World Trade Center neurotoxic exposures are associated with elevated plasma amyloid, total‐tau and neurofilament light in responders
Bibliographic record
Abstract
Abstract Background The collapse of the World Trade Center towers on September 11th 2001 resulted in a 16‐acre environmental toxic disaster. More than 1.2 million tons of construction material and carcinogens including polycyclic aromatic hydrocarbons, gypsum and metals coalesced, resulting in a highly alkaline dust cloud. Police and Law enforcement were among the most highly exposed group. Method In this retrospective cohort study, we included a subset of high exposure Responders (n=424) with cross sectional plasma samples of amyloid β40, amyloid β42, total‐tau, neurofilament light and a baseline evaluation of cognitive functioning assessed with the Montreal Cognitive Assessment (MoCA) to examine long‐term associations between WTC neurotoxic exposures (e.g. diesel exhaust, chemicals) with levels of proteins associated with neuropathological characteristics of Alzheimer’s disease and neurodegeneration. Spearman rho p values adjusted for multiple comparisons using the false discovery rate (FDR=0.05) examined associations with participant characteristics and plasma concentrations. Multivariate regressions ascertained independent effects of WTC neurotoxic exposures in predicting plasma biomarker concentrations. Result Responders were on average 54.3 years at blood draw. Worse performance on the baseline MoCA was associated with higher levels of Aβ40. Plasma Aβ40 and NfL were inversely correlated with dust exposure, Aβ42 and ratio Aβ42‐40 were inversely correlated with total hours on site during 9/11‐9/14 and working in enclosed work areas was associated with higher concentrations of Aβ40 and lower concentrations of ratio Aβ42‐40. Diesel exhaust exposure predicted levels of Aβ40, total tau and NfL whereas early exposure predicted Aβ42 concentrations and dust exposure predicted ratio Aβ42‐40. Conclusion Differences across inhaled neurotoxins and time of arrival may have differential long‐term effects on blood‐based protein biomarkers of neuropathology and brain health.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".