Depressive Symptoms and Mortality Among US Adults

Zhang, Zefeng; Jackson, Sandra L.; Gillespie, Cathleen; Merritt, Robert; Yang, Quanhe

doi:10.1001/jamanetworkopen.2023.37011

Cited by 37 publications

(8 citation statements)

References 52 publications

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“…Individuals engaged in nonagricultural occupations may be more susceptible to unhealthy lifestyle choices such as smoking, alcohol consumption, and lack of physical activity ( 108 – 110 ). These lifestyle factors can interact with symptoms of depression, collectively increasing the risk of mortality ( 109 , 111 ). Furthermore, nonagricultural work is often associated with heightened job stress and competition, which may exacerbate the development of depressive symptoms ( 112 ).…”

Section: Discussionmentioning

confidence: 99%

Depressive symptoms and all-cause mortality among middle-aged and older people in China and associations with chronic diseases

Zhu,

Wang,

et al. 2024

Front. Public Health

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IntroductionIt remains unclear whether depressive symptoms are associated with increased all-cause mortality and to what extent depressive symptoms are associated with chronic disease and all-cause mortality. The study aims to explore the relationship between depressive symptoms and all-cause mortality, and how depressive symptoms may, in turn, affect all-cause mortality among Chinese middle-aged and older people through chronic diseases.MethodsData were collected from the China Health and Retirement Longitudinal Study (CHARLS). This cohort study involved 13,855 individuals from Wave 1 (2011) to Wave 6 (2020) of the CHARLS, which is a nationally representative survey that collects information from Chinese residents ages 45 and older to explore intrinsic mechanisms between depressive symptoms and all-cause mortality. The Center for Epidemiological Studies Depression Scale (CES-D-10) was validated through the CHARLS. Covariates included socioeconomic variables, living habits, and self-reported history of chronic diseases. Kaplan–Meier curves depicted mortality rates by depressive symptom levels, with Cox proportional hazards regression models estimating the hazard ratios (HRs) of all-cause mortality.ResultsOut of the total 13,855 participants included, the median (Q1, Q3) age was 58.00 (51.00, 63.00) years. Adjusted for all covariates, middle-aged and older adults with depressive symptoms had a higher all-cause mortality rate (HR = 1.20 [95% CI, 1.09–1.33]). An increased rate was observed for 55–64 years old (HR = 1.23 [95% CI, 1.03–1.47]) and more than 65 years old (HR = 1.32 [95% CI, 1.18–1.49]), agricultural Hukou (HR = 1.44, [95% CI, 1.30–1.59]), and nonagricultural workload (HR = 1.81 [95% CI, 1.61–2.03]). Depressive symptoms increased the risks of all-cause mortality among patients with hypertension (HR = 1.19 [95% CI, 1.00–1.40]), diabetes (HR = 1.41[95% CI, 1.02–1.95]), and arthritis (HR = 1.29 [95% CI, 1.09–1.51]).ConclusionDepressive symptoms raise all-cause mortality risk, particularly in those aged 55 and above, rural household registration (agricultural Hukou), nonagricultural workers, and middle-aged and older people with hypertension, diabetes, and arthritis. Our findings through the longitudinal data collected in this study offer valuable insights for interventions targeting depression, such as early detection, integrated chronic disease care management, and healthy lifestyles; and community support for depressive symptoms may help to reduce mortality in middle-aged and older people.

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Section: Discussionmentioning

confidence: 99%

Depressive symptoms and all-cause mortality among middle-aged and older people in China and associations with chronic diseases

Zhu,

Wang,

et al. 2024

Front. Public Health

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“…These symptoms not only have an impact on health in their own right but also increase the risk of other adverse health outcomes, such as neurodegenerative disorders and suicide 3 . They can result in a substantial reduction in life expectancy (10-20 years) [9][10][11] . Since mental disorders are closely linked to poor health outcomes, it is crucial to comprehend the contributing factors in order to advance the prevention and treatment of these disorders.…”

Section: Introductionmentioning

confidence: 99%

“…Older adults frequently face underrecognition and undertreatment of their mental health conditions, yet early intervention at the rst signs of mental disorders can improve a range of outcomes 11 . Recent evidence has revealed changes in metabolic markers in mental disorders [12][13][14][15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Longitudinal trajectories of immunometabolic marker in various mental disorders and their relationship with brain structures

