Distinct grey matter patterns and their determinants in a Swedish birth cohort of septuagenarians
Poster No:
153
Submission Type:
Abstract Submission
Authors:
Giulia Lorenzon1, Anna Marseglia1, Konstantinos Poulakis1, Sebastian Muehlboeck1, Daniel Ferreira1, Miia Kivipelto1, Lina Ryden2, Silke Kern2, Sara Shams1, Anna Zettergren2, Ingmar Skoog2, Eric Westman1
Institutions:
1Karolinska Institutet, Stockholm, Sweden, 2University of Gothenburg, Gothenburg, Sweden
First Author:
Co-Author(s):
Introduction:
Dementia is a growing burden on global healthcare (Gauthier et al., 2022). Detecting individuals at risk in the early preclinical stage is crucial to implement prevention strategies. However, this is challenged by the high inter-individual neuropathological heterogeneity long before the clinical manifestation of dementia (Jack et al., 2013)(Jack et al., 2013). Distinct patterns of cortical and subcortical atrophy have been previously identified in neurodegenerative disorders including Alzheimer's Disease (AD) (Ferreira et al., 2020; Mohanty et al., 2022; Poulakis et al., 2022). However, knowledge of brain heterogeneity in the general population is still lacking, yet crucial to inform early detection and intervention. Furthermore, it is important to understand the factors contributing to such heterogeneity. The aim of this study is therefore to identify specific patterns of grey matter atrophy and their lifelong determinants among relatively cognitively intact older individuals.
Methods:
This cross-sectional study included 792 individuals from the Gothenburg H70-1944 Birth cohort identified through the Swedish Tax Agency's population register and living in Gothenburg (Sweden) who underwent clinical examinations and MRI between January 2014 and December 2016. We selected 746 septuagenarians without dementia or neuropsychiatric disorders, and with good quality MRI. Patterns (subtypes) of grey matter patterns were identified using unsupervised Random Forest applied to 34 regions assessing cortical thickness and 7 subcortical regions assessing volume (Poulakis et al., 2018). Next, we characterized the subtypes in relation to the following features: sociodemographic factors, cardiometabolic risk factors, cognitive function, risk gene (apolipoprotein e4 allele), and biomarkers of cerebrovascular pathology, neurodegeneration, inflammation, and lipid alterations. Linear and multinomial logistic regression models were used to compare the clusters pairwise (cluster 1 vs. remaining) and estimate their associations with the abovementioned features.
Results:
We identified 5 different grey matter clusters. Cluster 1 was the most prevalent (n=278, 37.3%). Cluster 2 (n=142, 19%) exhibited diffused but primarily frontal atrophy. Cluster 3 (n=121, 16.2%) and Cluster 4 (n=157, 21%) showed thicker frontotemporal and temporal thickness. Cluster 5 (n=48, 6.4%) mostly showed posterior atrophy. Small vessel disease, heart disease, alcohol consumption, smoking history, cardiometabolic disorders, lipid alterations, and depression were key determinants of the clusters. Cluster 2 showed a higher prevalence of diabetes, alcohol consumption and elevated C-reactive protein; Cluster 4 had less odds of elevated triglycerides, brain lacunes, smoking and depression history; Cluster 5 was associated with more heart disease, alcohol consumption, elevated homocysteine and t-tau, but less white matter lesions compared to the reference cluster.
Conclusions:
Our study uncovered GM heterogeneity in cognitively intact older adults and their associations with cardiometabolic and lifestyle factors. Our findings revealed the presence of distinct patterns of atrophy in elderly individuals otherwise cognitively healthy. Small vessel disease, cardiovascular and cardiometabolic risk factors, as well as inflammatory and neuropathological biomarkers may contribute to these patterns. These findings help understanding the potential mechanisms driving different atrophy patterns and highlight the importance of cardio- and cerebrovascular health to preserve cognitive function and brain structure in old age, with potential implications for early detection and prevention of cognitive decline.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Lifespan Development:
Aging
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Aging
Cerebrovascular Disease
Cognition
Cortex
Degenerative Disease
Modeling
MRI
Statistical Methods
STRUCTURAL MRI
Sub-Cortical
1|2Indicates the priority used for review
·Distinct patterns of grey matter cortical thickness (left) and subcortical volume (right) compared to Cluster 1 (reference). Cold color=greater thickness/volume; hot color=atrophy.
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