The Distinction of Amyloid-β Protein Precursor (AβPP) Ratio in Platelet Between Alzheimer’s Disease Patients and Controls: A Systematic Review and Meta-Analysis

Abstract

To systematically assess the clinical significance of platelet amyloid-β protein precursor (AβPP) ratio between Alzheimer’s disease (AD) patients and controls. 14 articles were selected in this analysis by search of databases including PubMed and Web of Science up to December 2016. Random effects models were used to calculate the standardized mean difference (SMD). Subgroup analyses were used to detect the cause of heterogeneity. The result showed a significant drop in platelet AβPP ratio in AD patients compared to controls [SMD: –1.871; 95% CI: (–2.33, –1.41); p < 0.001; I² = 88.0% ]. Subgroup analysis revealed races or the quality of studies may be the cause of high heterogeneity. This meta-analysis concluded that there is a close association between platelet AβPP ratio and AD. It is necessary to design a sizable sample study to further support that platelet AβPP ratio can be a biomarker of AD.

Keywords

Alzheimer’s disease amyloid-β precursor protein biomarker meta-analysis platelet

INTRODUCTION

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that leads to progressive cognitive deterioration. As the most common form of dementia, the worldwide prevalence of AD will grow to 106.8 million by 2050 [1]. The pathogenesis of AD is not completely understood, but the amyloid-β (Aβ) cascade hypothesis [2] is generally accepted. The analysis of three major biomarkers of cerebrospinal fluid (CSF), namely total tau (T-tau), phospho-tau (P-tau) and the 42 amino acid form of Aβ (Aβ42), can identify early stages of AD, and the finding of regional Aβ deposition with positron emission tomography (PET) has the same property [3 –5]. Although these two measures have been included in diagnostic criteria for AD [6, 7], difficulty of obtaining CSF by lumber puncture and the high price of positron electron tomography (PET) imaging are still unsolved problems preventing these two means from being carried out in communities to screen for preclinical AD patients. Currently, neuropsychological examination and brain magnetic resonance imaging (MRI) are used as primary auxiliary methods to diagnose AD, but they are not relatively accurate in detecting the early stage of AD. Therefore, there has been much research to identify new, available biomarkers of AD for routine examinations. Over the past decade, many biomarkers had been identified in peripheral body fluids, for example in blood [8 –12], saliva [13], and urine [14, 15], that may be correlates of the changes in brain. In particular, because some biomarkers of peripheral blood can reflect, to some degree, primary pathological alterations of AD, this approach has attracted attention [16], most notably in platelets [17 –21].

Aβ peptide, a main component of senile plaque in the brain of AD patients, is produced by amyloid-β protien precursor (AβPP). Platelets contain α-, β-, and γ-secretases to accomplish all metabolites from the AβPP processing [22], which is same as the complete enzymatic machinery in the central nervous system [23]. In fact, the platelet is the most important source of circulating Aβ in peripheral blood [24 –26], and it can be an available peripheral model to detect valid biomarkers for achieving the early prediction of AD [27]. Platelet AβPP isoforms ratio (120–130 kDa/106–110 kDa), the proportion of two major platelet AβPP isoforms, is known to be a promising biomarker for the prediction of AD. A number of reports from different scientific groups demonstrated the change of AβPP ratio certainly occur in the progress of AD: several studies have shown that AβPP ratio was decreased in patients with mild cognitive impairment (MCI) compared to the healthy elderly individuals [28, 29], and the result of the AβPP ratio in AD patients was significantly lower than MCI patients [30, 31] or healthy controls [22 , 32–42]. Moreover, one study shown that platelet AβPP ratio could be partially reversed in AD patients when using cholinesterase inhibitors [43]. Therefore, AβPP ratio may be associated with cognition in AD patients. However, individual study has yielded inconsistent findings possibly due to differences in techniques resulted in 130 kDa AβPP isoform partly degrading, or increased activation of platelets [44]. Hence, it is still uncertain whether AβPP ratio can serve as a valuable biomarker for the early diagnosis of AD.

The aim of this work is to systematically review and analyze the evidence on the association between AβPP ratio and AD, and assess the diagnostic effect of AβPP ratio as a biomarker of AD.

