*Review* **Fluid Candidate Biomarkers for Alzheimer's Disease: A Precision Medicine Approach**

#### **Eleonora Del Prete \*, Maria Francesca Beatino, Nicole Campese, Linda Giampietri, Gabriele Siciliano, Roberto Ceravolo and Filippo Baldacci**

Neurology Unit, Clinical and Experimental Medicine Department, University of Pisa, 56126 Pisa, Italy<EMAIL_ADDRESS>(M.F.B<EMAIL_ADDRESS>(N.C<EMAIL_ADDRESS>(L.G<EMAIL_ADDRESS>(G.S<EMAIL_ADDRESS>(R.C<EMAIL_ADDRESS>(F.B.) **\*** Correspondence<EMAIL_ADDRESS>
Received: 11 September 2020; Accepted: 5 November 2020; Published: 11 November 2020

**Abstract:** A plethora of dynamic pathophysiological mechanisms underpins highly heterogeneous phenotypes in the field of dementia, particularly in Alzheimer's disease (AD). In such a faceted scenario, a biomarker-guided approach, through the implementation of specific fluid biomarkers individually reflecting distinct molecular pathways in the brain, may help establish a proper clinical diagnosis, even in its preclinical stages. Recently, ultrasensitive assays may detect different neurodegenerative mechanisms in blood earlier. ß-amyloid (Aß) peptides, phosphorylated-tau (p-tau), and neurofilament light chain (NFL) measured in blood are gaining momentum as candidate biomarkers for AD. P-tau is currently the more convincing plasma biomarker for the diagnostic workup of AD. The clinical role of plasma Aβ peptides should be better elucidated with further studies that also compare the accuracy of the different ultrasensitive techniques. Blood NFL is promising as a proxy of neurodegeneration process *tout court*. Protein misfolding amplification assays can accurately detect α-synuclein in cerebrospinal fluid (CSF), thus representing advancement in the pathologic stratification of AD. In CSF, neurogranin and YKL-40 are further candidate biomarkers tracking synaptic disruption and neuroinflammation, which are additional key pathophysiological pathways related to AD genesis. Advanced statistical analysis using clinical scores and biomarker data to bring together individuals with AD from large heterogeneous cohorts into consistent clusters may promote the discovery of pathophysiological causes and detection of tailored treatments.

**Keywords:** biomarkers; Alzheimer's disease; neurodegeneration; cerebrospinal fluid; mild cognitive impairment; synaptic biomarkers; neuroinflammation; neurofilament light chain

#### **1. Introduction**

Alzheimer's disease (AD) is the most common neurodegenerative disease (NDD), with 5.8 million Americans aged 65 years and older living with AD in 2020 [1]. Since Alois Alzheimer's first description of the typical histological alterations of neuritic plaques (NP) and neurofibrillary tangles (NFT) in 1906 [2], more than eighty years have passed before amyloid beta (Aβ) and phosphorylated-tau (p-tau) were identified as the main component of NP and NFT, respectively [3]. In 1984, the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) [4] set postmortem examination as the reference standard of AD diagnosis. Since then, the broad phenotypical variability of neurodegenerative diseases (NDDs) has pushed the efforts toward developing a classification based on the main misfolded protein deposition [5,6]. Nevertheless, the occurrence of these aggregates in multiple combinations is frequent, and NDDs are rather emerging as a spectrum of disorders characterized by the loss of proteostasis [7].

Due to the failure of numerous trials against amyloid pathology, the idea of "one drug fits all" treatment as an ultimate solution for an AD cure is fading [8]. In brief, the current framework on

AD is more complex than previously thought because AD is not a mere plaque and tangle disorder. The following pathophysiological pathways leading to neurodegeneration have been recognized as clearly implicated in AD pathogenesis: (1) accumulation of misfolded proteins in the brain (Aβ peptides, tau and p-tau proteins, other co-pathologies), (2) vascular dysfunction, (3) synaptic disruption, and (4) neuroinflammation. The discovery of biomarkers indicating the modification of these processes at different levels in space and time is gaining momentum, especially in design tailored disease-modifying trials (Figure 1).

**Figure 1.** Alzheimer's disease fluid biomarkers. The major pathophysiological processes involved in Alzheimer's disease (in bold) with validated and proposed fluid biomarkers are schematically represented. Fluid biomarkers of vascular dysfunction, and of TAR DNA binding protein 43 (TDP-43) and α-syn pathologies are still missing. Abbreviations: *A*β, β-amyloid, α*-syn*, α-synuclein; *NFL*, neurofilament light chain; *Ng*, neurogranin; *p-tau*, phosphorylated tau protein; *t-tau*, total tau protein, synaptosomal-associated protein 25 (SNAP-25), and triggering receptor expressed on myeloid cells 2 (TREM2).

Our aim is to review the development of novel candidate fluid biomarkers tracking these key pathophysiological mechanisms in different matrices, especially cerebrospinal fluid (CSF) and blood. In relation to AD, we mainly focused on the diagnostic and prognostic value of these biomarkers, with particular attention to the novel ultrasensitive techniques.

#### **2. Literature Search Methods**

We performed a narrative review of literature focusing on novel candidate fluid biomarkers for AD. A systematic review of literature focused on plasma biomarkers detected by means of novel ultrasensitive techniques was performed in PubMed. We used the combination of the keywords "plasma", "serum", "amyloid-β", "NFL" (neurofilament light chain), "p-tau", "p-tau181", "phopsho-tau181", "phosphorylated tau181", "t-tau", "Simoa", "immunoassay", "immunomagnetic reduction", "fully automated", "immuno-infrared sensor", "mass spectrometry", and "multimer detection system". Only papers in English published between 2014 and July 2020 and focused on AD were included in the final analysis. Overall, we identified 21 studies that provided relevant diagnostic and/or prognostic information (Figure 2). Among them, 10 were focused on amyloid-β peptides, 7 were focused on p-tau or tau or both, and 4 were focused on NFL. For each paper, the study population, the study design (cross-sectional, perspective, retrospective), and the diagnostic and/or

prognostic value of the investigated biomarker were analyzed. We classified the diagnostic value of each biomarker according to previously published recommendations as follows: "excellent" (area under ROC curve [AuROC] 0.90–1.00), "good" (AuROC 0.80–0.89), "fair or moderate" (AuROC 0.70–0.79), "poor" (AuROC 0.60–0.69), or "fail or insufficient" (i.e., no discriminatory capacity) (AuROC 0.50–0.59).

**Figure 2.** Flowchart displaying the article selection process.

#### **3. Toward a Pathophysiological Definition of Alzheimer's Disease**

With the 1984 NINCDS-ADRDA criteria, the accuracy for probable AD diagnosis was suboptimal, with sensitivity between 70.9% and 87.3% and specificity between 44.3% and 70.8% [9]. The definite diagnosis relied on postmortem examination, with obvious limitations, since it is not applicable in vivo. For this reason, the InternationalWorking Group (IWG) [10] and later the National Institute on Aging and Alzheimer's Association (NIA-AA) [11] published novel criteria for the diagnosis of AD incorporating in vivo biomarkers. According to the 2007 IWG criteria, AD can be identified in vivo by the presence of amnestic syndrome of the hippocampal type, which is characterized by low free recall that does not improve with cueing. Moreover, biomarkers must be consistent with AD pathology. These biomarkers are pathophysiological and topographical. The pathophysiological ones are low CSF Aβ1-42 peptide concentration, high CSF total tau (t-tau) or p-tau levels, and an increased cerebral uptake of amyloid tracers (e.g., Pittsburgh compound) with PET. The formers are hippocampal atrophy on volumetric Magnetic Resonance Imnaging (MRI) and cortical regional hypometabolism on fluorodeoxyglucose FDG-PET, involving bilateral temporal parietal regions and posterior cingulate. IWG criteria managed to move from the static and binary/dichotomic vision of AD as a clinicopathological entity to its current dynamic clinical-radio-biological description [10]. The subsequent 2010 revision of IWG criteria overtook the amnestic-centered concept of AD and broadened the spectrum, adding the rarer atypical forms of AD, such as primary progressive non-fluent aphasia, in particular logopenic aphasia, posterior cortical atrophy, and frontal variant AD. The-so-called "asymptomatic at risk of AD"

condition without clinical symptoms but with positive biomarkers of AD pathology was stated out, as well as the concept of "mixed AD", implying the co-occurrence of clinical and biological features of other disease, such as parkinsonism (e.g., Lewy body pathology) or cerebrovascular disease [12]. Later on, these concepts were implemented in the IWG-2 criteria (2014) [13], where clinical diagnosis required specifying whether typical or atypical AD phenotypes occurred. Furthermore, the condition of a preclinical AD stage (for asymptomatic at risk and presymptomatic subjects) was defined in the presence of cognitive normal individuals with biomarkers indicative of AD pathophysiological process. Topographical biomarkers were used only for disease staging and monitoring. In parallel with IWG criteria, the NIA-AA diagnostic guidelines developed in 2011 [14–16] moved forward, defining the concept of mild cognitive impairment (MCI) due to AD (clinical MCI individuals with biomarkers indicating AD pathology). In fact, MCI due to AD had a high likelihood of developing AD over time. Subsequently, in 2016, the joint IWG-Alzheimer's Association (IWG-AA) formalized a purely biological definition of AD, based on the positivity of biomarkers of both amyloidosis and tauopathy [17]. In the same years, the "A/T/N" classification system for AD was published. In this classification, the validated AD biomarkers were reported into three binary categories (presence or absence) based on the nature of the pathophysiology. "A" refers to the ß-amyloid pathology (cerebral amyloid PET or CSF Aß42); "T," refers to taupathology (CSF p-tau, or cerebral tau PET); and "N" refers to neurodegeneration or neuronal injury *tout court* ([18F]-fluorodeoxyglucose-PET, structural MRI, or CSF total tau [18]). This unbiased biomarker-based scheme was recently incorporated in the current NIA-AA criteria published in 2018, with the addition of C for clinical change, to integrate the biomarkers condition with clinical cognitive status [19]. All A+ individuals are considered part of the "Alzheimer's continuum", while only A+ and T+ define AD. Non AD-specific parameters, namely neurodegenerative/neuronal injury biomarkers (N) and cognitive symptoms (C), define staging [19]. A- individuals fall either in the "normal AD biomarker" category with A-T-(N-), or "Suspected non-Alzheimer's pathophysiology" (SNAP) with A-T+(N)-, A-T-(N)+, or A-T+(N)+.

