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Morphological and genetic decoding shows heterogeneous patterns of brain aging in chronic musculoskeletal pain

Nature Mental Health volume 2, pages 435–449 (2024)Cite this article

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Abstract

Chronic musculoskeletal pain (CMP), a prevalent and heterogeneous condition characterized by persistent pain in various body parts, is a leading cause of disability worldwide and greatly affects a patient’s brain. Apart from experiencing pain, older adults with CMP also have accelerated cognitive decline and higher dementia risk with limited understanding of biological mechanism underlying the associations between CMP and dementia risk. A multiscale study to disentangle pathological brain aging from normal brain aging may reveal the underlying mechanisms. Using large-scale, cross-sectional and longitudinal cohorts (N = 9,344), we have developed an MRI-based brain age model (N = 6,725) to evaluate the difference between brain age and chronological age, termed ‘predicted age difference’ (PAD), across several common types of CMP (N = 2,427). Our study unveils significantly increased PAD in knee osteoarthritis (KOA) cohorts versus healthy controls, and validates it in an independent dataset (N = 192), suggesting a pattern of brain-aging acceleration in KOA. This acceleration was contributed by the hippocampus in both datasets and predicted memory decline and dementia incidents during follow-up. The SLC39A8 gene showed pleiotropy between brain-aging accelerations and KOA and exhibited spatially transcriptional associations with the regional contributions to brain-aging accelerations. The genes exhibiting spatially strong transcriptional associations with regional contributions were highly expressed in microglial cells and astrocytes, and were mainly enriched in synaptic structure and neurodevelopment. These findings highlight a heterogeneous pattern of brain aging in CMP and reveal a heritable morphological pattern that links brain-aging acceleration to cognitive decline and an elevated risk of dementia in KOA.

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Fig. 1: Overview of research questions, participants and analysis pipeline.
Fig. 2: Training a brain age model and applying it to cohorts with CMP in dataset 1.
Fig. 3: Brain-aging acceleration in KOA and its cognitive relevance in dataset 2.
Fig. 4: Pleiotropic genes between KOA and brain-aging acceleration.
Fig. 5: Enrichment analysis on genes with high correlations with KOA neuroimaging phenotypes.

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Data availability

The MRI data are from multiple sources. The data acquired from UKB are publicly available upon third-party authorization (https://www.ukbiobank.ac.uk/register-apply). The GWAS summary statistics of KOA were downloaded from https://www.ebi.ac.uk/gwas/publications/30664745. The GWAS summary statistics for chronic hip pain, chronic back pain and chronic neck pain were downloaded from https://gwas.mrcieu.ac.uk/datasets with the category codes as ukb-b-133, ukb-b-8463 and ukb-b-16118, respectively. Human brain gene expression data in AHBA can be downloaded from http://human.brain-map.org/static/download. Individual data used to estimate brain age in dataset 2 can be accessed via ScienceDB (https://doi.org/10.57760/sciencedb.psych.00120).

Code availability

The publicly available software for the analyses has been described in the Methods of our manuscript. Our custom analysis code and the developed brain age model can be accessed at https://github.com/tulab-brain/BrainAgeCP.

References

  1. El-Tallawy, S. N. et al. Management of musculoskeletal pain: an update with emphasis on chronic musculoskeletal pain. Pain Ther. 10, 181–209 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Moriarty, O., McGuire, B. E. & Finn, D. P. The effect of pain on cognitive function: a review of clinical and preclinical research. Prog. Neurobiol. 93, 385–404 (2011).

    Article  PubMed  Google Scholar 

  3. Rouch, I. et al. Association between chronic pain and long-term cognitive decline in a population-based cohort of elderly participants. Pain 162, 552–560 (2021).

    Article  PubMed  Google Scholar 

  4. Tian, J. et al. Association between chronic pain and risk of incident dementia: findings from a prospective cohort. BMC Med. 21, 169 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Whitlock, E. L. et al. Association between persistent pain and memory decline and dementia in a longitudinal cohort of elders. JAMA Intern. Med. 177, 1146–1153 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bethlehem, R. A. I., Seidlitz, J. & White, S. R. Brain charts for the human lifespan. Nature. 604, 525–533 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Xia, X. et al. Aging and Alzheimer’s disease: comparison and associations from molecular to system level. Aging Cell 17, e12802 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Ferrucci, L. & Kuchel, G. A. Heterogeneity of aging: individual risk factors, mechanisms, patient priorities and outcomes. J. Am. Geriatr. Soc. 69, 610–612 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Nguyen, Q. D. et al. Health heterogeneity in older adults: exploration in the Canadian longitudinal study on aging. J. Am. Geriatr. Soc. 69, 678–687 (2021).

