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Record W4386485383 · doi:10.1016/j.bbi.2023.09.003

Anti-satellite glia cell IgG antibodies in fibromyalgia patients are related to symptom severity and to metabolite concentrations in thalamus and rostral anterior cingulate cortex

2023· article· en· W4386485383 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBrain Behavior and Immunity · 2023
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsMcGill University
FundersKnut och Alice Wallenbergs StiftelseFoundation for Research in RheumatologyVetenskapsrådetCanadian Institutes of Health ResearchStockholms Läns LandstingAlex och Eva Wallströms Stiftelse för Vetenskaplig Forskning och UtbildningKarolinska InstitutetEuropean Commission
KeywordsAnterior cingulate cortexMedicineFibromyalgiaCingulate cortexAntibodyThalamusFunctional magnetic resonance imagingInternal medicineImmunologyNeuroscienceCentral nervous systemPsychologyCognitionPsychiatry

Abstract

fetched live from OpenAlex

Recent translational work has shown that fibromyalgia might be an autoimmune condition with pathogenic mechanisms mediated by a peripheral, pain-inducing action of immunoglobulin G (IgG) antibodies binding to satellite glia cells (SGC) in the dorsal root ganglia. A first clinical assessment of the postulated autoimmunity showed that fibromyalgia subjects (FMS) had elevated levels of antibodies against SGC (termed anti-SGC IgG) compared to healthy controls and that anti-SGC IgG were associated with a more severe disease status. The overarching aim of the current study was to determine whether the role of anti-SGC IgG in driving pain is exclusively through peripheral mechanisms, as indirectly shown so far, or could be attributed also to central mechanisms. To this end, we wanted to first confirm, in a larger cohort of FMS, the relation between anti-SGC IgG and pain-related clinical measures. Secondly, we explored the associations of these autoantibodies with brain metabolite concentrations (assessed via magnetic resonance spectroscopy, MRS) and pressure-evoked cerebral pain processing (assessed via functional magnetic resonance imaging, fMRI) in FMS. Proton MRS was performed in the thalamus and rostral anterior cingulate cortex (rACC) of FMS and concentrations of a wide spectrum of metabolites were assessed. During fMRI, FMS received individually calibrated painful pressure stimuli corresponding to low and high pain intensities. Our results confirmed a positive correlation between anti-SGC IgG and clinical measures assessing condition severity. Additionally, FMS with high anti-SGC IgG levels had higher pain intensity and a worse disease status than FMS with low anti-SGC IgG levels. Further, anti-SGC IgG levels negatively correlated with metabolites such as scyllo-inositol in thalamus and rACC as well as with total choline and macromolecule 12 in thalamus, thus linking anti-SGC IgG levels to the concentration of metabolites in the brain of FMS. However, anti-SGC IgG levels in FMS were not associated with the sensitivity to pressure pain or the cerebral processing of evoked pressure pain. Taken together, our results suggest that anti-SGC IgG might be clinically relevant for spontaneous, non-evoked pain. Our current and previous translational and clinical findings could provide a rationale to try new antibody-related treatments in FMS.

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.

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 score0.066
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.298
Teacher spread0.283 · 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