IL-6 Response to Psychosocial Stress Predicts 12-month Changes in Cardiometabolic Biomarkers in Perimenopausal Women
Bibliographic record
Abstract
OBJECTIVE: Cardiometabolic diseases are the number one cause of mortality, accounting for over one third of all deaths in the United States. Cardiometabolic risk further increases with psychosocial stress exposure and during menopausal transition in women. Because disease risk and stress burden are associated with aberrant immune signaling, we hypothesized that responses of interleukin-6 (IL-6) to psychosocial stress may predict longitudinal cardiometabolic outcomes in perimenopausal women. METHODS: We conducted post hoc analyses in 151 perimenopausal or early postmenopausal women participants in a previously completed study. At study onset, participants underwent the Trier Social Stress Test (TSST), and plasma IL-6 was measured repeatedly before and during the 1 hour post-TSST. Subsequently, participants were randomly assigned to either hormonal treatment (HT) or placebo and followed for 12 months to determine longitudinal changes in cardiometabolic biomarkers. RESULTS: Greater IL-6 reactivity to stress, measured with baseline-adjusted area under the curve, predicted 12-month decrease in flow-mediated dilatation of the brachial artery (P = 0.0005), a measure of endothelial-dependent vascular function, but not in endothelial-independent function measured with nitroglycerin-mediated dilatation (P = 0.17). Greater baseline IL-6 levels predicted 12-month increase in insulin resistance based on the homeostatic model assessment of insulin resistance score (P = 0.0045) and in the number of criteria met for metabolic syndrome (P = 0.0008). These predictions were not moderated by HT. CONCLUSIONS: Greater baseline IL-6 levels as well as its reactivity to stress may predict worsening in distinct cardiometabolic biomarkers as women transition to menopause. Interleukin-6 reactivity predicts decline in endothelial-dependent vascular function, whereas baseline IL-6 presages accumulation of metabolic 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 | 0.001 | 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".