Association Between Telomere Length and Risk of Lung Cancer in an Asian Population: A Mendelian Randomization Study
Bibliographic record
Abstract
Background: Several traditional observational studies and Mendelian randomization (MR) studies have indicated an association between leukocyte telomere length (LTL) and the risk of lung cancer in the European population. However, the results in the Asian population are still unclear. The objective was to reveal the genetic causal association between LTL and the risk of lung cancer in the Asian population. Methods: We conducted a two-sample MR analysis using summary statistics. Instrumental variables (IVs) were obtained from the genome-wide association studies (GWAS) of LTL (n = 23,096) and lung cancer (n = 212,453) of Asian ancestry. We applied the random-effects inverse-variance weighted (IVW) model as the main method. As well, several other models were performed as complementary methods to assess the impact of potential MR assumption violations, including MR-Egger regression, weighted median, and weighted mode models. Results: We included eight single-nucleotide polymorphisms (SNPs) as IVs for LTL and found that LTL was significantly associated with the risk of lung cancer in the IVW model (odds ratio (OR): 1.60; 95% confidence interval (CI): 1.31 - 1.97; P = 5.96 × 10 -6 ), which was in line with the results in the weighted median and weighted mode models. However, the relationship was not statistically significant in the MR-Egger regression model (OR: 1.44; 95% CI: 0.92 - 2.26; P = 0.160). Sensitivity analyses indicated the robustness of the results. Conclusions: This two-sample MR study confirmed that longer telomere length significantly increased the risk of lung cancer in the Asian population, which was in accord with findings in the Western population. World J Oncol. 2023;14(4):277-284 doi: https://doi.org/10.14740/wjon1624
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".