Research Review By Dr. Joshua Plener©


Download MP3

Date Posted:

March 2021

Study Title:

Accelerated brain aging in chronic low back pain


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.

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.

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.

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.

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.

Study Strengths / Weaknesses:

  • The results of this study provide an avenue for future research to expand and better understand the relationships identified.
  • 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:

  1. James SL et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392(10159): 1789–1858.
  2. Meucci RD, Fassa AG, Faria NM. Prevalence of chronic low back pain: systematic review. Rev. Saude Publ. 2015. 49.
  3. Buckalew N, Haut M, Aizenstein H, et al. Differences in brain structure and function in older adults with self-reported disabling and nondisabling chronic low back pain. Pain Med 2010; 11(8): 1183-97.
  4. Ivo R, Nicklas A, Dargel J, et al. Brain structural and psychometric alterations in chronic low back pain. Eur Spine J 2013; 22: 1958–1964.
  5. Malfliet A, Coppieters I, Wilgen P et al. Brain changes associated with cognitive and emotional factors in chronic pain: a systematic review. Eur J Pain 2017; 21: 769–786.
  6. Wand M, Parkitny L, O’Connell, et al. Cortical changes in chronic low back pain: current state of the art and implications for clinical practice. Man Ther 2011; 16: 15–20.
  7. Apkarian AV, Sosa Y, Sonty S, et al. Chronic back pain is associated with decreased prefrontal and thalamic gray matter density. J Neurosci 2004; 24: 10410–10415.
  8. Kregel J, Meeus M, Malfilet A, et al. Structural and functional brain abnormalities in chronic low back pain: A systematic review. Semin. Arthritis Rheum 2015; 45: 229–237.
  9. Schmidt-Wilcke T, Leinisch E, Ganssbauer S, et al. Affective components and intensity of pain correlate with structural differences in gray matter in chronic back pain patients. Pain 2006; 125: 89–97.
  10. Seminowicz DA, Wideman T, Naso L et al. Effective treatment of chronic low back pain in humans reverses abnormal brain anatomy and function. J. Neurosci 2011; 31: 7540–7550.
  11. Seminowicz DA, Shpaner M, Keaser M, et al. Cognitive-behavioral therapy increases prefrontal cortex gray matter in patients with chronic pain. J Pain 2013; 14: 1573–1584.
  12. Eavani H, Habes M, Satterthwaite T, et al. Heterogeneity of structural and functional imaging patterns of advanced brain aging revealed via machine learning methods. Neurobiol Aging 2018; 71: 41–50.
  13. Ly M, Yu G, Karin H et al. Improving brain age prediction models: incorporation of amyloid status in Alzheimer’s disease. Neurobiol Aging 2020 Mar; 87: 44-48.
  14. Cruz-Almeida Y, Fillingim R, Riley J et al. Chronic pain is associated with a brain aging biomarker in community-dwelling older adults. Pain 2019; 160: 1119–1130.
  15. Flor H, Denke C, Schaefer M, Grüsser S. Effect of sensory discrimination training on cortical reorganisation and phantom limb pain. Lancet. 2001 Jun 2;357(9270):1763-4.
  16. Karp J, Shega J, Morone N, et al. Advances in understanding the mechanisms and management of persistent pain in older adults. Br J Anaesth 2008; 101: 111–120.
  17. Fields HL. 2000. Pain modulation: expectation, opioid analgesia and virtual pain. Prog. Brain Res. 122, 245–253.
  18. Ploghaus A, Tracey I, Gati J, et al. Dissociating pain from its anticipation in the human brain. Science 1999; 284: 1979–1981.
  19. Beck AT, Steer RA, Brown GK. 1996. Beck depression inventory-II. San Antonio. 78, 490-498.

Contact Tech Support  Contact Dr. Shawn Thistle
RRS Education on Facebook Dr. Shawn Thistle on Twitter Dr. Shawn Thistle on LinkedIn Find RRS Education on Instagram RRS Education (Research Review Service)