Abstract
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. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.
Keywords: 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 narrative review, we 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).
Figure 1.
Classification of AD biomarkers. The figure illustrates the classes of blood-based AD biomarkers discussed in this review: 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. Methods
To review the potential roles of lipids, metabolites, oxidative stress, inflammatory molecules, ncRNAs, vitamins, gut microbiota, and proteins to function as potential blood biomarkers for AD, we carried out an extensive search in PubMed (U.S. National Library of Medicine) publication database. The following terms were used alone, or in combination, as keywords under the heading “Title/Abstract” to collect and sort our references: “Alzheimer”, “blood”, “serum”, “plasma”, “biomarker/s”, “lipid/s”, “metabolite/s”, “oxidative stress”, “inflammation”, “cytokines”, “inflammatory molecule/s”, “miRNA”, “lncRNA”, “ncRNA”, “vitamin/s”, “microbiota”, “protein/s”. To then systematize the biomarker-category-related literature, the fixed keywords “Alzheimer”, “biomarker/s” and “blood” or “plasma” or ”serum” were combined with each category-related term, according to the 8 sections present in the text. Although recent publications were preferred, our research was not limited by publication date. Finally, book chapters and institutional websites have also been consulted as possible integrative material.
3. Results
3.1. Long-Studied and Well-Known Biomarkers: Amyloid-β Peptides and Tau
Several studies indicate the potential for the plasma levels of different amyloid-β (Aβ) variants to function as AD biomarkers, due to their accuracy and predictivity [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36]. This is not surprising considering that CSF Aβ peptides represent one of the core biomarkers in AD diagnosis, and that, at the same time, there is an urgent need to identify more accessible ones [37,38]. On this path, Janelidze et al. suggested that the Aβ1–42/Aβ1–40 ratio in the plasma could be used as a screening diagnostic marker, followed, when necessary, by more specific tests, such as amyloid PET or CSF Aβ1–42/Aβ1–40 ratio [39]. This concept is further emphasized by recent discoveries pointing towards a possible association between central nervous system (CNS) Aβ accumulation and increased amounts of several plasma proteins and metabolites (e.g., interleukin 17, α2-macroglobulin, pancreatic polypeptide Y, chemokine ligand 13, vascular cell adhesion protein 1, IgM, apolipoprotein A1, fibrinogen gamma chain, other interleukins, and complement proteins), which may derive from a systemic response to Aβ accumulation [40]. Furthermore, novel, fully automated assays that measure plasma Aβ1–42 and Aβ1–40 (i.e., Elecsys immunoassays) have been shown to be capable of predicting Aβ pathology in mild cognitive impaired (MCI) as well as AD subjects from both BioFINDER and German biomarker studies, thus encouraging their applications in the context of AD clinical trial prescreenings [41].
Another core biomarker suitable for early screening and prognosis is represented by tau, the main constituent of fibrillary tangles, which can be easily detected via PET and in CSF [42,43,44,45]. Nonetheless, again, the invasive nature of CSF biomarkers remains a concern, which prevents them from being used in large-cohort screenings. However, with the recently acquired ability to quantify plasma tau, particularly plasma-tau181 and plasma-tau217, many studies have underscored their feasible use to screen for tau pathology in AD [46,47,48,49]. Of interest, a very recent longitudinal study conducted on elderly subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) reported that increased plasma levels of p-tau paralleled Aβ pathology in the brain [50], thus suggesting that amyloid plaque deposition is linked to dysregulated tau metabolism, with subsequent release of the soluble p-tau181 in circulation [50]. Of note, data from Karikari et al. demonstrated the high diagnostic accuracy of p-tau181 in identifying AD patients as well as predicting future dementia in a multicenter study conducted on more than 1000 individuals from the ADNI cohort [51].
Assessing the ability of blood-based biomarkers to detect the early stages of the disease is pivotal. In this respect, Janelidze et al. conducted an investigation on 176 MCI, 89 subjective cognitive decline (SCD), and 225 healthy individuals from the BioFINDER-2 cohort, and reported that increased levels of plasma of p-tau217 could discriminate preclinical stages of AD prior to any deposition of PET-detectable neurofibrils [52]. Moreover, further evidence from BioFINDER-1 and BioFINDER-2 studies showed that a combination of p-tau217 and plasma Aβ1–42/Aβ1–40 ratio was able to detect Aβ pathology both in MCI and healthy individuals [53].
Overall, these data support the use of plasma p-tau181 and p-tau217 as non-invasive biomarkers for clinical trial recruitment, disease-modifying trial monitoring, prognosis, and diagnosis of any stage of AD progression.
Recent promising results also include DYRK1A kinase, known to be involved in tau phosphorylation and neurofibrillary tangle formation [54]. In this respect, data from the INSIGHT-preAD study reported that plasma DYRK1A levels increase during human aging, but this age-associated rise is blocked in elderly individuals with high brain amyloid load, likely reflecting early brain changes associated with AD during aging [55]. These data point to DYRK1A as a promising theragnostic molecule to be used both as a treatment [54] and as a biomarker to identify people who could benefit from early treatment, as well as for risk stratification [55].
3.2. Plasma Neurofilament Light
Neurofilament light (NfL) has been listed among the most important AD-associated biomarkers in the Alzbiomarker Database, and NfL levels has been found to increase even at the prodromal stage [56]. However, the fact that high NfL levels are found in association with all neurodegenerative diseases makes this marker less specific to be applied for AD diagnosis [57]. Nevertheless, the recent opportunity of measuring NfL in the plasma through the Simoa assay, in even smaller quantities than before, has made NfL a valuable peripheral biomarker to assess cognitive decline and to identify individuals at risk of neurodegeneration and brain atrophy [58,59]. Accordingly, a recent study by Mattsson et al. on the ADNI cohort, which included volunteers with dementia, MCI, and healthy controls, showed a substantial increase in blood NfL in AD cases compared to controls, and this correlated with disease assessments based on CSF measurements, PET imaging, and cognitive tests [60]. Furthermore, data from Weston et al., comparing familial AD (FAD) mutation carriers and non-carriers, reveal that familial AD patients and presymptomatic carriers both show enhanced circulating NfL compared to non-carriers, with the levels of this protein approximately correlating with the expected time of clinical disease onset [61]. However, given that increased levels of plasma NfL are also found in association with many neurodegenerative disorders [62,63], plasma NfL can be used in the future as a potential screening test to detect neurodegeneration at the primary care unit, while a combination of NfL and other AD biomarkers might be used to monitor disease progression in a clinical trial setting [15,33].
3.3. Inflammation
3.3.1. Inflammatory Molecules
Inflammation has been considered an important contributor to AD pathogenesis and progression [64,65,66,67,68,69]. Gradual Aβ plaque deposition in the brain and the accumulation of neurofibrillary tangles induce microglia and astrocyte activation, two cell types involved in important physiological roles, such as synaptogenesis, synaptic plasticity, and neuronal support [69]. Although the glial response in physiological conditions is protective, if excessive, it induces a switch from an anti-inflammatory to a pro-inflammatory glial phenotype, thus fostering AD progression [69].
Given the central role of inflammation in the development of neurodegenerative diseases, several inflammatory-based fluid biomarkers have been proposed [8,70]. Although most of the data available relate to CSF, many studies have also recently investigated the variation of these molecules in the blood, with promising results [8]. The triggering receptor expressed on myeloid cells 2 (TREM2) is one of the most-studied neuroinflammatory biomarkers [8]. Expressed in microglia, it exerts important physiological functions, such as phagocytosis modulation, cytokine production, and cell division [71]. Recently, increased TREM2 mRNA levels in peripheral blood mononuclear cells (PBMCs) have been found to characterize AD patients compared to controls, and to be dependent on the APOE genotype, in accordance with data obtained using transgenic AD mouse models [72,73,74]. Similarly, evidence from another study of 80 AD patients, 30 amnestic MCI, and 86 healthy volunteers reported enhanced peripheral TREM2 mRNA in AD compared to amnestic MCI, suggesting the ability of this biomarker to discriminate between disease stages [75]. When TREM2 protein expression in circulating monocytes was considered, a test with nearly 70% diagnostic accuracy was obtained by Hu et al., consistent with RNA-based observations [76]. Similar to TREM2, leukocyte mRNA levels of the triggering receptor expressed on myeloid cells 1 (TREM1), which is closely related to TREM2, also follows the same trend, thus representing another possible biomarker [77]. TREM2 levels can also be measured in the form of the so-called soluble TREM2 (sTREM2), the secreted ectodomain of TREM2. Low plasma sTREM2 has been associated with β-amyloid accumulation and CSF p-tau level, but a similar decrease has also been reported in the context of vascular dementia, thus questioning the specificity of this biomarker [71,78]. Alternatively, regarding the plasma levels of soluble TREM1 (sTREM1), opposite results have been reported by Jiang et al., with a gradual rise in this biomarker correlating with AD severity [79]. Although evidence is accumulating, blood-TREM-based biomarkers are still far from clinical application. Indeed, while clear data are emerging on the correlation between CSF sTREM2 and AD, results from a recent meta-analysis have shown there to be no significant difference in plasma sTREM2 levels among AD, MCI, or preclinical AD patients, suggesting that more research is needed to better clarify the role of this biomarker in the blood [80].
YKL-40, also known as chitinase-3-like protein 1 (CHI3L1), is a pro-inflammatory glycoprotein expressed in differentiated glial cells, and is considered a marker of neuroinflammation [81,82]. Recently, serum levels of YKL-40 have been shown to be a promising marker for early MCI diagnosis and patient selection, as it is capable of discriminating between cognitive normality and mild cognitive impairment with 85% sensitivity and specificity. However, it was not as good a marker for disease progression [83,84]. Results from a multicentre study have shown that plasma YKL-40 concentration is higher in AD-related dementia, similar to what has been observed with YKL-40 in the CSF [82]. However, it should be noted that high levels of blood YKL-40 have also been reported in aging, vascular dementia, frontotemporal dementia, sporadic Creutzfeldt–Jakob disease, and Lewy body dementia, as well as to vary according to sex, thus almost excluding its applicability as a specific and differential AD biomarker [81,85].
Peripheral monocyte chemotactic protein (MCP)-1 and MCP-3 have also been reported to be higher in AD patients than in healthy patients [86], with MCP-1 also being elevated in MCI patients relative to healthy subjects [86,87]. Nevertheless, the statistical significance of the MCP-1 result contrasts with another meta-analysis conducted by Olsson et al. in 2016 [56]. Interestingly, Morgan et al. recently showed that a panel of ten proteins, including cytokines eotaxin-1, MCP-1, and MIP-1β, was able to significantly differentiate AD, MCI, and healthy control groups, with subsets of this panel also successful in discerning patients from controls when tested in a discovery cohort [70].
C-reactive protein (CRP) is an acute-phase protein, the level of which rises during inflammation; however, evidence for its use as an AD biomarker remains inconclusive. Indeed, while some studies indicate that blood CRP levels correlate with Mini-Mental State Examination (MMSE) score, other results suggest that this is valid only among APOEε4 homozygote AD patients [88,89]. Still, no association with cognitive decline has been reported from independent data, leaving CRP as a debatable biomarker [90,91].
Chemokines are a set of chemo-attractant cytokines that participate in the inflammatory process, and are involved in dementia development [92,93]. In this respect, while blood CX3CL1, also called fractalkine, has been found to be upregulated both in MCI and AD, plasma CCL23 seems to better predict MCI-to-AD progression [93,94]. Plasma concentrations of the C-C chemokine ligand (or RANTES) have been reported to be elevated in AD and to correlate with an increased inflammatory burden [95,96,97]. However, since changes in RANTES have also been noted in other neurodegenerative and metabolic diseases, further research is needed to better understand the specificity of this marker [95,98,99].
The complement system plays a key role in innate immune defence, and a strong inflammatory response is produced upon its activation [100]. In this respect, while increased circulating clusterin (a member of the small heat shock protein family also involved in complement-mediated cell lysis) has been measured in AD compared to controls, a combination of clusterin, factor I, and terminal complement complex evaluation can discriminate between MCI subjects that will develop dementia and those who will remain stable [101,102]. On the contrary, significantly lower levels of complement component 3 (C3) have been reported in the serum of AD patients compared to healthy volunteers, but no correlation was found when complement component 4 (C4) was considered [89].
Other inflammatory proteins have also been hypothesized as possible AD biomarkers, but data remain uncertain. For example, Suidan et al. found that young AD patients are characterized by delayed clotting [103]. However, since the clotting profile may change considerably in many different conditions, additional data on larger cohorts are warranted [103]. Similarly, interferon-γ-induced protein 10 (IP-10), an important player in inflammation and angiogenesis [104], has been reported to be the plasma analyte from the ADNI cohort showing the highest abnormality levels [105]. However, no correlation between serum levels of IP-10 and AD or MCI was found in an independent study by Galimberti et al. [106], although positive results had previously been described concerning CSF IP-10 content [107]. Regarding immunoglobulins, while serum IgA and IgG levels have been shown to be significantly higher in AD compared to controls, no differences were found for IgM [89]. Finally, since a single inflammatory molecule might often lack specificity, and is subject to interpersonal variations, a combination of possible biomarkers might represent an option to strengthen predictive capacity, as already reported [108,109,110].
3.3.2. Circulating Cytokines
Dysregulation of inflammatory cytokines has been shown in the brain tissue of AD patients in post mortem analyses as well as in the CSF of MCI and AD patients [56,111]. These results led to increasing interest in the role of circulating cytokines in AD, since they may circumvent the need for invasive diagnostic procedures.
A sizable body of evidence has been accumulated on how blood cytokine levels differ across AD, MCI, and non-demented patients, although disagreements over the conclusions persist. In their meta-analysis, Bradburn et al. established blood IL-6 as a risk factor for cognitive decline in MCI patients, with high IL-6 levels being associated with an increased risk of an AD diagnosis at a follow-up visit within 2–7 years (odds ratio, 1.42) [112]. La Rosa et al., following MCI patients for 2 years, found that blood samples collected at baseline had higher PBMC mRNA levels of IL-1β and IL-6 in AD converters than in non-converters only if the samples were stimulated with Aβ, suggesting that an inflammatory milieu may contribute specifically to the onset of AD [113]. Elevation in pro-inflammatory cytokines, such as interleukin (IL)-6, tumor necrosis factor (TNF)-α, IL-1β, transforming growth factor (TGF)-β, IL-12, and IL-18, in the peripheral blood of AD patients compared to control subjects was also reported by Swardfager et al. in a meta-analysis comprising 44 studies [114]. Moreover, in a more recent study by Lai et al., in addition to the previous markers, circulating IL-2, interferon (IFN)-γ, CRP, and CXCL10 were found to be elevated in AD patients compared to healthy controls, while IL-6 levels were inversely correlated with cognitive function, in contrast with other studies [115]. Some of these findings were confirmed in another meta-analysis, which also found that soluble TNF receptor (sTNFR)-1 and sTNFR-2 are overexpressed in the blood of AD patients relative to healthy controls or MCI patients [87]. However, yet another review on peripheral IL-1β, IL-6, TNF-α, and CRP found no statistically significant difference between AD patients and controls in any of these markers [116]. A possible explanation for this discrepancy is that the meta-analysis by Ng et al. included far fewer studies than that by Lai et al. Still, Nesham et al. reported the opposite trend in a study of 60 subjects, in which mRNA levels of IFN-γ and TNF-α in PBMCs of AD subjects were, instead, decreased compared with non-demented controls [117].
Studies have also investigated whether other circulating cytokines can predict the conversion of MCI to AD. One study showed that the absence of IL-33 was more common in MCI patients who converted to AD at 1-year follow-up visits than in those who did not convert to AD; overall, the cognitive function in patients expressing IL-33 was better preserved than in patients who did not express it [118]. Similarly, one report suggested that osteopontin (a matricellular protein originally isolated from bone, also functioning as a pro-inflammatory cytokine) is more highly expressed in the blood of recently diagnosed AD patients than in those that have had AD for more than 2 years [119]. Furthermore, it was also found that MCI patients had higher blood osteopontin levels at diagnosis of AD progression [119]. Moreover, within a panel consisting of 29 cytokines, and including total tau protein, p-tau181, Aβ1–40, and Aβ1–42, a high level of circulating IL-2 was found to be the best-performing biomarker to predict a slower cognitive decline in MCI patients (measured by a two-point decrease, or more, in the MMSE), though no reliable biomarker was found in AD patients [120].
3.4. Metabolism
Metabolites are defined as the intermediate and final products of metabolic reactions. Usually, this term is used to indicate relatively small biomolecules involved in various biological processes, such as cell growth, reproduction, food breakdown, and chemical detoxification, and they constitute the building blocks of many other biological components [121]. Disruptions to many biochemical pathways, such as amyloid precursor protein metabolism, tau protein phosphorylation, oxidative stress, mitochondrial function, inflammation, lipid metabolism, and neurotransmitter pathways, occur in AD patients [122]. Therefore, metabolomics analysis (MA) may represent a new method to investigate a multifactorial disease, such as AD, because of its ability to detect hundreds of metabolites rapidly and synchronously [123,124].
Furthermore, research has highlighted that the biochemical mechanisms underlying AD start decades before the clinical onset of dementia [125], which offers the opportunity to use biomarkers as an adjunctive tool for early AD diagnosis [126]. According to these discoveries, biomarkers can be added into the diagnostic procedure to recognize specific phases of disease progression, to assist doctors in monitoring the course of AD, and to improve the accuracy of the diagnosis [127,128].
A recent study conducted by Sun et al. on 30 AD patients, 32 MCI patients, and 40 controls found 11 metabolites able to discern between AD patients and controls [129]. In particular, 1,4-butanediamine and L-ornithine, compared to the other metabolites, turned out to have a higher diagnostic capacity [129]. Outcomes of this study suggest that irregular energy metabolism, oxidative stress, and metabolic disorders of lipids and amino acids in patients affected by AD or anamnestic mild cognitive impairment (aMCI) might occur [129]. Furthermore, recent evidence by Piubelli et al. suggests that both serum D serine concentrations and D-/total serine levels are indicative of disease progression, and can represent new advanced biomarkers [130].
Several studies indicate that AD is highly prevalent in adults with Down syndrome (DS), and, therefore, biomarker discovery in this population is of interest [131,132]. In this context, Gross et al. examined plasma samples from 78 patients with Down syndrome who met the diagnostic criteria for AD (DS-AD) and 68 individuals with Down syndrome who did not (DS-NAD) [133]. Outcomes revealed remarkably higher levels of lactic, pyruvic, and methyladipic acids in the DS-AD group in comparison to the DS-NAD group, suggesting that, in this population, AD is accompanied by a switch from aerobic respiration to fermentative, less efficient metabolism [133]. In addition, markedly decreased levels of uridine were noticed in the DS-AD group, without evidence of hypoxia [133]. However, since all participants were affected by Down syndrome, the absence of healthy controls could represent a possible limitation to this study given the similar pathological aspects and cellular dysfunctions in DS and AD [131,134]. Of note, dysfunctions in mitochondrial bioenergetics accompanied by a shift in glucose metabolism have also been reported to long precede the onset of neurotypical AD, suggesting that the observations regarding the DS-AD population may be applicable even in AD patients without other comorbidities [135,136,137,138].
Significantly increased plasma levels of lithocholic acid (LCA) have been detected in AD patients in comparison to healthy controls [139]. The same study, in which the levels of 20 bile acid metabolites were quantified in plasma, also reported higher levels of glycochenodeoxycholic acid (GCDCA), glycodeoxycholic acid (GDCA), and glycolithocholic acid (GLCA) in AD compared to MCI patients. However, although LCA and GDCA may be useful to routinely diagnose AD using plasma samples, this analysis revealed a limited specificity, sensitivity, and accuracy compared to other plasma markers. Thus, these two bile acid markers measured in the plasma could be helpful to diagnose AD in combination with other biomarkers [139].
Associations between circulating metabolites and neocortical amyloid positivity were also investigated [140]. In this regard, a panel consisting of anandamide and its isotope, phosphatidylethanolamine, phosphatidylcholine, and an unidentified metabolite with a median mass/charge ratio of 829.66, was found to be able to predict PET neocortical amyloid burden with 72% accuracy, with the potential to develop a simple blood test to diagnose AD even at the prodromal or preclinical stages [9,140]. Although a promising candidate for monitoring the progression of amyloid pathology in anti-amyloid trials, the ability of this signature to differentiate demented from non-demented individuals remains to be validated, as there are subjects who show the amyloid signature neuropathologically, yet are cognitively intact [141,142,143].
Results concerning the Alcadeins (Alcs) family are also of interest [144]. Given that the quantity of p3-Alcα in plasma mirrors the pathological process of Aβ build-up in AD patients, it was hypothesized that the quantity of p3-Alcα could be used as a plasma biomarker in AD [145]. Indeed, it has been reported that plasma p3-Alcα concentrations in AD and MCI patients were significantly enhanced in comparison with controls. Elevated p3-Alcα plasma levels turned out to be remarkably correlated with AD risk, despite adjustment for confounding factors, including age, gender, ApoE-ε4, and renal function [145].
Acylcarnitines are a large class of metabolites that play key roles in long-chain and branched-chain fatty acid metabolism, insulin resistance, cellular stress responses, and cholinergic neurotransmission [146,147,148,149]. Of interest, three of these acylcarnitines, decanoylcarnitine [C10], pimelylcarnitine [C7–DC], and tetradecadienylcarnitine [C14:2], were predictive of a lower risk of AD onset [150]. However, a possible weakness of this study may be the fact that lower plasma concentrations of decanoylcarnitine and tetradecadienylcarnitine were also found in individuals with schizophrenia compared with healthy controls, thus limiting the specificity of these biomarkers [151]. Following this analysis, several other studies have designed interesting diagnostic panels. A cross-sectional study found several metabolites, the levels of which were altered both in AD patients and MCI patients [18]. All these data were used to create a logistic regression model that precisely discriminates AD from normal controls [18]. Seven metabolites composed the final panel: one non-esterified fatty acid (22:6n − 3, DHA), one bile acid (deoxycholic acid), one sphingomyelin (SM(39:1)), three amino acids (glutamic acid, alanine, and aspartic acid), and one phosphatidylethanolamine (PE(36:4)). This metabolic signature was even able to distinguish between MCI and normal control patients, suggesting that it may be a powerful resource for early-stage diagnosis [18]. Another biomarker panel consisting of six plasma metabolites belonging to amino acid metabolism, one-carbon metabolism, and fatty acid and nucleic acid metabolism (arachidonic acid, N,N-dimethylglycine, thymine, glutamine, glutamic acid, and cytidine) was able to discriminate AD patients from normal controls [152]. Overall, these results supply a broad global plasma metabolite profile, and may strengthen early diagnosis [153].
Finally, data from a combined omics analysis performed on the INSIGHT-preAD cohort showed that a combination of metabolomic and transcriptomic features was able to discriminate between amyloid-negative and amyloid-positive individuals, with the potential to be applied in early screenings and in risk stratification assessments [154].
In conclusion, blood MA provides hope for a better comprehension of AD, as well as for early diagnosis and prompt therapy, but more research is required to address its specificity and reproducibility [151,155].
3.5. Oxidative Stress
Oxidative stress is characterized by the loss of balance between reactive oxygen species (ROS) and antioxidant defenses, leading to protein and DNA oxidation, lipid peroxidation, glycoxidation, and altered glucose metabolism [156]. Since the brain has a high rate of oxygen consumption, is constituted by lipids that can be easily oxidized, and contains less antioxidant molecules than other organs, it is considered particularly exposed to oxidative damage [157]. Several studies have reported that oxidative stress can cause early brain alterations, and thus hypothesized a central role of oxidative damage in the pathogenesis of many neurodegenerative diseases, including AD [158,159,160]. Up to now, lipid peroxidation and many oxidative-stress-related molecules have been detected as differentially expressed in AD brain, urine, and/or CSF compared to controls, such as 3-nitrotyrosine, 4-hydroxynonenal, and 8-hydroxy-2-deoxyguanosine [161,162]. Recently, blood has also been considered a source for oxidative-stress-based biomarkers, and Table 1 summarizes the main findings [10,163,164,165,166,167,168,169,170,171,172,173,174,175,176].
Table 1.
Oxidative-stress-related blood AD biomarkers.
| Ref | Study Cohort and Design | Plasma/Serum | Measurement Methods | Results | Cohort of Variation | Biomarker/s Proposed |
|---|---|---|---|---|---|---|
| Han et al., 2021 [164] | Aβ1–42-treated PC12 cells, brain and hippocampus of APP/PS1 mouse, and the serum of AD patients | Serum | Probe 1 *, ELISA assay or LC–MS | ↑ Hcy ↓ Cys and GSH |
AD vs. HC | Hcy, Cys, and GSH changes in the serum |
| Evlice et al., 2017 [165] | 30 AD (15 females and 15 males) and 10 HC (7 males and 3 females) | Serum | Activity and quantitative G6PD kit | ↑ serum G6PD | AD vs. HC | Serum G6PD levels |
| Peña-Bautista et al., 2021 [166] |
12 preclinical AD and 31 HC | Plasma | Chromatography and mass spectrometry | ↓ lipid peroxidation -15-F2t-IsoP correlates with p-tau -15-F2t-IsoP correlates with t-tau |
AD vs. HC (non-significant) | Plasma isoprostanoids (combination of 10 biomarkers) |
| Zengi et al., 2012 [168] | 21 AD (10 men and 11 women) and 20 HC (11 men and 9 women) | Serum | PON1 activity absorbance assay | ↓ serum PON1 | AD vs. HC | Serum PON1 activity |
| López et al., 2013 [176] | 36 AD, 18 MCI, and 33 aged HC | Blood | ↑ Copper and MDA | AD and MCI vs. HC | Blood copper, MDA, and SOD | |
| Pradhan et al., 2022 [175] | 47 AD, 43 MCI, and 48 HC | Serum | SPR and Western blot | ↓ SIRT1, SIRT3, and SIRT6 | AD vs. MCI and HC | Serum SIRT1, SIRT3, and SIRT6 concentration |
| Cardoso et al., 2014 [170] | 27 AD, 17 MCI, and 28 HC | Plasma | Hydride generation atomic absorption spectroscopy | ↓ plasma Se ↓ erythrocyte Se |
-AD vs. MCI and HC -AD and MCI vs. HC |
Plasma Se levels |
| García et al., 2021 [10] | 20 MCI (13 males and 7 females), 20 AD (11 males and 9 females), and 15 PD (12 males and 3 females) and HC (age and sex matched). | Plasma | Electrochemical immunosensor | ↑ Unfolded p53 ↑ Unfolded p53 |
-MCI, AD, and PD vs. HC -AD vs. MCI and PD |
Plasma unfolded p53 |
| Peña-Bautista et al., 2021 [167] |
6 AD and 13 MCI | Plasma | LC–MS | ↑ dihomo-isoprostanes (17-epi-17-F2t-dihomo-IsoP, 17-F2t-dihomo-IsoP, Ent-7(RS)-7-F2t-dihomo-IsoP) and neuroprostanes (10-epi-10-F4t-NeuroP) | AD vs. MCI | Plasma isoprostanoids levels |
| Picco et al., 2014 [169] | 23 SCI, 28 MCI, and 34 mild AD | Plasma | Spectrophotometric analysis | ↓ eSOD activity ↓ CAT activity = GPx activity |
-AD vs. SCI -AD vs. MCI and SCI |
Plasma eSOD, CAT, and GPx activity combined with functional neuroimaging |
| Lin et al., 2021 [163] | 49 MCI and 16 HC | Plasma | Commercially available assay kit | ↓ plasma GSH | MCI vs. HC | Plasma GSH levels |
| Li et al., 2021 [173] | 839 HC | Serum | ↑ Serum uric acid = Serum uric acid in healthy individuals with or without tau pathology |
Preclinical AD vs. HC | Serum uric acid | |
| Du et al., 2019 [172] | 113 aMCI and 832 HC | Serum | Commercial ELISA kit | ↑ Serum IMA and IMA/albumin | aMCI vs. HC | Serum IMA |
| Wu et al., 2021 [171] | 88 HC, 201 with cognitive impairment and no dementia (CIND) and 207 with dementia (160 AD and 47 vascular dementia) | Plasma | LC–MS/MS | ↓ plasma | Dementia vs. CIND and HC | Plasma ergothioneine levels |
Abbreviations: AD, Alzheimer’s disease; aMCI, amnestic mild cognitive impairment; APP/PS1, double transgenic mouse model of AD; CAT, catalase; ELISA, enzyme-linked immunosorbent assay; eSOD, extracellular superoxide dismutase; G6PD, glucose-6-phosphate dehydrogenase; GPx, glutathione peroxidase; GSH, glutathione; HC, healthy controls; Hcy, homocysteine; IMA, ischemia-modified albumin; LC–MS, liquid chromatography–mass spectrometry; MCI, mild cognitive impairment; MDA, malondialdehyde; PD, Parkinson’s disease; PON1, paraoxonase 1; SCI, subjective cognitive impairment; SEC–ICP–MS, size exclusion chromatography–inductively coupled plasma–mass spectrometry; SPR, surface plasmon resonance; * Probe 1, ethyl (E)-3-(9-chloro-11-oxo-2,3,6,7-tetrahydro-1H,5H,11H-pyrano [2,3-f] pyrido [3,2,1-ij] quinolin-10-yl)-2-cyanoacrylate; ↓, decrease; ↑, increase.
