Aim: The acute effects of maximal endurance exercise (15 min) on lipid and lipoprotein levels were examined, in order to determine whether the level of response produced could be affected by maximal exercise intensity (incremental stress test).
Methods: Participants in this cross sectional study were male athletes (n=78) of national level: basketball (n=10), swimming (n=9), long distance (LD) running (n=23) and wrestling (n=35); also a group of non-athlete volunteers as controls (n=19). Athletes had trained an average of 3 h/day and 5 years. The ergometric test used for the determination of maximal oxygen uptake on a treadmill was based on a stepwise stress protocol.
Results: Immediately after a maximal effort all groups (controls included) showed significant increases in total cholesterol (TC), triglycerides (TG), phospholipids (PL) and high-density cholesterol (HDL-C), from rest values. Low-density cholesterol (LDL-C) increased significantly in basketball, LD running and wrestling, while it did not change significantly in swimming and controls, after maximal effort. Hemoconcentration was of the order of +12.5%.
Conclusions: The level of these increases might be directly related to the intensity and duration of exercise performed. Acute endurance exercise (100% VO(2max)) may induce acute modification of the above lipid parameters potentiating the systematic chronic exercise effects already present.
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Pulmonology
December 2025
Sorbonne Université, INSERM, UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, Paris, France.
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January 2025
Department of Sport Games, Józef Piłsudski University of Physical Education in Warsaw, Marymoncka 34, Warsaw, 00-968, Poland.
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Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA. Electronic address:
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February 2025
Motion and Exercise Science, University of Stuttgart, Stuttgart, Germany.
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requires precise segmentation of structures. In this study we developed a novel automatic machine learning approach for volumetric segmentation and quantitative assessment of MRI volumes and investigated the efficacy of using a machine learning algorithm to assess muscle, subcutaneous adipose tissue (SAT), and bone volume of the thigh before and after a strength training.
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