Biomarkers for Alzheimer’s Disease Diagnosis and Progression
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This entry is adapted from the peer-reviewed paper 10.3390/cells11081367

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ1–42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. 

Alzheimer’s disease biomarker diagnosis oxidative stress gut microbiota miRNA lipid vitamin tau amyloid-beta

1. Introduction

Alzheimer’s disease (AD) affects approximately 50,000,000 people worldwide, and is one of the most prevalent and compelling causes of dementia in the geriatric population [1]. Characterized by the extracellular deposition of amyloid-β (Aβ) peptide fibrils and intracellular neurofibrillary tangles, AD has a multifactorial etiology and complex pathogenesis that are still not fully understood [1][2]. To date, no therapy has proved effective against AD, and the high failure rate observed in clinical trials may be due to study design, inclusion criteria, and attempts at treatment when the disease is already at an advanced stage [3][4][5][6]. However, since molecular alterations far precede the onset of neurodegenerative signs, the discovery of new biomarkers associated with early disease stages is of utmost importance [1][2][7]. A biomarker can be defined as a biological marker capable of indicating molecular changes both at a physiological and pathological level [8][9]. An ideal biomarker should be reproducible, highly accurate, non-invasive, cost-effective, easy and quick to measure, and capable of distinguishing between similar conditions without exaggerated technical demand [8][9][10]. Regarding AD, although extensive research has been carried out on Aβ and tau protein alteration in the cerebrospinal fluid (CSF) and via positron emission tomography (PET), high invasiveness and considerable costs remain a concern, thus preventing the implementation of large-scale population screenings [11]. In this respect, the discovery of new minimally invasive blood-based AD biomarkers may be beneficial in presymptomatic diagnosis, disease progression monitoring, drug discovery and development, patient stratification, and targeted therapy [12][13][14][15]. Furthermore, the use of biomarkers to guide preclinical disease stage trials in the context of personalized medicine for neurodegenerative diseases has recently been proposed by the Alzheimer’s Precision Medicine Initiative (APMI), and could represent a breakthrough in AD treatment [13]. Currently, the amyloid-based PrecivityAD™ test is the only recently approved blood test for AD, although phosphorylated tau tests are also promising [7]. However, limitations related to specificity, accuracy, counseling, and interpretation still exist, and solutions based on the combination of several biomarkers belonging to different categories in a single test could strengthen the results [7][16][17][18]. Although extensive research has been conducted, a comprehensive and up-to-date overview of the main emerging blood-based AD biomarker candidates is still lacking. Since several pathways are altered in AD compared to healthy people [1][2], in this research, the researchers analyze the potential of lipids, metabolites, vitamins, inflammatory molecules and cytokines, non-coding RNAs, oxidative stress, and gut-microbiome-derived molecules as possible new blood-based AD biomarkers, thus giving insight into early diagnosis and progression monitoring for this devastating neurodegenerative disease (Figure 1).
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Figure 1. Classification of AD biomarkers. The figure illustrates the classes of blood-based AD biomarkers discussed in this research: long-studied and well-known proteins, inflammatory molecules, lipids, metabolites, oxidative-stress-related molecules, non-coding RNAs, vitamins, and gut-microbiota-based circulating molecules.

