Platelet-Derived Amyloid-β Protein Precursor as a Biomarker of Alzheimer’s Disease

Abstract

Background:

Platelet proteins may be associated with Alzheimer’s disease (AD) pathology.

Objective:

To investigate the relationship between platelet proteins and cerebrospinal fluid (CSF) biomarkers of AD and cognition in individuals with memory decline to identify effective screening methods for detecting the early stages of the disease.

Methods:

We classified 68 participants with subjective memory decline according to the ATN framework determined by CSF amyloid-β (A), CSF p-tau (T), and t-tau (N). All participants underwent Mini-Mental State Examination (MMSE) and platelet-related protein content testing.

Results:

Eighteen participants had normal AD biomarkers (NCs), 24 subjects had non-AD pathologic changes (non-AD), and 26 subjects fell within the Alzheimer’s continuum (AD). The platelet amyloid-β protein precursor (AβPP) ratio in the AD group was significantly lower than in the non-AD and NCs groups, and positively correlated with MMSE scores and CSF amyloid-β₄₂ level, which could affect MMSE scores through CSF amyloid-β₄₂. Levels of platelet phosphorylated-tau 231 and ser396/404 phosphorylated tau were elevated in both AD and non-AD compared to NCs. Additionally, the receiver operating characteristic analysis demonstrated that the platelet AβPP ratio was a sensitive identifier for differentiating the AD from NCs (AUC = 0.846) and non-AD (AUC = 0.768). And ser396/404 phosphorylated tau could distinguish AD from NCs.

Conclusion:

Our study was the first to find an association between platelet AβPP ratio and CSF biomarkers of AD, which contribute to the understanding of the peripheral changes in AD. These findings may help to discover potential feasible and effective screening tools for AD.

Keywords

Alzheimer’s disease amyloid-β protein precursor ratio ATN framework cerebrospinal fluid platelet

INTRODUCTION

Alzheimer’s disease (AD) is a neurodegenerative condition characterized by progressive cognitive impairment and memory decline [1]. Dementia patients in China account for about 25% of the entire number of worldwide cases, and to date, an estimated 9 million Chinese people are living with AD, the most common cause of dementia [2, 3]. In 2018, the National Institute on Aging and Alzheimer’s Association (NIA-AA) proposed a framework for research wherein cerebrospinal fluid (CSF) and imaging [magnetic resonance (MRI) and positron emission tomography (PET)]-based Aβ (A), tau (T), and neurodegeneration (N) measurements could be compiled into an ATN classification system [4, 5]. Eight different ATN biomarker profiles could be classified into three general biomarker categories as follows: normal AD biomarkers (A–T–N–), non-AD pathologic changes (A–T+N–, A–T–N+, or A–T+N+), and the Alzheimer’s continuum (A+T–N–, A+T–N+, A+T+N–, or A+T+N+). In this way, subjects with clinically subjective cognitive decline (SCD) could be biologically diagnosed with AD when they presented with A+T+N–or A+T+N–profiles. The NIA-AA framework has a great advantage in the early diagnosis of AD. However, the methods used to detect ATN biomarkers are hardly acceptable to potential patients because PET biomarkers are expensive and involve radiation, and CSF biomarkers are invasive. Therefore, finding blood biomarkers associated with central pathological changes is of great significance for the early diagnosis of AD.

Previous studies found increased levels of platelet Aβ [6, 7] and a decreased amyloid-β protein precursor (AβPP) ratio [6 , 9] in AD patients compared to healthy controls (HCs). We previously showed a lower platelet AβPP ratio and higher levels of phosphorylated tau in subjects with mild cognitive impairment (MCI) compared to HCs [10]. In addition, the platelet AβPP ratio was positively correlated with cognitive impairment in patients with AD [8, 11]. Importantly, several recent studies found that platelet proteins could predict cognitive decline in MCI and AD patients [12] and could also discriminate patients with AD from HCs [13]. A reduced AβPP ratio could predict conversion from MCI to AD [14].

Platelets are widely regarded as an available peripheral neuronal-like cellular system [15 –17]. Once activated, platelets release a variety of biochemically active factors including cytokines, neurotransmitters, and Aβ peptides [18 –20]. Very recent studies demonstrated that platelets could disrupt the permeability of the blood-brain barrier [21, 22] and transfer Aβ from blood vessels to the central nervous system [22]. More importantly, platelets were also shown to secrete various processed forms of AβPP and other substances while infiltrating the brain, which resulted in the growth of Aβ depositions in mice brains [22]. Thus, platelets have also been suggested as a powerful peripheral matrix to search for biomarkers to objectively predict AD in the early stages [23, 24]. However, no studies have explored the association between platelet proteins and CSF biomarkers in AD, especially in the early stages of AD, such as the SCD stage.

