Household Air Pollution and Telomere Length in Rural Chinese Women Using Biomass Stoves
Bibliographic record
Abstract
Background: Short telomeres are associated with chronic disease and early mortality. Urban and traffic pollution has been associated with telomere attrition in adults and children. The associations of household air pollution from biomass stoves and telomere length are unknown.Objectives: To investigate the associations between household air pollution and telomere length in adult women.Methods: We conducted a repeated-measure (one summer and two winter seasons) panel study of 137 rural Chinese women (mean age=55 y), measured their 48-h personal exposure to fine particulate matter (PM2.5) and black carbon (BC), and collected their oral DNA that was analyzed for relative telomere length (RTL). Non-linear associations were assessed using natural cubic splines (2-4 DF). Multivariates mixed models were used to examine the relationship between air pollution exposure and telomere length.Results: Air pollution exposures ranged from 13-945 µg/m3 (arithmetic mean=141, SE: 11) for PM2.5 and 0.1-139 µg/m3 (mean=4.1, SE: 0.4) for BC. Spline models illustrated that the associations between air pollution and RTL were non-linear. A 1-ln (µg/m3) increase in air pollution was associated with lower RTL (PM2.5 [-0.45, 95% CI: -0.88, -0.02, p=0.04], BC [-0.81, 95% CI: -1.21, -0.39, p<0.001]) after adjusting for age, waist circumference, ethnicity, secondhand smoke, sodium intake, day of the week, and time of day. Additionally adjusting for ambient temperature reduced the effect of air pollution by 23-62% (BC [-0.62, 95% CI: -1.09, -0.15, p=0.01]; PM2.5 [-0.17, 95% CI: -0.72, 0.39], p=0.54), which may be due to more frequent biomass burning to heat homes when the outdoor temperature is colder.Conclusion: Household air pollution exposure is associated with shorter telomeres in rural Chinese women cooking with biomass fuel, with stronger associations for BC than PM2.5 mass.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".