Sex differences in the relationship of cognition and brain in midlife adults at risk for dementia
Poster No:
203
Submission Type:
Abstract Submission
Authors:
Qing Qi1,2, Feng Deng1,2, Maria-Eleni Dounavi3, Graciela Muniz-Terrera4,5, Ivan Koychev6, Paresh Malhotra7,8, Craig Ritchie4,9, John O'Brien10, Brian Lawlor1,2, Lorina Naci1,2
Institutions:
1Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland, 2Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland, 3Department of Psychiatry, School of Clinical Medicine, niversity of Cambridge, Cambridge, United Kingdom, 4Edinburgh Dementia Prevention, University of Edinburgh, Edinburgh, United Kingdom, 5Department of Social Medicine, Ohio University, Athens, United States, 6Department of Psychiatry, Oxford University, Oxford, United Kingdom, 7Department of Brain Science, Imperial College London, London, United Kingdom, 8UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London, United Kingdom, 9Scottish Brain Sciences, Edinburgh, United Kingdom, 10Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
First Author:
Qing Qi
Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin|Global Brain Health Institute, Trinity College Dublin
Dublin, Ireland|Dublin, Ireland
Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin|Global Brain Health Institute, Trinity College Dublin
Dublin, Ireland|Dublin, Ireland
Co-Author(s):
Feng Deng
Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin|Global Brain Health Institute, Trinity College Dublin
Dublin, Ireland|Dublin, Ireland
Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin|Global Brain Health Institute, Trinity College Dublin
Dublin, Ireland|Dublin, Ireland
Maria-Eleni Dounavi
Department of Psychiatry, School of Clinical Medicine, niversity of Cambridge
Cambridge, United Kingdom
Department of Psychiatry, School of Clinical Medicine, niversity of Cambridge
Cambridge, United Kingdom
Graciela Muniz-Terrera
Edinburgh Dementia Prevention, University of Edinburgh|Department of Social Medicine, Ohio University
Edinburgh, United Kingdom|Athens, United States
Edinburgh Dementia Prevention, University of Edinburgh|Department of Social Medicine, Ohio University
Edinburgh, United Kingdom|Athens, United States
Paresh Malhotra
Department of Brain Science, Imperial College London|UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey
London, United Kingdom|London, United Kingdom
Department of Brain Science, Imperial College London|UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey
London, United Kingdom|London, United Kingdom
Craig Ritchie
Edinburgh Dementia Prevention, University of Edinburgh|Scottish Brain Sciences
Edinburgh, United Kingdom|Edinburgh, United Kingdom
Edinburgh Dementia Prevention, University of Edinburgh|Scottish Brain Sciences
Edinburgh, United Kingdom|Edinburgh, United Kingdom
John O'Brien
Department of Psychiatry, School of Clinical Medicine, University of Cambridge
Cambridge, United Kingdom
Department of Psychiatry, School of Clinical Medicine, University of Cambridge
Cambridge, United Kingdom
Brian Lawlor
Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin|Global Brain Health Institute, Trinity College Dublin
Dublin, Ireland|Dublin, Ireland
Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin|Global Brain Health Institute, Trinity College Dublin
Dublin, Ireland|Dublin, Ireland
Lorina Naci
Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin|Global Brain Health Institute, Trinity College Dublin
Dublin, Ireland|Dublin, Ireland
Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin|Global Brain Health Institute, Trinity College Dublin
Dublin, Ireland|Dublin, Ireland
Introduction:
Two-thirds of Alzheimer's Disease (AD) cases occur in women [1]. Compared to men, women exhibit more rapid cognitive decline and brain atrophy in the presence of AD-related neuropathology [2]. It is now acknowledged that AD processes are present decades before the onset of clinical symptoms [3]. However, whether there are sex differences in cognition-brain structure coupling and how AD risk affects their relationships in midlife remain unclear. In this study, we investigated associations between sex, AD risk, brain structure and cognition.
Methods:
Participants: 701 cognitively unimpaired middle-aged participants (40–59 years) were recruited in the PREVENT-Dementia study from five study sites: West London, Edinburgh, Cambridge, Oxford and Dublin. In total, 614 participants (233 M/ 381 F) who had completed cognitive, clinical and structural Magnetic Resonance Imaging (sMRI) data were included in this study.
Risk factors: The Apolipoprotein E [APOE] Ɛ4 risk is determined by ≥1 APOE Ɛ4 allele. The Cardiovascular Risk Factors Aging and Dementia (CAIDE) score is calculated based on eight variables [4], with higher scores indicating greater risk.
MRI data acquisition and processing: sMRI data were acquired using a T1-weighted MPRAGE sequence (TR = 2.3 s, TE = 2.98 ms, 160 slices, flip angle = 9°, voxel size = 1 mm3 isotropic). Freesurfer v7.1.0 was used for data processing [5]. The recon-all pipeline was run with default settings for each participant. The cortical thickness in 68 regions was quantified based on the Desikan-Killiany atlas [6]. The global cortical thickness (CT) was obtained by averaging the values from the bilateral hemispheres for each participant. We chose nine regions of interest (ROIs) relating to AD from previous studies [7-9], and the mean CT within each ROI was calculated by averaging the values from the bilateral hemispheres.
