Volume loading augments cutaneous vasodilatation and cardiac output of heat stressed older adults
Bibliographic record
Abstract
Key points Age‐related changes in cutaneous microvascular and cardiac functions limit the extent of cutaneous vasodilatation and the increase in cardiac output that healthy older adults can achieve during passive heat stress. However, it is unclear if these age‐related changes in microvascular and cardiac functions maximally restrain the levels of cutaneous vasodilatation and cardiac output that healthy older adults can achieve during heat stress. We observed that rapid volume loading, performed during passive heat stress, augments both cutaneous vasodilatation and cardiac output in healthy older humans. These findings demonstrate that the microcirculation of healthy aged skin can further dilate during passive heat exposure, despite peripheral limitations to vasodilatation. Furthermore, healthy older humans can augment cardiac output when cardiac pre‐load is increased during heat stress. Abstract Primary ageing markedly attenuates cutaneous vasodilatation and the increase in cardiac output during passive heating. However, it remains unclear if these responses are maximally restrained by age‐related changes in cutaneous microvascular and cardiac functions. We hypothesized that rapid volume loading performed during heat stress would increase cardiac output in older adults without parallel increases in cutaneous vasodilatation. Twelve young (Y: 26 ± 5 years) and ten older (O: 69 ± 3 years) healthy adults were passively heated until core temperature increased by 1.5°C. Cardiac output (thermodilution), forearm vascular conductance (FVC, venous occlusion plethysmography) and cutaneous vascular conductance (CVC, laser‐Doppler) were measured before and after rapid infusion of warmed saline (15 mL kg −1 , ∼7 min). While heat stressed, but prior to saline infusion, cardiac output (O: 6.8 ± 0.4 vs . Y: 9.4 ± 0.6 L min −1 ), FVC (O: 0.08 ± 0.01 vs . Y: 0.17 ± 0.02 mL (100 mL min −1 mmHg −1 ) −1 ), and CVC (O: 1.29 ± 0.34 vs . Y: 1.93 ± 0.30 units mmHg −1 ) were lower in older adults (all P < 0.01). Rapid saline infusion increased cardiac output (O: +1.9 ± 0.3, Y: +1.8 ± 0.7 L min −1 ), FVC (O: +0.015 ± 0.007, Y: +0.048 ± 0.013 mL (100 mL min −1 mmHg −1 ) −1 ), and CVC (O: +0.28 ± 0.10, Y: +0.29 ± 0.16 units mmHg −1 ) in both groups (all P < 0.01). The absolute increase in cardiac output and CVC were similar between groups, whereas FVC increased to a greater extent in young adults ( P < 0.01). These results demonstrate that healthy older adults can achieve greater levels of cutaneous vasodilatation and cardiac output during passive heating.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".