Multi-Omic Blood Biomarkers in Late-Onset Alzheimer’s Disease

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Oneil Girish Bhalala	--	4151	2024-02-08 00:11:41	\|
2	layout & references	Sirius Huang	Meta information modification	4151	2024-02-08 03:05:07	\|

This entry is adapted from the peer-reviewed paper 10.3390/ijms25021231

Late-onset Alzheimer’s disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer’s disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer’s disease at various timepoints within the disease course, including at the preclinical stage.

Alzheimer’s disease genomics proteomics metabolomics risk prediction

1. Introduction

Late-onset Alzheimer’s disease (AD), the leading cause of dementia worldwide, is projected to affect more than 150 million individuals globally by 2050, with the increase mainly driven by population growth and ageing ^[1]. AD diagnosis is made clinically based on a syndrome of progressive cognitive impairment, typically with a predominant amnestic profile, though the constellation of symptoms can vary considerably. Diagnosis can be supported with the use of biomarkers, such as those derived from biofluids and brain imaging. However, there is significant global variation in the use of these ancillary investigations partly due to the invasiveness of some tests, such as obtaining cerebral spinal fluid (CSF), and the accessibility of advanced imaging including magnetic resonance imaging (MRI) and positron emission tomography (PET) ^[2]. The clinical interpretation of these biomarkers within different disease stages and in the setting of comorbid cerebral disease also remains challenging. These barriers have thus far prevented the widespread implementation of biomarkers into a unified diagnostic pathway for AD, though there is a movement towards defining and diagnosing AD biologically through the presence of biomarkers ^[3].

2. Blood-Based Protein Biomarkers as a Measure of Dynamic Risk

While considerable progress has been made in uncovering the genetic architecture of AD through large GWASs, the resulting PRSs represent a static risk that is embedded within an individual’s genetics. The analysis of more dynamic biomarkers may provide further insights into the real-time risk of AD at a particular stage of life. Technological improvements in biomarker detection have led to improved utility for AD diagnosis. In particular, ultrasensitive detection is possible with the use of single molecule array (Simoa) and mass spectrometry, allowing for the quantification of ultralow concentrations (pico- and femtomolar ranges) ^[4]^[5]^[6]. Blood levels of AD-implicated proteins such as neurofilament light chain (NfL), Aβ and tau (including hyperphosphorylated species) demonstrate strong concordance with CSF levels ^[7]^[8]^[9], thereby increasing their utility in diagnosing AD. Potential applications of different protein blood biomarkers in assessing AD risk are discussed below.

2.1. Neurofilament Light Chain

NfL is a cytoskeletal protein found predominantly in neuronal axons with a role in axonal growth and stability, with CSF and blood levels increasing after axonal damage ^[10]. Elevated plasma NfL levels are detected in a wide range of neurodegenerative diseases ^[11] including AD ^[12]^[13], frontotemporal dementia (FTD) ^[14], amyotrophic lateral sclerosis ^[15] and HIV-associated dementia ^[16]. However, NfL can also be elevated in acute non-neurodegenerative causes of brain injury including stroke ^[17] and encephalitis ^[18], highlighting that clinical context is important for biomarker interpretation. The role of NfL may be in discriminating between neurological disorders and psychiatric disorders, both of which can present with cognitive and memory dysfunction, a common clinical dilemma ^[19]^[20].

The ‘real world’ utility of blood NfL levels for assessing AD risk has been studied in memory clinics. In one prospective study of over 100 patients assessed in a tertiary memory clinic, physicians found knowledge of serum NfL levels diagnostically useful in patients under 62 years of age (60% useful) and in male patients (62% useful), as well as in those with a diagnostic uncertainty (67% vs. 51% useful in those patients with no diagnostic uncertainty) ^[21]. These findings were independent of knowledge of CSF NfL levels, suggesting that there is growing confidence in the use of blood NfL levels amongst physicians. Analysis of plasma NfL levels in over 550 patients with established diagnoses in a retrospective memory clinic study also found higher values in those with neurodegenerative conditions compared to non-neurodegenerative conditions ^[22]. Moreover, the increase in NfL levels correlates with the degree of cognitive impairment (higher levels seen in dementia compared to those with MCI, which was in turn higher than those with subjective cognitive impairment) ^[22]^[23]^[24]. However, the addition of plasma NfL levels to a diagnostic model based on clinical factors (age, cognitive test scores and APOE status) does not significantly increase the area under the receiver operating characteristic curve (AUC, 0.83 [0.78–0.87] versus 0.81 [0.77–0.85] for a model without NfL levels) for discriminating neurodegenerative from non-neurodegenerative conditions in patients with established cognitive impairment, suggesting that knowledge of blood NfL level may not be as relevant once substantial symptoms have developed ^[22]. Further, longitudinal blood NfL levels are not predictive of conversion to AD ^[25]^[26].

While NfL is emerging as an attractive biomarker for neurodegenerative conditions, its low specificity reduces its ability to serve as an AD-specific biomarker ^[4]^[27]. Moreover, cut-off values have not been well established for clinical use, though NfL levels increase in an age-dependent manner ^[27]^[28] and age-adjusted models have been proposed ^[29]. Importantly, the effects of ancestry on normative values for NfL are unclear with conflicting results ^[20]^[30]^[31]^[32]. These open questions need to be addressed before NfL can be used robustly in a clinical setting to identify neurodegeneration ^[33].

2.2. Amyloid-Beta

Accumulation of Aβ plaques is a key component of AD pathology, with levels of Aβ1-42 changing decades before symptom onset. While a decrease in Aβ1-42 levels within the CSF has been robustly associated with AD ^[34], the association of blood levels was less clear due to concentrations being up to 100-fold lower in blood compared to CSF. Early meta-analyses of blood Aβ1-42 levels measured using plate-based immunoassays such as ELISA did not find significant differences between AD and healthy controls ^[35]. Measurements using mass spectrometry demonstrate a stronger correlation between plasma and CSF Aβ1-42 levels ^[36]^[37], suggesting that earlier equivocal results for Aβ likely reflect technological challenges rather than true pathobiology. Still, plasma Aβ levels and robustness differ significantly based on the assay used, limiting widespread uptake as a useful clinical blood biomarker ^[36].

To assess how plasma Aβ1-42 may serve as a biomarker for AD risk, a subgroup analysis of the Rotterdam study of over 450 older individuals (mean age of 68 years) found that lower plasma Aβ1-42 levels were associated with increased dementia incidence (HR = 1.27 [1.02–1.58]), especially among those individuals that were non-APOE ε4 carriers (HR = 1.47 [1.09–1.99]) ^[38]. In older individuals with subjective cognitive concerns but without a dementia diagnosis on the initial visit, a lower plasma Aβ42/40 ratio (value of Aβ1-42/Aβ1-40) demonstrated a steeper cognitive decline over a median follow-up of 3.9 years compared to those individuals with a higher Aβ42/40 ratio ^[39]^[40]. A lower plasma Aβ42/40 ratio is also seen in individuals with MCI who develop dementia compared to those who do not ^[41]. Moreover, individuals with the highest Aβ42/40 ratio have a significantly lower dementia risk (HR 0.52 [0.31–0.86]) over a 3-year period. Of note, plasma levels of Aβ (either as Aβ1-42, Aβ1-40 or Aβ42/40 ratio) do not significantly differ along the AD continuum (from cognitively unimpaired Aβ+ individuals to MCI to AD), indicating that it may not be useful in prognosticating disease progression ^[26]^[42].

As discussed above, Aβ deposition within the brain starts well before symptom onset. Detecting deposition non-invasively in vivo is possible with Aβ-PET emerging as a powerful imaging tool ^[43]. While diagnostically sensitive, Aβ-PET is resource intensive and not easily accessible in many countries. Plasma Aβ1-42 levels show excellent performance characteristics with high AUC values (above 0.9) in predicting Aβ-PET levels ^[44]^[45]. Consequently, new and efficient investigation pathways can be developed for individuals suspected for AD. For example, by using plasma Aβ to screen individuals with cognitive concerns and only proceeding to a Aβ-PET scan if plasma levels are abnormal, there would be over a 50% reduction in the number of PET scans needed to diagnose AD via PET imaging ^[46]^[47]. These findings highlight the potential role of blood Aβ measurements as a population-screening tool for AD, especially in those populations with a lower prevalence of AD.