Statistical analyses: Linear regression models were used to investigate the association of CAIDE with global CT and episodic and relational memory separately. To investigate the relationships between CT and cognition, and the moderating role of the sex variable, we used the linear regression model for episodic and relational memory, with global CT and nine regional CT as the independent variable (in independent models), sex as the moderator, and age, years of education, ICV and study sites included as covariates. The same moderation regression analysis was repeated in APOE Ɛ4+ and APOE Ɛ4− groups separately to further delineate the sex moderation effect on cognition-CT relationships in participants belonging to different risk groups. Multiple comparisons correction was carried out using the Bonferroni method.
Risk factors: The Apolipoprotein E [APOE] Ɛ4 risk is determined by ≥1 APOE Ɛ4 allele. The Cardiovascular Risk Factors Aging and Dementia (CAIDE) score is calculated based on eight variables [4], with higher scores indicating greater risk.
MRI data acquisition and processing: sMRI data were acquired using a T1-weighted MPRAGE sequence (TR = 2.3 s, TE = 2.98 ms, 160 slices, flip angle = 9°, voxel size = 1 mm3 isotropic). Freesurfer v7.1.0 was used for data processing [5]. The recon-all pipeline was run with default settings for each participant. The cortical thickness in 68 regions was quantified based on the Desikan-Killiany atlas [6]. The global cortical thickness (CT) was obtained by averaging the values from the bilateral hemispheres for each participant. We chose nine regions of interest (ROIs) relating to AD from previous studies [7-9], and the mean CT within each ROI was calculated by averaging the values from the bilateral hemispheres.
Statistical analyses: Linear regression models were used to investigate the association of CAIDE with global CT and episodic and relational memory separately. To investigate the relationships between CT and cognition, and the moderating role of the sex variable, we used the linear regression model for episodic and relational memory, with global CT and nine regional CT as the independent variable (in independent models), sex as the moderator, and age, years of education, ICV and study sites included as covariates. The same moderation regression analysis was repeated in APOE Ɛ4+ and APOE Ɛ4− groups separately to further delineate the sex moderation effect on cognition-CT relationships in participants belonging to different risk groups. Multiple comparisons correction was carried out using the Bonferroni method.
Results:
CAIDE was negatively associated with global CT (Fig. 1a), and negatively associated with episodic and relational memory (Fig.1b). We didn't find a significant association between global CT and cognition (Fig. 2a) but found a sex-specific coupling of global CT and episodic and relational memory (Fig. 2b). Males showed a positive association between global CT and cognition, while females showed no relationship between them. Such sex-specific coupling between global CT and cognition was absent in APOE Ɛ4 carriers, and only shown in APOE Ɛ4 non-carriers (Fig. 2c, 2d). Among the nine ROIs, we only found a significant sex-specific coupling of episodic and relational memory and precuneus CT after Bonferroni correction. Furthermore, cognition was decoupled from the precuneus CT in APOE Ɛ4 carriers, and only coupled with CT of precuneus in APOE Ɛ4 non-carriers (Fig. 2e, 2f).
Conclusions:
We found inherent sex-specific differences in the coupling between brain structure and cognition. Our results suggest that these sex-specific differences are being eroded by APOE Ɛ4 carriership in mid-life. Longitudinal follow-up in this cohort will shed light on the long-term sex-specific impact of APOE genotype on brain structure and cognition in preclinical populations with risk for AD.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Cognition
STRUCTURAL MRI
Other - Alzheimer's disease; Risk factors; Midlife
1|2Indicates the priority used for review
Provide references using author date format
[2] Gamache, J., Yun, Y., & Chiba-Falek, O. (2020). Sex-dependent effect of APOE on Alzheimer's disease and other age-related neurodegenerative disorders. Disease models & mechanisms, 13(8), dmm045211.
[3] Jack CR Jr, Knopman DS, Jagust WJ, et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12(2):207-216.
[4] Kivipelto M, Ngandu T, Laatikainen T, Winblad B, Soininen H, Tuomilehto J. Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study. Lancet Neurol. 2006;5(9):735-741.
[5] Fischl, B. (2012). FreeSurfer. Neuroimage, 62(2), 774-781.
[6] Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., ... & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31(3), 968-980.
[7] Jack Jr, C. R., Wiste, H. J., Weigand, S. D., Knopman, D. S., Mielke, M. M., Vemuri, P., ... & Petersen, R. C. (2015). Different definitions of neurodegeneration produce similar amyloid/neurodegeneration biomarker group findings. Brain, 138(12), 3747-3759.
[8] Cieri, F., Zhuang, X., Cordes, D., Kaplan, N., Cummings, J., Caldwell, J., & Alzheimer’s Disease Neuroimaging Initiative (ADNI). (2022). Relationship of sex differences in cortical thickness and memory among cognitively healthy subjects and individuals with mild cognitive impairment and Alzheimer disease. Alzheimer's Research & Therapy, 14(1), 36.
[9] Dickerson, B. C., Bakkour, A., Salat, D. H., Feczko, E., Pacheco, J., Greve, D. N., ... & Buckner, R. L. (2009). The cortical signature of Alzheimer's disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cerebral cortex, 19(3), 497-510.