Genetic risk scores enhance the diagnostic value of plasma biomarkers of brain amyloidosis

Abstract

Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrollment, and treatment monitoring in Alzheimer’s disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer’s disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity. Analyzing 962 individuals from a population-based sample, we observed that an AD-GRS was independently associated with amyloid PET levels (an early marker of AD pathophysiology) over and above APOE ε4 or plasma p-tau181, Aβ42/40, GFAP, or NfL. Among individuals with a high or moderately high plasma p-tau181, integrating AD-GRS data significantly improved classification accuracy of amyloid PET positivity, including the finding that the combination of a high AD-GRS and high plasma p-tau181 outperformed p-tau181 alone in classifying amyloid PET positivity (88% vs. 68%; p = 0.001). A machine learning approach incorporating plasma biomarkers, demographics, and the AD-GRS was highly accurate in predicting amyloid PET levels (90% training set; 89% test set), and Shapley value analyses (an explainer method based in cooperative game theory) indicated that the AD-GRS and plasma biomarkers had differential importance in explaining amyloid deposition across individuals. Polygenic risk for AD dementia appears to account for a unique portion of disease heterogeneity which could noninvasively enhance the interpretation of blood-based AD biomarker profiles in the population.

Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer’s Disease

“…Similarly, in a prospective study of over 350 patients attending a Spanish memory clinic, plasma p-tau181 demonstrated Class I evidence for correlation with AD [118]. Moreover, by taking a multi-omics approach and incorporating an AD-PRS with plasma p-tau181, prediction of Aβ-PET status improved from 68% (for p-tau181 alone) to 88% (p = 0.001) [209]. Similarly, the prediction accuracy of the underlying cause of cognitive impairment in individuals from specialized memory clinics followed over a 4-year period was higher with a diagnostic model combining p-tau217 or p-tau181 with cognitive tests and APOE status (AUC 0.90 [0.86-0.94]), compared to predictions made by memory clinic specialists (AUC 0.72 [0.65-0.78], p < 0.001) [210].…”

Age-related polygenic effects of Alzheimer's disease on brain structures and cognition in middle aged and elderly adults from UK Biobank

“…Studies supporting the dependence have demonstrated associations between APOE and levels of pathological markers in CSF [54], as well as links to WM integrity and WMHV [21], and suggested that the exclusion of APOE loci or regions from AD-PRS would decrease its statistical power in explaining variations in brain volumes [17,55] and WM integrity [16]. Conversely, other studies suggested that the effects of the AD-PRS on Aβ burden [14,19], tau burden [13,14,56], brain structure changes [13,16,21] and cognitive function [18, 56] might be independent of APOE. Some studies even failed to detect signi cant intergroup differences in regional GM volumes among individuals with different APOE genotypes [21,53].…”

“…In addition to single genes, AD-GWASs have enabled the assessment of individualised risk through calculating the polygenic risk score for AD (AD-PRS), which incorporates risk pro les from multiple Single Nucleotide Polymorphisms (SNPs) across the entire genome [10,11]. The AD-PRS has been shown to correlate with neuropathological markers [12][13][14], brain structural degeneration [15][16][17], and cognitive decline [13,15,18] along the continuum of AD, with the ability to enhance the e cacy of prediction models [19,20]. However, the ndings on the associated brain regions and the dependence of the AD-PRS on APOE remain inconsistent, likely stemming from variations in study samples and statistical methodologies.…”

Blood biomarker profiles in young-onset neurocognitive disorders: a cohort study

“…13 Leveraging multi-omic data by combining the static risk captured by genetic analyses such as the PRS, the dynamic risk identified by protein biomarkers, and standard clinical assessments can improve diagnostic accuracy for late-onset neurodegenerative conditions such as LOAD. [15][16][17] However, there is a paucity of data on how such an approach applies to diagnosing individuals with young-onset neurocognitive symptoms. Identifying clinical variables and blood biomarkers that are useful in discriminating young-onset dementia due to neurodegenerative causes from PPD may improve diagnostic accuracy and timeliness, thereby potentially reducing morbidity.…”