Aβ deposition is not restricted to AD and is detected in non-AD causes of dementia, including dementia with Lewy bodies (DLB), Parkinson’s disease dementia (PDD), FTD and vascular dementia (VaD). In these non-AD cases, Aβ levels, as measured by Aβ-PET, also increase with age and APOE ε4 carrier status but vary in cortical distribution relative to the underlying dementia diagnosis ^[48]^[49]. With respect to blood biomarkers, plasma Aβ levels also vary amongst non-AD dementia types and may be higher in VaD compared to AD ^[50]^[51]^[52]^[53]. However, it is not well established how accurately blood Aβ levels, either as Aβ1-42 or as Aβ42/40, can discriminate between dementia subtypes early in the disease process and studies are needed to further investigate this.

2.3. Tau

Tau tangles, like Aβ, are a quintessential feature of AD, with tau-hyperphosphorylation leading to significant pathology ^[54]. Total-tau (t-tau) increases in the CSF and plasma following various causes of neuronal injury such as ischemic stroke and cardiac injury, as well as in neurodegenerative conditions including AD, DLB and FTD. The non-specific nature of elevated t-tau limits its ability to discriminate between AD and non-AD dementia.

In contrast to CSF, blood t-tau levels reflect production from the central nervous system (CNS) as well as from peripheral tissues (such as liver, heart and kidney), explaining why blood t-tau levels are not considered diagnostic. Given that only one-fifth of plasma t-tau originates from the CNS ^[55], assays that specifically measure brain-derived tau levels are needed. By exploiting the fact that peripherally derived tau contains exon 4a, which is not found in CNS-derived tau109, a unique tau antibody has been developed to specifically measure plasma levels of brain-derived t-tau (BD-tau) ^[56]. Using this antibody, BD-tau levels correlate with CSF t-tau levels and are able to differentiate between autopsy-confirmed AD vs. non-AD cases (AUC = 0.86 [0.76–0.97]). When tested in memory clinics, BD-tau analysis is able to differentiate AD from non-AD neurodegenerative causes with AUC ranging from 0.78 (for progressive supranuclear palsy) to 0.99 (for the agrammatic variant of primary progressive aphasia due to a progranulin mutation).

In addition to t-tau and BD-tau, there are nearly 100 known post-translational modifications of tau ^[54]. Some of the tau species are phosphorylated at unique threonine sites (p-tau) and have been found to be highly specific for AD ^[57]^[58]. The role of these p-tau species in AD prediction is highlighted below.

2.3.1. P-Tau181

Tau phosphorylated at threonine 181 (p-tau181) is one of the most studied tau species in AD. Using mass spectrometry and ultra-sensitive immunoassays, blood p-tau181 levels can differentiate between cognitively unimpaired individuals and those that have MCI or AD. In a prospective cohort study of 589 individuals from the Swedish BioFINDER cohort, plasma p-tau181 levels were strongly correlated with CSF p-tau181 levels, Aβ-PET and tau-PET ^[59]. Plasma p-tau181 was elevated in preclinical AD cases (individuals who were cognitively normal but with Aβ-PET positivity), and was able to discriminate between AD and non-AD dementia cases (AUC = 0.94 [0.90–0.99]). Similarly, in a retrospective North American cohort study of over 400 individuals, plasma p-tau181 levels were 3.5-fold higher in AD than cognitively unimpaired individuals and successfully discriminated between both clinically diagnosed and autopsy-confirmed AD and FTD cases (AUC = 0.87–0.89) ^[60]. The discriminatory power of blood p-tau181 was again demonstrated in a UK cohort of ^[60] individuals (AUC = 0.97 [0.94–1.00], autopsy-confirmed AD vs. non-AD dementia) ^[61] as well in separate North American and Swedish cohorts, with an AUC = 0.83–1.00 for AD vs. FTD and AUC = 0.92 for clinically diagnosed AD vs. VaD ^[62]. Importantly, p-tau181 tracks along the AD continuum (as measured by CSF Aβ levels and Aβ-PET load) and with cognitive decline, further supporting its role as a dynamic AD risk marker ^[26]^[42].

The robustness of plasma p-tau181 across ancestries was demonstrated by a prospective Spanish cohort study of 349 individuals (AUC = 0.96 for clinically diagnosed AD vs. cognitively unimpaired individuals) ^[63] and in a small Thai cohort study of 51 individuals (AUC = 0.84 [0.73–0.94]) ^[64]. However, p-tau181 performance was reduced among non-Hispanic White Americans (AUC = 0.69 [0.59–0.80]) and Black Americans (AUC = 0.63 [0.51–0.74]), and was considerably lower in Hispanic Americans (AUC = 0.51 [0.40–0.64]) ^[65]. More studies are needed to assess the generalizability of plasma p-tau181 amongst patients with different ancestral backgrounds.

2.3.2. P-Tau217

Other phosphorylated tau species are being investigated for their ability to discriminate between AD and non-AD dementia along the disease continuum. One such species is p-tau217 (phosphorylated at threonine 217), which has demonstrated improved performance in CSF, compared to CSF p-tau181, in distinguishing clinically diagnosed AD from non-AD dementia ^[66]^[67]. Similarly, plasma p-tau217 has also been found to better discriminate between autopsy-confirmed AD and non-AD cases (AUC = 0.89 [0.81–0.97]) compared to plasma p-tau181 (AUC = 0.72 [0.60–0.84]) ^[68]. Similarly, p-tau217 was shown to be superior to p-tau181 in discriminating between clinically diagnosed AD and non-AD cases (p-tau217 AUC = 0.96 [0.93–0.98], p-tau181 AUC = 0.81 [0.74–0.87], p < 0.001) as well as being more specific for Aβ-PET positivity than p-tau181 ^[55]. The specificity of p-tau217 for AD compared to other neurodegenerative tauopathies is further supported by a North American multicohort study where p-tau217 differentiated clinically diagnosed AD from FTD, with an AUC = 0.93 (0.91–0.96), compared to an AUC = 0.91 (0.88–0.94) for p-tau181 (p = 0.01) ^[69]. With respect to p-tau performances amongst individuals from different ancestral backgrounds, similar to p-tau181, plasma p-tau217 performed better in non-Hispanic White Americans (AUC = 0.71 [0.61–0.82]) and Black Americans (AUC = 0.68 [0.57–0.78]), but had a poor performance in Hispanic Americans (AUC = 0.52 [0.40–0.64]), considerably lower accuracy than those seen in European studies ^[65].

With respect to PET imaging, plasma p-tau217 levels can distinguish cognitively unimpaired individuals who are Aβ-PET positive from Aβ-PET negative cognitively unimpaired individuals even when the former individuals’ tau-PET scans are negative in the entorhinal cortex, a region involved early in AD-related tau pathology ^[70]. Furthermore, individuals with negative tau-PET imaging within the entorhinal cortex demonstrate a 2.2% increase per year of tracer uptake if they have higher baseline levels of plasma p-tau217. Interestingly, plasma p-tau217 levels in PSEN1 E280A mutation carriers (a form of autosomal dominant Alzheimer’s disease) are significantly altered at 24.9 years of age compared to non-carriers, which is approximately 20 years earlier than the expected age for symptom onset within the PSEN1 mutation carrier population ^[68]. This finding has been replicated in another cohort of cognitively unimpaired PSEN1 E280A mutation carriers, where plasma p-tau217 levels are higher compared to non-carriers and predict a higher burden of Aβ and tau as measured using PET imaging ^[71].

P-tau217 is also associated with AD progression risk. In cognitively unimpaired individuals who are Aβ-PET positive, a higher baseline plasma p-tau217 level confers an increased risk of conversion to clinically defined AD over a median follow-up time of 6 years (HR = 2.03 [1.57–2.63], p < 0.001]) ^[72]. Longitudinal plasma p-tau217 measurements demonstrate a greater increase in individuals with MCI who convert to AD compared to those who did not ^[73]. These studies demonstrate the potential utility of p-tau217 in identifying individuals at risk for AD well before symptoms and PET imaging changes emerge.

2.3.3. P-Tau231

P-tau231 (phosphorylated at threonine 231) is emerging as another potential blood biomarker which is highly sensitive for AD. Using a newly developed ultra-sensitive Simoa assay, p-tau231 was detected in all clinical stages of AD, including in individuals with MCI and sub-threshold signals in Aβ-PET, with levels increasing alongside disease progression ^[74]. Plasma p-tau231 is also seen in the early stages of AD and able to differentiate between Braak 0 (no deposition of Aβ plaque) and Braak I-II (Aβ plaque confined to the transentorhinal region) stages, which has not been observed with p-tau181 ^[74]^[75]. However, plasma p-tau231 is similar to p-tau181 in differentiating between AD and non-AD neurodegenerative cases, including limbic age-related TDP-43 encephalopathy and hippocampal sclerosis, both of which can clinically present similarly to AD ^[75]^[76]. Further studies are needed to elucidate the role of p-tau231 in dynamic AD risk profiling and how it differs from the other p-tau species.

