A high-fat diet increases risk of ventricular arrhythmia in female rats: enhanced arrhythmic risk in the absence of obesity or hyperlipidemia
Bibliographic record
Abstract
Obesity increases the incidence of cardiac arrhythmias and impairs wound healing. However, it is presently unknown whether a high-fat diet affects arrhythmic risk or wound healing before the onset of overt obesity or hyperlipidemia. After 8 wk of feeding a high-fat diet to adult female rats, a nonsignificant increase in body weight was observed and associated with a normal plasma lipid profile. Following ischemia/reperfusion injury, scar length (standard diet 0.29 +/- 0.09 vs. high-fat 0.32 +/- 0.13 cm), thickness (standard diet 0.047 +/- 0.02 vs. high-fat 0.059 +/- 0.01 cm), and collagen alpha(1) type 1 content (standard diet 0.21 +/- 0.04 vs. high-fat 0.20 +/- 0.04 arbitrary units/mm(2)) of infarcted hearts were not altered by the high-fat diet. However, the mortality rate was greatly increased 24 h postinfarction (from 5% to 46%, P < 0.01 for ischemia/reperfusion rats; from 20% to 89%, P < 0.0001, in complete-occlusion rats) in high-fat fed rats, in association with a higher prevalence of ventricular arrhythmias. Ventricular arrhythmia inducibility was also significantly increased in noninfarcted rats fed a high-fat diet. In the hearts of rats fed a high-fat diet, connexin-40 expression was absent, connexin-43 was hypophosphorylated and lateralized, and neurofilament-M immunoreactive fiber density (standard diet 2,020 +/- 260 vs. high-fat diet 2,830 +/- 250 microm(2)/mm(2)) and tyrosine hydroxylase protein expression were increased (P < 0.05). Thus, in the absence of overt obesity and hyperlipidemia, sympathetic hyperinnervation and an aberrant pattern of gap junctional protein expression and regulation in the heart of female rats fed a high-fat diet may have contributed in part to the higher incidence of inducible cardiac arrhythmias.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".