Glutathione (GSH), a tripeptide composed of cysteine, glutamate, and glycine, represents the most abundant and dominant endogenous antioxidant in the body; its homeostasis has been reported to be dysregulated in neurodegenerative diseases [177]. Regarding AD, while serum GSH levels are significantly lower in patients than controls, its plasma concentration has been correlated with cognitive decline, and it is able to discriminate between MCI subjects and healthy volunteers [163,164]. Despite these data, L-cysteine prodrug supplementation, or oral γ-glutamylcysteine administration, did not prevent AD alterations, nor did it restore GSH and oxidative markers, thus underscoring the need for further studies [178]. Similarly, serum glucose-6-phosphate dehydrogenase (G6PD), an enzyme that protects red blood cells from oxidative stress, has been found to nearly double in AD subjects relative to healthy individuals, although more studies are required to strengthen these results [165]. Plasma levels and the activity of other enzymatic antioxidants, such as extracellular superoxide dismutase, catalase, and glutathione peroxidase, were shown to decrease progressively according to the severity of the cognitive impairment [169,176]. Likewise, when Zengi et al. compared 21 moderate AD subjects with 20 healthy volunteers, significantly low blood levels of the high-density lipoprotein-associated antioxidant enzyme paraoxonase 1 have been detected in the AD group, thus confirming ROS/antioxidant imbalance as an AD signature [168]. Alternatively, among mild cognitive impaired type 2 diabetic patients, enhanced plasma activity of the enzyme dipeptidyl peptidase-4 (DPP4), known to enhance inflammation and oxidative stress, is negatively linked with circulating brain-derived neurotrophic factor (BDNF) levels, and positively correlates with inflammatory markers (i.e., IL-6, CRP) and cognitive impairment [179,180].
Isoprostanoids are the result of non-enzymatic oxidation of polyunsaturated fatty acids, and are the secondary product of lipid peroxidation [181]. However, while some evidence suggests increased plasma dihomo-isoprostanes and neuroprostanes in AD patients, other studies show no significant difference from controls [166,182].
Several minerals and vitamins are also known to exert an antioxidant effect, although evidence for their potential use as AD biomarkers remains debated [183]. Indeed, while plasma and erythrocyte selenium concentrations have been reported to decrease in cognitively impaired individuals, other evidence shows that serum levels of the same mineral seem not to change in overt AD [170,184]. Similarly, alterations in iron, zinc, and copper have also been described [185,186,187,188]. For instance, Mueller et al. reported that an increased serum copper/non-heme iron ratio can predict the progression from mild cognitive impairment to overt dementia, thus representing a promising early diagnostic biomarker [187]. However, results from another study conducted on 36 AD patients, 18 MCI individuals, and 33 controls did not find copper to have the capacity to differentially diagnose AD and MCI conditions, thus calling for new studies [176].
Other potential markers directly or indirectly associated with oxidative stress imbalance, such as sialic acid deficiency, increased protein carbonylation, plasma unfolded p53, acetylcholinesterase (AChE) expression, serum thiol–disulfide balance, and serum ischemia-modified albumin (IMA) concentration, have been investigated in independent studies, but their results need to be replicated [10,172,189,190,191]. Interestingly, when 113 patients with aMCI were compared to 832 controls, serum IMA amount, and the IMA/albumin ratio, were shown to be capable of detecting AD at the prodromal stage, suggesting the potential for this molecule to detect early disease onset [172].
Other possible oxidative-stress-related biomarkers have also been investigated. For example, serum uric acid levels have been found elevated in the preclinical stage of AD, and this increase was particularly pronounced in people with amyloid pathology [173]. Since some studies indicate that hyperuricemia may act as an antioxidant [192], these results suggest that increased uric acid levels may represent an antioxidant response of the body against amyloid load, which is not only present at the preclinical AD stage, but also characterizes the clinical stage. Recently, results from another cross-sectional study carried out on a total of 496 individuals show that low plasma ergothioneine levels, an uncommon sulfur-containing derivative of the amino acid histidine with antioxidant properties, typify demented patients and inversely correlate with disease severity, although it could not distinguish between AD and vascular dementia [171]. Moreover, altered levels of redox-reactive antiphospholipid antibodies (directed against the plasma protein β2-glycoprotein I and not phospholipids), decreased serum concentrations of some sirtuins (SIRT1, SIRT3, and SIRT6), and reduced serum coenzyme Q10 concentrations have also been considered as disease markers, although, regarding coenzyme Q10, discordant data have been published [174,175,193,194].
Finally, erythrocyte morphology, membrane protein composition, and oxidative stress hallmarks have been proposed as possible circulating biomarkers, but more research is needed [195].
Overall, when considering the potential use of oxidative-stress-related molecules as possible biomarkers, it should be noted that oxidative damage is a common hallmark of all neurodegenerative diseases, and is found in several other conditions, thus making it difficult to find a specific AD marker [161]. Nevertheless, the need for non-invasive disease biomarkers coupled with these promising emerging data should encourage new large, comprehensive, and confirmatory studies to be undertaken.
3.6. Circulating Non-Coding RNAs
3.6.1. miRNAs—Alzheimer’s Disease
MicroRNAs (miRNAs) are short non-coding RNAs (approximately 21 bp long), with an important role in post-transcriptional gene modulation [196,197,198]. They can circulate in the blood, either as cell-free miRNAs bound to specific proteins or encapsulated in microvesicles, typically exosomes [199]. RNA sequencing and next-generation sequencing (NGS), microarray analysis, and quantitative reverse transcription–polymerase chain reaction (RT–PCR) are all suitable techniques typically used to detect miRNA levels in the bloodstream [199,200]. Dysregulations in blood and CSF miRNA levels have been reported during aging and in age-related diseases, including neurodegeneration [200,201,202,203,204,205,206,207]. This aspect, in association with the fact that they are stable in biofluids, makes miRNAs ideal non-invasive biomarkers for early diagnosis, disease progression monitoring, population screenings, and even therapy [199,200,208,209]. Regarding AD, early evidence came from Kumar et al., who reported that a miRNA signature consisting of seven plasma miRNAs (hsa-let-7d-5p, hsa-let-7g-5p, hsa-miR-15b-5p, hsa-miR-142-3p, hsa-miR-191-5p, hsa-miR-301a-3p, and hsa-miR-545-3p) can differentiate AD and healthy individuals with more than 95% accuracy [210]. Soon after, Kiko et al. proposed that the decrease in plasma miR-34a and miR-146a found in AD patients compared to controls could be used to non-invasively detect the disease [211]. Subsequent investigations have shown that upregulation of miR-590-5p and miR-142-5p, along with downregulation of miR-194-5p, is distinctive of AD subjects compared to healthy volunteers [212]. Moreover, enhanced plasma concentrations of hsa-let7d-5p and hsa-let7g-5p were detected in a study comprising 50 AD and 50 age- and gender-matched controls [213].
PBMCs have also been considered a source of potential miRNA-based biomarkers [214]. For instance, decreased hsa-miR-29b in PBMCs has been correlated with lower SP1 expression, a transcription factor that regulates the transcription and translation of proteins involved in AD [215]. Although both hsa-miR-29b and hsa-miR-375 are closely related to SP1 regulation, the results are not significant [215]. Instead, data derived from an array analysis carried out on PBMCs from 16 AD patients and 16 gender-, age-, and ethnicity-matched controls showed significant upregulation of miR-34a and miR-181b in demented subjects, while the expression of several other miRNAs changed according to APOE genotype [214].
As far as pathophysiological modifications are concerned, miRNAs have been reported to modulate Aβ levels through the regulation of the amyloid precursor protein (APP) and tau phosphorylation, as described for miR-455-3p and miR-483-5p, respectively [216,217]. In this context, plasma levels of miR-2000-3p, a neuroprotective ncRNA against Aβ-mediated toxicity, were reported to be lower in AD compared to controls, both in vitro and in vivo [218]. Moreover, levels of circulating miR-15a have been shown to correlate with plaque score [219]. In parallel, Geekiyanage et al. proposed four downregulated serum miRNAs involved in Aβ and tau phosphorylation pathways (miR-137, miR-181c, miR-9, and miR-29a/b) as a potential panel for early disease screenings [220]. However, while further investigations conducted on 105 AD and 150 healthy individuals led to the validation of Aβ-regulated miR-181c as being decreased in the serum of AD patients compared to controls, contrasting evidence was found for miR-9, with increased levels of this biomarker also reported in demented individuals [72]. BACE1, SP1, NCSTN, PTEN, and SIRT1 are also regulated by miRNAs implicated in AD, with miR-9, miR-16, miR-34a, miR-106a, miR-107, miR-125b, miR146, and miR-181c presenting the highest level of interactions in the network [221]. In support of this, consistent decreases in circulating miR-29c levels, a miRNA negatively correlated to BACE1 expression, were found to characterize AD patients compared to age-matched controls [222]. Moreover, the downregulation of three miRNAs that modulate target proteins related to AD, such as APP and CaMKK2 (hsa-miR-9-5p, hsa-miR-106a-5p, and hsa-miR-106b-5p), have been correlated with disease severity; hsa-miR-106a-5p alone reaching statistic values of 93% specificity and 68% sensitivity in AD diagnosis [223]. Other possible biomarkers have been identified by Liu et al. in a study comprising 50 AD patients, 20 individuals with vascular dementia (VD), and 50 healthy controls, in which a significant decrease in the circular RNA hsa-circ-0003391 in peripheral blood was correlated with a rise in miR-574-5p in AD compared to both controls and VD subjects [224]. When machine learning approaches were applied to a total of 465 subjects, including AD and controls, circulating levels of miR-532-5p showed the highest correlation with neurodegeneration (AUC 87,6%), but miR-26a/26b-5p were the best predictors of MMSE score [225].
Since immune modulation is of central importance in AD pathophysiology, the circulating miRNAs involved in these pathways could represent a source of novel early AD biomarkers. In this respect, an increase in circulating miR-206 levels in AD patients has been found to correlate with enhanced inflammation and reduced expression of the neuroprotective factor IGF1 [226]. In addition, miR-146b-5p and miR-15b-5p downregulation, two miRNAs involved in innate immune system regulation and cell cycle control, have been linked to AD after performing RNA sequencing on 40 amyloid-positive AD patients and 31 amyloid-negative healthy controls [227]. Given the large amount of data usually generated when comparing the expression of hundreds of miRNAs in large cohorts, recently, machine learning approaches have also been considered and miRNA-based biosignatures have been proposed [228]. When random forest-based machine learning approaches were used to account for miRNA dysregulation, brain volume, comorbidities, and demography, three blood miRNAs related to cellular senescence and inflammation were found to be the best predictors of cognitive impairment: miR-140-5p, miR-197-3p, and miR-501-3p [229]. Moreover, a deregulation in blood miRNAs involved in neuroinflammatory pathways has been reported by Yuen et al. in based on their results from a meta-analysis followed by machine learning techniques [230]. To date, serum levels of miR-125 are among the most promising ncRNA-based biomarkers [72]. In this respect, upon analyzing 84 AD and 62 healthy subjects, Jia et al. reported a significant decline in serum miR-125b and miR-223, both involved in immune regulation, with a combination of both being more predictive than either miRNA alone [231]. Interestingly, serum miR-125b levels have also been reported to be capable of discriminating AD patients from both healthy controls and subjects characterized by inflammation, thus excluding neuroinflammation as a possible confounding effect [232]. Finally, levels of the closely related miR-34c, implicated in repressing cell survival and antioxidant defense, were found to be upregulated in plasma from AD individuals compared to age-matched controls, in an independent study [233].
Diminished concentrations of other ncRNAs, such as hsa-miR-501-3p, were reported to correlate with MMSE score, while an opposite trend has been observed for miR-455-3p [234,235]. Notably, contrasting results have been obtained when analyzing the levels of the DNA replication modulator hsa-miR-501-3p in the brain of AD subjects, suggesting an intermittent concordant trend between serum and other compartments [234].
Despite promising results, reproducibility and validating issues often pose problems in identifying one or several miRNAs that can be used in disease diagnosis. Therefore, using a group of biomarkers might represent a strategy to strengthen the results and decrease interindividual variability. For example, after performing NGS on blood samples from 49 AD, 20 MCI, 90 multiple sclerosis (MS) patients, and 55 controls, Keller et al. proposed a pool of 68 miRNAs as an AD diagnostic set [236]. In addition, when conducting a genome-wide serum microRNA screening with NGS, and subsequent RT–PCR, on a discovery cohort and a validation cohort, six miRNAs were differentially expressed between patients and controls (miR-98-5p, miR-885-5p, miR-483-3p, miR-342-3p, miR-191-5p, and miR-let-7d-5p), with miR-342-3p presenting the highest sensitivity and specificity [237]. Results from an integrated analysis conducted on 12 miRNA datasets identified 37 dysregulated miRNAs in AD compared to controls, with has-miR-93, has-miR-26b, has-miR-34a, has-miR-98-5p, and has-miR-15b-5p being the key nodes when analyzing miRNA–mRNA interactions and modulation [238]. Notably, machine learning techniques can also be applied to analyze peripheral blood miRNA signatures, with recent data proposing machine learning models reaching up to 92% and 90,9% accuracy in the serum and plasma, respectively [239]. Lastly, a combination of 12 miRNAs has been shown to be able to differentiate AD and controls with an accuracy of 93% and a specificity of 95%, thus clearly improving the statistical strength [17]. However, predictive values of the same signature decreased to 74–78% accuracy when used to distinguish between AD, MCI, Parkinson’s disease (PD), depression, bipolar disorder, and schizophrenia [17].
Regarding blood-based miRNA platforms dedicated to differential diagnosis, results from a multicenter study showed that serum is even better than CSF to discriminate and classify patients with the sporadic behavioral variant of FTD from AD, with upregulated miR-223-3p and downregulated miR-15a-5p seeming to characterize the former [240,241]. Furthermore, AD and VD might be differentially diagnosed by measuring miR-31, miR-93, and miR-146a serum concentrations [242], while other miRNAs can be used alone or in combination to distinguish between AD, ALS, and controls [241]. A miRNA signature based on 37 brain-enriched and plasma miRNAs also proved able to distinguish between AD, PD, frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS), albeit with varying levels of accuracy [243]. In addition, results from a study performed on 120 AD, 120 PD, and 120 healthy controls revealed that, while both plasma miR-103 and miR-107 are lower in AD compared to controls, and are both correlated with MMSE score, only miRNA-103 is capable of significantly differentiating AD from PD [244].
Overall, miR-125b seems to be one of the best-characterized circulating AD ncRNAs, with recent evidence supporting its use as a theragnostic biomarker [72,245]. More research is needed to better clarify the role of other potential ncRNAs in early disease detection.
Table 2 relates the main findings on circulatory ncRNA dysregulation in AD [17,210,211,212,213,218,220,222,223,224,225,226,227,231,232,233,234,235,237,238,242,246,247,248,249,250,251,252,253,254,255,256,257,258,259].
Table 2.
Circulating ncRNAs as AD biomarkers.
| Ref | Study Cohort | Plasma/ Serum/Blood |
Upregulated | Downregulated | Cohort of ncRNA Variation | Method |
|---|---|---|---|---|---|---|
| Dakterzada et al., 2021 [252] | Discovery cohort (n = 19, mild AD) and validation cohort (n = 53, mild AD) | Plasma | miR-342-5p | Severe AD | RT–PCR | |
| Poursaei et al., 2022 [213] | 50 AD and 50 HC | Plasma | hsa-let7d-5p hsa-let7g-5p |
AD | RT–PCR | |
| Galimberti et al., 2014 [232] | 22 AD, 18 NINDCs, 8 NIDCs, and 10 FTD | Serum | miR-125b miR-23a miR-26b |
AD | RT–PCR | |
| Kumar et al., 2017 [235] | Discovery cohort (10 AD, 6 MCI, and 14 HC) and validation cohort (11 AD, 20 MCI, and 18 HC) | Serum | miR-455-3p miR-4668-5p |
AD | RT–PCR | |
| Yilmaz et al., 2016 [223] | 172 AD and 109 HC | Whole blood | hsa-miR-9-5p hsa-miR-106a-5p hsa-miR-106b-5p hsa-miR-107 |
AD | RT–PCR | |
| Wang et al., 2020 [244] | 120 AD, 120 PD, and 120 HC | Plasma | miR-107 | miR-103 | AD | RT–PCR |
| Barbagallo et al., 2020 [259] | 30 AD, 30 PD, 24 VD, 25 VP, and 30 HC | Serum | miR-22 miR-23a miR-29a miR-125b |
AD | RT–PCR | |
| Fotuhi et al., 2019 [258] | 45 AD and 36 HC | Whole plasma | lncRNA BACE1-AS | AD | RT–PCR | |
| Feng et al., 2018 [257] | 88 AD and 72 HC | Plasma | lncRNA BACE1 | AD | RT–PCR | |
| Yang et al., 2015 [222] | 30 AD and 30 HC | Blood | miR-29c | AD | RT–PCR | |
| Bhatnagar et al., 2014 [233] | 110 AD and 123 HC | Plasma | miR-34c | AD | RT–PCR | |
| Leidinger et al., 2013 [17] | 106 AD, 18 MCI, 16 CIS, 9 PD, 15 DEP, 15 BD, 14 SCHIZ, and 22 HC | Blood | hsa-miR-30d-5p | hsa-miR-144-5p | AD | NGS and RT–PCR |
| Zhu et al., 2015 [256] | 26 AD, 30 MCI, and 42 HC | Serum | miRNA-210 | AD | RT–PCR | |
| Kiko et al., 2014 [211] | Plasma | miR-34a miR-146a |
AD | RT–PCR | ||
| Xing et al., 2016 [226] | 30 AD and 30 HC | Blood | miR-206 | AD | RT–PCR | |
| Wu et al., 2020 [227] | 40 AD (amyloid positive) and 31 controls (amyloid negative) | Blood | miR-146b-5p miR-15b-5p |
AD | Small RNA sequencing | |
| Kumar et al., 2013 [210] | 11 AD, 9 MCI, and 20 HC | Plasma | hsa-miR-191-5p hsa-miR-15b-5p hsa-let-7d-5p hsa-let-7g-5p hsa-miR-142-3p |
AD | nCounter miRNA expression assay v1 and RT–PCR | |
| Geekiyanage et al., 2012 [220] | 7 AD and 7 HC | Serum | miR-137 miR-181c miR-9 miR-29a/b |
AD | RT–PCR | |
| Tan et al., 2014 [72] | 105 AD and 150 HC | Serum | miR-9 | miR-125b miR-181c |
AD | RT–PCR |
| Sørensen et al., 2016 [212] | 10 AD and 10 VD/FTD or LBD | Plasma | miR-590-5p miR-142-5p |
miR-194-5p | AD | RT–PCR |
| Ludwig et al., 2019 [225] | AD, MCI, HC, and ODN (total subjects 465) | Blood | miR-532-5p | AD | RT–PCR | |
| Liu et al., 2014 [247] | 32 MCI, 45 AD, and 50 HC | Serum | miR-384 | AD | RT–PCR | |
| Wang et al., 2019 [218] | 7 AD and 5 HC | Plasma | miR-200a-3p | AD | Microarray miRNA profile | |
| Liu et al., 2020 [224] | 50 AD, 20 VD, and 50 HC | Blood | miR-574-5p | hsa-circ-0003391 | AD | Microarray analysis |
| Hara et al., 2017 [234] | 27 AD and 18 HC | Serum | hsa-miR-501-3p hsa-let-7f-5p hsa-miR-26b-5p |
AD | RT–PCR | |
| Jia et al., 2016 [231] | 84 AD and 62 HC | Serum | miR-519 | miR-29, miR-125b, miR-223 |
AD | RT–PCR |
| Cosín-Tomás et al., 2017 [249] | HC, AD, PAD (n = 35 per group), and PD (n = 20) | Plasma | miR-34a-5p miR-545-3p |
AD | RT–PCR | |
| Nagaraj et al., 2017 [248] | 15 MCI, 20 AD, and 15 HC | Plasma | miR-483-5p miR-486-5p miR-200a-3p miR-142-3P |
miR-30b-5p | AD and MCI | RT–PCR |
| Dong et al., 2015 [242] | 127 AD, 30 MCI, and 30 VD | Serum | miR-93 miR-146a |
miR-31 miR-93 miR-143 miR-146a |
AD and MCI | Solexa sequencing and RT–PCR |
| Siedlecki-Wullich et al., 2019 [251] | 56 AD, 26 MCI, 38 HC, and 27 FTD | Plasma | miR-92a-3p miR-181c-5p miR-210-3p |
AD and MCI | RT–PCR | |
| Sabry et al., 2020 [246] | 40 MCI and AD, and 20 HC | Plasma | miRNA-483-5p | AD and MCI | RT–PCR | |
| Zhang et al., 2021 [254] | 75 MCI and 52 HC | Serum | hsa-let-7g-5p hsa-miR-107 hsa-miR-186-3p |
MCI | RT–PCR | |
| Shi et al., 2020 [253] | 71 aMCI and 69 HC | Serum | miR-34c | aMCI | RT–PCR | |
| He et al., 2021 [250] | Discovery cohort (n = 10), analysis cohort (n = 30), and validation cohort (n = 80) | Plasma | miR-1185-2-3p miR-1909-3p miR-22-5p miR-134-3p |
aMCI | Microarray sequencing | |
| Wang et al., 2015 [255] | 97 AD, 116 aMCI, and 81 HC | Plasma | miR-107 | aMCI | RT–PCR |
Abbreviations: AD, Alzheimer’s disease; aMCI, amnestic mild cognitive impairment; BD, bipolar disorder; CIS, clinically isolated syndrome; DEP, major depression; FTD, frontotemporal dementia; HC, healthy controls; INDCs, inflammatory neurological controls; LBD, Lewy body dementia; MoCA, Montreal Cognitive Assessment; NINDCs, non-inflammatory neurological controls; PAD, preclinical AD; PD, Parkinson’s disease; SCHIZ, schizophrenia; VD, vascular dementia; VP, vascular parkinsonism.
3.6.2. miRNAs—Early Diagnosis
Since early diagnosis in AD is of utmost importance, and different AD stages (mild, moderate, and severe) are reported to be characterized by a distinct set of dysregulated serum miRNAs, many studies have been carried out to differentiate early disease onset from late AD stage and/or healthy controls (Table 2) [206]. Regarding MCI, while reduced serum miR-31, miR-143, miR-93, and miR-146a have been found capable of discriminating between AD and controls, the last two were also shown to be upregulated in MCI versus healthy individuals [242]. Moreover, Sheinerman et al. reported that two plasma miRNA families, miR-132 and miR-134, are both independently capable of distinguishing between MCI and aged-matched controls, with no gender differences [260,261]. Two pairs of plasma miRNAs (hsa-miR-191, hsa-miR-101 and has-miR-103, has-miR-222) have also been shown to have high accuracy for detecting MCI [262]. Of note, enhanced serum concentration of miR-34c was correlated with the aMCI stage, similar to that observed in the plasma of AD patients [233,253]. Concerning the potentiality of using ncRNA to monitor disease onset and progression, while diminished levels of serum miR-384 and miR-210 have been reported to correlate with disease severity [247,256], plasma levels of miR-342-5p inversely correlated with cognitive manifestations at a 2-year follow-up in another study [252]. Furthermore, of the plasma miR-15b-5p, miR-142-3p, miR-34a-5p, and miR-545-3p reported to distinguish AD from controls, only the last two could discriminate preclinical AD from AD and healthy individuals. However, the lack of corroboration regarding these data in the validation cohort underlines the need for deeper investigation [249].
Combinations of other plasma biomarkers have also been proposed. Nagaraj et al. reported that, among 15 miRNAs prioritized from a wider screening, six plasma miRNAs were able to detect AD at an early stage compared to healthy individuals [248]. A prodromal AD biosignature consisting of five plasma miRNAs (miR-1185-2-3p, miR-1909-3p, miR-22-5p, miR-134-3p, and miR-107) was instead proposed by He et al. based on microarray sequencing performed on three different datasets (discovery, analysis, and validation cohorts) [250]. Finally, a combinatorial signature comprising diet, gut microbiota, and serum miRNA biomarkers has also been reported to distinguish between MCI and controls, suggesting that joining different biomarkers in the same diagnostic test could reduce unpredictable variabilities [254].
Despite the promising results, clearly differentiating MCI from AD remains a challenge. Indeed, even though plasma miR-92a-3p, miR-181c-5p, and miR-210-3p were all reported to be higher in MCI than AD, they are significantly increased in both MCI and AD when compared to controls [251]. Similarly, although plasma miR-483-5p was found to be elevated in MCI compared to AD, both conditions presented a significant rise in this miRNA in plasma compared to healthy individuals, thus making it difficult to distinguish between the two stages [246].
3.6.3. miRNAs—Exosomes
Exosomes are highly stable small membrane-enclosed vesicles (diameter of 30–100 nm) originating from the cellular biosynthetic secretory pathway, and are used to transport RNAs, proteins, and lipids in circulation [263,264,265]. Being able to resist the activity of ribonucleases, they offer protection to their cargoes, thus representing a source of biomarkers less susceptible to interference compared to cell-free blood miRNAs [263]. Due to these characteristics, CNS-derived blood exosomes have already been considered a diagnostic tool in different neurodegenerative diseases, such as AD, PD, and ALS [263,264,265,266]. In AD, both plasma- and serum-derived exosomes have been investigated as a source of novel AD biomarkers, sometimes obtaining discordant results compared with the respective cell-free miRNA levels [259]. Concerning plasma, in 2015, Lugli et al. reported a panel of 21 exosomal-derived miRNAs differentially expressed between AD and controls, among which seven were highly discriminating, reaching an accuracy of 83–85% for AD detection [267]. Moreover, while downregulation of miR-212 and miR-132-3p in plasma extracellular vesicles was reported to discriminate between AD and controls, the latter was not found capable of differentiating MCI from healthy individuals [268]. Diminished levels of circulating ex-miR-342-3p, ex-miR-125a-5p, ex-miR-125b-5p, and ex-miR-451a were also detected in AD patients [269]. However, it should be noted that a similar deregulation in exosomal miRNAs has also been reported during normal aging, thus evidencing the importance of age-matched controls [269]. Results from another exploratory study conducted in 2019 showed that reduced levels of hsa-miR-23a-3p, hsa-miR-126-3p, hsa-let-7i-5p, and hsa-miR-151a-3p could efficiently distinguish AD from healthy subjects, while hsa-miR-451a and hsa-miR-21-5p performed better in differentially diagnosing between AD and Lewy body dementia [270]. Regarding other pathologies, different signatures were also established from large and small extracellular vesicle-derived miRNAs to distinguish between AD, PD, ALS, and FTD, thus underscoring the potentiality of these biomarkers [271].
Concerning serum, there is evidence of machine learning methods, based on random forests and accounting for neuroimaging and clinical data, that are capable of predicting AD upon defining the optimal miRNA signature [272]. In addition, while ex-miR-223 was found to be downregulated in AD, upregulation of ex-miR-135a was related to both MCI and AD in a study comprising 131 MCI, 198 AD, and 30 healthy controls [273,274]. However, other results show that, while rising serum ex-miR-135a is a feature of AD patients, a combination of ex-miR135a, ex-miR-384, and ex-miR-193b seems to better define MCI individuals, thus evidencing the need for further analysis [275].
3.6.4. miRNAs—Limitations
Although miRNAs present real promise for future biomarker discovery, several limitations remain to be addressed. First, although many miRNAs are significantly dysregulated, the lack of reproducibility between studies prevents consistency, with only one or a few miRNAs being replicated by independent groups [276]. In addition, gender and age differences should also be considered, as some miRNA levels vary in a sex-dependent manner [243]. Secondly, since dysregulations in similar miRNAs, such as miR-29, miR-26, and let-7, have also been reported in PD [277], and a plethora of ncRNAs have been implicated in the pathogenesis of several neurodegenerative diseases [278], careful analysis of their ability to differentially diagnose these conditions is required. Concerning technologies, it should also be emphasized that high throughput methods can analyze hundreds of miRNAs at the same time, but can have low sensitivity and high variability depending on the extraction method and on the normalization applied [208]. In this respect, the analysis of single nucleotide polymorphism variation in the miRNA biogenesis pathway and microfluidic-based quantitative PCR platforms has recently been proposed to partially solve these issues [279,280].
