Blood biomarkers for memory: toward early detection of risk for Alzheimer disease, pharmacogenomics, and repurposed drugs

Abstract

Short-term memory dysfunction is a key early feature of Alzheimer's disease (AD). Psychiatric patients may be at higher risk for memory dysfunction and subsequent AD due to the negative effects of stress and depression on the brain. We carried out longitudinal within-subject studies in male and female psychiatric patients to discover blood gene expression biomarkers that track short term memory as measured by the retention measure in the Hopkins Verbal Learning Test. These biomarkers were subsequently prioritized with a convergent functional genomics approach using previous evidence in the field implicating them in AD. The top candidate biomarkers were then tested in an independent cohort for ability to predict state short-term memory, and trait future positive neuropsychological testing for cognitive impairment. The best overall evidence was for a series of new, as well as some previously known genes, which are now newly shown to have functional evidence in humans as blood biomarkers: RAB7A, NPC2, TGFB1, GAP43, ARSB, PER1, GUSB, and MAPT. Additional top blood biomarkers include GSK3B, PTGS2, APOE, BACE1, PSEN1, and TREM2, well known genes implicated in AD by previous brain and genetic studies, in humans and animal models, which serve as reassuring de facto positive controls for our whole-genome gene expression discovery approach. Biological pathway analyses implicate LXR/RXR activation, neuroinflammation, atherosclerosis signaling, and amyloid processing. Co-directionality of expression data provide new mechanistic insights that are consistent with a compensatory/scarring scenario for brain pathological changes. A majority of top biomarkers also have evidence for involvement in other psychiatric disorders, particularly stress, providing a molecular basis for clinical co-morbidity and for stress as an early precipitant/risk factor. Some of them are modulated by existing drugs, such as antidepressants, lithium and omega-3 fatty acids. Other drug and nutraceutical leads were identified through bioinformatic drug repurposing analyses (such as pioglitazone, levonorgestrel, salsolidine, ginkgolide A, and icariin). Our work contributes to the overall pathophysiological understanding of memory disorders and AD. It also opens new avenues for precision medicine- diagnostics (assement of risk) as well as early treatment (pharmacogenomically informed, personalized, and preventive).

Fig. 1

Steps 1-3: discovery, prioritization and validation/testing. a Cohorts used in study, depicting flow of discovery, prioritization, and testing of biomarkers. b Differential gene expression in the discovery cohort -number of genes identified with differential expression (DE) and absent–present (AP) methods with an internal score of 2 and above. Red—increased in expression in high memory, blue—decreased in expression in high memory. Pyramid on the left depicts the number of discovery step probesets, identified based on their score for tracking memory, with a maximum of internal points of 6 (33% (2 pt), 50% (4 pt) and 80% (6 pt)). Pyramid on the right depicts prioritization with CFG for prior evidence of involvement in AD. In the prioritization step probesets are converted to their associated genes using Affymetrix annotation and GeneCards. Genes are prioritized and scored using CFG for AD evidence with a maximum of 12 external points. Genes scoring at least ten points out of a maximum possible of 18 total internal and external scores points are carried to the testing step

Fig. 2

Best predictive biomarkers. a For state-low memory retention state. b For trait-future positive neuropsychological testing. From among the top candidate biomarker list (CFG score ≥ 10, n = 138 probesets). Bold- top CFG scoring biomarkers on the list (CFG ≥ 12, n = 23 probesets). Bar graph shows best predictive biomarkers in each group. * Nominally significant p < 0.05. Table underneath the figures displays the actual number of biomarkers for each group whose ROC AUC p-values (a) and Cox Regression Odds Ratio p-values (b) are at least nominally significant. Some female diagnostic group are missing from the graph as they did not have subjects to be tested or any significant biomarkers. Cross-sectional is based on levels at one visit. Longitudinal is based on levels at multiple visits (integrates levels at most recent visit, maximum levels, slope into most recent visit, and maximum slope). Dividing lines represent the cutoffs for a test performing at chance levels (white), and at the same level as the best biomarkers for all subjects in cross-sectional (gray) and longitudinal (black) based predictions. All biomarkers perform better than chance. Biomarkers performed better when personalized by gender and diagnosis

Fig. 3

Convergent functional evidence for involvement in memory and AD. Genes from Table 3

Fig. 4

Pharmacogenomics: Top biomarkers (from Table 3) modulated by existing drugs