Among the mimics of typical AD-type dementia, Primary Age-Related Tauopathy (PART) should be mentioned [20,21]. PART identifies individuals with cerebral NFT indistinguishable from those of AD, in the absence of Aβ plaques; notably, NFT are restricted to the medial temporal lobe, basal forebrain, brainstem, and olfactory areas [21]. At a clinical level, associated manifestations range from normal cognition to amnesic cognitive impairment, but they are rarely a frank dementia. Similarly, a recently described entity is the limbic-predominant age-related TDP-43 encephalopathy (LATE) [22]. LATE is a common TDP-43 proteinopathy that generally affects older adults, and it is frequently associated with hippocampal sclerosis. Aβ plaques or tauopathy may also coexist. Generally, co-pathologies in AD subjects are common with approximately 30% of AD patients showing a cerebrovascular disease [23]. The concomitant deposition of Aβ and α-syn proteins is also described in postmortem examination in about 30% of AD individuals [24,25] but also in up to 40% of patients with Parkinson's disease (PD), Parkinson disease dementia (PDD), and dementia with Lewy bodies (DLB) clinical diagnoses [26–28].

Thus, in this scenario, it is likely that no single biomarker could reach a 100% diagnostic accuracy, being AD biologically multifaceted with a clinical picture reflecting pathology only in terms of probabilistic association. Despite these intrinsic limitations, the use of core biomarkers in the AD diagnostic workup improves accuracy (up to 90%) with a relevant impact on AD stratification and selection for disease-modifying trials tailored against Aß and tau pathologies [29].

To date, the neuropathological hallmarks of AD remain extracellular Aβ plaques and NFTs [30,31]. First proposed in 1992, the "amyloid cascade hypothesis" [32] has been later corroborated by genetic and biochemical data and currently represents the dominant pathogenetic model of AD. According to this hypothesis, the deposition of fibrillar Aβ plaques within the brain promotes the accumulation of NFTs, synaptic disintegration and neuronal death by inflammatory mechanisms, modification of ions homeostasis, kinase/phosphatase activity, and oxidative stress [33]. In particular, Aβ plaques create an unique environment that facilitates tau aggregation, initially as dystrophic neurites surrounding Aβ plaques, followed by the formation and spread throughout the brain in a prion-like manner of NFTs

and neuropil threads [34]. NFTs are characteristic of AD and are composed of hyperphosphorylated tau [35–37]. The hyperphosphorylation of tau protein reduces its affinity for microtubules and promotes its capacity to aggregate and fibrillize [38]. Therefore, microtubules are destabilized, and axonal transport is impaired [39].

The hyperphosphorylated tau could also migrate in the somatodendridic compartments where it interacts with Aβ and enhances synaptotoxicity [40], finally causing cell death due to a toxic gain of function mechanism [41,42].

At the same time, much interest is growing around the role of inflammation in the pathogenesis of AD. The contribution of inflammation to the pathophysiology of AD has been already hypothesized more than 20 years ago [43–45]. The attention has been focused especially on microglia activation, which seems to occur decades before AD onset [46–48]. Furthermore, a correlation between neuroinflammation and amyloid or tau accumulation in the human brain has been reported in several investigations [46,49–51]. The microglial activation produces two different phenotypes. The microglial "pro-inflammatory" phenotype (M1) displays pro-inflammatory cytokines (IL-1β, IL-6, IL-12, tumor necrosis factor (TNF)-α, CCL2), nitric oxide, reactive oxygen and nitrogen species. The "anti-inflammatory" one (M2) sustains the production of IL-10 and TGF-β, and it increases the expression of neurotrophic factors (nerve-derived growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophins, glial cell–derived neurotrophic factor (GDNF)), and several other signals involved in downregulation, protection, or repair processes [52]. The chronic stimulus on microglia by Aβ peptides accumulation is likely to lead to a protracted inflammation, and, in turn, increase Aβ deposition, in a vicious circle [53]. The inflammatory state would promote the production and release of pro-inflammatory cytokines, which could themselves have a detrimental effect by inducing neuronal cell death.

Another relevant key pathophysiological mechanism that contributes to AD is vascular dysregulation [54]. Several pieces of evidence support the role of chronic cerebral hypoperfusion as the *primum movens* of AD pathology [55,56]. Hypoxia can activate β-secretase-1 and γ-secretase as well as increase Aβ peptides accumulation [57]. Furthermore, the reduced supply of oxygen and nutrients affects neurons per se, and, in turn, it promotes blood–brain barrier dysfunction, increasing oxidative stress and inflammation [58]. Since Aβ deposition derives essentially from an imbalance between production and removal, clearing system impairment is emerging as a further key pathophysiological mechanism leading to AD. In particular, this mechanism involves the alteration of astroglial-mediated interstitial fluid (ISF) bulk flow or glymphatic system [59,60]. This pathway is mainly modulated by the sleep–wake cycle and seems to be important for the sleep-driven clearance of Aβ [61]. Vascular pathology seems to be additive or even synergic to AD pathology as a cause of cognitive impairment [62,63]. This cross-talk is most evident for cerebral amyloid angiopathy (CAA), which shares Aβ deposition with AD typical neurotic plaques that are localized within leptomeningeal and intracortical arteries, arterioles, and capillaries. CAA is commonly found in AD brains: up to approximately 50% of subjects with severe NP load [64]. CAA can affect perivascular drainage impairing glymphatic circulation, thus reducing a major route of Aβ clearance from the brain [59]. Intracranial atherosclerosis was found to be an additional, although not strictly neurodegenerative, strong risk factor for AD dementia [65].

#### **4. Fluid Biomarkers: Ultrasensitive Measurement Techniques**

Due to several advantages over invasive (e.g., CSF Aβ peptides), expensive and scarcely available (e.g., cerebral amyloid-PET) diagnostic tools, technologies aiming at quantifying NDDs biomarkers in blood are gathering momentum.

However, the discovery of CNS-related biomarkers in blood presents challenging issues: (a) the concentration of a biomarker released in CNS is lower than in CSF, considering that it has to cross the blood–brain barrier and that the blood volume is consistently larger than the CSF one, (b) biomarkers could also be directly expressed peripherally, and the contribution of CNS might be difficult to quantify, (c) proteolytic degradation of the analytes by plasma proteases and confounding blood proteins may interfere with biomarker measurement [66].

The traditional enzyme-linked immunosorbent assay (ELISA) was extensively used in the last few decades. It showed a substantial intrinsic variability in the quantification of plasma/serum biomarkers and provided overlapping results in the discrimination between NDDs and cognitively healthy subjects [67,68]. The large sample volume required in the analysis combined with a sensitivity limited to the picomolar range could be addressed as the main weakness of this method. Therefore, ultrasensitive techniques often representing ELISA-based evolutions have been developed for blood biomarker discovery.

The automated xMAP (multi-analyte profiling) Luminex technology, a flow cytometric method, allows the adaptation of several immunoassay formats to simultaneously detect multiple analytes on different sets of microspheres in a single well [69]. Through pre-made calibrators, it reduces measurement variability, partially overcoming some limitations of conventional ELISA methods (Table 1).

Another emerging technique is the single-molecule array (Simoa), which is essentially a digital ELISA. This fully automated method is based on capturing antibody-coated paramagnetic beads loaded in arrays of femtomolar-sized reaction chambers with a volume 2 billion times smaller than conventional ELISA. Ultimately, by acquiring fluorescence images, an increase in signal will reflect the presence of single enzyme-associated immunocomplexes [70] (Table 1). This method is a candidate prescreening tool but may be potentially useful throughout the whole AD spectrum [71]. Large-scale longitudinal multicenter studies are anyway needed for the standardization and harmonization of preanalytical and analytical variables [72].

Combining the unique advantages of highly specific immunoreactions and electrochemiluminescence (ECL) biosensors, ECL immunoassays (ECLIA) have been implemented in several automated platforms. A wide dynamic detection range, low background noise, and simple optical set-ups are the strengths of this technique [73,74] (Table 1).

For protein analysis, immunoprecipitation has also been coupled with mass-spectrometry (IP-MS) providing a robust quantitative tool to identify antigens based on their intrinsic chemicophysical properties [75]. A significant advantage of this method is the possibility to analyze complex mixtures of Aβ peptides at very low concentrations in a single assay. However, multi-step structured analysis strategies are required in order to reduce the influence of non-specific binders and improve the signal quality (Table 1) [76]. A tailored approach for the identification of oligomers has been adopted in the Multimer Detection System (MDS), which is a modified sandwich ELISA originally designed to detect prion proteins. As opposed to the conventional method, this strategy relies on two epitope-overlapping antibodies for capturing and detecting an epitope, so that only multimers will bind to both antibodies, allowing their selective detection over monomers, which conversely will only bind to one of them (Table 1) [77,78].

**Table 1.** Key points of ultrasensitive techniques for the detection of putative blood biomarkers for AD.

**Table 1.** *Cont.*

*Abbreviations:* Aβ: amyloid β; Aβ1-40: amyloid β-peptide 1-40; Aβ1-42: amyloid β-peptide 1-42; AD: Alzheimer's disease; CSF: cerebrospinal fluid; ECLIA: electrochemiluminescence immunoassay; ELISA: enzyme-linked immunosorbent assay; IMR: immunomagnetic reduction; IP-MS: immunoprecipitation coupled with mass spectrometry; MDS: multimer detection system; MSD: meso scale discovery; NFL: neurofilament light chain; p-tau: phosphorylated-tau; Simoa: single molecule array; t-tau: total tau; xMAP: multi-analyte profiling.

