Relationship between remnant cholesterol and risk of heart failure in participants with diabetes mellitus
Bibliographic record
Abstract
BACKGROUND: Evidence about the association between calculated remnant cholesterol (RC) and risk of heart failure (HF) in participants with diabetes mellitus (DM) remains sparse and limited. METHODS: We included a total of 22 230 participants with DM from the UK Biobank for analyses. Participants were categorized into three groups based on their baseline RC measures: low (with a mean RC of 0.41 mmol/L), moderate (0.66 mmol/L), and high (1.04 mmol/L). Cox proportional hazards models were used to evaluate the relationship between RC groups and HF risk. We performed discordance analysis to evaluate whether RC was associated with HF risk independently of low-density lipoprotein cholesterol (LDL-C). RESULTS: During a mean follow-up period of 11.5 years, there were a total of 2232 HF events observed. The moderate RC group was significantly related with a 15% increased risk of HF when compared with low RC group (hazard ratio [HR] = 1.15, 95% confidence interval [CI]: 1.01-1.32), while the high RC group with a 23% higher HF risk (HR = 1.23, 95% CI: 1.05-1.43). There was significant relationship between RC as a continuous measure and the increased HF risk (P < 0.01). The association between RC and risk of HF was stronger in participants with HbA1c level ≥ 53 mmol/mol when compared with HbA1c < 53 mmol/mol (P for interaction = 0.02). Results from discordance analyses showed that RC was significantly related to HF risk independent of LDL-C measures. CONCLUSIONS: Elevated RC was significantly associated with risk of HF in patients with DM. Moreover, RC was significantly related to HF risk independent of LDL-C measures. These findings may highlight the importance of RC management to HF risk in patients with DM.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".