These studies highlight that novel blood tau antibody development, including different assay systems that target known p-tau species ^[77], can further improve the utility of p-tau and brain-specific tau as sensitive blood biomarkers for AD prediction.

2.4. YKL-40

The role of neuroinflammation in AD is becoming increasingly recognized ^[78]. YKL-40, also known as chitinase 3-like protein 1 (CHI3L1), is a highly conserved acute-phase glycoprotein involved in inflammation-activated remodeling and may be an indicator of neuroinflammation, but its exact function within the brain is not completely understood ^[79]. Interest in YKL-40 as a biomarker for AD was generated by early studies that found higher CSF YKL-40 levels in those with MCI and mild AD compared to cognitively unimpaired individuals; these levels also correlated well with CSF Aβ and p-tau181 ^[80]. Similarly, blood YKL-40 levels are higher in individuals with AD compared to healthy control, with levels increasing with disease severity ^[81]^[82]^[83]^[84]. Interestingly, CHI3L1-associated SNPs correlated with blood protein levels and AD risk in a Han Chinese population ^[81]. Blood YKL-40 levels are also negatively correlated with structural (regional volume and cortical thickness) MRI changes in individuals with AD, but not with cognitive decline, suggesting it may serve as a generic marker of neurodegeneration ^[83]^[85]^[86]. The exact association of blood YKL-40 and AD needs further study as prospective analyses in cognitively unimpaired individuals suggest higher levels may be potentially protective (with reduced Aβ accumulation and improved cognitive testing) ^[87], while other studies suggest YKL-40 as a detrimental marker ^[88]^[89] and possibly specific to certain ethnic groups ^[90].

2.5. Soluble Triggering Receptor Expressed on Myeloid Cells 2

Triggering receptor expressed on myeloid cells 2 (TREM2) is a transmembrane receptor expressed in many immune-related cells, including CNS microglia ^[91]. Rare SNPs in this gene are second only to APOE in terms of magnitude of associated genetic risk of AD, with the R47H variant of TREM2 increasing AD risk two- to three-fold ^[92]^[93]. While there has been immense interest in TREM2 with respect to the pathogenesis of AD, its use as a peripheral biomarker has been recently proposed given the finding of an increase in blood TREM2 expression in AD patients compared to those cognitively unimpaired ^[94]^[95]. However, the association of AD status with levels of the soluble form of TREM2 (sTREM2) within the CSF and blood is inconsistent, possibly reflecting technological challenges in detecting this protein ^[96]^[97]^[98]^[99]^[100]. Considerably more work is needed to determine if TREM2 and/or sTREM2 are robust biomarkers for AD risk.

2.6. Glial Fibrillary Acidic Protein

Glial fibrillary acidic protein (GFAP) is an abundant intermediate filament cytoskeletal protein highly expressed in astrocytes, with a role in neuro-inflammation and astrocyte reactivity. As such, GFAP’s use as an AD blood biomarker is promising as higher levels are found in individuals with AD compared to those cognitively unimpaired ^[101]^[102] and MCI ^[103]. Moreover, plasma GFAP levels predict conversion to AD in individuals with MCI over a 5 year period, independent of APOE ε4 status and age (AUC = 0.84 [0.77–0.91]) ^[104]. Plasma GFAP levels also correlate with Aβ-PET positivity ^[105]^[106], but not tau-PET ^[107]^[108], and can more accurately reflect CNS Aβ levels than other markers of inflammation such as YKL-40 or sTREM2 ^[107]. Interestingly, plasma GFAP may discriminate Aβ-PET positivity better than CSF GFAP (plasma AUC = 0.69–0.86 vs. CSF AUC = 0.59–0.76), as well as demonstrating a higher magnitude of change along the AD continuum ^[109]. Of note, a rise in plasma GFAP levels is also seen in Lewy body dementia ^[102] and FTD ^[110], suggesting that plasma GFAP levels are reflective of the reactive astrogliosis occurring in neurodegeneration more broadly. Nonetheless, the growing importance of GFAP in AD risk is evidenced by its potential inclusion in revised diagnostic criteria for AD ^[3].

2.7. Comparing Blood-Based Protein Biomarkers as Risk Predictors

As blood biomarkers demonstrate improved diagnostic performance, a natural question arises as to which of these protein biomarker(s) is/are most powerful in identifying AD risk. Recent studies have compared the various biomarkers at different stages along the AD continuum (Table 1). With the increasing rate of publications in this field, publicly available databases such as AlzBiomarker ^[111] provide updated biomarker meta-analyses, allowing for comparisons of multiple proteins of interest.

Table 1. Illustrative performance comparisons of blood protein biomarkers for classifying Alzheimer’s disease from recent large studies.

Disease Comparison	Study	Blood Biomarker Performance (AUC [95% Confidence Interval Range])
Preclinical AD vs. CU	Chatterjee et al., 2022 ^[112] (n = 95)	GFAP 0.79 (0.69–0.89)	NfL 0.61 (0.47–0.74)	t-tau 0.61 (0.48–0.75)		p-tau181 0.74 (0.63–0.85)	p-tau231 0.77 (0.68–0.87)
Preclinical AD vs. CU	Chatterjee et al., 2022 ^[112] (n = 95)	p < 0.005		p < 0.05 vs. GFAP		Not significant vs. GFAP
AD vs. CU	Simren et al., 2021 ^[103] (n = 202)	p-tau181 0.91 (0.86–0.96)	NfL 0.82 (0.79–0.92)	GFAP 0.69 (0.57–0.77)		t-tau 0.70 (0.61–0.79)	Aβ_42/40 0.67 (0.58–0.76)
AD vs. CU	Simren et al., 2021 ^[103] (n = 202)	p < 0.001		Not significant vs. p-tau181
MCI Conversion to AD vs. Non-Conversion	Simren et al., 2021 ^[103] (n = 107)	p-tau181 0.77 (0.61–0.84)	Aβ_42/40 0.67 (0.51–0.82)	NfL 0.62 (0.45–0.78)		t-tau 0.60 (0.42–0.79)	GFAP 0.61 (0.54–0.72)
MCI Conversion to AD vs. Non-Conversion	Simren et al., 2021 ^[103] (n = 107)	p < 0.05		Not significant vs. p-tau181
AD vs. FTD	Thijssen et al., 2021 ^[69] (n = 349)	p-tau217 0.93 (0.91–0.96)			p-tau181 0.91 (0.88–0.94)
AD vs. FTD	Thijssen et al., 2021 ^[69] (n = 349)	p = 0.01
AD vs. non-AD Dementia	Palmqvist et al., 2020 ^[68] (n = 81)	p-tau217 0.89 (0.81–0.97)			p-tau181 0.72 (0.60–0.84)
AD vs. non-AD Dementia	Palmqvist et al., 2020 ^[68] (n = 81)	p = 0.04

Bolded blood protein biomarkers are those significant compared to biomarkers in grey boxes. AD, Alzheimer’s disease; CU, cognitively unimpaired; MCI, mild cognitive impairment; FTD, frontotemporal dementia; AUC, area under the ROC curve; GFAP, glial fibrillary acidic protein; NfL, neurofilament light chain; t-tau, total tau; p-tau, phosphor-tau; Aβ, amyloid-β.

In a study of over 300 individuals of European background, plasma p-tau181 outperformed other blood biomarkers (GFAP, NfL, t-tau and Aβ42/40) when distinguishing between clinically diagnosed AD and cognitively unimpaired individuals (AUC = 0.91 [0.86–0.96] vs. AUC = 0.67–0.82 for other blood biomarkers) as well between individuals with MCI who converted to AD and those who did not (AUC = 0.77 [0.61–0.84] vs. AUC = 0.60–0.67 for other blood biomarkers) ^[103]. Interestingly, combining p-tau181 with the other biomarkers did not increase the diagnostic accuracy in this study. Contrastingly, three separate observational cross-sectional studies totaling over 800 individuals with North American and European backgrounds found no significant difference in diagnostic accuracy of AD between p-tau181 (AUC 0.67–0.87) and GFAP (AUC 0.69–0.86) ^[109]. In preclinical AD (defined as Aβ-PET positive with normal cognitive profiles), GFAP (AUC = 0.79 [0.69–0.89]) was also not statistically different to p-tau181 (AUC = 0.74 [0.63–0.85]) in discriminating against Aβ-PET negative cognitively unimpaired individuals ^[112]. In a prospective study of over 110 Swedish and North American preclinical AD individuals, plasma p-tau217 was superior to p-tau181, p-tau231 and GFAP in predicting cognitive decline ^[72].

