Association of chronic diseases with depressive symptom trajectories in postmenopausal women: a population-based cohort study

Technical Summary: Association of Chronic Diseases with Depressive Symptom Trajectories in Postmenopausal Women

Problem Statement
Postmenopausal women face a heightened susceptibility to both chronic diseases and depressive symptoms. While cross-sectional studies have established a link between multimorbidity and depression, the longitudinal dynamics of this relationship remain unclear. Specifically, it is not well understood how chronic diseases influence the trajectories of depressive symptoms over time—whether they lead to chronicity, remission, or increasing severity—and through what biological or behavioral mechanisms these associations operate. Existing literature often relies on average group-level changes, potentially obscuring the heterogeneity of individual adaptation to chronic illness in this specific demographic.

Methodology
This study utilized a longitudinal design based on data from the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort.

• Population: The analysis included 6,337 postmenopausal women aged ≥45 years with complete follow-up data across five survey waves (2011, 2013, 2015, 2018, and 2020).
• Exposure: Chronic disease status was ascertained via self-report at baseline (2011). Conditions included hypertension, dyslipidemia, diabetes, lung disease, liver disease, heart disease, stroke, kidney disease, digestive diseases, arthritis/rheumatism, and asthma. Multimorbidity was categorized by the count of conditions (0, 1, 2, ≥3).
• Outcome: Depressive symptoms were measured using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) at all five waves.
• Analytical Approach:
  • Group-Based Trajectory Modeling (GBTM): A semiparametric, data-driven method was employed to identify distinct latent subgroups of depressive symptom trajectories over the nine-year period. Model selection was guided by the Bayesian Information Criterion (BIC) and average posterior probabilities.
  • Association Analysis: Inverse Probability of Treatment Weighting (IPTW) was applied to balance baseline covariates (age, education, marital status, smoking, BMI, waist circumference). Subsequently, multivariable logistic regression was used to estimate the odds of belonging to specific trajectory groups based on chronic disease burden and specific disease types.
  • Mediation Analysis: The study examined whether sleep duration and cognitive function mediated the relationship between the number of chronic diseases and depressive symptoms.

Key Results

1. Trajectory Identification: Four distinct depressive symptom trajectories were identified:
   • No Depressive Symptoms (NDS): 62.2% of participants.
   • Increasing Depressive Symptoms (IDS): 19.7%.
   • Remitting Depressive Symptoms (RDS): 10.0%.
   • Persistently Severe Depressive Symptoms (PDS): 8.2%.
2. Impact of Chronic Disease Burden: There was a dose-response relationship between the number of chronic diseases and adverse trajectories. Compared to women with no chronic diseases, those with ≥3 diseases had significantly higher odds of belonging to the PDS group (OR = 5.20, 95% CI: 3.96–6.83). Even a single chronic disease significantly increased the risk of adverse trajectories.
3. Specific Disease Risks: Certain conditions were identified as strong risk factors for the PDS trajectory, including stroke (OR = 3.79), kidney disease (OR = 3.69), arthritis/rheumatism (OR = 3.54), digestive system diseases, and memory-related diseases. Hypertension showed more modest associations, while diabetes and liver disease showed significance primarily for RDS and PDS groups.
4. Demographic Modifiers: Lower education, rural residence, single status, and a history of premature menopause were independently associated with higher odds of belonging to adverse trajectories (IDS, RDS, or PDS).
5. Mediating Mechanisms: Sleep duration and cognitive function were identified as significant partial mediators. A higher chronic disease count was associated with shorter sleep duration and poorer cognitive function, which in turn were linked to higher depressive symptom scores. The indirect effects were statistically significant for both sleep (ES = 0.209) and cognition (ES = 0.097).

Key Contributions

• Longitudinal Trajectory Mapping: This study moves beyond cross-sectional snapshots to map the heterogeneous long-term trajectories of depressive symptoms in postmenopausal women, distinguishing between those who remain symptom-free, those whose symptoms worsen, and those who experience remission or persistent severity.
• Granular Disease Analysis: It differentiates the impact of specific chronic conditions versus the cumulative burden of multimorbidity, highlighting that certain diseases (e.g., stroke, kidney disease) exert a disproportionately large influence on persistent severe depression.
• Mechanistic Insight: By identifying sleep duration and cognitive function as mediators, the study elucidates potential pathways through which chronic illness exacerbates depression in the context of postmenopausal physiological changes (e.g., estrogen decline).
• Methodological Rigor: The application of IPTW to balance confounders and GBTM to capture individual heterogeneity strengthens the validity of the observed associations in a large, nationally representative sample.

Significance and Claims
The authors claim that this is the first study to examine the association between longitudinal changes in chronic diseases and depressive symptom trajectories specifically within postmenopausal women. The findings underscore that the long-term trajectory of depressive symptoms is strongly shaped by the burden of chronic disease.

The paper posits that these results highlight the necessity for an integrated multidisciplinary treatment model that bridges gynecological, geriatric, and mental health services. Clinicians are encouraged to implement targeted risk stratification and management strategies based on a patient's depressive symptom trajectory and chronic disease profile. The study concludes that addressing the cumulative burden of chronic diseases, alongside sleep and cognitive health, is critical for preventing adverse depressive outcomes in this population.

Note: The authors acknowledge limitations, including the reliance on self-reported data (potential recall bias), the possibility of residual confounding from unmeasured factors, and the observational nature of the study which precludes definitive causal conclusions.