Among the recently developed biosensors in AD research, the immuno-infrared sensor represents a promising label-free technique not aiming at discriminating particular Aβ species, but rather aiming at identifying the secondary structure distribution of all misfolded peptides. Thus, it is potentially exploitable in a preclinical setting (Table 1) [79–81].

A further virtuous application of the immunoassay principles is part of the immunomagnetic reduction (IMR) technique, in which magnetic antibody-coated nanoparticles dispersed in aqueous solution oscillate under external multiple alternating current (AC) magnetic fields. The association of target molecules determines a reduction in the AC magnetic susceptibility of capturing nanoparticles that will be as high as the concentration of the analytes (Table 1) [82]. Compared to ELISA-based techniques (e.g., Simoa), this method does not make use of beads to purify or concentrate antigens, and it is virtually able to quantify smaller proteins in higher number. Whether this could represent an advantage to detect Aβ peptides in plasma is still to be elucidated (Table 1).

Huge efforts have been made to develop and refine these technologies. Nevertheless, there is an urgent need to promote unbiased cross-platform evaluations for an effective method standardization.

In view of a targeted-oriented approach to AD, the adoption of guidelines to systemize preanalytical and postanalytical procedures across laboratories, aiming at finding consensus on a high-performance scalable platform for the discovery and approval of blood biomarkers, would be strongly recommended.

#### **5. Biomarkers Tracking Amyloid Pathology**

Accumulating evidence from the clinical research consistently supports that CSF Aβ1–42 peptide shows an inverse correlation with plaque load in the brain [97–101] and provides important diagnostic information throughout the continuum of the AD spectrum. Therefore, this biomarker is currently integrated in the diagnostic criteria of AD; it is used for subject selection in clinical trials and approved in medical practice as well [13,14,19]. On the contrary, CSF Aβ1–40 peptide alone, albeit prevailing over the other Aβ species in both CNS and periphery, showed no relevant correlation with AD dementia [76,87–89]. Notably, the ratio of CSF Aβ1–42/Aβ1–40 has been found to predict cortical amyloid-PET positivity more accurately than CSF Aβ1–42 alone [90–92], improving the discrimination of AD vs. non-AD demented patients (Table 2) [93,94].

**Table 2.** Overview on the possible context of use of fluid biomarkers in AD.

Legend: plus sign (+): potential use, supportive data available. Abbreviations: Aβ: amyloid beta; t-tau: total tau; p-tau: phosphorylated-tau; YKL-40; Ng: neurogranin; NFL: neurofilaments; CSF: cerebrospinal fluid.

Some investigations suggest that also the CSF Aβ1–42/Aβ1–38 ratio turned out to improve cerebral amyloid deposition compared with CSF Aβ1–42 alone [94–96]. In addition, in pharmacological trials, short Aβ peptides detection in CSF may help monitor patients receiving drugs that modulate γ-secretase (Table 2) [97,98]. Among Aβ species, Aβ oligomers that are likely to play a key role in AD pathogenesis could be potentially used as preclinical biomarkers in CSF. Unfortunately, the detection of Aβ species and Aβ oligomers is challenging due to their polymorphous and unstable nature. Moreover, their concentration in biofluid is low, and they compete with other proteins and Aβ monomers. For the aforementioned reasons, most of the existing techniques are not satisfactory and reporting conflicting results so far [99]. As it also emerged from the Olsson and colleagues meta-analysis in 2016, Aβ11–42 and Aβ1–40 peptides measured in blood with traditional ELISA methods did not discriminate AD from healthy controls [95].

Undoubtedly, the validation of relatively new technologies in the last few years—e.g., Simoa [100], immunoprecipitation-mass spectrometry (IP-MS) assays [74], stable labeling kinetics protocols [101], multimer detection system (MDS) [102], xMAP technology [103], immuno-infrared sensor [104], electrochemiluminescence immunoassays (ECLIA) [105–112]—led to a significant increased sensitivity in amyloid peptides detection in periphery when compared to the conventional ELISA technique, with drastically lower concentrations (up to the femtolitre) in blood than in CSF [65]. Particularly, in a 2017 study based on an IP-MS method, the plasma Aβ1-42/Aβ1-40 ratio was significantly lower in amyloid-PET positive compared with amyloid-PET negative participants. This ratio reported a good accuracy in distinguishing the two populations [101] (Table 3) [113–131].

In 2018, Nakamura and colleagues used the same technique to measure Aβ peptides in plasma of cognitive normal individuals, MCI and AD with dementia subjects, finding higher levels of plasma Aβ1–40/Aβ1–42 ratio in amyloid-PET positive compared with amyloid-PET negative individuals [75]. Regardless of the clinical diagnosis, the ratio of Aβ1–40 and Aβ1–42 peptides (Aβ1–40/Aβ1–42), and that of Aβ precursor protein fragment (APP669–711) and Aβ1–42 (APP669–711/Aβ1–42), had an excellent diagnostic accuracy in discriminating cerebral amyloid-PET positive and amyloid-PET negative subjects (Table 3). Similarly, in another study including a cohort of subjective memory complainers, a condition at risk for AD, the plasma Aβ1–40/Aβ1–42 ratio, turned out to be the best predictor of cerebral amyloidosis among a series of tested candidate biomarkers (e.g., β-site amyloid precursor protein cleaving enzyme 1 or BACE1, t-tau, NFL [72] (Table 3). Additional investigations performed using the Simoa technique reported a moderate accuracy of Aβ1–42/Aβ1–40 ratio in identifying the amyloid status. The Aβ1–42/Aβ1–40 ratio was lower in amyloid-PET positive compared with amyloid-PET negative participants [122,132] (Table 3). A further study in which Aβ peptides concentrations were assessed with a fully automated ECLIA technique confirmed the good discriminative role of Aβ1–42/Aβ1–40 ratio, in both validation and discovery cohorts [92] (Table 3). Similarly, Hanon and colleagues in a large investigation including 1040 MCI or AD participants reported that plasma Aβ1–42 and Aβ1–40 concentrations assessed by means of a kit based on a multiplex xMAP technique were lower in AD than in both MCI and non amnestic MCI (naMCI), suggesting a gradual decrease of these peripheral biomarkers with the course of the disease, in accordance to previous findings [109,133,134]. Conversely, in some studies where IMR was used, plasma Aβ1–42 concentrations are higher in AD patients than in healthy subjects [95,121]. Lue and colleagues reported a moderate diagnostic accuracy of this plasma biomarker in one cohort and excellent in the other [121] (Table 3). Moreover, in a 2018 study carried out by Nabers and colleagues using an immuno-infrared sensor, when compared with controls, not only were the concentrations of β-sheet-enriched Aβ peptides higher in severe AD dementia patients, as previously demonstrated [80], but they were also higher in prodromal AD patients, reaching a good diagnostic accuracy in identifying the amyloid status assessed by PET (Table 3) [80]. A recently developed ELISA method detecting Aβ multimers from monomers (MDS) showed a good accuracy of plasma Aβ oligomers in distinguishing AD patients from healthy controls [118] (Table 3). In parallel, in a subsequent study in which traditional ELISA was applied, plasma BACE-1 increase has emerged as another surrogate hallmark of AD progression [135]. In an effort to assess the plasma concentrations of Aβ1–38, Aβ1–40 and Aβ1–42 simultaneously, Shahpasand-Kroner and colleagues found that the Aβ1–42/Aβ1–40 and Aβ1–42/Aβ1–38 ratios are significantly lower in patients with AD dementia than in patients with dementia due to other causes and have a good accuracy in differentiating the two groups [120] (Table 3).

**3.**DiagnosticandprognosticroleofbloodAβpeptides,p-tau,t-tau,andNFLproteinsmeasuredwithultrasensitive

neuropathology

 autopsy as reference standard

 frontotemporal dementia; FTLD: frontotemporal lobar degeneration; HR: hazard ratio; IMR: immunomagnetic reduction; IP: immunoprecipitation; IP MS: immunoprecipitation coupled to mass spectrometry; MCI: mild cognitive impairment; MDS: multimer detection system; MS: mass spectrometry; MSA: multiple system atrophy; NA: not assessed; NFL: neurofilament light chain; nfvPPA: non-fluent variant primary progressive aphasia; PD: Parkinson's disease; PPA: primary progressive aphasia; PSP: progressive supranuclear palsy; p-tau181: phospho-tau181; Simoa: single molecule array; SMC: subjective memory complainers; svPPA: semantic variant primary progressive aphasia; t-tau: total-tau; xMAP: multi-analyte profiling.

In summary, quite concordant results regarding low plasmatic concentrations of Aβ1–42 and low Aβ1–42 /Aβ1–40 ratio in AD are reported in most of the studies in which an ultrasensitive technique has been applied. Overall, recent data suggest a low plasma Aβ1–42 and Aβ1–42/Aβ1–40 ratio as being a specific feature of AD patients with a weak to moderate and a moderate to high concordance with amyloid-PET outcomes, respectively [136] (Table 2). Further investigations comparing the different ultrasensitive techniques in the same populations will provide more accurate information regarding advantages and drawbacks.

#### **6. Biomarkers for Tau Pathology**

Together with CSF Aβ1–42, CSF t-tau and p-tau are considered as core biomarkers for AD diagnosis [11,137], and they are currently used for subject selection in clinical trials. Both CSF tau species are higher in patients compared to non-demented individuals. P-tau is more specific than t-tau for AD pathology and plays a main role in differential diagnosis being substantially normal in non-AD dementias [138]. Recent studies have shown that CSF tau can predict disease progression in both cognitively unimpaired and MCI subjects (Table 2) [139–141]. In 1999, Hulstaert and colleagues reported that the combined measurements of CSF Aβ1–42 and tau had a better outcome than the individual biomarker in differentiating AD patients from controls and other dementias [142]. In an effort to establish the utility of both CSF t-tau/Aβ1–42 and p-tau/Aβ1–42 ratios, several authors ended up confirming those preliminary findings [98,143,144]. Moreover, the ability of both ratios to predict disease onset and progression was proven in other studies, including normal individuals and MCI subjects, respectively [140,145,146].