Predicting levels of amyloid burden, a risk factor for AD development, is important to facilitate screening of preclinical AD individuals. Plasma p-tau217 and p-tau231 are emerging as potential blood biomarkers for detecting low but abnormal levels of amyloid burden (as quantified by Aβ-PET), compared to p-tau181, GFAP and NfL. The 168 Plasma p-tau231 levels are abnormal at a significantly lower Aβ-PET Centiloids (26.4) than p-tau217 (35.4 Centiloids). The Centiloid scale is a standardized metric for the amyloid signal in Aβ-PET imaging, with 30 Centiloids considered to a cut-off for Aβ-PET positivity. Both p-tau217 and p-tau231 also demonstrate the strongest association with disease progression (compared to p-tau181, GFAP and NfL) ^[113]. Interestingly, p-tau231 is significantly elevated at lower thresholds of Aβ-PET Centiloids compared to p-tau217, p-tau181, GFAP and NfL ^[114]. However, it is p-tau217, and not p-tau231, that demonstrates longitudinal changes (over 4 years) that are correlated with AD progression. These findings suggest that p-tau231 may be useful in identifying at-risk individuals for AD early in the disease process, while p-tau217 is useful in tracking disease progression, both of which have implications for dynamic AD risk profiling.

Other combinations of blood biomarkers demonstrate limited utility. In cognitively unimpaired individuals, a biomarker combination of plasma p-tau181 and p-tau217 along with the APOE genotype is not significantly better at predicting conversion to AD (AUC = 0.88 [0.82–0.95]) compared to p-tau181 alone (AUC = 0.84 [0.77–0.92]) ^[115]. In individuals with MCI, a combination of p-tau181, p-tau217 and Aβ42/40 ratio more accurately predicts AD conversion (AUC = 0.87 [0.82–0.92]) compared to that of any single biomarker, but it was not statistically different than a model with five blood biomarkers (p-tau181, p-tau217, Aβ42/40, NfL and APOE ε4 status) with an AUC = 0.89 [0.85–0.93] (p = 0.10) ^[115]. A combination of p-tau217 levels and Aβ42/40 ratio measured in antemortem plasma strongly predicts AD amyloid and tau load in postmortem analysis with an AUC = 0.89 [0.82–0.96], but not significantly better than p-tau217 alone (p = 0.124) ^[116]. While further studies are needed, these findings suggest that simply testing more biomarkers will not necessarily improve diagnostic accuracy. Instead, in order to maximize the predictive power of the test, selecting the appropriate biomarker(s) may depend on the stage along the AD continuum at which an individual is being assessed ^[117].

2.8. Dynamic Changes in Blood-Based Protein Biomarkers in Response to Anti-Amyloid Therapy

The emergence of anti-amyloid therapies is making disease modification in AD a possibility. Three monoclonal antibodies (aducanumab, lecanemab and donanemab) targeting various Aβ species demonstrate a slowing in progression in early clinical stages of AD, with lecanemab demonstrating up to a 35% reduction in the rate of cognitive decline ^[118]^[119]^[120]. While the main biomarkers used in these studies included Aβ-PET, plasma blood biomarkers were used as exploratory endpoints and showed dynamic changes in response to anti-amyloid therapies. For example, a steady increase in plasma Aβ42/40 ratio and decrease in p-tau181 and GFAP were observed with lecanemab treatment compared to placebo; a lesser magnitude of change was observed with plasma NfL ^[120]. With donanemab, there was nearly a one-third reduction in levels of p-tau217 and a one-sixth reduction in levels of GFAP compared to placebo ^[119]^[121]. Similarly, high-dose aducanumab led to a 20% reduction in levels of p-tau181 compared to placebo ^[118]. While these studies have demonstrated how blood biomarkers can be used to monitor for responses to anti-amyloid therapy, it is unclear if blood biomarkers further change (in either direction) after therapy is discontinued, indicating remission or progression of AD pathology, and how these changes correspond to AD symptom progression. Nonetheless, the new anti-amyloid therapies are facilitating a paradigm shift in how blood biomarkers are helping to understand AD pathology and treatment. Such a shift in the utilization of blood biomarkers may also shed light on the effects of existing treatments, such as cholinesterase inhibitors and memantine, as well as molecules currently under investigation—an area of research where insufficient data exist ^[122]^[123].

