispim; Thays Ataide-Silva; Gustavo Gomes de Araujo
Journal:  Eur J Appl Physiol       Date:  2021-11-05       Impact factor: 3.078

7.  Effects of 16 weeks of pyramidal and polarized training intensity distributions in well-trained endurance runners.

Authors:  Luca Filipas; Matteo Bonato; Gabriele Gallo; Roberto Codella
Journal:  Scand J Med Sci Sports       Date:  2021-11-25       Impact factor: 4.645

8.  The Effect of Polarized Training (SIT, HIIT, and ET) on Muscle Thickness and Anaerobic Power in Trained Cyclists.

Authors:  Paulina Hebisz; Rafał Hebisz
Journal:  Int J Environ Res Public Health       Date:  2021-06-18       Impact factor: 3.390

9.  Assessment of Metabolic Flexibility by Means of Measuring Blood Lactate, Fat, and Carbohydrate Oxidation Responses to Exercise in Professional Endurance Athletes and Less-Fit Individuals.

Authors:  Iñigo San-Millán; George A Brooks
Journal:  Sports Med       Date:  2018-02       Impact factor: 11.928

10.  Physiological extremes of the human blood metabolome: A metabolomics analysis of highly glycolytic, oxidative, and anabolic athletes.

Authors:  Daniela Schranner; Martin Schönfelder; Werner Römisch-Margl; Johannes Scherr; Jürgen Schlegel; Otto Zelger; Annett Riermeier; Stephanie Kaps; Cornelia Prehn; Jerzy Adamski; Quirin Söhnlein; Fabian Stöcker; Florian Kreuzpointner; Martin Halle; Gabi Kastenmüller; Henning Wackerhage
Journal:  Physiol Rep       Date:  2021-06
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