Polypharmacy in mice disrupts left ventricular function and structure and promotes proteome reorganisation in an age‐ and sex‐specific fashion
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Most older people use polypharmacy (≥5 medications), particularly those with cardiovascular disease. The effects of polypharmacy on the cardiovascular system are not well described. We examined the effect of a chronic polypharmacy regimen on left ventricular (LV) function, structure and proteome in young and old mice of both sexes. EXPERIMENTAL APPROACH: Young (4 months) and old (23 months) C57BL/6JArc male and female mice were treated with an oral polypharmacy regimen (therapeutic doses of oxybutynin, oxycodone, citalopram, simvastatin and metoprolol) or control. Blood pressure and echocardiography were assessed after 4 and 9 weeks of treatment, respectively. After 10 weeks of treatment, LV histology and proteome was assessed. KEY RESULTS: Polypharmacy reduced heart rate in all groups, whereas its effect on LV structure varied by age and sex. Relative LV wall thickness increased, and LV diameter decreased with polypharmacy in older males, consistent with concentric hypertrophy. The LV proteome showed dysregulation in structural proteins, calcium handling proteins, metabolic enzymes and antioxidants. When comparing polypharmacy against age- and sex-matched controls, 195 unique differentially expressed proteins were identified, most in old males (141 proteins). Co-expression network analysis linked LV structural changes with expression of proteins involved in protein folding and trafficking and linked heart rate effects with desmosomal protein expression. CONCLUSIONS AND IMPLICATIONS: These findings suggest that, in mice, polypharmacy affected cardiac function and structure differently depending upon age and sex, with older males being the most affected. Further investigation may elucidate mechanisms and inform personalised medicine.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".