Extreme Air Pollution Conditions Adversely Affect Blood Pressure and Insulin Resistance
Bibliographic record
Abstract
Mounting evidence supports that fine particulate matter adversely affects cardiometabolic diseases particularly in susceptible individuals; however, health effects induced by the extreme concentrations within megacities in Asia are not well described. We enrolled 65 nonsmoking adults with metabolic syndrome and insulin resistance in the Beijing metropolitan area into a panel study of 4 repeated visits across 4 seasons since 2012. Daily ambient fine particulate matter and personal black carbon levels ranged from 9.0 to 552.5 µg/m(3) and 0.2 to 24.5 µg/m(3), respectively, with extreme levels observed during January 2013. Cumulative fine particulate matter exposure windows across the prior 1 to 7 days were significantly associated with systolic blood pressure elevations ranging from 2.0 (95% confidence interval, 0.3-3.7) to 2.7 (0.6-4.8) mm Hg per SD increase (67.2 µg/m(3)), whereas cumulative black carbon exposure during the previous 2 to 5 days were significantly associated with ranges in elevations in diastolic blood pressure from 1.3 (0.0-2.5) to 1.7 (0.3-3.2) mm Hg per SD increase (3.6 µg/m(3)). Both black carbon and fine particulate matter were significantly associated with worsening insulin resistance (0.18 [0.01-0.36] and 0.22 [0.04-0.39] unit increase per SD increase of personal-level black carbon and 0.18 [0.02-0.34] and 0.22 [0.08-0.36] unit increase per SD increase of ambient fine particulate matter on lag days 4 and 5). These results provide important global public health warnings that air pollution may pose a risk to cardiometabolic health even at the extremely high concentrations faced by billions of people in the developing world today.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".