Background
Dementia, a major global challenge in the twenty-first century, affects approximately 50 million people worldwide and is predicted to reach 152 million by 2050 [
1]. As the most common type of dementia, Alzheimer’s disease (AD) is a complex and polygenic disease with a considerable hereditary component (60–80%) [
2]. It is a progressive neurodegenerative disorder characterized by concealed onset, and individuals often have significant cognitive impairment and histopathological changes in the brain before overt clinical diagnosis. Given the severe consequences, much attention must be paid to improving risk prediction and facilitating the prevention of the condition.
APOE ε4 allele is the strongest common genetic risk factor for AD [
3], which can elevate the risk of AD and dementia by approximately threefold and advanced age of onset [
4,
5]. However, additional common single nucleotide polymorphisms (SNPs) have been discovered by several recent AD genome-wide association studies (GWAS), which may reveal underlying biological mechanistic pathways and offer new perspectives into brain pathology involved in AD-related cognitive decline [
6,
7]. Though the individual effect size is minor, a significant modification to AD risk can be achieved when combining these SNPs together. The polygenic risk score (PRS) is developed to quantitatively represent the combined effect of genetic variants on disease risk. It is reported that PRS based on the additive effect of multiple AD-related loci has the potential to work as a valuable predictor of AD risk or pathological trajectories [
8,
9].
Magnetic resonance imaging (MRI) markers of brain structure consistently find that the AD-specific symptoms probably result from atrophy and loss of neurons and synapses in particular brain regions [
10‐
12], including the entorhinal cortex, hippocampus, parahippocampal cortex, inferior parietal lobule, cuneus, and precuneus [
10,
11]. Moreover, brain white matter shrinkage is also one of the earliest pathophysiological changes detected in AD patients [
13]. In general, these results suggest that brain abnormalities might manifest decades before overt clinical symptoms [
14‐
16].
PRS combined with neuroimaging data may provide valuable insights into identifying markers of early risk for AD [
17,
18], which will enable strategies for early diagnosis, prevention, and treatment. However, current evidence has mainly focused on the association of AD PRS with global gray and white matter [
19,
20] or specific parts such as the hippocampus [
21‐
25], and the investigations linking MRI metrics of comprehensive brain structures at the regional level to genetic variation have so far received much less attention. Furthermore, several studies failed to detect any significant association between PRS and brain measures at a global or regional scale [
19,
23]. In addition, only a few studies have investigated these associations in infants [
26] or adolescents [
27]; the dearth of hard evidence could be due to the undersized number of subjects in earlier genetic neuroimaging studies (
N < 2000).
In the current study, we analyzed genotype and multimodal MRI data from participants in the UK Biobank (UKB,
N ~ 23 000) and Adolescent Brain Cognitive Development Study (ABCD,
N ~ 4660), to assess the associations of genetic loading for AD with multiple MRI metrics of the whole brain. In particular, our study investigated these associations in children and adults by using two cognitively normal populations spanning wide age ranges, as the neural mechanisms during adolescence may vary from those during adulthood. Furthermore, to evaluate the
APOE impacts on AD PRS, PRS in this study is constructed based on AD-related SNPs, including and excluding the
APOE region, respectively. We anticipate that the combination of using the existing large GWAS datasets (if applicable) as our training data [
6,
7] and using two large populations with MRI imaging data mentioned above as our target data would improve the statistical power to clarify the extent to which these structures evolve at different stages of brain development.
Discussion
Using two different populations with age spans of 9–11 years and 46–82 years (total N ~ 27,660), this study examined the strength of the association between AD PRS and different MRI metrics including morphometric and histological measures of comprehensive brain structures in children and adults. This research showed that in adulthood, PRS of AD had associations with regional structure reduction in the rostral anterior cingulate, pars orbitalis, lateral orbitofrontal, superior frontal cortex, hippocampus, thalamus, amygdala, and striatum (caudate, putamen, accumbens), while the brain expansion was concentrated near the occipital lobe (superior parietal, cuneus, and precuneus). Compared to those with lower PRSs, adolescents with higher PRSs had thinner cortex in the caudal anterior cingulate and supramarginal. Additionally, individuals with higher PRSs had widespread microstructural abnormalities in both adolescents and adults, indicated by decreased FA or increased MD in extensive white matter tracts.