Since 2013, it has been reported that plasma t-tau levels, measured through an assay based on digital array technology, were higher in AD participants compared with both MCI and healthy subjects, but they did not show significant modifications in subsequent longitudinal evaluations (Table 2) [116,147]. Shortly thereafter, a large meta-analysis demonstrated the increase of plasma t-tau levels to be strongly associated with AD (Table 2) [115].

 Indeed, Mattsson and colleagues to assess plasma t-tau concentration in two separate cohorts applied the Simoa technique. In AD patients compared with both MCI and healthy controls, an increase of plasma tau was demonstrated but with overlapping results. More interestingly, longitudinal evaluations revealed that high baseline levels of this biomarker were predictive of cognitive decline, higher atrophy rates at MRI, and hypometabolism at 18F-FDG-PET as well [148]. A longitudinal study carried out using the same technique found increased plasma tau levels associated with a higher risk of MCI and cognitive decline in MCI subjects, irrespective of the total Aβ-burden in the brain [123] (Table 3). In 2019, Park and colleagues highlighted that both plasma t-tau measured by the Simoa technique as well and the plasma t-tau/Aβ<sup>42</sup> ratio positively correlated with cerebral tau-PET uptake. Moreover, plasma tau-related biomarkers concentrations were significantly higher in Tau-PET+ subjects compared with Tau-PET- subjects and could differentiate the two groups with good accuracy (Table 3). It is noteworthy that the plasma t-tau and t-tau/Aβ<sup>42</sup> ratio could predict the cerebral accumulation of this misfolded protein after a 2-year follow-up [149]. A clear association between plasma and CSF levels of p-tau was also found in Aβ+ patients, including the presymptomatic stage (Aβ+ cognitively unimpaired), but not in Aβ− individuals [90]. Overall, these data suggest to some extent that plasma t-tau concentration is high in AD patients, but the substantial overlap with normal controls hinders its diagnostic utility (Table 2). Interestingly, an additional study found out that plasma p-tau concentrations improve diagnostic accuracy significantly compared to plasma t-tau by reaching a good capability in the discrimination of amyloid-positive and amyloid-negative subjects (Table 3) [124]. Moreover, plasma p-tau levels assessed by IMR have shown a good accuracy in differentiating unimpaired and MCI subjects [125] (Table 3).

The potential diagnostic role of plasma p-tau has been outlined in three recent investigations. In a first study performed with a novel ECLIA technique, plasma p-tau concentrations not only could

discriminate AD and frontotemporal lobar degeneration (FTLD) participants with good accuracy, but they also identified amyloid-PET positive participants among elderly and MCI, and they predicted the rate of cognitive decline in AD and MCI over a 2-year follow-up (Table 3) [91]. The second study including three separate cohorts with 589 individuals (controls, MCI, AD, and non-AD NDDs) revealed that plasma p-tau levels likewise assessed by means of the MSD platform increase with disease progression (from preclinical to frank dementia phases encompassing prodromal/MCI stage) and can distinguish AD dementia from non-AD dementia with excellent accuracy (Table 3). Furthermore, plasma p-tau concentration was more elevated in Aβ+ cognitively unimpaired individuals than in Aβ− ones and in Aβ+ MCI who progressed to AD dementia compared to those who did not convert [90]. These results were confirmed in another study involving four independent cohorts (1131 total subjects) in different clinical contexts. Actually, plasma p-tau concentration measured by the Simoa technique discriminated AD dementia patients from both cognitively unimpaired subjects and other NDDs (including tauopathies such as Progressive Supranuclear Palsy and Corticobasal Syndrome) with optimal diagnostic accuracy (Table 3). Moreover, plasma p-tau predicted future cognitive decline over time. Interestingly, plasma p-tau concentration strongly correlates with cerebral amyloid-PET burden, even in amyloid-PET positive but tau-PET negative subjects, suggesting its crucial role in detecting the earliest disease phases. Thus, this biomarker might represent a screening tool implementable in different clinical settings and contexts of use [127].

#### **7. Biomarkers for Neuroinflammation**

The role of inflammation in AD pathogenesis was first suggested more than 20 years ago. Microglia, astrocytes, cytokines, and chemokines play a central role in disease pathogenesis since early phases [45,150]. Furthermore, amyloid and tau accumulation is linked to neuroinflammation [46,49,50,151], and Aβ accumulation evokes an exaggerated or heightened microglial response inducing and amplifying inflammatory reactions [152]. Therefore, biomarkers of neuroinflammation are gaining momentum in preclinical stages of AD and are useful to establish the eligibility of patients into new clinical trials [153–156].

A potential biomarker of neuroinflammation is the microglia/astrocyte-expressed protein YKL-40. YKL-40 is 45 a glycoprotein belonging to the family of 18 glycosyl hydrolases, and it is alternatively named human cartilage glycoprotein-39 (HC gp-39) or chitinase-3-like-1 protein (CHI3L1) [157]. CSF YKL-40 concentration is able to differentiate patients with typical AD dementia from cognitively normal controls with fair diagnostic accuracy [158,159]. Limited data regarding the ability of CSF YKL-40 to discriminate different NDDs are available so far. Actually, CSF YKL-40 differentiated AD from DLB, PD [160], FTLD [161], and non-AD MCI [162] with only a moderate diagnostic accuracy. Furthermore, CSF YKL-40 concentration is higher in AD versus Aβ-positive MCI subjects [163], and it significantly increases over time in the former (Table 2) [163]. CSF YKL-40 showed no ability in differentiating stable and progressing MCI [164,165], although it may predict progression to overt dementia in MCI [165] (Table 2). CSF YKL-40 levels negatively correlated with cortical thickness in specific AD-vulnerable areas, such as middle and inferior temporal areas in Aβ42-positive subjects [166] and grey matter volume in *APOE* ε4 carriers [167]. Interestingly, a positive association between CSF YKL-40 and t-tau has been reported in asymptomatic preclinical stages of AD and other NDDs [110,168], thus suggesting a link of YKL-40 with an underlying tau-driven neurodegenerative mechanisms [169].

YKL-40 has also been investigated in plasma, and elevated levels have been reported in patients with mild AD [170] and early AD [171] compared with controls. Unfortunately, plasma YKL-40 did not, so far, demonstrate utility as a diagnostic biomarker and for predicting cognitive decline (Table 2) [170,172]. To sum up, YKL-40 is an unspecific pathophysiological biomarker tracking immune/inflammatory response in NDDs, and it could be helpful as a monitoring biomarker for targeted anti-inflammatory therapies [169].

Another emerging biomarker of inflammation is "Triggering Receptor Expressed on Myeloid cells 2" (TREM2). TREM2 receptors play an important role in the pathogenesis of AD [173]. In the early stages of AD, TREM2 seems to be upregulated, probably in a protective intent [174]. However, due to the activation of inflammatory response, a detrimental role may prevail in later stages [175]. Some TREM2 genetic variants are related to AD possibly impairing microglia Aβ phagocytic ability and reducing, as a consequence, the cerebral Aß peptides clearance [167]. TREM2 has been proposed as AD biomarker in CSF, but with conflicting results so far. Some studies found higher CSF levels of TREM2 in AD [176–179] and MCI [176] compared to controls, and in subjects with MCI due to AD (or prodromal AD) compared with preclinical AD and AD dementia patients (Table 2) [179]. However, another study showed no difference between AD or MCI patients and cognitively normal controls [180]. A link between high CSF TREM2 value and neurodegeneration was proposed in MCI, considering that it positively correlated with gray matter volume and a negative correlation with mean diffusivity was detected [181]. Higher levels of TREM2 mRNA and TREM2 protein expressed in peripheral blood mononuclear cells were identified in AD patients compared to controls, with an inverse correlation with MMSE [182]. Moreover, TREM2 gene expression was found to be higher in MCI than AD patients [183]. Finally, a possible role of TREM2 as CSF and blood biomarker for AD has been suggested, but few data are currently available, and additional research is needed.

Another interesting candidate inflammatory biomarker is the monocyte chemoattractant protein-1 (MCP-1), which is a member of the C-C chemokine family and a potent chemotactic factor for monocytes [184]. Elevated CSF MCP-1 levels were found in AD patients compared to controls [185]. Noteworthy, also in blood, MCP-1 could be higher in MCI subjects than in controls [185].

Several other inflammatory biomarkers in CSF and blood have been investigated for their potential use as biomarkers for AD. A large metanalysis exploring inflammatory biomarkers in CSF demonstrated that AD patients could express higher levels of TGF-β compared with controls [350 Molievo]. TGF-ß1 is a neurotrophic, anti-inflammatory factor, and it enhances Aβ clearance by microglia activation. Since the early phases of disease, a reduced expression of TGF-ß has been described both in postmortem AD studies [186,187] and in animal models [188–190]. Two recently published meta-analyses investigating inflammatory biomarkers in blood reported an elevated tumor necrosis factor (TNF)-α, IL-12 [191], IL-1β, IL-2, IL-6, IL-18 [191,192], and reduced IL-1 receptor antagonist concentration in AD patients compared with controls. Mounting data about IL-6 as an AD biomarker are available. Blood IL-6 levels are associated with severity of cognitive decline in AD [193] and positively correlated with the cerebral ventricular volumes [194] and with matched CSF samples [195]. Blood IL-6 concentration was even higher in MCI individuals [135], suggesting that this biomarker is altered also in prodromal AD stages.

#### **8. Biomarkers for Synaptic Dysfunction**

Synaptic dysfunction is a core feature of AD, occurring early in the disease course. Synaptic density is strictly correlated with cognitive impairment and with Aβ and tau accumulation in AD, thus suggesting a central role in the underlying neurodegenerative process [196]. Based on these observations, several synaptic proteins have been investigated as potential diagnostic and prognostic biomarkers in AD. These include the quite well-characterized Neurogranin (Ng) and other emerging biomarkers such as Synaptosomal-Associated Protein 25 (SNAP-25), Synaptotagmin 1 and 2 (SYT-1 and SYT-2), Neuropentraxin 2 (NPTX-2), and Growth Associated Protein 43 (GAP-43) [197,198].

