The Influence of Alcohol Consumption, Cigarette Smoking, and Physical Activity on Leukocyte Telomere Length
Bibliographic record
Abstract
BACKGROUND: Telomeres protect from DNA degradation and maintain chromosomal stability. Short telomeres have been associated with an increased risk of cancer at several sites. However, there is limited knowledge about the lifestyle determinants of telomere length. We aimed to determine the effect of three factors, known to be important in cancer etiology, on relative leukocyte telomere length (rLTL): alcohol consumption, smoking, and physical activity. METHODS: This cross-sectional study included 477 healthy volunteers ages 20 to 50 years who completed a questionnaire and provided a fasting blood sample. Multiplex quantitative real-time PCR (qPCR) was used to measure rLTL. Regression coefficients were calculated using multiple linear regression while controlling for important covariates. RESULTS: There was no association between alcohol consumption and rLTL. Daily smokers and those in the middle and lower tertile of pack-years smoking had shorter rLTL than never daily smokers (P = 0.02). Data were suggestive of a linear trend with total physical activity (P = 0.06). Compared with the lowest quartile, the highest quartile of vigorous physical activity was associated with longer rLTL. A significant linear trend of increasing rLTL with increasing vigorous physical activity was observed (P = 0.02). CONCLUSIONS: Cigarette smoking and vigorous physical activity have an impact on telomere length. Smoking was related to shorter telomere length while vigorous physical activity was related to longer telomeres. IMPACT: The findings from this study suggest that lifestyle may play an important role in telomere dynamics and also suggest that engaging in healthy behaviors may mitigate the effect of harmful behaviors on telomere length.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".