2. Current Insights

As confirmed by numerous scientific evidence, the neuropathology associated with AD is already traceable many years before clinical onset. For this reason, the research of the preclinical phases, that is, of cognitively healthy subjects at risk of developing dementia due to the presence of the neuropathological signs of AD, is of particular importance. In 2018, the National Institute on Aging (NIA) and the Alzheimer’s Association (AA) proposed a new clinically unbiased classification system of the disease based on the presence (or absence) of three processes: amyloidosis, tauopathy, and neurodegeneration (ATN classification); detectable by examination of the CSF, and via PET and MRI [19][20]. The ATN classification thus identifies eight possible risk profiles for AD, from completely negative A − T − N − to completely positive A + T + N +. At present, however, it is not known which of these profiles is associated with an increased risk of AD or cognitive decline. A first possible answer to this question comes from a recent study that combined the data of four cohorts, for a total of 814 participants, followed for an average follow-up period of 7 years, and classified according to the ATN scheme [21][22]. The results revealed that only subjects classified as A + T + N + show marked cognitive impairment compared to subjects classified as A − T − N −. The same data emerged using a previous classification of the NIA–AA group, based on the presence (or absence) of amyloidosis and tau, leading to the conclusion that the concomitant presence of amyloidosis and tau pathology is required to increase the risk of developing cognitive impairment in the future [23]. However, the high invasiveness and the elevated costs of CSF sampling, as well as the imaging methods, have recently led scientists to search for new minimally invasive and cost-effective blood-based biomarkers to be used in broad population screenings [11]. In this respect, the intrinsic multifactorial etiology of AD offers the possibility to search for a large number of biomarkers belonging to different categories. The application of these biomarkers in AD diagnosis and prognosis ranges from common bench tests to molecular biology; therefore, their affordability depends on the goals the biomedical expert aims to reach. Proteomics [24] and transcriptomics [25] are to be considered fundamental in discovering and understanding the complex correlations of the many active biomarkers with brain pathology; for example, the mitochondrial signature of AD [26]. The most promising research concerns new possible molecular biology techniques with which early diagnosis can be made. AD is a disease that has a very slow development process: the obvious dementia symptoms are the tip of the iceberg of brain changes, while the “invisible” biological correlates in the AD subject start up to 20 years earlier. It is evident that, without a correct diagnosis and without knowing why these changes occur (why do some proteins, such as beta-amyloid and tau, accumulate? And are these the cause of the disease, or a consequence of it?), the pharmacological approach may be ineffective and imprecise, relying on a symptomatic approach. Therefore, the future of research is focusing on techniques that allow abnormalities to be identified before they are irreversible.
The affordability, feasibility, and cost-effectiveness of many molecular biology kits and assays, which should enable physicians to diagnose AD at the earliest stage, have to be compared with the huge costs of caring for the AD patient. From worldwide estimates, ADI (Alzheimer’s Disease International) reported over 9.9 million new cases of AD-caused dementia per year in 2015, that is, a new case every 3.2 s.
In this research, the researchers summarized the main findings regarding dysregulations in lipids, metabolites, oxidative stress, inflammation, gut microbiota, vitamins, and non-coding RNAs in AD patients compared to controls. The huge amount of data and evidence reported in this research, however, may lack sufficient elaboration to allow the reader to grasp the overriding value of the enormous amount of data reported in the results. This is not only a limitation of the research, which should gather as many novelties in the field as possible, but it represents a weakness of the AD research worldwide, overinflated with the enormous crowding of biomolecular data, yet showing scant ability in using this data as an orchestrated methodology to narrow the time between earliest symptoms or signs and diagnosis. A recent systematic review by van der Schaar et al. proposed that a starting point for clinicians is to deepen the discussion about biomarkers, more than personal views or thoughts from societal contexts, particularly to diagnose AD before dementia [27]. This should make this research particularly important to accurately know what is currently discussed in the neurobiology of AD diagnosis.
However, several limitations still exist and need to be addressed before clinical application. First, as broadly discussed in the text, specificity remains a concern. Hypovitaminosis, oxidative stress, ncRNAs fluctuations, high levels of pro-inflammatory cytokines and systemic inflammation, alterations in metabolic and lipidomic profiles, and dysbiosis are common to many different conditions [28][29][30][31][32][33][34]. Second, studies including age- and gender-matched cohorts should be preferred, as physiological alterations in fluid biomarkers have been reported during aging and between males and females [35]. Of note, more advanced biomarkers with the potential for clinical application do not seem exempt from age and sex impact, as demonstrated by recent investigations from the APMI and the INSIGHT-preAD research [36].
Interpersonal changes due to comorbidities, genetic background, and lifestyle should also be accounted for, and, in this respect, studies with very large numbers of participants are encouraged [37][38][39]. Moreover, the use of standardized tests, shared inclusion criteria, and consistent statistical analysis are of major importance to ensure reproducibility, as often independent studies are not able to replicate previous data, thus limiting clinical advancement [40][41].
Furthermore, the recent introduction of machine learning (ML) for the diagnosis of AD and the prediction of MCI, represents an advancement in the availability of tools able to reach high performance in AD diagnosis [42][43]. In this respect, ML can support the diagnostic investigation of MCI progression from the metabolic signature pattern [44].
Yet, some particularly advanced and cutting-edge techniques, such as peripheral lipidomics, triple quadrupole mass spectrometry, and isobaric tagging methods, are particularly burdensome for clinical routine analysis, and here were described for completeness, whereas others are very rarely applied [45][46][47].
Lastly, as several authors mainly focus on a single molecule, it would be interesting to investigate whether a combination of multiple biomarkers from different categories could strengthen early diagnostic accuracy, potentially offering the opportunity to establish distinct panels of biomarkers for distinct stages of AD onset and progression.
Overall, although promising data have been recently reported, more research is required to ensure the specificity, sensitivity, cost-effectiveness, and reproducibility of blood-based AD biomarkers, with the ultimate goal of helping diagnosis and improving therapy.

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