A reliable blood-based assay is required to properly diagnose and monitor Alzheimer’s disease (AD). Many attempts have been made to develop such a diagnostic tool by measuring amyloid-β oligomers (AβOs) in the blood, but none have been successful in terms of method reliability. We present a multimer detection system (MDS), initially developed for the detection of prion oligomers in the blood, to detect AβOs. To characterize Aβ in the blood, plasma was spiked with synthetic amyloid-β (Aβ) and incubated over time. Then, the MDS was used to monitor the dynamic changes of AβO levels in the plasma. Increasing concentrations of AβOs were observed in the plasma of patients with AD but not in the plasma of normal control subjects. The plasma from patients with AD (n = 27) was differentiated from that of the age-matched normal control subjects (n = 144) with a sensitivity of 83.3% and a specificity of 90.0%. Synthetic Aβ spiked into the blood plasma of patients with AD, but that of not elderly normal control subjects, induced dynamic changes in the formation of AβOs over time. AβOs were detected by the MDS, which is a useful blood-based assay with high sensitivity and specificity for AD diagnosis.

Background: Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. Methods: Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimer’s disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. Results: The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The ‘time orientation’ and ‘3-word recall’ score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. Conclusions: The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.

Presenilin 1 (PSEN1) is a part of the gamma secretase complex with several interacting substrates, including amyloid precursor protein (APP), Notch, adhesion proteins and beta catenin. PSEN1 has been extensively studied in neurodegeneration, and more than 300 PSEN1 mutations have been discovered to date. In addition to the classical early onset Alzheimer’s disease (EOAD) phenotypes, PSEN1 mutations were discovered in several atypical AD or non-AD phenotypes, such as frontotemporal dementia (FTD), Parkinson’s disease (PD), dementia with Lewy bodies (DLB) or spastic paraparesis (SP). For example, Leu113Pro, Leu226Phe, Met233Leu and an Arg352 duplication were discovered in patients with FTD, while Pro436Gln, Arg278Gln and Pro284Leu mutations were also reported in patients with motor dysfunctions. Interestingly, PSEN1 mutations may also impact non-neurodegenerative phenotypes, including PSEN1 Pro242fs, which could cause acne inversa, while Asp333Gly was reported in a family with dilated cardiomyopathy. The phenotypic diversity suggests that PSEN1 may be responsible for atypical disease phenotypes or types of disease other than AD. Taken together, neurodegenerative diseases such as AD, PD, DLB and FTD may share several common hallmarks (cognitive and motor impairment, associated with abnormal protein aggregates). These findings suggested that PSEN1 may interact with risk modifiers, which may result in alternative disease phenotypes such as DLB or FTD phenotypes, or through less-dominant amyloid pathways. Next-generation sequencing and/or biomarker analysis may be essential in clearly differentiating the possible disease phenotypes and pathways associated with non-AD phenotypes.

Kim SM, et al. Dement Neurocognitive Disord. 2018 Jun;17(2):57-65. https://doi.org/10.12779/dnd.2018.17.2.57

Park KH, et al. Dement Neurocogn Disord. 2024 Oct;23(4):165-187. https://doi.org/10.12779/dnd.2024.23.4.165

TREM2 (triggering receptor expressed on myeloid cells 2) is a membrane-bound receptor primarily expressed on microglia in the central nervous system (CNS). TREM2 plays a crucial role in regulating immune responses, phagocytosis, lipid metabolism, and inflammation. Mutations in the TREM2 gene have been linked to various neurodegenerative diseases, including Alzheimer’s disease (AD), frontotemporal dementia (FTD), Parkinson’s disease (PD), and Nasu–Hakola disease (NHD). These mutations are suggested to impair microglial activation and reduce the ability to clear amyloid aggregates, leading to exacerbated neuroinflammatory responses and accelerating disease progression. This review provides an overview of TREM2 structure, functions, and known pathogenic variants—including Arg47His, Arg62His, His157Tyr, Tyr38Cys, and Thr66Met. Furthermore, the molecular and cellular consequences of TREM2 mutations are introduced, such as impaired ligand binding, altered protein folding and trafficking, enhanced TREM2 shedding, and dysregulated inflammatory signaling. We also highlight recent advances in therapeutic strategies aimed at modulating TREM2 signaling. These include monoclonal antibodies (e.g., AL002, CGX101), small molecule agonists, and gene/cell-based therapies that seek to restore microglial homeostasis, enhance phagocytosis, and reduce neuroinflammation. While these approaches show promise in in vivo/in vitro studies, their clinical translation may be challenged by disease heterogeneity and mutation-specific responses. Additionally, determining the appropriate timing and precise dosing will be essential.