Added “Age-like” Brain Patterns with Increasing Pain Impact in Persons with Knee Osteoarthritis Pain: Considerations by Race

Toward MR protocol-agnostic, unbiased brain age predicted from clinical-grade MRIs

The difference between the estimated brain age and the chronological age ('brain-PAD') could become a clinical biomarker. However, most brain age models were developed for research-grade high-resolution T1-weighted MRIs, limiting their applicability to clinical-grade MRIs from various protocols. We adopted a dual-transfer learning strategy to develop a model agnostic to modality, resolution, or slice orientation. We retrained a convolutional neural network (CNN) using 6281 clinical MRIs from 1559 patients, among 7 modalities and 8 scanner models. The CNN was trained to estimate brain age from synthetic research-grade magnetization-prepared rapid gradient-echo MRIs (MPRAGEs) generated by a 'super-resolution' method. The model failed with T2-weighted Gradient-Echo MRIs. The mean absolute error (MAE) was 5.86-8.59 years across the other modalities, still higher than for research-grade MRIs, but comparable between actual and synthetic MPRAGEs for some modalities. We modeled the "regression bias" in brain age, for its correction is crucial for providing unbiased summary statistics of brain age or for personalized brain age-based biomarkers. The bias model was generalizable as its correction eliminated any correlation between brain-PAD and chronological age in new samples. Brain-PAD was reliable across modalities. We demonstrate the feasibility of brain age predictions from arbitrary clinical-grade MRIs, thereby contributing to personalized medicine.

Toward MR protocol-agnostic, bias-corrected brain age predicted from clinical-grade MRIs

The predicted brain age minus the chronological age ('brain-PAD') could become a clinical biomarker. However, most brain age methods were developed to use research-grade high-resolution T1-weighted MRIs, limiting their applicability to clinical-grade MRIs from multiple protocols. To overcome this, we adopted a double transfer learning approach to develop a brain age model agnostic to modality, resolution, or slice orientation. Using 6,224 clinical MRIs among 7 modalities, scanned from 1,540 patients using 8 scanners among 15 + facilities of the University of Florida's Health System, we retrained a convolutional neural network (CNN) to predict brain age from synthetic research-grade magnetization-prepared rapid gradient-echo MRIs (MPRAGEs) generated by a deep learning-trained 'super-resolution' method. We also modeled the "regression dilution bias", a typical overestimation of younger ages and underestimation of older ages, which correction is paramount for personalized brain age-based biomarkers. This bias was independent of modality or scanner and generalizable to new samples, allowing us to add a bias-correction layer to the CNN. The mean absolute error in test samples was 4.67-6.47 years across modalities, with similar accuracy between original MPRAGEs and their synthetic counterparts. Brain-PAD was also reliable across modalities. We demonstrate the feasibility of clinical-grade brain age predictions, contributing to personalized medicine.

Pain interference mediates the association between epigenetic aging and grip strength in middle to older aged males and females with chronic pain

Introduction

Chronic pain is one of the leading causes of disability that may accelerate biological aging and reduce physical function. Epigenetic clocks provide an estimate of how the system ages and can predict health outcomes such as physical function. Physical function declines may be attributed to decreases in muscle quality due to disuse that can be measured quickly and noninvasively using grip strength. The purpose of this study was to explore the associations among self-reported pain, grip strength, and epigenetic aging in those with chronic pain.

Methods

Participants (57.91 ± 8.04 years) completed pain questionnaires, a blood draw and hand grip strength task. We used an epigenetic clock previously associated with knee pain (DNAmGrimAge), and used the subsequent difference of predicted epigenetic age from chronological age (DNAmGrimAge-Difference).

Results

Exploratory pathway analyses revealed that pain intensity mediated the association between DNAmGrimAge-difference and handgrip strength in males only (β = -0.1115; CI [-0.2929, -0.0008]) and pain interference mediated the association between DNAmGrimAge-difference and handgrip strength in males β = -0.1401; CI [-0.3400, -0.0222]), and females (β = -0.024; CI [-0.2918, -0.0020]).

Discussion

Chronic knee pain may accelerate epigenetic aging processes that may influence handgrip strength in older age adults. Chronic pain could be a symptom of the aging body thus contributing to declines in musculoskeletal function in later life.

