Genomics and pharmacogenomics of cluster headache: implications for personalized management? A systematic review
Bibliographic record
Abstract
The role of genetic factors in cluster headache etiology, suggested by familial and twin studies, remains ill-defined, with the exact pathophysiological mechanisms still largely elusive. This systematic review aims to synthesize current knowledge on cluster headache genetics and explore its implications for personalized treatment and prediction of treatment response. Thus, we searched PubMed, Scopus, and the Cochrane Library databases and reference lists of identified research articles, meta-analyses, and reviews to identify relevant studies up to 10 July 2024. The quality of the evidence was assessed using Newcastle-Ottawa Scale for case control studies and NIH Quality Assessment tool for Observational Cohort and Cross-Sectional Studies. The protocol of this study was registered via the Open Science Framework ( https://osf.io/cd4s3 ). Fifty-one studies were selected for the qualitative synthesis: 34 candidate gene studies, 5 GWAS, 7 gene expression studies, 4 pharmacogenetic association studies, and 1 whole genome sequencing study. The bulk of genetic evidence in cluster headache underscores the involvement of genes associated with chronobiological regulation. The most studied gene in cluster headache is the HCRTR2 , which is expressed in the hypothalamus; however, findings across studies continue to be inconclusive. Recent GWAS have uncovered novel risk loci for cluster headache, marking a significant advancement for the field. Nevertheless, there remains a need to investigate various genes involved in specific mechanisms and pathways.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".