Cigarette smoking and structural brain deficits in patients with atrial fibrillation
Bibliographic record
Abstract
Abstract Background Brain lesions are common in patients with atrial fibrillation (AF). Whether cigarette smoking is associated with an increased risk of brain lesions and reduced brain volume in patients with AF is currently unknown. Purpose In this analysis, we aimed to investigate the association of cigarette smoking habits with cerebral small vessel disease and overall brain volume. Methods For this cross-sectional analysis, we included AF patients from a multicentre cohort study. Smoking habits were defined by smoking status categories (never, former, current), duration (tertiles of years), cigarettes per day (CPD as obtained at baseline), packyears (PKY, tertiles) and time since smoking cessation (≤5 years, 6-10 years, 11-15 and ≥16 years). A standardized brain magnetic resonance imaging was done to assess the prevalence of small vessel disease white matter lesions (WML, graded according to the Fazekas scale) and small non-cortical infarcts (SNCI). Additionally, MRI was used to quantify the volumes of WML and SNCI; as well as to measure grey matter (GM) and white matter (WM) brain volumes. Multivariable adjusted logistic and linear regression analyses were applied. Results 1746 patients were enrolled (mean age 72.6 years, 27.5% females). 131 (7.5%) were current smokers, 846 (48.5%) former and 769 (44%) never smokers. Patients with a longer smoking duration, higher amount of CPD and PKY had reduced GM brain volume (p for linear trend 0.05, 0.005 and 0.02, respectively). Patient with higher CPD also had a reduced WM brain volume (p for linear trend 0.04). Compared to patients with a smoking duration in the lowest tertile (≤18 years), patients who had smoked for 19-34 years (second tertile) and more than 35 years (third tertile) had a higher risk for WML Fazekas ≥2 with an OR (95% CI) of 1.85 (1.29; 2.66, p=0.001) and 1.46 (1.01; 2.09, p=0.04) (Figure 1). Additionally, increasing smoking duration tertiles exhibited larger WML volumes, with a significant linear trend (p=0.05). AF patients who quit smoking ≥16 years ago had a lower odds for SNCI (OR 0.47 (0.24; 0.94, p= 0.03), less WML volume (β: -0.451 (-0.8; -0.11, p=0.01), and a higher WM brain volume (β: 16.9 (0.3; 33.6, p= 0.05) compared to patients who quit 5 years prior to the study enrolment (Figure 2). We found no significant difference in any structural brain deficit between current and former smokers compared to never smokers. Conclusion In a large AF population, we found that smoking duration and amount were associated with the loss of brain volumes. Smoking duration and time since smoking cessation were associated with small vessel disease.
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 | 0.001 |
| 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 | 0.000 |
| 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".