METHODS

Search strategy

According to the PRISMA guidelines [45], we manually searched eligible literatures for systematic reviews and meta-analyses. Searches were restricted to published articles in English. We carried out this work through PubMed and Web of Science up to December 2016 with the search terms: (amyloid precursor protein OR AβPP) AND (platelet) AND (Alzheimer’s disease OR dementia). Any unconformity was solved by consultation of a researcher. We screened the titles and abstracts of all possible relevant papers on the basis of the following criteria.

Selection criteria

Articles were included if they synchronously satisfied the following criteria: the study (1) contained an AD cohort and a control cohort, (2) used the authoritative diagnostic criteria for AD and had cognitive screening tests, such as the Mini-Mental State Examination (MMSE), for distinguishing between AD and control, (3) had an original data in cross-sectional study or a baseline data in longitudinal study for subsequent analyses, (4) reported mean and standard deviation (SD) for both AD patients and controls, (5) obtained the result of AβPP ratio in platelet through western blot analysis, and (6) included a sample size of ≥10 for each group.

Data extraction

Data were abstracted using a predefined data extraction form: first author, publication year, criteria for AD assessment, races of subjects, sample size, basic information of participants (gender, age, the levels of MMSE and AβPP ratio) and the quality score of studies. Disagreements were resolved by discussion.

Quality assessment

The quality of the included studies was evaluated by using the Newcastle Ottawa Scale (NOS) [46]. The NOS categorizes into three primary items including Selection, Comparability, and Exposure. Every category contains some subordinate items. A study can be evaluated a maximum of one point for each subordinate item within the Selection and Exposure categories. In Comparability category, a maximum of two points can be given for one study. The total score of the NOS ranges from zero to nine.

Statistical analysis

Analysis was performed using STATA version 12.0 (StataCorp, College Station, TX). The standard mean difference (SMD) was used to assess the pooled effect size of the selected studies. Since the included studies might have different heterogeneity, a random effects model was used to calculate the variance. In this study, heterogeneity was evaluated by the I² statistic and 95% confidence interval (CI) was used as an effect measure for continuous variables. Sensitivity analysis was selected to estimate the stability of the results of meta-analysis by omitting a certain study each time. Subgroup analyses were utilized to explore potential cause of heterogeneity. Publication bias was assessed using funnel plots and the Begg’s test was implemented to detect proof of publication bias.

RESULTS

We screened 221 unique records from two main databases (PubMed and Web of Science). Of these, we deleted 198 articles on the basis of exclusion criteria. 14 articles fulfilled all the conditions and were included in the present meta-analysis after reviewing the full text of remaining 23 articles. The selection process is described in Fig. 1.

Fig.1

Detail of the study selection process and screening.

As Table 1 shows, 16 studies were included in this meta-analysis. Since two articles [22, 28], respectively, classified the AD cohort to two separate subcohorts according to different severity of AD, we marked them as crowd 1 and crowd 2. These studies recruited a total of 959 volunteers, including 470 AD patients and 489 controls [22 , 44].

