Proteomic analysis of cognitive impairment in responders traumatized by the World Trade Center disaster
Bibliographic record
Abstract
Abstract Background WTC responders, particularly those with long‐term PTSD, have an increased incidence of mild cognitive impairment (MCI), a prognostic marker of later dementia. In this study, we used proteomics to interrogate the interaction between PTSD and MCI in a cohort of disaster responders. Method The sample included 181 responders to the World Trade Center (WTC) disaster, including 73 with probable PTSD, 61 with MCI, and 81 WTC traumatized controls with neither condition. 34 responders had comorbid PTSD and MCI. PTSD was measured with the Posttraumatic Stress Disorder Checklist (PCL; cut‐point >44). MCI was defined according to NIA‐AA criteria using the Montreal Cognitive Assessment (MoCA; cut‐point <23). In total, we profiled 282 proteins with known involvement in relevant biological processes using the Olink Proseek Multiplex Platform. Specifically, we focused on markers of neurodevelopmental processes, cellular regulation, immunological function, cardiovascular disease, inflammatory processes, neurological diseases. Differential expression analysis was conducted to identify protein biomarkers of PTSD, MCI, and co‐occurring PTSD/MCI. Results In total, 46 unique proteins were identified from the combined list of PTSD and MCI associated proteins. Thirty‐seven proteins were differentially expressed in PTSD versus exposed controls at p < 0.05; nine were significant after correcting for multiple comparisons using the false discovery rate (FDR < 0.1). Twenty‐five proteins were differentially expressed in MCI versus exposed controls (p < 0.05); 6 attained an FDR < 0.1. After controlling for PTSD, effect size directions for these 25 MCI‐related proteins were unchanged; 13 remained significant at p < 0.05. Conclusion The current study identified several novel protein biomarkers for PTSD and MCI, which were associated with disease burden characterized by co‐occurrence of the phenotypes. Results indicated that half of the proteins associated with PTSD, and vice versa proteins associated with MCI were found in comorbid PTSD and MCI. The results suggest that identification of protein biomarkers might be useful in development of a plasma‐based assay for early detection of MCI in traumatized patients, especially those showing signs of MCI.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".