Current and Future Trends in Biomarkers for the Early Detection of Alzheimer's Disease in Asia: Expert Opinion

AI-based differential diagnosis of dementia etiologies on multimodal data

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

Association of soluble TREM2 with Alzheimer's disease and mild cognitive impairment: a systematic review and meta-analysis

Objective

Soluble triggering receptor expressed on myeloid cells 2 (sTREM2) is a potential neuroinflammatory biomarker linked to the pathogenesis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Previous studies have produced inconsistent results regarding sTREM2 levels in various clinical stages of AD. This study aims to establish the correlation between sTREM2 levels and AD progression through a meta-analysis of sTREM2 levels in cerebrospinal fluid (CSF) and blood.

Methods

Comprehensive searches were conducted in PubMed, Embase, Web of Science, and the Cochrane Library to identify observational studies reporting CSF and blood sTREM2 levels in AD patients, MCI patients, and healthy controls. A random effects meta-analysis was used to calculate the standardized mean difference (SMD) and 95% confidence intervals (CIs).

Results

Thirty-six observational studies involving 3,016 AD patients, 3,533 MCI patients, and 4,510 healthy controls were included. CSF sTREM2 levels were significantly higher in both the AD [SMD = 0.28, 95% CI (0.15, 0.41)] and MCI groups [SMD = 0.30, 95% CI (0.13, 0.47)] compared to the healthy control group. However, no significant differences in expression were detected between the AD and MCI groups [SMD = 0.09, 95% CI (-0.09, 0.26)]. Furthermore, increased plasma sTREM2 levels were associated with a higher risk of AD [SMD = 0.42, 95% CI (0.01, 0.83)].

Conclusion

CSF sTREM2 levels are positively associated with an increased risk of AD and MCI. Plasma sTREM2 levels were notably higher in the AD group than in the control group and may serve as a promising biomarker for diagnosing AD. However, sTREM2 levels are not effective for distinguishing between different disease stages of AD. Further investigations are needed to explore the longitudinal changes in sTREM2 levels, particularly plasma sTREM2 levels, during AD progression.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024514593.

AI-based differential diagnosis of dementia etiologies on multimodal data

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an AI model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations, and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a micro-averaged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the micro-averaged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in various clinical settings and drug trials, with promising implications for person-level management.

A hybrid multimodal machine learning model for Detecting Alzheimer's disease

Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF) assays for enhanced AD characterization. The model incorporates a hybrid algorithm combining enhanced Harris Hawks Optimization (HHO) algorithm referred to as ILHHO, with Kernel Extreme Learning Machine (KELM) classifier for simultaneous feature selection and classification. ILHHO enhances HHO's search efficiency by integrating iterative mapping (IM) to improve population diversity and local escaping operator (LEO) to balance exploration-exploitation. Comparative analysis with other improved HHO algorithms, classic meta-heuristic algorithms (MHAs), and state-of-the-art MHAs on IEEE CEC2014 benchmark functions indicates that ILHHO achieves superior optimization performance compared to other comparative algorithms. The synergistic ILHHO-KELM model is evaluated on 202 AD Neuroimaging Initiative (ADNI) subjects. Results demonstrate superior multimodal classification accuracy over single modalities, validating the importance of fusing heterogeneous biomarkers. MRI + PET + CSF achieves 99.2 % accuracy for AD vs. normal control (NC), outperforming conventional and proposed methods. Discriminative feature analysis provides further insights into differential AD-related neurodegeneration patterns detected by MRI and PET. The differential PET and MRI features demonstrate how the two modalities provide complementary biomarkers. The neuroanatomical relevance of selected features supports ILHHO-KELM's potential for extracting sensitive AD imaging signatures. Overall, the study showcases the advantages of capitalizing on complementary multimodal data through advanced feature learning techniques for improving AD diagnosis.

