Ambient particulate air pollution is associated with higher risk of microvascular complications and diabetic ketoacidosis among persons with type 1 diabetes mellitus in an observational survival study
Bibliographic record
Abstract
Aims The burden of Type 1 Diabetes Mellitus (DM, T1D) is growing and represents a major health care cost. This study investigated the relationship between ambient particulate matter (PM 2.5 ) exposure and T1D complications. The research was motivated by the potential for air pollution's known inflammatory effects to exacerbate T1D's microvascular harms (i.e., damaged peripheral tissues from poor glucose control). Methods In a cohort of 12, 925 Utah participants, competing-risk Cox proportional hazards regression analyzed the increased risk of PM 2.5 exposure and diabetic ketoacidosis (DKA), along with kidney, ophthalmic, and neurological complications. Results An interquartile range increase in one-year PM 2.5 exposure was associated with increased risk of DKA by 28.8 % (95 % CI: 14.3, 45.2), ophthalmic complications by 33.5 % (95 % CI: 10.0, 62.0), and neurological complications by 35.1 % (95 % CI: 14.3, 59.6). No statistically significant effect was found for diabetic kidney complications. Acute exposures of 30, 60, and 90 days was also associated with increased risk of diabetic ketoacidosis, although less than one-year exposure. Conclusions We hypothesize these effects stem from PM 2.5 -induced oxidative stress and systemic inflammation, which may exacerbate metabolic disruptions already present in hyperglycemic individuals. Clients and providers may want to consider environmental factors, like air pollution, in T1D management.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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 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".