Therefore, we recruited SCD subjects for the present research. Based on the subjects, who were grouped according to the ATN research framework, we aimed to explore whether the platelet AβPP ratio, p-tau231, and ser396/404 phosphorylated tau levels were altered in patients classified in the Alzheimer’s continuum, and whether they would correlate with the altered levels of CSF AD biomarkers (Aβ₄₂, t-tau, and p-tau). We hypothesized that platelet proteins could reflect part of the pathological state of AD. We also investigated whether changes in platelet protein levels could be attributed to alterations in cognition function. This may help to discover potential indicators for AD screening.

METHODS

Participants

A total of 238 participants who complained of memory decline were recruited through community health screening from the neurology outpatient clinic of the Affiliated Zhongda Hospital of Southeast University and the Affiliated Brain Hospital of Nanjing Medical University from January 2017 to December 2020. All participants underwent a standardized clinically related interview and had their blood drawn. Cognitive function was measured using the Mini-Mental State Examination (MMSE) scale. In addition, routine blood examination and brain MRI scan were also performed. Then, the subjects were grouped according to their clinical diagnosis at baseline into the following groups: SCD (n = 109), MCI (n = 84), and AD (n = 45). The final study population included 68 participants who agreed to lumbar puncture and CSF examination and included 27 SCD subjects, five MCI subjects, and 36 AD subjects.

The inclusion criteria were: (i) age between 45 and 85 years old; (ii) biologically right-handed; and (iii) normal vision and hearing. The MMSE was used as the standard for clinical classification. Subjects with MMSE scores greater than or equal to 26 were categorized into the SCD group, scores between 24 and 25 were categorized into the MCI group, and the others were grouped into the AD group. The exclusion criteria for all subjects included: (i) other neurologic diseases (e.g., Parkinson’s disease) or any psychiatric disorder (e.g., depressive disorder), (ii) significant cerebrovascular disorders, (iii) history of brain trauma, (iv) significant medical problems (e.g., impaired liver or kidney function), (v) currently taking medication that affected platelet function (e.g., anticoagulants, corticosteroids, or antiplatelet agents), (vi) drug abuse or substance addiction, or (vii) any contraindications for lumbar puncture. T2-weighted images were obtained to exclude subjects with major white matter changes, cerebral infarction, or other lesions. Please refer to the Supplementary Material for the MRI scanning parameters.

The present study was approved by the Research Ethics Committee of the Affiliated Zhongda Hospital, Southeast University, and the Affiliated Brain Hospital of Nanjing Medical University. All subjects provided written informed consent.

CSF collection and analysis

An experienced neurological physician performed all lumbar punctures (LPs) in the early afternoon. About 10 mL of CSF was collected in polypropylene tubes and immediately centrifuged for 10 min at 2000 g at 4°C within 2 h. Lastly, 300μL CSF samples were aliquoted into 0.5 mL polypropylene tubes and stored at –80°C.

The concentrations of CSF Aβ₄₂, p-tau, and t-tau were measured using sandwich enzyme-linked immunosorbent assays (ELISAs) (INNOTEST, Fujirebio, Belgium) according to the kit instructions, and professionally trained technicians who were blinded to the clinical data performed all assays. CSF samples belonging to the same subject were measured in triplicate on the same day with the same standard, and the samples were measured again if the intra-assay coefficients of variation were >11.0% for Aβ₄₂, 13.2% for t-tau, and 8.9% for p-tau. To obtain the definitive diagnosis of each subject according to the ATN framework [4 , 25], the recommended criteria for CSF neuropathological biomarkers (A+: Aβ₄₂ <550 ng/L; T+: p-tau >52 ng/L; N+: t-tau >375 ng/L) from a previous study [26] that applied the identical ELISA kits was used in the present study. CSF Aβ₄₀ was not measured and, therefore, the amyloid ratio was not used in the analyses. All subjects were divided into three groups of normal controls (NCs) with normal AD biomarkers levels, non-AD pathologic changes (non-AD group), and patients in the Alzheimer’s continuum (the AD group). The AD continuum denoted definite AD pathologic changes and AD, and non-AD pathologic changes denoted the lack of a specific AD biomarker, i.e., evidence of Aβ deposition, at this point in time [4].

Platelet preparation

The protocol for platelet preparation was as previously reported [10]. Here, a brief introduction of the procedure was provided to maintain the scientific integrity of the present study. The collected venous blood samples were centrifuged for 10 min at 200 g at 4°C to obtain platelet-rich plasma, which was transferred to a new tube and immediately centrifuged for 20 min at 3,000 g at 4°C to obtain platelets. The bottom platelet pellets were washed twice with Advanced Tyrode’s Solution (without calcium) (Solarbio) and centrifuged for 10 min at 1,500 g at 4°C after each wash. Subsequently, platelet-derived proteins were collected, and the concentrations were measured using the Bradford method (Solarbio).

