Smoking Behavior Change and the Risk of Heart Failure in Patients With Type 2 Diabetes: Nationwide Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is one of the most common initial manifestations of cardiovascular disease in patients with type 2 diabetes. Although smoking is an independent risk factor for HF, there is a lack of data for the incidence of HF according to changes in smoking behaviors in patients with type 2 diabetes. OBJECTIVE: We aimed to examine the association between interval changes in smoking behavior and the risk of HF among patients with type 2 diabetes. METHODS: We conducted a retrospective cohort study using the National Health Insurance Service database. We identified 365,352 current smokers with type 2 diabetes who had 2 consecutive health screenings (2009-2012) and followed them until December 31, 2018, for the incident HF. Based on smoking behavior changes between 2 consecutive health screenings, participants were categorized into quitter, reducer I (≥50% reduction) and II (<50% reduction), sustainer (reference group), and increaser groups. RESULTS: During a median follow-up of 5.1 (IQR 4.0-6.1) years, there were 13,879 HF cases (7.8 per 1000 person-years). Compared to sustainers, smoking cessation was associated with lower risks of HF (adjusted hazard ratio [aHR] 0.90, 95% CI0.86-0.95), whereas increasers showed higher risks of HF than sustainers; heavy smokers who increased their level of smoking had a higher risk of HF (aHR 1.13, 95% CI 1.04-1.24). In the case of reducers, the risk of HF was not reduced but rather increased slightly (reducer I: aHR 1.14, 95% CI 1.08-1.21; reducer II: aHR 1.03, 95% CI 0.98-1.09). Consistent results were noted for subgroup analyses including type 2 diabetes severity, age, and sex. CONCLUSIONS: Smoking cessation was associated with a lower risk of HF among patients with type 2 diabetes, while increasing smoking amount was associated with a higher risk for HF than in those sustaining their smoking amount. There was no benefit from reduction in smoking amount.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".