Progress in blood biomarkers of subjective cognitive decline in preclinical Alzheimer's disease : Chinese Medical Journal

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Progress in blood biomarkers of subjective cognitive decline in preclinical Alzheimer's disease

Yu, Xianfeng1; Shao, Kai1; Wan, Ke2; Li, Taoran1; Li, Yuxia3; Zhu, Xiaoqun2; Han, Ying1,4,5,6

Editor(s): Ji, Yuanyuan

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Chinese Medical Journal ():10.1097/CM9.0000000000002566, March 14, 2023. | DOI: 10.1097/CM9.0000000000002566
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Dementia is a general term for a series of symptoms caused by diseases that affect the brain as well as memories, thoughts, behaviors, and emotions.[1] Neurodegenerative diseases, such as Alzheimer's disease (AD), are the main causes of dementia, accounting for approximately 50% to 60% of cases.[2] A new case of dementia occurs every 3 s globally, and current estimates indicate that >55 million people worldwide have dementia.[3,4] China has the largest number of patients with dementia, placing a heavy burden on the public and healthcare system.[5] Despite improved access to health services, the diagnosis and management of dementia are often inadequate, especially in rural areas.[5] By 2060, the number of patients may grow to 13.8 million unless medical breakthroughs are developed to prevent, slow down, or cure AD. Official death certificates recorded 121,499 AD-related deaths in 2019, the latest year for which data are available.[2] Therefore, AD is now widely considered one of the most severe diseases.

In 2018, the National Institute on Aging-Alzheimer's Association workgroup published the latest diagnostic criteria for AD,[6] which divided the Alzheimer's continuum into six stages. Two stages are characterized as preclinical: in the first stage, only amyloid-β (Aβ) is positive, and in the second stage, self-sensory cognition declines; upon objective examination, self-sensory cognition is normal, or only slight decreases in objective cognition or slight mental and behavioral abnormalities are found. However, during these two stages, AD has not yet progressed to the mild cognitive impairment (MCI) stage, that is, subjective cognitive decline (SCD). This is an important node for AD prevention and treatment [Figure 1]. However, most people's understanding of AD is mainly limited to the moderate and severe stages. During these stages, patients’ cognition deteriorates, and medication fails to restore it. To sum up the experience and lessons gained from previous failures in the development of monoclonal antibodies, the most important possibility is that the timing of drug intervention is too delayed.[1] In the early clinical stage, patients’ cognitive function is still preserved; treatment is needed to block the course of the disease so that it does not progress to the MCI stage and objective cognitive impairment does not occur.

Figure 1:
AD is a continuum. The schematic diagram shows that the symptomatic stages of AD are in accordance with the framework of the NIA-AA study. These stages are only applicable to individuals in the Alzheimer's continuum, defined by biomarker evidence of amyloidosis, including or excluding tau lesions, and are not associated with neurodegenerative states. The color map shows the continuous progression of individual cognitive impairment from the absence of objective cognitive impairment (pale red) to severe objective cognitive impairment (deep red), lasting approximately 15 to 25 years. According to this framework, AD symptoms and manifestations are divided into six stages. Stage 1 refers to asymptomatic disease. Stage 2, also known as the transitional stage, includes individuals who present with the first subtle sign of AD as a combination of one or more of the following: decreased subjective cognition, subtle objective decline, or mild behavioral symptoms. Stages 1 and 2 are classified as preclinical AD, according to the 2011 NIA-AA criteria. Furthermore, stage 3 reflects MCI, stage 4 reflects mild dementia, stage 5 reflects moderate dementia, and stage 6 reflects severe dementia. AD: Alzheimer's disease; CDR: Clinical Dementia Rating; MCI: Mild cognitive impairment; MTA: Medial temporal lobe atrophy; NIA-AA: National Alzheimer's Association Institute.

An accurate and early diagnosis of AD enables patients to receive early medical intervention, potentially delaying the onset of AD or disease progression. Cerebrospinal fluid (CSF) Aβ42/40 (or CSF Aβ42) and Aβ-positron emission tomography (PET) are associated with detecting Aβ pathology in the brain and are considered the earliest biomarkers of AD. However, CSF collection by lumbar puncture is considered by many to be a more invasive diagnostic approach, and Aβ-PET scans are costly and involve a potential exposure to gamma radiation for both the physicians and participants. Additionally, the advanced equipment required for Aβ-PET scanning is only available in select hospitals worldwide.[5] Therefore, the urgent need for relatively inexpensive, easily accessible, and non-invasive methods for diagnosing AD has become apparent. In recent years, continuous developments in blood biomarker research to identify patients with preclinical AD offer the best chance for successful treatment and prevention of cognitive decline before clinical symptoms appear. However, from a clinical perspective, the differential diagnosis of AD may be challenging, even for experts in dementia. Accurate prognosis and disease monitoring are also challenging with clinical information alone. Therefore, biomarkers play an increasingly important role in this field.

SCD is generally regarded as the first manifestation of the AD continuum. It refers to self-experienced cognitive decline without objective evidence,[7,8] which results in such persons generally being considered healthy in clinical practice. Also, SCD is considered to be present at a relatively late stage of AD before clinical manifestation and is associated with abnormal AD biomarkers. Thus, SCD increased risk of future cognitive decline and dementia,[6] making it a high-risk condition for dementia. Despite the lack of evidence of objective cognitive impairment, the subjective decline in cognitive function experienced by individuals may become increasingly important for clinicians, as the number of individuals with such concerns seeking medical help and advice is increasing.

An increasing number of biomarker studies have demonstrated that SCD symptoms are associated with specific and unique potential pathological events. For example, owing to the rapid development of ultra-sensitive assays, trace levels of brain-specific proteins can now be measured in the blood.[9] Plasma phosphorylated tau (P-tau), along with Aβ42/40, appears to be the best candidate marker during symptomatic AD (i.e., prodromal AD and dementia) and preclinical AD.[10,11] Although not AD-specific, blood neurofilament light (NfL) seems to be useful in identifying neurodegeneration, showing promise as a non-disease-specific tool for recognizing neurodegeneration and for detecting the effectiveness of disease improvement therapies.[12] AD is a chronic disease with a long preclinical and prodromal period (20 years) and an average clinical duration of 8 to 10 years. Blood abnormalities occur several years earlier than brain imaging changes.[13] Analysis of cross-sectional and longitudinal data in large AD cohorts suggests that the pathological process of AD begins >2 decades before the onset of clinical symptoms. The accumulation of Aβ in the brain (approximately 15–20 years before clinical onset) is followed by a decline in cortical metabolism (approximately 10–15 years before clinical onset) and cerebral atrophy (approximately 5–10 years before clinical onset).[14,15] In patients with familial AD, the plasma concentrations of P-tau at threonine 181 (P-tau181) begin to increase approximately 15 years before clinical onset,[16] and plasma P-tau181 concentrations in patients with sporadic disease at necropsy predict neuropathological changes in the disease at least 8 years in advance.[17] Unlike genetics, biomarkers can only indicate the presence of pathology in AD after it has been triggered, that is, changes in biomarkers mark the beginning of a pathological process. Therefore, therapeutic interventions should be initiated as early as possible in the pre-symptomatic stage of the disease, that is, the SCD stage [Figure 2].

Figure 2:
Blood biomarkers in the continuum of AD. In this figure, cognitive function is represented on the y-axis (from top to bottom, unaffected by dementia), and clinical disease stages are represented on the x-axis (preclinical, MCI, and dementia, with clinical traces being used to identify these three defined and overlapping stages associated with a normal aging process). Cognitive function deteriorates with age to a greater extent than expected. One hypothetical model shows a time series of blood biomarker detection thresholds, indicated by arrows. AD: Alzheimer's disease; NfL: Neurofilamen light; GFAP: Glial fibrillary acidic protein; MCI: Mild cognitive impairment; nEV: neuronal-derived extracellular vesicle.

Standard terminology and diagnostic criteria of SCD

The concept of SCD was first introduced by Reisberg et al[18] in the early 1980s to define the early stages of AD and was initially assessed using the Global Deterioration Scale. The concept of SCD has remained relatively flat for >20 years since its proposal. Until 2005, many descriptive terms were used, including subjective cognitive complaints, subjective memory complaints, subjective memory impairments, subjective cognitive impairments, and pre-mild cognitive impairments, but they all referred to the same concept. Although increasing attention has been paid to SCD, progress is hindered by the lack of a common nomenclature, and no unified standard to define SCD exists. In 2014, the SCD Initiative proposed common terminology and a conceptual framework for AD-related SCD research.[8] The framework unifies multiple descriptors into the single term, SCD, and proposes a set of SCD characteristics caused by AD, called SCD plus, to facilitate the comparison of study results, data pools, meta-analyses, and collaborative multi-center studies. These characteristics include onset within 5 years, age ≥60 years, concern that one is performing worse than others of the same age, declining cognitive ability confirmed by informants, possession of the apolipoprotein E (APOE) ε4 genotype, and biomarker evidence for the presence of AD. Recently, two additional characteristics of SCD have been proposed: persistent complaints over time and seeking medical assistance for SCD.[7] However, uncertainty regarding whether self- or informant-reported data are helpful in identifying early AD-related cognitive decline prevails.[8] The updated features of SCD, published in 2020 by the SCD International Working Group led by Professor Jessen, were revised and supplemented based on the 2014 framework.[7] Items I (Self-sensory memory decreases) and IV (Those who were worried about their memory decline, the cognitive decline in the future would be two times as fast as those who did not worry and those who were normal aging) are necessary for the diagnosis of SCD. Notably, SCD has the most robust prediction ability in the late preclinical stage of AD, when the ability to detect objective cognitive impairment is poor owing to the limited sensitivity of standard neuropsychological tests.[8] Jessen et al[19] speculated that as predictions enter the early stages of disease, the predictive power of subjective cognitive reports increases and that of objective tests decreases. This may be related to successful compensation in the early stages of disease.

Currently, the development of core blood biomarkers of SCD in preclinical AD lags behind that of PET and CSF. The application of biomarker technology makes it possible to understand the neuropathological mechanism of SCD in AD and to provide potential pathological and blood biomarkers for the early detection and prediction of AD. We discuss different groups of blood-based biomarker candidates, their advantages and limitations, and the way forward, from identification and analysis to clinical validation. In the future, less invasive or cheaper blood-based biomarker testing, as well as genetic, clinical, and demographic information, may play an essential role in selecting individuals for more expensive and invasive biomarker testing. There is keen interest in exploring the potential role of SCD as an early sign of neurodegenerative disease. This study may be of interest if SCD can be combined with biomarker-based neurodegenerative disease detection and early intervention in the future.

In summary, SCD is considered to be one of the earliest clinical manifestations of AD and occurs before cognitive impairment, when individuals only suffer from slight neuronal damage and can make functional compensation.[8] SCD is heterogeneous in etiology and complex in phenomenology, and research on SCD has been hampered by a lack of common terminology and research procedures. Thus, it is imperative to identify blood biomarkers that can objectively reflect the different predictive values of SCD at various stages of cognitive impairment and to predict its outcomes. This review aimed to provide an overview of the literature on the progress of studies on the diagnosis and prognostic outcomes of patients with SCD in the context of AD, including studies using different blood biomarkers. The limitations of the current study and future directions are also discussed. A detailed summary of the relevant findings in the recent 5 years is shown in Table 1.

