Epigenetic aging, knee pain and physical performance in community-dwelling middle-to-older age adults

High-Impact Pain Is Associated With Epigenetic Aging Among Middle-Aged and Older Adults: Findings From the Health and Retirement Study

BACKGROUND

Chronic pain has been associated with accelerated biological aging, which may be related to epigenetic alterations. We evaluated the association of high-impact pain (ie, pain that limits activities and function) with epigenetic aging, a measure of biological aging, in a nationally representative sample of middle-aged and older adults in the United States.

METHODS

Cross-sectional analysis of adults 50 years of age and older from the 2016 Health and Retirement Study. Epigenetic aging was derived from 13 epigenetic clocks based on DNA methylation patterns that predict aging correlates of morbidity and mortality. Ordinary least squares regressions were performed to test for differences in the epigenetic clocks, adjusting for the complex survey design, as well as biological, social, and behavioral factors.

RESULTS

The analysis consisted of 3 855 adults with mean age of 68.5 years, including 59.8% with no pain and 25.8% with high-impact pain. Consistent with its operational definition, high-impact pain was associated with greater functional and activity limitations. High-impact pain was associated with accelerated epigenetic aging compared to no pain, as measured via second (Zhang, PhenoAge, GrimAge) and third (DunedinPoAm) generation epigenetic clocks. Additionally, GrimAge was accelerated in high-impact pain as compared to low-impact pain.

CONCLUSIONS

High-impact pain is associated with accelerated epigenetic aging among middle-aged and older adults in the United States. These findings highlight aging-associated epigenetic alterations in high-impact chronic pain and suggest a potential for epigenetic therapeutic approaches for pain management and the preservation of physical function in older adults.

Clinical prediction models for knee pain in patients with knee osteoarthritis: a systematic review

OBJECTIVE

To identify and describe existing models for predicting knee pain in patients with knee osteoarthritis.

METHODS

The electronic databases PubMed, EMBASE, CINAHL, Web of Science, and Cochrane Library were searched from their inception to May 2023 for any studies to develop and validate a prediction model for predicting knee pain in patients with knee osteoarthritis. Two reviewers independently screened titles, abstracts, and full-text qualifications, and extracted data. Risk of bias was assessed using the PROBAST. Data extraction of eligible articles was extracted by a data extraction form based on CHARMS. The quality of evidence was graded according to GRADE. The results were summarized with descriptive statistics.

RESULTS

The search identified 2693 records. Sixteen articles reporting on 26 prediction models were included targeting occurrence (n = 9), others (n = 7), progression (n = 5), persistent (n = 2), incident (n = 1), frequent (n = 1), and flares (n = 1) of knee pain. Most of the studies (94%) were at high risk of bias. Model discrimination was assessed by the AUROC ranging from 0.62 to 0.81. The most common predictors were age, BMI, gender, baseline pain, and joint space width. Only frequent knee pain had a moderate quality of evidence; all other types of knee pain had a low quality of evidence.

CONCLUSION

There are many prediction models for knee pain in patients with knee osteoarthritis that do show promise. However, the clinical extensibility, applicability, and interpretability of predictive tools should be considered during model development.

Mechanisms of pain in aging and age-related conditions: Focus on caregivers

Pain is a complex, subjective experience that can significantly impact quality of life, particularly in aging individuals, by adversely affecting physical and emotional well-being. Whereas acute pain usually serves a protective function, chronic pain is a persistent pathological condition that contributes to functional deficits, cognitive decline, and emotional disturbances in the elderly. Despite substantial progress that has been made in characterizing age-related changes in pain, complete mechanistic details of pain processing mechanisms in the aging patient remain unknown. Pain is particularly under-recognized and under-managed in the elderly, especially among patients with Alzheimer's disease (AD), Alzheimer's disease-related dementias (ADRD), and other age-related conditions. Furthermore, difficulties in assessing pain in patients with AD/ADRD and other age-related conditions may contribute to the familial caregiver burden. The purpose of this article is to discuss the mechanisms and risk factors for chronic pain development and persistence, with a particular focus on age-related changes. Our article also highlights the importance of caregivers working with aging chronic pain patients, and emphasizes the urgent need for increased legislative awareness and improved pain management in these populations to substantially alleviate caregiver burden.

