Evidence that meal fat content does not impact hemodynamic reactivity to or recovery from repeated mental stress tasks
Bibliographic record
Abstract
The magnitude (reactivity) and duration (recovery) of hemodynamic stress responses are predictive of cardiovascular risk, and fat intake has been shown to enhance hemodynamic reactivity to psychological stress tasks. The objective of this study was to determine the impact of a high-fat meal (HFM) on the magnitude and stability of hemodynamic stress reactivity and recovery. This was assessed by: (i) the peak changes from baseline to during stress for heart rate (HR); mean, systolic, and diastolic blood pressure; cardiac output; and total peripheral resistance; and (ii) the residual arousal in hemodynamic parameters at 2 points post-stress ("early" and "late" recovery). On different days, 10 healthy males (aged 23.2 ± 3.3 years) consumed either a HFM (54 g fat) or low-fat meal (LFM; 0 g fat) (∼1000 calories each), followed by 4 hourly 10-min stress tasks (mental arithmetic and speech tasks). Pre-stress (baseline) parameters did not differ between HFM and LFM conditions (all P > 0.05). Plasma triglycerides were greater following the HFM versus the LFM (P = 0.023). No reactivity or recovery parameters differed between meals (all P > 0.05). Stress reactivity and recovery parameters were stable over the 4 stress tasks (main effects of time, all P > 0.05), with the exception of HR (P < 0.05). Contrary to previous reports, meal fat content did not impact hemodynamic reactivity to laboratory stressors. These data also provide the first evidence that meal fat content does not impact hemodynamic recovery from repeated mental stress tasks.
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) | 0.002 | 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".