Biomarkers for Alzheimer’s Disease in the Current State: A Narrative Review

Abstract

Alzheimer’s disease (AD) has become a problem, owing to its high prevalence in an aging society with no treatment available after onset. However, early diagnosis is essential for preventive intervention to delay disease onset due to its slow progression. The current AD diagnostic methods are typically invasive and expensive, limiting their potential for widespread use. Thus, the development of biomarkers in available biofluids, such as blood, urine, and saliva, which enables low or non-invasive, reasonable, and objective evaluation of AD status, is an urgent task. Here, we reviewed studies that examined biomarker candidates for the early detection of AD. Some of the candidates showed potential biomarkers, but further validation studies are needed. We also reviewed studies for non-invasive biomarkers of AD. Given the complexity of the AD continuum, multiple biomarkers with machine-learning-classification methods have been recently used to enhance diagnostic accuracy and characterize individual AD phenotypes. Artificial intelligence and new body fluid-based biomarkers, in combination with other risk factors, will provide a novel solution that may revolutionize the early diagnosis of AD.

Hippocampal Estrogen Signaling Mediates Sex Differences in Retroactive Interference

“…For example, the levels of interference vulnerability might also be used as a tool to help characterize AD progression and choose the therapeutic strategy. Indeed, few treatment options are presently available for AD, and none of them is differentiated for sex [ 70 ], while many compounds that are able to ameliorate memory, at least in preclinical models, work well in one sex and not in the other. For instance, vitamin A and its metabolite, retinoic acid, have been shown to modulate the effects of estrogen signaling by increasing aromatase and ERβ expression in rat hippocampal cultured slices [ 71 ] and by reducing tau hyperphosphorylation and amyloid toxic fragments in the hippocampus of male 3×Tg-AD mice, a widely used AD mouse model.…”

Investigating the Potential Impact of Air Pollution on Alzheimer’s Disease and the Utility of Multidimensional Imaging for Early Detection

“…These issues may be resolved by the rapidly developing field of using AI in conjunction with neuroimaging to diagnose AD. AI has the capacity to integrate complicated multimodal data and enhance the precision of biomarker-based testing, and it holds great promise for providing reliable and widely available early AD diagnosis. , STRING and gene ontology techniques are thus employed to evaluate novel gene candidates identified by artificial intelligence in the frontal brain and cerebellum of AD patients . One of the essential techniques to produce input information on the available EEG data and aid in the differentiation of AD, MCI, and HC persons is generative adversarial networks and variational auto-encoder networks .…”

Should artificial intelligence be used in conjunction with Neuroimaging in the diagnosis of Alzheimer’s disease?

“…Brain imaging modalities like MRI, CT, PET, as well as molecular biomarkers, such as amyloid plaques and tau in CSF, are used in clinical settings to identify a patient's cognitive status (Gunes et al, 2022). However, as noted, limitations exist when using neuroimaging alone to identify AD.…”

“…The use of AI with neuroimaging for the diagnosis of AD is a rapidly emerging field and has the potential to solve these problems. AI has the ability to integrate complex multimodal data, improve the accuracy of biomarker-based testing, and has a promising future of providing accurate and widely accessible early diagnosis of AD (Gunes et al, 2022). Below is a review of studies (see Table 1) that have developed AI-based algorithms for classifying, monitoring, and diagnosing AD as well as studies that have identified non-invasive, early AD biomarkers.…”