Regarding exosomes, although the methods used for their isolation and purification are expected to be reproducible, several procedural differences among distinct laboratories need to be revised to reach a consensus protocol and reduce background noise [281]. Overall, standardized measurement techniques, reproducible and universal protocols for sample collection and purification, large cohorts, clear criteria for the classification and design of the study, and standard statistical analysis with defined cut-offs are of utmost importance, and should be considered when designing new investigations [265,276].
3.6.5. Long Non-Coding RNAs
Long non-coding RNAs are a subset of non-coding RNAs (ncRNAs) characterized by long transcripts (>200 nucleotides) devoid of protein-coding function [282]. They have been implicated in the regulation of several biological processes, such as proliferation, transcriptional and post-transcriptional regulation, malignancies, and apoptosis [282,283,284]. Concerning AD, recent insights have shown the involvement of ncRNAs and lncRNAs in disease pathogenesis, therefore creating interest around the possibility of their use as biomarkers (Table 2) [285,286,287]. Although there is currently less evidence than for miRNAs, altered brain expression of the RNA polymerase III-dependent ncRNA (i.e., NDM29, of the β-site amyloid precursor protein cleaving enzyme-1-antisense lncRNA (i.e., BACE1-AS), and of the intronic ncRNA (i.e., S1A) is known to be correlated to Aβ formation [287,288,289]. Moreover, increased cerebral tissue transcription of two lncRNAs, linc00507 and 17A, has been implicated in tau phosphorylation and GABA B alternative splicing, respectively [290,291,292]. Concerning possible circulating biomarkers, Feng et al. report evidence for significant plasma lncRNA BACE1 upregulation in 88 AD patients when compared to 72 controls [257]. However, no significant differences were found regarding plasma lncRNA 17A, S1A, or BC200, despite previous evidence showing alterations in some of these lncRNAs in the brain [257,289,291]. These data were also confirmed in 2020 by Wang et al., who reported a consistent rise in plasma exosomal lncRNA BACE1-AS in a study on 72 AD and 62 healthy individuals, reaching 87.5% sensitivity and 61.3% specificity [293]. However, another study comparing 45 AD and 36 control subjects reported low BACE1-AS in the pre-AD stage, while these levels dramatically increased in the full-AD condition, thus excluding the possibility of an early diagnosis, and showing the importance of distinguishing the disease stage when designing a screening study [258]. Of note, these differences were only observed when considering the whole plasma samples, while no changes were found in free plasma and plasma-derived exosomes alone [258].
Overall, despite these promising data, more research is certainly needed before lncRNA-based AD diagnostics reach the clinic. Moreover, combinations between lncRNAs and other circulating biomarkers, as well as morphological and physical features of brain tissues, should be also considered to strengthen the results, as already suggested [293].
3.7. Lipids
Lipids constitute around 50% of dry brain weight and they exert key roles in basic brain functions, such as blood–brain barrier (BBB) integrity, myelination, vesicle trafficking, APP processing, and neuroinflammation [294,295]. Since alterations in these processes have been implicated in the pathophysiology of several brain disorders, including AD, a proposal has been made for their use as markers [296]. However, standard imaging methods (i.e., PET or MRI) are not suitable for lipid detection, and brain biopsies remain inapplicable, thus leaving the CSF as a potential source of biomarkers [297,298]. More recently, as changes in the circulating lipids seem to mirror the dysregulation of their profile in the brain, blood has become a viable alternative to invasive CSF sampling [299,300,301]. At the same time, the emergence of cutting-edge techniques, such as peripheral lipidomics, triple quadrupole mass spectrometry, and isobaric tagging methods, have allowed the establishment of lipid signatures potentially associated with AD, even at the prodromal and preclinical stages [302,303,304]. Lipid peroxidation, caused by oxidative stress, is one of the most-studied markers of disease, and various molecules have been proposed as potential circulating biomarkers (See Section 3.5 Oxidative Stress) [166,167,182,305,306,307]. Concerning fatty acids, dysregulation in their profile is linked to increased risk of dementia, with hexacosanoid acid (C26:0) being the most upregulated both in plasma and red blood cells of AD patients [303,308,309]. A correlation between the primary fatty acid amide level in plasma and Aβ pathology, hippocampal volume, and cognitive score was also reported [310]. Instead, opposite trends were observed regarding the serum content of two saturated fatty acids (palmitic and myristic acids) and three unsaturated fatty acids (oleic, linolenic, and docosahexaenoic acids), with docosahexaenoic acid being the most significantly decreased in AD compared to controls [311,312].
Phospholipids and sphingolipids have also been proposed as potential AD biomarkers [313,314]. A study conducted by Kim et al., applying mass spectrometry and ultra-performance liquid chromatography to plasma samples from 205 AD patients and 207 healthy subjects, revealed that enhanced levels of circulating ceramides (Cer16:0, Cer18:0 and Cer24:1) and phosphatidylcholines (PC36:5 and PC38:6) are associated to cognitive decline, with PC36:5 mostly correlating with the younger AD cohort [315]. Diminished concentrations of phosphatidylcholine (PC) and phosphatidylethanolamine (PE) in the serum of AD patients were also reported by other studies [316]. In particular, while decreased serum PE and enhanced lysoPE were found to predict the rate of progression from MCI to AD [314], the ratio between plasma PC aa C34:4 (a phosphatidylcholine with diacyl residue C34:4) and lysoPC a C18:2 (a lysophosphatidylcholine with acyl residue C18:2) was able to differentially diagnose MCI, AD, and healthy individuals, reaching up to 85% accuracy [317]. Interestingly, in 2014, a panel of ten peripheral-blood lipids (PCaaC36:6, PCaaC38:0, PCaaC38:6, PCaeC40:6, lysoPCaC18:2, C3, C16:1-OH, PCaaC40:2, PCaaC40:1, and PCaaC40:6) was found to predict AD or MCI onset 2–3 years in advance, with over 90% accuracy; however, these results remain to be validated by independent studies [318]. More recently, while a combination of plasma PCs (PC16:0/20:5, PC16:0/22:6, and PC18:0/22:6) has been linked to poor cognitive scores [319], a group of three serum lipid metabolites (SM (OH) C24:1, SM C24:0, and PC ae C44:3) has been demonstrated as being capable of distinguishing between early AD and MCI subjects, though no control cohort was included in the study [320].
In addition to its important role in atherosclerosis and cardiovascular disease, cholesterol has been implicated in the pathophysiology of AD and MCI [321]. For instance, a decreased plasma desmosterol/cholesterol ratio has been found to correlate with MMSE performance and the rapidity of AD progression [322]. Moreover, results from a meta-analysis comprising a total of 6127 healthy individuals and 3423 AD patients showed an inverse correlation between serum LDL, and total cholesterol levels, versus cognitive performance [323]. These data were later confirmed by further analysis [321,324]. Alternatively, among those with MCI, while similar trends were observed concerning TC, no significant differences in serum LDL were retrieved compared to healthy controls [321]. However, reproducibility issues remain, particularly concerning the criteria used for patient distribution into subgroups. For example, evidence from a cross-sectional study conducted on 1889 Chinese participants reported an inverse U-shaped association between total cholesterol levels and cognitive score only in the subgroup of patients characterized by normal levels of homocysteine [325], while Huang et al. could reach statistical significance only in late-life obese APOE-ε 4 non-carriers [326]. Furthermore, similar to the U-shaped relationship observed for total cholesterol, higher plasma HDL levels have been found in AD patients versus controls in two prospective population-based investigations [327], while very low circulating HDL content was reported to be associated with cognitive decline [328]. Given the central role of apolipoprotein E in the pathogenesis of AD [329], biomarkers based on its alteration should be investigated. In this respect, while diminished plasma ApoE was reported to predict AD development, ApoA1, ApoH, and ApoJ were found altered in MCI subjects compared to healthy controls [301,311]. Finally, serum 24-hydroxycholesterol, a marker of cholesterol metabolism in the brain [330], is decreased in AD compared to controls, with its plasma esters being lower in MCI individuals progressing towards AD than in non-progressing individuals [299,300]. However, similar findings have also been described in the context of PD, thus questioning the specificity of this biomarker [331].
Overall, these results show how promising lipid biomarkers are, especially when used in combination, but reproducibility and validation is needed before reaching any clinical application.
3.8. Vitamins
Vitamins are essential constituents of our diet, and are involved in many physiological and pathological mechanisms [332,333]. Hypovitaminosis is implicated in the pathogenesis of various disorders [334,335], and there is increasing evidence that vitamins also play a key role in neurodegenerative diseases, leading to hypotheses concerning their use as disease biomarkers [336,337]. Regarding AD, although preliminary data are certainly encouraging, and the role of vitamins as biomarkers for AD has been widely investigated in literature (Table 3) [188,297,338,339,340,341,342,343,344,345,346,347,348,349,350,351], some limitations in terms of consistency, reproducibility, and specificity remain to be solved, and further research is needed before hypothesizing a clinical application.
Table 3.
Vitamin-based biomarkers for AD.
| Ref | Study Cohort and Design | Analysis Performed | Results | Cohort of Variation | Biomarker/s Proposed |
|---|---|---|---|---|---|
| Glasø et al., 2004 [338] | AD (n = 20), HC (n = 18) | Analysis on serum and blood | ↓ Blood thiamine ↓ Blood TDP |
AD | Vit B1 |
| dos Santos et al., 2020 [344] | AD (n = 60), HC (n = 60) | Complete blood count and Vit B12 levels assessment | ↓ Vit B12 | AD | Vit B12 |
| Lanyau-Domínguez et al., 2020 [351] | AD (n = 43), MCI (n = 131), HC (n = 250) | Spectrophotometry and high-resolution liquid chromatography on plasma |
↓ Vit A and vit C | AD | Combination of vit A and vit C |
| Gold et al., 1995 [339] | AD (n = 17), n-AD (n = 17) | Microbiologic assay on plasma and RBC | ↓ Plasma thiamine -No correlation between RBC thiamine and AD |
AD | Vit B1 |
| Wang et al., 2018 [340] | AD (n = 90), HC (n = 90) | HPLC on whole blood samples | ↓ TDP | Female AD vs. male AD | TDP as protective factor for AD |
| D’Cunha et al., 2019 [341] | AD (n = 63), HC (n = 63) | ELISA kit to determine APOE4 on serum | ↓ Vit B2 dietary intake | AD without APOE4 genotype | Vit B2 and folate |
| Dursun et al., 2016 [346] | EOAD (n = 22), LOAD (n = 72), MCI (n = 32), HC (n = 70) | Chemiluminescent immunoassay on serum | ↓ 25(OH)D | LOAD ApoEε4 non-carriers | Vit D (in ApoEε4 allele non-carriers) |
| Ouma et al., 2018 [347] | AD (mild: n = 41, moderate: n = 35, severe: n = 32), MCI (n = 61), HC (n = 61) | Competitive radioimmunoassay on serum | ↓ 25(OH)D3 | MCI and AD | 25(OH)D3 |
| Blasko et al., 2021 [297] | Non-converting HC (n = 13), HC converting to MCI (n = 6), HC converting to AD (n = 6), MCI converting to AD (n = 8), MCI converting to HC (n = 8) and stable MCI (n = 7) | Competitive immunoassay on serum | ↓ Folate | MCI–AD converting pt | Folate |
| An et al., 2019 [342] | 2533 participants followed for an average of 2.3 y | Immunoassay on serum | ↑ Folate, vit B6, and vit B12 intake | Pt with better cognitive reserve | B vitamins and folate |
| Murdaca et al., 2021 [343] | AD (n = 108) | Machine learning approach to correlate blood vitamin levels with MMSE score | ↓ Vit D and folic acid | Pt with lower MMSE score | Combination of vit D and folic acid |
| Baldacci et al., 2020 [345] | SMC (n = 316) | Aβ-PET (n = 316, at baseline and 2 y follow-up). Lumbar puncture (n = 40 at baseline). Immunoassay on plasma (n = 79, at baseline, 1 y and 3 y follow-up) |
↓ Vit B12 | Pt with higher plasma total Tau levels | Vit B12 |
| de Leeuw et al., 2020 [188] | SCD (n = 149), MCI (n = 150). | Analysis on serum and plasma | ↑ 1,25(OH)2D3 | SCD | 1,25(OH)2D3 |
| Hooshmand et al., 2014 [348] | AD (n = 18), MCI (n = 28), SCI (n = 29) | Immunoassay on plasma, ELISA on CSF, MRI scans | ↑ 25(OH)D3
↑ 25(OH)D3 |
-Pt with higher CSF Aβ1–42 levels -Pt with greater brain volumes |
Vit D |
| Al-Amin et al., 2019 [349] | MCI (n = 54) | Analysis on serum MRtrix and NBS on MRI scans |
↓ 25(OH)D3 | Pt with reduction in total hippocampal volume and connection deficit | Vit D |
| Raszewski et al., 2015 [350] | AD (n = 33), n-AD (n = 31) | HPLC on serum | ↓ Vit A and vit E | n-AD | Combination of vit A and vit E |
Abbreviations: AD, Alzheimer’s disease; CSF, cerebrospinal fluid; ELISA, enzyme-linked immunosorbent assay; EOAD, early-onset AD; HC, healthy controls; HPLC, high-performance liquid chromatography; LOAD, late-onset AD; MCI, mild cognitive impairment; n-AD, non-Alzheimer’s dementia; NBS, network-based statistic; pt, patients; RBC, red blood cells; SCD, subjective cognitive decline; SCI, subjective cognitive impairment; SMC, subjective memory complaints; TDP, thiamine diphosphate; y, years; ↓, decrease; ↑, increase.
3.8.1. Water-Soluble Vitamins: Vitamins B and C
Several studies have shown the importance of B vitamins for proper physiological and neurological functioning [334]. Concerning AD, some associations were found between low plasma levels of vitamin B1 (thiamine) and AD development [352], with high levels of its active form (thiamine diphosphate) being reported to be a protective factor against neurodegeneration [340]. Similarly, insufficient dietary intake of vitamin B2 (riboflavin) was associated with an augmented incidence rate of AD in patients not presenting the APOE-ε 4 genotype [341]. Interestingly, more significant changes in both vitamin B1 and B2 levels have been described in AD women compared to men, thus showing the importance of considering gender differences when designing a large-cohort study [340,341].
High concentrations of homocysteine in the blood (hyperhomocysteinemia) have been linked to cardiovascular and neurological disorders [353]. In this respect, vitamin B6 supplementation was observed to lower homocysteine levels in Aβ1–42-treated PC12 cells and in the brains of APP/PS1 mice, thus exerting a positive impact on an established biomarker for AD [164]. However, while a study involving 2533 participants showed a link between reduced cognitive decline and appropriate intake of vitamins B9 (folate), B6, and B12 [342], no significant differences were reported in another study comprising 202 AD patients when accounting for age, gender, education, and other covariates [354]. Nonetheless, a small study conducted on 48 patients followed up after 7–9 years showed a diminished amount of folate in the blood of MCI patients converting to AD [297]. When using machine learning approaches, the concomitant findings of reduced blood levels of folic acid and vitamin D were confirmed to be predictive of worse MMSE scores (that is, a more pronounced cognitive impairment), thus suggesting a possible link with disease severity [343]. Moreover, diminished levels of circulating vitamin B12 in patients with AD were reported in another recent case-control study [344]. Interestingly, vitamin B12 and total tau plasma levels were inversely related in a longitudinal study, thus confirming the correlation between vitamins and AD pathogenesis [345]. Again, this association was stronger for women, who might have a higher probability than males to convert to AD [355,356]. Despite these promising data, however, the role of vitamin B12 as a possible biomarker is far from being established. A recent study analyzing data from the ADNI cohort and the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) has shown contradictory results regarding the impact of vitamin B12 on neurological functions, probably depending on the clinical condition [357].
Similar to B vitamins, some evidence suggests that blood concentrations of vitamin C are decreased in AD patients compared to controls, thus representing another possible disease biomarker [351].
3.8.2. Liposoluble Vitamins: Vitamins D, A, and E
Over time, low concentrations of vitamin D were observed to be linked to neurodegeneration, and to a greater likelihood of cognitive impairment and dementia [346,358,359]. In particular, low 25(OH)D3, unlike 1,25(OH)2D3, was demonstrated to be remarkably associated with MCI and AD [188,347]. Similarly, it has been observed that 25(OH)D3 in the plasma might correlate with Aβ1–42 in CSF and cognitive status, thus linking blood and CSF biomarkers [348,360]. Furthermore, an augmented plasma concentration of 25(OH)D3 was linked to more pronounced cerebral volumes, especially white matter and medial temporal lobe structures, while decreased 25(OH)D3 in serum was associated with deficits in brain connectivity and smaller hippocampal sizes in MCI subjects [348,349,361]. Interestingly, if both genotype and vitamin D levels were considered in the same study, it has been found that, among late-onset AD patients, 25(OH)D3 insufficiency was observed only in ApoE-ɛ 4 non-carriers, thus suggesting the use of vitamin D as a possible disease biomarker only in this category of patients [346,362]. Nonetheless, serum levels of vitamin D remain a controversial candidate as an AD biomarker; indeed, unstandardized data and inconsistency among analytical methods still present an issue, and further evidence is needed to clarify the role of 25(OH)D3 serum levels in MCI and AD before considering its clinical application [363].
Regarding vitamins A and E, while data from a cross-sectional study reported decreased blood concentrations of vitamin A in AD participants [351], in another study, serum deficiency of both vitamin A and E was correlated with cognitive impairment in patients with dementia other than Alzheimer’s [350], thus emphasizing that additional studies are required to better assess both the specificity and consistency of these potential biomarkers.
3.9. Gut Microbiota
Gut microbiota alterations have been reported in several different conditions, including neurodegeneration and AD [364,365,366,367,368,369,370,371,372,373,374]. Overall, AD patients seem to be characterized by dysbiosis, a condition of bacterial imbalance with a predominance of pro-inflammatory taxa and a decrease in beneficial anti-inflammatory species [375,376,377,378]. Dysbiosis is normally accompanied by an altered gut immune response, favoring epithelial cell leakage, increased bacterial translocation, and enhanced systemic inflammation [379]. Though data are still inconclusive, gut bacteria family and genus shifts might represent promising tools as novel biomarkers for the diagnosis and progression of AD, and the restoration of gut microbiota balance a potential therapeutic approach [380]. Several bacterial metabolites are already used as fecal biomarkers to characterize dysbiosis in AD patients; for instance, higher levels of trimethylamine N-oxide (a microbial metabolite implicated in immune response activation), enhanced oxidative stress, and intestinal barrier dysfunction have been identified in MCI and AD patients compared to cognitively unimpaired individuals [381,382].
Some microbiota-derived molecules present in the systemic circulation have also been considered as potential blood biomarkers for AD. For example, increased levels of circulating LPS, the main component of the outer membrane in Gram-negative bacteria, have been identified in MCI and AD patients [383]. Indeed, LPS can induce the activation of pro-inflammatory pathways, promoting BBB disruption and neuroinflammation [384,385]. Furthermore, short-chain fatty acids (SCFAs), usually beneficial for their anti-inflammatory and antioxidant properties in the intestinal lumen, can be released in the blood upon dysbiosis, reach the cerebral circulation, and cause potentially harmful effects on brain function [383,386,387]. Moreover, given that gut dysbiosis may induce neuroinflammation, other inflammatory factors, such as IL-6 and INF-γ, have also been considered as potential AD biomarkers [386].
Although blood biomarkers are non-invasive and rapid diagnostic tools, and the preliminary results appear promising, several limitations still exist, making microbiota-derived blood biomarkers inconclusive, and not yet effective tools for AD diagnosis. Indeed, large-cohort studies are needed, ideally considering a combination of several biomarkers, which include bacterial composition and microbiota-derived metabolites. Additionally, since gut microbiome composition and function are affected by lifestyle, age, gender, dietary intake, and geography, it is important to at least limit all the possible confounding factors that can influence study results [388,389]. Overall, the increasing research on the role of gut microbiota in disease pathogenesis and progression could also prove useful in the discovery of novel minimally invasive biomarkers, although more studies are still required to better address this point.
4. Discussion
As confirmed by numerous scientific evidence, the neuropathology associated with AD is already traceable many years before clinical onset. For this reason, the study 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 [390,391]. 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 [392,393]. 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 [394]. 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 [395] and transcriptomics [396] 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 [397]. 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 narrative review, we 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 paper, 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 our extensive review, 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 [398]. This should make this review 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 [399,400,401,402,403,404,405]. 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 [406]. 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 study [345].
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 [407,408,409]. 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 [410,411].
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 [412,413]. In this respect, ML can support the diagnostic investigation of MCI progression from the metabolic signature pattern [414].
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 [302,303,304].
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.
5. Conclusions
Despite the vast amount of data on novel AD biomarkers, medicine is still getting to grips with the full extent of bioanalytical and imaging tools to diagnose AD at its earliest stage, and to differentiate AD from other cognitive impairments and neurodegenerative conditions. This report attempts to provide a thorough review of the many different kinds of biomarkers for AD that are being studied, and that shed light on the fundamental and diverse pathological mechanisms of AD. These insights from scientific bench studies can hopefully be translated into clinical diagnostics and medical therapeutics for this devastating disease.
