Current issue
Archive
Manuscripts accepted
About the Journal
Editorial office
Editorial board
Section Editors
Abstracting and indexing
Subscription
Contact
Ethical standards and procedures
Most read articles
Instructions for authors
Article Processing Charge (APC)
Regulations of paying article processing charge (APC)
RESEARCH PAPER
Cardiometabolic Multimorbidity and Depressive Symptom Trajectories in middle-aged and older adults with Chronic Pain: Evidence from the CHARLS and ELSA Cohorts
1
Ningxia Medical University, China
Submission date: 2026-03-23
Final revision date: 2026-04-20
Acceptance date: 2026-04-29
Online publication date: 2026-07-25
KEYWORDS
TOPICS
ABSTRACT
Introduction:
The co-occurrence of chronic pain and cardiometabolic multimorbidity (CMM) is increasingly prevalent among middle-aged and older adults, yet the extent to which CMM shapes the longitudinal course of depressive symptomatology in this population remains poorly understood.
Material and methods:
Data were drawn from the China Health and Retirement Longitudinal Study (CHARLS; participants aged 45 years and older) and the English Longitudinal Study of Ageing (ELSA; participants aged 50 years and older). Group-based trajectory modeling (GBTM) was applied to identify distinct depressive symptom trajectories, and multinomial logistic regression was used to examine associations between CMM and trajectory group membership, with subgroup analyses conducted to assess potential effect modification.
Results:
Three stable depressive symptom trajectories were identified in both cohorts: persistently low-, moderate-, and high-symptom groups. In ELSA, CMM was significantly associated with both moderate- (OR: 1.56, 95% CI: 1.24–1.96) and high-symptom trajectories (OR: 2.20, 95% CI: 1.61–3.02) after full covariate adjustment. In CHARLS, the association was confined to the high-symptom trajectory (OR: 1.39, 95% CI: 1.03–1.86). Subgroup analyses indicated a more pronounced association among female participants in CHARLS and among those younger than 65 years in ELSA.
Conclusions:
CMM is significantly associated with more severe depressive symptom trajectories among middle-aged and older adults with chronic pain, with cross-cohort heterogeneity reflecting differences in population characteristics and sociocultural context. Systematic CMM screening within chronic pain management may help identify individuals at elevated risk for adverse psychological outcomes.
The co-occurrence of chronic pain and cardiometabolic multimorbidity (CMM) is increasingly prevalent among middle-aged and older adults, yet the extent to which CMM shapes the longitudinal course of depressive symptomatology in this population remains poorly understood.
Material and methods:
Data were drawn from the China Health and Retirement Longitudinal Study (CHARLS; participants aged 45 years and older) and the English Longitudinal Study of Ageing (ELSA; participants aged 50 years and older). Group-based trajectory modeling (GBTM) was applied to identify distinct depressive symptom trajectories, and multinomial logistic regression was used to examine associations between CMM and trajectory group membership, with subgroup analyses conducted to assess potential effect modification.
Results:
Three stable depressive symptom trajectories were identified in both cohorts: persistently low-, moderate-, and high-symptom groups. In ELSA, CMM was significantly associated with both moderate- (OR: 1.56, 95% CI: 1.24–1.96) and high-symptom trajectories (OR: 2.20, 95% CI: 1.61–3.02) after full covariate adjustment. In CHARLS, the association was confined to the high-symptom trajectory (OR: 1.39, 95% CI: 1.03–1.86). Subgroup analyses indicated a more pronounced association among female participants in CHARLS and among those younger than 65 years in ELSA.
Conclusions:
CMM is significantly associated with more severe depressive symptom trajectories among middle-aged and older adults with chronic pain, with cross-cohort heterogeneity reflecting differences in population characteristics and sociocultural context. Systematic CMM screening within chronic pain management may help identify individuals at elevated risk for adverse psychological outcomes.
REFERENCES (52)
1.
Chen, Q.-F. et al. Global burden of disease and its risk factors for adults aged 70 and older across 204 countries and territories: a comprehensive analysis of the Global Burden of Disease Study 2021. BMC Geriatr 25, 462 (2025).
2.
Yang, L. et al. Stage-specific risk factors of cardiometabolic multimorbidity: A systematic review and meta-analysis from incidence to mortality. Ageing Research Reviews 114, 102991 (2026).
3.
Lindstrom, M. et al. Global Burden of Cardiovascular Diseases and Risks Collaboration, 1990-2021. Journal of the American College of Cardiology 80, 2372–2425 (2022).
4.
Chowdhury, S. R., Chandra Das, D., Sunna, T. C., Beyene, J. & Hossain, A. Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis. eClinicalMedicine 57, 101860 (2023).
5.
Asogwa, O. A. et al. Multimorbidity of non-communicable diseases in low-income and middle- income countries: a systematic review and meta-analysis. Open access.
