Accelerated Brain Aging in Chronic Low Back Pain +MP3
Research Review By Dr. Joshua Plener©
Audio:
Date Posted:
March 2021
Study Title:
Accelerated brain aging in chronic low back pain
Authors:
Yu G, Ly M, Karin H et al.
Author's Affiliations:
Department of Bioengineering; Center for Neuroscience; Department of Psychiatry – all at the University of Pittsburgh, USA
Publication Information:
Brain Research 2021; 1755: 147263. doi: 10.1016/j.brainres.2020.147263.
Background Information:
Low back pain (LBP) is the leading cause of disability worldwide (1), with approximately two-thirds of patients still reporting pain after twelve months (2). The literature has suggested that chronic LBP may negatively affect brain structure, which can lead to impairment in attention, mental flexibility, language skills, and emotional decision making (3-6).
It has been demonstrated that in chronic LBP, there are gray matter density changes in multiple regions, most notably the prefrontal cortex, thalamus, brainstem, corpus callosum, and total gray matter volume (4, 7-9). Furthermore, treatment appears to help normalize these gray matter alterations (10, 11).
One machine-learning approach to assessing the gray matter density of an individual compared to an age-matched healthy peer is called brain age. Brain age serves as a holistic measurement of numerous regional structural changes and can be especially useful to define structural changes in terms of accelerated aging (12). Of note, past research has shown significant differences of brain age in a chronic pain population compared to healthy individuals (13, 14).
The aim of this study is to apply a brain age prediction model to a chronic low back pain cohort without depression. This study investigated the association between brain age and factors of chronic LBP duration and pain severity, as well as an analysis to identify regions with altered gray matter density.
It has been demonstrated that in chronic LBP, there are gray matter density changes in multiple regions, most notably the prefrontal cortex, thalamus, brainstem, corpus callosum, and total gray matter volume (4, 7-9). Furthermore, treatment appears to help normalize these gray matter alterations (10, 11).
One machine-learning approach to assessing the gray matter density of an individual compared to an age-matched healthy peer is called brain age. Brain age serves as a holistic measurement of numerous regional structural changes and can be especially useful to define structural changes in terms of accelerated aging (12). Of note, past research has shown significant differences of brain age in a chronic pain population compared to healthy individuals (13, 14).
The aim of this study is to apply a brain age prediction model to a chronic low back pain cohort without depression. This study investigated the association between brain age and factors of chronic LBP duration and pain severity, as well as an analysis to identify regions with altered gray matter density.
Pertinent Results:
There were 63 participants in this study – 31 with chronic LBP and 32 healthy controls. Compared to the healthy control group, the chronic LBP group was not significantly different in participant age or sex, but had significantly greater pain, pain duration, and depressive symptoms.
Overall, the multivariable linear model predicted brain age and explained 32% of the variance. The results demonstrate that there is a significant interaction between the effect of chronic LBP status and chronological age for a person’s predicted brain age (p = 0.031). Within the chronic LBP group, sex, current pain, pain duration, and depressive symptoms were not associated with brain age.
Healthy controls showed greater gray matter density in the cerebellum compared to the chronic LBP group, but lower gray matter density in the left cuneus and superior occipital gyrus.
Overall, the multivariable linear model predicted brain age and explained 32% of the variance. The results demonstrate that there is a significant interaction between the effect of chronic LBP status and chronological age for a person’s predicted brain age (p = 0.031). Within the chronic LBP group, sex, current pain, pain duration, and depressive symptoms were not associated with brain age.
Healthy controls showed greater gray matter density in the cerebellum compared to the chronic LBP group, but lower gray matter density in the left cuneus and superior occipital gyrus.
Clinical Application & Conclusions:
The results of this study appear to demonstrate that chronic LBP patients age at an additional 1.8 months per year of life compared to their healthy counterparts!
Sex, depressive symptoms, duration of pain and current pain levels were not significantly associated with brain age, therefore this study demonstrates an alternative driving factor not encompassed by these variables. However, the total duration of pain and current pain levels may be an imperfect representation of an individual patient’s trajectory within chronic LBP, as it is possible that pain intensity can change over time, while the duration which a patient experiences higher levels of pain may also contribute to activation and enhancement within pain related brain regions (6, 15). Previous literature suggests that normal and pathological brain changes may contribute to the patient’s experience of chronic LBP due to the impairment of descending inhibition, implicating a bi-directional relationship between structural changes and chronic pain (16). These factors suggest the trajectory of chronic LBP and its relationship with brain changes are more complex than only measuring the duration of pain and pain at one time point (15).
Similar to chronic LBP, in the natural aging process, there are changes to similar brain structures such as lower gray matter density in the prefrontal cortex, thalamus and brainstem (4, 7, 8). It has been demonstrated in previous literature that older adults with chronic LBP have differences in brain structure and function when compared to healthy controls (4). Therefore, the discrepancy in the brain age between these two groups would theoretically be larger at a greater chronological age. Future research could help us understand if older adults with chronic LBP are at a greater risk for brain morphometric changes given their duration of pain.
