Ambient fine particulate matter is an independent predictor of insulin resistance in non-diabetic adults in the PURSE-HIS Cohort, Tamil Nadu, India
Bibliographic record
Abstract
Background: Chronic exposure to ambient fine particulate matter (less than 2.5 mm in aerodynamic diameter, PM2.5) has been shown in animal models to induce insulin resistance (IR) through alterations in inflammatory pathways. Here we assess the association between concentrations of ambient – PM2.5 and IR in the Population Study of Urban Semi-urban Rural Endovascular Disease and Holistic Intervention Study (PURSE-HIS) cohort in Tamil Nadu India. Methods: The study included 6246 randomly selected geolocated participants without diabetes (mean age 42 years; 58% women). We used PM2.5 estimates developed for Global Burden of Disease (2010), which combined satellite-based estimates, chemical transport model simulations, and ground measurements to produce global estimates of annual average PM2.5 concentrations at ~ 10 x 10 km resolution. PM2.5 estimates were applied to the study population spread over 80 km x 80 km grid in both urban and rural areas. IR was assessed by homeostasis model assessment of IR (HOMA-IR). Linear regression models were used controlling for age, gender, BMI, physical activity, energy intake, smoking, stress, and anxiety. Results: The PM2.5 exposure estimates ranged from 18µg/m3 to 35µg/m3 in the study area. The mean HOMA-IR levels in men and women was 2.4 mg/dl (± 2.2 mg/dl) and 2.2 mg/dl (± 2.1 mg/dl), respectively. In multivariate models, an inter quartile range (IQR) change in PM2.5 was associated with a 0.22 mg/dl (95%CI: 0.13, 0.34) and 0.33 mg/dl (95%CI: 0.22, 0.43) increase in HOMA-IR levels for males and females, respectively. Conclusion: PM2.5 was an independent predictor of IR. We are currently performing ground-based PM2.5 assessments to validate the global PM2.5 estimates and refine the initial estimates for its association with IR.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".