6.
Zhao, X. Independent and joint associations of cardiometabolic multimorbidity and depression on cognitive function: findings from multi-regional cohorts and generalisation from community to clinic.
7.
Zhou, Y. et al. Bidirectional Associations Between Cardiometabolic Multimorbidity and Depression and Mediation of Lifestyles. JACC: Asia 4, 657–671 (2024).
8.
Zhao, Y. W. et al. The effect of multimorbidity on functional limitations and depression amongst middle-aged and older population in China: a nationwide longitudinal study. Age and Ageing 50, 190–197 (2021).
9.
Yin, X., Chen, Y., Zhou, L., Yang, H. & Wang, Y. Association between chronic pain and risk of cardiometabolic multimorbidity: a prospective cohort study. Reg Anesth Pain Med 50, 858–864 (2025).
10.
Han, Y. Duration-dependent impact of cardiometabolic diseases and multimorbidity on all-cause and cause-specific mortality: a prospective cohort study of 0.5 million participants. (2023).
11.
Qiu, Y. et al. The prevalence and economic burden of pain on middle-aged and elderly Chinese people: results from the China health and retirement longitudinal study. BMC Health Serv Res 20, 600 (2020).
12.
Werneck, A. O. & Stubbs, B. Bidirectional relationship between chronic pain and depressive symptoms in middle-aged and older adults. General Hospital Psychiatry 89, 49–54 (2024).
13.
Zeng, J. et al. Chronic pain in multiple sites is associated with depressive symptoms in US adults: A cross-sectional study. Journal of Psychiatric Research 183, 212–218 (2025).
14.
Rydin, A. O. et al. Trajectories of depressive symptoms, metabolic syndrome, inflammation, and cardiometabolic diseases: A longitudinal Bayesian network approach. Brain, Behavior, and Immunity 130, 106120 (2025).
15.
eQTLGen et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 50, 668–681 (2018).
16.
Zhao, Y., Hu, Y., Smith, J. P., Strauss, J. & Yang, G. Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS). International Journal of Epidemiology 43, 61–68 (2014).
17.
Steptoe, A., Breeze, E., Banks, J. & Nazroo, J. Cohort Profile: The English Longitudinal Study of Ageing.
18.
Zhao, Z., Cheng, S. & Li, S. Dynamic patterns of depression and their association with stroke: insights from CHARLS, ELSA, and HRS. International Journal of Surgery 112, 2341–2350 (2026).
19.
Ruiz, M., Hu, Y., Martikainen, P. & Bobak, M. Life course socioeconomic position and incidence of mid–late life depression in China and England: a comparative analysis of CHARLS and ELSA. J Epidemiol Community Health 73, 817–824 (2019).
20.
Hamer, M., Batty, G. D. & Kivimaki, M. Risk of future depression in people who are obese but metabolically healthy: the English longitudinal study of ageing. Mol Psychiatry 17, 940–945 (2012).
21.
Li, C. et al. Early-life exposure to severe famine and subsequent risk of depressive symptoms in late adulthood: the China Health and Retirement Longitudinal Study. Br J Psychiatry 213, 579–586 (2018).
22.
Zhang, Y. et al. Cardiometabolic multimorbidity, social activity, and joint trajectories of physical disability, depressive symptom, and cognitive function in mid-to-late life: a multicohort study. BMC Med 23, 577 (2025).
23.
Jiao, J. et al. Long-term trajectories of memory, depression, and mobility independence before death: a multi-cohort study. Transl Psychiatry 16, 221 (2026).
24.
American Diabetes Association. 6. Glycemic Targets: Standards of Medical Care in Diabetes—2021. Diabetes Care 44, S73–S84 (2021).
25.
Joint committee for guideline revision. 2016 Chinese guidelines for the management of dyslipidemia in adults. J Geriatr Cardiol 15, 1–29 (2018).
26.
Lange-Maia, B. S. et al. Impact of Chronic Medical Condition Development on Longitudinal Physical Function from Mid- to Early Late-Life: The Study of Women’s Health Across the Nation. The Journals of Gerontology: Series A 75, 1411–1417 (2020).
27.
Liu, L. et al. Trajectories of depressive symptoms 10 years after stroke and associated risk factors: a prospective cohort study. The Lancet 402, S64 (2023).
28.
Shi, S., Xue, F., Jiang, T. & Ling, L. Association between trajectory of triglyceride-glucose index and all-cause mortality in critically ill patients with atrial fibrillation: a retrospective cohort study. Cardiovasc Diabetol 24, 278 (2025).
29.
Belhassen, M., Nolin, M., Jacoud, F., Marant Micallef, C. & Van Ganse, E. Trajectories of Controller Therapy Use Before and After Asthma-Related Hospitalization in Children and Adults: Population-Based Retrospective Cohort Study. JMIR Public Health Surveill 9, e50085 (2023).
