Biomarkers in cluster headache: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To systematically investigate previously examined biomarkers in blood, urine, cerebrospinal fluid, tear fluid, and saliva of patients with cluster headache. BACKGROUND: Cluster headache is a condition with extensive clinical challenges in terms of diagnosis and treatment. Identification of a biomarker with diagnostic implications or as a potential treatment target is highly warranted. METHODS: We conducted a systematic review including peer reviewed full text of studies that measured biochemical compounds in either blood, urine, cerebrospinal fluid, tear fluid, or saliva of patients with cluster headache diagnosed after the implementation of the International Classification of Headache Disorders (1988) written in English, Danish, Swedish, or Norwegian. Inclusion required a minimum of five participants. The search was conducted in PubMed and EMBASE, in September 2022, and extracted data were screened by two authors. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for reporting systematic reviews were followed. The Newcastle-Ottawa Scale was used to assess the risk of bias in case-controlled studies. RESULTS: We included 40 studies involving 832 patients with cluster headache and 872 controls, evaluating 80 potential biomarkers. The risk of bias for case-controlled studies was a median of 6 (range: 3-8) and 20 studies out of 40 (50%) were of fair or good quality. Most studies were identified within three groups: hypothalamic-regulated hormones, inflammatory markers, and neuropeptides. Among the hypothalamic hormones, cortisol was the most frequently investigated (N = 7) and was elevated in cluster headache in most of the studies. The most frequently examined inflammatory marker was interleukin 1 (N = 3), but findings were divergent. Calcitonin gene-related peptide was the most investigated neuropeptide (N = 9) and all studies found increased levels during attacks. CONCLUSION: Biomarker findings have been inconsistent and widely non-specific for cluster headache, which explains why none of the previous studies succeeded in identifying a unique biomarker for cluster headache, but instead contributed to substantiating the underlying pathophysiologic mechanisms. Several of the examined biomarkers could hold promise as markers for disease activity but are unfit for a clear distinction from both controls and other headaches.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".