Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort
Bibliographic record
Abstract
Abstract Background Neuroinflammation (NI) has been implicated in both the pathogenesis of and neuroprotection against neurodegenerative diseases (NDs)(Psenicka et al., 2021). Plasma glial fibrillary acidic protein (GFAP), and Neurofilament light (NFL) are measures of astrogliosis and neurodegeneration, respectively. Amyloid beta (Aβ)42/40 ratio (Aβ42 concentration to total Aβ concentration) below 0.068 is associated with AD pathology (Baldeiras et al., 2018). Neuroimaging‐based inflammatory biomarkers have been proposed, including free‐water diffusion (FWD)(Pasternak et al., 2009). Here we investigated FWD as a candidate biomarker for NI in AD compared to non‐AD dementia using Aβ42/40 ratio in a subset of data from the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Method FWD maps were generated in 370 subjects (126 non‐AD and 244 AD). MRI processing included ICVmapp3r for brain extraction and bias field correction, Synb0, Topup and Eddy for dMRI preprocessing and MATLAB for freewater mapping. Plasma Aβ42, Aβ40, GFAP and NFL were measured using the Simoa Human Neurology 4‐Plex E assay, and cognition was estimated using the Montreal Cognitive Assessment (MoCA). Linear regression was used to estimate the ability for FWD in the left (LcGM) and right (RcGM) cortical grey matter to predict GFAP, NFL and MoCA score in AD and nonAD based on Aβ42/40 threshold of 0.068. Result FWD correlated with GFAP (LcGM; R = 0.4, p = 0.0013 and RcGM; R = 0.37, p = 0.0069), and MoCA total score (LcGM; R = ‐0.29, p = 0.001 and RcGM; R = ‐0.27, p = 0.001), but not with NFL across the whole group. This relationship was largely driven by the AD group wherein FWD predicted GFAP (LcGM: R = 0.12, p = 0.02, RcGM approaching significance R = 0.1, p = 0.06), and MoCA Total score (LcGM: R = ‐0.33, p<0.0001), RcGM: R = ‐0.25, p<0.0001). The nonAD group did not show this relationship. FWD did not predict NFL in the AD and nonAD group. Conclusion In patients with Aβ42/40<0.068, suggestive of AD, FWD in cGM was more strongly related to GFAP than NFL, and predicted cognition, a pattern that was not observed in nonAD patients. Our results suggest distinct patterns of NI in AD compared with nonAD that can be detected with FWD and with a multi‐modal approach could further understanding of differences in pathophysiology across NDs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".