Table 1

Characteristics of studies used for analysis of AβPP ratio in platelet in AD

First author, year	Criteria of diagnosis	Study design	race	AD				Control				NOS
				N(M/F)	Age	MMSE	AβPP ratio	N (M/F)	Age	MMSE	AβPP ratio
Jelic et al. [39]	NINCDS- ADRDA	Cross-sectional study	Caucasian	30 (12/19)	67.0±9.9	25.4±2.77	5.9±1.37	23 (9/14)	68.2±9.6	28.65±1.37	10.3±2.86	6
Srisawat,Chatchawan et al. [59]	NINCDS- ADRDA	Cross-sectional study	Asian	13 (4/9)	79.3±8.1	20.3±2.2^*	7.32±1.29	27 (6/2^*1)	68.3±6.6	27.6±1.8^*	9.13±3.00	5
Zainaghi et al. [32]	NINCDS-ADRDA, DSM-IV	Cross-sectional study	Mixed	23 (8/15)	74.4±9.0	18.6±5.1	1.01±0.21	29 (7/22)	70±5.8	29.0±1.1	1.24±0.21	6
Liu et al. [44]	NINCDS-ADRDA	Longitudinal study	Asian	66 (41/25)	76.5±9.9	13.6±8.2	1.35±0.81	46 (27/19)	72.5±10.9	28.9±1.0	1.44±0.79	5
Sanchez-Gonzalez	NINCDS-ADRDA,	Cross-sectional	Mixed	26 (17/9)	60.96±	14.64±	0.3662±	46 (15/31)	67.73±	28.33±	0.6769±	5
et al. [54]	DSM-IV	study	12.85	5.31	0.1891	9.46	1.96	0.1021
Colciaghi et al. [22] (crowd 1) #	NINCDS-ADRDA, DSM-IV	Cross-sectional study	Caucasian	20 (8/12)	68.0±7.5	21.36±4.03	0.419±0.168	15 (6/9)	67.7±4.2	29.30±1.97	0.706±0.241	5
Colciaghi et al. [22] (crowd 2) #	NINCDS-ADRDA, DSM-IV	Cross-sectional study	Caucasian	11 (4/7)	67.8±6.3	25.70±0.78	0.447±0.160	15 (6/9)	67.7±4.2	29.30±1.97	0.706±0.241	5
Borroni et al. [36]	NINCDS-ADRDA, DSM-IV	Cross-sectional study	Caucasian	40 (23/17)	68.06±7.3	22.5±2.5	0.46±0.26	40 (18/22)	68.3±9	29.6±0.5	0.91±0.30	6
Colciaghi et al. [37]	NINCDS-ADRDA	Cross-sectional study	Caucasian	33 (13/20)	68.15±6.18	18.0±3.7	0.31±0.19	26 (9/17)	63.19±6.10	29.28±1.1	0.90±0.29	6
Padovani et al. [28] (crowd1) #	NINCDS-ADRDA, DSM-IV	Cross-sectional study	Caucasian	35 (12/23)	69.2±8.5	20.0±1.8	0.44±0.24	25 (14/11)	69.8±9.0	29.4±1.0	0.93±0.30	6
Padovani et al. [28] (crowd2) #	NINCDS-ADRDA, DSM-IV	Cross-sectional study	Caucasian	21 (9/12)	66.2±6.9	24.9±0.9	0.49±0.30	25 (14/11)	69.8±9.0	29.4±1.0	0.93±0.30	6
Padovani et al. [40]	NINCDS-ADRDA, DSM-IV	Cross-sectional study	Caucasian	85 (35/50)	68.0±10.1	14.2±6.7	0.35±0.18	95 (52/43)	59.3±17.4	23.6±7.5	0.92±0.38	6
Baskin et al. [35]	NINCDS-ADRDA	Longitudinal study	Mixed	10 (NA/NA)	NA	16.40±1.56	5.83±0.51	11 (NA/NA)	NA	29.50±0.20	8.09±0.61	5
Di Luca et al. [38]	NINCDS-ADRDA	Cross-sectional study	Caucasian	32 (13/19)	71.8±10.3	12.7±7.3	0.31±0.15	25 (18/7)	67.6±13.5	28.2±3.6	0.84±0.24	5
Rosenberg et al. [33]	NINCDS-ADRDA	Cross-sectional study	Mixed	15 (NA/NA)	NA	NA	5.61±0.41	19 (NA/NA)	NA	NA	7.72±0.57	4
Di Luca et al. [34]	NINCDS-ADRDA, DSM-IV	Cross-sectional study	Caucasian	10 (3/7)	67.3±12.9	19.6±1.8	0.16±0.048	15 (7/8)	61.0±14.0	29.4±0.6	0.76±0.280	6

Data are presented as mean±SD. # denotes articles contain two studies included in present meta-analysis respectively. AD, Alzheimer’s disease; NINCDS-ADRDA, National Institute of Neurological Disorders and Stroke-Alzheimer Disease and Related Disorders Association; MMSE, Mini-Mental State Examination; NOS, Newcastle Ottawa Scale; NA, the relative information is not published in the article.

