Brain health and depression in vascular cognitive impairment and dementia
Poster No:
149
Submission Type:
Abstract Submission
Authors:
Valerie Lohner1, Markus Schirmer2
Institutions:
1University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Cardiology, Cologne, Germany, 2Harvard Medical School, Massachusetts General Hospital, Department of Neurology, Boston, MA
First Author:
Valerie Lohner
University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Cardiology
Cologne, Germany
University of Cologne, Faculty of Medicine and University Hospital Cologne, Department of Cardiology
Cologne, Germany
Co-Author:
Markus Schirmer
Harvard Medical School, Massachusetts General Hospital, Department of Neurology
Boston, MA
Harvard Medical School, Massachusetts General Hospital, Department of Neurology
Boston, MA
Introduction:
Cognitive reserve (CR) is a surrogate measure of brain health and reflects the ability to cope with neurodegenerative processes, and is crucial for maintaining independence and quality of life, especially in older adults [6]. Cerebral small vessel disease (SVD) is a common degenerative condition contributing to vascular cognitive impairment and dementia (VCID) [3]. Both CR and SVD have individually been linked to cognitive performance [2,4] and symptoms of depression [1,2,8], however, their combination to generate a comprehensive measure of functional brain health has not fully been explored. In this work, we aim to explore associations of cognitive reserve as a marker of brain health with depression in individuals with SVD, leveraging a latent variable approach.
Methods:
We used cross-sectional data from a large publicly available cohort of 1113 individuals with SVD (MarkVCID study [7]). Depressive symptoms were assessed using the 15-item Geriatric Depression Scale (GDS-15), prevalent depression was defined as scores ≥ 5. White matter hyperintensities (WMHs), as a marker of SVD, were assessed with the Fazekas scale. Cognitive status included normal cognition, impaired cognition but not mild cognitive impairment (MCI), MCI, and dementia. First, we modelled CR using a latent variable approach, including education (years), tobacco use (>100 cigarettes), and overall Fazekas scale (range: 0 (none) to 3 (confluent WMHs)). We then modeled cognitive diagnosis using CR, sex, and age as independent predictors. Parameters were estimated using the R package LAVAAN [5]. Using these estimates, we subsequently calculated CR for each participant and tested its associations with depression utilising ordinal regression. All analyses were performed in R and significance was set to p<0.05.
Results:
Median age of the study population was 72.5 [IQR: 67.4, 78.3] years, 651 (60%) were women. The majority of participants had confluent WMHs (median Fazekas score 3 [IQR: 1, 3]), and one in nine had depression (n=116, 11%). Most of the participants presented with normal cognition (59%), 5% had cognitive deficits but were not classified as having MCI, 27% had MCI, and 9% dementia. Education, tobacco use, and SVD measured through the Fazekas score were significant contributors to CR (p<0.001; see Figure 1) and CR reduced the severity of cognitive diagnosis (b (standard error): -0.40 (0.08); p<0.001). Additionally, higher CR was associated with reduced odds of presenting with depression (odds ratio (95% confidence interval) 0.90 (0.86-0.95)).
·Figure 1
Conclusions:
In this study, we demonstrated that SVD significantly affects CR. Brain health, quantified as cognitive reserve, and female sex demonstrated protective effects on, while older age was negatively associated with, the severity of cognitive dysfunction. Additionally, we demonstrated in this population that individuals with an overall high burden of SVD, higher brain health has a protective effect on the risk of having depression in individuals with SVD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Lifespan Development:
Aging
Keywords:
Cerebrovascular Disease
Psychiatric Disorders
Other - Brain health; Cognitive reserve; VCID; depression; small vessel disease
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
Which processing packages did you use for your study?
Provide references using APA citation style.
[2] Clancy, U., et al. (2020). Clinical management of cerebral small vessel disease: a call for a holistic approach. Chin Med J (Engl), 134(2), 127-142. https://doi.org/10.1097/cm9.0000000000001177
[3] Duering, M., et al. (2023). Neuroimaging standards for research into small vessel disease-advances since 2013. The Lancet. Neurology, 22(7), 602-618. https://doi.org/https://dx.doi.org/10.1016/S1474-4422(23)00131-X (Erratum in: Lancet Neurol. 2023 Sep;22(9):e10 PMID: 37453436 [https://www.ncbi.nlm.nih.gov/pubmed/37453436] Erratum in: Lancet Neurol. 2023 Sep;22(9):e10 PMID: 37475121 [https://www.ncbi.nlm.nih.gov/pubmed/37475121])
[4] Farina, M., et al. (2018). Cognitive Reserve in Elderly and Its Connection with Cognitive Performance: A Systematic Review. Ageing International, 43(4), 496-507. https://doi.org/10.1007/s12126-017-9295-5
[5] Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1 - 36. https://doi.org/10.18637/jss.v048.i02
[6] Rost, N. S et al. (2023). The Brain Health Imperative in the 21st Century—A Call to Action. Neurology, 101(13), 570-579. https://doi.org/doi:10.1212/WNL.0000000000207739
[7] Wilcock, D., et al. (2021). MarkVCID cerebral small vessel consortium: I. Enrollment, clinical, fluid protocols. Alzheimers Dement, 17(4), 704-715. https://doi.org/10.1002/alz.12215
[8] Zijlmans, J. L., et al. (2023). The role of cognitive and brain reserve in late-life depressive events: The Rotterdam Study. Journal of Affective Disorders, 320, 211-217. https://doi.org/https://doi.org/10.1016/j.jad.2022.09.145