Trajectories of depressive symptoms and subsequent cognitive decline in older adults: a pooled analysis of two longitudinal cohorts
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Background: the course of depression is variable, but it is unknown how this variability over time affects long-term cognitive decline. Objective: to examine the relationship of different trajectories of depressive symptoms on rates of subsequent cognitive decline in older adults. Design: population-based cohort study. Setting: communities in the USA and England. Subjects: 17,556 older adults from the Health and Retirement Study and the English Longitudinal Study of Ageing. Methods: depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale, and trajectories were calculated using group-based trajectory modelling. Global cognitive function and three cognitive domains of memory, executive function and temporal orientation were assessed for up to 18 years. Results: five trajectories of depressive symptoms were identified. Compared with the ‘non-depressed’ trajectory, the ‘worsening depressive symptoms’ trajectory (pooled β = −0.016 standard deviation (SD)/year, 95% confidence interval (CI): −0.021 to −0.010), ‘persistent depressive symptoms’ trajectory (pooled β = −0.016 SD/year, 95% CI: −0.024 to −0.008), and ‘mild depressive symptoms’ trajectory (pooled β = −0.008 SD/year, 95% CI: −0.014 to −0.003) were associated with faster rates of cognitive decline, while no such association was found for the ‘improving depressive symptoms’ trajectory (pooled β = 0.001 SD/year, 95% CI: −0.010 to 0.012). Conclusions: subthreshold depressive symptoms are associated with an increased rate of cognitive decline, while individuals who show improving depressive symptoms do not exhibit accelerated cognitive decline. These findings raise the possibility that maintaining depressive symptoms as low as possible and ignoring the clinical threshold, might mitigate cognitive decline in older adults.
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ML and AI techniques, such as deep learning and longitudinal modeling, can analyze large, complex datasets spanning multiple life stages to identify patterns and trajectories of health and disease over time.
“ML and AI techniques, such as deep learning and longitudinal modeling, can analyze large, complex datasets spanning multiple life stages to identify patterns and trajectories of health and disease over time [30, 31].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [30]
Unsupervised learning techniques, such as clustering and latent class analysis, can uncover distinct subgroups of individuals with similar developmental trajectories, which may inform the timing of interventions.
“Unsupervised learning techniques, such as clustering and latent class analysis, can uncover distinct subgroups of individuals with similar developmental trajectories, which may inform the timing of interventions [30, 31].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [30]
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“The details of cognitive assessment have been previously reported (24–26).”
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“This observation underscores the importance of addressing depressive symptoms of any severity (Zhu et al., 2022).”
Editorial: Epidemiology and clinical researches on neuropsychiatric disorders in aging
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“While prior longitudinal evidence suggests that depressive symptoms predict cognitive decline among primarily White populations [13–15], our findings suggest among middle to late midlife adulthood, depressive symptoms take a toll on the cognitive functioning among ethnoracial minority individuals…”
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“” The fi ve answers to items 1, 4, 7, 9, and 12 were scored as 3 points, the 10 answers to items 2, 3, 5, 6, 8, 10, 11, 13, 14, and 15 received 1 point, and all answers to items “ no ” were scored as 0 points.”
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“Moreover, a study of two longitudinal cohorts reported that the presence of persistent or deteriorating depressive symptoms were related to faster cognitive decline, even among individuals with mild symptoms [15].”
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“However, most studies, with only a few exceptions (Jia, Du, et al., 2020; Mukadam et al., 2019), have mainly focused on antecedents of one or two life domains (Mao et al., 2020; Yin et al., 2017; C.-E. Zhu et al., 2022).”
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“Such results resonate with the recent research that Zhu Y [31] presented, which showed that depressive symptoms’ trajectory was associated with faster rates of cognitive decline.”
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“First, our study constitutes one of the largest and most comprehensive analyses to date on PM pollution exposures and depressive symptoms in middle-aged 62 Third, this study used a WQS regression model to assess the effect of PM pollutant mixture on depressive symptom trajectories.”
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“However, the relationship between depression and cognitive function in this study was inconsistent with previous research, which reported that more depressed participants experienced a signi fi cantly steeper decline in cognitive function (Form´anek et al., 2020; Zhu et al., 2022).”
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“Based on previous studies, we selected a series of covariates, including demographic characteristics such as age [23–25], gender [26,27], type of residential address [28], and marital states [29,30], as well as factors that have been validated by research to be highly correlated with depressive…”
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- Authors
- Zhu, Y., Li, C., Xie, W., Zhong, B., Wu, Y., & Blumenthal, J. A.
- Affiliations
- Peking University (CN)
- Affiliated Wuhan Mental Health Center, Tongji Medical College of Huazhong University of Science & Technology, Wuhan, China
- Duke Medical Center (US)
- Corresponding author
- Xie, Wuxiang
- Venue
- Age and Ageing
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-10-16
- Volume
- 51
- Issue
- 1
- ISSN
- 0002-0729 (ISSN-L)
- 1468-2834
- OpenAlex
- W3206498095
- Cited by
- 112
- References
- 37
- Citations by year
- 2026: 24 · 2025: 28 · 2024: 31 · 2023: 23 · 2022: 6
- FWCI
- 10.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Age and Ageing (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Health Research and Educational Trust
- Academy of Medical Sciences (NIF001-1005-P56804)
- National Natural Science Foundation of China (81974489, 81974490)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Aging, Health, and Disability
- Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Aging and Gerontology Research
- Keywords
- depressive symptoms, cognitive decline, older adults, subthreshold depression, mild depressive symptoms, persistent depressive symptoms, memory, executive function, time orientation, global cognitive function
- MeSH terms
- Aged, Cognition, Depression, Humans, Longitudinal Studies, Cohort Studies, Cognitive Dysfunction
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