Table 1 - Summary of blood biomarkers association studies in preclinical AD in recent 5 years (according to the ATN framework).
Years Study Biomarkers Assays/platforms Subjects (numbers) Follow-up (years) Main results
“A” in blood biomarkers
 2018 Verberk et al [34] Plasma Aβ42 and Aβ42/40 Simoa 248 SCD 3 ± 2 Plasma Aβ42/40 ratio has potential as a prescreener to identify AD pathological changes in cognitively normal individuals with SCD.
 2018 de Rojas et al [26] Plasma Aβ42, Aβ40, and Aβ42/40 ATN-X, ELISA and microRNA 200 SCD / Brain and plasma Aβ levels are partially correlated in individuals diagnosed with SCD. Aβ plasma measurements, particularly the TP 42/40 ratio, could generate a new recruitment strategy independent of the APOE genotype that would improve the identification of SCD subjects with brain amyloidosis and reduce the rate of screening failures in preclinical AD studies.
 2019 Youn et al [27] Plasma Aβ oligomerization MSD 92 NC, 17 SCD, 14 MCI, and 39 AD / Findings suggested that a substantial correlation exists between amyloid β oligomerization in the blood and brain volume reduction in the form of AD.
 2019 Iulita et al [36] Plasma Aβ40 and Aβ42 Multiplex arrays 28 AD, 30 MCI, 30 SMI, and 19 NC 3 Plasma amyloid, MMP, and inflammatory biomarkers demonstrated differences in individuals with cognitive deterioration and/or progression to MCI/pAD.
 2019 Chen et al [32] Plasma Aβ and tau ELISA 31 HC, 33 MCI, and 18 AD 0.5 Plasma Aβ and tau levels change in a dynamic, nonlinear, and nonparallel manner over the AD continuum. Changes in plasma Aβ concentration are time-dependent, whereas changes in hyperP-tau protein levels paralleled the clinical progression of MCI.
 2021 Wang et al [33] Plasma Aβ42 and Aβ40 MSD 142 SCD / The study demonstrated different WM microstructures between subjects with low and high Aβ40 levels among individuals with SCD. The findings suggest that plasma Aβ40 levels could reflect central neurodegeneration and may represent a valuable biomarker to predict different trajectories of aging in individuals with SCD.
 2021 Palmqvist et al [49] Plasma Aβ42 and Aβ40 ELISA, Simoa, and MSD 164 SCD and 176 MCI 4 In BioFINDER (a swedish study), where plasma Aβ42/Aβ40 was available, it was selected only for the prediction within 6 years, and removing it from the model reduced the AUC by less than 0.02.
 2021 Simrén et al [47] Plasma Aβ42/40 Simoa 99 CU, 107 MCI, and 103 AD 4 Plasma biomarker amyloid-β (Aβ42/40) was altered in AD dementia. A significant association was observed between baseline Aβ42/Aβ40 and Aβ42 with GM loss in the orbitofrontal cortex.
 2021 Raffin et al [63] Plasma Aβ42, Aβ40, and Aβ42/40 Immunoprecipitation with chromatography-mass spectrometry 465 non-AD 4 Mixed-model linear regressions demonstrated that the reverse relationship between PA and cognitive decline tended to be more pronounced as plasma Aβ42/40 increased.
 2022 Gerards et al [24] Plasma Aβ42/ Aβ40 Simoa 54 DAT, 57 MCI, and 33 SCD / The ratios plasma Aβ42/Aβ40 and plasma P-tau181/Aβ42, are biomarkers, which can differentiate diagnostic groups in a memory clinic setting outside of research studies.
 2022 Pan et al [35] Plasma Aβ42 and Aβ40 Simoa 183 NC, 77 SCD, 111 MCI, 56 AD, and 22 non-AD dementia / The present study demonstrated that plasma Aβ42 and Aβ42/40 ratio were significantly lower in AD patients. In the AD continuum, plasma Aβ42 showed a significantly increasing trend from NC to SCD before decreasing in the groups of MCI and AD.
“T” in blood biomarkers
 2017 Müller et al [50] Plasma tau Simoa 111 SCD and 134 NC / In conclusion, plasma tau is not altered in the examined cohort of subjects at increased risk for AD.
 2021 Janelidze et al [48] Plasma P-tau217 MSD 225 NC, 89 SCD, and 176 MCI 0.7–2.1 The study results suggest that in AD, plasma P-tau217 becomes abnormal before tau-PET and that plasma P-tau217 may be considered an early AD biomarker.
 2021 Palmqvist et al [49] Plasma P-tau217 and P-tau181 ELISA, Simoa, and MSD 164 SCD and 176 MCI 4 Plasma P-tau, in combination with brief cognitive tests and APOE genotyping, might greatly improve the diagnostic prediction of AD and facilitate recruitment for AD trials.
 2021 Simrén et al [47] Plasma P-tau181 Simoa 99 CU, 107 MCI, and 103 AD 4 P-tau181 detected AD at MCI and dementia stages and was strongly associated with cognitive decline and gray matter loss.
 2021 Thomas et al [54] Plasma P-tau181 Simoa 402 CU, 199 SCD, and 346 MCI 4 The results of this study add support to the potential use of the Obj-SCD classification in clinical research as a tool to assist with early detection, and may also support the use of this classification to identify and recruit research and clinical trial participants at risk for future disease progression.
 2021 Ashton et al [46] Plasma P-tau231 Simoa 189 CU, 54 MCI, 42 AD, and 26 non-AD 4.2 The study results suggest that in AD, plasma P-tau217 becomes abnormal before tau-PET and that plasma P-tau217 may be considered as an early AD biomarker.
 2022 Gerards et al [24] Plasma P-tau181 Simoa 54 DAT, 57 MCI, and 33 SCD 0 Plasma pTau181 and NfL, as well as the ratios Aβ42/40 and P-tau181/Aβ42, are biomarkers, which can differentiate diagnostic groups in a memory clinic setting outside of research studies.
“N” in blood biomarkers
 2017 Mattsson et al [12] Plasma NfL Simoa 193 NC, 197 MCI, and 180 AD 2 Plasma NfL may be a promising noninvasive biomarker for AD.
 2018 Lewczuk et al [67] Plasma NfL Simoa 41 NC, 25 MCI-AD, and 33 ADD / This study confirms increased concentrations of plasma NfL in patients with AD compared with non-demented controls.
 2018 Verberk et al [34] Plasma T-tau Simoa 248 SCD 3 ± 2 Plasma Aβ42/40 ratio has potential as a prescreener to identify AD pathological changes in cognitively normal individuals with SCD.
 2018 Chatterjee et al [68] Plasma NfL Simoa 76 SCD and 24 non-SCD / Plasma NfL is inversely associated with cognitive performance in elderly individuals. While plasma NfL may not reflect NAL in individuals with normal global cognition, the current observations indicate that onset of axonal injury, reflected by increased plasma NfL, within the preclinical phase of AD may contribute to the pathogenesis of AD.
 2019 Kenny et al [81] Plasma microRNA-206, OpenArray platform 31 NC, 30 MCI, and 25 AD 5 The present study demonstrates blood-based microRNAs’ diagnostic and prognostic potential for MCI and AD.
 2019 Iulita et al [36] plasma MMP-1, MMP-3, MMP-9, IFN-γ, TNF-α, IL-6, IL-8, and IL-10 Multiplex arrays 28 AD, 30 MCI, 30 SMI, and 19 NC 3 The montreal Cognitive Assessment (MoCA)/Cambridge Cognitive Examination (CAMCOG)-based trends in MMP-1, MMP-3, IL-8, IL-10 and TNF-α were associated with a final diagnosis of pAD.
 2020 Hu et al [91] Serum ApoB Immunonephelometry 288 NC and 219 SCD / This study was the first to find some protective associations of serum ApoB, but not ApoA1, with CSF AD core biomarkers in the preclinical stage of AD. This finding indicated that ApoB might play different roles in different stages of AD.
 2020 Baldacci et al [28] Plasma NfL and T-tau Simoa 316 SCD 2 The study found that plasma NfL and T-tau longitudinal trajectories are affected by age and female sex, respectively, in SMC individuals.
 2020 Bergland et al [88] Plasma SM Reversed phase chromatography mass spectrometry 50 NC, 50 Aβ+MCI, and 49 Aβ–MCI Average of 2.04 Reduced plasma concentrations of SM were associated with AD.
 2020 de Leeuw et al [90] Blood LDL cholesterol and uridine MS, HPLC, and competitive luminescence immunoassay 204 SCD, 130 MCI, and 194 AD 2.1 ± 0.87 This study found associations between higher LDL cholesterol levels in SCD and lower uridine in AD with clinical progression.
 2020 Zhao et al [78] Plasma NDE Aβ1–42 ELISA 87 MCI, 80 HC, and 88 AD 2–3 A combination of two biomarkers of NDEs (Aβ1–42) and SS-16 predicted the conversion of MCI to AD dementia more accurately.
 2021 Palmqvist et al [49] Plasma NfL ELISA, Simoa, and MSD 164 SCD and 176 MCI 4 In BioFINDER, the best model using the primary outcome to predict AD dementia within 4 years included the predictors plasma P-tau217, number of APOE ε4 alleles, executive function, memory function, cortical thickness and plasma NfL.
 2021 Simrén et al [47] Plasma NfL, T-tau, and GFAP Simoa 99 CU, 107 MCI, and 103 AD 4 Plasma NfL, T-tau and GFAP were altered in AD dementia. Plasma NfL and MMSE were significantly associated at baseline. Within the AD group, baseline T-tau and GFAP were associated with GM volume change.
 2021 He et al [70] Plasma NfL MSD 223 NC and 281 MCI 4 Plasma NfL can be a promising biomarker of progressive cognition decline in older adults with MCI.
 2021 Raffin et al [63] Plasma NfL immunoprecipitation with chromatography-mass spectrometry 465 non-AD 4 It suggests that PA may attenuate the negative association between amyloid load and cognition, while having high NfL levels mitigates the favorable relationship between PA and cognition.
 2021 Liu et al [75] Serum exosomal microRNA-135a ELISA 165 SCD, 143 MCI, 202 DAT, and 30 HC / Clinical research results showed that the sensitivity of microRNA-135a in the diagnosis of AD has been greatly improved after the detection of specific exosomes using the ABCA1-labeled exosome capture technology.
 2021 Bangen et al [69] Plasma NfL Simoa 81 NC, 46 Obj–SCD, and 167 MCI 5 Findings demonstrate the utility of plasma NfL as a biomarker of early AD-related changes, provide support for the use of Obj-SCD criteria in clinical research to better capture subtle cognitive alteration related changes.
 2022 Gerards et al [24] Plasma T-tau and NfL Simoa 54 DAT, 57 MCI, and 33 SCD 0 Plasma P-tau181 and NfL, as well as the ratios Aβ42/Aβ40 and P-tau181/Aβ42, are biomarkers, which can differentiate diagnostic groups in a memory clinic setting outside of research studies.
 2022 Pan et al [35] Plasma T-tau Simoa 183 NC, 77 SCD, 111 MCI, 56 AD, and 22 non-AD dementia / The present study demonstrated that plasma Aβ42 and Aβ42/Aβ40 ratio were significantly lower in AD patients. In the AD continuum, plasma Aβ42 showed a significantly increasing trend from NC to SCD before decreasing in the groups of MCI and AD.
 2022 Ebenau et al [92] Serum NfL, GFAP Simoa 401 SCD 3.8 ± 2.8 The results showed that NfL and GFAP predicted clinical progression, and have added value beyond Aβ and P-tau. However, our results do not reveal a single most suitable biomarker for N.
AD: Alzheimer's disease; ADD: Alzheimer's disease dementia; Apo: Apolipoprotein; Aβ40: Amyloid-beta40; Aβ42: Amyloid-beta42; CSF: Cerebrospinal fluid; CU: Cognitively unimpaired; DAT: Dementia of the Alzheimer type; GFAP: Glial fibrillary acidic protein; HPLC: High-performance liquid chromatography; LDL: Low-density lipoprotein; MCI: Mild cognitive impairment; MMP: Metallo-proteinase; MS: Mass spectrometry; MSD: Meso Scale Diagnostics; NC: Normal controls; NDE: Plasma neuron-derived exosome; NfL: Neurofilament light; Obj-SCD: Objectively defined subtle cognitive decline; PA: Physical activity; pAD: Probable AD; P-tau: Phosphorylated tau; SM: Sphingomyelin; SMI: Subjective memory impairment; TP: Total plasma; T-tau: Total-tau; AUC: area under the curve; SCD: subjective cognitive decline; APOE: Apolipoprotein E; IFN: Inborn errors of interferon; HC: Healthy controls; WM: White matter; TNF: Tumor Necrosis Factor; SMC: Subjective memory complaint; ELISA: Enzyme-linked immunosorbent assay; PET: positron emission tomography; IL: Interleukin; N: Neurodegeneration; MMSE: Mini-Mental State Examination; GM: Gray matte; SS: Sniffin sticks; ABCA: ATP-binding cassette transporter A; NAL: Neocortical amyloid-β load; /: Not applicable.