References

Collaborators, G.D.F. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. Lancet Public Health 2022, 7, e105–e125.
Judge, D.; Roberts, J.; Khandker, R.K.; Ambegaonkar, B.; Black, C.M. Physician Practice Patterns Associated with Diagnostic Evaluation of Patients with Suspected Mild Cognitive Impairment and Alzheimer’s Disease. Int. J. Alzheimer’s Dis. 2019, 2019, 4942562.
NIA-AA. NIA-AA Revised Diagnostic Criteria: A Biological Definition of Alzheimer’s Disease. Available online: https://aaic.alz.org/nia-aa.asp (accessed on 28 September 2023).
Teunissen, C.E.; Verberk, I.M.W.; Thijssen, E.H.; Vermunt, L.; Hansson, O.; Zetterberg, H.; van der Flier, W.M.; Mielke, M.M.; Del Campo, M. Blood-based biomarkers for Alzheimer’s disease: Towards clinical implementation. Lancet Neurol. 2022, 21, 66–77.
Leuzy, A.; Mattsson-Carlgren, N.; Palmqvist, S.; Janelidze, S.; Dage, J.L.; Hansson, O. Blood-based biomarkers for Alzheimer’s disease. EMBO Mol. Med. 2022, 14, e14408.
Mankhong, S.; Kim, S.; Lee, S.; Kwak, H.B.; Park, D.H.; Joa, K.L.; Kang, J.H. Development of Alzheimer’s Disease Biomarkers: From CSF- to Blood-Based Biomarkers. Biomedicines 2022, 10, 850.
Planche, V.; Bouteloup, V.; Pellegrin, I.; Mangin, J.F.; Dubois, B.; Ousset, P.J.; Pasquier, F.; Blanc, F.; Paquet, C.; Hanon, O.; et al. Validity and Performance of Blood Biomarkers for Alzheimer Disease to Predict Dementia Risk in a Large Clinic-Based Cohort. Neurology 2023, 100, e473–e484.
Baiardi, S.; Quadalti, C.; Mammana, A.; Dellavalle, S.; Zenesini, C.; Sambati, L.; Pantieri, R.; Polischi, B.; Romano, L.; Suffritti, M.; et al. Diagnostic value of plasma p-tau181, NfL, and GFAP in a clinical setting cohort of prevalent neurodegenerative dementias. Alzheimer’s Res. Ther. 2022, 14, 153.
Aschenbrenner, A.J.; Li, Y.; Henson, R.L.; Volluz, K.; Hassenstab, J.; Verghese, P.; West, T.; Meyer, M.R.; Kirmess, K.M.; Fagan, A.M.; et al. Comparison of plasma and CSF biomarkers in predicting cognitive decline. Ann. Clin. Transl. Neurol. 2022, 9, 1739–1751.
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.
Bacioglu, M.; Maia, L.F.; Preische, O.; Schelle, J.; Apel, A.; Kaeser, S.A.; Schweighauser, M.; Eninger, T.; Lambert, M.; Pilotto, A.; et al. Neurofilament Light Chain in Blood and CSF as Marker of Disease Progression in Mouse Models and in Neurodegenerative Diseases. Neuron 2016, 91, 494–496.
Jack, C.R.; Bennett, D.A.; Blennow, K.; Carrillo, M.C.; Dunn, B.; Haeberlein, S.B.; Holtzman, D.M.; Jagust, W.; Jessen, F.; Karlawish, J.; et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s Dement. 2018, 14, 535–562.
Mattsson, N.; Andreasson, U.; Zetterberg, H.; Blennow, K.; Alzheimer’s Disease Neuroimaging, I. Association of Plasma Neurofilament Light with Neurodegeneration in Patients with Alzheimer Disease. JAMA Neurol. 2017, 74, 557–566.
Rohrer, J.D.; Woollacott, I.O.; 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.
Gaiani, A.; Martinelli, I.; Bello, L.; Querin, G.; Puthenparampil, M.; Ruggero, S.; Toffanin, E.; Cagnin, A.; Briani, C.; Pegoraro, E.; et al. Diagnostic and Prognostic Biomarkers in Amyotrophic Lateral Sclerosis: Neurofilament Light Chain Levels in Definite Subtypes of Disease. JAMA Neurol. 2017, 74, 525–532.
Gisslen, 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.
Gendron, T.F.; Badi, M.K.; Heckman, M.G.; Jansen-West, K.R.; Vilanilam, G.K.; Johnson, P.W.; Burch, A.R.; Walton, R.L.; Ross, O.A.; Brott, T.G.; et al. Plasma neurofilament light predicts mortality in patients with stroke. Sci. Transl. Med. 2020, 12, eaay1913.
Kammeyer, R.; Mizenko, C.; Sillau, S.; Richie, A.; Owens, G.; Nair, K.V.; Alvarez, E.; Vollmer, T.L.; Bennett, J.L.; Piquet, A.L. Evaluation of Plasma Neurofilament Light Chain Levels as a Biomarker of Neuronal Injury in the Active and Chronic Phases of Autoimmune Neurologic Disorders. Front. Neurol. 2022, 13, 689975.
Eratne, D.; Loi, S.M.; Walia, N.; Farrand, S.; Li, Q.X.; Varghese, S.; Walterfang, M.; Evans, A.; Mocellin, R.; Dhiman, K.; et al. A pilot study of the utility of cerebrospinal fluid neurofilament light chain in differentiating neurodegenerative from psychiatric disorders: A ‘C-reactive protein’ for psychiatrists and neurologists? Aust. N. Z. J. Psychiatry 2020, 54, 57–67.
Barker, W.; Quinonez, C.; Greig, M.T.; Behar, R.; Chirinos, C.; Rodriguez, R.A.; Rosselli, M.; Rodriguez, M.J.; Cid, R.C.; Rundek, T.; et al. Utility of Plasma Neurofilament Light in the 1Florida Alzheimer’s Disease Research Center (ADRC). J. Alzheimer’s Dis. 2021, 79, 59–70.
Willemse, E.A.J.; Scheltens, P.; Teunissen, C.E.; Vijverberg, E.G.B. A neurologist’s perspective on serum neurofilament light in the memory clinic: A prospective implementation study. Alzheimer’s Res. Ther. 2021, 13, 101.
Gotze, K.; Vrillon, A.; Bouaziz-Amar, E.; Mouton-Liger, F.; Hugon, J.; Martinet, M.; Dumurgier, J.; Cognat, E.; Zetterberg, H.; Blennow, K.; et al. Plasma neurofilament light chain in memory clinic practice: Evidence from a real-life study. Neurobiol. Dis. 2023, 176, 105937.
Preische, O.; Schultz, S.A.; Apel, A.; Kuhle, J.; Kaeser, S.A.; Barro, C.; Graber, S.; Kuder-Buletta, E.; LaFougere, C.; Laske, C.; et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat. Med. 2019, 25, 277–283.
Mattsson, N.; Cullen, N.C.; Andreasson, U.; Zetterberg, H.; Blennow, K. Association between Longitudinal Plasma Neurofilament Light and Neurodegeneration in Patients with Alzheimer Disease. JAMA Neurol. 2019, 76, 791–799.
Sugarman, M.A.; Zetterberg, H.; Blennow, K.; Tripodis, Y.; McKee, A.C.; Stein, T.D.; Martin, B.; Palmisano, J.N.; Steinberg, E.G.; Simkin, I.; et al. A longitudinal examination of plasma neurofilament light and total tau for the clinical detection and monitoring of Alzheimer’s disease. Neurobiol. Aging 2020, 94, 60–70.
Chatterjee, P.; Pedrini, S.; Doecke, J.D.; Thota, R.; Villemagne, V.L.; Dore, V.; Singh, A.K.; Wang, P.; Rainey-Smith, S.; Fowler, C.; et al. Plasma Abeta42/40 ratio, p-tau181, GFAP, and NfL across the Alzheimer’s disease continuum: A cross-sectional and longitudinal study in the AIBL cohort. Alzheimer’s Dement. 2022, 19, 1117–1134.
Ashton, N.J.; Janelidze, S.; Al Khleifat, A.; Leuzy, A.; van der Ende, E.L.; Karikari, T.K.; Benedet, A.L.; Pascoal, T.A.; Lleo, A.; Parnetti, L.; et al. A multicentre validation study of the diagnostic value of plasma neurofilament light. Nat. Commun. 2021, 12, 3400.
Valentino, P.; Marnetto, F.; Martire, S.; Malucchi, S.; Bava, C.I.; Popovic, M.; Bertolotto, A. Serum neurofilament light chain levels in healthy individuals: A proposal of cut-off values for use in multiple sclerosis clinical practice. Mult. Scler. Relat. Disord. 2021, 54, 103090.
Harp, C.; Thanei, G.A.; Jia, X.; Kuhle, J.; Leppert, D.; Schaedelin, S.; Benkert, P.; von Budingen, H.C.; Hendricks, R.; Herman, A. Development of an age-adjusted model for blood neurofilament light chain. Ann. Clin. Transl. Neurol. 2022, 9, 444–453.
Sapkota, S.; Erickson, K.; Harvey, D.; Tomaszewski-Farias, S.E.; Olichney, J.M.; Johnson, D.K.; Dugger, B.N.; Mungas, D.M.; Fletcher, E.; Maillard, P.; et al. Plasma biomarkers predict cognitive trajectories in an ethnoracially and clinically diverse cohort: Mediation with hippocampal volume. Alzheimer’s Dement. 2022, 14, e12349.