Socioeconomic Status, Knee Pain, and Epigenetic Aging in Community-Dwelling Middle-to-Older Age Adults

Chronic musculoskeletal pain is often associated with lower socioeconomic status (SES). SES correlates with psychological and environmental conditions that could contribute to the disproportionate burden of chronic stress. Chronic stress can induce changes in global DNA methylation and gene expression, which increases risk of chronic pain. We aimed to explore the association of epigenetic aging and SES in middle-to-older age individuals with varying degrees of knee pain. Participants completed self-reported pain, a blood draw, and answered demographic questions pertaining to SES. We used an epigenetic clock previously associated with knee pain (DNAmGrimAge) and the subsequent difference of predicted epigenetic age (DNAmGrimAge-Diff). Overall, the mean DNAmGrimAge was 60.3 (±7.6), and the average DNAmGrimAge-diff was 2.4 years (±5.6 years). Those experiencing high-impact pain earned less income and had lower education levels compared to both low-impact and no pain groups. Differences in DNAmGrimAge-diff across pain groups were found, whereby individuals with high-impact pain had accelerated epigenetic aging (∼5 years) compared to low-impact pain and no pain control groups (both ∼1 year). Our main finding was that epigenetic aging mediated the associations of income and education with pain impact, as such the relationship between SES and pain outcomes may occur through potential interactions with the epigenome reflective of accelerated cellular aging. PERSPECTIVE: Socioeconomic status (SES) has previously been implicated in the pain experience. The present manuscript aims to present a potential social-biological link between SES and pain via accelerated epigenetic aging.

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.

Brain predicted age difference mediates pain impact on physical performance in community dwelling middle to older aged adults

The purpose of the study was to examine associations between physical performance and brain aging in individuals with knee pain and whether the association between pain and physical performance is mediated by brain aging. Participants (n=202) with low impact knee pain (n=111), high impact knee pain (n=60) and pain-free controls (n=31) completed self-reported pain, magnetic resonance imaging (MRI), and a Short Physical Performance Battery (SPPB) that included balance, walking, and sit to stand tasks. Brain predicted age difference, calculated using machine learning from MRI images, significantly mediated the relationships between walking and knee pain impact (CI: -0.124; -0.013), walking and pain-severity (CI: -0.008; -0.001), total SPPB score and knee pain impact (CI: -0.232; -0.025), and total SPPB scores and pain-severity (CI: -0.019; -0.001). Brain-aging begins to explain the association between pain and physical performance, especially walking. This study supports the idea that a brain aging prediction can be calculated from shorter duration MRI sequences and possibly implemented in a clinical setting to be used to identify individuals with pain who are at risk for accelerated brain atrophy and increased likelihood of disability.

Nomogram model based on radiomics signatures and age to assist in the diagnosis of knee osteoarthritis

BACKGROUND

Knee osteoarthritis (KOA) is a common disease in the elderly. An effective method for accurate diagnosis could affect the management and prognosis of patients.

OBJECTIVES

To develop a nomogram model based on X-ray imaging data and age, and to evaluate its effectiveness in the diagnosis of KOA.

METHODS

A total of 4403 knee X-rays from 1174 patients (July 2017 to November 2018) were retrospectively analyzed. Radiomics features were extracted and selected from the X-ray image data to quantify the phenotypic characteristics of the lesion region. Feature selection was performed in three steps to enable the derivation of robust and effective radiomics signatures. Then, logistic regression (LR), support vector machine (SVM) AdaBoost, gradient boosting decision tree (GBDT), and multi-layer perceptron (MLP) was adopted to verify the performance of radiomics signatures. In addition, a nomogram model combining age with radiomics signatures was constructed. At last, receiver operating characteristic (ROC) curve, calibration and decision curves were used to evaluate the discriminative performance.

RESULTS

The LR model has the best classification performance among the four radiomics models in testing cohort (LR AUC vs. SVM AUC: 0.843 vs. 0.818, DeLong test P = 0.0024; LR AUC vs. GBDT AUC: 0.843 vs. 0.821, P = 0.0028; LR AUC vs. MLP AUC: 0.843 vs. 0.822, P = 0.0019). The nomogram model achieved better predictive efficacy than the radiomics model in testing cohort compared to radiomics models although the statistical difference was not significant (Nomogram AUC vs. Radiomics AUC: 0.847 vs. 0.843, P = 0.06). The decision curve analysis revealed that the constructed nomogram had clinical usefulness.

CONCLUSION

The nomogram model combining radiomics signatures with age has good performance for the accurate diagnosis of KOA and may help to improve clinical decision-making.

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.