Search strategy

We searched the PubMed and Science Direct databases for articles published between January 1996 and June 2022 that described changes in blood biomarkers in patients with SCD associated with AD. The search terms used were either “((“subjective cognitive decline” [Title/Abstract]) OR (“subjective memory decline” [Title/Abstract]) OR (“subjective cognitive complaint” [Title/Abstract]) OR (“subjective cognitive complaints” [Title/Abstract]) OR (“subjective memory impairment” [Title/Abstract]) OR (“subjective memory impairments” [Title/Abstract]) OR (“subjective memory complaint” [Title/Abstract]) OR (“subjective memory complaints” [Title/Abstract]) OR (“cognitive complaints” [Title/Abstract]) OR (“subjective cognitive impairment” [Title/Abstract]) OR (“subjective cognitive impairments” [Title/Abstract] OR SCD) AND (AD OR Alzheimer∗) AND (“biological marker” OR biomarker OR amyloid OR Abeta OR TAU OR TTAU OR T-TAU OR PTAU OR P-TAU OR NFL OR NEFL OR NfL OR neurofilament) AND (plasma OR serum OR blood)).” The retrieved studies and references to relevant reviews, systematic reviews, and meta-analyses were manually searched for additional relevant articles. Our study was not limited by publication date. Finally, book chapters and institutional websites were consulted as possible synthesis material.

Selection criteria

These studies were included based on the following enrollment criteria: (1) studies describing changes in blood biomarkers in patients with SCD associated with AD; (2) patients with SCD who performed well on standard neuropsychological tests and who did not have other medical or psychiatric reasons for perceived cognitive alterations; and (3) original studies that were published in English, with availability of full text, regardless of study environment. The following types of studies were excluded: (1) case reports, meeting summaries, reviews, and study designs or protocols; (2) interventional/experimental study design; and (3) risk factors associated with impaired cognitive function (i.e., changes in sleep patterns, depressions, and nutritional status) that focused on subjects that were not relevant to our objectives. A detailed evaluation led to the inclusion and review of 116 studies. A detailed description of the item selection process is provided in a flowchart [Figure 3]. We discussed and summarized the dynamic changes in SCD-related biomarkers in the context of AD, detection techniques and methods, and predictions of disease outcomes and actual outcomes.

Figure 3:
Flow chart of the search and screening process for articles included in the review. PubMed and ScienceDirect databases were searched for articles describing changes in blood biomarkers associated with AD in patients with SCD published between January 1996 and June 2022. Finally, a detailed evaluation resulted in the inclusion and review of 113 studies. AD: Alzheimer's disease; SCD: Subjective cognitive decline.

Potential Biomarkers

Aβ pathological markers

Aβ is the main pathological marker of AD; CSF and PET biomarkers of Aβ pathology indicate abnormalities decades before the onset of dementia symptoms.[20] A meta-analysis from The Lancet Neurology showed no difference between plasma Aβ42 and Aβ40 in normal controls (NC) or patients with AD.[21] However, recent results are more promising owing to the development of highly sensitive assays and techniques. Over the past 20 years, many studies have evaluated plasma Aβ42 as a biomarker of AD, typically using immunoassays with relatively high variance and uncertain specificity, and have found poor and inconsistent performance overall. The ratio of plasma Aβ42 to Aβ40 was measured with high precision, demonstrating a high correlation with cerebral amyloidosis.[11] Compared with amyloidosis, plasma Aβ42/40 may be more consistent with plasma Aβ42 and Aβ40, as the ratio may be normalized because of pre-analysis variability or differences in Aβ levels related to the circadian rhythm or other biological variations not related to brain amyloidosis.[22] Some studies have shown that, owing to their accuracy and predictability, plasma levels of different Aβ variants may play a role as AD biomarkers.[23] Not surprisingly, given that the CSF Aβ peptide is one of the core biomarkers for diagnosing AD, an urgent need to identify more readily available biomarkers exists. Thus, researchers have suggested that plasma Aβ42/40 could be used as a diagnostic marker for screening.[24] In addition, novel fully automated assays for the measurement of plasma Aβ42 and Aβ40 (i.e., the Elecsys immunoassay) have been shown to predict MCI and Aβ pathology in patients with AD in BioFINDER and German biomarker studies, encouraging their use in the pre-screening of AD clinical trials.[25]

Studies have shown that Aβ plasma measurements, particularly the total plasma 42/40 ratio, could generate a new recruitment strategy independent of the APOE genotype that would improve the identification of patients with SCD with brain amyloidosis and reduce the rate of screening failure in preclinical AD studies.[26] In a study of the SCD phase of the AD continuum, plasma Aβ42/40 was validated as a biomarker of AD because it assessed the ability of plasma Aβ42/40 to detect early AD. Pre-screening with plasma Aβ42/40 has been estimated to reduce the number of required amyloid PET scans by 62%, significantly reducing the recruitment time and cost. In addition to using Aβ alone, studies have found that plasma Aβ42/40, especially in combination with age and APOE ε4 status, can be used to accurately diagnose brain amyloidosis and to screen for brain amyloidosis in individuals with normal cognition.[13] Voxel-based morphometry study identified specific brain atrophy and its location concerning plasma Multimer Detection System-Oligomeric Amyloid-β.[27,28]. Individuals who are negative in amyloid PET scans and positive for plasma Aβ42/40 have an increased risk of developing amyloid PET positivity, which might be similar to the correlation between CSF biomarkers and amyloid PET.[29,30]

Predictive efficacy of clinical conversion

Blood-based biomarkers can screen potential participants more quickly and inexpensively, especially in prevention testing.[29,31] Recent reports have shown that high-resolution determination of plasma Aβ42/40 levels has a strong predictive effect on cerebral amyloidosis.[10,11] Other researchers have included Aβ42/40, APOE ε4 copy number, and participant age in mass spectrum-based plasma biomarker models and have found strong diagnostic performance and area under the curve (AUC) (0.90; 95% confidence interval = 0.87–0.93) and accurate (86%) results. The model can accurately distinguish between brain amyloid positive and negative individuals. The diagnostic evaluation process may contribute to the early detection of AD and may improve the efficiency of drug trials for AD. Analysis of longitudinal data revealed the evolution of Aβ-related biomarkers in patients over time. In the MCI stage, Aβ42 levels initially increased steadily and then decreased before the onset of clinical AD. Plasma Aβ and tau levels varied in a dynamic, nonlinear, and non-parallel manner across the AD continuum. The change in plasma Aβ concentration is time-dependent, and the change in hyperphosphorylated tau (hyper P-tau) protein level is parallel to the clinical progress of MCI.[32] Plasma Aβ40 levels can reflect central nervous degeneration and may represent a valuable biomarker for predicting different aging trajectories in patients with SCD.[33] Verberk et al[34] found that the plasma Aβ42/40 ratio has the potential to serve as a pre-screening index for identifying the earliest pathological changes in AD continuity among individuals with SCD and normal cognition. The plasma Aβ42/40 ratio combined with age and APOE ε4 status yielded >80% accuracy. This indicates that future pre-screening based on blood tests will reduce the need for invasive or expensive amyloid measurement methods such as lumbar puncture or PET scans. Furthermore, a lower plasma Aβ42/40 ratio was associated with a two-fold increased risk of MCI or clinical progression of dementia. Unfortunately, a large proportion of studies did not group participants by cognitive level (no SCD, MCI, or AD groups).

Predictive efficacy of objective cognitive decline

In the AD continuum, plasma Aβ42 showed a significantly increasing trend from NC to SCD, before decreasing in the MCI and AD stages. Furthermore, the predictive value of plasma Aβ for brain amyloid deposition is inconsistent throughout cognitive decline.[35] In a retrospective clinical study, lower Aβ40 and Aβ42 levels were associated with more significant cognitive decline, based on the patient's 3-year changes in the Montreal Cognitive Assessment and Cambridge Cognitive Examination.[36] The significant positive correlation between elevated plasma Aβ levels and improvements in subjective and objective cognitive indicators may reflect a functional relationship similar to that between cerebral Aβ burden and memory complaints and/or cognitive performance in cross-sectional studies of older adults without dementia. In a longitudinal study examining the association between plasma Aβ42/40 of cognitively unimpaired (CU) individuals and MCI or the risk of progression to AD dementia using a time-dependent receiver operating characteristic with up to 6 years of follow-up, taking into account inter-individual differences in follow-up and transition time, Simoa-based plasma Aβ42/40 AUC values were found to be ≥0.85 (ranging from 0.85 to 0.92) at all annual time points.[37] Plasma Aβ42/40 is shown to be associated with MCI and the progression of AD dementia.[34,38] Low (abnormal) plasma Aβ42/40 levels were also shown to be associated with more significant decreases in composite cognitive scores over time (adjusted for sex, age, education, treatment group, body mass index, and the clinical dementia rating scale and geriatric depression scale scores).[39] This finding was also observed when longitudinal Mini-Mental State Examination (MMSE) scores were used. Similar results have been reported using domain-specific findings including attention, memory, language, and executive function.[40] Interventions to increase plasma Aβ levels, including certain anti-diabetic drugs, may also improve cognitive outcomes.