Windon, C.; Iaccarino, L.; Mundada, N.; Allen, I.; Boxer, A.L.; Byrd, D.; Rivera-Mindt, M.; Rabinovici, G.D.; ADNI. Comparison of plasma and CSF biomarkers across ethnoracial groups in the ADNI. Alzheimer’s Dement. 2022, 14, e12315.
O’Bryant, S.; Petersen, M.; Hall, J.; Johnson, L.; Yaffe, K.; Braskie, M.; Toga, A.W.; Rissman, R.A.; HABLE Study Team. Characterizing plasma NfL in a community-dwelling multi-ethnic cohort: Results from the HABLE study. Alzheimer’s Dement. 2022, 18, 240–250.
Jung, Y.; Damoiseaux, J.S. The potential of blood neurofilament light as a marker of neurodegeneration for Alzheimer’s disease. Brain 2023, 147, 12–25.
Tapiola, T.; Alafuzoff, I.; Herukka, S.K.; Parkkinen, L.; Hartikainen, P.; Soininen, H.; Pirttila, T. Cerebrospinal fluid beta-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain. Arch Neurol. 2009, 66, 382–389.
Olsson, B.; Lautner, R.; Andreasson, U.; Ohrfelt, A.; Portelius, E.; Bjerke, M.; Holtta, M.; Rosen, 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.
Brand, A.L.; Lawler, P.E.; Bollinger, J.G.; Li, Y.; Schindler, S.E.; Li, M.; Lopez, S.; Ovod, V.; Nakamura, A.; Shaw, L.M.; et al. The performance of plasma amyloid beta measurements in identifying amyloid plaques in Alzheimer’s disease: A literature review. Alzheimer’s Res. Ther. 2022, 14, 195.
Teunissen, C.E.; Chiu, M.J.; Yang, C.C.; Yang, S.Y.; Scheltens, P.; Zetterberg, H.; Blennow, K. Plasma Amyloid-Beta (Abeta42) Correlates with Cerebrospinal Fluid Abeta42 in Alzheimer’s Disease. J. Alzheimer’s Dis. 2018, 62, 1857–1863.
Hilal, S.; Wolters, F.J.; Verbeek, M.M.; Vanderstichele, H.; Ikram, M.K.; Stoops, E.; Ikram, M.A.; Vernooij, M.W. Plasma amyloid-beta levels, cerebral atrophy and risk of dementia: A population-based study. Alzheimer’s Res. Ther. 2018, 10, 63.
Giudici, K.V.; de Souto Barreto, P.; Guyonnet, S.; Li, Y.; Bateman, R.J.; Vellas, B.; Group, M.D. Assessment of Plasma Amyloid-beta42/40 and Cognitive Decline Among Community-Dwelling Older Adults. JAMA Netw. Open 2020, 3, e2028634.
Verberk, I.M.W.; Hendriksen, H.M.A.; van Harten, A.C.; Wesselman, L.M.P.; Verfaillie, S.C.J.; van den Bosch, K.A.; Slot, R.E.R.; Prins, N.D.; Scheltens, P.; Teunissen, C.E.; et al. Plasma amyloid is associated with the rate of cognitive decline in cognitively normal elderly: The SCIENCe project. Neurobiol. Aging 2020, 89, 99–107.
Hanon, O.; Vidal, J.S.; Lehmann, S.; Bombois, S.; Allinquant, B.; Baret-Rose, C.; Treluyer, J.M.; Abdoul, H.; Gele, P.; Delmaire, C.; et al. Plasma amyloid beta predicts conversion to dementia in subjects with mild cognitive impairment: The BALTAZAR study. Alzheimer’s Dement. 2022, 18, 2537–2550.
Guo, Y.; Shen, X.N.; Wang, H.F.; Chen, S.D.; Zhang, Y.R.; Chen, S.F.; Cui, M.; Cheng, W.; Dong, Q.; Ma, T.; et al. The dynamics of plasma biomarkers across the Alzheimer’s continuum. Alzheimer’s Res. Ther. 2023, 15, 31.
Masdeu, J.C. Neuroimaging of Diseases Causing Dementia. Neurol. Clin. 2020, 38, 65–94.
Nakamura, A.; Kaneko, N.; Villemagne, V.L.; Kato, T.; Doecke, J.; Dore, V.; Fowler, C.; Li, Q.X.; Martins, R.; Rowe, C.; et al. High performance plasma amyloid-beta biomarkers for Alzheimer’s disease. Nature 2018, 554, 249–254.
Doecke, J.D.; Perez-Grijalba, V.; Fandos, N.; Fowler, C.; Villemagne, V.L.; Masters, C.L.; Pesini, P.; Sarasa, M.; Group, A.R. Total Abeta(42)/Abeta(40) ratio in plasma predicts amyloid-PET status, independent of clinical AD diagnosis. Neurology 2020, 94, e1580–e1591.
Verberk, I.M.W.; Slot, R.E.; Verfaillie, S.C.J.; Heijst, H.; Prins, N.D.; van Berckel, B.N.M.; Scheltens, P.; Teunissen, C.E.; van der Flier, W.M. Plasma Amyloid as Prescreener for the Earliest Alzheimer Pathological Changes. Ann. Neurol. 2018, 84, 648–658.
Keshavan, A.; Pannee, J.; Karikari, T.K.; Rodriguez, J.L.; Ashton, N.J.; Nicholas, J.M.; Cash, D.M.; Coath, W.; Lane, C.A.; Parker, T.D.; et al. Population-based blood screening for preclinical Alzheimer’s disease in a British birth cohort at age 70. Brain 2021, 144, 434–449.
Ossenkoppele, R.; Jansen, W.J.; Rabinovici, G.D.; Knol, D.L.; van der Flier, W.M.; van Berckel, B.N.; Scheltens, P.; Visser, P.J.; Amyloid, P.E.T.S.G.; Verfaillie, S.C.; et al. Prevalence of amyloid PET positivity in dementia syndromes: A meta-analysis. JAMA 2015, 313, 1939–1949.
Edison, P.; Rowe, C.C.; Rinne, J.O.; Ng, S.; Ahmed, I.; Kemppainen, N.; Villemagne, V.L.; O’Keefe, G.; Nagren, K.; Chaudhury, K.R.; et al. Amyloid load in Parkinson’s disease dementia and Lewy body dementia measured with PIB positron emission tomography. J. Neurol. Neurosurg. Psychiatry 2008, 79, 1331–1338.
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 beta-amyloid in Alzheimer’s disease and vascular disease. Sci. Rep. 2016, 6, 26801.
Chojdak-Lukasiewicz, J.; Malodobra-Mazur, M.; Zimny, A.; Noga, L.; Paradowski, B. Plasma tau protein and Abeta42 level as markers of cognitive impairment in patients with Parkinson’s disease. Adv. Clin. Exp. Med. 2020, 29, 115–121.
Donaghy, P.C.; Firbank, M.; Petrides, G.; Lloyd, J.; Barnett, N.; Olsen, K.; Heslegrave, A.; Zetterberg, H.; Thomas, A.J.; O’Brien, J.T. The relationship between plasma biomarkers and amyloid PET in dementia with Lewy bodies. Park. Relat. Disord. 2022, 101, 111–116.
Chouliaras, L.; Thomas, A.; Malpetti, M.; Donaghy, P.; Kane, J.; Mak, E.; Savulich, G.; Prats-Sedano, M.A.; Heslegrave, A.J.; Zetterberg, H.; et al. Differential levels of plasma biomarkers of neurodegeneration in Lewy body dementia, Alzheimer’s disease, frontotemporal dementia and progressive supranuclear palsy. J. Neurol. Neurosurg. Psychiatry 2022, 93, 651–658.
Holper, S.; Watson, R.; Yassi, N. Tau as a Biomarker of Neurodegeneration. Int. J. Mol. Sci. 2022, 23, 7307.
Barthelemy, N.R.; Horie, K.; Sato, C.; Bateman, R.J. Blood plasma phosphorylated-tau isoforms track CNS change in Alzheimer’s disease. J. Exp. Med. 2020, 217, e20200861.
Gonzalez-Ortiz, F.; Turton, M.; Kac, P.R.; Smirnov, D.; Premi, E.; Ghidoni, R.; Benussi, L.; Cantoni, V.; Saraceno, C.; Rivolta, J.; et al. Brain-derived tau: A novel blood-based biomarker for Alzheimer’s disease-type neurodegeneration. Brain 2023, 146, 1152–1165.
Wesseling, H.; Mair, W.; Kumar, M.; Schlaffner, C.N.; Tang, S.; Beerepoot, P.; Fatou, B.; Guise, A.J.; Cheng, L.; Takeda, S.; et al. Tau PTM Profiles Identify Patient Heterogeneity and Stages of Alzheimer’s Disease. Cell 2020, 183, 1699–1713.
Ossenkoppele, R.; van der Kant, R.; Hansson, O. Tau biomarkers in Alzheimer’s disease: Towards implementation in clinical practice and trials. Lancet Neurol. 2022, 21, 726–734.
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.
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.
Lantero Rodriguez, J.; Karikari, T.K.; Suarez-Calvet, M.; Troakes, C.; King, A.; Emersic, A.; Aarsland, D.; Hye, A.; Zetterberg, H.; Blennow, K.; et al. Plasma p-tau181 accurately predicts Alzheimer’s disease pathology at least 8 years prior to post-mortem and improves the clinical characterisation of cognitive decline. Acta Neuropathol. 2020, 140, 267–278.
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.
Sarto, J.; Ruiz-Garcia, R.; Guillen, N.; Ramos-Campoy, O.; Falgas, N.; Esteller, D.; Contador, J.; Fernandez, G.; Gonzalez, Y.; Tort-Merino, A.; et al. Diagnostic Performance and Clinical Applicability of Blood-Based Biomarkers in a Prospective Memory Clinic Cohort. Neurology 2023, 100, e860–e873.