30.
Li, J.-J., Liu, H.-H. & Li, S. Landscape of cardiometabolic risk factors in Chinese population: a narrative review. Cardiovasc Diabetol 21, 113 (2022).
31.
Wang, T. et al. Age-specific modifiable risk factor profiles for cardiovascular disease and all-cause mortality: a nationwide, population-based, prospective cohort study. The Lancet Regional Health - Western Pacific 17, 100277 (2021).
32.
Abner, S. et al. Consultation rates in people with type 2 diabetes with and without vascular complications: a retrospective analysis of 141,328 adults in England. Cardiovasc Diabetol 21, 8 (2022).
33.
Maschke, V., Lohner, V. & Mons, U. Linking cardiometabolic multimorbidity to depressive symptoms in the oldest-old: results from a cross-sectional study in Germany. BMC Public Health 25, 1720 (2025).
34.
Huang, Z.-T. et al. Patterns of cardiometabolic multimorbidity and the risk of depressive symptoms in a longitudinal cohort of middle-aged and older Chinese. Journal of Affective Disorders 301, 1–7 (2022).
35.
Ditmars, H. L. et al. Associations between depression and cardiometabolic health: A 27-year longitudinal study. Psychol. Med. 52, 3007–3017 (2022).
36.
Yang, W. et al. Association of cardiometabolic multimorbidity with risk of late-life depression: a nationwide twin study. Eur. Psychiatr. 67, e58 (2024).
37.
Vyas, A., Pan, X. & Sambamoorthi, U. Chronic Condition Clusters and Polypharmacy among Adults.
38.
Katon, W. J. Clinical and health services relationships between major depression, depressive symptoms, and general medical illness. Biological Psychiatry 54, 216–226 (2003).
39.
Asman, A. G., Hoogendoorn, C. J., McKee, M. D. & Gonzalez, J. S. Assessing the association of depression and anxiety with symptom reporting among individuals with type 2 diabetes. J Behav Med 43, 57–68 (2020).
40.
Scherer, M. et al. Association between multimorbidity patterns and chronic pain in elderly primary care patients: a cross-sectional observational study. BMC Fam Pract 17, 68 (2016).
41.
Tsai, S. A., Xiao, L., Lv, N., Liu, Y. & Ma, J. Association of the Cardiometabolic Staging System with Individual Engagement and Quality of Life in the US Adult Population. Obesity 25, 1540–1548 (2017).
42.
J. Katon, Wayne. Epidemiology and treatment of depression in patients with chronic medical illness. Dialogues in Clinical Neuroscience 13, 7–23 (2011).
43.
Nadrowski, P. et al. Associations between cardiovascular disease risk factors and IL-6 and hsCRP levels in the elderly. Experimental Gerontology 85, 112–117 (2016).
44.
Kemp, A. H., Quintana, D. S., Felmingham, K. L., Matthews, S. & Jelinek, H. F. Depression, Comorbid Anxiety Disorders, and Heart Rate Variability in Physically Healthy, Unmedicated Patients: Implications for Cardiovascular Risk. PLoS ONE 7, e30777 (2012).
45.
Xu, S. et al. Endothelial Dysfunction in Atherosclerotic Cardiovascular Diseases and Beyond: From Mechanism to Pharmacotherapies. Pharmacological Reviews 73, 924–967 (2021).
46.
Guidance on the management of pain in older people. Age and Ageing 42, i1–i57 (2013).
47.
Ahrend, J. N., Jobski, K., Bantel, C. & Hoffmann, F. Pain intensity and comorbid depressive symptoms in the general population: An analysis of the German Health Update Study (GEDA 2019/2020‐EHIS). European Journal of Pain 29, e4745 (2025).
48.
Stubbs, B. et al. Depression and pain: primary data and meta-analysis among 237 952 people across 47 low- and middle-income countries. Psychol. Med. 47, 2906–2917 (2017).
49.
Jiang, R. et al. The inflammatory and genetic mechanisms underlying the cumulative effect of co-occurring pain conditions on depression. Science AdvAnceS (2025).
50.
Sălcudean, A. et al. Neuroinflammation—A Crucial Factor in the Pathophysiology of Depression—A Comprehensive Review. Biomolecules 15, 502 (2025).
51.
Hassamal, S. Chronic stress, neuroinflammation, and depression: an overview of pathophysiological mechanisms and emerging anti-inflammatories. Front. Psychiatry 14, 1130989 (2023).
52.
Maes, M., Almulla, A. F., You, Z. & Zhang, Y. Neuroimmune, metabolic and oxidative stress pathways in major depressive disorder. Nat Rev Neurol 21, 473–489 (2025).
Share
RELATED ARTICLE
| eISSN: | 1896-9151 |
| ISSN: | 1734-1922 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