Tau isolated from coarse tangles in the AD brain was first shown to be “abnormally” phosphorylated >30 years ago. However, the relationship between tau phosphorylation and AD remains poorly understood. Controversy remains whether phosphorylation is a causative factor, an incidental association, or a protective response that delays AD pathogenesis. Different tau phosphorylation sites have also been postulated to play different roles in AD pathophysiology. For example, some phosphorylation sites inhibit pathological aggregation, whereas others promote it.[41] Phosphorylation plays a vital role in the physiological function of tau, mainly by changing its microtubule-binding affinity to regulate axonal growth, plasticity, and transport. Although total tau (T-tau) is a validated clinical biomarker of the AD continuum, more studies are needed to fully exploit tau's potential as a diagnostic biomarker specific for AD. Currently, most T-tau assays are based on detecting antibodies around the tau's N-terminal. These fragments fail to differentiate between full-length tau and fragments lacking either N- or C-terminal regions. This raises concerns about whether current T-tau tests may fail to elucidate molecular information and whether these measurements represent T-tau. Entanglements containing the full-length or truncated forms of P-tau are the second major pathological feature of AD. Studies to date that measured plasma T-tau levels have found too much overlap between clinical groups, suggesting its limited diagnostic value.[42,43] This is in contrast with the recent results obtained in the analysis of post-translational modifications of tau. Tau contains >70 post-translational modification sites, including >40 phosphorylation sites and several truncated forms.[44] Different forms of P-tau can be detected in the CSF and plasma. Similar to Aβ, amyloid and tau pathology can be detected with high accuracy (80%–98%) using different methods, such as mass spectrometry and immune-based methods.[45-47] In a recent study, plasma levels of P-tau217 were increased in early preclinical AD and altered before tau-PET positivity. In patients with normal tau-PET, high plasma P-tau217 levels are associated with higher future increases in tau-PET signals in the olfactory region of interest.[48] These findings suggest that plasma P-tau217 is a promising biomarker of early AD and may be particularly useful for patient selection and as an indicator for monitoring drug response in clinical trials, including in patients with preclinical AD. One area of ongoing research is to explore the utility of tau fragments as biomarkers for the AD continuum.

However, tau is a complex protein in the physiology and progression of AD and should not be considered as a single biomarker classified as T-tau and P-tau. Many different forms of tau (we call it the “tauosome”) can contribute to the complex and multi-targeted panels needed for diagnostic interrogation to improve AD management.

Predictive efficacy of clinical conversion

A combination of plasma P-tau and other available biomarkers can accurately predict the risk of SCD progressing to AD. Using CSF P-tau, Aβ42/40, and nerve mercerization instead of plasma biomarkers do not significantly improve predictive accuracy. Plasma P-tau combined with simple cognitive testing and APOE genotyping may significantly improve diagnostic predictions for AD (AUC = 0.83).[49] In a study by Simrén et al,[47] P-tau181 was more capable of predicting the transformation to AD dementia in CU or MCI cases than other plasma biomarkers, and longitudinal observation showed that the increase in plasma P-tau181 was mainly related to the decrease in gray matter volume of the medial and lateral temporal lobes, as well as the posterior cingulate and cingulate cortices (P < 0.05). Plasma P-tau231, similar to plasma P-tau181, has been shown to have high diagnostic accuracy in detecting AD in the dementia phase, including in cases of both AD and non-AD neurodegenerative diseases, as confirmed by neuropathology. Plasma P-tau231 also provides a high-resolution distinction between Aβ-positive CU and MCI cases and Aβ-negative older patients with CU, and is closely related to Aβ and tau PET. Plasma P-tau231 has now been demonstrated to (1) increase before Aβ PET positivity and be associated with Aβ PET, (2) significantly increase earlier in individuals with CU than plasma P-tau181, (3) isolate Aβ PET in a quartile superior to plasma P-tau181 and CSF P-tau217, and (4) detect early tau deposition, which was not observed with plasma P-tau181.[46] Plasma P-tau231 concentrations increase in the preclinical phase of the disease, with concentrations gradually increasing from younger to older patients with CU with MCI and AD dementia (P < 0.0001), and plasma P-tau231 levels in AD were approximately two times higher than those of the CU combination (AD was three times higher than Aβ negativity in older patients with CU). Plasma P-tau231 levels were 2.6-fold higher in AD cases than in non-AD dementia cases. Plasma P-tau231 also distinguishes AD from MCI cases without underlying AD pathology (AUC = 0.89), a potential challenge observed in clinical settings.[46] Previous studies have reported increased plasma tau levels in patients with AD and AD-induced MCI. However, in a study of 111 SCD cohorts, it was found that plasma tau was not altered in participants at an increased risk of AD.[50] A combination of plasma P-tau and other available biomarkers can accurately predict the risk of AD dementia. Plasma P-tau181 has demonstrated superior diagnostic and prognostic utility than other putative plasma biomarkers of AD (Aβ42/40, NfL, T-tau, and glial fibrillary acidic protein [GFAP]). In addition, plasma P-tau181 may have been observed preclinically with plasma Aβ42/40 and can be used to monitor disease progression in clinical trials.[47]

Predictive efficacy of objective cognitive decline

A study by Janelidze et al[51] found that elevated baseline plasma P-tau181 levels (adjusted for age, sex, and education) in individuals with CU increased the risk of progression to AD dementia (with a mean follow-up of 4.9 years) at a risk ratio similar to that of CSF P-tau181. Similar findings have been observed in a 60-month longitudinal study. Longitudinal increases in plasma P-tau181 have been associated with future cognitive decline in individuals with CU.[52] P-tau181 is detected in MCI and dementia stages and is closely associated with cognitive decline and gray matter loss. P-tau181 was significantly superior to other biomarkers (AUC = 0.91) and was the best predictor of decreased cognitive ability compared with changes in neural mercerization, Aβ (Aβ42/40), T-tau, and GFAP in AD dementia, confirming the results of earlier studies. The finding remains true even when plasma T-tau, P-tau, Aβ42/40, and NfL were adjusted for. High plasma P-tau181 has also been shown to carry an increased risk of AD dementia in Aβ-positive CU cases within 84 months and in a shorter interval of 48 months than Aβ-negative CU cases. Over a 100-month interval, plasma P-tau181-positive CU individuals at baseline exhibited faster cognitive decline than P-tau181-negative CU individuals.[51] These findings highlight plasma P-tau181's potential as a noninvasive, cost-effective biomarker for the diagnosis and prognosis of AD. In a study that investigated the association between longitudinal plasma P-tau217 and longitudinal cognition,[52] high plasma P-tau217 in CU individuals was associated with cognitive deterioration according to the MMSE and Preclinical Alzheimer Cognitive Composite. Plasma P-tau181 and P-tau217 levels were associated with a longitudinal decline in MMSE scores in analyses that combined CU and MCI individuals[53]; however, P-tau217 provided the best fit to the data and a larger effect size than P-tau181. When plasma P-tau181, P-tau217, Aβ42/40, and NfL were combined in a multivariate model, only P-tau217 was an important independent predictor of cognitive decline. Thomas et al[54] found that objectively defined subtle cognitive decline (Obj-SCD) and plasma P-tau181 are markers of early AD. Moreover, within the 4 years of follow-up, participants who were Obj-SCD/P-tau181-positive had the fastest incidence of cognitive and functional decline.

In a 3-year longitudinal study, researchers found a higher increase in plasma T-tau concentration over time in women than in men, and the rate of change in baseline plasma T-tau was inversely proportional to cognitive scores.[28] Ashton et al[46] showed that an increase in P-tau231 concentration can predict the decline in cognitive function to some extent. In addition to its predictive function, plasma P-tau231 has a certain ability to identify the stage of the continuous spectrum of AD diseases, which can accurately distinguish patients with AD from Aβ-negative older patients with CU (AUC = 0.92–0.94). P-tau231 also distinguishes patients with AD from those with non-AD neurodegenerative diseases (AUC = 0.93) or with Aβ-negative MCI (AUC = 0.89). Before the abnormal threshold of β-PET was reached, plasma P-tau231 began to increase in an “amyloid precursor.” In addition, for preclinical AD, plasma P-tau231 is superior to plasma P-tau181 and CSF P-tau217; therefore, plasma P-tau181 is better at distinguishing AD dementia from Aβ-positive older patients with CU.[46] Compared with P-tau181, the diagnostic accuracy of serum P-tau231 for AD is slightly lower, which seems to replicate the preclinical findings of P-tau231 in the brain, CSF, and plasma,[46,55] as the biomarker-negative control group is most likely to include patients with AD pathology.

Biomarkers of neurodegeneration and inflammation

In addition to Aβ and Tau proteins, there are some biomarkers of neurodegeneration and inflammation, such as NfL and C-reactive protein (CRP). NfL is an axonal scaffold protein that is one of the two core neurofilament proteins (NfL+α-internexin) and is a cross-disease biomarker of neurogenesis. Neurofilaments are essential for the growth and stability of axons as well as for synaptic organization and function of the central nervous system (CNS). The biomarker, which can be measured in CSF and blood, is the first nerve-specific biomarker, and its clinical value has been confirmed in numerous publications after the development of ultrasensitive analysis.[56] The increased levels of nerve mercerization in the blood are similar to those in the CSF, making the clinical implementation of this marker possible. NfL has been listed as one of the most important AD-related biomarkers in the ALZ Biomarkers database, and NfL levels have been found to increase even during the prodromal phase.[21] However, high NfL levels are associated with all neurodegenerative diseases, making this marker less specific for AD diagnosis.[57] Elevated plasma NfL levels, particularly in frontotemporal dementia, amyotrophic lateral sclerosis (ALS), and atypical Parkinson's disease, mean that plasma NfL may be used in the future as a potential screening test to detect neurodegeneration in primary healthcare units.[57,58] The combination of NfL and other AD biomarkers can be used to monitor disease progression in clinical trials.[23] However, the opportunity to measure plasma NfL using Simoa assays has recently been reduced to a smaller extent, making NfL a valuable peripheral biomarker for assessing cognitive decline and identifying individuals at risk for neurodegeneration and brain atrophy. The current results suggest that blood NfL levels can serve as a biomarker for monitoring neurodegeneration and disease progression in pre-symptomatic AD, such as during the prodromal phase[12] of sporadic AD, early stage of dementia, and autosomal dominant AD.[59] In neuroinflammation and neurodegenerative diseases, the correlation between CSF and blood levels is good or excellent (0.70–0.97).[60] However, the age-associated increase in NfL complicates the interpretation of these results. In clinical research and practice, NfL is a general biomarker of axonal injury or degeneration, regardless of the underlying cause. It can be used to indicate neurodegenerative processes in patients with psychiatric symptoms.[61] Therefore, the biomarker could serve as an initial diagnostic test, and a positive result could indicate additional testing with a more specific biomarker to better understand the underlying etiology.