Western blot analysis

Details of the western blotting procedure are described in the Supplementary Material and in our previous study [10]. A standard western blot assay was performed. The primary antibodies used in this study were monoclonal 22C11 antibody (Merck, dilution 1:2,000), phospho-Tau231 (Abcam, dilution 1:5,000), PHF1 (Abcam, dilution 1:1,000), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Abcam, dilution 1:4,000). The 22C11 antibody can recognize immature ∼110 kDa and mature ∼130 kDa isoforms of AβPP. The phospho-Tau231 antibody recognizes threonine-231 phosphorylated tau protein and the PHF1 antibody recognizes tau phosphorylated at serines 396 and 404. The gray value of the target band was quantified using GAPDH as the internal reference. The protein level measurements were repeated in triplicate.

Statistical analysis

Statistical analyses were performed using MedCalc Statistical Software version 15.2.2 (MedCalc Software bvba, Ostend, Belgium). Statistical significance was defined as p < 0.05 (two-tailed). The Kolmogorov-Smirnov test was used to assess the normal distribution of the data. Categorical variables were analyzed using a chi-squared test, and continuous variables using a one-way ANOVA (Bonferroni’s correction was used for the post-hoc test). The partial correlation analysis was used to investigate the correlation between each platelet-related indicator and CSF biomarker levels and the assessment of cognitive function adjusted for age, gender, and education years. Mediation analysis was performed to determine whether CSF neuropathological biomarkers could mediate the association between the levels of platelet-related indicators and the cognitive assessment, which was based on a standard three-variable mediation model that was used in our previous studies [27, 28]. Finally, a receiver operating characteristic (ROC) curve was performed to compute the area under the curve (AUC) to determine the identification accuracy of the platelet-related indicators. The Youden index [29] was used to obtain optimal sensitivity and specificity.

RESULTS

Clinical features

Based on the CSF biomarker levels, 68 subjects were divided into three groups: 18 NCs, 24 non-AD patients, and 26 AD patients. The clinical features and CSF neuropathological biomarker levels of all subjects are presented in Table 1. Three groups showed significantly different MMSE scores and CSF biomarker levels, with no difference in age, gender, or education years.

Table 1

Comparison of clinical features, cognitive assessment, and levels of CSF biomarkers among AD, non-AD, and NC groups

Variables	AD group	Non-AD group	NC group	F / χ²	p
Sample size	26	24	18
Age (y)	62.15±11.44	65.00±10.71	61.61±10.47	0.625	0.539
Gender (M / F)	11 / 15	7 / 17	6 / 12	0.985	0.611
Education (y)	10.35±3.59	10.38±2.81	11.67±3.05	1.111	0.335
MMSE scores	15.12±6.85	21.46±8.16	27.39±2.93	18.588	<0.001
CSF Aβ₄₂ level (ng/L)	433.44±72.91	1017.18±291.17	1065.00±253.85	60.224	<0.001
CSF p-tau level (ng/L)	60.08±32.43	83.51±25.04	48.17±7.48	10.880	<0.001
CSF t-tau level (ng/L)	450.16±281.05	545.74±295.65	171.99±42.63	12.169	<0.001

Data are presented as the mean±standard deviation. One-way ANOVA/chi-square test was used for the data analysis. AD, Alzheimer’s disease; NC, normal control; M, male; F, female; MMSE, Mini-Mental State Examination; CSF, cerebrospinal fluid; Aβ₄₂, amyloid-β 42.

Comparison of platelet-related indicator levels in the three groups

The platelet AβPP ratio was calculated as the density of the 130 kDa band divided by that of the 110 kDa band. Compared to the NC group and non-AD group, the platelet AβPP ratio was significantly reduced in the AD group (Fig. 1A). In addition, levels of platelet p-tau231 and ser396/404 phosphorylated tau in AD and non-AD patients were significantly higher than in the NCs. However, there was no significant difference in these two platelet-related tau species between the AD and non-AD groups (Fig. 1B, C).

Fig. 1

Western blot analysis showing the expression of platelet-related indicators in AD, non-AD, and NC groups. (A) Platelet AβPP ratio, (B) platelet p-tau231, and (C) platelet ser396/404 phosphorylated tau. AβPP, amyloid-β protein precursor.

Correlation between platelet-related indicator levels and the assessment of cognitive function and CSF neuropathological biomarker levels

In the AD group, significant, positive correlations were found between platelet AβPP ratios and MMSE scores and CSF Aβ₄₂ levels, but no significant correlations were found between platelet AβPP ratios and the levels of CSF p-tau and CSF t-tau (Fig. 2A, B, Table 2). Additionally, there was a significant negative correlation between relative platelet p-tau231 levels and MMSE scores in AD and non-AD patients (Fig. 2C, D, Table 2).

