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Sinclair LI, Mohr A, Morisaki M, Edmondson M, Chan S, Bone-Connaughton A, Turecki G, Love S. Is later-life depression a risk factor for Alzheimer's disease or a prodromal symptom: a study using post-mortem human brain tissue? Alzheimers Res Ther 2023; 15:153. [PMID: 37700368 PMCID: PMC10496415 DOI: 10.1186/s13195-023-01299-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 08/24/2023] [Indexed: 09/14/2023]
Abstract
BACKGROUND Depression and dementia are both common diseases. Although new cases of depression are more common in younger adults, there is a second peak at the age of 50 years suggesting a different pathological process. Late-life depression (LLD) is associated with dementia. However, it remains unclear whether depression represents a dementia prodrome or is a true risk factor for its development. LLD is thought to have a vascular component and this may be a possible link between depression and dementia. We hypothesised that later-life depression is a prodromal manifestation of dementia and would therefore be associated with more AD, and/or ischaemic brain abnormalities that are present in earlier-life depression or in age- and sex-matched controls. METHODS We assessed post-mortem orbitofrontal cortex and dorsolateral pre-frontal cortex from 145 individuals in 4 groups: 28 18-50-year-olds with depression, 30 older individuals (ages 51-90) with depression, 28 with early AD (Braak tangle stages III-IV) and 57 matched controls (17 early-life, 42 later-life). Levels of Aβ, phospho-tau and α-synuclein were assessed by immunohistochemistry and ELISA. To quantify chronic ischaemia, VEGF, MAG and PLP1 were measured by ELISA. To assess pericyte damage, PDGFRB was measured by ELISA. For blood-brain barrier leakiness, JAM-A, claudin 5 and fibrinogen were measured by ELISA. To quantity endothelial activation, the ratio of ICAM1:collagen IV was assessed by immunohistochemistry. RESULTS There was no evidence of chronic cerebral hypoperfusion or increased Aβ/tau in either depression group. There was also no indication of pericyte damage, increased blood-brain barrier leakiness or endothelial activation in the OFC or DLPFC in the depression groups. CONCLUSIONS Contrary to some previous findings, we have not found evidence of impaired vascular function or increased Aβ in LLD. Our study had a relatively small sample size and limitations in the availability of clinical data. These results suggest that depression is a risk factor for dementia rather than an early manifestation of AD or a consequence of cerebral vascular insufficiency.
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Affiliation(s)
- Lindsey I Sinclair
- Dementia Research Group, Faculty of Health Sciences, University of Bristol, Southmead Hospital, Level 1 Learning & Research Building, Bristol, BS10 5NB, UK.
| | - Asher Mohr
- Dementia Research Group, Faculty of Health Sciences, University of Bristol, Southmead Hospital, Level 1 Learning & Research Building, Bristol, BS10 5NB, UK
| | - Mizuki Morisaki
- Dementia Research Group, Faculty of Health Sciences, University of Bristol, Southmead Hospital, Level 1 Learning & Research Building, Bristol, BS10 5NB, UK
| | - Martin Edmondson
- Dementia Research Group, Faculty of Health Sciences, University of Bristol, Southmead Hospital, Level 1 Learning & Research Building, Bristol, BS10 5NB, UK
| | - Selina Chan
- Dementia Research Group, Faculty of Health Sciences, University of Bristol, Southmead Hospital, Level 1 Learning & Research Building, Bristol, BS10 5NB, UK
- Douglas Institute, Department of Psychiatry, McGill University, Montreal, Canada
| | - A Bone-Connaughton
- Dementia Research Group, Faculty of Health Sciences, University of Bristol, Southmead Hospital, Level 1 Learning & Research Building, Bristol, BS10 5NB, UK
| | - Gustavo Turecki
- Department of Life Sciences, Warwick University, Warwick, UK
| | - Seth Love
- Dementia Research Group, Faculty of Health Sciences, University of Bristol, Southmead Hospital, Level 1 Learning & Research Building, Bristol, BS10 5NB, UK
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Huang ST, Hsiao FY, Tsai TH, Chen PJ, Peng LN, Chen LK. Using Hypothesis-Led Machine Learning and Hierarchical Cluster Analysis to Identify Disease Pathways Prior to Dementia: Longitudinal Cohort Study. J Med Internet Res 2023; 25:e41858. [PMID: 37494081 PMCID: PMC10413246 DOI: 10.2196/41858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 04/08/2023] [Accepted: 05/27/2023] [Indexed: 07/27/2023] Open
Abstract
