Exposure to household and outdoor air pollution and fasting plasma glucose as a marker of diabetes risk in Chinese adults
Bibliographic record
Abstract
Background and Aim: Household air pollution (HAP) from solid fuel stoves is a widespread environmental exposure. Exposure to outdoor air pollution can increase risk of diabetes but studies of HAP and diabetes are limited. Methods: We modelled the relation of personal exposure to two HAP components – fine particulate matter (P2.5) and black carbon (BC), and stationary measures of outdoor PM2.5, with fasting plasma glucose (FPG) as a marker of diabetes risk in a cross-sectional study of 663 adults (40-79 years) in three provinces of China. We applied linear mixed regression models with a village-level random intercept to assess associations between pollutants and FPG levels in the same season as blood collection (winter) and for annual weighted mean exposure estimated across two seasons, adjusted for covariates. Results: We observed higher FPG (mmol/L) with winter outdoor PM2.5 (2.4%, 95% CI 0.2 to 4.5% per 100µg/m3 increase), driven by an increase in the southern non-heating province of Guangxi (10.5%, 95% CI -2.6% to 23.6% per 100µg/m3 increase). We observed higher FPG in association with winter and annual outdoor PM2.5 among participants with no hypertension and among those with BMI below the median (<25.1kg/m2). We found no association with winter or annual personal PM2.5 and BC exposures, however in sub-group analyses excluding those taking diabetes treatment, we found higher FPG levels with personal winter BC exposure (0.8%, 95% CI -0.2% to 1.7% per 1µg/m3 increase), an association that was stronger among participants with a BMI below the median (1.9%, 95% CI 0.7% to 3.1% per 1µg/m3 increase). Conclusions: We found some evidence of detrimental associations between air pollutants and FPG levels. Longitudinal investigations of HAP exposure and markers of diabetes risk are needed in settings where solid fuels are an important energy source for cooking and heating. Keywords: air pollution, diabetes
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".