Thanapornsangsuth, P.; Booncharoen, K.; Luechaipanit, W.; Supharatpariyakorn, T.; Sarutikriangkri, Y.; Tangnimitchok, S.; Likitjaroen, Y.; Sukprakun, C.; Tepmongkol, S.; Hemachudha, T. Prospective evaluation of plasma phosphorylated tau in a real-life memory clinic in Thailand. Alzheimer’s Dement. 2023, 19, 2745–2749.
Brickman, A.M.; Manly, J.J.; Honig, L.S.; Sanchez, D.; Reyes-Dumeyer, D.; Lantigua, R.A.; Lao, P.J.; Stern, Y.; Vonsattel, J.P.; Teich, A.F.; et al. Plasma p-tau181, p-tau217, and other blood-based Alzheimer’s disease biomarkers in a multi-ethnic, community study. Alzheimer’s Dement. 2021, 17, 1353–1364.
Janelidze, S.; Stomrud, E.; Smith, R.; Palmqvist, S.; Mattsson, N.; Airey, D.C.; Proctor, N.K.; Chai, X.; Shcherbinin, S.; Sims, J.R.; et al. Cerebrospinal fluid p-tau217 performs better than p-tau181 as a biomarker of Alzheimer’s disease. Nat. Commun. 2020, 11, 1683.
Mielke, M.M.; Aakre, J.A.; Algeciras-Schimnich, A.; Proctor, N.K.; Machulda, M.M.; Eichenlaub, U.; Knopman, D.S.; Vemuri, P.; Graff-Radford, J.; Jack, C.R., Jr.; et al. Comparison of CSF phosphorylated tau 181 and 217 for cognitive decline. Alzheimer’s Dement. 2022, 18, 602–611.
Palmqvist, S.; Janelidze, S.; Quiroz, Y.T.; Zetterberg, H.; Lopera, F.; Stomrud, E.; Su, Y.; Chen, Y.; Serrano, G.E.; Leuzy, A.; et al. Discriminative Accuracy of Plasma Phospho-tau217 for Alzheimer Disease vs Other Neurodegenerative Disorders. JAMA 2020, 324, 772–781.
Thijssen, E.H.; La Joie, R.; Strom, A.; Fonseca, C.; Iaccarino, L.; Wolf, A.; Spina, S.; Allen, I.E.; Cobigo, Y.; Heuer, H.; et al. Plasma phosphorylated tau 217 and phosphorylated tau 181 as biomarkers in Alzheimer’s disease and frontotemporal lobar degeneration: A retrospective diagnostic performance study. Lancet Neurol. 2021, 20, 739–752.
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, 149–156.
Aguillon, D.; Langella, S.; Chen, Y.; Sanchez, J.S.; Su, Y.; Vila-Castelar, C.; Vasquez, D.; Zetterberg, H.; Hansson, O.; Dage, J.L.; et al. Plasma p-tau217 predicts in vivo brain pathology and cognition in autosomal dominant Alzheimer’s disease. Alzheimer’s Dement. 2022, 19, 2585–2594.
Mattsson-Carlgren, N.; Salvado, G.; Ashton, N.J.; Tideman, P.; Stomrud, E.; Zetterberg, H.; Ossenkoppele, R.; Betthauser, T.J.; Cody, K.A.; Jonaitis, E.M.; et al. Prediction of Longitudinal Cognitive Decline in Preclinical Alzheimer Disease Using Plasma Biomarkers. JAMA Neurol. 2023, 80, 360–369.
Mattsson-Carlgren, N.; Janelidze, S.; Palmqvist, S.; Cullen, N.; Svenningsson, A.L.; Strandberg, O.; Mengel, D.; Walsh, D.M.; Stomrud, E.; Dage, J.L.; et al. Longitudinal plasma p-tau217 is increased in early stages of Alzheimer’s disease. Brain 2020, 143, 3234–3241.
Ashton, N.J.; Pascoal, T.A.; Karikari, T.K.; Benedet, A.L.; Lantero-Rodriguez, J.; Brinkmalm, G.; Snellman, A.; Scholl, M.; Troakes, C.; Hye, A.; et al. Plasma p-tau231: A new biomarker for incipient Alzheimer’s disease pathology. Acta Neuropathol. 2021, 141, 709–724.
Smirnov, D.S.; Ashton, N.J.; Blennow, K.; Zetterberg, H.; Simren, J.; Lantero-Rodriguez, J.; Karikari, T.K.; Hiniker, A.; Rissman, R.A.; Salmon, D.P.; et al. Plasma biomarkers for Alzheimer’s Disease in relation to neuropathology and cognitive change. Acta Neuropathol. 2022, 143, 487–503.
Pilotto, A.; Parigi, M.; Bonzi, G.; Battaglio, B.; Ferrari, E.; Mensi, L.; Benussi, A.; Caratozzolo, S.; Cosseddu, M.; Turrone, R.; et al. Differences Between Plasma and Cerebrospinal Fluid p-tau181 and p-tau231 in Early Alzheimer’s Disease. J. Alzheimer’s Dis. 2022, 87, 991–997.
Suarez-Calvet, M.; Karikari, T.K.; Ashton, N.J.; Lantero Rodriguez, J.; Mila-Aloma, M.; Gispert, J.D.; Salvado, G.; Minguillon, C.; Fauria, K.; Shekari, M.; et al. Novel tau biomarkers phosphorylated at T181, T217 or T231 rise in the initial stages of the preclinical Alzheimer’s continuum when only subtle changes in Abeta pathology are detected. EMBO Mol. Med. 2020, 12, e12921.
Leng, F.; Edison, P. Neuroinflammation and microglial activation in Alzheimer disease: Where do we go from here? Nat. Rev. Neurol. 2021, 17, 157–172.
Wilczynska, K.; Waszkiewicz, N. Diagnostic Utility of Selected Serum Dementia Biomarkers: Amyloid beta-40, Amyloid beta-42, Tau Protein, and YKL-40: A Review. J. Clin. Med. 2020, 9, 3452.
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.
Dai, Q.H.; Gong, D.K. Association of the Polymorphisms and Plasma Level of CHI3L1 with Alzheimer’s Disease in the Chinese Han Population: A Case-Control Study. Neuropsychobiology 2019, 77, 29–37.
Choi, J.; Lee, H.W.; Suk, K. Plasma level of chitinase 3-like 1 protein increases in patients with early Alzheimer’s disease. J. Neurol. 2011, 258, 2181–2185.
Brosseron, F.; Maass, A.; Kleineidam, L.; Ravichandran, K.A.; Kolbe, C.C.; Wolfsgruber, S.; Santarelli, F.; Hasler, L.M.; McManus, R.; Ising, C.; et al. Serum IL-6, sAXL, and YKL-40 as systemic correlates of reduced brain structure and function in Alzheimer’s disease: Results from the DELCODE study. Alzheimer’s Res. Ther. 2023, 15, 13.
Wilczynska, 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.
Villar-Pique, 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.
Muszynski, P.; Groblewska, M.; Kulczynska-Przybik, A.; Kulakowska, A.; Mroczko, B. YKL-40 as a Potential Biomarker and a Possible Target in Therapeutic Strategies of Alzheimer’s Disease. Curr. Neuropharmacol. 2017, 15, 906–917.
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-beta levels, memory performance, and sex in subjective memory complainers. Neurobiol. Aging 2020, 96, 22–32.
Gonzales, M.M.; Short, M.I.; Satizabal, C.L.; O’Bryant, S.; Tracy, R.P.; Zare, H.; Seshadri, S. Blood biomarkers for dementia in Hispanic and non-Hispanic White adults. Alzheimer’s Dement. 2021, 7, e12164.
Gonzales, M.M.; Wang, C.P.; Short, M.I.; Parent, D.M.; Kautz, T.; MacCarthy, D.; Satizabal, C.L.; Gonzalez, D.A.; Royall, D.R.; Zare, H.; et al. Blood biomarkers for cognitive decline and clinical progression in a Mexican American cohort. Alzheimer’s Dement. 2022, 14, e12298.
Grewal, R.; Haghighi, M.; Huang, S.; Smith, A.G.; Cao, C.; Lin, X.; Lee, D.C.; Teten, N.; Hill, A.M.; Selenica, M.B. Identifying biomarkers of dementia prevalent among amnestic mild cognitively impaired ethnic female patients. Alzheimer’s Res. Ther. 2016, 8, 43.
Shi, Y.; Holtzman, D.M. Interplay between innate immunity and Alzheimer disease: APOE and TREM2 in the spotlight. Nat. Rev. Immunol. 2018, 18, 759–772.
Jonsson, T.; Stefansson, H.; Steinberg, S.; Jonsdottir, I.; Jonsson, P.V.; Snaedal, J.; Bjornsson, S.; Huttenlocher, J.; Levey, A.I.; Lah, J.J.; et al. Variant of TREM2 associated with the risk of Alzheimer’s disease. N. Engl. J. Med. 2013, 368, 107–116.
Guerreiro, R.; Wojtas, A.; Bras, J.; Carrasquillo, M.; Rogaeva, E.; Majounie, E.; Cruchaga, C.; Sassi, C.; Kauwe, J.S.; Younkin, S.; et al. TREM2 variants in Alzheimer’s disease. N. Engl. J. Med. 2013, 368, 117–127.
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. 2014, 38, 497–501.
Mori, Y.; Yoshino, Y.; Ochi, S.; Yamazaki, K.; Kawabe, K.; Abe, M.; Kitano, T.; Ozaki, Y.; Yoshida, T.; Numata, S.; et al. TREM2 mRNA Expression in Leukocytes Is Increased in Alzheimer’s Disease and Schizophrenia. PLoS ONE 2015, 10, e0136835.