The gray matter density maps demonstrated that there was widespread gray matter density contribution to brain age. Most notably, the healthy control participants showed greater gray matter density in the cerebellum compared to chronic LBP participants. This may be important, as the cerebellum plays a role in nociceptive processing, including voluntary motor inhibition during pain and anticipation of pain (17, 18).
The results of this study are very interesting and demonstrate the impact chronic LBP can have on an individual. In order to strengthen this study’s results and continue to understand the association of chronic LBP and brain age, it is important to replicate this study in a larger sample size, with a longitudinal analysis.
Similar to chronic LBP, in the natural aging process, there are changes to similar brain structures such as lower gray matter density in the prefrontal cortex, thalamus and brainstem (4, 7, 8). It has been demonstrated in previous literature that older adults with chronic LBP have differences in brain structure and function when compared to healthy controls (4). Therefore, the discrepancy in the brain age between these two groups would theoretically be larger at a greater chronological age. Future research could help us understand if older adults with chronic LBP are at a greater risk for brain morphometric changes given their duration of pain.
The gray matter density maps demonstrated that there was widespread gray matter density contribution to brain age. Most notably, the healthy control participants showed greater gray matter density in the cerebellum compared to chronic LBP participants. This may be important, as the cerebellum plays a role in nociceptive processing, including voluntary motor inhibition during pain and anticipation of pain (17, 18).
The results of this study are very interesting and demonstrate the impact chronic LBP can have on an individual. In order to strengthen this study’s results and continue to understand the association of chronic LBP and brain age, it is important to replicate this study in a larger sample size, with a longitudinal analysis.
Study Methods:
The duration of chronic LBP was self-assessed in years, with pain levels identified by a visual analog scale (VAS) administered to assess their pain on the day of the MRI scan. As well, depressive symptoms were self-scored using the Beck Depression Inventory (19).
The brain MRI was segmented into three tissues: gray matter, white matter, and cerebrospinal fluid. A gray matter density map was created, which is associated with both gray matter volume and cortical thickness. Individuals with thick cortical gray matter and large cortical volumes had a high gray matter density and on the other hand, participants with thin cortical gray matter and small cortical volumes had a lower gray matter density. All images were registered on a single template to account for head size differences.
A previously developed brain age estimation algorithm has been created to estimate chronological age based on gray matter density maps. This model was created by including 757 healthy individual’s images who were 20-85 years of age with normal cognitive function, negative beta-amyloid status, and no history of psychosis or neurological disorders. For this current study, chronic LBP groups were compared to the gray matter density maps of these healthy controls.
The brain MRI was segmented into three tissues: gray matter, white matter, and cerebrospinal fluid. A gray matter density map was created, which is associated with both gray matter volume and cortical thickness. Individuals with thick cortical gray matter and large cortical volumes had a high gray matter density and on the other hand, participants with thin cortical gray matter and small cortical volumes had a lower gray matter density. All images were registered on a single template to account for head size differences.
A previously developed brain age estimation algorithm has been created to estimate chronological age based on gray matter density maps. This model was created by including 757 healthy individual’s images who were 20-85 years of age with normal cognitive function, negative beta-amyloid status, and no history of psychosis or neurological disorders. For this current study, chronic LBP groups were compared to the gray matter density maps of these healthy controls.
Statistical Analysis:
To test the effect of chronic LBP on the association between chronological age and predicted brain age, a multivariable linear regression was used. Sex, subclinical depressive symptoms, pain duration and current pain were assessed to see if these variables moderated the association between chronological age and brain age within the chronic LBP group.
To identify differences in gray matter density between groups, a t-test of the gray matter density maps between chronic LBP and healthy controls were used. In addition, to identify which region’s gray matter density correlates with brain age not explained by age and sex, a regression analysis was utilized.
To test the effect of chronic LBP on the association between chronological age and predicted brain age, a multivariable linear regression was used. Sex, subclinical depressive symptoms, pain duration and current pain were assessed to see if these variables moderated the association between chronological age and brain age within the chronic LBP group.
To identify differences in gray matter density between groups, a t-test of the gray matter density maps between chronic LBP and healthy controls were used. In addition, to identify which region’s gray matter density correlates with brain age not explained by age and sex, a regression analysis was utilized.
Study Strengths / Weaknesses:
Strengths:
- The results of this study provide an avenue for future research to expand and better understand the relationships identified.
Limitations:
- The sample size used for this study was small.
- This study is a cross-sectional design, limiting our ability to infer cause and effect.
- There was a lack of complementary parameters including interventions for pain such as nonpharmacological therapies and psychological function.
Additional References:
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