Fig. 2

Scatter plots showing the correlation between platelet indicator levels and MMSE scores, and CSF biomarker levels in AD and non-AD groups. The platelet AβPP ratio correlated with MMSE scores (A) and CSF Aβ₄₂ levels (B) in the AD group, whereas platelet p-tau231 correlated with MMSE scores in the AD group (C) and non-AD group (D). The correlation analyses were performed by partial correlation analysis controlling for age, gender, and education years. AD, Alzheimer’s disease; MMSE, Mini-Mental State Examination; CSF, cerebrospinal fluid; AβPP, amyloid-β protein precursor; Aβ₄₂, amyloid-β 42.

Table 2

Correlation coefficients of platelet indicators’ levels with MMSE scores, three CSF biomarkers’ levels in AD and non-AD patients

Variables	AβPP ratio		P-tau231		Ser396/404 phosphorylated tau
	AD	Non-AD	AD	Non-AD	AD	Non-AD
MMSE score	R = 0.437	R = –0.040	R = –0.445	R = –0.510	R = 0.282	R = –0.251
	p = 0.037	p = 0.862	p = 0.034	p = 0.018	p = 0.192	p = 0.272
CSF Aβ₄₂ level	R = 0.438	R = 0.235	R = –0.123	R = –0.312	R = 0.273	R = 0.209
	p = 0.037	p = 0.305	p = 0.575	p = 0.168	p = 0.208	p = 0.363
CSF p-tau level	R = 0.254	R = 0.060	R = –0.072	R = –0.214	R = –0.057	R = –0.428
	p = 0.243	p = 0.796	p = 0.744	p = 0.351	p = 0.795	p = 0.053
CSF t-tau level	R = 0.263	R = 0.067	R = –0.061	R = –0.127	R = 0.110	R = –0.052
	p = 0.225	p = 0.771	p = 0.783	p = 0.582	p = 0.617	p = 0.823

Partial correlation analysis for data analysis by controlling age, gender, and education years. AD, Alzheimer’s disease; MMSE, Mini-Mental State Examination; CSF, cerebrospinal fluid; AβPP, amyloid-β protein precursor; Aβ₄₂, amyloid-β 42.

Mediation analysis

In AD patients, the exploratory mediation analysis indicated that CSF Aβ₄₂ could mediate the association between the platelet AβPP ratio and MMSE assessment where the covariates of age, gender, and education years were controlled (Fig. 3).

Fig. 3

Mediation analysis identified that the effects of the platelet AβPP ratio on the MMSE assessment were mediated through CSF Aβ₄₂ in the AD group. Aβ₄₂, amyloid-β 42; AβPP, amyloid-β protein precursor; MMSE, Mini-Mental State Examination; CSF, cerebrospinal fluid.

ROC curve analysis

For distinguishing AD patients from NCs, the AUCs of the platelet AβPP ratio, ser396/404 phosphorylated tau, and p-tau231 were 0.846 (sensitivity = 88.9%, specificity = 73.1%), 0.853 (sensitivity = 94.4%, specificity = 61.5%), and 0.705 (sensitivity = 100.0%, specificity = 42.3%), respectively (Fig. 4A). Except for the platelet AβPP ratio (AUC = 0.768, sensitivity = 100.0%, specificity = 42.3%), platelet ser396/404 phosphorylated tau and p-tau231 showed lower power to differentiate between the AD and non-AD groups (ser396/404 phosphorylated tau: AUC = 0.529, sensitivity = 58.3%, specificity = 57.7%; p-tau231: AUC = 0.548, sensitivity = 87.5%, specificity = 38.5%, Fig. 4B).

Fig. 4

The receiver operating characteristic analysis of platelet-related indicators. (A) AD versus NCs, (B) AD versus non-A, and (C) non-AD versus NCs. AβPP, amyloid-β protein precursor.

In addition, platelet-related indicators (ser396/404 phosphorylated tau and p-tau231) could significantly differentiate patients with non-AD from NCs (Fig. 4C), and the AUC was 0.880 (sensitivity = 75.0%, specificity = 88.9%) for ser396/404 phosphorylated tau and 0.806 (sensitivity = 87.5%, specificity = 66.7%) for p-tau231. However, the platelet AβPP ratio performed poorly in distinguishing non-AD patients from NCs (AUC = 0.597, sensitivity = 37.5%, specificity = 88.9%).

DISCUSSION

To our knowledge, this study was the first to explore the associations between the platelet AβPP ratio, p-tau231 protein, ser396/404 phosphorylated tau protein, and CSF AD biomarkers in participants with complaints of memory decline. The current findings were as follows: (i) the platelet AβPP ratio was significantly reduced in the AD group compared to both the non-AD group and the HC group and was positively correlated with MMSE scores and CSF Aβ₄₂ levels, which could affect MMSE scores through CSF Aβ₄₂. In addition, the platelet AβPP ratio differentiated the AD patient group from NCs with better classification accuracy (AUC = 0.846) and the AD patient group from the non-AD group with moderate classification accuracy (AUC = 0.768), and (ii) p-tau231 and ser396/404 phosphorylated tau levels were significantly elevated in AD and non-AD patients compared to NCs. Moreover, platelet p-tau231 levels were negatively correlated with MMSE scores in both the AD and non-AD groups. In addition, ser396/404 phosphorylated tau showed better performance (AUC = 0.853) only for distinguishing AD patients from NCs. These findings provided new evidence for the association of platelet proteins with CSF core biomarkers of AD pathology, suggesting that the platelet AβPP ratio may serve as a screening tool for AD in future studies.

