Leukocyte Telomere Length Predicts Cancer Risk in Barrett's Esophagus
Bibliographic record
Abstract
PURPOSE: Leukocyte telomere length has gained attention as a marker of oxidative damage and age-related diseases, including cancer. We hypothesize that leukocyte telomere length might be able to predict future risk of cancer and examined this in a cohort of patients with Barrett's esophagus, who are at increased risk of esophageal adenocarcinoma and thus were enrolled in a long-term cancer surveillance program. PATIENTS AND METHODS: In this prospective study, telomere length was measured by quantitative PCR in baseline blood samples in a cohort of 300 patients with Barrett's esophagus followed for a mean of 5.8 years. Leukocyte telomere length hazard ratios (HR) for risk of esophageal adenocarcinoma were calculated using multivariate Cox models. RESULTS: Shorter telomeres were associated with increased esophageal adenocarcinoma risk (age-adjusted HR between top and bottom quartiles of telomere length, 3.45; 95% confidence interval, 1.35-8.78; P = 0.009). This association was still significant when individually or simultaneously adjusted for age, gender, nonsteroidal anti-inflammatory drug (NSAID) use, cigarette smoking, and waist-to-hip ratio (HR, 4.18; 95% confidence interval, 1.60-10.94; P = 0.004). The relationship between telomere length and cancer risk was particularly strong among NSAID nonusers, ever smokers, and patients with low waist-to-hip ratio. CONCLUSION: Leukocyte telomere length predicts risk of esophageal adenocarcinoma in patients with Barrett's esophagus independently of smoking, obesity, and NSAID use. These results show the ability of leukocyte telomere length to predict the risk of future cancer and suggest that it might also have predictive value in other cancers arising in a setting of chronic inflammation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 teacher head, 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".