Random effect model was used to analyze the included studies. Compared with the control cohorts, the AβPP ratio levels were significantly lower in AD cohorts [SMD: –1.871; 95% CI: (–2.33,–1.41); p < 0.001; I² = 88.0%. Forest plot shown in Fig. 2]. With sensitivity analysis subtracting one study each time, we demonstrated no study had significant effect on the pooled SMD (Supplementary Figure 1).

Fig.2

Forest plot for platelet AβPP ratio levels in AD and controls with random effects model.

Most of the studies were included within the scope of the funnel plot, however, individual studies with certain influential factors generated publication bias (Fig. 3). Egger’s test was performed to assess publication bias (t = –2.52, p = 0.025).

Fig.3

Funnel plots of assessment of publication bias. Each point represents a separate study for the indicated association. Funnel plots for platelet AβPP ratio levels in AD and controls.

According to the categories of NOS, we formulated a new scoring rubric for the present meta-analysis. Authoritative diagnostic criteria of AD and detailed exclusion criteria are necessary to assess adequate definition and obvious representativeness of cases. Meanwhile control subjects were stated clearly that they were healthy, cognitively normal participants, which can make certain definition of controls within Selection category. Age was selected as the most important factor and the years of education as a second factor should be modified for the analysis of Comparability. MMSE was regarded as the method of assessment for cognition within the Exposure category. After evaluating the quality, the score of each article was recorded in Table 1.

Some suspicious factors were selected in subgroup analyses subsequently, including study design, races, and the quality of studies (data shown in the Supplementary Material). Subgroup analyses by study showed there are still higher heterogeneity in the cross-sectional study subgroup (I² = 73.4%) and the longitudinal study subgroup (I² = 95.7%). But subgroup analyses by races showed decreased heterogeneity occurred in the Caucasian subgroup (I² = 45.2%), and the result of 6 points subgroup (I² = 49.3%) from subgroup analyses indicated the quality of studies may also be a cause for high heterogeneity.

DISCUSSION

Meta-analysis has been recognized as a valid tool to answer many clinical problems via summarizing and reviewing prior studies. In this study, we analyzed the association between platelet AβPP ratio and AD using comprehensive meta-analysis to gain a cogent conclusion. To the best of our knowledge, this is the first meta-analysis providing powerful evidence about the effects of AβPP ratio as the biomarker of AD. The pooled results of the selected studies displayed a significant association between platelet AβPP ratio and AD, and that lower AβPP ratio may be a risk marker of AD.

The exact cause of the alteration of platelet AβPP ratio in patients with AD is yet to be debated. The result may be explained by the following hypotheses. AβPP is a transmembrane protein with three major isoforms (AβPP695, AβPP751, and AβPP770) derived from the same messenger RNA [47]. The two longer forms (AβPP751 and AβPP770) are the most abundant isoforms in platelets though AβPP695 is also present [48]. The observed change in platelet AβPP isoforms may be caused by the release of the mature full-length AβPP751/770 from activated platelets in AD [49, 50]. In fact, an increasing 130 kDa AβPP had been identified in AD plasma [51] and the total AβPP in platelet was not different between AD patients and controls [23]. Additionally, altered α- and β-secretase levels [19 , 52] that induce decreased soluble AβPP or altered membrane fluidity of platelet [53 –55] that accelerate a transport of the protein also may be potential causes. Overall, platelet AβPP ratio changes significantly in AD and fundamental mechanism of it needs to be explored further.

Although the result of meta-analysis is significant, the assessment of publication bias suggested that the missing studies might have an effect on the reliability of this result (Fig. 3). Furthermore, because of the heterogeneity of the pooled effect size was higher, we attempted to confirm the probable cause of it. The results of subgroup analyses indicated study design did not have a significant effect (Supplementary Figure 2). But we found either the difference in participants’ races or the quality of studies may lead to high pooled heterogeneity by subgroup analyses (Supplementary Figuess 3 and 4). Of note, some included studies lacked strict exclusion criteria [33 , 44], which resulted in certain complications that were not controlled completely and volunteers were provided with the poorer homogeneity. Besides, the ascertainment of AD staging in most studies was done by the MMSE score. This may lead to different stages of AD being included in the present analysis, which could influence the homogeneity of the results of AβPP ratio and may induce high heterogeneity. Although the most important cause was not found, we tried our best to find probably causes to reduce the heterogeneity.