The combination of two completely different age-spanning populations enabled us to identify the highly distinct pattern of brain changes and the influence of the APOE genotype between adolescents and middle adulthood. This suggested that AD-related brain abnormalities may be partly accounted for genomic vulnerability decades before significant manifestation of clinical symptoms, and caution is needed when assessing the impact across age spans.
Genetic associations across macrostructural MRI phenotypes of cortex and subcortex
In the cortex, multiple metrics associations with PRS were concentrated in the cingulate and prefrontal lobe, which have previously been reported to show significant cortical atrophy in studies of AD [
10,
49‐
53]. However, the small increased macrostructural measurements in the occipital regions were unexpected. Even though it was also observed by a prior Mendelian randomization study suggesting a causal relationship between AD and greater volume of the occipital lobe [
50], the mainstream studies have provided insight into the accelerated rate of occipital lobe atrophy in patients with AD. As massive amyloid deposits can be found in AD patients’ occipital cortex [
54], this could be due to its space-occupying effects. It may also be a manifestation of structural brain improvement, as the brain is an active combatant to resist impairment, which may mechanistically result from compensatory neurogenesis or the plasticity in axonal sprouting [
55]. Before strong conclusions about different cortical atrophy patterns of AD can be drawn, further analysis would be needed to replicate this finding.
In the subcortex, the results of our exploratory analysis in adolescents did not withstand correction for multiple testing. When taking the lack of significance into account, our analyses either showed that the impact of these risk variants on subcortical structures in children was not significant enough to be detected or it could be attributed to the smaller sample size of ABCD and insufficient statistical test efficacy. For middle-aged and older adults in UKB, the reduced hippocampal volume explains the most variance, a result consistent with the consensus of the hippocampus being the primary focus of neural loss in AD patients [
10]. Moreover, the volumetric decline of the amygdala, thalamus, and striatum (caudate, putamen) has also been observed to strongly correlate with PRS, in keeping with previous observational studies of AD demonstrating abnormal shape change [
10,
56‐
58]. In general, our finding reflects the neurodegenerative effect of genetic risk for AD in subcortical areas before clinical manifestations in adults [
19,
24,
25].
Genetic risk and white matter tracts
We demonstrate that in both adolescents and adults, individuals with higher PRSs have increased MD or decreased FA of widespread white matter. The changes in these two metrics possibly represent the loss of neurons, dendrites, and axons in neurodegenerative illnesses like AD, since they reflect less restricted movement of water molecules around the axons’ longitudinal axis and elevated diffusivity of water in all directions [
59]. The non-significant results of PRS and FA in adolescents may be due to less susceptibility to white matter structural damage in the younger population. We postulated that adolescents with high genetic risk for AD have a density or activity reduction of terminal neuronal fields in preferentially affected brain regions. These underlying processes would provide a developmental foothold for subsequent pathogenic changes [
26], and MD is potentially a more sensitive biomarker to detect compared to FA [
59,
60].
Abnormal microstructure of extensive white matter tracts, especially superior and inferior longitudinal fasciculus, cingulate cingulum, corticospinal tract, anterior thalamic radiation, and uncinate fasciculus, has been frequently reported in AD research [
23,
61,
62]. These alterations can impact complex networks relevant to episodic memory and other cognitive processes, which have been proven to be associated with immune response genes within AD genetic risk [
20]. One possible mechanism is the gene regulatory activation of microglia to alter myelination and axonal growth in immunosurveillance and immune activation manner during development and adulthood [
20].