BACKGROUND Dementia development is a complex process in which the occurrence and sequential relationships of different diseases or conditions may construct specific patterns leading to incident dementia. OBJECTIVE This study aimed to identify patterns of disease or symptom clusters and their sequences prior to incident dementia using a novel approach incorporating machine learning methods. METHODS Using Taiwan's National Health Insurance Research Database, data from 15,700 older people with dementia and 15,700 nondementia controls matched on age, sex, and index year (n=10,466, 67% for the training data set and n=5234, 33% for the testing data set) were retrieved for analysis. Using machine learning methods to capture specific hierarchical disease triplet clusters prior to dementia, we designed a study algorithm with four steps: (1) data preprocessing, (2) disease or symptom pathway selection, (3) model construction and optimization, and (4) data visualization. RESULTS Among 15,700 identified older people with dementia, 10,466 and 5234 subjects were randomly assigned to the training and testing data sets, and 6215 hierarchical disease triplet clusters with positive correlations with dementia onset were identified. We subsequently generated 19,438 features to construct prediction models, and the model with the best performance was support vector machine (SVM) with the by-group LASSO (least absolute shrinkage and selection operator) regression method (total corresponding features=2513; accuracy=0.615; sensitivity=0.607; specificity=0.622; positive predictive value=0.612; negative predictive value=0.619; area under the curve=0.639). In total, this study captured 49 hierarchical disease triplet clusters related to dementia development, and the most characteristic patterns leading to incident dementia started with cardiovascular conditions (mainly hypertension), cerebrovascular disease, mobility disorders, or infections, followed by neuropsychiatric conditions. CONCLUSIONS Dementia development in the real world is an intricate process involving various diseases or conditions, their co-occurrence, and sequential relationships. Using a machine learning approach, we identified 49 hierarchical disease triplet clusters with leading roles (cardio- or cerebrovascular disease) and supporting roles (mental conditions, locomotion difficulties, infections, and nonspecific neurological conditions) in dementia development. Further studies using data from other countries are needed to validate the prediction algorithms for dementia development, allowing the development of comprehensive strategies to prevent or care for dementia in the real world.
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Affiliation(s)
- Shih-Tsung Huang
- Department of Pharmacy, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Fei-Yuan Hsiao
- Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan
- School of Pharmacy, College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Pharmacy, National Taiwan University Hospital, Taipei, Taiwan
| | | | - Pei-Jung Chen
- Advanced Tech Business Unit, Acer, New Taipei City, Taiwan
| | - Li-Ning Peng
- Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Liang-Kung Chen
- Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
- Taipei Municipal Gan-Dau Hospital (Managed by Taipei Veterans General Hospital), Taipei, Taiwan
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Twait EL, Basten M, Gerritsen L, Gudnason V, Launer LJ, Geerlings MI. Late-life depression, allostatic load, and risk of dementia: The AGES-Reykjavik study. Psychoneuroendocrinology 2023; 148:105975. [PMID: 36423561 PMCID: PMC11060697 DOI: 10.1016/j.psyneuen.2022.105975] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND The current study aimed to assess if the relation between depression and dementia could be explained by allostatic load (AL) profiles, as well as assessing their risk on incident all-cause dementia, Alzheimer's disease (AD), and non-AD dementias. METHODS The study included individuals without dementia at baseline from the population-based AGES-Reykjavik Study. Depressive symptoms assessed with the Geriatric Depression Scale-15 and AL markers were collected at baseline. Latent profile analysis (LPA) was performed on the AL markers. Incident dementia was measured during 12-years of follow-up. Cox regressions adjusted for AL profiles were performed to evaluate if AL could explain the relation between depressive symptoms and incident dementia. Additional Cox regressions exploring the interaction with depressive symptoms and AL profiles were also performed. RESULTS LPA revealed four profiles based on AL factors: 'Low cardiovascular dysregulation' (43 %), 'Average' (42 % prevalence), 'High cardiovascular dysregulation' (11 %), and 'Multisystem dysregulation' (4 %). Cox regression analyses found an increased risk for dementia in the 'Multisystem dysregulation' group (HR 1.72; 95 % CI 1.26-2.33), as well as for AD (HR 1.75; 95 % CI: 1.12-2.71) and non-AD dementias (HR 1.87; 95 % CI: 1.23-2.84). AL profiles did not mediate the risk of all-cause dementia with depressive symptoms; however, there was evidence of additive interaction with depressive symptoms and the 'Multisystem dysregulation' profile and all-cause dementia (RERI 0.15; 95 % CI 0.03-0.26). CONCLUSION AL profiles and depressive symptoms were independently related to dementia. Individuals with multisystem dysregulation could be more susceptible to the negative effects of depressive symptomology on incident dementia.