Ng is a post-synaptic protein largely expressed in the excitatory neurons of the hippocampus and cerebral cortex that acts as a calcium-sensitive modulator of post-synaptic signaling pathways and of long-term potentiation (LTP) [199]. Two recently published meta-analyses reported higher CSF Ng levels in AD compared to MCI and normal controls, thus supporting a role of CSF Ng as a useful diagnostic tool (Table 2) [200,201]. In particular, Ng reported good or even optimal diagnostic accuracy in differentiating AD patients with a full-blown clinical picture from cognitively normal subjects [202]. However, CSF Ng concentration discriminates between stable and converting MCI with poor diagnostic accuracy [200,201,203]. As regards its prognostic value, higher baseline CSF Ng levels are detected in controls and in MCI subjects who will convert to AD compared to non-converters, indicating a role of Ng in predicting progression to AD dementia in both cognitively normal [204]

and MCI individuals (Table 2) [205,206]. CSF Ng could be also a reliable biomarker in the diagnostic workup of dementia being specifically more elevated in AD than non-AD dementias (FTD, DLB, but also VaD) [168,207–210]. Intriguingly, CSF Ng levels are high in AD patients with a typical amnesic phenotype, suggesting its role in the stratification and identification of AD subtypes, as a selective indicator of hippocampal degeneration [207]. In addition to Ng, other synaptic proteins have been explored as candidate biomarkers for AD. In particular, SNAP-25, a pre-synaptic protein involved in vesicle docking and neurotransmitter release, showed good accuracy in differentiating AD and MCI from normal controls (Table 2) [211–213]. Furthermore, high baseline CSF SNAP-25 levels predict future conversion to AD in MCI individuals [212]. Other pre-synaptic proteins such as SYT-1, SYT-2, NPTX-2, and GAP-43 are candidate as biomarkers to differentiate AD, MCI, and cognitive normal controls [197,213]. Importantly, an inverse correlation between CSF and neuron-derived plasma exosomes (NDE) levels of Ng, SYT-2, and GAP-43 has been observed [200,214]. Even if these results would deserve further supporting evidence, NDE may represent a window on the early synaptic dysfunction in AD and pave the way to a minimally invasive assessment (blood sample) of synaptic biomarkers in cognitively impaired and unimpaired subjects.

#### **9. Biomarkers of Neuronal Injury**

NFL is a subunit of neurofilaments that are neural cytoplasmic proteins designated to the structural stability of neurons; they are present in dendrites, soma, and also in axons. Axons physiologically release a low amount of NFL proteins that increase with aging [215]. The concentration of NFL significantly increases in both CSF and in blood as a result of axonal injury or neurodegeneration [216–219]. NFL in CSF is usually measured by sandwich ELISA technology. On the contrary, blood NFL concentration is 40-fold lower than in CSF, and it is below the sensitivity of ELISA or electrochemiluminescence assay technology [215]. Promising results came from recently developed ultrasensitive techniques capable of detecting even low concentrations of NFL in blood (Simoa) [220].  However, growing data showed that CSF and, above all, blood NFL identifies neurodegeneration from early stages [215]. Indeed, NFL (CSF and blood) showed a good diagnostic accuracy in differentiating AD and FTD from healthy controls (Table 3) [128,129,131,208,221,222]. According to these results, a possible context-of-use of this biomarker is to rule out neurodegeneration in mimics such as psychiatric disturbances, or to early detect, within screening programs, the neurodegenerative process in a specific population at high risk (e.g., diabetes, elderly, genetic mutation carriers). Increased blood NFL concentration could also help clinicians to proceed or not with more invasive and expensive examinations in individuals with subjective memory complaints [215]. Moreover, CSF NFL but not t-tau, p-tau, and Ng might be a reliable risk biomarker being associated with a threefold higher risk to develop MCI over a median follow-up of 3.8 years in a population of cognitively healthy individuals [223]. CSF [224,225] and blood [128,226,227] NFL tightly correlated each other and with disease severity. In this regard, in a prospective case-control study including normal controls, MCI, and AD dementia patients, plasma NFL correlated with CSF NFL, poor cognition, cerebral atrophy, and brain hypometabolism [128].

Serum NFL concentration correlated with the estimated years to symptom onset and disease severity in autosomal dominant AD mutation carriers, suggesting its possible role as a risk biomarker in subjects with autosomal genetic mutations for AD (Table 3) [227]. Promising data concern the role of NFL in the differential diagnosis between FTD and AD. Actually, CSF NFL is higher in FTD patients compared to early onset AD, and the addition of NFL analysis improves the diagnostic accuracy of the traditional core biomarkers (p-tau181 and Aß42) up to a sensitivity of 86% and a specificity of 100% [228]. Similar findings were also reported in an autopsy-confirmed AD and FTD study (Table 3) [229]. Moreover, serum NFL could help in the differentiation of Primary Progressive Aphasia (PPA) variants. Indeed, serum NFL is increased in PPA compared to controls and discriminates between nfvPPA/svPPA (with a more likely FTD pathology) and lvPPA (where an AD pathology is expected in more than 50% of cases) with 81% and 67% of sensitivity and specificity, respectively [230].

Visinin-like protein 1 (VILIP-1) is emerging as a surrogate of signaling disruption and neuronal injury. VILIP-1 is a neuronal calcium sensor protein involved in signaling pathways related to synaptic plasticity [231]. A high intracellular concentration of Ca2+ induces the reversible translocation of VILIP-1 to the membrane components of the cell modulating signaling cascade in the neurons via the activation of specific membrane-bound targets [232]. The dysregulation of Ca2+ homeostasis is involved in AD neurodegeneration, bringing to a reduced intracellular expression of VILIP-1 and a quite selective damage of VILIP-1-containing neurons (cortical pyramidal cells, interneurons, septal, subthalamic, and hippocampal neurons) [232]. Therefore, this biomarker significantly increases in CSF [231]. Since VILIP-1 contributes to an altered Ca2+ homeostasis leading to neuronal loss [232], it is mainly considered a biomarker of neuronal injury. CSF VILIP-1 is higher in AD than in controls, but its diagnostic accuracy remains limited, especially in the prodromal stage (Table 2) [115,233,234]. Although VILIP-1 tightly correlated with p-tau and t-tau in CSF, conflicting results concern its relationship with Aß peptides [235,236]. CSF VILIP-1 and the VILIP-1/Aβ-42 ratio negatively correlate with MMSE and with the cerebral amyloid load, and they may predict a cognitive decline over time [233,234,236–240].

#### **10. Toward Alternative Pathophysiological Pathways and Novel Matrices**

The research on novel putative biomarkers in AD recently focused on two main directions: the exploration of new still under-characterized pathophysiological pathways, including mixed neuropathology models, and the identification of alternative easily accessible matrices.

TAR DNA-binding protein 43 (TDP43) is a DNA and RNA binding protein involved in transcription and splicing. TDP-43 contributes to neuroinflammation and may play a role in mitochondrial and neural dysfunction. In ALS and FTLD, its hyperphosphorylated and/or ubiquitinated cytoplasmic inclusions are detected [241,242] but also 20–50% of AD patients may show concomitant TDP-43 pathology [243–245]. Interestingly, TDP-43 pathology can be triggered by Aβ peptides [244]. In AD, increased plasma TDP-43 levels have been found compared to normal controls [246]; furthermore, plasma levels were increased also in the preclinical stage of subjects who subsequently progressed to AD dementia [247]. However, the evidence of a diagnostic and prognostic role of TDP-43 in AD is currently quite limited as well as its role in differentiating AD from other dementia mainly involving the hippocampus and memory (e.g., LATE) [22].

Lewy-related pathology (LRP), primarily consisting of α-synuclein (α-syn) aggregates, has been detected in more than half of autopsied AD brains, and higher levels of α-syn in the CSF of patients with MCI and AD have been associated with AD pathology and cognitive decline [248,249]. Moreover, CSF total α-syn (t-α-syn) and oligomeric α-synuclein (o-α-syn) levels were higher in AD [250] compared to PD, PD dementia and DLB individuals [251,252]. The use of standard ELISA methods to assess CSF α-syn levels does not ensure good diagnostic accuracy in discriminating AD from synucleinopathies [250]. Nevertheless, RT-QuIC [253] and protein misfolding cyclic amplification (PMCA) [254] are promising tools to identify AD individuals with α-syn co-pathology. Furthermore, growing interest toward the evaluation of α-syn heterocomplexes with Aβ1–42 (α-syn/Aβ) or tau (α-syn/tau) measured in red blood cells (RBCs) as peripheral pathophysiological markers of NDDs has been displayed [254]. Despite both α-syn alone, α-syn/Aβ and α-syn/tau heteroaggregates being found lower in AD compared to cognitive normal controls when isolated from red blood cells (RBC), only RBC α-syn/Aβ and α-syn/tau heterodimers discriminated AD from controls with fair accuracy [254].

Exploring alternative easily accessible matrices as a source of putative biomarkers is another key point of the search for novel fluid biomarkers. In this frame, exosomes represent an innovative and promising non-invasive tool to track early neurodegenerative changes occurring within the central nervous system. Exosomes are vesicles containing potential biomarkers for NDDs released into the extracellular space (that can be isolated from several body fluids) [243,254]. Proteins reflecting key events of the neurodegenerative process have been isolated in exosomes extracted from CSF and blood by using proteomic analysis [244–246]; in particular, p-tau was isolated in CSF exosomes from patients with mild AD (Braak stage 3) [247], and increased levels of exosomes-associated tau and Aβ were found in AD patients compared to controls [248]. Finally, other easily accessible matrices such as the retina may represent an open window on early neurodegenerative events in AD [249]. Amyloid pathology was demonstrated in the retina, and high-resolution non-invasive retinal imaging [47–51] represents an in vivo approach for visualizing Aβ deposits [250–252]. Indeed, retinal Aβ accumulation positively correlated with cerebral amyloid plaques [8]. Furthermore, decreased flow velocities in the retinal central veins were found in both MCI and AD compared to controls, thus suggesting a strict correlation with the underlying early neurodegenerative changes [253]. However, this field of research remains in its pathfinding stages, and a consensus on retinal imaging modalities, methodologies, and measures is still missing [253].