CRP is an acute-phase protein whose levels increase during inflammation; however, evidence for its use as a biomarker of AD remains inconclusive. Hooper et al[62] found that chronically elevated CRP was inversely associated with Aβ (P = 0.040) and the relationship is specific to APOE ε4 carriers (P = 0.027). The results of this study suggest that inflammation may be beneficial in the early stages of AD, as the immune system seeks to counteract Aβ pathology, particularly in APOE ε4 carriers. Studying the temporal relationship between cerebral Aβ and inflammatory markers will provide further evidence for the possibility that chronic inflammation regulates amyloid production in vivo. However, the clinical relevance of these measures has been proven to be relatively low and will not be described here.

Predictive efficacy of clinical conversion

Plasma NfL concentrations increase in AD, even during the prodromal phase, and are associated with important disease features, as detected through cognitive testing, neuroimaging, and CSF biomarker measurements. In clinical trial scenarios, plasma NfL levels can be used to predict longitudinal disease progression.[12] Future studies should test plasma NfL as a longitudinal, noninvasive agent for neurodegeneration. Preische et al[59] confirmed that the kinetics of NfL in serum predicted disease progression and cranial nerve degeneration in the pre-symptomatic stage of familial AD, which supported its potential utility as a clinically useful biomarker. Unfortunately, SCD in NC was not grouped separately.

Raffin et al[63] indicated that physical activity (PA) is related to the production of blood NfL. In addition, PA might alleviate cognitive decline related to amyloid protein, while a higher NfL level would assuage the beneficial relationship between PA and cognition. Longitudinal analysis of NfL levels by Niklas correlated the baseline characteristics of AD (including clinical diagnosis, CSF biomarkers, imaging measurements, and cognitive testing) with future increases and longitudinal NfL levels with further changes in other measurements.[12] These associations vary according to clinical stage. For example, only some baseline measures (including lower Aβ42 and higher P-tau levels) were associated with a longitudinal increase in NfL levels in CU individuals, while most measures (atrophy, cognitive, and CSF biomarkers) were associated with longitudinal increases in NfL levels in patients with MCI, but only poor cognition (ADAS-Cog) was associated with longitudinal increases in NfL levels in patients with AD dementia.[64] Both decreased brain volume and loss of spinal motor neurons were associated with higher NfL concentrations.[65]

Predictive efficacy of cognitive decline

Among liquid biomarkers of neurodegeneration, NfL is one of the most promising. NfL has been classified as a surrogate N (neurodegeneration) in the ATN framework.[66] Recent developments have enabled the measurement of NfL concentrations in blood samples. Elevated plasma NfL levels predict cranial nerve degeneration, and are associated with future atrophy, metabolic decline, and cognitive decline. Therefore, plasma NfL may be considered to be a blood-based biomarker for screening and tracking neurodegeneration in AD.[59,64,67,68] An increase in peripheral NfL in the blood and CSF is non-specific to the etiology of the disease, and there is still limited research on the exact mechanism of its release and transport from the CNS to peripheral blood.[56]

Patients with Obj-SCD and MCI have been shown to have increased baseline plasma NfL relative to the NC group. Elevated NfL predicts a faster rate of cognitive and functional decline. These results demonstrate the utility of plasma NfL as a biomarker of early AD-related changes and support the use of the Obj-SCD standard in clinical studies to better capture subtle cognitive changes.[69] This elevation was confirmed in a recent longitudinal, within-person analysis of serum NfL dynamics (n = 196) and further revealed that the rate of change in serum NfL distinguishes between mutation carriers and non-mutation carriers, almost 10 years before the cross-sectional absolute NfL level (i.e., 16.2 years vs. 6.8 years before the estimated symptom onset). Serum NfL predicts the rate of cortical thinning and cognitive changes assessed using simple mental state and logical memory tests.[59] Raffin et al[63] indicated that PA might alleviate cognitive decline related to amyloid protein, while higher NfL levels would assuage the good relationship between PA and cognition. He et al[70] found that plasma NfL was significantly associated with cognitive function only in the MCI group and not in CU adults (i.e., NC group). This result was consistent with the findings of Mattsson et al.[12] This points to the possibility of using plasma NfL as a marker to predict individual cognitive decline in MCI. Although some studies have shown that blood CRP levels are associated with MMSE scores, others have shown that this association is only significant in patients with AD homozygous for APOE ε4.[71] Nevertheless, independent data have not been reported to be associated with decreased cognitive ability; therefore, CRP level remains a controversial biomarker.

Other emerging biomarkers

Neuronal-derived extracellular vesicle

Exosomes are highly stable small membrane-enclosed vesicles (30–100 nm in diameter) that originate from the cellular biosynthesis and secretion pathway and are used to transport ribonucleic acid (RNA), proteins, and lipids in circulation.[72] As they protect cargo against ribonuclease activity, they represent a less susceptible source of biomarkers than cell-free blood microRNA (miRNA).[72] Because of these features, blood exosomes derived from the CNS have been considered diagnostic tools for different neurodegenerative diseases, such as AD, PD, and ALS.[73] Plasma- and serum-derived exosomes have been studied as sources of novel AD biomarkers, sometimes yielding inconsistent results compared with their respective acellular miRNA levels.[74] Exosomes, a nanometer subtype of extracellular vesicles, function as messengers of intercellular communication and are an emerging tool for fluid biopsy, used in various diseases, including AD.[75] The levels of circulating exosomes (i.e., mRNA, protein, and miRNA) in the blood have shown promising results as biomarkers of AD. Many studies of various miRNAs in plasma or serum neuronal-derived extracellular vesicles (nEVs) have shown that the development of AD biomarkers is promising.[76] However, the lack of a standardized method for the collection and preparation of biological fluids, of an optimized EV isolation method with high purity and biological fluid yield, and of good analytical performance for EV biomolecules, such as miRNA and protein, retard the clinical application of EV biomarkers. In addition, extensive and novel elements in exosomes were identified through methodological and analytical differences between studies. Li et al[77] suggested the potential use of plasma nEVs Aβ42 levels for diagnosing AD-induced cognitive impairment in Aβ-positive NCs. This biomarker reflects cortical amyloid deposition, and predicts cognitive decline and entorhinal atrophy. Other studies have shown that a combination of two biomarkers of plasma neuronal-derived exosome (Aβ1–42) and sniffin sticks predicted the conversion of MCI to AD dementia more accurately in combination.[78] Although the methods used for isolation and purification are expected to be repeatable, some procedural differences between different laboratories need to be standardized toward a consensus protocol and to reduce background noise. Overall, standardized measurement techniques, repeatable and common protocols for sample collection and purification, large cohorts, well-defined criteria for study classification and design, and standard statistical analysis with well-defined thresholds are essential and should be considered in the design of new studies.[79]


MiRNAs have recently become promising, cost-effective, and non-invasive biomarkers of AD because they can be easily detected in different biological fluids, especially in blood (free form or in exosome).[80] Studies have shown that an increase in plasma miRNA-206 predicts a decline in cognitive ability and the onset of dementia in MCI subjects. MiRNA-206 level is closely related to the decline in MMSE score over time.[81] Some studies have shown that potential miRNAs in the blood can be used as biomarkers for AD and MCI.[80] For example, miR-125b, miR-455-3p, and miR-501-3p are thought to have the potential to distinguish patients with AD from healthy individuals with high sensitivity and specificity.[82]

In addition, miRNAs are introduced as a combination of panels in the blood. A recent systematic evaluation showed that circulating miRNAs are excellent biomarkers for the clinical diagnosis of AD.[83] Although miRNAs in peripheral circulation are closely related to the pathophysiology of AD and can serve as ideal biomarkers, they have recently been found to be related to other diseases (i.e., miR-125b and miR-455-3p involved in cancer), and there is an inconsistency between different studies (i.e., miR-210-3p).[84] Library construction is also a good option; however, the limitations of miRNA discovery using library construction and next generation sequencing include relatively low total RNA content in blood samples, miRNA library construction bias, and variability in sample processing, which may result in unreliable results. Unlike core AD biomarkers, blood miRNA biomarkers require longitudinal studies to identify standard and repeatable features before they can become mature AD biomarkers with clinical efficacy.


Lipids account for approximately 50% of the dry brain weight and play key roles in basic brain functions, such as blood-brain barrier (BBB) integrity, myelin formation, vesicle transport, APP treatment, and neuroinflammation.[85] Recently, because changes in circulating lipids seem to reflect lipid dysregulation in the brain, blood has become a viable alternative to invasive CSF sampling. Phospholipids and sphingolipids have also been proposed as potential biomarkers of AD.[86] Several candidate lipids have been introduced and have shown a strong correlation with gold-standard AD biomarkers (i.e., CSF P-tau/Aβ42 ratio).

In a study conducted by Kim et al[87], increased circulating ceramide and phosphatidylcholine (PC) levels were associated with decreased cognitive ability. Other studies have also reported decreased serum PC and phosphatidylethanolamine (PE) concentrations in patients; in particular, decreases in serum PE and increases in lysin PE predicted the rate of progression from MCI to AD.[86] Bergland et al[88] found that the plasma sphingomyelin concentration, especially SM(d43:2), in MCI Aβ-positive patients was lower than that in the control group, and the plasma sphingomyelin concentration in MCI Aβ-positive patients was also lower than that in MCI Aβ-negative patients. In addition, sphingomyelin (SM(d43:2)) was the only lipid related to MCI Aβ-positive patients. Therefore, we can conclude that a decrease in plasma SM concentration is related to AD and has the potential to be used as an early predictor of AD. A systematic review showed changes in plasma cholesterol levels, triglycerides, sphingolipids, phospholipids, and various cholesterol derivatives in patients.[89] In addition, changes in lipids in the blood can be observed at the earliest stages of AD, providing insights into the biochemical processes involved in the pathogenesis of AD. However, the pathology of these lipids (i.e., cholesterol, sphingolipids, phospholipids) and the origin of brain and peripheral lipids require further in-depth investigation. Combining several lipids into one panel may have advantages over single lipid measurement.

