Blunted Cardiac Output from Overtraining Is Related to Increased Arterial Stiffness
Bibliographic record
Abstract
PURPOSE: Moderate overtraining has been characterized by decreased exercising HR and recently decreased exercising stroke volume (SV), independent of alterations to blood volume. The aim of this study was to assess changes in arterial stiffness and central hemodynamics, and their relationship to exercising SV, after 3 wk of overload training. METHODS: Twenty-six cyclists and triathletes completed 3 wk of either regular training (CON; n = 13) or overload training (OL; n = 13). Testing took place before (PRE) and after regular or overload training (POST). Resting measures included brachial blood pressure, HR, carotid-femoral pulse wave velocity (PWV) to assess arterial stiffness, and carotid pulse wave analysis to assess wave reflections and central hemodynamics. An incremental cycle test was used to assess peak power, maximal HR, and maximal lactate to assess overtraining status. Cardiac output (Q˙), SV, and HR were assessed using cardiac impedance. RESULTS: Resting arterial stiffness was unaltered in CON but increased with OL after increased training (CON -0.1 ± 0.6 m·s vs OL +0.5 ± 0.8 m·s, P = 0.04). Resting blood pressure and central hemodynamics, including aortic pressures, augmentation index, and subendocardial viability ratio, did not change (all P > 0.05). Maximal SV (CON +3 mL vs OL -9 mL, P = 0.04), HR (CON -2 ± 4 bpm vs OL -9 ± 3 bpm, P < 0.001), and Q˙ (CON +0.32 L·min vs OL -1.75 L·min, P = 0.01) decreased with OL from PRE to POST. A significant inverse relationship existed between changes in PWV and maximal Q˙ (r = -0.44, P = 0.04) and changes in PWV and peak power (r = -0.48, P = 0.01), and trended for SV and PWV (r = -0.41, P = 0.055). CONCLUSIONS: Overload training results in increased resting arterial stiffness and reduced SV during exercise, with no changes to resting central hemodynamics.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".