The ATN framework has emphasized biomarkers over traditional clinical evaluations to improve antemortem predictions of AD pathology using biomarkers for Aβ deposition (A), pathologic tau (T), and neurodegeneration (N) [5]. The ATN classification can help to identify which individuals with SCD are at risk of dementia and which individuals with normal biomarkers are highly unlikely to show clinical progression over time [30].

For the first time, based on the ATN framework, we found that the platelet AβPP ratio was low in the AD group and was associated with declines in MMSE scores and CSF Aβ₄₂ levels, which indicated that a decreased platelet AβPP ratio could also be observed only in patients with aggregated Aβ pathology in the brain. Our findings were consistent with other studies that also found lower platelet AβPP ratios in AD patients compared to HCs [31, 32]. It is well known that platelets are the major source of peripheral Aβ in human blood [20]. Recent studies showed that platelets could disrupt the permeability of the BBB [21, 22] and transfer Aβ from blood vessels into the central nervous system [33]. Several studies also showed that platelets could secrete various processed forms of AβPP and other substances while infiltrating the brain, which resulted in the growth of Aβ depositions in the brain [22, 34]. In addition, the platelet AβPP ratio was positively correlated with cognitive decline, i.e., the lower the ratio, the more severe the disease [35, 36]. Circulating Aβ₄₀, the major Aβ form secreted from platelets [37], mediates oxidative stress and neurovascular dysfunction, leading to brain dysfunction and cognitive impairment [38, 39]. A previous study proposed a hypothesis using major depression as a model, suggesting that individuals with major depression would be exposed to elevations in platelet AβPP arising from platelet activation and develop brain structural abnormalities and cognitive deficits [40]. When the hypothesis model was applied to AD, our results provided evidence in support of that model. This also indicates the reliability of the results in the current study.

The study also showed that the effects of the platelet AβPP ratio on the MMSE assessment were mediated through CSF Aβ₄₂ in the AD group. This observation is consistent with the platelet activation hypothesis [41, 42]. Peripheral Aβ peptides spread to the brain from the blood, and induce brain Aβ production and deposition, showing a decrease in the platelet AβPP ratio, accompanied by a decrease in CSF Aβ₄₂ levels in vivo. Therefore, it is not surprising that patients with terminal events, namely neuronal death, and cognitive impairment, had lower MMSE scores. The results suggest that platelet inhibitors may have a role in preventing or delaying AD onset disorders associated with platelet hyperactivity such as major depression [40]. Furthermore, using ROC curve analysis, we found that the platelet AβPP ratio showed better performance in distinguishing AD patients from non-AD patients or NCs. Thus, we could speculate that platelet AβPP contributes to Aβ accumulation in AD brains and our study indicated that the platelet AβPP ratio may serve as a screening tool for AD in future studies.

Consistent with our previous findings [10], this study showed that platelet p-tau231 and ser396/404 phosphorylated tau levels were elevated in the AD and non-AD groups compared to the NCs. While an earlier study did not observe any significant differences in ser396/404 phosphorylated tau between the AD group and NCs, which may have been due to the small sample size and the primary antibody was not purchased from a trustworthy company [43]. In contrast to the low platelet AβPP ratio, the levels of platelet p-tau231 and ser396/404 phosphorylated tau were not specific for the AD group, which mirrored the CSF findings [44]. It has been increasingly recognized that the aggregation of tau protein underlies numerous human neurodegenerative diseases, rather than being specific for AD [45, 46]. Therefore, within the ATN framework, CSF t-tau can serve as a marker of disease severity, and p-tau is considered a biomarker of a pathologic state that is associated with paired helical filament tau deposits and tau formation [5]. Further studies should be conducted to explore the relationship between platelet phosphorylated tau and AD neuropathology in the brain. Moreover, elevated platelet p-tau231 levels were associated with decreased MMSE scores in the AD and non-AD groups. Ser396/404 phosphorylated tau showed better performance for distinguishing AD patients from NCs, indicating that platelet p-tau may have clinical utility.