Author Contributions
Conceptualization, A.V., G.R. and A.P.; writing—original draft preparation, A.V., S.P., V.F., E.P., A.C., V.G.P. and G.S.; writing—review and editing, A.V., S.C. and A.P.; supervision, A.V., G.R., C.E. and A.P. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
Footnotes
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Hodson R. Alzheimer’s Disease. Nature. 2018;559:S1. doi: 10.1038/d41586-018-05717-6. [DOI] [PubMed] [Google Scholar]
- 2.Mattson M.P. Pathways towards and away from Alzheimer’s Disease. Nature. 2004;430:631–639. doi: 10.1038/nature02621. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Bachurin S.O., Bovina E.V., Ustyugov A.A. Drugs in Clinical Trials for Alzheimer’s Disease: The Major Trends. Med. Res. Rev. 2017;37:1186–1225. doi: 10.1002/med.21434. [DOI] [PubMed] [Google Scholar]
- 4.Arnerić S.P., Batrla-Utermann R., Beckett L., Bittner T., Blennow K., Carter L., Dean R., Engelborghs S., Genius J., Gordon M.F., et al. Cerebrospinal Fluid Biomarkers for Alzheimer’s Disease: A View of the Regulatory Science Qualification Landscape from the Coalition Against Major Diseases CSF Biomarker Team. J. Alzheimer’s Dis. 2016;55:19–35. doi: 10.3233/JAD-160573. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Mattsson N., Carrillo M.C., Dean R.A., Devous M.D., Nikolcheva T., Pesini P., Salter H., Potter W.Z., Sperling R.S., Bateman R.J., et al. Revolutionizing Alzheimer’s Disease and Clinical Trials through Biomarkers. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 2015;1:412–419. doi: 10.1016/j.dadm.2015.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Schneider L.S., Mangialasche F., Andreasen N., Feldman H., Giacobini E., Jones R., Mantua V., Mecocci P., Pani L., Winblad B., et al. Clinical Trials and Late-Stage Drug Development for Alzheimer’s Disease: An Appraisal from 1984 to 2014. J. Intern. Med. 2014;275:251–283. doi: 10.1111/joim.12191. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Galasko D.R., Grill J.D., Lingler J.H., Heidebrink J.L. A Blood Test for Alzheimer’s Disease: It’s about Time or Not Ready for Prime Time? J. Alzheimer’s Dis. 2022. in press . [DOI] [PubMed]
- 8.Molinuevo J.L., Ayton S., Batrla R., Bednar M.M., Bittner T., Cummings J., Fagan A.M., Hampel H., Mielke M.M., Mikulskis A., et al. Current State of Alzheimer’s Fluid Biomarkers. Acta Neuropathol. 2018;136:821–853. doi: 10.1007/s00401-018-1932-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Burnham S.C., Rowe C.C., Baker D., Bush A.I., Doecke J.D., Faux N.G., Laws S.M., Martins R.N., Maruff P., Macaulay S.L., et al. Predicting Alzheimer Disease from a Blood-Based Biomarker Profile. Neurology. 2016;87:1093–1101. doi: 10.1212/WNL.0000000000003094. [DOI] [PubMed] [Google Scholar]
- 10.García S., Amor-Gutiérrez O., Palomares-Albarrán M., Toyos-Rodríguez C., Cuetos F., Martínez C., Costa-García A., Fernández-Sánchez M.T., de la Escosura-Muñiz A., Novelli A. Unfolded P53 as a Marker of Oxidative Stress in Mild Cognitive Impairment, Alzheimer’s and Parkinson’s Disease. Curr. Alzheimer Res. 2021;18:695–700. doi: 10.2174/1567205018666211117101216. [DOI] [PubMed] [Google Scholar]
- 11.Hampel H., O’Bryant S.E., Molinuevo J.L., Zetterberg H., Masters C.L., Lista S., Kiddle S.J., Batrla R., Blennow K. Blood-Based Biomarkers for Alzheimer Disease: Mapping the Road to the Clinic. Nat. Rev. Neurol. 2018;14:639–652. doi: 10.1038/s41582-018-0079-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hampel H., O’Bryant S.E., Castrillo J.I., Ritchie C., Rojkova K., Broich K., Benda N., Nisticò R., Frank R.A., Dubois B., et al. Precision medicine—The Golden Gate for Detection, Treatment and Prevention of Alzheimer’s Disease. J. Prev. Alzheimer’s Dis. 2016;3:243. doi: 10.14283/jpad.2016.112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Hampel H., O’Bryant S.E., Durrleman S., Younesi E., Rojkova K., Escott-Price V., Corvol J.-C., Broich K., Dubois B., Lista S. A Precision Medicine Initiative for Alzheimer’s Disease: The Road Ahead to Biomarker-Guided Integrative Disease Modeling. Climacteric. 2017;20:107–118. doi: 10.1080/13697137.2017.1287866. [DOI] [PubMed] [Google Scholar]
- 14.Hampel H., Lista S., Teipel S.J., Garaci F., Nisticò R., Blennow K., Zetterberg H., Bertram L., Duyckaerts C., Bakardjian H., et al. Perspective on Future Role of Biological Markers in Clinical Therapy Trials of Alzheimer’s Disease: A Long-Range Point of View beyond 2020. Biochem. Pharmacol. 2014;88:426–449. doi: 10.1016/j.bcp.2013.11.009. [DOI] [PubMed] [Google Scholar]
- 15.Blennow K., Zetterberg H. Biomarkers for Alzheimer’s Disease: Current Status and Prospects for the Future. J. Intern. Med. 2018;284:643–663. doi: 10.1111/joim.12816. [DOI] [PubMed] [Google Scholar]
- 16.Mahaman Y.A.R., Embaye K.S., Huang F., Li L., Zhu F., Wang J.-Z., Liu R., Feng J., Wang X. Biomarkers Used in Alzheimer’s Disease Diagnosis, Treatment, and Prevention. Ageing Res. Rev. 2022;74:101544. doi: 10.1016/j.arr.2021.101544. [DOI] [PubMed] [Google Scholar]
- 17.Leidinger P., Backes C., Deutscher S., Schmitt K., Mueller S.C., Frese K., Haas J., Ruprecht K., Paul F., Stähler C., et al. A Blood Based 12-MiRNA Signature of Alzheimer Disease Patients. Genome Biol. 2013;14:R78. doi: 10.1186/gb-2013-14-7-r78. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Olazarán J., Gil-de-Gómez L., Rodríguez-Martín A., Valentí-Soler M., Frades-Payo B., Marín-Muñoz J., Antúnez C., Frank-García A., Acedo-Jiménez C., Morlán-Gracia L., et al. A Blood-Based, 7-Metabolite Signature for the Early Diagnosis of Alzheimer’s Disease. J. Alzheimer’s Dis. 2015;45:1157–1173. doi: 10.3233/JAD-142925. [DOI] [PubMed] [Google Scholar]
- 19.Yun G., Kim H.J., Kim H.-G., Lee K.M., Hong I.K., Kim S.H., Rhee H.Y., Jahng G.-H., Yoon S.S., Park K.-C., et al. Association Between Plasma Amyloid-β and Neuropsychological Performance in Patients with Cognitive Decline. Front. Aging Neurosci. 2021;13:736937. doi: 10.3389/fnagi.2021.736937. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Shahpasand-Kroner H., Klafki H.-W., Bauer C., Schuchhardt J., Hüttenrauch M., Stazi M., Bouter C., Wirths O., Vogelgsang J., Wiltfang J. A Two-Step Immunoassay for the Simultaneous Assessment of Aβ38, Aβ40 and Aβ42 in Human Blood Plasma Supports the Aβ42/Aβ40 Ratio as a Promising Biomarker Candidate of Alzheimer’s Disease. Alzheimer’s Res. Ther. 2018;10:121. doi: 10.1186/s13195-018-0448-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Chen T.-B., Lin K.-J., Lin S.-Y., Lee Y.-J., Lin Y.-C., Wang C.-Y., Chen J.-P., Wang P.-N. Prediction of Cerebral Amyloid Pathology Based on Plasma Amyloid and Tau Related Markers. Front. Neurol. 2021;12:619388. doi: 10.3389/fneur.2021.619388. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Rembach A., Faux N.G., Watt A.D., Pertile K.K., Rumble R.L., Trounson B.O., Fowler C.J., Roberts B.R., Perez K.A., Li Q., et al. Changes in Plasma Amyloid Beta in a Longitudinal Study of Aging and Alzheimer’s Disease. Alzheimer’s Dement. 2014;10:53–61. doi: 10.1016/j.jalz.2012.12.006. [DOI] [PubMed] [Google Scholar]
- 23.Wang M.J., Yi S., Han J., Park S.Y., Jang J.-W., Chun I.K., Kim S.E., Lee B.S., Kim G.J., Yu J.S., et al. Oligomeric Forms of Amyloid-β Protein in Plasma as a Potential Blood-Based Biomarker for Alzheimer’s Disease. Alzheimer’s Res. Ther. 2017;9:98. doi: 10.1186/s13195-017-0324-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Lui J.K., Laws S.M., Li Q.-X., Villemagne V.L., Ames D., Brown B., Bush A.I., de Ruyck K., Dromey J., Ellis K.A., et al. Plasma Amyloid-β as a Biomarker in Alzheimer’s Disease: The AIBL Study of Aging. J. Alzheimer’s Dis. 2010;20:1233–1242. doi: 10.3233/JAD-2010-090249. [DOI] [PubMed] [Google Scholar]
- 25.Chong J.R., Ashton N.J., Karikari T.K., Tanaka T., Saridin F.N., Reilhac A., Robins E.G., Nai Y., Vrooman H., Hilal S., et al. Plasma P-tau181 to Aβ42 Ratio Is Associated with Brain Amyloid Burden and Hippocampal Atrophy in an Asian Cohort of Alzheimer’s Disease Patients with Concomitant Cerebrovascular Disease. Alzheimer’s Dement. 2021;17:1649–1662. doi: 10.1002/alz.12332. [DOI] [PubMed] [Google Scholar]
- 26.Chouraki V., Beiser A., Younkin L., Preis S.R., Weinstein G., Hansson O., Skoog I., Lambert J., Au R., Launer L., et al. Plasma Amyloid-β and Risk of Alzheimer’s Disease in the Framingham Heart Study. Alzheimer’s Dement. 2015;11:249. doi: 10.1016/j.jalz.2014.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Meng X., Li T., Wang X., Lv X., Sun Z., Zhang J., Su F., Kang S., Kim S., An S.S.A., et al. Association between Increased Levels of Amyloid-β Oligomers in Plasma and Episodic Memory Loss in Alzheimer’s Disease. Alzheimer’s Res. Ther. 2019;11:89. doi: 10.1186/s13195-019-0535-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Chatterjee P., Elmi M., Goozee K., Shah T., Sohrabi H.R., Dias C.B., Pedrini S., Shen K., Asih P.R., Dave P., et al. Ultrasensitive Detection of Plasma Amyloid-β as a Biomarker for Cognitively Normal Elderly Individuals at Risk of Alzheimer’s Disease. J. Alzheimer’s Dis. 2019;71:775–783. doi: 10.3233/JAD-190533. [DOI] [PubMed] [Google Scholar]
- 29.Jiao B., Liu H., Guo L., Liao X., Zhou Y., Weng L., Xiao X., Zhou L., Wang X., Jiang Y., et al. Performance of Plasma Amyloid β, Total Tau, and Neurofilament Light Chain in the Identification of Probable Alzheimer’s Disease in South China. Front. Aging Neurosci. 2021;13:749649. doi: 10.3389/fnagi.2021.749649. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Shi Y., Lu X., Zhang L., Shu H., Gu L., Wang Z., Gao L., Zhu J., Zhang H., Zhou D., et al. Potential Value of Plasma Amyloid-β, Total Tau, and Neurofilament Light for Identification of Early Alzheimer’s Disease. ACS Chem. Neurosci. 2019;10:3479–3485. doi: 10.1021/acschemneuro.9b00095. [DOI] [PubMed] [Google Scholar]
- 31.Stockmann J., Verberk I.M.W., Timmesfeld N., Denz R., Budde B., Lange-Leifhelm J., Scheltens P., van der Flier W.M., Nabers A., Teunissen C.E., et al. Amyloid-β Misfolding as a Plasma Biomarker Indicates Risk for Future Clinical Alzheimer’s Disease in Individuals with Subjective Cognitive Decline. Alzheimer’s Res. Ther. 2020;12:169. doi: 10.1186/s13195-020-00738-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Shen X., Li J., Wang H., Li H., Huang Y., Yang Y., Tan L., Dong Q., Yu J. Plasma Amyloid, Tau, and Neurodegeneration Biomarker Profiles Predict Alzheimer’s Disease Pathology and Clinical Progression in Older Adults without Dementia. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 2020;12:e12104. doi: 10.1002/dad2.12104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.de Wolf F., Ghanbari M., Licher S., McRae-McKee K., Gras L., Weverling G.J., Wermeling P., Sedaghat S., Ikram M.K., Waziry R., et al. Plasma Tau, Neurofilament Light Chain and Amyloid-β Levels and Risk of Dementia; a Population-Based Cohort Study. Brain. 2020;143:1220–1232. doi: 10.1093/brain/awaa054. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Lim Y.Y., Maruff P., Kaneko N., Doecke J., Fowler C., Villemagne V.L., Kato T., Rowe C.C., Arahata Y., Iwamoto S., et al. Plasma Amyloid-β Biomarker Associated with Cognitive Decline in Preclinical Alzheimer’s Disease. J. Alzheimer’s Dis. 2020;77:1057–1065. doi: 10.3233/JAD-200475. [DOI] [PubMed] [Google Scholar]
- 35.Nabers A., Perna L., Lange J., Mons U., Schartner J., Güldenhaupt J., Saum K., Janelidze S., Holleczek B., Rujescu D., et al. Amyloid Blood Biomarker Detects Alzheimer’s Disease. EMBO Mol. Med. 2018;10:e8763. doi: 10.15252/emmm.201708763. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Nakamura A., Kaneko N., Villemagne V.L., Kato T., Doecke J., Doré V., Fowler C., Li Q.-X., Martins R., Rowe C., et al. High Performance Plasma Amyloid-β Biomarkers for Alzheimer’s Disease. Nature. 2018;554:249–254. doi: 10.1038/nature25456. [DOI] [PubMed] [Google Scholar]
- 37.Villemagne V.L., Burnham S., Bourgeat P., Brown B., Ellis K.A., Salvado O., Szoeke C., Macaulay S.L., Martins R., Maruff P., et al. Amyloid β Deposition, Neurodegeneration, and Cognitive Decline in Sporadic Alzheimer’s Disease: A Prospective Cohort Study. Lancet Neurol. 2013;12:357–367. doi: 10.1016/S1474-4422(13)70044-9. [DOI] [PubMed] [Google Scholar]
- 38.Vos S.J., Xiong C., Visser P.J., Jasielec M.S., Hassenstab J., Grant E.A., Cairns N.J., Morris J.C., Holtzman D.M., Fagan A.M. Preclinical Alzheimer’s Disease and Its Outcome: A Longitudinal Cohort Study. Lancet Neurol. 2013;12:957–965. doi: 10.1016/S1474-4422(13)70194-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Janelidze S., Stomrud E., Palmqvist S., Zetterberg H., van Westen D., Jeromin A., Song L., Hanlon D., Tan Hehir C.A., Baker D., et al. Plasma β-Amyloid in Alzheimer’s Disease and Vascular Disease. Sci. Rep. 2016;6:26801. doi: 10.1038/srep26801. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Ashton N.J., Schöll M., Heurling K., Gkanatsiou E., Portelius E., Höglund K., Brinkmalm G., Hye A., Blennow K., Zetterberg H. Update on Biomarkers for Amyloid Pathology in Alzheimer’s Disease. Biomark. Med. 2018;12:799–812. doi: 10.2217/bmm-2017-0433. [DOI] [PubMed] [Google Scholar]
- 41.Palmqvist S., Janelidze S., Stomrud E., Zetterberg H., Karl J., Zink K., Bittner T., Mattsson N., Eichenlaub U., Blennow K., et al. Performance of Fully Automated Plasma Assays as Screening Tests for Alzheimer Disease–Related β-Amyloid Status. JAMA Neurol. 2019;76:1060. doi: 10.1001/jamaneurol.2019.1632. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Guo Y., Huang Y.-Y., Shen X.-N., Chen S.-D., Hu H., Wang Z.-T., Tan L., Yu J.-T. Characterization of Alzheimer’s Tau Biomarker Discordance Using Plasma, CSF, and PET. Alzheimer’s Res. Ther. 2021;13:93. doi: 10.1186/s13195-021-00834-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.McGrowder D.A., Miller F., Vaz K., Nwokocha C., Wilson-Clarke C., Anderson-Cross M., Brown J., Anderson-Jackson L., Williams L., Latore L., et al. Cerebrospinal Fluid Biomarkers of Alzheimer’s Disease: Current Evidence and Future Perspectives. Brain Sci. 2021;11:215. doi: 10.3390/brainsci11020215. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Chandra A., Valkimadi P., Pagano G., Cousins O., Dervenoulas G., Politis M. Applications of Amyloid, Tau, and Neuroinflammation PET Imaging to Alzheimer’s Disease and Mild Cognitive Impairment. Hum. Brain Mapp. 2019;40:5424–5442. doi: 10.1002/hbm.24782. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Milà-Alomà M., Salvadó G., Gispert J.D., Vilor-Tejedor N., Grau-Rivera O., Sala-Vila A., Sánchez-Benavides G., Arenaza-Urquijo E.M., Crous-Bou M., González-de-Echávarri J.M., et al. Amyloid Beta, Tau, Synaptic, Neurodegeneration, and Glial Biomarkers in the Preclinical Stage of the Alzheimer’s Continuum. Alzheimer’s Dement. 2020;16:1358–1371. doi: 10.1002/alz.12131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Mielke M.M., Hagen C.E., Xu J., Chai X., Vemuri P., Lowe V.J., Airey D.C., Knopman D.S., Roberts R.O., Machulda M.M., et al. Plasma Phospho-tau181 Increases with Alzheimer’s Disease Clinical Severity and Is Associated with Tau- and Amyloid-positron Emission Tomography. Alzheimer’s Dement. 2018;14:989–997. doi: 10.1016/j.jalz.2018.02.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Janelidze S., Mattsson N., Palmqvist S., Smith R., Beach T.G., Serrano G.E., Chai X., Proctor N.K., Eichenlaub U., Zetterberg H., et al. Plasma P-Tau181 in Alzheimer’s Disease: Relationship to Other Biomarkers, Differential Diagnosis, Neuropathology and Longitudinal Progression to Alzheimer’s Dementia. Nat. Med. 2020;26:379–386. doi: 10.1038/s41591-020-0755-1. [DOI] [PubMed] [Google Scholar]
- 48.Karikari T.K., Pascoal T.A., Ashton N.J., Janelidze S., Benedet A.L., Rodriguez J.L., Chamoun M., Savard M., Kang M.S., Therriault J., et al. Blood Phosphorylated Tau 181 as a Biomarker for Alzheimer’s Disease: A Diagnostic Performance and Prediction Modelling Study Using Data from Four Prospective Cohorts. Lancet Neurol. 2020;19:422–433. doi: 10.1016/S1474-4422(20)30071-5. [DOI] [PubMed] [Google Scholar]
- 49.Thijssen E.H., la Joie R., Wolf A., Strom A., Wang P., Iaccarino L., Bourakova V., Cobigo Y., Heuer H., Spina S., et al. Diagnostic Value of Plasma Phosphorylated Tau181 in Alzheimer’s Disease and Frontotemporal Lobar Degeneration. Nat. Med. 2020;26:387–397. doi: 10.1038/s41591-020-0762-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Moscoso A., Grothe M.J., Ashton N.J., Karikari T.K., Rodriguez J.L., Snellman A., Suárez-Calvet M., Zetterberg H., Blennow K., Schöll M. Time Course of Phosphorylated-Tau181 in Blood across the Alzheimer’s Disease Spectrum. Brain. 2021;144:325–339. doi: 10.1093/brain/awaa399. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Karikari T.K., Benedet A.L., Ashton N.J., Lantero Rodriguez J., Snellman A., Suárez-Calvet M., Saha-Chaudhuri P., Lussier F., Kvartsberg H., Rial A.M., et al. Diagnostic Performance and Prediction of Clinical Progression of Plasma Phospho-Tau181 in the Alzheimer’s Disease Neuroimaging Initiative. Mol. Psychiatry. 2021;26:429–442. doi: 10.1038/s41380-020-00923-z. [DOI] [PubMed] [Google Scholar]
- 52.Janelidze S., Berron D., Smith R., Strandberg O., Proctor N.K., Dage J.L., Stomrud E., Palmqvist S., Mattsson-Carlgren N., Hansson O. Associations of Plasma Phospho-Tau217 Levels with Tau Positron Emission Tomography in Early Alzheimer Disease. JAMA Neurol. 2021;78:149. doi: 10.1001/jamaneurol.2020.4201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Janelidze S., Palmqvist S., Leuzy A., Stomrud E., Verberk I.M.W., Zetterberg H., Ashton N.J., Pesini P., Sarasa L., Allué J.A., et al. Detecting Amyloid Positivity in Early Alzheimer’s Disease Using Combinations of Plasma Aβ42/Aβ40 and P-tau. Alzheimer’s Dement. 2022;18:283–293. doi: 10.1002/alz.12395. [DOI] [PubMed] [Google Scholar]
- 54.Stotani S., Giordanetto F., Medda F. DYRK1A Inhibition as Potential Treatment for Alzheimer’s Disease. Future Med. Chem. 2016;8:681–696. doi: 10.4155/fmc-2016-0013. [DOI] [PubMed] [Google Scholar]
- 55.Delabar J.M., Ortner M., Simon S., Wijkhuisen A., Feraudet-Tarisse C., Pegon J., Vidal E., Hirschberg Y., Dubois B., Potier M. Altered Age-linked Regulation of Plasma DYRK1A in Elderly Cognitive Complainers (INSIGHT-preAD Study) with High Brain Amyloid Load. Alzheimer’s Dement. Transl. Res. Clin. Interv. 2020;6:e12046. doi: 10.1002/trc2.12046. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Olsson B., Lautner R., Andreasson U., Öhrfelt A., Portelius E., Bjerke M., Hölttä M., Rosén C., Olsson C., Strobel G., et al. CSF and Blood Biomarkers for the Diagnosis of Alzheimer’s Disease: A Systematic Review and Meta-Analysis. Lancet Neurol. 2016;15:673–684. doi: 10.1016/S1474-4422(16)00070-3. [DOI] [PubMed] [Google Scholar]
- 57.Khalil M., Teunissen C.E., Otto M., Piehl F., Sormani M.P., Gattringer T., Barro C., Kappos L., Comabella M., Fazekas F., et al. Neurofilaments as Biomarkers in Neurological Disorders. Nat. Rev. Neurol. 2018;14:577–589. doi: 10.1038/s41582-018-0058-z. [DOI] [PubMed] [Google Scholar]
- 58.Gisslén M., Price R.W., Andreasson U., Norgren N., Nilsson S., Hagberg L., Fuchs D., Spudich S., Blennow K., Zetterberg H. Plasma Concentration of the Neurofilament Light Protein (NFL) Is a Biomarker of CNS Injury in HIV Infection: A Cross-Sectional Study. EBioMedicine. 2016;3:135–140. doi: 10.1016/j.ebiom.2015.11.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Kuhle J., Barro C., Andreasson U., Derfuss T., Lindberg R., Sandelius Å., Liman V., Norgren N., Blennow K., Zetterberg H. Comparison of Three Analytical Platforms for Quantification of the Neurofilament Light Chain in Blood Samples: ELISA, Electrochemiluminescence Immunoassay and Simoa. Clin. Chem. Lab. Med. 2016;54:1655–1661. doi: 10.1515/cclm-2015-1195. [DOI] [PubMed] [Google Scholar]
- 60.Mattsson N., Andreasson U., Zetterberg H., Blennow K. Association of Plasma Neurofilament Light With Neurodegeneration in Patients with Alzheimer Disease. JAMA Neurol. 2017;74:557. doi: 10.1001/jamaneurol.2016.6117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Weston P.S.J., Poole T., Ryan N.S., Nair A., Liang Y., Macpherson K., Druyeh R., Malone I.B., Ahsan R.L., Pemberton H., et al. Serum Neurofilament Light in Familial Alzheimer Disease. Neurology. 2017;89:2167–2175. doi: 10.1212/WNL.0000000000004667. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Rohrer J.D., Woollacott I.O.C., Dick K.M., Brotherhood E., Gordon E., Fellows A., Toombs J., Druyeh R., Cardoso M.J., Ourselin S., et al. Serum Neurofilament Light Chain Protein Is a Measure of Disease Intensity in Frontotemporal Dementia. Neurology. 2016;87:1329–1336. doi: 10.1212/WNL.0000000000003154. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Rojas J.C., Karydas A., Bang J., Tsai R.M., Blennow K., Liman V., Kramer J.H., Rosen H., Miller B.L., Zetterberg H., et al. Plasma Neurofilament Light Chain Predicts Progression in Progressive Supranuclear Palsy. Ann. Clin. Transl. Neurol. 2016;3:216–225. doi: 10.1002/acn3.290. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Calsolaro V., Edison P. Neuroinflammation in Alzheimer’s Disease: Current Evidence and Future Directions. Alzheimer’s Dement. 2016;12:719–732. doi: 10.1016/j.jalz.2016.02.010. [DOI] [PubMed] [Google Scholar]
- 65.Byrne L.M., Rodrigues F.B., Blennow K., Durr A., Leavitt B.R., Roos R.A.C., Scahill R.I., Tabrizi S.J., Zetterberg H., Langbehn D., et al. Neurofilament Light Protein in Blood as a Potential Biomarker of Neurodegeneration in Huntington’s Disease: A Retrospective Cohort Analysis. Lancet Neurol. 2017;16:601–609. doi: 10.1016/S1474-4422(17)30124-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Buchhave P., Blennow K., Zetterberg H., Stomrud E., Londos E., Andreasen N., Minthon L., Hansson O. Longitudinal Study of CSF Biomarkers in Patients with Alzheimer’s Disease. PLoS ONE. 2009;4:e6294. doi: 10.1371/journal.pone.0006294. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Brunden K.R., Zhang B., Carroll J., Yao Y., Potuzak J.S., Hogan A.-M.L., Iba M., James M.J., Xie S.X., Ballatore C., et al. Epothilone D Improves Microtubule Density, Axonal Integrity, and Cognition in a Transgenic Mouse Model of Tauopathy. J. Neurosci. 2010;30:13861–13866. doi: 10.1523/JNEUROSCI.3059-10.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Brosseron F., Traschütz A., Widmann C.N., Kummer M.P., Tacik P., Santarelli F., Jessen F., Heneka M.T. Characterization and Clinical Use of Inflammatory Cerebrospinal Fluid Protein Markers in Alzheimer’s Disease. Alzheimer’s Res. Ther. 2018;10:25. doi: 10.1186/s13195-018-0353-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Bronzuoli M.R., Iacomino A., Steardo L., Scuderi C. Targeting Neuroinflammation in Alzheimer’s Disease. J. Inflamm. Res. 2016;9:199–208. doi: 10.2147/JIR.S86958. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Morgan A.R., Touchard S., Leckey C., O’Hagan C., Nevado-Holgado A.J., Barkhof F., Bertram L., Blin O., Bos I., Dobricic V., et al. Inflammatory Biomarkers in Alzheimer’s Disease Plasma. Alzheimer’s Dement. 2019;15:776–787. doi: 10.1016/j.jalz.2019.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Ferri E., Rossi P.D., Geraci A., Ciccone S., Cesari M., Arosio B. The STREM2 Concentrations in the Blood: A Marker of Neurodegeneration? Front. Mol. Biosci. 2021;7:627931. doi: 10.3389/fmolb.2020.627931. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Tan L., Yu J.-T., Liu Q.-Y., Tan M.-S., Zhang W., Hu N., Wang Y.-L., Sun L., Jiang T., Tan L. Circulating MiR-125b as a Biomarker of Alzheimer’s Disease. J. Neurol. Sci. 2014;336:52–56. doi: 10.1016/j.jns.2013.10.002. [DOI] [PubMed] [Google Scholar]
- 73.Ochi S., Iga J., Funahashi Y., Yoshino Y., Yamazaki K., Kumon H., Mori H., Ozaki Y., Mori T., Ueno S. Identifying Blood Transcriptome Biomarkers of Alzheimer’s Disease Using Transgenic Mice. Mol. Neurobiol. 2020;57:4941–4951. doi: 10.1007/s12035-020-02058-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Casati M., Ferri E., Gussago C., Mazzola P., Abbate C., Bellelli G., Mari D., Cesari M., Arosio B. Increased Expression of TREM2 in Peripheral Cells from Mild Cognitive Impairment Patients Who Progress into Alzheimer’s Disease. Eur. J. Neurol. 2018;25:805–810. doi: 10.1111/ene.13583. [DOI] [PubMed] [Google Scholar]
- 75.Tan Y.J., Ng A.S.L., Vipin A., Lim J.K.W., Chander R.J., Ji F., Qiu Y., Ting S.K.S., Hameed S., Lee T.-S., et al. Higher Peripheral TREM2 MRNA Levels Relate to Cognitive Deficits and Hippocampal Atrophy in Alzheimer’s Disease and Amnestic Mild Cognitive Impairment. J. Alzheimer’s Dis. 2017;58:413–423. doi: 10.3233/JAD-161277. [DOI] [PubMed] [Google Scholar]
- 76.Hu N., Tan M.-S., Yu J.-T., Sun L., Tan L., Wang Y.-L., Jiang T., Tan L. Increased Expression of TREM2 in Peripheral Blood of Alzheimer’s Disease Patients. J. Alzheimer’s Dis. 2013;38:497–501. doi: 10.3233/JAD-130854. [DOI] [PubMed] [Google Scholar]