    Article  PubMed  Google Scholar 

  11. Ibanez, A. & Zimmer, E. R. Time to synergize mental health with brain health. Nat. Mental Health 1, 441–443 (2023).

    Article  Google Scholar 

  12. Pang, J. C. et al. Geometric constraints on human brain function. Nature 618, 566–574 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Antal, B. et al. Type 2 diabetes mellitus accelerates brain aging and cognitive decline: complementary findings from UK Biobank and meta-analyses. eLife 11, e73138 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Brouwer, R. M. et al. Genetic variants associated with longitudinal changes in brain structure across the lifespan. Nat. Neurosci. 25, 421–432 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Douaud, G. et al. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature 604, 697–707 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Cole, J. H. & Franke, K. Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 40, 681–690 (2017).

    Article  PubMed  Google Scholar 

  17. Boyle, R. et al. Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis. Brain Imaging Behav. 15, 327–345 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Cole, J. H. Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle and cognitive factors. Neurobiol. Aging 92, 34–42 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  19. de Lange, A. G. & Cole, J. H. Commentary: correction procedures in brain-age prediction. Neuroimage Clin. 26, 102229 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Constantinides, C. et al. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia Consortium. Mol. Psychiatry 28, 1201–1209 (2023).

    Article  PubMed  Google Scholar 

  21. Han, L. K. M. et al. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group. Mol. Psychiatry 26, 5124–5139 (2021).

    Article  PubMed  Google Scholar 

  22. Guggenmos, M. et al. Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence. Transl. Psychiatry 7, 1279 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zorina-Lichtenwalter, K. et al. Genetic predictors of human chronic pain conditions. Neuroscience. 338, 36–62 (2016).

    Article  PubMed  Google Scholar 

  24. Yimer, B. B. et al. Heterogeneity in the association between weather and pain severity among patients with chronic pain: a Bayesian multilevel regression analysis. Pain Rep. 7, e963 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Peek, A. L. et al. Increased GABA+ in people with migraine, headache and pain conditions—a potential marker of pain. J. Pain 22, 1631–1645 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  26. López-Otín, C. et al. Hallmarks of aging: an expanding universe. Cell 186, 243–278 (2023).

    Article  PubMed  Google Scholar 

  27. Huang, S. W. et al. Osteoarthritis increases the risk of dementia: a nationwide cohort study in Taiwan. Sci. Rep. 5, 10145 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Innes, K. E. & Sambamoorthi, U. The association of perceived memory loss with osteoarthritis and related joint pain in a large Appalachian population. Pain Med. 19, 1340–1356 (2018).

    Article  PubMed  Google Scholar 

  29. Du, J. et al. Association of APOE-ε4, osteoarthritis, β-amyloid and tau accumulation in primary motor and somatosensory regions in Alzheimer disease. Neurology 101, e40–e49 (2023).

    Article  PubMed  Google Scholar 

  30. Hung, P. S. et al. Differential expression of a brain aging biomarker across discrete chronic pain disorders. Pain 163, 1468–1478 (2022).

    Article  PubMed  Google Scholar 

  31. Yu, G. Z. et al. Accelerated brain aging in chronic low back pain. Brain Res. 1755, 147263 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kaufmann, T. et al. Common brain disorders are associated with heritable patterns of apparent aging of the brain. Nat. Neurosci. 22, 1617–1623 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tachmazidou, I. et al. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. Nat. Genet. 51, 230–236 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Ferreira, L. K. & Busatto, G. F. Resting-state functional connectivity in normal brain aging. Neurosci. Biobehav. Rev. 37, 384–400 (2013).

    Article  PubMed  Google Scholar 

  36. Zhu, Y. et al. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362, eaat8077 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Grubman, A. et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation. Nat. Neurosci. 22, 2087–2097 (2019).

    Article  PubMed  Google Scholar 

  38. Anderson, K. M. et al. Convergent molecular, cellular and cortical neuroimaging signatures of major depressive disorder. Proc. Natl Acad. Sci. USA 117, 25138–25149 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Beck, D. et al. Cardiometabolic risk factors associated with brain age and accelerate brain ageing. Hum. Brain Mapp. 43, 700–720 (2022).

    Article  PubMed  Google Scholar 

  41. Mishra, S., Beheshti, I. & Khanna, P. A review of neuroimaging-driven brain age estimation for identification of brain disorders and health conditions. IEEE Rev. Biomed. Eng. 16, 371–385 (2023).

    Article  PubMed  Google Scholar 

  42. de Lange, A. G. et al. Mind the gap: performance metric evaluation in brain-age prediction. Hum. Brain Mapp. 43, 3113–3129 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Lisman, J. et al. Viewpoints: how the hippocampus contributes to memory, navigation and cognition. Nat. Neurosci. 20, 1434–1447 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Caillaud, M. et al. Evidence of a relation between hippocampal volume, white matter hyperintensities, and cognition in subjective cognitive decline and mild cognitive impairment. J. Gerontol. B Psychol. Sci. Soc. Sci. 75, 1382–1392 (2020).