The present study also had several limitations. i) Multicenter samples are still necessary to verify the present findings and provide more strong evidence for the clinical translation of these platelet indicators. ii) A longitudinal study is essential to confirm whether the association between platelet-related indicators and CSF AD biomarkers is a “state” or “trait.” iii) According to the ATN framework [5], some subjects with non-AD pathological changes may translate to AD patients since these subjects may also show an aggregated Aβ pathologic state in follow-up visits. It is unknown whether these platelet-related indicators show dynamic changes during the development of AD. iv) Information regarding APOE status was not available and thus, could not be controlled for in the analysis. The present study had additional limitations. For example, we only used the MMSE to assess cognitive function. More sensitive and comprehensive cognitive testing should be used to evaluate the relationship between platelet-related indicators and multiple cognitive domains in future research. Taken together, platelet-related indicators are likely to become important members of a composite peripheral biomarker panel for AD screening.

Conclusion

In summary, our study was the first to find a robust association between the platelet AβPP ratio and CSF AD biomarkers. The platelet AβPP ratio could specifically distinguish the AD group from the non-AD group and NC group, whereas p-tau231 and ser396/404 phosphorylated tau could not possess. Although the underlying mechanisms were not entirely clear, the exploration of platelet AβPP, p-tau231, and ser396/404 phosphorylated tau protein may be a novel and useful supplement to understanding the peripheral changes in AD. Furthermore, because blood sampling is convenient and feasible, future studies in that direction will be more likely to find feasible and effective screening tools for AD.

Footnotes

ACKNOWLEDGMENTS

The study was supported by the National Key Research and Development Plan of China (Grant No. 2016YFC1306700 to ZJZ); the National Natural Science Key Foundation of China (Grant No. 81830040 and 82130042 to ZJZ, 82071204 to CMX), Science, and Technology Program of Guangdong (Grant No. 2018B030334001 to ZJZ); and the Program of Excellent Talents in Medical Science of Jiangsu Province (Grant No. JCRCA2016006 to ZJZ).

Authors’ disclosures available online ().

The supplementary material is available in the electronic version of this article: .

References

Joe

, Ringman

(2019) Cognitive symptoms of Alzheimer’s disease: Clinical management and prevention. BMJ 367, 16217.

Jia

, Quan

, Fu

, Zhao

, Li

, Wei

, Tang

, Qin

, Wang

, Qiao

, Shi

, Wang

, Du

, Zhang

, Luo

, Qu

, Zhou

, Gauthier

, Jia

(2020) Dementia in China: Epidemiology, clinical management, and research advances. . Lancet Neurol 19, 81–92.

Jia

, Wei

, Chen

, Li

, Tang

, Qin

, Zhao

, Jin

, Xu

, Wang

, Zhou

, Zuo

, Wu

, Han

, Huang

, Wang

, Li

, Chu

, Shi

, Gong

, Du

, Zhang

, Zhou

, Lv

, Xie

, Ji

, Li

, Yu

, Luo

, Wang

, Yang

, Qu

, Guo

, Liang

, Zhang

, Tan

, Shen

, Zhang

, Peng

, Tang

, Lv

, Fang

, Chu

, Jia

, Gauthier

(2018) The cost of Alzheimer’s disease in China and re-estimation of costs worldwide. . Alzheimers Dement 14, 483–491.

Knopman

, Siemers

, Bain

, Hendrix

, Carrillo

(2018) National Institute on Aging - Alzheimer’s Association Research Framework lays the groundwork for deeper understanding of Alzheimer’s disease. . Alzheimers Dement 14, 261–262.

Jack

, Jr. , Bennett

, Blennow

, Carrillo

, Dunn

, Haeberlein

, Holtzman

, Jagust

, Jessen

, Karlawish

, Liu

, Molinuevo

, Montine

, Phelps

, Rankin

, Rowe

, Scheltens

, Siemers

, Snyder

, Sperling

, Contributors (2018) NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement 14, 535–562.

Tang

, Hynan

, Baskin

, Rosenberg

(2006) Platelet amyloid precursor protein processing: A bio-marker for Alzheimer’s disease. J Neurol Sci 240, 53–58.

Gonzalez-Sanchez

, Diaz

, Pascual

, Antequera

, Herrero-San Martin

, Llamas-Velasco

, Villarejo-Galende

, Bartolome

, Carro

(2018) Platelet proteomic analysis revealed differential pattern of cytoskeletal- and immune-related proteins at early stages of Alzheimer’s disease. Mol Neurobiol 55, 8815–8825.

Di Luca

, Pastorino

, Bianchetti

, Perez

, Vignolo

, Lenzi

, Trabucchi

, Cattabeni

, Padovani

(1998) Differential level of platelet amyloid β precursor protein isoforms: An early marker for Alzheimer disease. Arch Neurol 55, 1195–1200.

Rosenberg

, Baskin

, Fosmire

, Risser

, Adams

, Svetlik

, Honig

S. L

, Cullum

, Weiner

(1997) Altered amyloid protein processing in platelets of patients with Alzheimer disease. Arch Neurol 54, 139–144.

10.

Shi

, Gu

, Wang

, Gao

, Zhu

, Lu

, Zhou

, Zhu

, Zhang

, Xie

, Zhang

(2020) Platelet amyloid-beta protein precursor (AbetaPP) ratio and phosphorylated tau as promising indicators for early Alzheimer’s disease. J Gerontol A Biol Sci Med Sci 75, 664–670.