- 77.Sao T., Yoshino Y., Yamazaki K., Ozaki Y., Mori Y., Ochi S., Yoshida T., Mori T., Iga J.-I., Ueno S.-I. TREM1 mRNA Expression in Leukocytes and Cognitive Function in Japanese Patients with Alzheimer’s Disease. J. Alzheimer’s Dis. 2018;64:1275–1284. doi: 10.3233/JAD-180418. [DOI] [PubMed] [Google Scholar]
- 78.Wang Q., Xu Y., Qi C., Liu A., Zhao Y. Association Study of Serum Soluble TREM2 with Vascular Dementia in Chinese Han Population. Int. J. Neurosci. 2020;130:708–712. doi: 10.1080/00207454.2019.1702548. [DOI] [PubMed] [Google Scholar]
- 79.Jiang T., Gong P.-Y., Tan M.-S., Xue X., Huang S., Zhou J.-S., Tan L., Zhang Y.-D. Soluble TREM1 Concentrations Are Increased and Positively Correlated with Total Tau Levels in the Plasma of Patients with Alzheimer’s Disease. Aging Clin. Exp. Res. 2019;31:1801–1805. doi: 10.1007/s40520-019-01122-9. [DOI] [PubMed] [Google Scholar]
- 80.Liu D., Cao B., Zhao Y., Huang H., McIntyre R.S., Rosenblat J.D., Zhou H. Soluble TREM2 Changes during the Clinical Course of Alzheimer’s Disease: A Meta-Analysis. Neurosci. Lett. 2018;686:10–16. doi: 10.1016/j.neulet.2018.08.038. [DOI] [PubMed] [Google Scholar]
- 81.Vergallo A., Lista S., Lemercier P., Chiesa P.A., Zetterberg H., Blennow K., Potier M.-C., Habert M.-O., Baldacci F., Cavedo E., et al. Association of Plasma YKL-40 with Brain Amyloid-β Levels, Memory Performance, and Sex in Subjective Memory Complainers. Neurobiol. Aging. 2020;96:22–32. doi: 10.1016/j.neurobiolaging.2020.07.009. [DOI] [PubMed] [Google Scholar]
- 82.Craig-Schapiro R., Perrin R.J., Roe C.M., Xiong C., Carter D., Cairns N.J., Mintun M.A., Peskind E.R., Li G., Galasko D.R., et al. YKL-40: A Novel Prognostic Fluid Biomarker for Preclinical Alzheimer’s Disease. Biol. Psychiatry. 2010;68:903–912. doi: 10.1016/j.biopsych.2010.08.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83.Wilczyńska K., Maciejczyk M., Zalewska A., Waszkiewicz N. Serum Amyloid Biomarkers, Tau Protein and YKL-40 Utility in Detection, Differential Diagnosing, and Monitoring of Dementia. Front. Psychiatry. 2021;12:725511. doi: 10.3389/fpsyt.2021.725511. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Wilczyńska K., Waszkiewicz N. Diagnostic Utility of Selected Serum Dementia Biomarkers: Amyloid β-40, Amyloid β-42, Tau Protein, and YKL-40: A Review. J. Clin. Med. 2020;9:3452. doi: 10.3390/jcm9113452. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 85.Villar-Piqué A., Schmitz M., Hermann P., Goebel S., Bunck T., Varges D., Ferrer I., Riggert J., Llorens F., Zerr I. Plasma YKL-40 in the Spectrum of Neurodegenerative Dementia. J. Neuroinflamm. 2019;16:145. doi: 10.1186/s12974-019-1531-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86.Su C., Zhao K., Xia H., Xu Y. Peripheral Inflammatory Biomarkers in Alzheimer’s Disease and Mild Cognitive Impairment: A Systematic Review and Meta-Analysis. Psychogeriatrics. 2019;19:300–309. doi: 10.1111/psyg.12403. [DOI] [PubMed] [Google Scholar]
- 87.Shen X.-N., Niu L.-D., Wang Y.-J., Cao X.-P., Liu Q., Tan L., Zhang C., Yu J.-T. Inflammatory Markers in Alzheimer’s Disease and Mild Cognitive Impairment: A Meta-Analysis and Systematic Review of 170 Studies. J. Neurol. Neurosurg. Psychiatry. 2019;90:590–598. doi: 10.1136/jnnp-2018-319148. [DOI] [PubMed] [Google Scholar]
- 88.Tao Q., Alvin Ang T.F., Akhter-Khan S.C., Itchapurapu I.S., Killiany R., Zhang X., Budson A.E., Turk K.W., Goldstein L., Mez J., et al. Impact of C-Reactive Protein on Cognition and Alzheimer Disease Biomarkers in Homozygous APOE Ɛ4 Carriers. Neurology. 2021;97:e1243–e1252. doi: 10.1212/WNL.0000000000012512. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 89.Hao J., Qiao Y., Li T., Yang J., Song Y., Jia L., Jia J. Investigating Changes in the Serum Inflammatory Factors in Alzheimer’s Disease and Their Correlation with Cognitive Function. J. Alzheimer’s Dis. 2021;84:835–842. doi: 10.3233/JAD-210552. [DOI] [PubMed] [Google Scholar]
- 90.Yarchoan M., Louneva N., Xie S.X., Swenson F.J., Hu W., Soares H., Trojanowski J.Q., Lee V.M.-Y., Kling M.A., Shaw L.M., et al. Association of Plasma C-Reactive Protein Levels with the Diagnosis of Alzheimer’s Disease. J. Neurol. Sci. 2013;333:9–12. doi: 10.1016/j.jns.2013.05.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Song I.-U., Chung S.-W., Kim Y.-D., Maeng L.-S. Relationship between the Hs-CRP as Non-Specific Biomarker and Alzheimer’s Disease According to Aging Process. Int. J. Med. Sci. 2015;12:613–617. doi: 10.7150/ijms.12742. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 92.Balkwill F.R. The Chemokine System and Cancer. J. Pathol. 2012;226:148–157. doi: 10.1002/path.3029. [DOI] [PubMed] [Google Scholar]
- 93.Kulczyńska-Przybik A., Słowik A., Mroczko P., Borawski B., Groblewska M., Borawska R., Mroczko B. Cerebrospinal Fluid and Blood CX3CL1 as a Potential Biomarker in Early Diagnosis and Prognosis of Dementia. Curr. Alzheimer Res. 2020;17:709–721. doi: 10.2174/1567205017666201109095657. [DOI] [PubMed] [Google Scholar]
- 94.Faura J., Bustamante A., Penalba A., Giralt D., Simats A., Martínez-Sáez E., Alcolea D., Fortea J., Lleó A., Teunissen C.E., et al. CCL23: A Chemokine Associated with Progression from Mild Cognitive Impairment to Alzheimer’s Disease. J. Alzheimer’s Dis. 2020;73:1585–1595. doi: 10.3233/JAD-190753. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 95.Vacinova G., Vejražkova D., Rusina R., Holmerová I., Vaňková H., Jarolímová E., Včelák J., Bendlová B., Vaňková M. Regulated upon Activation, Normal T Cell Expressed and Secreted (RANTES) Levels in the Peripheral Blood of Patients with Alzheimer’s Disease. Neural Regen. Res. 2021;16:796. doi: 10.4103/1673-5374.295340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 96.Reale M., Kamal M.A., Velluto L., Gambi D., di Nicola M., Greig N.H. Relationship between Inflammatory Mediators, Aβ Levels and ApoE Genotype in Alzheimer Disease. Curr. Alzheimer Res. 2012;9:447–457. doi: 10.2174/156720512800492549. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 97.Marksteiner J., Kemmler G., Weiss E.M., Knaus G., Ullrich C., Mechtcheriakov S., Oberbauer H., Auffinger S., Hinterhölzl J., Hinterhuber H., et al. Five out of 16 Plasma Signaling Proteins Are Enhanced in Plasma of Patients with Mild Cognitive Impairment and Alzheimer’s Disease. Neurobiol. Aging. 2011;32:539–540. doi: 10.1016/j.neurobiolaging.2009.03.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 98.Tang P., Chong L., Li X., Liu Y., Liu P., Hou C., Li R. Correlation between Serum RANTES Levels and the Severity of Parkinson’s Disease. Oxidative Med. Cell. Longev. 2014;2014:208408. doi: 10.1155/2014/208408. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 99.Mori F., Nisticò R., Nicoletti C.G., Zagaglia S., Mandolesi G., Piccinin S., Martino G., Finardi A., Rossini P.M., Marfia G.A., et al. RANTES Correlates with Inflammatory Activity and Synaptic Excitability in Multiple Sclerosis. Mult. Scler. J. 2016;22:1405–1412. doi: 10.1177/1352458515621796. [DOI] [PubMed] [Google Scholar]
- 100.Markiewski M.M., Lambris J.D. The Role of Complement in Inflammatory Diseases from Behind the Scenes into the Spotlight. Am. J. Pathol. 2007;171:715–727. doi: 10.2353/ajpath.2007.070166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Morgan A.R., Touchard S., O’Hagan C., Sims R., Majounie E., Escott-Price V., Jones L., Williams J., Morgan B.P. The Correlation between Inflammatory Biomarkers and Polygenic Risk Score in Alzheimer’s Disease. J. Alzheimer’s Dis. 2017;56:25–36. doi: 10.3233/JAD-160889. [DOI] [PubMed] [Google Scholar]
- 102.Hakobyan S., Harding K., Aiyaz M., Hye A., Dobson R., Baird A., Liu B., Harris C.L., Lovestone S., Morgan B.P. Complement Biomarkers as Predictors of Disease Progression in Alzheimer’s Disease. J. Alzheimer’s Dis. 2016;54:707–716. doi: 10.3233/JAD-160420. [DOI] [PubMed] [Google Scholar]
- 103.Suidan G.L., Singh P.K., Patel-Hett S., Chen Z.-L., Volfson D., Yamamoto-Imoto H., Norris E.H., Bell R.D., Strickland S. Abnormal Clotting of the Intrinsic/Contact Pathway in Alzheimer Disease Patients Is Related to Cognitive Ability. Blood Adv. 2018;2:954–963. doi: 10.1182/bloodadvances.2018017798. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 104.Liu M., Guo S., Hibbert J.M., Jain V., Singh N., Wilson N.O., Stiles J.K. CXCL10/IP-10 in Infectious Diseases Pathogenesis and Potential Therapeutic Implications. Cytokine Growth Factor Rev. 2011;22:121–130. doi: 10.1016/j.cytogfr.2011.06.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 105.Iturria-Medina Y., Sotero R.C., Toussaint P.J., Mateos-Pérez J.M., Evans A.C. Early Role of Vascular Dysregulation on Late-Onset Alzheimer’s Disease Based on Multifactorial Data-Driven Analysis. Nat. Commun. 2016;7:11934. doi: 10.1038/ncomms11934. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 106.Galimberti D., Venturelli E., Fenoglio C., Lovati C., Guidi I., Scalabrini D., Mariani C., Bresolin N., Scarpini E. IP-10 Serum Levels Are Not Increased in Mild Cognitive Impairment and Alzheimer’s Disease. Eur. J. Neurol. 2007;14:e3–e4. doi: 10.1111/j.1468-1331.2006.01637.x. [DOI] [PubMed] [Google Scholar]
- 107.Galimberti D., Schoonenboom N., Scheltens P., Fenoglio C., Bouwman F., Venturelli E., Guidi I., Blankenstein M.A., Bresolin N., Scarpini E. Intrathecal Chemokine Synthesis in Mild Cognitive Impairment and Alzheimer Disease. Arch. Neurol. 2006;63:538. doi: 10.1001/archneur.63.4.538. [DOI] [PubMed] [Google Scholar]
- 108.Popp J., Oikonomidi A., Tautvydaitė D., Dayon L., Bacher M., Migliavacca E., Henry H., Kirkland R., Severin I., Wojcik J., et al. Markers of Neuroinflammation Associated with Alzheimer’s Disease Pathology in Older Adults. Brain Behav. Immun. 2017;62:203–211. doi: 10.1016/j.bbi.2017.01.020. [DOI] [PubMed] [Google Scholar]
- 109.Delaby C., Gabelle A., Blum D., Schraen-Maschke S., Moulinier A., Boulanghien J., Séverac D., Buée L., Rème T., Lehmann S. Central Nervous System and Peripheral Inflammatory Processes in Alzheimer’s Disease: Biomarker Profiling Approach. Front. Neurol. 2015;6:181. doi: 10.3389/fneur.2015.00181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 110.Abe K., Shang J., Shi X., Yamashita T., Hishikawa N., Takemoto M., Morihara R., Nakano Y., Ohta Y., Deguchi K., et al. A New Serum Biomarker Set to Detect Mild Cognitive Impairment and Alzheimer’s Disease by Peptidome Technology. J. Alzheimer’s Dis. 2020;73:217–227. doi: 10.3233/JAD-191016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 111.Cacabelos R., Alvarez X.A., Fernández-Novoa L., Franco A., Mangues R., Pellicer A., Nishimura T. Brain Interleukin-1 Beta in Alzheimer’s Disease and Vascular Dementia. Methods Find. Exp. Clin. Pharmacol. 1994;16:141–151. [PubMed] [Google Scholar]
- 112.Bradburn S., Sarginson J., Murgatroyd C.A. Association of Peripheral Interleukin-6 with Global Cognitive Decline in Non-Demented Adults: A Meta-Analysis of Prospective Studies. Front. Aging Neurosci. 2018;9:438. doi: 10.3389/fnagi.2017.00438. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 113.la Rosa F., Saresella M., Baglio F., Piancone F., Marventano I., Calabrese E., Nemni R., Ripamonti E., Cabinio M., Clerici M. Immune and Imaging Correlates of Mild Cognitive Impairment Conversion to Alzheimer’s Disease. Sci. Rep. 2017;7:16760. doi: 10.1038/s41598-017-16754-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 114.Swardfager W., Lanctôt K., Rothenburg L., Wong A., Cappell J., Herrmann N. A Meta-Analysis of Cytokines in Alzheimer’s Disease. Biol. Psychiatry. 2010;68:930–941. doi: 10.1016/j.biopsych.2010.06.012. [DOI] [PubMed] [Google Scholar]
- 115.Lai K.S.P., Liu C.S., Rau A., Lanctôt K.L., Köhler C.A., Pakosh M., Carvalho A.F., Herrmann N. Peripheral Inflammatory Markers in Alzheimer’s Disease: A Systematic Review and Meta-Analysis of 175 Studies. J. Neurol. Neurosurg. Psychiatry. 2017;88:876–882. doi: 10.1136/jnnp-2017-316201. [DOI] [PubMed] [Google Scholar]
- 116.Ng A., Tam W.W., Zhang M.W., Ho C.S., Husain S.F., McIntyre R.S., Ho R.C. IL-1β, IL-6, TNF- α and CRP in Elderly Patients with Depression or Alzheimer’s Disease: Systematic Review and Meta-Analysis. Sci. Rep. 2018;8:12050. doi: 10.1038/s41598-018-30487-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 117.Neshan M., Malakouti S.K., Kamalzadeh L., Makvand M., Campbell A., Ahangari G. Alterations in T-Cell Transcription Factors and Cytokine Gene Expression in Late-Onset Alzheimer’s Disease. J. Alzheimer’s Dis. 2022;85:645–665. doi: 10.3233/JAD-210480. [DOI] [PubMed] [Google Scholar]
- 118.Liang C.-S., Su K.-P., Tsai C.-L., Lee J.-T., Chu C.-S., Yeh T.-C., Su M.-W., Lin G.-Y., Lin Y.-K., Chu H.-T., et al. The Role of Interleukin-33 in Patients with Mild Cognitive Impairment and Alzheimer’s Disease. Alzheimer’s Res. Ther. 2020;12:86. doi: 10.1186/s13195-020-00652-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 119.Sun Y., Yin X.S., Guo H., Han R.K., He R.D., Chi L.J. Elevated Osteopontin Levels in Mild Cognitive Impairment and Alzheimer’s Disease. Mediat. Inflamm. 2013;2013:615745. doi: 10.1155/2013/615745. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 120.Liang C.-S., Tsai C.-L., Lin G.-Y., Lee J.-T., Lin Y.-K., Chu C.-S., Sung Y.-F., Tsai C.-K., Yeh T.-C., Chu H.-T., et al. Better Identification of Cognitive Decline with Interleukin-2 Than with Amyloid and Tau Protein Biomarkers in Amnestic Mild Cognitive Impairment. Front. Aging Neurosci. 2021;13:670115. doi: 10.3389/fnagi.2021.670115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 121.Mamas M., Dunn W.B., Neyses L., Goodacre R. The Role of Metabolites and Metabolomics in Clinically Applicable Biomarkers of Disease. Arch. Toxicol. 2011;85:5–17. doi: 10.1007/s00204-010-0609-6. [DOI] [PubMed] [Google Scholar]
- 122.de la Monte S.M., Tong M. Brain Metabolic Dysfunction at the Core of Alzheimer’s Disease. Biochem. Pharmacol. 2014;88:548–559. doi: 10.1016/j.bcp.2013.12.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 123.Hassan-Smith G., Wallace G.R., Douglas M.R., Sinclair A.J. The Role of Metabolomics in Neurological Disease. J. Neuroimmunol. 2012;248:48–52. doi: 10.1016/j.jneuroim.2012.01.009. [DOI] [PubMed] [Google Scholar]
- 124.Wang Y.-Y., Sun Y.-P., Luo Y.-M., Peng D.-H., Li X., Yang B.-Y., Wang Q.-H., Kuang H.-X. Biomarkers for the Clinical Diagnosis of Alzheimer’s Disease: Metabolomics Analysis of Brain Tissue and Blood. Front. Pharmacol. 2021;12:700587. doi: 10.3389/fphar.2021.700587. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 125.Morris C.J. Early-Stage and Preclinical Alzheimer Disease. Alzheimer Dis. Assoc. Disord. 2005;19:163–165. doi: 10.1097/01.wad.0000184005.22611.cc. [DOI] [PubMed] [Google Scholar]
- 126.Bateman R.J., Xiong C., Benzinger T.L.S., Fagan A.M., Goate A., Fox N.C., Marcus D.S., Cairns N.J., Xie X., Blazey T.M., et al. Clinical and Biomarker Changes in Dominantly Inherited Alzheimer’s Disease. N. Engl. J. Med. 2012;367:795–804. doi: 10.1056/NEJMoa1202753. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 127.Hane F.T., Robinson M., Lee B.Y., Bai O., Leonenko Z., Albert M.S. Recent Progress in Alzheimer’s Disease Research, Part 3: Diagnosis and Treatment. J. Alzheimer’s Dis. 2017;57:645–665. doi: 10.3233/JAD-160907. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 128.Yang J., Chen T., Sun L., Zhao Z., Qi X., Zhou K., Cao Y., Wang X., Qiu Y., Su M., et al. Potential Metabolite Markers of Schizophrenia. Mol. Psychiatry. 2013;18:67–78. doi: 10.1038/mp.2011.131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 129.Sun C., Gao M., Wang F., Yun Y., Sun Q., Guo R., Yan C., Sun X., Li Y. Serum Metabolomic Profiling in Patients with Alzheimer Disease and Amnestic Mild Cognitive Impairment by GC/MS. Biomed. Chromatogr. 2020;34:e4875. doi: 10.1002/bmc.4875. [DOI] [PubMed] [Google Scholar]
- 130.Piubelli L., Pollegioni L., Rabattoni V., Mauri M., Princiotta Cariddi L., Versino M., Sacchi S. Serum D-Serine Levels Are Altered in Early Phases of Alzheimer’s Disease: Towards a Precocious Biomarker. Transl. Psychiatry. 2021;11:77. doi: 10.1038/s41398-021-01202-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 131.Hartley D., Blumenthal T., Carrillo M., DiPaolo G., Esralew L., Gardiner K., Granholm A.-C., Iqbal K., Krams M., Lemere C., et al. Down Syndrome and Alzheimer’s Disease: Common Pathways, Common Goals. Alzheimer’s Dement. 2015;11:700–709. doi: 10.1016/j.jalz.2014.10.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 132.Fortea J., Vilaplana E., Carmona-Iragui M., Benejam B., Videla L., Barroeta I., Fernández S., Altuna M., Pegueroles J., Montal V., et al. Clinical and Biomarker Changes of Alzheimer’s Disease in Adults with Down Syndrome: A Cross-Sectional Study. Lancet. 2020;395:1988–1997. doi: 10.1016/S0140-6736(20)30689-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 133.Gross T.J., Doran E., Cheema A.K., Head E., Lott I.T., Mapstone M. Plasma Metabolites Related to Cellular Energy Metabolism Are Altered in Adults with Down Syndrome and Alzheimer’s Disease. Dev. Neurobiol. 2019;79:622–638. doi: 10.1002/dneu.22716. [DOI] [PubMed] [Google Scholar]
- 134.Gomez W., Morales R., Maracaja-Coutinho V., Parra V., Nassif M. Down Syndrome and Alzheimer’s Disease: Common Molecular Traits beyond the Amyloid Precursor Protein. Aging. 2020;12:1011–1033. doi: 10.18632/aging.102677. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 135.Mosconi L., de Santi S., Li J., Tsui W.H., Li Y., Boppana M., Laska E., Rusinek H., de Leon M.J. Hippocampal Hypometabolism Predicts Cognitive Decline from Normal Aging. Neurobiol. Aging. 2008;29:676–692. doi: 10.1016/j.neurobiolaging.2006.12.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 136.Baik S.H., Kang S., Lee W., Choi H., Chung S., Kim J.-I., Mook-Jung I. A Breakdown in Metabolic Reprogramming Causes Microglia Dysfunction in Alzheimer’s Disease. Cell Metab. 2019;30:493–507.e6. doi: 10.1016/j.cmet.2019.06.005. [DOI] [PubMed] [Google Scholar]
- 137.Yao J., Rettberg J.R., Klosinski L.P., Cadenas E., Brinton R.D. Shift in Brain Metabolism in Late Onset Alzheimer’s Disease: Implications for Biomarkers and Therapeutic Interventions. Mol. Asp. Med. 2011;32:247–257. doi: 10.1016/j.mam.2011.10.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 138.Vlassenko A.G., Gordon B.A., Goyal M.S., Su Y., Blazey T.M., Durbin T.J., Couture L.E., Christensen J.J., Jafri H., Morris J.C., et al. Aerobic Glycolysis and Tau Deposition in Preclinical Alzheimer’s Disease. Neurobiol. Aging. 2018;67:95–98. doi: 10.1016/j.neurobiolaging.2018.03.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 139.Marksteiner J., Blasko I., Kemmler G., Koal T., Humpel C. Bile Acid Quantification of 20 Plasma Metabolites Identifies Lithocholic Acid as a Putative Biomarker in Alzheimer’s Disease. Metabolomics. 2018;14:1. doi: 10.1007/s11306-017-1297-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 140.Voyle N., Kim M., Proitsi P., Ashton N.J., Baird A.L., Bazenet C., Hye A., Westwood S., Chung R., Ward M., et al. Blood Metabolite Markers of Neocortical Amyloid-β Burden: Discovery and Enrichment Using Candidate Proteins. Transl. Psychiatry. 2016;6:e719. doi: 10.1038/tp.2015.205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 141.Aizenstein H.J., Nebes R.D., Saxton J.A., Price J.C., Mathis C.A., Tsopelas N.D., Ziolko S.K., James J.A., Snitz B.E., Houck P.R., et al. Frequent Amyloid Deposition Without Significant Cognitive Impairment Among the Elderly. Arch. Neurol. 2008;65:1509. doi: 10.1001/archneur.65.11.1509. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 142.Zolochevska O., Taglialatela G. Non-Demented Individuals with Alzheimer’s Disease Neuropathology: Resistance to Cognitive Decline May Reveal New Treatment Strategies. Curr. Pharm. Des. 2016;22:4063–4068. doi: 10.2174/1381612822666160518142110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 143.Giannakopoulos P., Herrmann F.R., Bussiere T., Bouras C., Kovari E., Perl D.P., Morrison J.H., Gold G., Hof P.R. Tangle and Neuron Numbers, but Not Amyloid Load, Predict Cognitive Status in Alzheimer’s Disease. Neurology. 2003;60:1495–1500. doi: 10.1212/01.WNL.0000063311.58879.01. [DOI] [PubMed] [Google Scholar]
- 144.Kamogawa K., Kohara K., Tabara Y., Takita R., Miki T., Konno T., Hata S., Suzuki T. Potential Utility of Soluble P3-Alcadeinα Plasma Levels as a Biomarker for Sporadic Alzheimer’s Disease. J. Alzheimer’s Dis. 2012;31:421–428. doi: 10.3233/JAD-2012-120601. [DOI] [PubMed] [Google Scholar]
- 145.Piao Y., Kimura A., Urano S., Saito Y., Taru H., Yamamoto T., Hata S., Suzuki T. Mechanism of Intramembrane Cleavage of Alcadeins by γ-Secretase. PLoS ONE. 2013;8:e62431. doi: 10.1371/journal.pone.0062431. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 146.Jones L.L., McDonald D.A., Borum P.R. Acylcarnitines: Role in Brain. Prog. Lipid Res. 2010;49:61–75. doi: 10.1016/j.plipres.2009.08.004. [DOI] [PubMed] [Google Scholar]
- 147.McCoin C.S., Knotts T.A., Adams S.H. Acylcarnitines—Old Actors Auditioning for New Roles in Metabolic Physiology. Nat. Rev. Endocrinol. 2015;11:617–625. doi: 10.1038/nrendo.2015.129. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 148.Mihalik S.J., Goodpaster B.H., Kelley D.E., Chace D.H., Vockley J., Toledo F.G.S., DeLany J.P. Increased Levels of Plasma Acylcarnitines in Obesity and Type 2 Diabetes and Identification of a Marker of Glucolipotoxicity. Obesity. 2010;18:1695–1700. doi: 10.1038/oby.2009.510. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 149.Schooneman M.G., Vaz F.M., Houten S.M., Soeters M.R. Acylcarnitines: Reflecting or Inflicting Insulin Resistance? Diabetes. 2013;62:1–8. doi: 10.2337/db12-0466. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 150.Huo Z., Yu L., Yang J., Zhu Y., Bennett D.A., Zhao J. Brain and Blood Metabolome for Alzheimer’s Dementia: Findings from a Targeted Metabolomics Analysis. Neurobiol. Aging. 2020;86:123–133. doi: 10.1016/j.neurobiolaging.2019.10.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 151.Harrigan G., Goodacre R. In: Metabolic Profiling: Its Role in Biomarker Discovery and Gene Function Analysis. Harrigan G.G., Goodacre R., editors. Springer; Boston, MA, USA: 2003. [Google Scholar]
- 152.Wang G., Zhou Y., Huang F.-J., Tang H.-D., Xu X.-H., Liu J.-J., Wang Y., Deng Y.-L., Ren R.-J., Xu W., et al. Plasma Metabolite Profiles of Alzheimer’s Disease and Mild Cognitive Impairment. J. Proteome Res. 2014;13:2649–2658. doi: 10.1021/pr5000895. [DOI] [PubMed] [Google Scholar]
- 153.Varma V.R., Oommen A.M., Varma S., Casanova R., An Y., Andrews R.M., O’Brien R., Pletnikova O., Troncoso J.C., Toledo J., et al. Brain and Blood Metabolite Signatures of Pathology and Progression in Alzheimer Disease: A Targeted Metabolomics Study. PLoS Med. 2018;15:e1002482. doi: 10.1371/journal.pmed.1002482. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 154.Xicota L., Ichou F., Lejeune F.-X., Colsch B., Tenenhaus A., Leroy I., Fontaine G., Lhomme M., Bertin H., Habert M.-O., et al. Multi-Omics Signature of Brain Amyloid Deposition in Asymptomatic Individuals at-Risk for Alzheimer’s Disease: The INSIGHT-PreAD Study. EBioMedicine. 2019;47:518–528. doi: 10.1016/j.ebiom.2019.08.051. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 155.Lee S.J., Teunissen C.E., Pool R., Shipley M.J., Teumer A., Chouraki V., Melo van Lent D., Tynkkynen J., Fischer K., Hernesniemi J., et al. Circulating Metabolites and General Cognitive Ability and Dementia: Evidence from 11 Cohort Studies. Alzheimer’s Dement. 2018;14:707–722. doi: 10.1016/j.jalz.2017.11.012. [DOI] [PubMed] [Google Scholar]
- 156.Gella A., Durany N. Oxidative Stress in Alzheimer Disease. Cell Adhes. Migr. 2009;3:88–93. doi: 10.4161/cam.3.1.7402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 157.Huang W.-J., Zhang X., Chen W.-W. Role of Oxidative Stress in Alzheimer’s Disease. Biomed. Rep. 2016;4:519–522. doi: 10.3892/br.2016.630. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 158.Praticò D., Uryu K., Leight S., Trojanoswki J.Q., Lee V.M.-Y. Increased Lipid Peroxidation Precedes Amyloid Plaque Formation in an Animal Model of Alzheimer Amyloidosis. J. Neurosci. 2001;21:4183–4187. doi: 10.1523/JNEUROSCI.21-12-04183.2001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 159.Markesbery W.R. Oxidative Stress Hypothesis in Alzheimer’s Disease. Free Radic. Biol. Med. 1997;23:134–147. doi: 10.1016/S0891-5849(96)00629-6. [DOI] [PubMed] [Google Scholar]
- 160.Smith M.A., Rottkamp C.A., Nunomura A., Raina A.K., Perry G. Oxidative Stress in Alzheimer’s Disease. Biochim. Biophys. Acta-Mol. Basis Dis. 2000;1502:139–144. doi: 10.1016/S0925-4439(00)00040-5. [DOI] [PubMed] [Google Scholar]