    Article  PubMed  Google Scholar 

  45. Vetere, G. et al. An inhibitory hippocampal–thalamic pathway modulates remote memory retrieval. Nat. Neurosci. 24, 685–693 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Roh, J. H. et al. Volume reduction in subcortical regions according to severity of Alzheimer’s disease. J. Neurol. 258, 1013–1020 (2011).

    Article  PubMed  Google Scholar 

  47. Jahn, H. Memory loss in Alzheimer’s disease. Dialogues Clin. Neurosci. 15, 445–454 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Bangen, K. J. et al. Baseline white matter hyperintensities and hippocampal volume are associated with conversion from normal cognition to mild cognitive impairment in the Framingham offspring study. Alzheimer Dis. Assoc. Disord. 32, 50–56 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Jokinen, H. et al. Global burden of small vessel disease-related brain changes on MRI predicts cognitive and functional decline. Stroke 51, 170–178 (2020).

    Article  PubMed  Google Scholar 

  50. Chen, H. H. et al. The diagnostic accuracy of the Ascertain Dementia 8 questionnaire for detecting cognitive impairment in primary care in the community, clinics and hospitals: a systematic review and meta-analysis. Fam. Pract. 35, 239–246 (2018).

    Article  PubMed  Google Scholar 

  51. Nebert, D. W. & Liu, Z. SLC39A8 gene encoding a metal ion transporter: discovery and bench to bedside. Hum. Genomics 13, 51 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Hermann, E. R. et al. Brain magnetic resonance imaging phenome-wide association study with metal transporter gene SLC39A8. Front. Genet. 12, 647946 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Grabert, K. et al. Microglial brain region-dependent diversity and selective regional sensitivities to aging. Nat. Neurosci. 19, 504–516 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Patir, A. et al. A core transcriptional signature of human microglia: derivation and utility in describing region-dependent alterations associated with Alzheimer’s disease. Glia 67, 1240–1253 (2019).

    Article  PubMed  Google Scholar 

  55. Fjell, A. M. & Walhovd, K. B. Structural brain changes in aging: courses, causes and cognitive consequences. Rev. Neurosci. 21, 187–221 (2010).

    Article  PubMed  Google Scholar 

  56. Elahi-Mahani, A. et al. Glial cells modulate hippocampal synaptic plasticity in morphine dependent rats. Brain Res. Bull. 140, 97–106 (2018).

    Article  PubMed  Google Scholar 

  57. Takeda, A. Zinc signaling in the hippocampus and its relation to pathogenesis of depression. J. Trace Elem. Med. Biol. 26, 80–84 (2012).

    Article  PubMed  Google Scholar 

  58. Leng, F. & Edison, P. Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat. Rev. Neurol. 17, 157–172 (2021).

    Article  PubMed  Google Scholar 

  59. Stephenson, J. et al. Inflammation in CNS neurodegenerative diseases. Immunology 154, 204–219 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Kapoor, M. et al. Role of proinflammatory cytokines in the pathophysiology of osteoarthritis. Nat. Rev. Rheumatol. 7, 33–42 (2011).

    Article  PubMed  Google Scholar 

  61. Papenberg, G. et al. Physical activity and inflammation: effects on gray-matter volume and cognitive decline in aging. Hum. Brain Mapp. 37, 3462–3473 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Marsland, A. L. et al. Interleukin-6 covaries inversely with hippocampal grey matter volume in middle-aged adults. Biol. Psychiatry 64, 484–490 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Maggio, N. & Vlachos, A. Tumor necrosis factor (TNF) modulates synaptic plasticity in a concentration-dependent manner through intracellular calcium stores. J. Mol. Med. 96, 1039–1047 (2018).

    Article  PubMed  Google Scholar 

  64. Paolicelli, R. C. et al. Synaptic pruning by microglia is necessary for normal brain development. Science 333, 1456–1458 (2011).

    Article  PubMed  Google Scholar 

  65. Weerasekera, A. et al. Thalamic neurometabolite alterations in patients with knee osteoarthritis before and after total knee replacement. Pain 162, 2014–2023 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Nakatomi, Y. et al. Neuroinflammation in patients with chronic fatigue syndrome/myalgic encephalomyelitis: an ¹¹C-(R)-PK11195 PET study. J. Nucl. Med. 55, 945–950 (2014).

    Article  PubMed  Google Scholar 

  67. von Elm, E. et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet 370, 1453–1457 (2007).