The present meta-analysis has the following limitations that must be taken into account. First, although subgroup analyses results revealed a difference in race or the quality of studies may be relative to the high heterogeneity, it should be noted that there was considerable heterogeneity. Second, the measure method of AβPP ratio was by western blot, a semi-quantitative method. The results of AβPP ratio were easily influenced by factors of operators. Third, because of we screened the literature only in English, some studies in other languages might be missed. Fourth, as only published articles were included in the present meta-analysis, publication bias was not a negligible issues. Finally, due to the scarcity of published articles on the platelet AβPP ratio in MCI cohorts, it was not possible to analyze adequate data to support the evidence that the platelet AβPP ratio can be used as a predictive biomarker in the early stage of AD.

Conclusion

This meta-analysis indicates that platelet AβPP ratio is associated with AD. It provides evidence from considerable samples that platelet AβPP ratio level was significantly lower in AD compared to controls, and it may be a valuable candidate biomarker of AD.

Finally, although several assays reported no significant difference in plasma Aβ level [12 , 56–58], these significant results of platelet AβPP gives many researchers great encouragement. We have reasons to believe that there are some predictive biomarkers that occur in peripheral blood. In the future, a large sample case-control study with rigorous design should be performed to confirm the findings of previous studies and the present study.

Footnotes

ACKNOWLEDGMENTS

This study was supported by the National Natural Science Foundation of China (No. 81671046), the Fundamental Research Funds for the Central Universities and the Scientific Research Innovation Program for College and University Graduates of Jiangsu Province (No. KYZZ16_0131).

Authors’ disclosures available online ().

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

References

Brookmeyer

, Johnson

, Ziegler-Graham

, Arrighi

(2007) Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement 3, 186–191.

Selkoe

(2002) Alzheimer’s disease is a synaptic failure. Science 298, 789–791.

Oboudiyat

, Gefen

, Varelas

, Weintraub

, Rogalski

, Bigio

, Mesulam

(2017) Cerebrospinal fluid markers detect Alzheimer’s disease in nonamnestic dementia. Alzheimers Dement 13, 598–601.

Blennow

, Mattsson

, Scholl

, Hansson

, Zetterberg

(2015) Amyloid biomarkers in Alzheimer’s disease. Trends Pharmacol Sci 36, 297–309.

Palmqvist

, Zetterberg

, Mattsson

, Johansson

, Minthon

, Blennow

, Olsson

, Hansson

(2015) Detailed comparison ofamyloid PET and CSF biomarkers for identifying earlyAlzheimer disease. Neurology 85, 1240–1249.

McKhann

, Knopman

, Chertkow

, Hyman

, Jack

Jr , Kawas

, Klunk

, Koroshetz

, Manly

, Mayeux

, Mohs

, Morris

, Rossor

, Scheltens

, Carrillo

, Thies

, Weintraub

, Phelps

(2011) 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 7, 263–269.

Dubois

, Feldman

, Jacova

, Hampel

, Molinuevo

, Blennow

, DeKosky

, Gauthier

, Selkoe

, Bateman

, Cappa

, Crutch

, Engelborghs

, Frisoni

, Fox

, Galasko

, Habert

, Jicha

, Nordberg

, Pasquier

, Rabinovici

, Robert

, Rowe

, Salloway

, Sarazin

, Epelbaum

, de Souza

, Vellas

, Visser

, Schneider

, Stern

, Scheltens

, Cummings

(2014) Advancing research diagnostic criteria for Alzheimer’s disease: The IWG-2 criteria. Lancet Neurol 13, 614–629.

Baird

, Westwood

, Lovestone

(2015) Blood-based proteomic biomarkers of Alzheimer’s disease pathology. Front Neurol 6, 236.

