Aerobic Metabolic Adaptations in Endurance Eccentric Exercise and Training: From Whole Body to Mitochondria
Bibliographic record
Abstract
A characteristic feature of eccentric as compared with concentric exercise is the ability to generate greater mechanical loads for lower cardiopulmonary demands. Current evidence concurs to show that eccentric training translates into considerable gains in muscle mass and strength. Less is known, however, regarding its impact on oxygen transport and on factors to be considered for optimizing its prescription and monitoring. This article reviews the existing evidence for endurance eccentric exercise effects on the components of the oxygen transport system from systemic to mitochondria in both humans and animals. In the studies reviewed, specially designed cycle-ergometers or downhill treadmill running were used to generate eccentric contractions. Observations to date indicate that overall, the aerobic demand associated with the eccentric training load was too low to significantly increase peak maximal oxygen consumption. By extension, it can be inferred that the very high eccentric power output that would have been required to solicit a metabolic demand sufficient to enhance peak aerobic power could not be tolerated or sustained by participants. The impact of endurance eccentric training on peripheral flow distribution remains largely undocumented. Given the high damage susceptibility of eccentric exercise, the extent to which skeletal muscle oxygen utilization adaptations would be seen depends on the balance of adverse and positive signals on mitochondrial integrity. The article examines the protection provided by repeated bouts of acute eccentric exercise and reports on the impact of eccentric cycling and downhill running training programs on markers of mitochondrial function and of mitochondrial biogenesis using mostly from animal studies. The summary of findings does not reveal an impact of training on skeletal muscle mitochondrial respiration nor on selected mitochondrial messenger RNA transcripts. The implications of observations to date are discussed within future perspectives for advancing research on endurance eccentric exercise physiological impacts and using a combined eccentric and concentric exercise approach to optimize functional capacity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".