11.

Baskin

, Rosenberg

, Iyer

, Hynan

, Cullum

(2000) Platelet APP isoform ratios correlate with declining cognition in AD. Neurology 54, 1907.

12.

, Liu

, He

, Chen

, He

, Yang

, Wang

(2021) Platelet biomarkers for a descending cognitive function: A proteomic approach. Aging Cell 20, e13358.

13.

Bram

JMF

, Talib

, Joaquim

HPG

, Sarno

, Gattaz

, Forlenza

(2019) Protein levels of ADAM10, BACE1, and PSEN1 in platelets and leukocytes of Alzheimer’s disease patients. Eur Arch Psychiatry Clin Neurosci 269, 963–972.

14.

Zainaghi

, Talib

, Diniz

, Gattaz

, Forlenza

(2012) Reduced platelet amyloid precursor protein ratio (APP ratio) predicts conversion from mild cognitive impairment to Alzheimer’s disease. J Neural Transm (Vienna) 119, 815–819.

15.

Akingbade

OES

, Gibson

, Kalaria

, Mukaetova-Ladinska

(2018) Platelets: Peripheral biomarkers of dementia? . J Alzheimers Dis 63, 1235–1259.

16.

Talib

, Joaquim

, Forlenza

(2012) Platelet biomarkers in Alzheimer’s disease. World J Psychiatry 2, 95–101.

17.

Borroni

, Agosti

, Marcello

, Di Luca

, Padovani

(2010) Blood cell markers in Alzheimer disease: Amyloid precursor protein form ratio in platelets. Exp Gerontol 45, 53–56.

18.

Maynard

DM HH

, Horne

, White

, Gahl

(2007) Proteomic analysis of platelet alpha-granules using mass spectrometry. J Thromb Haemost 5, 1945–1955.

19.

Qureshi

, Chaoji

, Maiguel

, Faridi

, Barth

, Salem

, Singhal

, Stoub

, Krastins

, Ogihara

, Zaki

, Gupta

(2009) Proteomic and phospho-proteomic profile of human platelets in basal, resting state: Insights into integrin signaling. PLoS One 4, e7627.

20.

Inyushin

, Zayas-Santiago

, Rojas

, Kucheryavykh

(2020) On the role of platelet-generated amyloid beta peptides in certain amyloidosis health complications. Front Immunol 11, 571083.

21.

Kopeikina

, Dukhinova

, Yung

AWY

, Veremeyko

, Kuznetsova

, Lau

TYB

, Levchuk

, Ponomarev

(2020) Platelets promote epileptic seizures by modulating brain serotonin level, enhancing neuronal electric activity, and contributing to neuroinflammation and oxidative stress. Prog Neurobiol 188, 101783.

22.

T CL

, Zhou

, Xu

, Guo

(2021) Platelets transport β-amyloid from the peripheral blood into the brain by destroying the blood-brain barrier to accelerate the process of Alzheimer’s disease in mouse models. Aging (Albany NY) 13, 7644–7659.

23.

Pluta

, Ułamek-Kozioł

(2019) Lymphocytes, platelets, erythrocytes, and exosomes as possible biomarkers for Alzheimer’s disease clinical diagnosis. Adv Exp Med Biol 1118, 71–82.

24.

Canobbio

, Abubaker

, Visconte

, Torti

, Pula

(2015) Role of amyloid peptides in vascular dysfunction and platelet dysregulation in Alzheimer’s disease. Front Cell Neurosci 9, 65.

25.

Jack

Jr , Bennett

, Blennow

, Carrillo

, Feldman

, Frisoni

, Hampel

, Jagust

, Johnson

, Knopman

, Petersen

, Scheltens

, Sperling

, Dubois

(2016) A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 87, 539–547.

26.

Mulder

, Verwey

, van der Flier

, Bouwman

, Kok

, van Elk

, Scheltens

, Blankenstein

(2010) Amyloid-beta(1-42), total tau, and phosphorylated tau as cerebrospinal fluid biomarkers for the diagnosis of Alzheimer disease. Clin Chem 56, 248–253.

27.

, Gong

, Yin

, Yuan

, Zhang

, Lv

, Zhang

, Soares

, Zhang

, Xie

, Zhang

(2019) Amygdala connectivity mediates the association between anxiety and depression in patients with major depressive disorder. Brain Imaging Behav 13, 1146–1159.

28.

, Bai

, Wang

, Fan

, Wang

, Liu

, Zhang

, Yao

, Xie

(2020) Identification of microRNA-9 linking the effects of childhood maltreatment on depression using amygdala connectivity. Neuroimage 224, 117428.

29.

Youden

(1950) Index for rating diagnostic tests. Cancer 3, 32–35.

30.

