Microglia activation predicts tau positivity beyond Aβ in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Deposition of amyloid‐beta (Aβ) plaques and hyperphosphorylated tau are known hallmarks of Alzheimer’s disease (AD). In addition, neuroinflammation or microglia activation plays a role in AD pathophysiology and may be a potential driver of abnormal tau deposition. This study demonstrated the important brain regions where microglia activation, in addition to Aβ, is driving tau positivity in AD. Method 120 participants from the TRIAD cohort underwent 18F‐AZD4694 amyloid‐PET, 18F‐MK620 tau‐PET, and 11C‐PBR28 neuroinflammation‐PET. All images were normalized to the ADNI template and quantified as SUVR in 35 regions‐of‐interest using cerebellar gray as the reference region. ANCOVA was used to assess the differences in levels of microglia activation between tau‐positive (T+; n=38) vs. tau‐negative (T‐; n=82), corrected for age, sex, APOE‐e4, education, and global Aβ. Tau status was based on both visual rating with elevated uptake in Braak1and2 and quantitative cut‐off of 1.22. In addition, step‐wise logistic regression was conducted to predict T+ in three steps: 1) the covariates, i.e. age, sex, APOEε4, and education, 2) covariates + global Aβ, and 3) covariates + global Aβ + activated microglia in each of the 35 ROIs, corrected for multiple comparisons using FDR. Our prediction model was trained based on a randomly selected 70%/30% training/test set. In order to investigate the stability of our model, this procedure was repeated 500 times. Result Levels of microglia activation were significantly increased in T+ compared to T‐ in the posterior cingulate (p=0.0001), middle (p=0.02) and inferior (p=0.0005) temporal, fusiform (p=0.0004), and orbitofrontal cortex (p=0.005). Based on logistic regression and a likelihood‐ratio test between each step, we found that the ‘covariates + global Aβ model’ performed significantly better in predicting T+ than the ‘covariates‐only’ model (χ2(1)=72.64, p<0.0001). In the 3‐step model, the addition of activated microglia showed a significant improvement of the model’s performance to predict T+, particularly the activated microglia in the hippocampus (p=0.001), middle (p=0.006) and inferior (p=0.001) temporal, and fusiform (p=0.001) cortex. Receiver operating characteristic curves (ROC) and AUCs are shown in Fig 1. Conclusion Our study demonstrated that activated microglia in the Braak2 to Braak4 regions play an important role in predicting tau positivity in addition to Aβ.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".