Association Between Toll‐Like Receptor 4 (<i>TLR4</i>) and Triggering Receptor Expressed on Myeloid Cells 2 (<i>TREM2</i>) Genetic Variants and Clinical Progression of Huntington's Disease
Bibliographic record
Abstract
BACKGROUND: Although Huntington's disease (HD) is caused by a single dominant gene, it is clear that there are genetic modifiers that may influence the age of onset and disease progression. OBJECTIVES: We sought to investigate whether new inflammation-related genetic variants may contribute to the onset and progression of HD. METHODS: We first used postmortem brain material from patients at different stages of HD to look at the protein expression of toll-like receptor 4 (TLR4) and triggering receptor expressed on myeloid cells 2 (TREM2). We then genotyped the TREM2 R47H gene variant and 3 TLR4 single nucleotide polymorphisms in a large cohort of HD patients from the European Huntington's Disease Network REGISTRY. RESULTS: We found an increase in the number of cells expressing TREM2 and TLR4 in postmortem brain samples from patients dying with HD. We also found that the TREM2 R47H gene variant was associated with changes in cognitive decline in the large cohort of HD patients, whereas 2 of 3 TLR4 single nucleotide polymorphisms assessed were associated with changes in motor progression in this same group. CONCLUSIONS: These findings identify TREM2 and TLR4 as potential genetic modifiers for HD and suggest that inflammation influences disease progression in this condition. © 2019 International Parkinson and Movement Disorder Society.
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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 | 0.000 |
| 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) | 0.003 | 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".