Circadian rhythmicity and biopsychosocial characteristics influence opioid use in chronic low back pain
Bibliographic record
Abstract
BACKGROUNDInter- and intraindividual fluctuations in pain intensity pose a major challenge to treatment efficacy, with a majority of people perceiving their pain relief as inadequate. Recent preclinical studies have identified circadian rhythmicity as a potential contributor to these fluctuations and a therapeutic target.METHODSWe therefore sought to determine the impact of circadian rhythms in people with chronic low back pain (CLBP) through a detailed characterization, including questionnaires to evaluate biopsychosocial characteristics, ecological momentary assessment (7 day e-diaries at 8:00/14:00/20:00) to observe pain fluctuations, and intraday blood transcriptomics (at 8:00/20:00) to identify genes/pathways of interest.RESULTSWhile most individuals displayed constant or variable/mixed pain phenotypes, a distinct subset had daily fluctuations of increasing pain scores (>30% change in intensity over 12 hours in ≥4/7 days). This population had no opioid users, better biopsychosocial profiles, and differentially expressed transcripts relative to other pain phenotypes. The circadian-governed neutrophil degranulation pathway was particularly enriched among arrhythmic individuals; the link between neutrophil degranulation and opioid use was further confirmed in a separate CLBP cohort.CONCLUSIONOur findings identified pain rhythmicity and the circadian expression of neutrophil degranulation pathways as indicators of CLBP outcomes, which may help provide a personalized approach to phenotyping biopsychosocial characteristics and medication use. This highlights the need to better understand the impact of circadian rhythmicity across chronic pain conditions.FUNDINGThis work was funded by grants from the Canadian Institutes of Health Research (CIHR; grant PJT-190170, to NG and MGP) and the CIHR-Strategy for Patient-Oriented Research Chronic Pain Network (grant SCA-145102, to NG, QD, LD, MGP, and MC). DT was funded by a MS Canada endMS Doctoral Research Award, AMZ by an Ontario Graduate Scholarship, HGMG by a CIHR Doctoral Research Award, MGP by a Junior 2 Research Scholarship from the Fonds de recherche du Québec - Santé, and LD by a Canadian Excellence Research Chairs and Pfizer Canada Professorship in Pain Research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".