    Article  Google Scholar 

  68. Flint, C. et al. Systematic misestimation of machine learning performance in neuroimaging studies of depression. Neuropsychopharmacology 46, 1510–1517 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Goyal, M. S. et al. Persistent metabolic youth in the aging female brain. Proc. Natl Acad. Sci. USA 116, 3251–3255 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Lin, J. Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theory 37, 145–151 (1991).

    Article  Google Scholar 

  71. Franke, K. et al. In vivo biomarkers of structural and functional brain development and aging in humans. Neurosci. Biobehav. Rev. 117, 142–164 (2020).

    Article  PubMed  Google Scholar 

  72. Yu, J., Li, J. & Huang, X. The Beijing version of the Montreal Cognitive Assessment as a brief screening tool for mild cognitive impairment: a community-based study. BMC Psychiatry 12, 156 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Gong, Y. & Wang, D. J. Handbook of Wechsler Memory Scale-Revised (Hunan Medical College, 1989).

  74. Galvin, J. E. et al. The AD8: a brief informant interview to detect dementia. Neurology 65, 559–564 (2005).

    Article  PubMed  Google Scholar 

  75. Broce, I. J. et al. Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer’s disease. Acta Neuropathol. 137, 209–226 (2019).

    Article  PubMed  Google Scholar 

  76. Elvsåshagen, T. et al. The genetic architecture of the human thalamus and its overlap with ten common brain disorders. Nat. Commun. 12, 2909 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Karadag, N. et al. Identification of novel genomic risk loci shared between common epilepsies and psychiatric disorders. Brain 146, 3392–3403 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Andreassen, O. A. et al. Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am. J. Hum. Genet. 92, 197–209 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Andreassen, O. A., Thompson, W. K. & Dale, A. M. Boosting the power of schizophrenia genetics by leveraging new statistical tools. Schizophr. Bull. 40, 13–17 (2014).

    Article  PubMed  Google Scholar 

  80. Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife 7, e34408 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Markello, R. D. et al. Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife 10, e72129 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Burt, J. B. et al. Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography. Nat. Neurosci. 21, 1251–1259 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Huntenburg, J. M., Bazin, P. L. & Margulies, D. S. Large-scale gradients in human cortical organization. Trends Cogn. Sci. 22, 21–31 (2018).

    Article  PubMed  Google Scholar 

  84. Burt, J. B. et al. Generative modeling of brain maps with spatial autocorrelation. Neuroimage 220, 117038 (2020).

    Article  PubMed  Google Scholar 

  85. Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

    Article  PubMed  Google Scholar 

  86. Seidlitz, J. et al. Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders. Nat. Commun. 11, 3358 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Korotkevich, G. et al. Fast gene set enrichment analysis. BioRxiv https://doi.org/10.1101/060012 (2016).

  88. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Martins, D. et al. Imaging transcriptomics: convergent cellular, transcriptomic and molecular neuroimaging signatures in the healthy adult human brain. Cell Rep. 37, 110173 (2021).

    Article  PubMed  Google Scholar 

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Acknowledgements

The present study utilized the UK Biobank Resource under application no. 71901. We sincerely thank all participants and researchers from the UK Biobank. The study was supported by the STI-2030 Major Project (2022ZD0206400, Y.T.), the National Natural Science Foundation of China (32171078, 32322035, Y.T.), the Scientific Foundation of the Institute of Psychology, Chinese Academy of Sciences (E0CX52 and E2CX4015, Y.T.) and the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology (E1KX0210, Y.T.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Lei Zhao, Jiao Liu.

Authors and Affiliations

  1. CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China

    Lei Zhao, Wenhui Zhao, Jie Chen & Yiheng Tu

  2. Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

    Lei Zhao, Wenhui Zhao, Jie Chen & Yiheng Tu

  3. School of Traditional Chinese Medicine, Capital Medical University, Beijing, China

    Jiao Liu

  4. National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China

    Jiao Liu

  5. School of Data Science, The Chinese University of Hong Kong, Shenzhen, China

    Jicong Fan

  6. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Tian Ge

  7. Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA

    Tian Ge

  8. Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Tian Ge

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Contributions

Y.T. and J.L. conceived and designed the study. L.Z., W.Z., J.C., T.G. and J.F. analyzed and interpreted the data. J.L. was involved in data collection. L.Z. and Y.T. wrote the initial manuscript. L.Z., Y.T. and T.G. were involved in writing, reviewing and editing the manuscript. Y.T. supervised the project. All authors edited and contributed to the final version of the manuscript.

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Correspondence to Yiheng Tu.

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Zhao, L., Liu, J., Zhao, W. et al. Morphological and genetic decoding shows heterogeneous patterns of brain aging in chronic musculoskeletal pain. Nat. Mental Health 2, 435–449 (2024). https://doi.org/10.1038/s44220-024-00223-3

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