Parasympathetic Response Patterns are Associated with Metabolic Syndrome Among Older Women but Not Men
Bibliographic record
Abstract
BACKGROUND: Little is known about the role of physiological stress responses in metabolic syndrome (MetS). PURPOSE: To examine whether patterns of autonomic response to psychological stress are associated with MetS and whether this association is moderated by sex. METHODS: 1121 men and women (Mage = 65.3 ± 6.77 years) with and without coronary artery disease (CAD) underwent an anger recall stressor task. Heart rate and heart-rate variability (HRV; HF, LF/HF) were assessed. Clusters of participants showing similar patterns of response across baseline, stress, and recovery periods were created using ACECLUS and FASTCLUS in SAS. Logistic regressions included clusters and interaction between clusters and sex as independent variables, controlling for relevant covariates. ANCOVAs were conducted in secondary analyses utilizing a continuous composite representation of MetS. RESULTS: Men and women showing greater tonic and phasic HR elevations were more likely to meet MetS criteria (OR = 1.45, [95% CI = 1.02-2.07], p = .037). HF-HRV cluster interacted significantly with sex (p < .001) to predict MetS. In women, those with significant parasympathetic withdrawal to stress and poor recovery were more likely to have MetS than women with a more moderate response (OR = 2.56, [95% CI = 1.23-5.41], p = .013). Women who displayed stress-related parasympathetic activation were also at greater risk of MetS (OR = 2.30, [95% CI = 1.30-4.07], p = .004). Results using a continuous measure of MetS were generally consistent with these findings. CONCLUSION: Among older participants with CAD or other noncardiovascular disease, hyperreactivity to stress was associated with greater prevalence of MetS, particularly in women. Consistent with emerging literature, women who showed blunting or activation of parasympathetic responses to stress were similarly at greater risk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".