#### **11. Conclusions**

Recent research efforts are expanding the array of biomarkers on detecting and stratifying NDDs. Since 2007, fluid biomarkers have been reported within the diagnostic criteria of AD. In particular, Aß42 peptide, p-tau, and t-tau proteins measured in CSF became essential for a "modern" AD definition. The conceptual shift from a phenotype to a biomarker-based (or a precision medicine) diagnostic approach allowed the inclusion of the atypical subtypes within the AD spectrum and the exclusion of AD-mimics. For instance, patients with early and predominant behavioral impairment but positive for core pathophysiological biomarkers are categorized as AD and not as FTD. By contrast, individuals showing cognitive impairment of the hippocampal type but negative for core biomarkers are not considered AD. Definitely, the identification of a specific pathophysiological process in vivo by one or more biomarkers prevails on clinical phenotype. Unfortunately, validated fluid biomarkers used for AD diagnosis are invasive, time-consuming, expensive, not easily repeatable and, most importantly, not applicable as screening tools in large asymptomatic populations. On the other hand, the preclinical or prodromal identification of AD is urgent for patient recruitment in future disease-modifying treatments. This is an "expert" opinion based on the current literature, reporting the diagnostic and prognostic value of fluid biomarkers in AD. Five candidate molecules—three in plasma measured using ultrasensitive techniques (Aβ peptides, p-tau, and NFL proteins) and two in CSF (Ng and YKL-40)—with different potential context-of-use (Table 2) may be proposed. These molecules may enrich the current array of fluid biomarkers—CSF Aβ42, t-tau, and p-tau—for a more precise management of AD, and, broadly speaking, NDDs. These biomarkers are useful to both classify patients in different diagnostic categories and to track the pathophysiological mechanisms underlying neurodegeneration. The blood biomarkers (Aβ peptides, p-tau, NFL) are probably not more accurate than the respective molecules measured in CSF. However, they may be easily repeated over time, proposed in screening programs, and monitor treatments in disease-modifying trials. On the other hand, CSF YKL-40 and Ng are proxies of additional pathophysiological mechanisms related to AD, namely neuroinflammation and synaptic disruption, that cannot be efficiently evaluated with peripheral blood biomarkers. Therefore, CSF YKL-40 and Ng may be used in a subsequent diagnostic step to better stratify patients with prodromal or definite AD.

Plasma p-tau is increased in AD patients compared to controls and MCI individuals, discriminating AD demented patients from both cognitively unimpaired subjects and other NDDs with optimal diagnostic accuracy [91]. In several recent studies from different research groups, its classificatory accuracy surprisingly overlaps with cerebral amyloid-PET [90,124]. Moreover, plasma p-tau predicts a future cognitive decline over time [91]. Therefore, plasma p-tau, being easily repeatable, could be proposed in screening, diagnostic, prognostic, and monitoring context-of-uses. Simoa is the ultrasensitive technique used with more successful results across the studies on plasma p-tau so far. Of course, additional studies are needed. In addition, plasma t-tau concentration correlated with future cognitive decline, increased atrophy rates measured by MRI, and cerebral hypometabolism in

FDG-PET images, but the results are less convincing and charted overlapping values among AD with dementia, prodromal, and preclinical AD groups [148].

Plasma Aβ peptides may represent a further significant improvement to facilitate the in vivo detection of amyloid pathology, substituting traditional core CSF biomarkers in next years. The combination of CSF traditional biomarkers (e.g., Aβ1–42/Aβ1–40 ratio, t-tau/Aβ1–42, and p-tau/Aβ1–42 ratios) can improve the diagnostic accuracy as well as the prediction of cognitive decline in AD patients. Similarly, the combination of plasma and serum biomarkers into ratios may increase the diagnostic power, although further evidence is needed [109,133]. Mounting data revealed that a low plasma Aβ1–42 and Aβ1–42/Aβ1–40 ratio are quite specific of AD pathology, although the concordance with cerebral amyloid-PET examination is variable and should be carefully evaluated in future studies [75,92,120]. In brief, further investigations should clarify in larger prospective studies: (1) the more accurate method to detect Aβ peptides and related by-products in plasma, (2) the pathophysiological role of plasma Aβ peptides and Aβ oligomers, and (3) their diagnostic and prognostic value as biomarker of AD.

NFL is mainly a marker of axonal degeneration and considered an unspecific indicator of neurodegeneration. Importantly, CSF and plasma NFL strictly correlated in all studies, suggesting that plasma NFL would be a reliable peripheral biomarker consistently reflecting modifications within the CNS. Indeed, many studies on NDDs demonstrated that diagnostic and prognostic accuracies of plasma and CSF NFL overlap [254]. NFL is a good example as a versatile biomarker for multiple context-of-use. It is useful to differentiate NDDs from mimics such as psychiatric disturbances or to early detect neurodegenerative processes in particular populations at risk (e.g., diabetes, elderly, genetic mutation carriers). NFL values were associated with a threefold higher risk to develop MCI, demonstrating a potential prognostic value [128,226,227]. Finally, the negative predictive value of plasma NFL might be used as a first step in screening programs for neurodegeneration, involving individuals with subjective memory complaints and late-onset psychiatric disorders. Concerning AD, plasma NFL showed a promising role in differentiating AD from bvFTD patients. It is likely that bvFTD individuals with an underlying TDP-43 pathology (related to amyotrophic lateral sclerosis) reported significantly higher plasma NFL value than AD subjects. Plasma NFL could early discriminate AD from more aggressive neurodegenerative dementia such as CJD [244]. Simoa was the only ultrasensitive technique used to measure plasma NFL in AD studies.

An increasing number of studies are focused on the development of precise biomarkers tracking additional key pathophysiological pathways leading to neurodegeneration, such as synaptic disruption and neuroinflammation. The pre-synaptic protein Ng measured in CSF is the most promising indicator of a synaptic dysfunction and hippocampal damage [202]. It could help stratify patients suffering from NDDs involving the hippocampus, including AD but also hippocampal sclerosis, LATE, and PART. LATE and PART have been recently defined in postmortem examinations, but in vivo diagnostic biomarkers are needed. CSF Ng could be also used as predictive indicator of an anticholinesterasic treatment response in patients showing a prevalent hippocampal impairment (typical AD phenotype, etc.) [200,201,205,206]. CSF Ng demonstrated from good to optimal diagnostic accuracy in discriminating AD dementia patients from the control group and a reliable prognostic value for AD conversion in MCI individuals.

Growing data reported an abnormal neuroinflammatory response in AD. Currently, CSF YKL-40 is the most promising fluid biomarker of glia activation, and it has been extensively investigated in other NDDs as well. Neuroinflammation is a common pathway of several NDDs, and not surprisingly, YKL-40 is an unspecific biomarker. This biomarker could be helpful in monitoring tailored anti-inflammatory trials in AD. Studies exploring a possible correlation between CSF YKL-40 concentration and cerebral inflammatory tracer as the translocator protein (TSPO)-PET uptake could clarify the role of this fluid biomarker as an indicator of neuroinflammation. CSF YKL-40 showed a fair diagnostic accuracy to discriminate AD patients from the control group and other neurodegenerative dementias. YKL-40 also reported a certain predictive value for MCI-AD progression.

In summary, we found that reliable fluid biomarkers might track three out of four of the main pathophysiological pathways of AD (Figure 1). The concentration in different biofluids of Aβ peptides and p-tau proteins reflect the cerebral misfolded protein deposition in AD. Moreover, plasma NFL might help the early identification of a general neurodegenerative process independently from the specific pathology. The diffusion of ultrasensitive techniques in the last few years is radically revolutionizing the context-of-use of these biomarkers in AD. The possibility to measure biomarkers in blood opens a completely novel scenario for the detection of multiple neurodegenerative mechanisms with a low cost and minimally invasive examination. This should encourage the development of screening tools in selected populations and improve the monitoring of disease-modifying trials. Of note, validated surrogates of co-pathologies such as α-syn and TDP-43 protein accumulation are currently not available as well as of cerebrovascular impairment. Finally, YKL-40 and Ng measured in CSF are promising proxies of neuroinflammation and synaptic disruption, respectively.

The studies described have several shortcomings. Many investigations reported different inclusion criteria and sometimes, they were not biomarker-based. Moreover, there is frequently a lack of data about comorbidities, especially cerebrovascular burden, contemporary pharmacological treatments, or stratification for age, gender, and genetic profiles [53]. Hepatic and kidney dysfunctions may impact biomarker levels as well as modifications of blood cell counts and plasma protein composition [67]. Nonetheless, these variables were not systematically considered, thus constituting a possible methodological bias, since individuals with AD present relevant vascular comorbidities.

From the prospective of a precision medicine approach, increasing attention is paid to find biomarkers associated to pathogenic pathways leading to neurodegeneration. The contemporary use of multiple biomarkers can help dissect the pathological mechanisms dynamically acting in space and time providing an accurate stratification of AD population.

AD is more a spectrum of different pathological mechanisms that brings a loss of proteostasis, which is the accumulation of several misfolded and aggregated proteins in multiple combinations rather than a single entity. Advanced statistical analysis, including unsupervised clustering strategies, combining clinical, biomarkers, and genetic data to collect subjects from large diversified cohorts into consistent clusters might be an innovative representation of AD [6] and other NDDs [254] and significantly contribute to the discovery of causes and tailored treatments. Novel data-driven classifications based on quantitative measurements of biomarkers and clinical information (e.g., standardized clinical scores) could improve the identification of effective and personalized therapies [253,254]. In conclusion, we assume that the identification and inclusion of AD patients in disease-modifying trials will be soon changed, mainly based on the demonstration of specific pathophysiological mechanisms and minimally influenced by phenotypes.