Point-Of-Care Ultra-Portable Single-Molecule Bioassays for One-Health

Screening asymptomatic organisms (humans, animals, plants) with a high-diagnostic accuracy using point-of-care-testing (POCT) technologies, though still visionary holds great potential. Convenient surveillance requires easy-to-use, cost-effective, ultra-portable but highly reliable, in-vitro-diagnostic devices that are ready for use wherever they are needed. Currently, there are not yet such devices available on the market, but there are a couple more promising technologies developed at readiness-level 5: the Clustered-Regularly-Interspaced-Short-Palindromic-Repeats (CRISPR) lateral-flow-strip tests and the Single-Molecule-with-a-large-Transistor (SiMoT) bioelectronic palmar devices. They both hold key features delineated by the World-Health-Organization for POCT systems and an occurrence of false-positive and false-negative errors <1-5% resulting in diagnostic-selectivity and sensitivity >95-99%, while limit-of-detections are of few markers. CRISPR-strip is a molecular assay that, can detect down to few copies of DNA/RNA markers in blood while SiMoT immunometric and molecular test can detect down to a single oligonucleotide, protein marker, or pathogens in 0.1mL of blood, saliva, and olive-sap. These technologies can prospectively enable the systematic and reliable surveillance of asymptomatic ones prior to worsening/proliferation of illnesses allowing for timely diagnosis and swift prognosis. This could establish a proactive healthcare ecosystem that results in effective treatments for all living organisms generating diffuse and well-being at efficient costs.

A Framework for Best Practices and Readiness in the Advent of Anti-Amyloid Therapy for Early Alzheimer's Disease in Asia

Advances in biomarker-based diagnostic modalities, recent approval of anti-amyloid monoclonal antibodies for early Alzheimer's disease (AD; mild cognitive impairment or mild dementia due to AD) and late-stage clinical development of other disease-modifying therapies for AD necessitate a significant paradigm shift in the early detection, diagnosis and management of AD. Anti-amyloid monoclonal antibodies target the underlying pathophysiological mechanisms of AD and have demonstrated a significant reduction in the rate of clinical decline in cognitive and functional outcome measures in patients with early AD. With growing recognition of the benefit of early interventions in AD, an increasing number of people may seek diagnosis for their subjective cognitive problems in an already busy medical system. Various factors such as limited examination time, lack of expertise for cognitive assessment and limited access to specialized tests can impact diagnostic accuracy and timely detection of AD. To overcome these challenges, a new model of care will be required. In this paper, we provide practical guidance for institutional readiness for anti-amyloid therapies for early AD in Asia, in terms of best practices for identifying eligible patients and diagnosing them appropriately, safe administration of anti-amyloid monoclonal antibodies and monitoring of treatment, managing potential adverse events such as infusion reactions and amyloid-related imaging abnormalities, and cross-disciplinary collaboration. Education and training will be the cornerstone for the establishment of new pathways of care for the identification of patients with early AD and delivery of anti-amyloid therapies in a safe and efficient manner to eligible patients.

Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers

Alzheimer's disease (AD) is the most common cause of dementia. Neuropathological changes in AD patients occur up to 10-20 years before the emergence of clinical symptoms. Specific diagnosis and appropriate intervention strategies are crucial during the phase of mild cognitive impairment (MCI) and AD. The detection of biomarkers has emerged as a promising tool for tracking the efficacy of potential therapies, making an early disease diagnosis, and prejudging treatment prognosis. Specifically, multiple neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography, optical imaging, and single photon emission-computed tomography, have provided a few potential biomarkers for clinical application. The MRI modalities described in this review include structural MRI, functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, and arterial spin labelling. These techniques allow the detection of presymptomatic diagnostic biomarkers in the brains of cognitively normal elderly people and might also be used to monitor AD disease progression after the onset of clinical symptoms. This review highlights potential biomarkers, merits, and demerits of different neuroimaging modalities and their clinical value in MCI and AD patients. Further studies are necessary to explore more biomarkers and overcome the limitations of multiple neuroimaging modalities for inclusion in diagnostic criteria for AD.