- 161.Buccellato F.R., D’Anca M., Fenoglio C., Scarpini E., Galimberti D. Role of Oxidative Damage in Alzheimer’s Disease and Neurodegeneration: From Pathogenic Mechanisms to Biomarker Discovery. Antioxidants. 2021;10:1353. doi: 10.3390/antiox10091353. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 162.Martins R.N., Villemagne V., Sohrabi H.R., Chatterjee P., Shah T.M., Verdile G., Fraser P., Taddei K., Gupta V.B., Rainey-Smith S.R., et al. Alzheimer’s Disease: A Journey from Amyloid Peptides and Oxidative Stress, to Biomarker Technologies and Disease Prevention Strategies—Gains from AIBL and DIAN Cohort Studies. J. Alzheimer’s Dis. 2018;62:965–992. doi: 10.3233/JAD-171145. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 163.Lin C.-H., Lane H.-Y. Plasma Glutathione Levels Decreased with Cognitive Decline among People with Mild Cognitive Impairment (MCI): A Two-Year Prospective Study. Antioxidants. 2021;10:1839. doi: 10.3390/antiox10111839. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 164.Han H., Wang F., Chen J., Li X., Fu G., Zhou J., Zhou D., Wu W., Chen H. Changes in Biothiol Levels Are Closely Associated with Alzheimer’s Disease. J. Alzheimer’s Dis. 2021;82:527–540. doi: 10.3233/JAD-210021. [DOI] [PubMed] [Google Scholar]
- 165.Evlice A., Ulusu N.N. Glucose-6-Phosphate Dehydrogenase a Novel Hope on a Blood-Based Diagnosis of Alzheimer’s Disease. Acta Neurol. Belg. 2017;117:229–234. doi: 10.1007/s13760-016-0666-6. [DOI] [PubMed] [Google Scholar]
- 166.Peña-Bautista C., Álvarez-Sánchez L., Ferrer I., López-Nogueroles M., Cañada-Martínez A.J., Oger C., Galano J.-M., Durand T., Baquero M., Cháfer-Pericás C. Lipid Peroxidation Assessment in Preclinical Alzheimer Disease Diagnosis. Antioxidants. 2021;10:1043. doi: 10.3390/antiox10071043. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 167.Peña-Bautista C., Álvarez L., Baquero M., Ferrer I., García L., Hervás-Marín D., Cháfer-Pericás C. Plasma Isoprostanoids Assessment as Alzheimer’s Disease Progression Biomarkers. J. Neurochem. 2021;157:2187–2194. doi: 10.1111/jnc.15183. [DOI] [PubMed] [Google Scholar]
- 168.Zengi O., Karakas A., Ergun U., Senes M., Inan L., Yucel D. Urinary 8-Hydroxy-2′-Deoxyguanosine Level and Plasma Paraoxonase 1 Activity with Alzheimer’s Disease. Clin. Chem. Lab. Med. 2012;50:529–534. doi: 10.1515/cclm.2011.792. [DOI] [PubMed] [Google Scholar]
- 169.Picco A., Polidori M.C., Ferrara M., Cecchetti R., Arnaldi D., Baglioni M., Morbelli S., Bastiani P., Bossert I., Fiorucci G., et al. Plasma Antioxidants and Brain Glucose Metabolism in Elderly Subjects with Cognitive Complaints. Eur. J. Nucl. Med. Mol. Imaging. 2014;41:764–775. doi: 10.1007/s00259-013-2638-x. [DOI] [PubMed] [Google Scholar]
- 170.Rita Cardoso B., Silva Bandeira V., Jacob-Filho W., Franciscato Cozzolino S.M. Selenium Status in Elderly: Relation to Cognitive Decline. J. Trace Elem. Med. Biol. 2014;28:422–426. doi: 10.1016/j.jtemb.2014.08.009. [DOI] [PubMed] [Google Scholar]
- 171.Wu L.-Y., Cheah I.K., Chong J.R., Chai Y.L., Tan J.Y., Hilal S., Vrooman H., Chen C.P., Halliwell B., Lai M.K.P. Low Plasma Ergothioneine Levels Are Associated with Neurodegeneration and Cerebrovascular Disease in Dementia. Free Radic. Biol. Med. 2021;177:201–211. doi: 10.1016/j.freeradbiomed.2021.10.019. [DOI] [PubMed] [Google Scholar]
- 172.Du L., Ma J., He D., Zhang X. Serum Ischaemia-modified Albumin Might Be a Potential Biomarker for Oxidative Stress in Amnestic Mild Cognitive Impairment. Psychogeriatrics. 2019;19:150–156. doi: 10.1111/psyg.12377. [DOI] [PubMed] [Google Scholar]
- 173.Li L.-L., Ma Y.-H., Bi Y.-L., Sun F.-R., Hu H., Hou X.-H., Xu W., Shen X.-N., Dong Q., Tan L., et al. Serum Uric Acid May Aggravate Alzheimer’s Disease Risk by Affecting Amyloidosis in Cognitively Intact Older Adults: The CABLE Study. J. Alzheimer’s Dis. 2021;81:389–401. doi: 10.3233/JAD-201192. [DOI] [PubMed] [Google Scholar]
- 174.Mcintyre J.A., Wagenknecht D.R., Ramsey C.J. Redox-Reactive Antiphospholipid Antibody Differences between Serum from Alzheimer’s Patients and Age-Matched Controls. Autoimmunity. 2009;42:646–652. doi: 10.3109/08916930903074833. [DOI] [PubMed] [Google Scholar]
- 175.Pradhan R., Singh A.K., Kumar P., Bajpai S., Pathak M., Chatterjee P., Dwivedi S., Dey A.B., Dey S. Blood Circulatory Level of Seven Sirtuins in Alzheimer’s Disease: Potent Biomarker Based on Translational Research. Mol. Neurobiol. 2022;59:1440–1451. doi: 10.1007/s12035-021-02671-9. [DOI] [PubMed] [Google Scholar]
- 176.López N., Tormo C., de Blas I., Llinares I., Alom J. Oxidative Stress in Alzheimer’s Disease and Mild Cognitive Impairment with High Sensitivity and Specificity. J. Alzheimer’s Dis. 2013;33:823–829. doi: 10.3233/JAD-2012-121528. [DOI] [PubMed] [Google Scholar]
- 177.Johnson W.M., Wilson-Delfosse A.L., Mieyal J.J. Dysregulation of Glutathione Homeostasis in Neurodegenerative Diseases. Nutrients. 2012;4:1399–1440. doi: 10.3390/nu4101399. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 178.Haddad M., Hervé V., ben Khedher M.R., Rabanel J.-M., Ramassamy C. Glutathione: An Old and Small Molecule with Great Functions and New Applications in the Brain and in Alzheimer’s Disease. Antioxid. Redox Signal. 2021;35:270–292. doi: 10.1089/ars.2020.8129. [DOI] [PubMed] [Google Scholar]
- 179.Zheng T., Liu H., Qin L., Chen B., Zhang X., Hu X., Xiao L., Qin S. Oxidative Stress-Mediated Influence of Plasma DPP4 Activity to BDNF Ratio on Mild Cognitive Impairment in Elderly Type 2 Diabetic Patients: Results from the GDMD Study in China. Metabolism. 2018;87:105–112. doi: 10.1016/j.metabol.2018.03.014. [DOI] [PubMed] [Google Scholar]
- 180.Chen B., Zheng T., Qin L., Hu X., Zhang X., Liu Y., Liu H., Qin S., Li G., Li Q. Strong Association between Plasma Dipeptidyl Peptidase-4 Activity and Impaired Cognitive Function in Elderly Population with Normal Glucose Tolerance. Front. Aging Neurosci. 2017;9:247. doi: 10.3389/fnagi.2017.00247. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 181.Signorini C., de Felice C., Galano J.-M., Oger C., Leoncini S., Cortelazzo A., Ciccoli L., Durand T., Hayek J., Lee J. Isoprostanoids in Clinical and Experimental Neurological Disease Models. Antioxidants. 2018;7:88. doi: 10.3390/antiox7070088. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 182.Mufson E.J., Leurgans S. Inability of Plasma and Urine F2A-Isoprostane Levels to Differentiate Mild Cognitive Impairment from Alzheimer’s Disease. Neurodegener. Dis. 2010;7:139–142. doi: 10.1159/000289224. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 183.Evans J.R., Lawrenson J.G. Antioxidant Vitamin and Mineral Supplements for Preventing Age-Related Macular Degeneration. Cochrane Database Syst. Rev. 2017;2017:CD000253. doi: 10.1002/14651858.CD000253.pub4. [DOI] [PubMed] [Google Scholar]
- 184.Cardoso B.R., Hare D.J., Bush A.I., Li Q.-X., Fowler C.J., Masters C.L., Martins R.N., Ganio K., Lothian A., Mukherjee S., et al. Selenium Levels in Serum, Red Blood Cells, and Cerebrospinal Fluid of Alzheimer’s Disease Patients: A Report from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) J. Alzheimer’s Dis. 2017;57:183–193. doi: 10.3233/JAD-160622. [DOI] [PubMed] [Google Scholar]
- 185.Ficiarà E., Munir Z., Boschi S., Caligiuri M.E., Guiot C. Alteration of Iron Concentration in Alzheimer’s Disease as a Possible Diagnostic Biomarker Unveiling Ferroptosis. Int. J. Mol. Sci. 2021;22:4479. doi: 10.3390/ijms22094479. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 186.Arnal N., Morel G.R., de Alaniz M.J.T., Castillo O., Marra C.A. Role of Copper and Cholesterol Association in the Neurodegenerative Process. Int. J. Alzheimer’s Dis. 2013;2013:414817. doi: 10.1155/2013/414817. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 187.Mueller C., Schrag M., Crofton A., Stolte J., Muckenthaler M.U., Magaki S., Kirsch W. Altered Serum Iron and Copper Homeostasis Predicts Cognitive Decline in Mild Cognitive Impairment. J. Alzheimer’s Dis. 2012;29:341–350. doi: 10.3233/JAD-2011-111841. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 188.de Leeuw F.A., van der Flier W.M., Tijms B.M., Scheltens P., Mendes V.M., Manadas B., Bierau J., van Wijk N., van den Heuvel E.G.H.M., Mohajeri M.H., et al. Specific Nutritional Biomarker Profiles in Mild Cognitive Impairment and Subjective Cognitive Decline Are Associated with Clinical Progression: The NUDAD Project. J. Am. Med. Dir. Assoc. 2020;21:1513.e1–1513.e17. doi: 10.1016/j.jamda.2019.12.009. [DOI] [PubMed] [Google Scholar]
- 189.Gündüztepe Y., Bukan N., Zorlu E., Karaman Y., Andaç Topkan T., Gurbuz N., Neşelioğlu S., Erel Ö. The Evaluation of Thiol-Disulfıte Balance, Ischemıa Albumın Modıfıcation and Seruloplazmine as a New Oxidatıve Stress in Mild Cognitive Impairment and Early Stage Alzheimer’s Disease Patients. J. Clin. Neurosci. 2020;75:188–194. doi: 10.1016/j.jocn.2019.12.026. [DOI] [PubMed] [Google Scholar]
- 190.Yadav J., Verma A.K., Garg R.K., Ahmad K., Shiuli, Mahdi A.A., Srivastava S. Sialic Acid Associated with Oxidative Stress and Total Antioxidant Capacity (TAC) Expression Level as a Predictive Indicator in Moderate to Severe Alzheimer’s Disease. Exp. Gerontol. 2020;141:111092. doi: 10.1016/j.exger.2020.111092. [DOI] [PubMed] [Google Scholar]
- 191.Işık M., Beydemir Ş. AChE MRNA Expression as a Possible Novel Biomarker for the Diagnosis of Coronary Artery Disease and Alzheimer’s Disease, and Its Association with Oxidative Stress. Arch. Physiol. Biochem. 2019;128:352–359. doi: 10.1080/13813455.2019.1683584. [DOI] [PubMed] [Google Scholar]
- 192.Engel B., Gomm W., Broich K., Maier W., Weckbecker K., Haenisch B. Hyperuricemia and Dementia—A Case-Control Study. BMC Neurol. 2018;18:131. doi: 10.1186/s12883-018-1136-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 193.Yamagishi K., Ikeda A., Moriyama Y., Chei C.-L., Noda H., Umesawa M., Cui R., Nagao M., Kitamura A., Yamamoto Y., et al. Serum Coenzyme Q10 and Risk of Disabling Dementia: The Circulatory Risk in Communities Study (CIRCS) Atherosclerosis. 2014;237:400–403. doi: 10.1016/j.atherosclerosis.2014.09.017. [DOI] [PubMed] [Google Scholar]
- 194.de Bustos F., Molina J.A., Jiménez-Jiménez F.J., García-Redondo A., Gómez-Escalonilla C., Porta-Etessam J., Berbel A., Zurdo M., Barcenilla B., Parrilla G., et al. Serum Levels of Coenzyme Q 10 in Patients with Alzheimer’s Disease. J. Neural Transm. 2000;107:233–239. doi: 10.1007/s007020050019. [DOI] [PubMed] [Google Scholar]
- 195.Stevenson A., Lopez D., Khoo P., Kalaria R.N., Mukaetova-Ladinska E.B. Exploring Erythrocytes as Blood Biomarkers for Alzheimer’s Disease. J. Alzheimer’s Dis. 2017;60:845–857. doi: 10.3233/JAD-170363. [DOI] [PubMed] [Google Scholar]
- 196.Cheng L., Quek C.Y.J., Sun X., Bellingham S.A., Hill A.F. The Detection of MicroRNA Associated with Alzheimer’s Disease in Biological Fluids Using next-Generation Sequencing Technologies. Front. Genet. 2013;4:150. doi: 10.3389/fgene.2013.00150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 197.Fabian M.R., Sonenberg N. The Mechanics of MiRNA-Mediated Gene Silencing: A Look under the Hood of MiRISC. Nat. Struct. Mol. Biol. 2012;19:586–593. doi: 10.1038/nsmb.2296. [DOI] [PubMed] [Google Scholar]
- 198.Krol J., Loedige I., Filipowicz W. The Widespread Regulation of MicroRNA Biogenesis, Function and Decay. Nat. Rev. Genet. 2010;11:597–610. doi: 10.1038/nrg2843. [DOI] [PubMed] [Google Scholar]
- 199.Kumar S., Reddy P.H. Are Circulating MicroRNAs Peripheral Biomarkers for Alzheimer’s Disease? Biochim. Biophys. Acta-Mol. Basis Dis. 2016;1862:1617–1627. doi: 10.1016/j.bbadis.2016.06.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 200.Kumar S., Vijayan M., Bhatti J.S., Reddy P.H. MicroRNAs as Peripheral Biomarkers in Aging and Age-Related Diseases. Prog. Mol. Biol. Transl. Sci. 2017;146:47–94. doi: 10.1016/bs.pmbts.2016.12.013. [DOI] [PubMed] [Google Scholar]
- 201.Wang Y., Yang Z., Le W. Tiny but Mighty: Promising Roles of MicroRNAs in the Diagnosis and Treatment of Parkinson’s Disease. Neurosci. Bull. 2017;33:543–551. doi: 10.1007/s12264-017-0160-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 202.Kume K., Iwama H., Deguchi K., Ikeda K., Takata T., Kokudo Y., Kamada M., Fujikawa K., Hirose K., Masugata H., et al. Serum MicroRNA Expression Profiling in Patients with Multiple System Atrophy. Mol. Med. Rep. 2017;17:852–860. doi: 10.3892/mmr.2017.7995. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 203.Patil K.S., Basak I., Dalen I., Hoedt E., Lange J., Lunde K.A., Liu Y., Tysnes O.-B., Forsgren L., Aarsland D., et al. Combinatory MicroRNA Serum Signatures as Classifiers of Parkinson’s Disease. Parkinsonism Relat. Disord. 2019;64:202–210. doi: 10.1016/j.parkreldis.2019.04.010. [DOI] [PubMed] [Google Scholar]
- 204.Regev K., Paul A., Healy B., von Glenn F., Diaz-Cruz C., Gholipour T., Mazzola M.A., Raheja R., Nejad P., Glanz B.I., et al. Comprehensive Evaluation of Serum MicroRNAs as Biomarkers in Multiple Sclerosis. Neurol.-Neuroimmunol. Neuroinflammation. 2016;3:e267. doi: 10.1212/NXI.0000000000000267. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 205.Müller M., Jäkel L., Bruinsma I.B., Claassen J.A., Kuiperij H.B., Verbeek M.M. MicroRNA-29a Is a Candidate Biomarker for Alzheimer’s Disease in Cell-Free Cerebrospinal Fluid. Mol. Neurobiol. 2016;53:2894–2899. doi: 10.1007/s12035-015-9156-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 206.Guo R., Fan G., Zhang J., Wu C., Du Y., Ye H., Li Z., Wang L., Zhang Z., Zhang L., et al. A 9-MicroRNA Signature in Serum Serves as a Noninvasive Biomarker in Early Diagnosis of Alzheimer’s Disease. J. Alzheimer’s Dis. 2017;60:1365–1377. doi: 10.3233/JAD-170343. [DOI] [PubMed] [Google Scholar]
- 207.Takousis P., Sadlon A., Schulz J., Wohlers I., Dobricic V., Middleton L., Lill C.M., Perneczky R., Bertram L. Differential Expression of MicroRNAs in Alzheimer’s Disease Brain, Blood, and Cerebrospinal Fluid. Alzheimer’s Dement. 2019;15:1468–1477. doi: 10.1016/j.jalz.2019.06.4952. [DOI] [PubMed] [Google Scholar]
- 208.Sheinerman K.S., Umansky S.R. Circulating Cell-Free MicroRNA as Biomarkers for Screening, Diagnosis and Monitoring of Neurodegenerative Diseases and Other Neurologic Pathologies. Front. Cell. Neurosci. 2013;7:150. doi: 10.3389/fncel.2013.00150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 209.Nguyen L.D., Chau R.K., Krichevsky A.M. Small Molecule Drugs Targeting Non-Coding RNAs as Treatments for Alzheimer’s Disease and Related Dementias. Genes. 2021;12:2005. doi: 10.3390/genes12122005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 210.Kumar P., Dezso Z., MacKenzie C., Oestreicher J., Agoulnik S., Byrne M., Bernier F., Yanagimachi M., Aoshima K., Oda Y. Circulating MiRNA Biomarkers for Alzheimer’s Disease. PLoS ONE. 2013;8:e69807. doi: 10.1371/journal.pone.0069807. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 211.Kiko T., Nakagawa K., Tsuduki T., Furukawa K., Arai H., Miyazawa T. MicroRNAs in Plasma and Cerebrospinal Fluid as Potential Markers for Alzheimer’s Disease. J. Alzheimer’s Dis. 2014;39:253–259. doi: 10.3233/JAD-130932. [DOI] [PubMed] [Google Scholar]
- 212.Sørensen S.S., Nygaard A.-B., Christensen T. MiRNA Expression Profiles in Cerebrospinal Fluid and Blood of Patients with Alzheimer’s Disease and Other Types of Dementia—An Exploratory Study. Transl. Neurodegener. 2016;5:6. doi: 10.1186/s40035-016-0053-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 213.Poursaei E., Abolghasemi M., Bornehdeli S., Shanehbandi D., Asadi M., Sadeghzadeh M., Rahmanpour D., Sadeh R.N. Evaluation of Hsa-Let-7d-5p, Hsa-Let-7g-5p and Hsa-MiR-15b-5p Plasma Levels in Patients with Alzheimer’s Disease. Psychiatr. Genet. 2022;32:25–29. doi: 10.1097/YPG.0000000000000303. [DOI] [PubMed] [Google Scholar]
- 214.Schipper H.M., Maes O.C., Chertkow H.M., Wang E. MicroRNA Expression in Alzheimer Blood Mononuclear Cells. Gene Regul. Syst. Biol. 2007;1:GRSB.S361. doi: 10.4137/GRSB.S361. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 215.Villa C., Ridolfi E., Fenoglio C., Ghezzi L., Vimercati R., Clerici F., Marcone A., Gallone S., Serpente M., Cantoni C., et al. Expression of the Transcription Factor Sp1 and Its Regulatory Hsa-MiR-29b in Peripheral Blood Mononuclear Cells from Patients with Alzheimer’s Disease. J. Alzheimer’s Dis. 2013;35:487–494. doi: 10.3233/JAD-122263. [DOI] [PubMed] [Google Scholar]
- 216.Kumar S., Reddy P.H. A New Discovery of MicroRNA-455-3p in Alzheimer’s Disease. J. Alzheimer’s Dis. 2019;72:S117–S130. doi: 10.3233/JAD-190583. [DOI] [PubMed] [Google Scholar]
- 217.Nagaraj S., Want A., Laskowska-Kaszub K., Fesiuk A., Vaz S., Logarinho E., Wojda U. Candidate Alzheimer’s Disease Biomarker MiR-483-5p Lowers TAU Phosphorylation by Direct ERK1/2 Repression. Int. J. Mol. Sci. 2021;22:3653. doi: 10.3390/ijms22073653. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 218.Wang L., Liu J., Wang Q., Jiang H., Zeng L., Li Z., Liu R. MicroRNA-200a-3p Mediates Neuroprotection in Alzheimer-Related Deficits and Attenuates Amyloid-Beta Overproduction and Tau Hyperphosphorylation via Coregulating BACE1 and PRKACB. Front. Pharmacol. 2019;10:806. doi: 10.3389/fphar.2019.00806. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 219.Bekris L.M., Lutz F., Montine T.J., Yu C.E., Tsuang D., Peskind E.R., Leverenz J.B. MicroRNA in Alzheimer’s Disease: An Exploratory Study in Brain, Cerebrospinal Fluid and Plasma. Biomarkers. 2013;18:455–466. doi: 10.3109/1354750X.2013.814073. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 220.Geekiyanage H., Jicha G.A., Nelson P.T., Chan C. Blood Serum MiRNA: Non-Invasive Biomarkers for Alzheimer’s Disease. Exp. Neurol. 2012;235:491–496. doi: 10.1016/j.expneurol.2011.11.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 221.Turk A., Kunej T., Peterlin B. MicroRNA-Target Interaction Regulatory Network in Alzheimer’s Disease. J. Pers. Med. 2021;11:1275. doi: 10.3390/jpm11121275. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 222.Yang G., Song Y., Zhou X., Deng Y., Liu T., Weng G., Yu D., Pan S. MicroRNA-29c Targets β-Site Amyloid Precursor Protein-Cleaving Enzyme 1 and Has a Neuroprotective Role in Vitro and in Vivo. Mol. Med. Rep. 2015;12:3081–3088. doi: 10.3892/mmr.2015.3728. [DOI] [PubMed] [Google Scholar]
- 223.Yılmaz Ş.G., Erdal M.E., Özge A.A., Sungur M.A. Can Peripheral MicroRNA Expression Data Serve as Epigenomic (Upstream) Biomarkers of Alzheimer’s Disease? OMICS: A J. Integr. Biol. 2016;20:456–461. doi: 10.1089/omi.2016.0099. [DOI] [PubMed] [Google Scholar]
- 224.Liu L., Chen X., Chen Y.-H., Zhang K. Identification of Circular RNA Hsa_Circ_0003391 in Peripheral Blood Is Potentially Associated with Alzheimer’s Disease. Front. Aging Neurosci. 2020;12:601965. doi: 10.3389/fnagi.2020.601965. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 225.Ludwig N., Fehlmann T., Kern F., Gogol M., Maetzler W., Deutscher S., Gurlit S., Schulte C., von Thaler A.-K., Deuschle C., et al. Machine Learning to Detect Alzheimer’s Disease from Circulating Non-Coding RNAs. Genom. Proteom. Bioinform. 2019;17:430–440. doi: 10.1016/j.gpb.2019.09.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 226.Xing H., Guo S., Zhang Y., Zheng Z., Wang H. Upregulation of MicroRNA-206 Enhances Lipopolysaccharide-Induced Inflammation and Release of Amyloid-β by Targeting Insulin-like Growth Factor 1 in Microglia. Mol. Med. Rep. 2016;14:1357–1364. doi: 10.3892/mmr.2016.5369. [DOI] [PubMed] [Google Scholar]
- 227.Wu H.Z.Y., Thalamuthu A., Cheng L., Fowler C., Masters C.L., Sachdev P., Mather K.A. Differential Blood MiRNA Expression in Brain Amyloid Imaging-Defined Alzheimer’s Disease and Controls. Alzheimer’s Res. Ther. 2020;12:59. doi: 10.1186/s13195-020-00627-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 228.Karaglani M., Gourlia K., Tsamardinos I., Chatzaki E. Accurate Blood-Based Diagnostic Biosignatures for Alzheimer’s Disease via Automated Machine Learning. J. Clin. Med. 2020;9:3016. doi: 10.3390/jcm9093016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 229.Gullett J.M., Chen Z., O’Shea A., Akbar M., Bian J., Rani A., Porges E.C., Foster T.C., Woods A.J., Modave F., et al. MicroRNA Predicts Cognitive Performance in Healthy Older Adults. Neurobiol. Aging. 2020;95:186–194. doi: 10.1016/j.neurobiolaging.2020.07.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 230.Yuen S.C., Liang X., Zhu H., Jia Y., Leung S. Prediction of Differentially Expressed MicroRNAs in Blood as Potential Biomarkers for Alzheimer’s Disease by Meta-Analysis and Adaptive Boosting Ensemble Learning. Alzheimer’s Res. Ther. 2021;13:126. doi: 10.1186/s13195-021-00862-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 231.Jia L.-H., Liu Y.-N. Downregulated Serum MiR-223 Servers as Biomarker in Alzheimer’s Disease. Cell Biochem. Funct. 2016;34:233–237. doi: 10.1002/cbf.3184. [DOI] [PubMed] [Google Scholar]
- 232.Galimberti D., Villa C., Fenoglio C., Serpente M., Ghezzi L., Cioffi S.M.G., Arighi A., Fumagalli G., Scarpini E. Circulating MiRNAs as Potential Biomarkers in Alzheimer’s Disease. J. Alzheimer’s Dis. 2014;42:1261–1267. doi: 10.3233/JAD-140756. [DOI] [PubMed] [Google Scholar]
- 233.Bhatnagar S., Chertkow H., Schipper H.M., Yuan Z., Shetty V., Jenkins S., Jones T., Wang E. Increased MicroRNA-34c Abundance in Alzheimer’s Disease Circulating Blood Plasma. Front. Mol. Neurosci. 2014;7:2. doi: 10.3389/fnmol.2014.00002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 234.Hara N., Kikuchi M., Miyashita A., Hatsuta H., Saito Y., Kasuga K., Murayama S., Ikeuchi T., Kuwano R. Serum MicroRNA MiR-501-3p as a Potential Biomarker Related to the Progression of Alzheimer’s Disease. Acta Neuropathol. Commun. 2017;5:10. doi: 10.1186/s40478-017-0414-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 235.Kumar S., Vijayan M., Reddy P.H. MicroRNA-455-3p as a Potential Peripheral Biomarker for Alzheimer’s Disease. Hum. Mol. Genet. 2017;26:3808–3822. doi: 10.1093/hmg/ddx267. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 236.Keller A., Backes C., Haas J., Leidinger P., Maetzler W., Deuschle C., Berg D., Ruschil C., Galata V., Ruprecht K., et al. Validating Alzheimer’s Disease Micro RNAs Using Next-generation Sequencing. Alzheimer’s Dement. 2016;12:565–576. doi: 10.1016/j.jalz.2015.12.012. [DOI] [PubMed] [Google Scholar]
- 237.Tan L., Yu J.-T., Tan M.-S., Liu Q.-Y., Wang H.-F., Zhang W., Jiang T., Tan L. Genome-Wide Serum MicroRNA Expression Profiling Identifies Serum Biomarkers for Alzheimer’s Disease. J. Alzheimer’s Dis. 2014;40:1017–1027. doi: 10.3233/JAD-132144. [DOI] [PubMed] [Google Scholar]
- 238.Wang Z., Shen L., Wang Y., Huang S. Integrated Analysis of MiRNA and MRNA Expression in the Blood of Patients with Alzheimer’s Disease. Mol. Med. Rep. 2020;22:1053–1062. doi: 10.3892/mmr.2020.11162. [DOI] [PubMed] [Google Scholar]
- 239.Xu A., Kouznetsova V.L., Tsigelny I.F. Alzheimer’s Disease Diagnostics Using MiRNA Biomarkers and Machine Learning. J. Alzheimer’s Dis. 2022;86:841–859. doi: 10.3233/JAD-215502. [DOI] [PubMed] [Google Scholar]
- 240.Denk J., Oberhauser F., Kornhuber J., Wiltfang J., Fassbender K., Schroeter M.L., Volk A.E., Diehl-Schmid J., Prudlo J., Danek A., et al. Specific Serum and CSF MicroRNA Profiles Distinguish Sporadic Behavioural Variant of Frontotemporal Dementia Compared with Alzheimer Patients and Cognitively Healthy Controls. PLoS ONE. 2018;13:e0197329. doi: 10.1371/journal.pone.0197329. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 241.Martinez B., Peplow P. MicroRNA Biomarkers in Frontotemporal Dementia and to Distinguish from Alzheimer’s Disease and Amyotrophic Lateral Sclerosis. Neural Regen. Res. 2022;17:1412. doi: 10.4103/1673-5374.330591. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 242.Dong H., Li J., Huang L., Chen X., Li D., Wang T., Hu C., Xu J., Zhang C., Zen K., et al. Serum MicroRNA Profiles Serve as Novel Biomarkers for the Diagnosis of Alzheimer’s Disease. Dis. Markers. 2015;2015:625659. doi: 10.1155/2015/625659. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 243.Sheinerman K.S., Toledo J.B., Tsivinsky V.G., Irwin D., Grossman M., Weintraub D., Hurtig H.I., Chen-Plotkin A., Wolk D.A., McCluskey L.F., et al. Circulating Brain-Enriched MicroRNAs as Novel Biomarkers for Detection and Differentiation of Neurodegenerative Diseases. Alzheimer’s Res. Ther. 2017;9:89. doi: 10.1186/s13195-017-0316-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 244.Wang J., Chen C., Zhang Y. An Investigation of MicroRNA-103 and MicroRNA-107 as Potential Blood-based Biomarkers for Disease Risk and Progression of Alzheimer’s Disease. J. Clin. Lab. Anal. 2020;34:e23006. doi: 10.1002/jcla.23006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 245.Zhuang J., Chen Z., Cai P., Wang R., Yang Q., Li L., Yang H., Zhu R. Targeting MicroRNA-125b Promotes Neurite Outgrowth but Represses Cell Apoptosis and Inflammation via Blocking PTGS2 and CDK5 in a FOXQ1-Dependent Way in Alzheimer Disease. Front. Cell. Neurosci. 2020;14:587747. doi: 10.3389/fncel.2020.587747. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 246.Sabry R., el Sharkawy R.E., Gad N.M. MiRNA -483-5p as a Potential Noninvasive Biomarker for Early Detection of Alzheimer’s Disease. Egypt. J. Immunol. 2020;27:59–72. [PubMed] [Google Scholar]