**Author Contributions:** E.D.P., M.F.B., N.C. and L.G. wrote the paper, F.B. substantially contributed to the conception and design of the article and interpreting the relevant literature, R.C. and G.S. revised the article critically for important intellectual content. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest relevant to this work.

#### **References**

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**3. Concrete productions**

Concrete construction demands land and hence the sourcing of materials for construction activities (aggregates, cement, waters, admixtures, etc.) from quarry sites and borrows pits can potentially end in the whole removal of vegetation and virgin materials. Additionally, the displacement of individual, concrete ingredients production resalted for losing of important ecological resources for local people, vegetation that gives watershed protection, and as a result the diminution of biodiversity of national or regional or global importance.

The aggregate manufacturing steps have many considerable environmental impacts. The foremost obvious environmental effect resulted from stone, aggregates, and mineral mines for industries is the degraded quality of air, and related health effects, sourcing from airborne emission from both stack and also disturbed areas at these mines. Natural deposits sources of aggregates are being depleted and causing a heavy threat to the environment and likewise to society. Because of a high rate of natural aggregates depletion from its source beds causing lots of problems such as loss of strata of water-retaining, bank slides, exposing water supply scheme intake wells, dropping underground water table levels which become a cause for agricultural effect and aquatic life disturbances.

In emerging nations like Ethiopia, due to rapid urbanization and infrastructure projects, there is a wide expansion of cement industries that releasing this pollutant to the surrounding. Apart from these environmental concerns regarding CO2 emission during cement manufacturing, natural resource-demanding also makes cement expensive when compared with aggregates and water for concrete productions. Consequently, to overcome these problems, searching for more

environmentally friendly and economical materials that have cement properties has prolonged attention in other such types of materials that can be used fully or partially substitute normal Portland cement.


*2.4. EEG Acquisition*

The EEG recordings were derived from ward recordings conducted during the patients' stay at the RPAH. The EEG was recorded using Compumedics hardware and software. The ward nurse applied the individual electrodes to the patient's head in the standard 10/20 format using the gold standard measurement process. Once the routine clinical recording was complete, the raw EEG files were obtained, and the seizure segments annotated by the EEG technician and reviewed by a senior neurologist. All seizures were then analysed in Curry 8 ("Curry", Compumedics Neuroscan) to obtain the functional connectomes. Curry is a neuroimaging software suite that allows the combination and analysis of multimodal data and is optimised for evaluating epilepsy-related data.

#### *2.5. EEG Processing to Obtain Functional Connectomes*

Curry was used to pre-process the EEG and obtain the sensor-based coherence matrices which represented the functional connectomes. First, we applied Curry's automated artifact reduction and filtering tool to obtain a clean signal. Next, Curry's coherence calculation process (shown in Figure 1, Step 1, b, ii) was used to generate coherence matrices for the first five seconds of each seizure. Specifically, using one-second non-overlapping windows starting from the annotated seizure onset time, the coherence matrices were computed from the cross-spectral densities *Gxy* and auto-spectral densities *Gxx* and *Gyy* of the channels *x* and *y*, using the equation

*Cxy* **= (***Gxy × Gxy***)**/**(***Gxx × Gyy***)**. The resulting coherence matrices were 21 *×* 21; row and column headers represented single electrodes. The reference electrodes and their corresponding scores were then removed, resulting in 19 *×* 19 matrices, which were used as the functional connectomes ("FC", Figure 1, Step 1, b, iii). Therefore, each electrode pair's corresponding value was a composite of the normalised maximum similarity between the waveforms and the time-shift (delay) when the maximum similarity occurred. The

electrode pair value represented the highest percentage of coherence achieved by that electrode pair in the one-second window after factoring in the signal time lag between the two electrodes.

#### *2.6. Mapping Cortical Regions to the Nearest Electrode*

This section details the processes in Step 2 of Figure 1, where we used our previously described method [30] to create a subject-specific electrode warp and map each subject's cortical regions from the DK atlas to the nearest electrode. First, using the ANTs nonlinear registration tool, 21 electrodes in the standard MNI template space were warped to each participant's T1 image that had been registered to the diffusion image space (Figure 1 Step 2, a, i–iii). Next, we applied our inverse square method, which incorporates the inverse square equation shown in Figure 1 (Step 2, b, i) to produce a subject-specific, one-to-one mapping of each cortical region to its nearest electrode. The inputs were each subject's electrode warp and the cortical structure labels from Step 1. The inverse square equation holds that the light intensity of a source is inversely proportional to the square of the distance from the source. Thus, the inverse square method enabled the consideration of MRI voxel intensity in assessing the distance of each cortical region from each electrode's centre. Voxel intensity may represent the cortex's topological arrangement, endorsing postulation of the EEG signal strength from a given region relative to that region's distance from the scalp. The matrix in Figure 1, (Step 2, b, ii) depicts each region with one electrode name assigned—this electrode was the closest to that region. Subcortical regions (such as the hippocampus) were not assigned electrodes as their physical distance from the scalp and positioning below other cortical regions deemed them inaccessible for accurate measurement; thus they were removed from the analysis.

## *2.7. Mapping the Structural Connectome to the Functional Connectome*

To enable the direct, one-to-one comparison of the values in the structural and functional connectomes, the structural connectome was first condensed to match the size of the functional connectomes (from 70 *×* 70 to 19 *×* 19). Only the upper triangle of the structural connectome was used in the calculation. The output file shown in Figure 1 (Step 2, b, ii) provided the electrode names and corresponding regions (and their values) for the new structural matrix. To calculate the new value for a given electrode pair in the condensed structural connectome, the values for all regions between that given electrode pair were summed. An example is provided in Figure 1, (Step 3, a), where all values between electrodes Fz ("E*x*") and Fp2 ("E*y*") are coloured in purple. The total sum of all values between E*x* and E*y* was used as the new value for Fz-Fp2 (black square) in the condensed structural connectome. Once new values were computed for all electrode pairs, the diagonal line (self correlations) was removed from the structural and functional connectomes, and the connectomes were converted to a 1D array for statistical analysis.


**6. Conclusion**

The homology modeling was performed to determine the best template, from which we concluded that 4m1m is preferred in *Aspergillus fumigatus, Aspergillus nidulans, Acinetobacter baumannii*, and *Candida albicans*. In *Cryptococcus neoformans* 4f4c is preferred, and 3wme is preferred in *Staphylococcus aureus*.

The molecular docking led us to conclude that bithionol, levofloxacin, e1210, tigecycline, and bithionol were the most efficient potential antibiotics for the organisms *Aspergillus fumigatus, Acinetobacter baumannii, Candida Albicans, Staphylococcus aureus*, and *Cryptococcus neoformans*, respectively. Each of the potential antibiotics was found to be more effective than a number of the known antibiotics in the treatment of that respective organism.

An analysis of the interacting residues showed us that:


*2.4. LOE Reduces Atherosclerotic Plaque Burden in apoE*−*/*− *Mice in WD-Fed apoE*−*/*− *Mice*

Hematoxylin and eosin (H&E) staining showed atherosclerotic plaques predominantly in the aortic sinus. The plaque area in the aortas of WD-fed apoE−/− mice was increased compared with that of aortas derived from C57BL/6 mice (Figure 5). While losartan failed to reduce the atherosclerotic lesion area in WD-fed apoE−/− mice (0.47 ± 0.07 vs. 0.55 ± 0.03 mm2 ), LOE treatment significantly reduced the aortic plaque area (0.33 ± 0.03 vs. 0.55 ± 0.03 mm2 ) (Figure 5).

*Plants* **2021**, *10*, x FOR PEER REVIEW 5 of 12

**Figure 4.** Effect of LOE and losartan treatments on plaque inflammation in the aortas of apoE−/− mice. LOE treatment significantly reduced the degree of plaque inflammation in western diet-fed apoE−/− mice, whereas losartan treatment did not. In vivo imaging of atherosclerotic plaque inflammation was assessed by fluorescence reflectance imaging (FRI) using AP-HGC-Cy5.5 nanoparticles. The extent of plaque inflammation was measured with an IVIS-200 FRI system. (**a**) Middle: FRI; bottom: merged images. (**b**) Corresponding cumulative data. The results are expressed as mean ± SEM (n = 4–6). \* *p* < 0.05 versus the C57BL/6 group and # *p* < 0.05 versus the apoE−/− vehicle group. **Figure 4.** Effect of LOE and losartan treatments on plaque inflammation in the aortas of apoE−/− mice. LOE treatment significantly reduced the degree of plaque inflammation in western diet-fed apoE−/− mice, whereas losartan treatment did not. In vivo imaging of atherosclerotic plaque inflammation was assessed by fluorescence reflectance imaging (FRI) using AP-HGC-Cy5.5 nanoparticles. The extent of plaque inflammation was measured with an IVIS-200 FRI system. (**a**) Middle: FRI; bottom: merged images. (**b**) Corresponding cumulative data. The results are expressed as mean ± SEM (*n* = 4–6). \* *p* < 0.05 versus the C57BL/6 group and # *p* < 0.05 versus the apoE−/− vehicle group. predominantly in the aortic sinus. The plaque area in the aortas of WD-fed apoE−/− mice was increased compared with that of aortas derived from C57BL/6 mice (Figure 5). While losartan failed to reduce the atherosclerotic lesion area in WD-fed apoE−/− mice (0.47 ± 0.07 vs. 0.55 ± 0.03 mm2), LOE treatment significantly reduced the aortic plaque area (0.33 ± 0.03 vs. 0.55 ± 0.03 mm2) (Figure 5).