There are also lipid peroxidation biomarkers that are worth considering as useful biomarkers for progressive AD, such as dihomo-isoprostanes (17-epi-17-F2t-dihomo-IsoP, 17-F2t-dihomo-IsoP, Ent-7(RS)-7-F2t-dihomo-IsoP), and neuroprostheses (10-epi-10-F4t-NeuroP), which had very high probabilities of exhibiting an increasing trend over time. In SCD, higher levels of low-density lipoprotein cholesterol are associated with disease progression.[90] Lipid changes associated with AD pathology may complement the proteinomics approach, which aims to develop a low-cost and safe method for identifying early AD pathology, progression, and potential new therapeutic modalities. A protective association of serum apolipoprotein B (ApoB) with CSF AD core biomarkers was first identified in 2020, particularly in individuals with SCD. It indicates that ApoB may be a potential biomarker of preclinical AD and may play different roles in different stages of AD.[91]


Currently, brain PET imaging and CSF biomarker analysis are the major methods used to examine the pathology of AD in patients. However, both methods are expensive and invasive, and are therefore limited in clinical practice. Therefore, non-invasive and easily accessible diagnostic tools are urgently needed to differentiate AD from other neurodegenerative diseases and predict the progression and prognosis of the AD continuum. Blood-based tests may be a breakthrough in AD diagnosis, improving medical decision-making and management, simplifying the grouping of AD clinical trials, and identifying patients who may benefit from AD-specific treatments. Evidence suggests that patients with SCD are at a greater risk for future cognitive decline and dementia than patients without impaired cognitive ability and SCD. SCD may also be the first symptom of an early neurodegenerative disease. This study may be meaningful if SCD can be combined with biomarker-based detection and early intervention for neurodegenerative diseases in the future.

Plasma biomarkers can be included in primary care to identify patients with cognitive symptoms who are at a high risk of AD. Patients could then be referred to a secondary facility for treatment and more advanced biomarker-based research, such as CSF or PET analyses. However, in many countries, most elderly patients with cognitive symptoms are only treated in primary care institutions. Although blood-based diagnostic algorithms may be sufficient to improve the accuracy of clinical AD dementia diagnosis and have a positive impact on patient management and care in primary care, further studies are needed to assess how best to utilize plasma biomarkers or other accessible and cost-effective measures, such as magnetic resonance imaging (MRI) and cognitive testing, to further develop predictive algorithms. In clinical trials, plasma biomarkers can be used to screen suitable individuals for inclusion as well as pharmacodynamic endpoint markers associated with the disease. Just as “TEST, TEST, TEST” became the slogan of the global fight against the COVID-19 pandemic, a similar goal could help roll back the relentless march of the global AD epidemic. Shifting the burden of such tests from specialist diagnostic units to primary care facilities allows more people to be screened, which offers the best chance to keep patients healthy through disease adjustment therapy.

However, the development of blood-based biomarkers for AD has various limitations that need to be resolved. For example, proteins originating in the CNS must cross the BBB for peripheral detection. Therefore, the concentration of the target proteins in the blood is often much lower than that in the CSF. Furthermore, peripherally expressed targeted proteins may represent systemic rather than pathological changes in the brain. They may undergo proteolytic degradation in the plasma for clearance in the liver or kidney, further reducing the amount of protein and increasing the difference to CSF levels. Therefore, many other proteins in the blood (such as albumin, immunoglobulins, autoantibodies, and xenophilic antibodies) may interfere with immunoassays and are present at much lower levels in CSF samples. Thus, measurements of blood-based biomarkers may provide contradictory results. We hope to identify truly representative biomarkers in AD that are stable, accurate, and have a clear mechanism.

Recent studies have shown that hippocampal volume, NfL, and GFAP are better predictors of clinical progression in SCD patients than A (amyloid) and T(tau) in the ATN framework, providing Class II evidence.[92] The addition of an “X(other)” in the A-T-N framework reflects the entire spectrum of AD pathology and elucidates its pathogenesis. The framework can also be used to track patients, with N and X being more valuable than diagnostics in tracking efficacy and monitoring drug efficacy. We believe that “X” is composed of heterogeneous and complex systems that are neither upstream nor downstream of A/T/N. It should be noted that in the ATN-X framework, the relationship between the elements is presented as an interactive and complex network rather than a simple random series. The ATN-X framework may also reflect different stages of preclinical SCD in preclinical AD development. The ATN-X framework may also be applied to treatment and related trials. Being treatment-oriented, all dimensions of the framework should refer to cocktail therapy, as the pathophysiological network is very complex and interconnected, and the ATN-X framework affects inclusion and exclusion criteria in clinical trials as a correction for clinical diagnosis. Recently, researchers have used progression to AD as an outcome to examine plasma P-tau, plasma Aβ42/40, plasma neural mercerization, APOE genotype, brief cognitive testing, and AD-specific MRI measurements to predict the probability of progression to AD.[49] In the future, the detection of the ATN-X framework in the peripheral system will be crucial, especially for blood detection based on ultrasensitive technology. Specifically, the mechanism of the corresponding peripheral ATN-X framework for biomarkers of AD should be elucidated. In addition, in multicenter studies, we need to unify the detection methods and critical points for blood biomarkers, and clinical trials for biomarkers should be further improved. Finally, a comprehensive biomarker-based model should be constructed to evaluate individuals suitable for further research and clinical applications, and this biomarker-based framework can be applied to other neurodegenerative diseases [Table 2].

Table 2 - Summary of ATN(X) criteria biomarkers.
Criteria aspect Pathology Neuroimaging biomarkers CSF biomarkers Blood biomarkers
A Amyloid PET 1–42 or Aβ1–42/1–40 1–42/1–40
T Tau Tau PET P-tau P-tau
N Neurodegeneration MRI or FDG-PET T-tau or NfL NfL
X GFAP, S100B, synaptic dysfunction, systemic immunity and inflammation, ChAT, glucose, lipids (cholesterols, triglycerides), aminoacids, vitamins (homocysteine, vitamins A, B12, C, D, E, folate), trace elements, and bacterial metabolites (lipopolysaccharide, valerate, acetate, butyrate), etc.
ChAT: Acetyltransferase; CSF: Cerebrospinal fluid; GFAP: Glial fibrillary acidic protein; MRI: Magnetic resonance imaging; NfL: Neurofilament light; PET: Positron emission tomography.

In conclusion, clinicians can identify SCD before AD based on the presence of pathophysiological biomarkers in blood, CSF, and neuroimaging analyses. Because of the minimal trauma, patients with suspected SCD may prefer neuroimaging and blood tests to CSF extraction. Changes in blood markers of AD quietly begin in CU individuals, whether Aβ PET-positive or not, long before the onset of symptoms. Success in disease-modulating therapies is expected to save people from progression to full-on AD. Moreover, neuroimaging is limited by the level and scale of hospitals and local economic conditions, especially in some remote and poor mountainous areas of our country, where the feasibility of promoting blood markers is greater. Blood-based biomarkers can facilitate the early detection of pathological changes in AD.


Currently, there may be several limitations in the research on the association between biomarkers of AD and SCD including the following: (1) Only a few studies have investigated this association, and these studies involve different research backgrounds and operations of SCD. (2) No longitudinal studies have monitored changes in AD biomarkers in CU individuals from the onset of SCD and decreased objective cognition, and throughout the subsequent process. (3) The lack of sensitivity of the analysis platform, and the differences in biological properties, sample handling, and storage, resulted in contradictory results for the frequently used single or multiplex ELISA platform in determining Aβ peptide levels in the blood. (4) More studies are needed to focus on markers other than amyloid and tau proteins in SCD patients, because these markers cannot explain SCD in the context of other neurodegenerative diseases.

Future studies must replicate recent findings and verify the clinical utility of identifying SCD. Regardless of the comments or criticisms of the proposed framework, any biological definition of AD is unlikely to be limited to changes in individual biomarkers. It is more likely that multimodal biological features appear at various stages of disease progression. In addition, detection methods for blood biomarkers are still at immature stages, and no unified standard has been established. Determining the cutoff value suitable for the Chinese population as soon as possible is necessary.


This work was supported by grants from the National Natural Science Foundation of China (Nos. 61633018, 82020108013, and 82001773).

