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Record W4400742844 · doi:10.1101/2024.07.16.24310509

Role of common and rare genetic variants in the aetiology of trigeminal neuralgia

2024· preprint· en· W4400742844 on OpenAlexaffabout
Kim J. Burchiel, Olga A. Korczeniewska, Fengshen Kuo, Ching‐Yu Huang, Ze’ev Seltzer, Scott R. Diehl

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicTrigeminal Neuralgia and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTrigeminal neuralgiaEtiologyMedicineConcomitantTrigeminal nerveNeurovascular bundlePain disorderNeuropathic painChronic painAnesthesiaPediatricsInternal medicineSurgeryPhysical therapy

Abstract

fetched live from OpenAlex

Summary Background Trigeminal neuralgia (TN) is characterized by repeated paroxysmal attacks of severe facial pain usually lasting 1-3 minutes. Lifetime prevalence is ca.3 per 1,000, more common in women, and with onset generally in middle age. Medications usually provide relief in the early stages of the disorder, but for many patients, severe drug side effects emerge and medically intractable pain returns, sometimes lasting for life. Some patients present with paroxysmal pain predominantly while others also experience substantial concomitant constant facial pain. Some patients have a history of a blood vessel compressing and damaging their trigeminal nerve (neurovascular compression, NVC). For these “classical” cases, surgery often provides complete or substantial pain relief for many years. “Idiopathic” cases without NVC or any other apparent cause also occur. NVC was previously observed to be less frequent in females who had early age of onset and these patients may constitute a unique subgroup. Our aim was to evaluate the role of inherited genetic variation in the aetiology of TN in patient subgroups based on age of onset, presence of NVC and sex. Methods To maximize aetiological homogeneity, only patients with predominantly paroxysmal pain and minimal concomitant continuous pain were included in the analysis. Conditions known to cause secondary TN such as tumors or multiple sclerosis were excluded. The GWAS analysis was based on 626 TN patients and 827 Control subjects of European ancestry recruited in Canada, the UK, and US. A Genome-Wide Association Study (GWAS) analysis was performed using Affymetrix’s Precision Medicine arrays yielding 7,781,254 biallelic DNA variants available after Quality Control (QC) and imputation. Rare damaging mutations in genes with functions relevant to the biology of TN were identified in Whole Genome Sequencing (WGS) genomic DNA of 100 patients using a novel strategy based on overlap of symptoms of TN with symptoms of known genetic disorders. Findings The GWAS analysis revealed associations at eight genome locations including near LRP1B (P-value 6.3 X 10 -15 ), a gene important for repair of myelin sheath injury that has been previously proposed as a target for the treatment of neuropathic pain. Associations were also found for the potassium channel gene KCNK10 , and for CHL1, CUX1, SGMS1 and ZNF804B genes, all genes with neural functions potentially relevant to the aetiology of TN. In addition, high-risk genotypes at the CUX1 and KCNK10 genes exhibit significant interactions with patients’ sex and the presence or absence of NVC (P-values 0.005 and 0.017, respectively). Whole genome sequencing of 100 TN patients revealed mutations in ion channel genes TRPM4 (six patients), SCN10A and SCNN1B (five patients), CACNA1F, CACNA1S and SCN5A (four patients) and CACNA1H , SCN2A and SCN9A (three patients). Female patients with onset prior to age 46 had more mutated genes with myelin-related functions (P-value 0.004) and associated with epilepsy or seizure (P-value 0.03) than older onset females and males of any onset age. Interpretation Risk of TN in patients presenting with paroxysmal pain only is associated with both common genetic variants and with rare mutations. Some high-risk genotypes have significant interactions with sex and NVC. Evidence of the condition’s heterogeneous genetic aetiology should be considered when evaluating novel therapies. Funding Grants from the William H. and Leila A. Cilker Genetics Research Program of the Facial Pain Research Foundation, The Foundation of the University of Medicine and Dentistry of New Jersey, and Rutgers School of Dental Medicine, Rutgers Health, Rutgers – The State University of New Jersey Contact Scott R Diehl, PhD, scott.diehl@rutgers.edu , 973-972-7053

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.289
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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