Piccio, L.; Deming, Y.; Del-Aguila, J.L.; Ghezzi, L.; Holtzman, D.M.; Fagan, A.M.; Fenoglio, C.; Galimberti, D.; Borroni, B.; Cruchaga, C. Cerebrospinal fluid soluble TREM2 is higher in Alzheimer disease and associated with mutation status. Acta Neuropathol. 2016, 131, 925–933.
Kleinberger, G.; Yamanishi, Y.; Suarez-Calvet, M.; Czirr, E.; Lohmann, E.; Cuyvers, E.; Struyfs, H.; Pettkus, N.; Wenninger-Weinzierl, A.; Mazaheri, F.; et al. TREM2 mutations implicated in neurodegeneration impair cell surface transport and phagocytosis. Sci. Transl. Med. 2014, 6, 243ra286.
Ohara, T.; Hata, J.; Tanaka, M.; Honda, T.; Yamakage, H.; Yoshida, D.; Inoue, T.; Hirakawa, Y.; Kusakabe, T.; Shibata, M.; et al. Serum Soluble Triggering Receptor Expressed on Myeloid Cells 2 as a Biomarker for Incident Dementia: The Hisayama Study. Ann. Neurol. 2019, 85, 47–58.
Ashton, N.J.; Suarez-Calvet, M.; Heslegrave, A.; Hye, A.; Razquin, C.; Pastor, P.; Sanchez-Valle, R.; Molinuevo, J.L.; Visser, P.J.; Blennow, K.; et al. Plasma levels of soluble TREM2 and neurofilament light chain in TREM2 rare variant carriers. Alzheimer’s Res. Ther. 2019, 11, 94.
Tsai, H.H.; Chen, Y.F.; Yen, R.F.; Lo, Y.L.; Yang, K.C.; Jeng, J.S.; Tsai, L.K.; Chang, C.F. Plasma soluble TREM2 is associated with white matter lesions independent of amyloid and tau. Brain 2021, 144, 3371–3380.
Elahi, F.M.; Casaletto, K.B.; La Joie, R.; Walters, S.M.; Harvey, D.; Wolf, A.; Edwards, L.; Rivera-Contreras, W.; Karydas, A.; Cobigo, Y.; et al. Plasma biomarkers of astrocytic and neuronal dysfunction in early- and late-onset Alzheimer’s disease. Alzheimer’s Dement. 2020, 16, 681–695.
Oeckl, P.; Halbgebauer, S.; Anderl-Straub, S.; Steinacker, P.; Huss, A.M.; Neugebauer, H.; von Arnim, C.A.F.; Diehl-Schmid, J.; Grimmer, T.; Kornhuber, J.; et al. Glial Fibrillary Acidic Protein in Serum is Increased in Alzheimer’s Disease and Correlates with Cognitive Impairment. J. Alzheimer’s Dis. 2019, 67, 481–488.
Simren, J.; Leuzy, A.; Karikari, T.K.; Hye, A.; Benedet, A.L.; Lantero-Rodriguez, J.; Mattsson-Carlgren, N.; Scholl, M.; Mecocci, P.; Vellas, B.; et al. The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer’s disease. Alzheimer’s Dement. 2021, 17, 1145–1156.
Cicognola, C.; Janelidze, S.; Hertze, J.; Zetterberg, H.; Blennow, K.; Mattsson-Carlgren, N.; Hansson, O. Plasma glial fibrillary acidic protein detects Alzheimer pathology and predicts future conversion to Alzheimer dementia in patients with mild cognitive impairment. Alzheimer’s Res. Ther. 2021, 13, 68.
Verberk, I.M.W.; Thijssen, E.; Koelewijn, J.; Mauroo, K.; Vanbrabant, J.; de Wilde, A.; Zwan, M.D.; Verfaillie, S.C.J.; Ossenkoppele, R.; Barkhof, F.; et al. Combination of plasma amyloid beta((1-42/1-40)) and glial fibrillary acidic protein strongly associates with cerebral amyloid pathology. Alzheimer’s Res. Ther. 2020, 12, 118.
Chatterjee, P.; Pedrini, S.; Stoops, E.; Goozee, K.; Villemagne, V.L.; Asih, P.R.; Verberk, I.M.W.; Dave, P.; Taddei, K.; Sohrabi, H.R.; et al. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer’s disease. Transl. Psychiatry 2021, 11, 27.
Pereira, J.B.; Janelidze, S.; Smith, R.; Mattsson-Carlgren, N.; Palmqvist, S.; Teunissen, C.E.; Zetterberg, H.; Stomrud, E.; Ashton, N.J.; Blennow, K.; et al. Plasma GFAP is an early marker of amyloid-beta but not tau pathology in Alzheimer’s disease. Brain 2021, 144, 3505–3516.
Shir, D.; Graff-Radford, J.; Hofrenning, E.I.; Lesnick, T.G.; Przybelski, S.A.; Lowe, V.J.; Knopman, D.S.; Petersen, R.C.; Jack, C.R., Jr.; Vemuri, P.; et al. Association of plasma glial fibrillary acidic protein (GFAP) with neuroimaging of Alzheimer’s disease and vascular pathology. Alzheimer’s Dement. 2022, 14, e12291.
Benedet, A.L.; Mila-Aloma, M.; Vrillon, A.; Ashton, N.J.; Pascoal, T.A.; Lussier, F.; Karikari, T.K.; Hourregue, C.; Cognat, E.; Dumurgier, J.; et al. Differences Between Plasma and Cerebrospinal Fluid Glial Fibrillary Acidic Protein Levels across the Alzheimer Disease Continuum. JAMA Neurol. 2021, 78, 1471–1483.
Benussi, A.; Ashton, N.J.; Karikari, T.K.; Gazzina, S.; Premi, E.; Benussi, L.; Ghidoni, R.; Rodriguez, J.L.; Emersic, A.; Binetti, G.; et al. Serum Glial Fibrillary Acidic Protein (GFAP) Is a Marker of Disease Severity in Frontotemporal Lobar Degeneration. J. Alzheimer’s Dis. 2020, 77, 1129–1141.
AlzBiomarker. Available online: https://www.alzforum.org/alzbiomarker (accessed on 29 September 2023).
Chatterjee, P.; Pedrini, S.; Ashton, N.J.; Tegg, M.; Goozee, K.; Singh, A.K.; Karikari, T.K.; Simren, J.; Vanmechelen, E.; Armstrong, N.J.; et al. Diagnostic and prognostic plasma biomarkers for preclinical Alzheimer’s disease. Alzheimer’s Dement. 2022, 18, 1141–1154.
Mila-Aloma, M.; Ashton, N.J.; Shekari, M.; Salvado, G.; Ortiz-Romero, P.; Montoliu-Gaya, L.; Benedet, A.L.; Karikari, T.K.; Lantero-Rodriguez, J.; Vanmechelen, E.; et al. Plasma p-tau231 and p-tau217 as state markers of amyloid-beta pathology in preclinical Alzheimer’s disease. Nat. Med. 2022, 28, 1797–1801.
Ashton, N.J.; Janelidze, S.; Mattsson-Carlgren, N.; Binette, A.P.; Strandberg, O.; Brum, W.S.; Karikari, T.K.; Gonzalez-Ortiz, F.; Di Molfetta, G.; Meda, F.J.; et al. Differential roles of Abeta42/40, p-tau231 and p-tau217 for Alzheimer’s trial selection and disease monitoring. Nat. Med. 2022, 28, 2555–2562.
Palmqvist, S.; Stomrud, E.; Cullen, N.; Janelidze, S.; Manuilova, E.; Jethwa, A.; Bittner, T.; Eichenlaub, U.; Suridjan, I.; Kollmorgen, G.; et al. An accurate fully automated panel of plasma biomarkers for Alzheimer’s disease. Alzheimer’s Dement. 2023, 19, 1204–1215.
Salvado, G.; Ossenkoppele, R.; Ashton, N.J.; Beach, T.G.; Serrano, G.E.; Reiman, E.M.; Zetterberg, H.; Mattsson-Carlgren, N.; Janelidze, S.; Blennow, K.; et al. Specific associations between plasma biomarkers and postmortem amyloid plaque and tau tangle loads. EMBO Mol. Med. 2023, 15, e2463.
Self, W.K.; Holtzman, D.M. Emerging diagnostics and therapeutics for Alzheimer disease. Nat. Med. 2023, 29, 2187–2199.
Budd Haeberlein, S.; Aisen, P.S.; Barkhof, F.; Chalkias, S.; Chen, T.; Cohen, S.; Dent, G.; Hansson, O.; Harrison, K.; von Hehn, C.; et al. Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer’s Disease. J. Prev. Alzheimer’s Dis. 2022, 9, 197–210.
Sims, J.R.; Zimmer, J.A.; Evans, C.D.; Lu, M.; Ardayfio, P.; Sparks, J.; Wessels, A.M.; Shcherbinin, S.; Wang, H.; Monkul Nery, E.S.; et al. Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial. JAMA 2023, 330, 512–527.
van Dyck, C.H.; Swanson, C.J.; Aisen, P.; Bateman, R.J.; Chen, C.; Gee, M.; Kanekiyo, M.; Li, D.; Reyderman, L.; Cohen, S.; et al. Lecanemab in Early Alzheimer’s Disease. N. Engl. J. Med. 2023, 388, 9–21.
Pontecorvo, M.J.; Lu, M.; Burnham, S.C.; Schade, A.E.; Dage, J.L.; Shcherbinin, S.; Collins, E.C.; Sims, J.R.; Mintun, M.A. Association of Donanemab Treatment with Exploratory Plasma Biomarkers in Early Symptomatic Alzheimer Disease: A Secondary Analysis of the TRAILBLAZER-ALZ Randomized Clinical Trial. JAMA Neurol. 2022, 79, 1250–1259.
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.
Huang, L.K.; Kuan, Y.C.; Lin, H.W.; Hu, C.J. Clinical trials of new drugs for Alzheimer disease: A 2020–2023 update. J. Biomed. Sci. 2023, 30, 83.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Neurosciences

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Oneil G. Bhalala

Rosie Watson

Nawaf Yassi

View Times: 587

Update Date: 08 Feb 2024

Table of Contents

Video Upload Options

Confirm