TRIANGULATING HETEROGENEITY IN DEMENTIA: An interaction between genetic variation, biological correlates and clinical symptoms

The goal of this thesis was to increase our understanding of the clinical and biological nature of Alzheimer’s disease (AD) and frontotemporal dementia (FTD), with the following three aims. First, we aimed to examine how patient characteristics (e.g., clinical symptoms, biomarker profiles) relate to disease development in AD and FTD (chapter 2). Second, we investigated the genetic architecture of FTD (chapter 3). Lastly, we aimed to identify biological processes through which genetic variants increase the risk of developing dementia (chapter 4). In chapter 2.1, we reviewed literature on personality characteristics as predictor or consequence of dementia. We found that neuroticism was associated with increased risk to develop dementia. In chapter 2.2, we compared longitudinal differences over a period of 3 years between disorders that can present with behavioural changes, including the behavioural variant of FTD (bvFTD), other neurodegenerative disorders and primary psychiatric disorders. Only bvFTD patients presented with worsening of frontal behavioural symptoms (e.g., disinhibition, apathy) over time, indicating that longitudinal monitoring of these symptoms might aid in differentiating bvFTD from other disorders. In chapter 2.3, we examined whether AD biomarker profiles showing abnormal and normal CSF total tau within the AD clinical spectrum were related to different clinical characteristics and biological factors. Individuals with abnormal tau (n=788, 57%) showed increased levels of proteins involved in neuronal plasticity (hyperplasticity), and individuals with normal tau (n=592, 43%) showed a decrease in neuronal plasticity proteins and an increase in blood-brain barrier dysfunction proteins (hypoplasticity). Our second aim was to improve our understanding of the genetic architecture underlying C9ORF72-FTD. We first performed a genome-wide association study (GWAS) on FTD, comparing frequencies of rare and common variants between FTD patients and controls in chapter 3.1. Here, we identified two genetic risk variants for FTD nearby the C9ORF72 gene that tag a rare C9ORF72 sub-haplotype of the Finnish founder haplotype of the repeat expansion. In chapter 3.2, we performed a transcriptome-wide association study (TWAS) on FTD, by integrating FTD summary statistics with cis gene expression panels on 53 tissue types (five consortia). Here, we identified 44 genes for which their predicted gene expression, mostly in the prefrontal cortex, was associated with an increased disease risk for FTD. Identified genes could function as potential candidates for functional follow-up studies. Lastly, in chapter 4, we aimed to identify which biological processes associated with measurements of genetic risk for AD, FTD and dementia with Lewy bodies (DLB). In chapter 4.1 we used a polygenic risk scores (PGRS)-based approach to assess the shared genetic overlap between AD and 615 unique metabolite blood levels. Most of the metabolites with a significant association with AD were lipids, including lipoproteins, glycerophospholipids, fatty acyls and sterols. A modest proportion of these associations were dependent on APOE-ε4-status. In chapter 4.2, we examined the association between the genetic liability for AD and CSF proteomic profiles in individuals with normal cognition, mild cognitive impairment and AD-type dementia. Out of 412 proteins, 201 were associated with polygenic risk scores (PGRS) for AD. We identified three clusters on proteins associated with PGRS, a protein cluster: (1) correlated with highly significant genetic variants, associated with amyloid-beta pathology; (2) including variants contributing to modest risk, involved in neural injury; (3) that also included less strongly associated variants, enriched with cytokine-cytokine interactions and cell adhesion molecules. In chapter 4.3 we aimed to identify which genetic risk loci for AD, FTD and DLB influence protein CSF levels, through so-called protein quantitative trait loci (pQTLs) analyses. We identified four pQTLS, of which three pQTLs related to AD and one pQTL related to DLB. No pQTLs for FTD were observed.