**Figure 5.** Effect of LOE and losartan treatments on the atherosclerotic plaque areas of aortic sections obtained from apoE−/− mice. LOE treatment significantly reduced the extent of aortic plaque, whereas losartan failed to reduce the area of atherosclerotic lesion in western diet-fed apoE−/− mice. The atherosclerotic plaque area (mm2 ) was measured as the area between the internal elastic lamina and the lumen and quantified by ImageJ after H&E staining. (**a**) H&E staining. Asterisk (\*) indicates atherosclerotic plaque. (**b**) Corresponding cumulative data. The scale bar represents 200 µm. The results are expressed as mean ± SEM (*n* = 4–6). \* *p* < 0.05 versus the C57BL/6 group, # *p* < 0.05 versus the apoE−/− vehicle group and †*p* < 0.05 versus the apoE−/− LOE group.

(**a**) (**b**)

### **3. Discussion**

The remnants of apolipoprotein B-containing lipoprotein within the arterial walls [25] and subsequent oxidative modification trigger an inflammatory response and endothelial dysfunction [26,27], leading to the formation of atherosclerotic plaques. Current therapeutics against atherosclerotic cardiovascular diseases cannot completely prevent vascular ROS-induced atherogenesis. The major findings of the present study indicate that LOE decreases vascular oxidative stress by suppressing the expression of NADPH oxidase, resulting in improved endothelial dysfunction and in the prevention of atherosclerotic inflammation and lesion progression in a mouse model of atherosclerosis.

A damaged endothelium disturbs the balance between vasoconstrictors and vasodilators and leads to multiple events that promote or exacerbate atherosclerosis [28]. Several studies have suggested that a number of polyphenol-rich natural products are capable of improving endothelium-dependent relaxation by enhancing the endothelial production of vasoprotective factors including NO and EDH and ultimately prevent endothelial dysfunction in cardiovascular diseases including hypertension [29–31]. Our previous studies revealed that LOE was a potential vasorelaxant acting via NO, and that LOEinduced relaxation was attenuated by PI3-kinase/Akt pathway inhibitors in isolated aortic rings. LOE induced a time-dependent phosphorylation of Akt at Ser473 and eNOS at Ser1177 in endothelial cells [22]. The findings suggest that LOE was an activator of the PI3-kinase/Akt-dependent eNOS phosphorylation at Ser 1177. In addition, LOE treatment attenuated endothelial dysfunction and hypertension induced by angiotensin II in rats [22]. Long-term administration of LOE to diabetic mice restored the abolished endothelium-dependent relaxation to Ach in aortic rings and ameliorated hyperglycemia partially [23]. The current findings based on aortic rings isolated from apoE−/− mice, an experimental model of atherosclerosis, clearly indicate that chronic LOE treatment improved endothelium-dependent vascular relaxation to Ach in WD-fed apoE−/− mice to a degree comparable to losartan. Losartan is a well-known angiotensin II receptor antagonist with anti-hypertensive activity and is used to treat hypertension and heart failure. Previous studies have shown that losartan exhibits anti-atherosclerotic effects in apoE−/− mice by reducing lipid accumulation and macrophage infiltration as well as by inhibiting LDL lipid peroxidation [32,33].

Endothelial dysfunction is triggered by high levels of NADPH oxidase-derived superoxide anions, which react with NO, thereby decreasing its bioavailability in the arterial wall. NADPH oxidase, which was detected in neutrophils, is found in vascular endothelial cells and smooth muscle cells, and essential source of superoxide formation in several animal models of vascular disease [34]. Furthermore, p22phox, one of the NADPH oxidase components, has been identified in atherosclerotic coronary arteries of humans [35]. In this study, we demonstrated that LOE inhibited the increased levels of vascular oxidative stress in apoE−/− mice as indicated by the pronounced DHE staining in all of the aortic plaques and the arterial wall as well as the excessive vascular expression of NADPH oxidase subunits, including p22phox and p47phox. The findings indicate that the potential effect of LOE intake on endothelium-dependent vascular relaxation is most likely associated with its ability to reduce vascular oxidative stress, partially via decreasing the NADPH oxidase expression in apoE−/− mice. Indeed, previous studies suggested that tea and grape-derived polyphenols downregulate the expression of NADPH oxidase subunits such as p22phox and nox1, and tea polyphenols induce upregulation of catalase expression in vascular cells [29,36]. Since endothelial dysfunction is identified ahead of structural alterations in the arterial wall, it most likely acts as an early signaling event in the pathological initiation and progression of atherosclerosis [28].

Inflammation, which is mediated by various factors such as cytokines, adhesion molecules, and NO, is considered a vital component of atherogenesis [1,3]. Several studies have shown that dietary polyphenols, especially theaflavin and quercetin, ameliorate atherosclerosis by improving inflammation and bioavailability of NO in apoE−/− mice [37,38]. We employed molecular FRI as a sensing platform to measure plaque inflammation after injecting AP-HGC-Cy5.5 nanoparticles as molecular imaging agents. These nanoparticles selectively distinguish atherosclerotic plaques by binding to the IL-4 receptor on macrophages, endothelial cells, and smooth muscle cells. Therefore, this molecular imaging tool facilitates the visualization of early atherosclerotic plaque lesions [39]. We detected that increased plaque inflammation in WD-fed apoE−/− mice and LOE treatment for 20 weeks resulted in marked regression of plaque inflammation, even more than losartan treatment. A reduction in plaque inflammation in LOE-treated apoE−/− mice decreased the aortic plaque area, suggesting anti-atherogenic and anti-inflammatory effects of LOE in apoE−/− mice.

### **4. Materials and Methods**


**9. Summary and conclusions**

442 International Perspectives on Global Environmental Change

**Final state –eq. (2) Scenario (a)**

AOSP 1.00 0.7103 0.8808 0.5 0.8800 0.2835 **Bd 1.00 0.0004 0.0005 0.5 0.0007 0.9974**  BW 1.00 0.7290 0.8069 0.5 0.8063 0.3024 CD 1.00 0.8113 0.9097 0.5 0.8868 0.3855 CW 1.00 0.8558 0.9721 0.5 0.9718 0.1635

D-Distrust to State 1.00 0.8284 0.9303 0.5 0.9295 0.3751

S- Urban Sewage 0 0.7332 0.8591 0 0.8572 0.3698 Si-Siltation 1.00 0.9169 0.9379 0.5 0.9378 0.0933 SLR 1.00 0.8314 0.8655 0.5 0.8653 0.1948 Sphi 1.00 0.9970 0.9953 0.5 0.9952 0.0061 Tourism 1.00 0.2055 0.1535 0.5 0.1520 0.7777 **ECOL 1.00 0.0026 0.0055 0.5 0.0056 0.9913**

Table 3. Initial and final concepts' state after 25 iterations for Scenario (a) and (b).

DFS 1.00 0.9903 0.9974 0.5 0.9972 0.0174 HAB 0 0.9938 0.9986 0 0.9986 0.0162 HA 0 0.7297 0.3376 0 0.3346 0.2689 IA 0 0.5125 0.7095 0 0.7049 0.4638 ISP 1.00 0.7903 0.8367 0.5 0.8365 0.2359 LF 1.00 0.7103 0.8162 0.5 0.8161 0.4114 M- Mining 1.00 1.0000 1.0000 0.5 0.5000 0.5000 MC 1.00 0.9842 0.9924 0.5 0.9922 0.0235 MR 1.00 0.3199 0.4806 0.5 0.4808 0.5738 MSW 0 0.9664 0.9964 0 0.9950 0.0955 PPP 1.00 0.7270 0.8281 0.5 0.8280 0.4026 PSA 1.00 0.7581 0.8754 0.5 0.8737 0.3187 RAW 1.00 1.0000 1.0000 0.5 0.5000 0.5000 RP 1.00 0.6315 0.7144 0.5 0.7140 0.3678

**Initial values-Scenario (b)**

**Final state – eq. (2) Scenario (b)**

**Final stateeq. (5) Scenario (b)**

**Final state –eq. (5) Scenario (a)**

**Concepts**

**Initial values-Scenario (a)**

In this chapter, the FCM methodology was presented and analyzed for the elicitation and understanding of individual and collective knowledge, preferences and beliefs. The aim was to present to the reader both a theoretical underpinning of FCMs as well as a grasp of their empirical modalities.

A cognition model, like FCM, represents a system in a form that corresponds closely to the way humans perceive it. Therefore, the model is easily understandable, even by a nonprofessional audience and each parameter has a perceivable meaning. The model can be easily altered to incorporate new phenomena, and if its behavior is different than expected, it is usually easy to find which factor should be modified and how. In this sense, a FCM is a dynamic modeling tool in which the resolution of the system representation can be increased by applying a further mapping. The FCM methodology developed makes it possible, if the initial mapping of the risk factors and future prospects of marine ecosystem is incomplete or incorrect, to make further additions to the map, and to predict the effects of the new parameters considered.

FCMs have some specific advantageous characteristics over traditional mapping methods: they capture more information in the relationships between concepts, are dynamic, combinable, and tunable, and express hidden relationships (Kosko, 1986, 1992). The resulting fuzzy model can be used to analyze, simulate, and test the influence of parameters and predict the behavior of the system. Summarizing, FCM helps describe the schematic structure, represent the causal relationships among the elements of a given decision environment, and the inference can be computed by a numeric matrix operation. With FCM it is usually easy to find which factor should be modified and in which way.

To illustrate the FCM methodology, an empirical application for modelling lay people perceptions is presented. We describe the main features of a FCM exercise designed to elicit the Black Sea stakeholder views/ perceptions about the risks that the Black Sea may face in the future 20 years. A generic model for environmental management is constructed by augmenting the individual FCMs drawn by lay people-stakeholders from Ukraine. The graph theoretical indices were calculated out of the individual cognitive maps and the collective cognitive map produced by augmentation. A number of scenarios were run using the FCM inference process to enable us to understand the complex structure of the Black Sea problems and the risks mainly affecting its marine ecosystem. This knowledge is further used to design policies that contribute in environmental management. The results show its functionality and demonstrate that the use of FCMs is reliable and efficient for this task.