EFFECT OF LONG-TERM POLYPHARMACY AND THE DRUG BURDEN INDEX (DBI) ON CARDIAC FUNCTION AND FIBROSIS IN AGED MICE
Bibliographic record
Abstract
Polypharmacy (use of ≥ 5 medications) and Drug Burden Index (DBI: measures cumulative exposure to anticholinergic and sedative drugs) impair function in older adults. Preclinical studies can provide a mechanistic understanding. We aim to evaluate the effect of chronic polypharmacy, medications with increasing DBI and deprescribing (cessation of medications) on cardiac function and histology in aged mice. Twelve-month-old male C57BL/6 mice received control feed or feeds/water containing therapeutic doses of drugs in regimens of polypharmacy with Zero DBI (simvastatin, metoprolol, omeprazole, paracetamol, irbesartan), Low DBI (simvastatin, metoprolol, omeprazole, paracetamol, citalopram), High DBI (simvastatin, metoprolol, oxybutynin, oxycodone, citalopram) or monotherapy with each of the five drugs from the High DBI diet. At 21 months, animals were re-randomised to continue treatment or be deprescribed. Blood pressure (BP) and rotarod performance (endurance) were assessed every 3 months and hearts were collected at 27 months. Compared to control, we observed a significant decrease in systolic and diastolic BP in Zero DBI, Low DBI, metoprolol and simvastatin treated mice and not in High DBI treated mice at 21 months (p<0.05). On rotarod performance, latency-to-fall declined in mice administered citalopram, compared to control (p<0.05) at all time points, with non-significant improvement after deprescribing. Preliminary histology (n=3) suggests a non-significant trend towards increased myocardial fibrosis in High DBI mice. Our results indicate that chronic High DBI diet may impair therapeutic effects of cardiac drugs and increase cardiac collagen. Citalopram reduces endurance and it can be reversed with deprescribing. Future studies will continue to address histological changes involved.
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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 | 0.000 |
| 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 | 0.002 |
| 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".