- 247.Liu C.-G., Wang J.-L., Li L., Wang P.-C. MicroRNA-384 Regulates Both Amyloid Precursor Protein and β-Secretase Expression and Is a Potential Biomarker for Alzheimer’s Disease. Int. J. Mol. Med. 2014;34:160–166. doi: 10.3892/ijmm.2014.1780. [DOI] [PubMed] [Google Scholar]
- 248.Nagaraj S., Laskowska-Kaszub K., Dębski K.J., Wojsiat J., Dąbrowski M., Gabryelewicz T., Kuźnicki J., Wojda U. Profile of 6 MicroRNA in Blood Plasma Distinguish Early Stage Alzheimer’s Disease Patients from Non-Demented Subjects. Oncotarget. 2017;8:16122–16143. doi: 10.18632/oncotarget.15109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 249.Cosín-Tomás M., Antonell A., Lladó A., Alcolea D., Fortea J., Ezquerra M., Lleó A., Martí M.J., Pallàs M., Sanchez-Valle R., et al. Plasma MiR-34a-5p and MiR-545-3p as Early Biomarkers of Alzheimer’s Disease: Potential and Limitations. Mol. Neurobiol. 2017;54:5550–5562. doi: 10.1007/s12035-016-0088-8. [DOI] [PubMed] [Google Scholar]
- 250.He H., Liu A., Zhang W., Yang H., Zhang M., Xu H., Liu Y., Hong B., Yan F., Yue L., et al. Novel Plasma MiRNAs as Biomarkers and Therapeutic Targets of Alzheimer’s Disease at the Prodromal Stage. J. Alzheimer’s Dis. 2021;83:779–790. doi: 10.3233/JAD-210307. [DOI] [PubMed] [Google Scholar]
- 251.Siedlecki-Wullich D., Català-Solsona J., Fábregas C., Hernández I., Clarimon J., Lleó A., Boada M., Saura C.A., Rodríguez-Álvarez J., Miñano-Molina A.J. Altered MicroRNAs Related to Synaptic Function as Potential Plasma Biomarkers for Alzheimer’s Disease. Alzheimer’s Res. Ther. 2019;11:46. doi: 10.1186/s13195-019-0501-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 252.Dakterzada F., David Benítez I., Targa A., Lladó A., Torres G., Romero L., de Gonzalo-Calvo D., Moncusí-Moix A., Tort-Merino A., Huerto R., et al. Reduced Levels of MiR-342-5p in Plasma Are Associated with Worse Cognitive Evolution in Patients with Mild Alzheimer’s Disease. Front. Aging Neurosci. 2021;13:705989. doi: 10.3389/fnagi.2021.705989. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 253.Shi Z., Zhang K., Zhou H., Jiang L., Xie B., Wang R., Xia W., Yin Y., Gao Z., Cui D., et al. Increased MiR-34c Mediates Synaptic Deficits by Targeting Synaptotagmin 1 through ROS-JNK-p53 Pathway in Alzheimer’s Disease. Aging Cell. 2020;19:e13125. doi: 10.1111/acel.13125. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 254.Zhang X., Wang Y., Liu W., Wang T., Wang L., Hao L., Ju M., Xiao R. Diet Quality, Gut Microbiota, and MicroRNAs Associated with Mild Cognitive Impairment in Middle-Aged and Elderly Chinese Population. Am. J. Clin. Nutr. 2021;114:429–440. doi: 10.1093/ajcn/nqab078. [DOI] [PubMed] [Google Scholar]
- 255.Wang T., Chen K., Li H., Dong S., Su N., Liu Y., Cheng Y., Dai J., Yang C., Xiao S. The Feasibility of Utilizing Plasma MiRNA107 and BACE1 Messenger RNA Gene Expression for Clinical Diagnosis of Amnestic Mild Cognitive Impairment. J. Clin. Psychiatry. 2015;76:135–141. doi: 10.4088/JCP.13m08812. [DOI] [PubMed] [Google Scholar]
- 256.Zhu Y., Li C., Sun A., Wang Y., Zhou S. Quantification of MicroRNA-210 in the Cerebrospinal Fluid and Serum: Implications for Alzheimer’s Disease. Exp. Ther. Med. 2015;9:1013–1017. doi: 10.3892/etm.2015.2179. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 257.Feng L., Liao Y.-T., He J.-C., Xie C.-L., Chen S.-Y., Fan H.-H., Su Z.-P., Wang Z. Plasma Long Non-Coding RNA BACE1 as a Novel Biomarker for Diagnosis of Alzheimer Disease. BMC Neurol. 2018;18:4. doi: 10.1186/s12883-017-1008-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 258.Fotuhi S.N., Khalaj-Kondori M., Hoseinpour Feizi M.A., Talebi M. Long Non-Coding RNA BACE1-AS May Serve as an Alzheimer’s Disease Blood-Based Biomarker. J. Mol. Neurosci. 2019;69:351–359. doi: 10.1007/s12031-019-01364-2. [DOI] [PubMed] [Google Scholar]
- 259.Barbagallo C., Mostile G., Baglieri G., Giunta F., Luca A., Raciti L., Zappia M., Purrello M., Ragusa M., Nicoletti A. Specific Signatures of Serum MiRNAs as Potential Biomarkers to Discriminate Clinically Similar Neurodegenerative and Vascular-Related Diseases. Cell. Mol. Neurobiol. 2020;40:531–546. doi: 10.1007/s10571-019-00751-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 260.Sheinerman K.S., Tsivinsky V.G., Crawford F., Mullan M.J., Abdullah L., Umansky S.R. Plasma MicroRNA Biomarkers for Detection of Mild Cognitive Impairment. Aging. 2012;4:590–605. doi: 10.18632/aging.100486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 261.Sheinerman K.S., Tsivinsky V.G., Abdullah L., Crawford F., Umansky S.R. Plasma MicroRNA Biomarkers for Detection of Mild Cognitive Impairment: Biomarker Validation Study. Aging. 2013;5:925–938. doi: 10.18632/aging.100624. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 262.Kayano M., Higaki S., Satoh J., Matsumoto K., Matsubara E., Takikawa O., Niida S. Plasma MicroRNA Biomarker Detection for Mild Cognitive Impairment Using Differential Correlation Analysis. Biomark. Res. 2016;4:22. doi: 10.1186/s40364-016-0076-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 263.Wang L., Zhang L. Circulating Exosomal MiRNA as Diagnostic Biomarkers of Neurodegenerative Diseases. Front. Mol. Neurosci. 2020;13:53. doi: 10.3389/fnmol.2020.00053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 264.Dong X., Zheng D., Nao J. Circulating Exosome MicroRNAs as Diagnostic Biomarkers of Dementia. Front. Aging Neurosci. 2020;12:580199. doi: 10.3389/fnagi.2020.580199. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 265.Hornung S., Dutta S., Bitan G. CNS-Derived Blood Exosomes as a Promising Source of Biomarkers: Opportunities and Challenges. Front. Mol. Neurosci. 2020;13:38. doi: 10.3389/fnmol.2020.00038. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 266.Cao X.-Y., Lu J.-M., Zhao Z.-Q., Li M.-C., Lu T., An X.-S., Xue L.-J. MicroRNA Biomarkers of Parkinson’s Disease in Serum Exosome-like Microvesicles. Neurosci. Lett. 2017;644:94–99. doi: 10.1016/j.neulet.2017.02.045. [DOI] [PubMed] [Google Scholar]
- 267.Lugli G., Cohen A.M., Bennett D.A., Shah R.C., Fields C.J., Hernandez A.G., Smalheiser N.R. Plasma Exosomal MiRNAs in Persons with and without Alzheimer Disease: Altered Expression and Prospects for Biomarkers. PLoS ONE. 2015;10:e0139233. doi: 10.1371/journal.pone.0139233. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 268.Cha D.J., Mengel D., Mustapic M., Liu W., Selkoe D.J., Kapogiannis D., Galasko D., Rissman R.A., Bennett D.A., Walsh D.M. MiR-212 and MiR-132 Are Downregulated in Neurally Derived Plasma Exosomes of Alzheimer’s Patients. Front. Neurosci. 2019;13:1208. doi: 10.3389/fnins.2019.01208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 269.Rani A., O’Shea A., Ianov L., Cohen R.A., Woods A.J., Foster T.C. MiRNA in Circulating Microvesicles as Biomarkers for Age-Related Cognitive Decline. Front. Aging Neurosci. 2017;9:323. doi: 10.3389/fnagi.2017.00323. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 270.Gámez-Valero A., Campdelacreu J., Vilas D., Ispierto L., Reñé R., Álvarez R., Armengol M.P., Borràs F.E., Beyer K. Exploratory Study on MicroRNA Profiles from Plasma-Derived Extracellular Vesicles in Alzheimer’s Disease and Dementia with Lewy Bodies. Transl. Neurodegener. 2019;8:31. doi: 10.1186/s40035-019-0169-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 271.Sproviero D., Gagliardi S., Zucca S., Arigoni M., Giannini M., Garofalo M., Olivero M., Dell’Orco M., Pansarasa O., Bernuzzi S., et al. Different MiRNA Profiles in Plasma Derived Small and Large Extracellular Vesicles from Patients with Neurodegenerative Diseases. Int. J. Mol. Sci. 2021;22:2737. doi: 10.3390/ijms22052737. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 272.Cheng L., Doecke J.D., Sharples R.A., Villemagne V.L., Fowler C.J., Rembach A., Martins R.N., Rowe C.C., Macaulay S.L., Masters C.L., et al. Prognostic Serum MiRNA Biomarkers Associated with Alzheimer’s Disease Shows Concordance with Neuropsychological and Neuroimaging Assessment. Mol. Psychiatry. 2015;20:1188–1196. doi: 10.1038/mp.2014.127. [DOI] [PubMed] [Google Scholar]
- 273.Wei H., Xu Y., Xu W., Zhou Q., Chen Q., Yang M., Feng F., Liu Y., Zhu X., Yu M., et al. Serum Exosomal MiR-223 Serves as a Potential Diagnostic and Prognostic Biomarker for Dementia. Neuroscience. 2018;379:167–176. doi: 10.1016/j.neuroscience.2018.03.016. [DOI] [PubMed] [Google Scholar]
- 274.Liu C.G., Meng S., Li Y., Lu Y., Zhao Y., Wang P.C. MicroRNA-135a in ABCA1-Labeled Exosome Is a Serum Biomarker Candidate for Alzheimer’s Disease. Biomed. Environ. Sci. 2021;34:19–28. doi: 10.3967/bes2021.004. [DOI] [PubMed] [Google Scholar]
- 275.Yang T.T., Liu C.G., Gao S.C., Zhang Y., Wang P.C. The Serum Exosome Derived MicroRNA-135a, -193b, and -384 Were Potential Alzheimer’s Disease Biomarkers. Biomed. Environ. Sci. 2018;31:87–96. doi: 10.3967/bes2018.011. [DOI] [PubMed] [Google Scholar]
- 276.Fransquet P.D., Ryan J. Micro RNA as a Potential Blood-Based Epigenetic Biomarker for Alzheimer’s Disease. Clin. Biochem. 2018;58:5–14. doi: 10.1016/j.clinbiochem.2018.05.020. [DOI] [PubMed] [Google Scholar]
- 277.Goh S.Y., Chao Y.X., Dheen S.T., Tan E.-K., Tay S.S.-W. Role of MicroRNAs in Parkinson’s Disease. Int. J. Mol. Sci. 2019;20:5649. doi: 10.3390/ijms20225649. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 278.Wu Y.-Y., Kuo H.-C. Functional Roles and Networks of Non-Coding RNAs in the Pathogenesis of Neurodegenerative Diseases. J. Biomed. Sci. 2020;27:49. doi: 10.1186/s12929-020-00636-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 279.Görücü Yilmaz Ş., Erdal M.E., Avci Özge A., Sungur M.A. SNP Variation in MicroRNA Biogenesis Pathway Genes as a New Innovation Strategy for Alzheimer Disease Diagnostics. Alzheimer Dis. Assoc. Disord. 2016;30:203–209. doi: 10.1097/WAD.0000000000000135. [DOI] [PubMed] [Google Scholar]
- 280.Takousis P. Analysis of Micro-RNA Expression by QPCR on a Microfluidics Platform for Alzheimer’s Disease Biomarker Discovery. Methods Mol. Biol. 2018;1750:283–292. doi: 10.1007/978-1-4939-7704-8_19. [DOI] [PubMed] [Google Scholar]
- 281.Nie C., Sun Y., Zhen H., Guo M., Ye J., Liu Z., Yang Y., Zhang X. Differential Expression of Plasma Exo-MiRNA in Neurodegenerative Diseases by Next-Generation Sequencing. Front. Neurosci. 2020;14:438. doi: 10.3389/fnins.2020.00438. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 282.Mercer T.R., Dinger M.E., Mattick J.S. Long Non-Coding RNAs: Insights into Functions. Nat. Rev. Genet. 2009;10:155–159. doi: 10.1038/nrg2521. [DOI] [PubMed] [Google Scholar]
- 283.Wang M.-Q., Zhu W.-J., Gao P. New Insights into Long Non-Coding RNAs in Breast Cancer: Biological Functions and Therapeutic Prospects. Exp. Mol. Pathol. 2021;120:104640. doi: 10.1016/j.yexmp.2021.104640. [DOI] [PubMed] [Google Scholar]
- 284.Lan Z., Chen Y., Jin J., Xu Y., Zhu X. Long Non-Coding RNA: Insight into Mechanisms of Alzheimer’s Disease. Front. Mol. Neurosci. 2022;14:821002. doi: 10.3389/fnmol.2021.821002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 285.Xu Y.-Z., Cheng M.-G., Wang X., Hu Y. The Emerging Role of Non-Coding RNAs from Extracellular Vesicles in Alzheimer’s Disease. J. Integr. Neurosci. 2021;20:239. doi: 10.31083/j.jin.2021.01.360. [DOI] [PubMed] [Google Scholar]
- 286.Han Z., Xue W., Tao L., Zhu F. Identification of Key Long Non-Coding RNAs in the Pathology of Alzheimer’s Disease and Their Functions Based on Genome-Wide Associations Study, Microarray, and RNA-Seq Data. J. Alzheimer’s Dis. 2019;68:339–355. doi: 10.3233/JAD-181051. [DOI] [PubMed] [Google Scholar]
- 287.Massone S., Ciarlo E., Vella S., Nizzari M., Florio T., Russo C., Cancedda R., Pagano A. NDM29, a RNA Polymerase III-Dependent Non Coding RNA, Promotes Amyloidogenic Processing of APP and Amyloid β Secretion. Biochim. Biophys. Acta-Mol. Cell Res. 2012;1823:1170–1177. doi: 10.1016/j.bbamcr.2012.05.001. [DOI] [PubMed] [Google Scholar]
- 288.Faghihi M.A., Modarresi F., Khalil A.M., Wood D.E., Sahagan B.G., Morgan T.E., Finch C.E., St. Laurent G., III, Kenny P.J., Wahlestedt C. Expression of a Noncoding RNA Is Elevated in Alzheimer’s Disease and Drives Rapid Feed-Forward Regulation of β-Secretase. Nat. Med. 2008;14:723–730. doi: 10.1038/nm1784. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 289.Ciarlo E., Massone S., Penna I., Nizzari M., Gigoni A., Dieci G., Russo C., Florio T., Cancedda R., Pagano A. An Intronic NcRNA-Dependent Regulation of SORL1 Expression Affecting Aβ Formation Is Upregulated in Post-Mortem Alzheimer’s Disease Brain Samples. Dis. Models Mech. 2012;6:424–433. doi: 10.1242/dmm.009761. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 290.Yan Y., Yan H., Teng Y., Wang Q., Yang P., Zhang L., Cheng H., Fu S. Long Non-coding RNA 00507/MiRNA-181c-5p/TTBK1/MAPT Axis Regulates Tau Hyperphosphorylation in Alzheimer’s Disease. J. Gene Med. 2020;22:3268. doi: 10.1002/jgm.3268. [DOI] [PubMed] [Google Scholar]
- 291.Massone S., Vassallo I., Fiorino G., Castelnuovo M., Barbieri F., Borghi R., Tabaton M., Robello M., Gatta E., Russo C., et al. 17A, a Novel Non-Coding RNA, Regulates GABA B Alternative Splicing and Signaling in Response to Inflammatory Stimuli and in Alzheimer Disease. Neurobiol. Dis. 2011;41:308–317. doi: 10.1016/j.nbd.2010.09.019. [DOI] [PubMed] [Google Scholar]
- 292.Aliperti V., Skonieczna J., Cerase A. Long Non-Coding RNA (LncRNA) Roles in Cell Biology, Neurodevelopment and Neurological Disorders. Non-Coding RNA. 2021;7:36. doi: 10.3390/ncrna7020036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 293.Wang D., Wang P., Bian X., Xu S., Zhou Q., Zhang Y., Ding M., Han M., Huang L., Bi J., et al. Elevated Plasma Levels of Exosomal BACE1-AS Combined with the Volume and Thickness of the Right Entorhinal Cortex May Serve as a Biomarker for the Detection of Alzheimer’s Disease. Mol. Med. Rep. 2020;22:227–238. doi: 10.3892/mmr.2020.11118. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 294.Kao Y.-C., Ho P.-C., Tu Y.-K., Jou I.-M., Tsai K.-J. Lipids and Alzheimer’s Disease. Int. J. Mol. Sci. 2020;21:1505. doi: 10.3390/ijms21041505. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 295.Chew H., Solomon V.A., Fonteh A.N. Involvement of Lipids in Alzheimer’s Disease Pathology and Potential Therapies. Front. Physiol. 2020;11:598. doi: 10.3389/fphys.2020.00598. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 296.Casas-Fernández E., Peña-Bautista C., Baquero M., Cháfer-Pericás C. Lipids as Early and Minimally Invasive Biomarkers for Alzheimer Disease. Curr. Neuropharmacol. 2021;19 doi: 10.2174/1570159X19666211102150955. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 297.Blasko I., Defrancesco M., Oberacher H., Loacker L., Kemmler G., Marksteiner J., Humpel C. Plasma Phosphatidylcholines and Vitamin B12/Folate Levels Are Possible Prognostic Biomarkers for Progression of Alzheimer’s Disease. Exp. Gerontol. 2021;147:111264. doi: 10.1016/j.exger.2021.111264. [DOI] [PubMed] [Google Scholar]
- 298.Fonteh A.N., Chiang A.J., Arakaki X., Edminster S.P., Harrington M.G. Accumulation of Cerebrospinal Fluid Glycerophospholipids and Sphingolipids in Cognitively Healthy Participants with Alzheimer’s Biomarkers Precedes Lipolysis in the Dementia Stage. Front. Neurosci. 2020;14:611393. doi: 10.3389/fnins.2020.611393. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 299.Benussi L., Ghidoni R., Dal Piaz F., Binetti G., di Iorio G., Abrescia P. The Level of 24-Hydroxycholesteryl Esters Is an Early Marker of Alzheimer’s Disease. J. Alzheimer’s Dis. 2017;56:825–833. doi: 10.3233/JAD-160930. [DOI] [PubMed] [Google Scholar]
- 300.Roy D., Chakrabarti S.S., Banerjee A., Sharma P., Biswas A., Chakrabarti S. Serum 24-hydroxycholesterol in Probable Alzheimer’s Dementia: Reexploring the Significance of a Tentative Alzheimer’s Disease Biomarker. Aging Med. 2019;2:74–81. doi: 10.1002/agm2.12068. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 301.Wellington C.L., Frikke-Schmidt R. Relation between Plasma and Brain Lipids. Curr. Opin. Lipidol. 2016;27:225–232. doi: 10.1097/MOL.0000000000000291. [DOI] [PubMed] [Google Scholar]
- 302.Sakr F., Dyrba M., Bräuer A., Teipel S. Association of Lipidomics Signatures in Blood with Clinical Progression in Preclinical and Prodromal Alzheimer’s Disease. J. Alzheimer’s Dis. 2022;85:1115–1127. doi: 10.3233/JAD-201504. [DOI] [PubMed] [Google Scholar]
- 303.Tokuoka S.M., Kita Y., Shimizu T., Oda Y. Isobaric Mass Tagging and Triple Quadrupole Mass Spectrometry to Determine Lipid Biomarker Candidates for Alzheimer’s Disease. PLoS ONE. 2019;14:e0226073. doi: 10.1371/journal.pone.0226073. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 304.Briganti S., Truglio M., Angiolillo A., Lombardo S., Leccese D., Camera E., Picardo M., di Costanzo A. Application of Serum Lipidomics to Biomarkers Discovery in Neurodegenerative Diseases. Metabolites. 2021;11:819. doi: 10.3390/metabo11120819. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 305.Praticò D., Clark C.M., Lee V.M., Trojanowski J.Q., Rokach J., FitzGerald G.A. Increased 8,12-Iso-IPF2alpha-VI in Alzheimer’s Disease: Correlation of a Noninvasive Index of Lipid Peroxidation with Disease Severity. Ann. Neurol. 2000;48:809–812. doi: 10.1002/1531-8249(200011)48:5<809::AID-ANA19>3.0.CO;2-9. [DOI] [PubMed] [Google Scholar]
- 306.Skoumalová A., Mádlová P., Topinková E. End Products of Lipid Peroxidation in Erythrocyte Membranes in Alzheimer’s Disease. Cell Biochem. Funct. 2012;30:205–210. doi: 10.1002/cbf.1836. [DOI] [PubMed] [Google Scholar]
- 307.Peña-Bautista C., Vigor C., Galano J.-M., Oger C., Durand T., Ferrer I., Cuevas A., López-Cuevas R., Baquero M., López-Nogueroles M., et al. Plasma Lipid Peroxidation Biomarkers for Early and Non-Invasive Alzheimer Disease Detection. Free Radic. Biol. Med. 2018;124:388–394. doi: 10.1016/j.freeradbiomed.2018.06.038. [DOI] [PubMed] [Google Scholar]
- 308.Zarrouk A., Riedinger J.-M., Ahmed S.H., Hammami S., Chaabane W., Debbabi M., ben Ammou S., Rouaud O., Frih M., Lizard G., et al. Fatty Acid Profiles in Demented Patients: Identification of Hexacosanoic Acid (C26:0) as a Blood Lipid Biomarker of Dementia. J. Alzheimer’s Dis. 2015;44:1349–1359. doi: 10.3233/JAD-142046. [DOI] [PubMed] [Google Scholar]
- 309.Peña-Bautista C., Álvarez-Sánchez L., Cañada-Martínez A.J., Baquero M., Cháfer-Pericás C. Epigenomics and Lipidomics Integration in Alzheimer Disease: Pathways Involved in Early Stages. Biomedicines. 2021;9:1812. doi: 10.3390/biomedicines9121812. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 310.Kim M., Snowden S., Suvitaival T., Ali A., Merkler D.J., Ahmad T., Westwood S., Baird A., Proitsi P., Nevado-Holgado A., et al. Primary Fatty Amides in Plasma Associated with Brain Amyloid Burden, Hippocampal Volume, and Memory in the European Medical Information Framework for Alzheimer’s Disease Biomarker Discovery Cohort. Alzheimer’s Dement. 2019;15:817–827. doi: 10.1016/j.jalz.2019.03.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 311.Wang D.-C., Sun C.-H., Liu L.-Y., Sun X.-H., Jin X.-W., Song W.-L., Liu X.-Q., Wan X.-L. Serum Fatty Acid Profiles Using GC-MS and Multivariate Statistical Analysis: Potential Biomarkers of Alzheimer’s Disease. Neurobiol. Aging. 2012;33:1057–1066. doi: 10.1016/j.neurobiolaging.2010.09.013. [DOI] [PubMed] [Google Scholar]
- 312.Sánchez-Campillo M., Ruiz-Pastor M.J., Gázquez A., Marín-Muñoz J., Noguera-Perea F., Ruiz-Alcaraz A.J., Manzanares-Sánchez S., Antúnez C., Larqué E. Decreased Blood Level of MFSD2a as a Potential Biomarker of Alzheimer’s Disease. Int. J. Mol. Sci. 2019;21:70. doi: 10.3390/ijms21010070. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 313.Mielke M.M., Haughey N.J. Could Plasma Sphingolipids Be Diagnostic or Prognostic Biomarkers for Alzheimer’s Disease? Clin. Lipidol. 2012;7:525–536. doi: 10.2217/clp.12.59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 314.Llano D.A., Devanarayan V. Serum Phosphatidylethanolamine and Lysophosphatidylethanolamine Levels Differentiate Alzheimer’s Disease from Controls and Predict Progression from Mild Cognitive Impairment. J. Alzheimer’s Dis. 2021;80:311–319. doi: 10.3233/JAD-201420. [DOI] [PubMed] [Google Scholar]
- 315.Kim M., Nevado-Holgado A., Whiley L., Snowden S.G., Soininen H., Kloszewska I., Mecocci P., Tsolaki M., Vellas B., Thambisetty M., et al. Association between Plasma Ceramides and Phosphatidylcholines and Hippocampal Brain Volume in Late Onset Alzheimer’s Disease. J. Alzheimer’s Dis. 2017;60:809–817. doi: 10.3233/JAD-160645. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 316.Llano D., Issa L., Devanarayan P., Devanarayan V. Hearing Loss in Alzheimer’s Disease Is Associated with Altered Serum Lipidomic Biomarker Profiles. Cells. 2020;9:2556. doi: 10.3390/cells9122556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 317.Klavins K., Koal T., Dallmann G., Marksteiner J., Kemmler G., Humpel C. The Ratio of Phosphatidylcholines to Lysophosphatidylcholines in Plasma Differentiates Healthy Controls from Patients with Alzheimer’s Disease and Mild Cognitive Impairment. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 2015;1:295–302. doi: 10.1016/j.dadm.2015.05.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 318.Mapstone M., Cheema A.K., Fiandaca M.S., Zhong X., Mhyre T.R., MacArthur L.H., Hall W.J., Fisher S.G., Peterson D.R., Haley J.M., et al. Plasma Phospholipids Identify Antecedent Memory Impairment in Older Adults. Nat. Med. 2014;20:415–418. doi: 10.1038/nm.3466. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 319.Simpson B.N., Kim M., Chuang Y.-F., Beason-Held L., Kitner-Triolo M., Kraut M., Lirette S.T., Windham B.G., Griswold M.E., Legido-Quigley C., et al. Blood Metabolite Markers of Cognitive Performance and Brain Function in Aging. J. Cereb. Blood Flow Metab. 2016;36:1212–1223. doi: 10.1177/0271678X15611678. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 320.Weng W.-C., Huang W.-Y., Tang H.-Y., Cheng M.-L., Chen K.-H. The Differences of Serum Metabolites Between Patients with Early-Stage Alzheimer’s Disease and Mild Cognitive Impairment. Front. Neurol. 2019;10:1223. doi: 10.3389/fneur.2019.01223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 321.Liu Y., Zhong X., Shen J., Jiao L., Tong J., Zhao W., Du K., Gong S., Liu M., Wei M. Elevated Serum TC and LDL-C Levels in Alzheimer’s Disease and Mild Cognitive Impairment: A Meta-Analysis Study. Brain Res. 2020;1727:146554. doi: 10.1016/j.brainres.2019.146554. [DOI] [PubMed] [Google Scholar]
- 322.Sato Y., Bernier F., Yamanaka Y., Aoshima K., Oda Y., Ingelsson M., Lannfelt L., Miyashita A., Kuwano R., Ikeuchi T. Reduced Plasma Desmosterol-to-cholesterol Ratio and Longitudinal Cognitive Decline in Alzheimer’s Disease. Alzheimer’s Dement. Diagn. Assess. Dis. Monit. 2015;1:67–74. doi: 10.1016/j.dadm.2014.11.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 323.Wu Y., Wang Z., Jia X., Zhang H., Zhang H., Li J., Zhang K. Prediction of Alzheimer’s Disease with Serum Lipid Levels in Asian Individuals: A Meta-Analysis. Biomarkers. 2019;24:341–351. doi: 10.1080/1354750X.2019.1571633. [DOI] [PubMed] [Google Scholar]
- 324.Tang Q., Wang F., Yang J., Peng H., Li Y., Li B., Wang S. Revealing a Novel Landscape of the Association Between Blood Lipid Levels and Alzheimer’s Disease: A Meta-Analysis of a Case-Control Study. Front. Aging Neurosci. 2020;11:370. doi: 10.3389/fnagi.2019.00370. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 325.Gao S., Cheng Y., Jin Y., Unverzagt F., Su L., Yang L., Ma F., Hake A., Kettler C., Chen C., et al. The Relationship between Cholesterol and Cognitive Function Is Homocysteine-Dependent. Clin. Interv. Aging. 2014;2014:1823–1829. doi: 10.2147/CIA.S64766. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 326.Huang S., Ma Y., Bi Y., Shen X., Hou X., Cao X., Ou Y., Zhao B., Dong Q., Tan L., et al. Metabolically Healthy Obesity and Lipids May Be Protective Factors for Pathological Changes of Alzheimer’s Disease in Cognitively Normal Adults. J. Neurochem. 2021;157:834–845. doi: 10.1111/jnc.15306. [DOI] [PubMed] [Google Scholar]
- 327.Kjeldsen E.W., Thomassen J.Q., Juul Rasmussen I., Nordestgaard B.G., Tybjærg-Hansen A., Frikke-Schmidt R. Plasma High-Density Lipoprotein Cholesterol and Risk of Dementia: Observational and Genetic Studies. Cardiovasc. Res. 2021;118:1330–1340. doi: 10.1093/cvr/cvab164. [DOI] [PubMed] [Google Scholar]