Eriksson

, Pedersen

, Reynolds

, Hong

, Prince

, Gatz

, Dickman

, Bennet

(2011) Associations of gene sequence variation and serum levels of C-reactive protein and interleukin-6 with Alzheimer’s disease and dementia. J Alzheimers Dis 23, 361–369.

10.

Rembach

, Faux

, Watt

, Pertile

, Rumble

, Trounson

, Fowler

, Roberts

, Perez

, Li

, Laws

, Taddei

, Rainey-Smith

, Robertson

, Vandijck

, Vanderstichele

, Barnham

, Ellis

, Szoeke

, Macaulay

, Rowe

, Villemagne

, Ames

, Martins

, Bush

, Masters

(2014) Changes in plasma amyloid beta in a longitudinal study of aging and Alzheimer’s disease. Alzheimers Dement 10, 53–61.

11.

Zetterberg

, Wilson

, Andreasson

, Minthon

, Blennow

, Randall

, Hansson

(2013) Plasma tau levels in Alzheimer’s disease. Alzheimers Res Ther 5, 9.

12.

Zhou

, Chan

, Chu

, Kwan

, Song

, Chen

, Ho

, Cheng

, Ho

, Lam

(2012) Plasma amyloid-beta oligomers level is a biomarker for Alzheimer’s disease diagnosis. Biochem Biophys Res Commun 423, 697–702.

13.

Shi

, Sui

, Peskind

, Li

, Hwang

, Devic

, Ginghina

, Edgar

, Pan

, Goodlett

, Furay

, Gonzalez-Cuyar

, Zhang

(2011) Salivary tau species are potential biomarkers of Alzheimer’s disease. J Alzheimers Dis 27, 299–305.

14.

, Chen

, Wang

, Han

, Zhang

, Dong

, Zhang

, Wu

, Zhao

(2015) The level of Alzheimer-associated neuronal thread protein in urine may be an important biomarker of mild cognitive impairment. J Clin Neurosci 22, 649–652.

15.

Youn

, Park

, Han

, Kim

(2011) Urine neural thread protein measurements in Alzheimer disease. J Am Med Dir Assoc 12, 372–376.

16.

O’Bryant

, Mielke

, Rissman

, Lista

, Vanderstichele

, Zetterberg

, Lewczuk

, Posner

, Hall

, Johnson

, Fong

Y-L

, Luthman

, Jeromin

, Batrla-Utermann

, Villarreal

, Britton

, Snyder

, Henriksen

, Grammas

, Gupta

, Martins

, Hampel

(2017) Perspective: Blood-based biomarkers in Alzheimer disease: Current state of the science and a novel collaborative paradigm for advancing from discovery to clinic. Alzheimers Dement 13, 45–58.

17.

Chatterjee

, Gupta

, Fagan

, Jasielec

, Xiong

, Sohrabi

, Dhaliwal

, Taddei

, Bourgeat

, Brown

, Benzinger

, Bateman

, Morris

, Martins

(2015) Decreased platelet APP isoform ratios in autosomal dominant Alzheimer’s disease: Baseline data from a DIAN cohort subset. Curr Alzheimer Res 12, 157–164.

18.

Liu

, Zhang

, Tan

, Chen

, Cao

(2015) Alterations in cholesterol and ganglioside GM1 content of lipid rafts in platelets from patients with Alzheimer disease. Alzheimer Dis Assoc Disord 29, 63–69.

19.

McGuinness

, Fuchs

, Barrett

, Passmore

, Johnston

(2016) Platelet membrane beta-secretase activity in mild cognitive impairment and conversion to dementia: A longitudinal study. J Alzheimers Dis 49, 1095–1103.

20.

Mukaetova-Ladinska

, Abdel-All

, Andrade

, Alves da Silva

, O’Brien

, Kalaria

(2015) Plasma and platelet clusterin ratio is altered in Alzheimer’s disease patients with distinct neuropsychiatric symptoms: Findings from a pilot study. Int J Geriatr Psychiatry 30, 368–375.

21.

Koc

, Uzar

, Cirak

, Parlak Demir

, Ilhan

(2014) The increase of mean platelet volume in patients with Alzheimer disease. Turk J Med Sci 44, 1060–1066.