Comparison of association pattern between AD risk gene and brain in children and adults
There have been inconsistent patterns of association between accelerated biological and brain aging in two populations with different age distributions. Specifically, genetically predicted neurodegenerative results in adults are more proximate to clinical brain pathophysiological changes in AD patients. The alterations associated with genetic risk for AD in adolescents aged 9–11 years appeared to be primarily located in the white matter and cingulate cortex when compared with the middle-aged and older adults and has not yet had a significant impact on subcortical structures. This suggests that specific genetic effects are not strongly exhibited during early neurodevelopment, which may be explained by the temporal and spatial abundance of gene expression.
Genetically mediated decreased CV occurs in different areas within the cingulate in both early and later life, with the former concentrated in the dorsal anterior cingulate and the latter in the rostral anterior cingulate. These two adjacent structures play distinct roles in conflict processing, task monitoring, emotional self-control, social cognitive, and executive functions [
63,
64], as also evidenced by their different connectivity with prefrontal and limbic regions. The difference, to some extent, indicates that attention and executive function were preferentially affected by genetically predicted AD in adolescents, followed by dysfunctional emotional information assessment as well as emotional response regulation in adults.
From the whole-brain perspective, the age-specific brain changes explained by AD genetic risk shown in our results are consistent with the classical pattern of damage observed in AD. Significant cortical atrophy was first detected in the cingulate in the early stages, followed by broad areas in the frontal cortex in progression to mild AD [
10]. Additionally, a large body of literature supports widespread white matter tract microstructural abnormalities. Notably, the cingulate cingulum, connecting the cingulate cortex and hippocampus, is one of the most severe imaging changes in early AD patients [
65]. We, therefore, speculate that AD pathology would partly spread along it from the cingulate cortex to the hippocampus and other brain regions.
Prior studies have noted the importance of different genetic associations with brain development, maturation, and atrophy between childhood and adulthood. For example, the brain-derived neurotrophic factor Val
66Met carriers have larger right hippocampal volume in children aged 6–10, which is inconsistent with adult findings [
66‐
68]. Margarida et al. also hold the view that during brain development, genes relevant to brain disorders show distinct temporal characteristics [
69]. Our results extend this literature by analyzing the correlation of AD PRS with the macro- and microstructure of the brain in two diverse age groups to identify age-specific patterns of brain development and senescence. Future research will need to determine whether these findings reflect distinguishable brain development and aging trajectories and indicate a clear link between spatiotemporal characters and the MRI phenotypic manifestations.
Moreover, the results of the subsequent analysis with the
APOE-independent PRS have also shown spatial and temporal specificity. The
APOE-independent PRS is mainly associated with white matter change in adolescence, while the effects of
APOE predominantly spread to the whole cerebral tissues during middle and older adulthood. These results generally agree with those obtained in previous studies demonstrating that
APOE has a pattern of brain expression most pronounced during adulthood rather than early development [
70]. This
APOE effect leads to a loss of microstructural organization and thus affects white matter integrity as a function of increasing age [
10,
70].
Strengths and limitations
This investigation is, to our knowledge, the first and largest investigation to examine the associations between AD PRS and whole brain structures at regional scales. Our study strength includes the utilization of two independent datasets with different age ranges, covering adolescence and adulthood to assess the differences. However, there are several limitations we must acknowledge. A major limitation is the predominantly European ancestry participants in our study, which minimized the population stratification bias but limited the generalizability of our findings to other populations [
71]. This common problem in such research is primarily due to the lack of large sample size GWAS and cohorts of other ethnicities, and future research should be warranted in other populations to fully understand the biological pathways of AD. Second, the limited age span (9–11 and 46–82 years) of two datasets from adolescents to middle adulthood cannot completely cover the stage of brain development and cognitive decline, thus preventing us from capturing the full trajectory of brain structure changes explained by AD-related genes. Third, given some uncontrollable differences between two datasets (such as image processing methods and sample size), making a direct quantitative comparison is not technically feasible. Future studies with even larger sample sizes, wider age spans, and more diverse MRI metrics are warranted to increase the sensitivity for detecting obvious genetic effects at early stages of brain development and validate such dynamic changes. Fourth, the additional variance that PRS accounted for we found was relatively small (< 1%), which is possibly due to the complex multidimensional properties of the brain.
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