Self-reported pain and fatigue are associated with physical and cognitive function in middle to older-aged adults

Persistent fatigue is often reported in those with chronic musculoskeletal pain. Separately, both chronic pain and chronic fatigue contribute to physical and cognitive decline in older adults. Concurrent pain and fatigue symptoms may increase disability and diminish quality of life, though little data exist to show this. The purpose of this study was to examine associations between self-reported pain and fatigue, both independently and combined, with cognitive and physical function in middle-older-aged adults with chronic knee pain. Using a cross-sectional study design participants (n = 206, age 58.0 ± 8.3) completed questionnaires on pain and fatigue, a physical performance battery to assess physical function, and the Montreal Cognitive Assessment. Hierarchical regressions and moderation analyses were used to assess the relationship between the variables of interest. Pain and fatigue both predicted physical function (β = -0.305, p < 0.001; β = -0.219, p = 0.003, respectively), however only pain significantly predicted cognitive function (β = -0.295, p <0.001). A centered pain*fatigue interaction was a significant predictor of both cognitive function (β = -0.137, p = 0.049) and physical function (β = -0.146, p = 0.048). These findings indicate that self-reported fatigue may contribute primarily to decline in physical function among individuals with chronic pain, and less so to decline in cognitive function. Future studies should examine the impact of both cognitive and physical function decline together on overall disability and health.

Epigenetic Aging Mediates the Association between Pain Impact and Brain Aging in Middle to Older Age Individuals with Knee Pain

Chronic musculoskeletal pain is a health burden that may accelerate the aging process. Accelerated brain aging and epigenetic aging have separately been observed in those with chronic pain. However, it is unknown whether these biological markers of aging are associated with each other in those with chronic pain. We aimed to explore the association of epigenetic aging and brain aging in middle-to-older age individuals with varying degrees of knee pain. Participants (57.91 ± 8.04 y) with low impact knee pain (n = 95), high impact knee pain (n = 53), and pain-free controls (n = 26) completed self-reported pain, a blood draw, and an MRI scan. We used an epigenetic clock previously associated with knee pain (DNAmGrimAge), the subsequent difference of predicted epigenetic and brain age from chronological age (DNAmGrimAge-Difference and Brain-PAD, respectively). There was a significant main effect for pain impact group (F (2,167) = 3.847, P = 0.023, r o t a t i o n a l   e n e r g y   =   1 / 2 I ω 2 = 0.038, ANCOVA) on Brain-PAD and DNAmGrimAge-difference (F (2,167) = 6.800, P = 0.001, I   =   m k 2 = 0.075, ANCOVA) after controlling for covariates. DNAmGrimAge-Difference and Brain-PAD were modestly correlated (r =0.198; P =0.010). Exploratory analysis revealed that DNAmGrimAge-difference mediated GCPS pain impact, GCPS pain severity, and pain-related disability scores on Brain-PAD. Based upon the current study findings, we suggest that pain could be a driver for accelerated brain aging via epigenome interactions.

Cross-Sectional Brain-Predicted Age Differences in Community-Dwelling Middle-Aged and Older Adults with High Impact Knee Pain

Purpose

Knee OA-related pain varies in impact across individuals and may relate to central nervous system alterations like accelerated brain aging processes. We previously reported that older adults with chronic musculoskeletal pain had a significantly greater brain-predicted age, compared to pain-free controls, indicating an "older" appearing brain. Yet this association is not well understood. This cross-sectional study examines brain-predicted age differences associated with chronic knee osteoarthritis pain, in a larger, more demographically diverse sample with consideration for pain's impact.

Patients and Methods

Participants (mean age = 57.8 ± 8.0 years) with/without knee OA-related pain were classified according to pain's impact on daily function (ie, impact): low-impact (n=111), and high-impact (n=60) pain, and pain-free controls (n=31). Participants completed demographic, pain, and psychosocial assessments, and T1-weighted magnetic resonance imaging. Brain-predicted age difference (brain-PAD) was compared across groups using analysis of covariance. Partial correlations examined associations of brain-PAD with pain and psychosocial variables.

Results

Individuals with high-impact chronic knee pain had significantly "older" brains for their age compared to individuals with low-impact knee pain (p < 0.05). Brain-PAD was also significantly associated with clinical pain, negative affect, passive coping, and pain catastrophizing (p's<0.05).

Conclusion

Our findings suggest that high impact chronic knee pain is associated with an older appearing brain on MRI. Future studies are needed to determine the impact of pain-related interference and pain management on somatosensory processing and brain aging biomarkers for high-risk populations and effective intervention strategies.