- 328.Yun S.-M., Park J.-Y., Seo S.W., Song J. Association of Plasma Endothelial Lipase Levels on Cognitive Impairment. BMC Psychiatry. 2019;19:187. doi: 10.1186/s12888-019-2174-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 329.Montagne A., Nation D.A., Sagare A.P., Barisano G., Sweeney M.D., Chakhoyan A., Pachicano M., Joe E., Nelson A.R., D’Orazio L.M., et al. APOE4 Leads to Blood–Brain Barrier Dysfunction Predicting Cognitive Decline. Nature. 2020;581:71–76. doi: 10.1038/s41586-020-2247-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 330.Lütjohann D., von Bergmann K. 24S-Hydroxycholesterol: A Marker of Brain Cholesterol Metabolism. Pharmacopsychiatry. 2003;36:102–106. doi: 10.1055/s-2003-43053. [DOI] [PubMed] [Google Scholar]
- 331.di Natale C., Monaco A., Pedone C., Tessitore A., de Mase A., Tedeschi G., Netti P.A., Abrescia P. The Level of 24-Hydroxycholesteryl Esters Decreases in Plasma of Patients with Parkinson’s Disease. Neurosci. Lett. 2018;672:108–112. doi: 10.1016/j.neulet.2018.02.041. [DOI] [PubMed] [Google Scholar]
- 332.Tardy A.-L., Pouteau E., Marquez D., Yilmaz C., Scholey A. Vitamins and Minerals for Energy, Fatigue and Cognition: A Narrative Review of the Biochemical and Clinical Evidence. Nutrients. 2020;12:228. doi: 10.3390/nu12010228. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 333.Mora J.R., Iwata M., von Andrian U.H. Vitamin Effects on the Immune System: Vitamins A and D Take Centre Stage. Nat. Rev. Immunol. 2008;8:685–698. doi: 10.1038/nri2378. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 334.Kennedy D. B Vitamins and the Brain: Mechanisms, Dose and Efficacy—A Review. Nutrients. 2016;8:68. doi: 10.3390/nu8020068. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 335.Caccamo D., Ricca S., Currò M., Ientile R. Health Risks of Hypovitaminosis D: A Review of New Molecular Insights. Int. J. Mol. Sci. 2018;19:892. doi: 10.3390/ijms19030892. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 336.Evans E., Piccio L., Cross A.H. Use of Vitamins and Dietary Supplements by Patients with Multiple Sclerosis. JAMA Neurol. 2018;75:1013. doi: 10.1001/jamaneurol.2018.0611. [DOI] [PubMed] [Google Scholar]
- 337.Ames B.N. Prolonging Healthy Aging: Longevity Vitamins and Proteins. Proc. Natl. Acad. Sci. USA. 2018;115:10836–10844. doi: 10.1073/pnas.1809045115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 338.Glasø M., Nordbø G., Diep L., Bøhmer T. Reduced Concentrations of Several Vitamins in Normal Weight Patients with Late-Onset Dementia of the Alzheimer Type without Vascular Disease. J. Nutr. Health Aging. 2004;8:407–413. [PubMed] [Google Scholar]
- 339.Gold M., Chen M.F., Johnson K. Plasma and Red Blood Cell Thiamine Deficiency in Patients with Dementia of the Alzheimer’s Type. Arch. Neurol. 1995;52:1081–1086. doi: 10.1001/archneur.1995.00540350075019. [DOI] [PubMed] [Google Scholar]
- 340.Wang C., Fei G., Pan X., Sang S., Wang L., Zhong C., Jin L. High Thiamine Diphosphate Level as a Protective Factor for Alzheimer’s Disease. Neurol. Res. 2018;40:432–442. doi: 10.1080/01616412.2018.1460704. [DOI] [PubMed] [Google Scholar]
- 341.D’Cunha N.M., Georgousopoulou E.N., Boyd L., Veysey M., Sturm J., O’Brien B., Lucock M., McKune A.J., Mellor D.D., Roach P.D., et al. Relationship Between B-Vitamin Biomarkers and Dietary Intake with Apolipoprotein E є4 in Alzheimer’s Disease. J. Nutr. Gerontol. Geriatr. 2019;38:173–195. doi: 10.1080/21551197.2019.1590287. [DOI] [PubMed] [Google Scholar]
- 342.An Y., Feng L., Zhang X., Wang Y., Wang Y., Tao L., Qin Z., Xiao R. Dietary Intakes and Biomarker Patterns of Folate, Vitamin B6, and Vitamin B12 Can Be Associated with Cognitive Impairment by Hypermethylation of Redox-Related Genes NUDT15 and TXNRD1. Clin. Epigenet. 2019;11:139. doi: 10.1186/s13148-019-0741-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 343.Murdaca G., Banchero S., Tonacci A., Nencioni A., Monacelli F., Gangemi S. Vitamin D and Folate as Predictors of MMSE in Alzheimer’s Disease: A Machine Learning Analysis. Diagnostics. 2021;11:940. doi: 10.3390/diagnostics11060940. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 344.dos Santos G.A.A., Pardi P.C. Biomarkers in Alzheimer’s Disease: Evaluation of Platelets, Hemoglobin and Vitamin B12. Dement. Neuropsychol. 2020;14:35–40. doi: 10.1590/1980-57642020dn14-010006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 345.Baldacci F., Lista S., Manca M.L., Chiesa P.A., Cavedo E., Lemercier P., Zetterberg H., Blennow K., Habert M.-O., Potier M.C., et al. Age and Sex Impact Plasma NFL and T-Tau Trajectories in Individuals with Subjective Memory Complaints: A 3-Year Follow-up Study. Alzheimer’s Res. Ther. 2020;12:147. doi: 10.1186/s13195-020-00704-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 346.Dursun E., Alaylıoğlu M., Bilgiç B., Hanağası H., Lohmann E., Atasoy I.L., Candaş E., Araz Ö.S., Önal B., Gürvit H., et al. Vitamin D Deficiency Might Pose a Greater Risk for ApoEε4 Non-Carrier Alzheimer’s Disease Patients. Neurol. Sci. 2016;37:1633–1643. doi: 10.1007/s10072-016-2647-1. [DOI] [PubMed] [Google Scholar]
- 347.Ouma S., Suenaga M., Bölükbaşı Hatip F.F., Hatip-Al-Khatib I., Tsuboi Y., Matsunaga Y. Serum Vitamin D in Patients with Mild Cognitive Impairment and Alzheimer’s Disease. Brain Behav. 2018;8:e00936. doi: 10.1002/brb3.936. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 348.Hooshmand B., Lökk J., Solomon A., Mangialasche F., Miralbell J., Spulber G., Annerbo S., Andreasen N., Winblad B., Cedazo-Minguez A., et al. Vitamin D in Relation to Cognitive Impairment, Cerebrospinal Fluid Biomarkers, and Brain Volumes. J. Gerontol. Ser. A. 2014;69:1132–1138. doi: 10.1093/gerona/glu022. [DOI] [PubMed] [Google Scholar]
- 349.Al-Amin M., Bradford D., Sullivan R.K.P., Kurniawan N.D., Moon Y., Han S.-H., Zalesky A., Burne T.H.J. Vitamin D Deficiency Is Associated with Reduced Hippocampal Volume and Disrupted Structural Connectivity in Patients with Mild Cognitive Impairment. Hum. Brain Mapp. 2019;40:394–406. doi: 10.1002/hbm.24380. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 350.Raszewski G., Chwedorowicz R., Chwedorowicz A., Gustaw Rothenberg K. Homocysteine, Antioxidant Vitamins and Lipids as Biomarkers of Neurodegeneration in Alzheimer’s Disease versus Non-Alzheimer’s Dementia. Ann. Agric. Environ. Med. 2015;23:193–196. doi: 10.5604/12321966.1196878. [DOI] [PubMed] [Google Scholar]
- 351.Lanyau-Domínguez Y., Macías-Matos C., Llibre-Rodríguez J.d.J., Pita-Rodríguez G.M., Suárez-Medina R., Quintero-Alejo M.E., Noriega-Fernández L., Guerra-Hernández M., Calvo-Rodríguez M., Sánchez-Gil Y., et al. Levels of Vitamins and Homocysteine in Older Adults with Alzheimer Disease or Mild Cognitive Impairment in Cuba. MEDICC Rev. 2020;22:40. doi: 10.37757/MR2020.V22.N4.14. [DOI] [PubMed] [Google Scholar]
- 352.Gibson G.E., Blass J.P. Thiamine-Dependent Processes and Treatment Strategies in Neurodegeneration. Antioxid. Redox Signal. 2007;9:1605–1620. doi: 10.1089/ars.2007.1766. [DOI] [PubMed] [Google Scholar]
- 353.Cordaro M., Siracusa R., Fusco R., Cuzzocrea S., di Paola R., Impellizzeri D. Involvements of Hyperhomocysteinemia in Neurological Disorders. Metabolites. 2021;11:37. doi: 10.3390/metabo11010037. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 354.Nelson C., Wengreen H.J., Munger R.G., Corcoran C.D. Dietary Folate, Vitamin B-12, Vitamin B-6 and Incident Alzheimer’s Disease: The Cache County Memory, Health, and Aging Study. J. Nutr. Health Aging. 2009;13:899–905. doi: 10.1007/s12603-009-0249-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 355.Lue L.-F., Sabbagh M.N., Chiu M.-J., Jing N., Snyder N.L., Schmitz C., Guerra A., Belden C.M., Chen T.-F., Yang C.-C., et al. Plasma Levels of Aβ42 and Tau Identified Probable Alzheimer’s Dementia: Findings in Two Cohorts. Front. Aging Neurosci. 2017;9:226. doi: 10.3389/fnagi.2017.00226. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 356.Alzheimer’s Association 2016 Alzheimer’s Disease Facts and Figures. Alzheimer’s Dement. 2016;12:459–509. doi: 10.1016/j.jalz.2016.03.001. [DOI] [PubMed] [Google Scholar]
- 357.McLimans K.E., Collazo Martinez A.D., Mochel J.P., Allenspach K., Willette A.A. Serum Vitamin B12 and Related 5-Methyltetrahydrofolate-Homocysteine Methyltransferase Reductase and Cubilin Genotypes Predict Neural Outcomes across the Alzheimer’s Disease Spectrum. Br. J. Nutr. 2020;124:135–145. doi: 10.1017/S0007114520000951. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 358.Dursun E., Gezen-Ak D. Vitamin D Basis of Alzheimer’s Disease: From Genetics to Biomarkers. Hormones. 2019;18:7–15. doi: 10.1007/s42000-018-0086-5. [DOI] [PubMed] [Google Scholar]
- 359.Annweiler C., Dursun E., Féron F., Gezen-Ak D., Kalueff A.V., Littlejohns T., Llewellyn D., Millet P., Scott T., Tucker K.L., et al. Vitamin D and Cognition in Older Adults: International Consensus Guidelines. Gériatrie Psychol. Neuropsychiatr. Viellissement. 2016;14:265–273. doi: 10.1684/pnv.2016.0613. [DOI] [PubMed] [Google Scholar]
- 360.Blennow K., Hampel H., Weiner M., Zetterberg H. Cerebrospinal Fluid and Plasma Biomarkers in Alzheimer Disease. Nat. Rev. Neurol. 2010;6:131–144. doi: 10.1038/nrneurol.2010.4. [DOI] [PubMed] [Google Scholar]
- 361.Soares J.Z., Pettersen R., Benth J.Š., Persson K., Strobel C., Selbæk G., Bogdanovic N. Vitamin D Levels, APOE Allele, and MRI Volumetry Assessed by NeuroQuant in Norwegian Adults with Cognitive Symptoms. J. Alzheimer’s Dis. 2021;79:311–321. doi: 10.3233/JAD-201018. [DOI] [PubMed] [Google Scholar]
- 362.Huebbe P., Nebel A., Siegert S., Moehring J., Boesch-Saadatmandi C., Most E., Pallauf J., Egert S., Müller M.J., Schreiber S., et al. APOEε4 Is Associated with Higher Vitamin D Levels in Targeted Replacement Mice and Humans. FASEB J. 2011;25:3262–3270. doi: 10.1096/fj.11-180935. [DOI] [PubMed] [Google Scholar]
- 363.Bivona G., lo Sasso B., Iacolino G., Gambino C.M., Scazzone C., Agnello L., Ciaccio M. Standardized Measurement of Circulating Vitamin D [25(OH)D] and Its Putative Role as a Serum Biomarker in Alzheimer’s Disease and Parkinson’s Disease. Clin. Chim. Acta. 2019;497:82–87. doi: 10.1016/j.cca.2019.07.022. [DOI] [PubMed] [Google Scholar]
- 364.Varesi A., Deumer U.-S., Ananth S., Ricevuti G. The Emerging Role of Gut Microbiota in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS): Current Evidence and Potential Therapeutic Applications. J. Clin. Med. 2021;10:5077. doi: 10.3390/jcm10215077. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 365.Ling Z., Liu X., Jia X., Cheng Y., Luo Y., Yuan L., Wang Y., Zhao C., Guo S., Li L., et al. Impacts of Infection with Different Toxigenic Clostridium Difficile Strains on Faecal Microbiota in Children. Sci. Rep. 2015;4:7485. doi: 10.1038/srep07485. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 366.Galicia-Garcia U., Benito-Vicente A., Jebari S., Larrea-Sebal A., Siddiqi H., Uribe K.B., Ostolaza H., Martín C. Pathophysiology of Type 2 Diabetes Mellitus. Int. J. Mol. Sci. 2020;21:6275. doi: 10.3390/ijms21176275. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 367.Abenavoli L., Scarpellini E., Colica C., Boccuto L., Salehi B., Sharifi-Rad J., Aiello V., Romano B., de Lorenzo A., Izzo A.A., et al. Gut Microbiota and Obesity: A Role for Probiotics. Nutrients. 2019;11:2690. doi: 10.3390/nu11112690. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 368.Schwabe R.F., Jobin C. The Microbiome and Cancer. Nat. Rev. Cancer. 2013;13:800–812. doi: 10.1038/nrc3610. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 369.Levy M., Kolodziejczyk A.A., Thaiss C.A., Elinav E. Dysbiosis and the Immune System. Nat. Rev. Immunol. 2017;17:219–232. doi: 10.1038/nri.2017.7. [DOI] [PubMed] [Google Scholar]
- 370.Jiang H., Ling Z., Zhang Y., Mao H., Ma Z., Yin Y., Wang W., Tang W., Tan Z., Shi J., et al. Altered Fecal Microbiota Composition in Patients with Major Depressive Disorder. Brain Behav. Immun. 2015;48:186–194. doi: 10.1016/j.bbi.2015.03.016. [DOI] [PubMed] [Google Scholar]
- 371.Gerhardt S., Mohajeri M. Changes of Colonic Bacterial Composition in Parkinson’s Disease and Other Neurodegenerative Diseases. Nutrients. 2018;10:708. doi: 10.3390/nu10060708. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 372.Sun M.-F., Shen Y.-Q. Dysbiosis of Gut Microbiota and Microbial Metabolites in Parkinson’s Disease. Ageing Res. Rev. 2018;45:53–61. doi: 10.1016/j.arr.2018.04.004. [DOI] [PubMed] [Google Scholar]
- 373.Gotkine M., Kviatcovsky D., Elinav E. Amyotrophic Lateral Sclerosis and Intestinal Microbiota—Toward Establishing Cause and Effect. Gut Microbes. 2020;11:1833–1841. doi: 10.1080/19490976.2020.1767464. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 374.Cenit M.C., Sanz Y., Codoñer-Franch P. Influence of Gut Microbiota on Neuropsychiatric Disorders. World J. Gastroenterol. 2017;23:5486. doi: 10.3748/wjg.v23.i30.5486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 375.Bello-Medina P.C., Hernández-Quiroz F., Pérez-Morales M., González-Franco D.A., Cruz-Pauseno G., García-Mena J., Díaz-Cintra S., Pacheco-López G. Spatial Memory and Gut Microbiota Alterations Are Already Present in Early Adulthood in a Pre-Clinical Transgenic Model of Alzheimer’s Disease. Front. Neurosci. 2021;15:595583. doi: 10.3389/fnins.2021.595583. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 376.Cattaneo A., Cattane N., Galluzzi S., Provasi S., Lopizzo N., Festari C., Ferrari C., Guerra U.P., Paghera B., Muscio C., et al. Association of Brain Amyloidosis with Pro-Inflammatory Gut Bacterial Taxa and Peripheral Inflammation Markers in Cognitively Impaired Elderly. Neurobiol. Aging. 2017;49:60–68. doi: 10.1016/j.neurobiolaging.2016.08.019. [DOI] [PubMed] [Google Scholar]
- 377.Vogt N.M., Kerby R.L., Dill-McFarland K.A., Harding S.J., Merluzzi A.P., Johnson S.C., Carlsson C.M., Asthana S., Zetterberg H., Blennow K., et al. Gut Microbiome Alterations in Alzheimer’s Disease. Sci. Rep. 2017;7:13537. doi: 10.1038/s41598-017-13601-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 378.Zhuang Z.-Q., Shen L.-L., Li W.-W., Fu X., Zeng F., Gui L., Lü Y., Cai M., Zhu C., Tan Y.-L., et al. Gut Microbiota Is Altered in Patients with Alzheimer’s Disease. J. Alzheimer’s Dis. 2018;63:1337–1346. doi: 10.3233/JAD-180176. [DOI] [PubMed] [Google Scholar]
- 379.Alhasson F., Das S., Seth R., Dattaroy D., Chandrashekaran V., Ryan C.N., Chan L.S., Testerman T., Burch J., Hofseth L.J., et al. Altered Gut Microbiome in a Mouse Model of Gulf War Illness Causes Neuroinflammation and Intestinal Injury via Leaky Gut and TLR4 Activation. PLoS ONE. 2017;12:e0172914. doi: 10.1371/journal.pone.0172914. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 380.Varesi A., Pierella E., Romeo M., Piccini G.B., Alfano C., Bjørklund G., Oppong A., Ricevuti G., Esposito C., Chirumbolo S., et al. The Potential Role of Gut Microbiota in Alzheimer’s Disease: From Diagnosis to Treatment. Nutrients. 2022;14:668. doi: 10.3390/nu14030668. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 381.Vogt N.M., Romano K.A., Darst B.F., Engelman C.D., Johnson S.C., Carlsson C.M., Asthana S., Blennow K., Zetterberg H., Bendlin B.B., et al. The Gut Microbiota-Derived Metabolite Trimethylamine N-Oxide Is Elevated in Alzheimer’s Disease. Alzheimer’s Res. Ther. 2018;10:124. doi: 10.1186/s13195-018-0451-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 382.Yan Y., Gao Y., Fang Q., Zhang N., Kumar G., Yan H., Song L., Li J., Zhang Y., Sun J., et al. Inhibition of Rho Kinase by Fasudil Ameliorates Cognition Impairment in APP/PS1 Transgenic Mice via Modulation of Gut Microbiota and Metabolites. Front. Aging Neurosci. 2021;13:755164. doi: 10.3389/fnagi.2021.755164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 383.Wu L., Han Y., Zheng Z., Peng G., Liu P., Yue S., Zhu S., Chen J., Lv H., Shao L., et al. Altered Gut Microbial Metabolites in Amnestic Mild Cognitive Impairment and Alzheimer’s Disease: Signals in Host–Microbe Interplay. Nutrients. 2021;13:228. doi: 10.3390/nu13010228. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 384.Valeri F., dos Santos Guilherme M., He F., Stoye N.M., Schwiertz A., Endres K. Impact of the Age of Cecal Material Transfer Donors on Alzheimer’s Disease Pathology in 5xFAD Mice. Microorganisms. 2021;9:2548. doi: 10.3390/microorganisms9122548. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 385.Zhao Y., Jaber V., Lukiw W.J. Secretory Products of the Human GI Tract Microbiome and Their Potential Impact on Alzheimer’s Disease (AD): Detection of Lipopolysaccharide (LPS) in AD Hippocampus. Front. Cell. Infect. Microbiol. 2017;7:318. doi: 10.3389/fcimb.2017.00318. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 386.Gu X., Zhou J., Zhou Y., Wang H., Si N., Ren W., Zhao W., Fan X., Gao W., Wei X., et al. Huanglian Jiedu Decoction Remodels the Periphery Microenvironment to Inhibit Alzheimer’s Disease Progression Based on the “Brain-Gut” Axis through Multiple Integrated Omics. Alzheimer’s Res. Ther. 2021;13:44. doi: 10.1186/s13195-021-00779-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 387.O’Riordan K.J., Collins M.K., Moloney G.M., Knox E.G., Aburto M.R., Fülling C., Morley S.J., Clarke G., Schellekens H., Cryan J.F. Short Chain Fatty Acids: Microbial Metabolites for Gut-Brain Axis Signalling. Mol. Cell. Endocrinol. 2022;546:111572. doi: 10.1016/j.mce.2022.111572. [DOI] [PubMed] [Google Scholar]
- 388.Mullane K., Williams M. Alzheimer’s Disease beyond Amyloid: Can the Repetitive Failures of Amyloid-Targeted Therapeutics Inform Future Approaches to Dementia Drug Discovery? Biochem. Pharmacol. 2020;177:113945. doi: 10.1016/j.bcp.2020.113945. [DOI] [PubMed] [Google Scholar]
- 389.David L.A., Maurice C.F., Carmody R.N., Gootenberg D.B., Button J.E., Wolfe B.E., Ling A.V., Devlin A.S., Varma Y., Fischbach M.A., et al. Diet Rapidly and Reproducibly Alters the Human Gut Microbiome. Nature. 2014;505:559–563. doi: 10.1038/nature12820. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 390.Hampel H., Cummings J., Blennow K., Gao P., Jack C.R., Vergallo A. Developing the ATX(N) Classification for Use across the Alzheimer Disease Continuum. Nat. Rev. Neurol. 2021;17:580–589. doi: 10.1038/s41582-021-00520-w. [DOI] [PubMed] [Google Scholar]
- 391.Jack C.R., Bennett D.A., Blennow K., Carrillo M.C., Feldman H.H., Frisoni G.B., Hampel H., Jagust W.J., Johnson K.A., Knopman D.S., et al. A/T/N: An Unbiased Descriptive Classification Scheme for Alzheimer Disease Biomarkers. Neurology. 2016;87:539–547. doi: 10.1212/WNL.0000000000002923. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 392.Soldan A., Pettigrew C., Fagan A.M., Schindler S.E., Moghekar A., Fowler C., Li Q.-X., Collins S.J., Carlsson C., Asthana S., et al. ATN Profiles among Cognitively Normal Individuals and Longitudinal Cognitive Outcomes. Neurology. 2019;92:e1567–e1579. doi: 10.1212/WNL.0000000000007248. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 393.Vos S.J.B., Duara R. The Prognostic Value of ATN Alzheimer Biomarker Profiles in Cognitively Normal Individuals. Neurology. 2019;92:643–644. doi: 10.1212/WNL.0000000000007223. [DOI] [PubMed] [Google Scholar]
- 394.Burnham S.C., Coloma P.M., Li Q.-X., Collins S., Savage G., Laws S., Doecke J., Maruff P., Martins R.N., Ames D., et al. Application of the NIA-AA Research Framework: Towards a Biological Definition of Alzheimer’s Disease Using Cerebrospinal Fluid Biomarkers in the AIBL Study. J. Prev. Alzheimer’s Dis. 2019;6:248–255. doi: 10.14283/jpad.2019.25. [DOI] [PubMed] [Google Scholar]
- 395.Bai B., Vanderwall D., Li Y., Wang X., Poudel S., Wang H., Dey K.K., Chen P.-C., Yang K., Peng J. Proteomic Landscape of Alzheimer’s Disease: Novel Insights into Pathogenesis and Biomarker Discovery. Mol. Neurodegener. 2021;16:55. doi: 10.1186/s13024-021-00474-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 396.Bagyinszky E., van Giau V., An S.A. Transcriptomics in Alzheimer’s Disease: Aspects and Challenges. Int. J. Mol. Sci. 2020;21:3517. doi: 10.3390/ijms21103517. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 397.Wang H., Dey K.K., Chen P.-C., Li Y., Niu M., Cho J.-H., Wang X., Bai B., Jiao Y., Chepyala S.R., et al. Integrated Analysis of Ultra-Deep Proteomes in Cortex, Cerebrospinal Fluid and Serum Reveals a Mitochondrial Signature in Alzheimer’s Disease. Mol. Neurodegener. 2020;15:43. doi: 10.1186/s13024-020-00384-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 398.van der Schaar J., Visser L.N.C., Bouwman F.H., Ket J.C.F., Scheltens P., Bredenoord A.L., van der Flier W.M. Considerations Regarding a Diagnosis of Alzheimer’s Disease before Dementia: A Systematic Review. Alzheimer’s Res. Ther. 2022;14:31. doi: 10.1186/s13195-022-00971-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 399.Pernigoni N., Zagato E., Calcinotto A., Troiani M., Mestre R.P., Calì B., Attanasio G., Troisi J., Minini M., Mosole S., et al. Commensal Bacteria Promote Endocrine Resistance in Prostate Cancer through Androgen Biosynthesis. Science. 2021;374:216–224. doi: 10.1126/science.abf8403. [DOI] [PubMed] [Google Scholar]
- 400.Bian X., Liu R., Meng Y., Xing D., Xu D., Lu Z. Lipid Metabolism and Cancer. J. Exp. Med. 2021;218:e20201606. doi: 10.1084/jem.20201606. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 401.Anastasiadou E., Jacob L.S., Slack F.J. Non-Coding RNA Networks in Cancer. Nat. Rev. Cancer. 2018;18:5–18. doi: 10.1038/nrc.2017.99. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 402.Liu N., Wang Z.-Z., Zhao M., Zhang Y., Chen N.-H. Role of Non-Coding RNA in the Pathogenesis of Depression. Gene. 2020;735:144276. doi: 10.1016/j.gene.2019.144276. [DOI] [PubMed] [Google Scholar]
- 403.Yang L., Chen Y., Liu Y., Xing Y., Miao C., Zhao Y., Chang X., Zhang Q. The Role of Oxidative Stress and Natural Antioxidants in Ovarian Aging. Front. Pharmacol. 2021;11:617843. doi: 10.3389/fphar.2020.617843. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 404.Salim S. Oxidative Stress and the Central Nervous System. J. Pharmacol. Exp. Ther. 2017;360:201–205. doi: 10.1124/jpet.116.237503. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 405.D’Amelio P., Quacquarelli L. Hypovitaminosis D and Aging: Is There a Role in Muscle and Brain Health? Nutrients. 2020;12:628. doi: 10.3390/nu12030628. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 406.Milà-Alomà M., Suárez-Calvet M., Molinuevo J.L. Latest Advances in Cerebrospinal Fluid and Blood Biomarkers of Alzheimer’s Disease. Ther. Adv. Neurol. Disord. 2019;12:1756286419888819. doi: 10.1177/1756286419888819. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 407.Chaudhry A., Rizig M. Comparing Fluid Biomarkers of Alzheimer’s Disease between African American or Black African and White Groups: A Systematic Review and Meta-Analysis. J. Neurol. Sci. 2021;421:117270. doi: 10.1016/j.jns.2020.117270. [DOI] [PubMed] [Google Scholar]
- 408.Wang H.-F., Tan L., Hao X.-K., Jiang T., Tan M.-S., Liu Y., Zhang D.-Q., Yu J.-T. Effect of EPHA1 Genetic Variation on Cerebrospinal Fluid and Neuroimaging Biomarkers in Healthy, Mild Cognitive Impairment and Alzheimer’s Disease Cohorts. J. Alzheimer’s Dis. 2015;44:115–123. doi: 10.3233/JAD-141488. [DOI] [PubMed] [Google Scholar]
- 409.Cicognola C., Chiasserini D., Parnetti L. Preanalytical Confounding Factors in the Analysis of Cerebrospinal Fluid Biomarkers for Alzheimer’s Disease: The Issue of Diurnal Variation. Front. Neurol. 2015;6:143. doi: 10.3389/fneur.2015.00143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 410.Mattsson-Carlgren N., Palmqvist S., Blennow K., Hansson O. Increasing the Reproducibility of Fluid Biomarker Studies in Neurodegenerative Studies. Nat. Commun. 2020;11:6252. doi: 10.1038/s41467-020-19957-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 411.Mattsson N., Blennow K., Zetterberg H. Inter-Laboratory Variation in Cerebrospinal Fluid Biomarkers for Alzheimer’s Disease: United We Stand, Divided We Fall. Clin. Chem. Lab. Med. 2010;48:131. doi: 10.1515/CCLM.2010.131. [DOI] [PubMed] [Google Scholar]
- 412.Syaifullah A.H., Shiino A., Kitahara H., Ito R., Ishida M., Tanigaki K. Machine Learning for Diagnosis of AD and Prediction of MCI Progression from Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation. Front. Neurol. 2021;11:576029. doi: 10.3389/fneur.2020.576029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 413.Castellazzi G., Cuzzoni M.G., Cotta Ramusino M., Martinelli D., Denaro F., Ricciardi A., Vitali P., Anzalone N., Bernini S., Palesi F., et al. A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features. Front. Neuroinform. 2020;14:25. doi: 10.3389/fninf.2020.00025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 414.Sala A., Caprioglio C., Santangelo R., Vanoli E.G., Iannaccone S., Magnani G., Perani D. Brain Metabolic Signatures across the Alzheimer’s Disease Spectrum. Eur. J. Nucl. Med. Mol. Imaging. 2020;47:256–269. doi: 10.1007/s00259-019-04559-2. [DOI] [PubMed] [Google Scholar]
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