Chronic pain is associated with a brain aging biomarker in community-dwelling older adults

Abstract

Chronic pain is associated with brain atrophy with limited evidence on its impact in the older adult's brain. We aimed to determine the associations between chronic pain and a brain aging biomarker in persons aged 60 to 83 years old. Participants of the Neuromodulatory Examination of Pain and Mobility Across the Lifespan (NEPAL) study (N = 47) completed demographic, psychological, and pain assessments followed by a quantitative sensory testing battery and a T1-weighted magnetic resonance imaging. We estimated a brain-predicted age difference (brain-PAD) that has been previously reported to predict overall mortality risk (brain-PAD, calculated as brain-predicted age minus chronological age), using an established machine-learning model. Analyses of covariances and Pearson/Spearman correlations were used to determine associations of brain-PAD with pain, somatosensory function, and psychological function. Individuals with chronic pain (n = 33) had "older" brains for their age compared with those without (n = 14; F[1,41] = 4.9; P = 0.033). Greater average worst pain intensity was associated with an "older" brain (r = 0.464; P = 0.011). Among participants with chronic pain, those who reported having pain treatments during the past 3 months had "younger" brains compared with those who did not (F[1,27] = 12.3; P = 0.002). An "older" brain was significantly associated with decreased vibratory (r = 0.323; P = 0.033) and thermal (r = 0.345; P = 0.023) detection, deficient endogenous pain inhibition (F[1,25] = 4.6; P = 0.044), lower positive affect (r = -0.474; P = 0.005), a less agreeable (r = -0.439; P = 0.020), and less emotionally stable personality (r = -0.387; P = 0.042). Our findings suggest that chronic pain is associated with added "age-like" brain atrophy in relatively healthy, community-dwelling older individuals, and future studies are needed to determine the directionality of our findings. A brain aging biomarker may help identify people with chronic pain at a greater risk of functional decline and poorer health outcomes.

Figure 1.

Study methods. A) Data used in the study comprising the ‘brain-age’ training sample of n=2,646 healthy individuals and the current experiment cohort (n=47) comprising those with chronic pain (n=33) and those without (n=14). B) Image pre-processing, applied to all images, used SPM12 software to segment T1-weighted MRIs into gray and white matter probability maps. These were then spatially-normalized using DARTEL non-linear registration to a custom template in MNI152 space, with 1.5mm³ voxels, using 4mm spatial smoothing. These normalized 3D images were converted into 1D vectors and the gray and white matter vectors concatenated. C) Machine learning age prediction involved generating a linear kernel by calculating the dot-product of all pairs of image vectors across all participants, resulting in a similarity matrix. The similarity matrix was used as input into a Gaussian Processes regression to predict chronological from the image vectors. The model trained on the full training set was then applied to the n=47 participants from the chronic pain study to generate a brain-predicted age value for each participant. D) Statistical analysis was conducted to evaluate performance of the regression model performance using ten-fold cross-validation. Brain-predicted age difference (brain-PAD) was then calculated for the chronic pain study participants; whereby chronological age was subtracted from brain-predicted age. Brain-PAD was then used for subsequent statistical analysis of pain-related variables.

Figure 2.

Screening and enrollment for the NEPAL study.

Figure 3.

Predicted brain age difference (predicted brain age – chronological age) across the groups (n=47) adjusted for chronological age, sex and exercise.

Figure 4.

Location of worst pain reported by our sample (n=33).

Figure 5.

Brain-PAD in pain participants who reported having any treatments or trying any self-remedies (something they may have done at home) to relieve their worst pain during the past 3 months (n=19) compared to those that did not (n=14).

Figure 6.

Associations between brain-PAD and psychological function in older individuals with chronic pain (n=33).

Figure 7.

Associations between brain-PAD and somatosensory function in our sample (n=47).

Figure 8.

Associations between brain-PAD and CPM in older individuals with chronic pain (n=33).

Figure 9.

CPM in a subset of pain participants who had a younger appearing brain (n=16) compared to those that had an older appearing brain (n=11). More negative numbers reflect better pain inhibition.