Ebenau

, Timmers

, Wesselman

LMP

, Verberk

IMW

, Verfaillie

SCJ

, Slot

RER

, van Harten

, Teunissen

, Barkhof

, van den Bosch

, van Leeuwenstijn

, Tomassen

, Braber

, Visser

, Prins

, Sikkes

SAM

, Scheltens

, van Berckel

BNM

, van der Flier

(2020) ATN classification and clinical progression in subjective cognitive decline: The SCIENCe project. Neurology 95, e46–e58.

31.

Sarno

, Talib

, Joaquim

, Bram

, Gattaz

, Forlenza

(2017) Protein expression of BACE1 is downregulated by donepezil in Alzheimer’s disease platelets. J Alzheimers Dis 55, 1445–1451.

32.

Liu

, Wang

, Ko

, Wang

, Chi

, Hong

, Lin

, Liu

(2007) Correlation between platelet amyloid precursor protein isoform ratio and cognition in Alzheimer’s disease. J Alzheimers Dis 11, 77–84.

33.

, Xiang

, Jin

, Wang

, Shen

, Huang

, Zhang

, Liu

, Zeng

, Liu

, Sun

, Zhuang

, Chen

, Yao

, Giunta

, Shan

, Tan

, Chen

, Dong

, Zhou

, Song

, Wang

(2018) Blood-derived amyloid-β protein induces Alzheimer’s disease pathologies. Mol Psychiatry 23, 1948–1956.

34.

Espinosa-Parrilla

Y G-BC

, Fuentes

, Palomo

, Alarcón

(2019) Decoding the role of platelets and related microRNAs in aging and neurodegenerative disorders. Front Aging Neurosci 11, 151.

35.

Zainaghi

, Forlenza

, Gattaz

(2007) Abnormal APP processing in platelets of patients with Alzheimer’s disease: Correlations with membrane fluidity and cognitive decline. Psychopharmacology (Berl) 192, 547–553.

36.

Borroni

, Colciaghi

, Archetti

, Marcello

, Caimi

, Di Luca

, Padovani

(2004) Predicting cognitive decline in Alzheimer disease. Role of platelet amyloid precursor protein. Alzheimer Dis Assoc Disord 18, 32–34.

37.

Skovronsky

, Lee

, Pratico

(2001) Amyloid precursor protein and amyloid beta peptide in human platelets. Role of cyclooxygenase and protein kinase C. J Biol Chem 276, 17036–17043.

38.

Park

, Zhou

, Koizumi

, El Jamal

, Previti

, Van Nostrand

, Carlson

, Iadecola

(2013) Brain and circulating levels of Abeta1-40 differentially contribute to vasomotor dysfunction in the mouse brain. Stroke 44, 198–204.

39.

Park

, Uekawa

, Garcia-Bonilla

, Koizumi

, Murphy

, Pistik

, Younkin

, Zhou

, Carlson

, Anrather

, Iadecola

(2017) Brain perivascular macrophages initiate the neurovascular dysfunction of Alzheimer Abeta peptides. Circ Res 121, 258–269.

40.

Pomara

, Murali Doraiswamy

(2003) Does increased platelet release of Aβ peptide contribute to brain abnormalities in individuals with depression? Med Hypotheses 60, 640–643.

41.

Carbone

, Pagni

, Tagliarini

, Imbimbo

, Pomara

(2021) Can platelet activation result in increased plasma Abeta levels and contribute to the pathogenesis of Alzheimer’s disease? Ageing Res Rev 71, 101420.

42.

Carbone

, Pagni

, Tagliarini

, Marazziti

, Pomara

(2021) Platelet APP processing: Is it a tool to explore the pathophysiology of Alzheimer’s disease? A systematic review. Life (Basel) 11, 750.

43.

Neumann

, Farías

, Slachevsky

, Perez

, Maccioni

(2011) Human platelets tau: A potential peripheral marker for Alzheimer’s disease. J Alzheimers Dis 25, 103–109.

44.

Suarez-Calvet

, Karikari

, Ashton

, Lantero Rodriguez

, Mila-Aloma

, Gispert

, Salvado

, Minguillon

, Fauria

, Shekari

, Grau-Rivera

, Arenaza-Urquijo

, Sala-Vila

, Sanchez-Benavides

, Gonzalez-de-Echavarri

, Kollmorgen

, Stoops

, Vanmechelen

, Zetterberg

, Blennow

, Molinuevo

, ALFA Study (2020) Novel tau biomarkers phosphorylatedat T181, T217 or T231 rise in the initial stages of the preclinical Alzheimer’s continuum when only subtle changes in Abeta pathology are detected. EMBO Mol Med 12, e12921.

45.

Song

, Wells

, Robinson

(2021) Critical molecular and cellular contributors to tau pathology. Biomedicines 9, 190.

46.

Spillantini

, Goedert

(2013) Tau pathology and neurodegeneration. Lancet Neurol 12, 609–622.