Conflicts of interest



1. Ismail Z, Creese B, Aarsland D, Kales HC, Lyketsos CG, Sweet RA, et al. Psychosis in Alzheimer disease – mechanisms, genetics and therapeutic opportunities. Nat Rev Neurol 2022;18:131–144. doi: 10.1038/s41582-021-00597-3.
2. 2022 Alzheimer's disease facts and figures. Alzheimers Dement 2022;18:700–789. doi: 10.1002/alz.12638.
3. Alzheimer's Disease International (ADI). World Alzheimer Report 2021. London, UK: Alzheimer's Disease International, 2021.
4. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011;7:263–269. doi: 10.1016/j.jalz.2011.03.005.
5. Jia L, Quan M, Fu Y, Zhao T, Li Y, Wei C, et al. Dementia in China: epidemiology, clinical management, and research advances. Lancet Neurol 2020;19:81–92. doi: 10.1016/s1474-4422(19)30290-x.
6. Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement 2018;14:535–562. doi: 10.1016/j.jalz.2018.02.018.
7. Jessen F, Amariglio RE, Buckley RF, van der Flier WM, Han Y, Molinuevo JL, et al. The characterisation of subjective cognitive decline. Lancet Neurol 2020;19:271–278. doi: 10.1016/s1474-4422(19)30368-0.
8. Jessen F, Amariglio RE, van Boxtel M, Breteler M, Ceccaldi M, Chételat G, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimers Dement 2014;10:844–852. doi: 10.1016/j.jalz.2014.01.001.
9. Andreasson U, Blennow K, Zetterberg H. Update on ultrasensitive technologies to facilitate research on blood biomarkers for central nervous system disorders. Alzheimers Dement (Amst) 2016;3:98–102. doi: 10.1016/j.dadm.2016.05.005.
10. Ovod V, Ramsey KN, Mawuenyega KG, Bollinger JG, Hicks T, Schneider T, et al. Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement 2017;13:841–849. doi: 10.1016/j.jalz.2017.06.2266.
11. Nakamura A, Kaneko N, Villemagne VL, Kato T, Doecke J, Doré V, et al. High performance plasma amyloid-β biomarkers for Alzheimer's disease. Nature 2018;554:249–254. doi: 10.1038/nature25456.
12. Mattsson N, Andreasson U, Zetterberg H, Blennow K. Alzheimer's Disease Neuroimaging Initiative. Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease. JAMA Neurol 2017;74:557–566. doi: 10.1001/jamaneurol.2016.6117.
13. Schindler SE, Bollinger JG, Ovod V, Mawuenyega KG, Li Y, Gordon BA, et al. High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis. Neurology 2019;93:e1647–e1659. doi: 10.1212/wnl.0000000000008081.
14. Gordon BA, Blazey TM, Su Y, Hari-Raj A, Dincer A, Flores S, et al. Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study. Lancet Neurol 2018;17:241–250. doi: 10.1016/s1474-4422(18)30028-0.
15. McDade E, Wang G, Gordon BA, Hassenstab J, Benzinger TLS, Buckles V, et al. Longitudinal cognitive and biomarker changes in dominantly inherited Alzheimer disease. Neurology 2018;91:e1295–e1306. doi: 10.1212/wnl.0000000000006277.
16. O’Connor A, Karikari TK, Poole T, Ashton NJ, Lantero Rodriguez J, Khatun A, et al. Plasma phospho-tau181 in presymptomatic and symptomatic familial Alzheimer's disease: a longitudinal cohort study. Mol Psychiatry 2021;26:5967–5976. doi: 10.1038/s41380-020-0838-x.
17. Lantero Rodriguez J, Karikari TK, Suárez-Calvet M, Troakes C, King A, Emersic A, 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. doi: 10.1007/s00401-020-02195-x.
18. Reisberg B, Ferris SH, de Leon MJ, Crook T. The Global Deterioration Scale for assessment of primary degenerative dementia. Am J Psychiatry 1982;139:1136–1139. doi: 10.1176/ajp.139.9.1136.
19. Jessen F, Wolfsgruber S, Wiese B, Bickel H, Mösch E, Kaduszkiewicz H, et al. AD dementia risk in late MCI, in early MCI, and in subjective memory impairment. Alzheimers Dement 2014;10:76–83. doi: 10.1016/j.jalz.2012.09.017.
20. Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE, et al. Alzheimer's disease. Lancet 2021;397:1577–1590. doi: 10.1016/s0140-6736(20)32205-4.
21. Olsson B, Lautner R, Andreasson U, Öhrfelt A, Portelius E, Bjerke M, et al. CSF and blood biomarkers for the diagnosis of Alzheimer's disease: a systematic review and meta-analysis. Lancet Neurol 2016;15:673–684. doi: 10.1016/s1474-4422(16)00070-3.
22. Willemse E, van Uffelen K, Brix B, Engelborghs S, Vanderstichele H, Teunissen C. How to handle adsorption of cerebrospinal fluid amyloid β (1-42) in laboratory practice? Identifying problematic handlings and resolving the issue by use of the Aβ(42)/Aβ(40) ratio. Alzheimers Dement 2017;13:885–892. doi: 10.1016/j.jalz.2017.01.010.
23. de Wolf F, Ghanbari M, Licher S, McRae-McKee K, Gras L, Weverling GJ, et al. Plasma tau, neurofilament light chain and amyloid-β levels and risk of dementia; a population-based cohort study. Brain 2020;143:1220–1232. doi: 10.1093/brain/awaa054.
24. Gerards M, Schild AK, Meiberth D, Rostamzadeh A, Vehreschild JJ, Wingen-Heimann S, et al. Alzheimer's disease plasma biomarkers distinguish clinical diagnostic groups in memory clinic patients. Dement Geriatr Cogn Disord 2022;51:182–192. doi: 10.1159/000524390.
25. Palmqvist S, Janelidze S, Stomrud E, Zetterberg H, Karl J, Zink K, et al. Performance of fully automated plasma assays as screening tests for Alzheimer disease-related β-amyloid status. JAMA Neurol 2019;76:1060–1069. doi: 10.1001/jamaneurol.2019.1632.
26. de Rojas I, Romero J, Rodríguez-Gomez O, Pesini P, Sanabria A, Pérez-Cordon A, et al. Correlations between plasma and PET beta-amyloid levels in individuals with subjective cognitive decline: the Fundació ACE Healthy Brain Initiative (FACEHBI). Alzheimers Res Ther 2018;10:119. doi: 10.1186/s13195-018-0444-1.
27. Youn YC, Kang S, Suh J, Park YH, Kang MJ, Pyun JM, et al. Blood amyloid-β oligomerization associated with neurodegeneration of Alzheimer's disease. Alzheimers Res Ther 2019;11:40. doi: 10.1186/s13195-019-0499-7.
28. Baldacci F, Lista S, Manca ML, Chiesa PA, Cavedo E, Lemercier P, et al. Age and sex impact plasma NFL and t-Tau trajectories in individuals with subjective memory complaints: a 3-year follow-up study. Alzheimers Res Ther 2020;12:147. doi: 10.1186/s13195-020-00704-4.
29. Schindler SE, Gray JD, Gordon BA, Xiong C, Batrla-Utermann R, Quan M, et al. Cerebrospinal fluid biomarkers measured by Elecsys assays compared to amyloid imaging. Alzheimers Dement 2018;14:1460–1469. doi: 10.1016/j.jalz.2018.01.013.
30. Hansson O, Seibyl J, Stomrud E, Zetterberg H, Trojanowski JQ, Bittner T, et al. CSF biomarkers of Alzheimer's disease concord with amyloid-β PET and predict clinical progression: a study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement 2018;14:1470–1481. doi: 10.1016/j.jalz.2018.01.010.
31. Sperling RA, Donohue MC, Raman R, Sun CK, Yaari R, Holdridge K, et al. Association of factors with elevated amyloid burden in clinically normal older individuals. JAMA Neurol 2020;77:735–745. doi: 10.1001/jamaneurol.2020.0387.
32. Chen TB, Lai YH, Ke TL, Chen JP, Lee YJ, Lin SY, et al. Changes in plasma amyloid and tau in a longitudinal study of normal aging, mild cognitive impairment, and Alzheimer's disease. Dement Geriatr Cogn Disord 2019;48:180–195. doi: 10.1159/000505435.
33. Wang X, Zhao M, Lin L, Han Y. Plasma β-amyloid levels associated with structural integrity based on diffusion tensor imaging in subjective cognitive decline: the SILCODE study. Front Aging Neurosci 2021;12:592024. doi: 10.3389/fnagi.2020.592024.
34. Verberk IMW, Slot RE, Verfaillie SCJ, Heijst H, Prins ND, van Berckel BNM, et al. Plasma amyloid as prescreener for the earliest Alzheimer pathological changes. Ann Neurol 2018;84:648–658. doi: 10.1002/ana.25334.
35. Pan FF, Huang Q, Wang Y, Wang YF, Guan YH, Xie F, et al. Non-linear character of plasma amyloid beta over the course of cognitive decline in Alzheimer's continuum. Front Aging Neurosci 2022;14:832700. doi: 10.3389/fnagi.2022.832700.
36. Iulita MF, Ganesh A, Pentz R, Flores Aguilar L, Gubert P, Ducatenzeiler A, et al. Identification and preliminary validation of a plasma profile associated with cognitive decline in dementia and at-risk individuals: a retrospective cohort analysis. J Alzheimers Dis 2019;67:327–341. doi: 10.3233/jad-180970.
37. Stockmann J, Verberk IMW, Timmesfeld N, Denz R, Budde B, Lange-Leifhelm J, et al. Amyloid-β misfolding as a plasma biomarker indicates risk for future clinical Alzheimer's disease in individuals with subjective cognitive decline. Alzheimers Res Ther 2020;12:169. doi: 10.1186/s13195-020-00738-8.
38. Janelidze S, Teunissen CE, Zetterberg H, Allué JA, Sarasa L, Eichenlaub U, et al. Head-to-head comparison of 8 plasma amyloid-β 42/40 assays in Alzheimer disease. JAMA Neurol 2021;78:1375–1382. doi: 10.1001/jamaneurol.2021.3180.
39. Giudici KV, de Souto Barreto P, Guyonnet S, Li Y, Bateman RJ, Vellas B, et al. Assessment of plasma amyloid-β42/40 and cognitive decline among community-dwelling older adults. JAMA Netw Open 2020;3:e2028634. doi: 10.1001/jamanetworkopen.2020.28634.
40. Verberk IMW, Hendriksen HMA, van Harten AC, Wesselman LMP, Verfaillie SCJ, van den Bosch KA, 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. doi: 10.1016/j.neurobiolaging.2020.01.007.
41. Strang KH, Sorrentino ZA, Riffe CJ, Gorion KM, Vijayaraghavan N, Golde TE, et al. Phosphorylation of serine 305 in tau inhibits aggregation. Neurosci Lett 2019;692:187–192. doi: 10.1016/j.neulet.2018.11.011.
42. Foiani MS, Woollacott IO, Heller C, Bocchetta M, Heslegrave A, Dick KM, et al. Plasma tau is increased in frontotemporal dementia. J Neurol Neurosurg Psychiatry 2018;89:804–807. doi: 10.1136/jnnp-2017-317260.
43. Illán-Gala I, Lleo A, Karydas A, Staffaroni AM, Zetterberg H, Sivasankaran R, et al. Plasma tau and neurofilament light in frontotemporal lobar degeneration and Alzheimer disease. Neurology 2021;96:e671–e683. doi: 10.1212/wnl.0000000000011226.
44. Wesseling H, Mair W, Kumar M, Schlaffner CN, Tang S, Beerepoot P, et al. Tau PTM profiles identify patient heterogeneity and stages of Alzheimer's disease. Cell 2020;183:1699–1713.e13. doi: 10.1016/j.cell.2020.10.029.
45. Karikari TK, Benedet AL, Ashton NJ, Lantero Rodriguez J, Snellman A, Suárez-Calvet M, et al. Diagnostic performance and prediction of clinical progression of plasma phospho-tau181 in the Alzheimer's disease neuroimaging initiative. Mol Psychiatry 2021;26:429–442. doi: 10.1038/s41380-020-00923-z.
46. Ashton NJ, Pascoal TA, Karikari TK, Benedet AL, Lantero-Rodriguez J, Brinkmalm G, et al. Plasma P-tau231: a new biomarker for incipient Alzheimer's disease pathology. Acta Neuropathol 2021;141:709–724. doi: 10.1007/s00401-021-02275-6.
47. Simrén J, Leuzy A, Karikari TK, Hye A, Benedet AL, Lantero-Rodriguez J, et al. The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer's disease. Alzheimers Dement 2021;17:1145–1156. doi: 10.1002/alz.12283.