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Affiliation(s)
- Emma L Twait
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Maartje Basten
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Lotte Gerritsen
- Department of Psychology, Utrecht University, Utrecht, the Netherlands
| | - Vilmundur Gudnason
- Department of Psychology, Utrecht University, Utrecht, the Netherlands; Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Lenore J Launer
- National Institute on Aging, Laboratory for Epidemiology and Population Sciences, Baltimore, MD, USA
| | - Mirjam I Geerlings
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; National Institute on Aging, Laboratory for Epidemiology and Population Sciences, Baltimore, MD, USA; Amsterdam UMC, location University of Amsterdam, Department of General Practice, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health, Aging & Later life, and Personalized Medicine, Amsterdam, the Netherlands; Amsterdam Neuroscience, Neurodegeneration, and Mood, Anxiety, Psychosis, Stress, and Sleep, Amsterdam, the Netherlands.
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Abstract
Depression is common in older individuals and is associated with high disability and increased mortality, yet the factors predicting late-life depression (LLD) are poorly understood. The relationship between of depressive disorder, age- and disease-related processes have generated pathogenic hypotheses and provided new treatment options. LLD syndrome is often related to a variety of vascular mechanisms, in particular hypertension, cerebral small vessel disease, white matter lesions, subcortical vascular impairment, and other processes (e.g., inflammation, neuroimmune regulatory dysmechanisms, neurodegenerative changes, amyloid accumulation) that may represent etiological factors by affecting frontolimbic and other neuronal networks predisposing to depression. The "vascular depression" hypothesis suggests that cerebrovascular disease (CVD) and vascular risk factors may predispose, induce or perpetuate geriatric depressive disorders. It is based on the presence of various cerebrovascular risk factors in many patients with LLD, its co-morbidity with cerebrovascular lesions, and the frequent development of depression after stroke. Other findings related to vascular depression are atrophy of the medial temporal cortex or generalized cortical atrophy that are usually associated with cognitive impairment. Other pathogenetic hypotheses of LLD, such as metabolic or inflammatory ones, are briefly discussed. Treatment planning should consider there may be a modest response to antidepressants, but several evidence-based and novel treatment options for LLD exist, such as electroconvulsive therapy, transcranial magnetic stimulation, neurobiology-based psychotherapy, as well as antihypertension and antiinflammatory drugs. However, their effectiveness needs further investigation, and new methodologies for prevention and treatment of depression in older individuals should be developed.
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Affiliation(s)
- Kurt A Jellinger
- Institute of Clinical Neurobiology, Alberichgasse 5/13, 1150, Vienna, Austria.
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Abstract
The incidence of sporadic Alzheimer's disease (AD) is increasing in recent years. Studies have shown that in addition to some genetic abnormalities, the majority of AD patients has a history of long-term exposure to risk factors. Neuroendocrine related risk factors have been proved to be strongly associated with AD. Long-term hormone disorder can have a direct detrimental effect on the brain by producing an AD-like pathology and result in cognitive decline by impairing neuronal metabolism, plasticity and survival. Traditional Chinese Medicine(TCM) may regulate the complex process of endocrine disorders, and improve metabolic abnormalities, as well as the resulting neuroinflammation and oxidative damage through a variety of pathways. TCM has unique therapeutic advantages in treating early intervention of AD-related neuroendocrine disorders and preventing cognitive decline. This paper reviewed the relationship between neuroendocrine and AD as well as the related TCM treatment and its mechanism. The advantages of TCM intervention on endocrine disorders and some pending problems was also discussed, and new insights for TCM treatment of dementia in the future was provided.
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Affiliation(s)
- Chujun Deng
- Department of Traditional Chinese Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Huize Chen
- Department of Traditional Chinese Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Zeyu Meng
- The Second Clinical Medical College, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Shengxi Meng
- Department of Traditional Chinese Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
- *Correspondence: Shengxi Meng,
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