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Can Genetic Markers Predict the Sporadic Form of Alzheimer's Disease? An Updated Review on Genetic Peripheral Markers.

ABSTRACT: Alzheimer's disease (AD) is the most common form of dementia that affects millions of individuals worldwide. Although the research over the last decades has provided new insight into AD pathophysiology, there is currently no cure for the disease. AD is often only diagnosed once the symptoms have become prominent, particularly in the late-onset (sporadic) form of AD. Consequently, it is essential to further new avenues for early diagnosis. With recent advances in genomic analysis and a lower cost of use, the exploration of genetic markers alongside RNA molecules can offer a key avenue for early diagnosis. We have here provided a brief overview of potential genetic markers differentially expressed in peripheral tissues in AD cases compared to controls, as well as considering the changes to the dynamics of RNA molecules. By integrating both genotype and RNA changes reported in AD, biomarker profiling can be key for developing reliable AD diagnostic tools.

SUBMITTER: Theron D

PROVIDER: S-EPMC10488021 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Can Genetic Markers Predict the Sporadic Form of Alzheimer's Disease? An Updated Review on Genetic Peripheral Markers.

Theron Danelda D Hopkins Lloyd N LN Sutherland Heidi G HG Griffiths Lyn R LR Fernandez Francesca F

International journal of molecular sciences 20230830 17

Alzheimer's disease (AD) is the most common form of dementia that affects millions of individuals worldwide. Although the research over the last decades has provided new insight into AD pathophysiology, there is currently no cure for the disease. AD is often only diagnosed once the symptoms have become prominent, particularly in the late-onset (sporadic) form of AD. Consequently, it is essential to further new avenues for early diagnosis. With recent advances in genomic analysis and a lower cost ...[more]

PMID: 37686283

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Project description:See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article.Alzheimer's disease is preceded by a lengthy 'preclinical' stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer's disease. In individuals with autosomal dominant genetic Alzheimer's disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer's disease to test whether an individual's symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer's disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent's symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer's disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer's Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer's dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.

Project description:BackgroundIncreasing evidence links the gut microbiota (GM) to Alzheimer's disease (AD) but the mechanisms through which gut bacteria influence the brain are still unclear. This study tests the hypothesis that GM and mediators of the microbiota-gut-brain axis (MGBA) are associated with the amyloid cascade in sporadic AD.MethodsWe included 34 patients with cognitive impairment due to AD (CI-AD), 37 patients with cognitive impairment not due to AD (CI-NAD), and 13 cognitively unimpaired persons (CU). We studied the following systems: (1) fecal GM, with 16S rRNA sequencing; (2) a panel of putative MGBA mediators in the blood including immune and endothelial markers as bacterial products (i.e., lipopolysaccharide, LPS), cell adhesion molecules (CAMs) indicative of endothelial dysfunction (VCAM-1, PECAM-1), vascular changes (P-, E-Selectin), and upregulated after infections (NCAM, ICAM-1), as well as pro- (IL1β, IL6, TNFα, IL18) and anti- (IL10) inflammatory cytokines; (3) the amyloid cascade with amyloid PET, plasma phosphorylated tau (pTau-181, for tau pathology), neurofilament light chain (NfL, for neurodegeneration), and global cognition measured using MMSE and ADAScog. We performed 3-group comparisons of markers in the 3 systems and calculated correlation matrices for the pooled group of CI-AD and CU as well as CI-NAD and CU. Patterns of associations based on Spearman's rho were used to validate the study hypothesis.ResultsCI-AD were characterized by (1) higher abundance of Clostridia_UCG-014 and decreased abundance of Moryella and Blautia (p < .04); (2) elevated levels of LPS (p < .03), upregulation of CAMs, Il1β, IL6, and TNFα, and downregulation of IL10 (p < .05); (3) increased brain amyloid, plasma pTau-181, and NfL (p < 0.004) compared with the other groups. CI-NAD showed (1) higher abundance of [Eubacterium] coprostanoligenes group and Collinsella and decreased abundance of Lachnospiraceae_ND3007_group, [Ruminococcus]_gnavus_group and Oscillibacter (p < .03); (2) upregulation of PECAM-1 and TNFα (p < .03); (4) increased plasma levels of NfL (p < .02) compared with CU. Different GM genera were associated with immune and endothelial markers in both CI-NAD and CI-AD but these mediators were widely related to amyloid cascade markers only in CI-AD.ConclusionsSpecific bacterial genera are associated with immune and endothelial MGBA mediators, and these are associated with amyloid cascade markers in sporadic AD. The physiological mechanisms linking the GM to the amyloid cascade should be further investigated to elucidate their potential therapeutic implications.

Dataset Information

Can Genetic Markers Predict the Sporadic Form of Alzheimer's Disease? An Updated Review on Genetic Peripheral Markers.

Publications

Can Genetic Markers Predict the Sporadic Form of Alzheimer's Disease? An Updated Review on Genetic Peripheral Markers.

Similar Datasets

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