Abstract 13736: Whole Body versus Small Muscle Mass Exercise Training in Patients With Heart Failure With Preserved Ejection Fraction: Impact on Peripheral Responses to Exercise
Bibliographic record
Abstract
Background: Patients with heart failure with preserved ejection fraction (HFpEF) have reduced peak aerobic power (VO 2 ) during whole body exercise due in part to smaller increases in blood flow (BF) and arterial-to-venous oxygen difference (Δa-vO 2 ). We recently demonstrated that 8wks of single leg knee extension (SLKE) exercise training was effective in improving whole body VO 2 by increasing Δa-vO 2 in HFpEF; whether SLKE training induces greater peripheral adaptations compared to traditionally prescribed whole-body (i.e., cycle) training is unknown. Hypothesis: SLKE training will result in larger improvements in peak leg VO 2 , leg BF, and Δa-vO 2 during a SLKE peak exercise test vs cycle training. Methods: We recruited 16 patients with HFpEF (71 ± 6yr, 5 males) and randomized them to cycle or SLKE training 3-4 days/wk for 16wk. Before and after the intervention we quantified leg BF (ultrasound), femoral venous O 2 partial pressure (PvO 2 ) and saturation (IV catheter) during incremental SLKE peak exercise to determine peak leg VO 2, Δa-vO 2, and skeletal muscle diffusive conductance (DMO 2 ; leg VO 2 /[2x leg PvO 2 ]). Results: SLKE (n=9) and cycle (n=7) groups were matched for age and sex. Both groups increased peak workload during the SLKE exercise test (34±8%; mean ± SE; p=0.005). There was a main effect of training such that peak DMO 2 improved in both groups (cycle: 45±19%; SLKE: 12±3%; see Figure 1 ). Peak Δa-vO 2 also improved (main effect of training, p=0.002); however, this was greater in response to cycle compared to SLKE training (12±3% vs 3±2%; interaction effect, p=0.035). Conclusion: These preliminary data indicate that both types of training improve leg DMO 2 ; however, greater improvements in O 2 extraction occurred with cycle training. The differences in the peripheral adaptations may be attributed to a larger exercise “dose” in the cycle trained group. This data highlights that both modalities are effective components of exercise prescription for patients with HFpEF.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".