Cheng,

Yujia,

You

et al. 2024

Preprint

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Background: Studies have identified immunometabolic biomarkers for various mental disorders, but their temporal evolution and relationship with brain structure remain unclear. This study aimed to assess the long-term trajectories of immunometabolic biomarkers across 10 years leading up to and following diagnosis and explore their association with brain structure. Methods: We conducted a nested case-control study using prospective community-based cohort data from UK Biobank, which included both psychiatric assessments and immunometabolic markers. Cases were defined as individuals with a diagnosis of mental disorders at baseline and during follow-up. Nearly 5 controls without any mental disorders were matched for each case. Multivariable linear regression was used to assess the divergence evolution between cases and controls for each psychiatric assessment and immunometabolic biomarker. Results: In comparison to controls, 6, 15, 10, and 47 immunometabolic markers exhibited significant changes over time in bipolar disorder, anxiety, schizophrenia, and depression, respectively. These biomarkers could be grouped into distinct clusters with complex, non-linear trajectories. Some clusters displayed monotonic changes, while others reversed near the time of diagnosis. The identified immunometabolic markers were associated with brain structure in the general population, including orbitofrontal, precuneus, and amygdala regions. Conclusions: These findings provide novel insights into the temporal dynamics of immunometabolic biomarkers in various mental disorders within 10 years before and after clinical diagnosis, as well as their correlation with brain structure. Monitoring and managing these biomarkers could potentially carry significant implications for the early detection and prevention of mental disorders in older adults.

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“…Major depressive disorder (MDD) is diagnosed when an individual experiences five of nine symptoms (either low / depressed mood or anhedonia, and four of the following: feelings of guilt / worthlessness, lack of energy, poor concentration, appetite changes, psychomotor retardation or agitation, sleep disturbances, or suicidal thoughts; American Psychiatric Association, 2013 [1]). It is an extremely common and devastating disease in terms of suffering, mortality and cost to society [2][3][4][5]. The challenge of MDD is also growing.…”

Section: Introductionmentioning

confidence: 99%

Examining factors related to low performance of predicting remission in participants with major depressive disorder using neuroimaging data and other clinical features

Wang,

Wu,

DeLorenzo

et al. 2024

PLoS ONE

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Major depressive disorder (MDD), a prevalent mental health issue, affects more than 8% of the US population, and almost 17% in the young group of 18–25 years old. Since Covid-19, its prevalence has become even more significant. However, the remission (being free of depression) rates of first-line antidepressant treatments on MDD are only about 30%. To improve treatment outcomes, researchers have built various predictive models for treatment responses and yet none of them have been adopted in clinical use. One reason is that most predictive models are based on data from subjective questionnaires, which are less reliable. Neuroimaging data are promising objective prognostic factors, but they are expensive to obtain and hence predictive models using neuroimaging data are limited and such studies were usually in small scale (N<100). In this paper, we proposed an advanced machine learning (ML) pipeline for small training dataset with large number of features. We implemented multiple imputation for missing data and repeated K-fold cross validation (CV) to robustly estimate predictive performances. Different feature selection methods and stacking methods using 6 general ML models including random forest, gradient boosting decision tree, XGBoost, penalized logistic regression, support vector machine (SVM), and neural network were examined to evaluate the model performances. All predictive models were compared using model performance metrics such as accuracy, balanced accuracy, area under ROC curve (AUC), sensitivity and specificity. Our proposed ML pipeline was applied to a training dataset and obtained an accuracy and AUC above 0.80. But such high performance failed while applying our ML pipeline using an external validation dataset from the EMBARC study which is a multi-center study. We further examined the possible reasons especially the site heterogeneity issue.

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Depressive Symptoms and Mortality Among US Adults

Cited by 37 publications

References 52 publications

Depressive symptoms and all-cause mortality among middle-aged and older people in China and associations with chronic diseases

Depressive symptoms and all-cause mortality among middle-aged and older people in China and associations with chronic diseases

Longitudinal trajectories of immunometabolic marker in various mental disorders and their relationship with brain structures

Examining factors related to low performance of predicting remission in participants with major depressive disorder using neuroimaging data and other clinical features

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