501: Accelerated brain aging mediates the impact of insomnia on chronic pain interference

Topic: Clinical Science
Subtopic: Pain category (ICD-11), Chronic primary orofacial pain, Psychology, Epidemiology, Imaging (e.g. fMRI, PET, MRS)

Background and Aims: Chronic pain is highly prevalent1 yet remains undertreated.2 A major challenge in pain management is its frequent comorbidity with other conditions that complicate treatment. Insomnia, a common sleep disturbance characterized by difficulties initiating or maintaining sleep, has been shown to exacerbate chronic pain severity.3-5 However, the neural mechanisms underlying the insomnia and chronic pain relationship remain poorly understood. Advanced brain-age derived from neuroimaging data6 has been linked to chronic pain outcomes,6 but how insomnia is involved has not been explored. This study examined brain-age to elucidate the neural underpinnings linking insomnia severity to chronic pain outcomes.

Methods: Sixty participants with chronic orofacial pain completed high-resolution T1-weighted structural MRI scans (Siemens Prisma 3T scanner). Insomnia severity was assessed with the Insomnia Severity Index (ISI),7 and chronic pain intensity and interference were measured using the Graded Chronic Pain Scale (GCPS).8 Brain age was estimated from T1 images using an unbiased deep learning convolutional neural network model (CNN) trained and validated in healthy, pain-free cohorts.9 Linear regression analyses controlling for sex and race, as well as mediation analyses, were conducted to examine whether brain age mediated the effects of insomnia on chronic pain outcomes.

Results: Estimated brain-age of chronic pain participants (mean=58.61, SD=7.29) was older than the chronological age (mean=42.58, SD=14.40, paired-t(59)=14.17, p<0.001). Additionally, older estimated brain-age was associated with greater pain intensity (β=0.35, p=0.009) and interference (β=0.32, p=0.017). Greater insomnia severity was associated with older brain-age (β=0.32, p=0.015, R2change=0.08), even after adjusting for demographic variables and baseline pain intensity. Subsequent mediation analysis indicated that brain-age significantly mediated the effect of insomnia on pain interference (a*b=0.10, 90% CI=[0.01, 0.21]) but not pain intensity (a*b=0.08, 90% CI=[-0.01, 0.18]).

Conclusions: These findings suggest that insomnia severity accounts for approximately 8% of the variance in accelerated brain aging, independent of chronic pain severity, highlighting a unique contribution of insomnia to neurobiological aging processes in chronic pain populations. Moreover, accelerated brain aging may represent a central nervous system mechanism through which insomnia exacerbates pain-related interference. However, this preliminary finding was based on a relatively small sample and relied on a single survey to assess insomnia severity, lacking an in-depth characterization of participants’ sleep patterns. Future studies with integrated sleep assessments and larger clinical samples are warranted to validate the current findings.

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6 Cundiff-O'Sullivan, R. L. et al. Clinical pain increased with older brain age, yet placebo effects were preserved. Pain, 10.1097 (2022).
7 Bastien, C. H., Vallieres, A. & Morin, C. M. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med 2, 297-307 (2001). https://doi.org:10.1016/s1389-9457(00)00065-4
8 Von Korff, M., Dworkin, S. F. & Le Resche, L. Graded chronic pain status: an epidemiologic evaluation. Pain 40, 279-291 (1990).
9 Dartora, C. et al. A deep learning model for brain age prediction using minimally preprocessed T1w images as input. Frontiers in Aging Neuroscience 15, 1303036 (2024).

Presenting Author: Yang Wang
Submitting Author: Yang Wang

Senior Author: Luana Colloca

IASP 2026 Global Year theme of Neuropathic Pain: No

• Yang Wang: PhD - Department of Pain and Translational Symptom Sciences, School of Nursing, University of Maryland, Baltimore, US
• Rachel L. Cundiff-O’Sullivan: PhD - Department of Pain and Translational Symptom Sciences, School of Nursing, University of Maryland, Baltimore, US
• Jerry Li: PhD - Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Canada
• Shuo Chen: PhD - School of Medicine, University of Maryland, Baltimore, US
• Mojgan Hodaie: PhD - Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Canada
• Luana Colloca: MD, PhD, MS - Department of Pain and Translational Symptom Sciences, School of Nursing, University of Maryland, Baltimore, US