48. Janelidze S, Berron D, Smith R, Strandberg O, Proctor NK, Dage JL, et al. Associations of plasma phospho-tau217 levels with tau positron emission tomography in early Alzheimer disease. JAMA Neurol 2021;78:149–156. doi: 10.1001/jamaneurol.2020.4201.
49. Palmqvist S, Tideman P, Cullen N, Zetterberg H, Blennow K, Dage JL, et al. Prediction of future Alzheimer's disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med 2021;27:1034–1042. doi: 10.1038/s41591-021-01348-z.
50. Müller S, Preische O, Göpfert JC, Yañez VAC, Joos TO, Boecker H, et al. Tau plasma levels in subjective cognitive decline: Results from the DELCODE study. Sci Rep 2017;7:9529. doi: 10.1038/s41598-017-08779-0.
51. Janelidze S, Mattsson N, Palmqvist S, Smith R, Beach TG, Serrano GE, et al. Plasma P-tau181 in Alzheimer's disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer's dementia. Nat Med 2020;26:379–386. doi: 10.1038/s41591-020-0755-1.
52. Moscoso A, Grothe MJ, Ashton NJ, Karikari TK, Lantero Rodríguez J, Snellman A, et al. Longitudinal associations of blood phosphorylated tau181 and neurofilament light chain with neurodegeneration in Alzheimer disease. JAMA Neurol 2021;78:396–406. doi: 10.1001/jamaneurol.2020.4986.
53. Pereira JB, Janelidze S, Stomrud E, Palmqvist S, van Westen D, Dage JL, et al. Plasma markers predict changes in amyloid, tau, atrophy and cognition in non-demented subjects. Brain 2021;144:2826–2836. doi: 10.1093/brain/awab163.
54. Thomas KR, Bangen KJ, Edmonds EC, Weigand AJ, Walker KS, Bondi MW, et al. Objective subtle cognitive decline and plasma phosphorylated tau181: early markers of Alzheimer's disease-related declines. Alzheimers Dement (Amst) 2021;13:e12238. doi: 10.1002/dad2.12238.
55. Suárez-Calvet M, Karikari TK, Ashton NJ, Lantero Rodríguez J, Milà-Alomà M, Gispert JD, 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 Aβ pathology are detected. EMBO Mol Med 2020;12:e12921. doi: 10.15252/emmm.202012921.
56. Gafson AR, Barthélemy NR, Bomont P, Carare RO, Durham HD, Julien JP, et al. Neurofilaments: neurobiological foundations for biomarker applications. Brain 2020;143:1975–1998. doi: 10.1093/brain/awaa098.
57. Khalil M, Teunissen CE, Otto M, Piehl F, Sormani MP, Gattringer T, et al. Neurofilaments as biomarkers in neurological disorders. Nat Rev Neurol 2018;14:577–589. doi: 10.1038/s41582-018-0058-z.
58. Rohrer JD, Woollacott IO, Dick KM, Brotherhood E, Gordon E, Fellows A, et al. Serum neurofilament light chain protein is a measure of disease intensity in frontotemporal dementia. Neurology 2016;87:1329–1336. doi: 10.1212/wnl.0000000000003154.
59. Preische O, Schultz SA, Apel A, Kuhle J, Kaeser SA, Barro C, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer's disease. Nat Med 2019;25:277–283. doi: 10.1038/s41591-018-0304-3.
60. Gaetani L, Blennow K, Calabresi P, Di Filippo M, Parnetti L, Zetterberg H. Neurofilament light chain as a biomarker in neurological disorders. J Neurol Neurosurg Psychiatry 2019;90:870–881. doi: 10.1136/jnnp-2018-320106.
61. Willemse EAJ, Scheltens P, Teunissen CE, Vijverberg EGB. A neurologist's perspective on serum neurofilament light in the memory clinic: a prospective implementation study. Alzheimers Res Ther 2021;13:101. doi: 10.1186/s13195-021-00841-4.
62. Hooper C, De Souto Barreto P, Cantet C, Cesari M, Payoux P, Salabert AS, et al. Chronically raised C-reactive protein is inversely associated with cortical β-amyloid in older adults with subjective memory complaints. Exp Gerontol 2018;108:226–230. doi: 10.1016/j.exger.2018.04.014.
63. Raffin J, Rolland Y, Aggarwal G, Nguyen AD, Morley JE, Li Y, et al. Associations between physical activity, blood-based biomarkers of neurodegeneration, and cognition in healthy older adults: the MAPT Study. J Gerontol A Biol Sci Med Sci 2021;76:1382–1390. doi: 10.1093/gerona/glab094.
64. Mattsson N, Cullen NC, 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. doi: 10.1001/jamaneurol.2019.0765.
65. Khalil M, Pirpamer L, Hofer E, Voortman MM, Barro C, Leppert D, et al. Serum neurofilament light levels in normal aging and their association with morphologic brain changes. Nat Commun 2020;11:812. doi: 10.1038/s41467-020-14612-6.
66. Alawode DOT, Heslegrave AJ, Ashton NJ, Karikari TK, Simrén J, Montoliu-Gaya L, et al. Transitioning from cerebrospinal fluid to blood tests to facilitate diagnosis and disease monitoring in Alzheimer's disease. J Intern Med 2021;290:583–601. doi: 10.1111/joim.13332.
67. Lewczuk P, Ermann N, Andreasson U, Schultheis C, Podhorna J, Spitzer P, et al. Plasma neurofilament light as a potential biomarker of neurodegeneration in Alzheimer's disease. Alzheimers Res Ther 2018;10:71. doi: 10.1186/s13195-018-0404-9.
68. Chatterjee P, Goozee K, Sohrabi HR, Shen K, Shah T, Asih PR, et al. Association of plasma neurofilament light chain with neocortical amyloid-β load and cognitive performance in cognitively normal elderly participants. J Alzheimers Dis 2018;63:479–487. doi: 10.3233/jad-180025.
69. Bangen KJ, Thomas KR, Weigand AJ, Edmonds EC, Clark AL, Solders S, et al. Elevated plasma neurofilament light predicts a faster rate of cognitive decline over 5 years in participants with objectively-defined subtle cognitive decline and MCI. Alzheimers Dement 2021;17:1756–1762. doi: 10.1002/alz.12324.
70. He L, Morley JE, Aggarwal G, Nguyen AD, Vellas B, de Souto Barreto P, et al. Plasma neurofilament light chain is associated with cognitive decline in non-dementia older adults. Sci Rep 2021;11:13394. doi: 10.1038/s41598-021-91038-0.
71. Tao Q, Alvin Ang TF, Akhter-Khan SC, Itchapurapu IS, Killiany R, Zhang X, et al. Impact of C-reactive protein on cognition and Alzheimer disease biomarkers in homozygous apolipoprotein e ε4 carriers. Neurology 2021;97:e1243–e1252. doi: 10.1212/wnl.0000000000012512.
72. Wang L, Zhang L. Circulating exosomal miRNA as diagnostic biomarkers of neurodegenerative diseases. Front Mol Neurosci 2020;13:53. doi: 10.3389/fnmol.2020.00053.
73. Dong X, Zheng D, Nao J. Circulating exosome microRNAs as diagnostic biomarkers of dementia. Front Aging Neurosci 2020;12:580199. doi: 10.3389/fnagi.2020.580199.
74. Barbagallo C, Mostile G, Baglieri G, Giunta F, Luca A, Raciti L, et al. Specific signatures of serum miRNAs as potential biomarkers to discriminate clinically similar neurodegenerative and vascular-related diseases. Cell Mol Neurobiol 2020;40:531–546. doi: 10.1007/s10571-019-00751-y.
75. Liu CG, Meng S, Li Y, Lu Y, Zhao Y, Wang PC. MicroRNA-135a in ABCA1-labeled exosome is a serum biomarker candidate for Alzheimer's disease. Biomed Environ Sci 2021;34:19–28. doi: 10.3967/bes2021.004.
76. Dong Z, Gu H, Guo Q, Liang S, Xue J, Yao F, et al. Profiling of serum exosome MiRNA reveals the potential of a MiRNA panel as diagnostic biomarker for Alzheimer's disease. Mol Neurobiol 2021;58:3084–3094. doi: 10.1007/s12035-021-02323-y.
77. Li TR, Yao YX, Jiang XY, Dong QY, Yu XF, Wang T, et al. β-Amyloid in blood neuronal-derived extracellular vesicles is elevated in cognitively normal adults at risk of Alzheimer's disease and predicts cerebral amyloidosis. Alzheimers Res Ther 2022;14:66. doi: 10.1186/s13195-022-01010-x.
78. Zhao A, Li Y, Yan Y, Qiu Y, Li B, Xu W, et al. Increased prediction value of biomarker combinations for the conversion of mild cognitive impairment to Alzheimer's dementia. Transl Neurodegener 2020;9:30. doi: 10.1186/s40035-020-00210-5.
79. Fransquet PD, Ryan J. Micro RNA as a potential blood-based epigenetic biomarker for Alzheimer's disease. Clin Biochem 2018;58:5–14. doi: 10.1016/j.clinbiochem.2018.05.020.
80. Zhao Y, Jaber V, Alexandrov PN, Vergallo A, Lista S, Hampel H, et al. microRNA-based biomarkers in Alzheimer's disease (AD). Front Neurosci 2020;14:585432. doi: 10.3389/fnins.2020.585432.
81. Kenny A, McArdle H, Calero M, Rabano A, Madden SF, Adamson K, et al. Elevated plasma microRNA-206 levels predict cognitive decline and progression to dementia from mild cognitive impairment. Biomolecules 2019;9:734. doi: 10.3390/biom9110734.
82. Hara N, Kikuchi M, Miyashita A, Hatsuta H, Saito Y, Kasuga K, et al. Serum microRNA miR-501-3p as a potential biomarker related to the progression of Alzheimer's disease. Acta Neuropathol Commun 2017;5:10. doi: 10.1186/s40478-017-0414-z.
83. Takousis P, Sadlon A, Schulz J, Wohlers I, Dobricic V, Middleton L, et al. Differential expression of microRNAs in Alzheimer's disease brain, blood, and cerebrospinal fluid. Alzheimers Dement 2019;15:1468–1477. doi: 10.1016/j.jalz.2019.06.4952.
84. Sun L, Xu M, Zhang G, Dong L, Wu J, Wei C, et al. Identification of circulating exosomal miR-101 and miR-125b panel act as a potential biomarker for hepatocellular carcinoma. Int J Genomics 2021;2021:1326463. doi: 10.1155/2021/1326463.
85. Kao YC, Ho PC, Tu YK, Jou IM, Tsai KJ. Lipids Alzheimer's disease. Int J Mol Sci 2020;21:1505. doi: 10.3390/ijms21041505.
86. Llano DA, Devanarayan V. Serum phosphatidylethanolamine and lysophosphatidylethanolamine levels differentiate Alzheimer's disease from controls and predict progression from mild cognitive impairment. J Alzheimers Dis 2021;80:311–319. doi: 10.3233/jad-201420.
87. Kim M, Nevado-Holgado A, Whiley L, Snowden SG, Soininen H, Kloszewska I, et al. Association between plasma ceramides and phosphatidylcholines and hippocampal brain volume in late onset Alzheimer's disease. J Alzheimer's Dis 2017;60:809–817.
88. Bergland AK, Proitsi P, Kirsebom BE, Soennesyn H, Hye A, Larsen AI, et al. Exploration of plasma lipids in mild cognitive impairment due to Alzheimer's disease. J Alzheimers Dis 2020;77:1117–1127. doi: 10.3233/jad-200441.
89. Agarwal M, Khan S. Plasma lipids as biomarkers for Alzheimer's disease: a systematic review. Cureus 2020;12:e12008. doi: 10.7759/cureus.12008.
90. de Leeuw FA, Tijms BM, Doorduijn AS, Hendriksen HMA, van de Rest O, de van der Schueren MAE, et al. LDL cholesterol and uridine levels in blood are potential nutritional biomarkers for clinical progression in Alzheimer's disease: the NUDAD project. Alzheimers Dement (Amst) 2020;12:e12120. doi: 10.1002/dad2.12120.
91. Hu H, Tan L, Bi YL, Xu W, Tan L, Shen XN, et al. Association of serum apolipoprotein B with cerebrospinal fluid biomarkers of Alzheimer's pathology. Ann Clin Transl Neurol 2020;7:1766–1778. doi: 10.1002/acn3.51153.
92. Ebenau JL, Pelkmans W, Verberk IMW, Verfaillie SCJ, van den Bosch KA, van Leeuwenstijn M, et al. Association of CSF, plasma, and imaging markers of neurodegeneration with clinical progression in people with subjective cognitive decline. Neurology 2022;98:e1315–e1326. doi: 10.1212/wnl.0000000000200035.

Alzheimer's disease; Biomarker; Blood; Evolving technologies; Subjective cognitive decline

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