22.

Colciaghi

, Marcello

, Borroni

, Zimmermann

, Caltagirone

, Cattabeni

, Padovani

, Di Luca

(2004) Platelet APP, ADAM 10 and BACE alterations in the early stages of Alzheimer disease. Neurology 62, 498–501.

23.

Bush

, Martins

, Rumble

, Moir

, Fuller

, Milward

, Currie

, Ames

, Weidemann

, Fischer

, Multhaup

, Beyreuther

, Masters

(1990) The amyloid precursor protein of Alzheimer’s disease is released by human platelets. J Biol Chem 265, 15977–15983.

24.

Evin

, Li

(2012) Platelets and Alzheimer’s disease: Potential of APP as a biomarker. World J Psychiatry 2, 102–113.

25.

Gowert

, Donner

, Chatterjee

, Eisele

, Towhid

, Munzer

, Walker

, Ogorek

, Borst

, Grandoch

, Schaller

, Fischer

, Gawaz

, Weggen

, Lang

, Jucker

, Elvers

(2014) Blood platelets in the progression of Alzheimer’s disease. PLoS One 9, e90523.

26.

Smith

(1997) Stimulated release of the beta-amyloid protein of Alzheimer’s disease by normal human platelets. Neurosci Lett 235, 157–159.

27.

Plagg

, Marksteiner

, Kniewallner

, Humpel

(2015) Platelet dysfunction in hypercholesterolemia mice, two Alzheimer’s disease mouse models and in human patients with Alzheimer’s disease. Biogerontology 16, 543–558.

28.

Padovani

, Borroni

, Colciaghi

, Pettenati

, Cottini

, Agosti

, Lenzi

, Caltagirone

, Trabucchi

, Cattabeni

, Di Luca

(2002) Abnormalities in the pattern of platelet amyloid precursor protein forms in patients with mild cognitive impairment and Alzheimer disease. Arch Neurol 59, 71–75.

29.

Bermejo-Bescos

, Martin-Aragon

, Jimenez-Aliaga

, Benedi

, Felici

, Gil

, Manuel Ribera

, Maria Villar

(2013) Processing of the platelet amyloid precursor protein in the mild cognitive impairment (MCI). Neurochem Res 38, 1415–1423.

30.

Borroni

, Colciaghi

, Caltagirone

, Rozzini

, Broglio

, Cattabeni

, Di Luca

, Padovani

(2003) Platelet amyloidprecursor protein abnormalities in mild cognitive impairmentpredict conversion to dementia of Alzheimer type: A 2-yearfollow-up study. Arch Neurol 60, 1740–1744.

31.

Zainaghi

, Talib

, Diniz

, Gattaz

, Forlenza

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

32.

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.

33.

Rosenberg

, Baskin

, Fosmire

, Risser

, Adams

, Svetlik

, Honig

, Cullum

, Weiner

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

34.

Di Luca

, Pastorino

, Cattabeni

, Zanardi

, Scarone

, Racagni

, Smeraldi

, Perez

(1996) Abnormal pattern of platelet APP isoforms in Alzheimer disease and Down syndrome. Arch Neurol 53, 1162–1166.

35.

Baskin

, Rosenberg

, Iyer

, Hynan

, Cullum

(2000) Platelet APP isoform ratios correlate with declining cognition in AD. Neurology 54, 1907–1909.

36.

Borroni

, Colciaghi

, Corsini

, Akkawi

, Rozzini

, Del Zotto

, Talarico

, Cattabeni

, Lenzi

, Di Luca

, Padovani

(2002) Early stages of probable Alzheimer disease are associated with changes in platelet amyloid precursor protein forms. Neurol Sci 23, 207–210.

37.

Colciaghi

, Borroni

, Pastorino

, Marcello

, Zimmermann

, Cattabeni

, Padovani

, Di Luca

(2002) [alpha]-Secretase ADAM10 as well as [alpha]APPs is reduced in platelets and CSF of Alzheimer disease patients. Mol Med 8, 67–74.

38.

Di Luca

, Pastorino

, Bianchetti

, Perez

, Vignolo

, Lenzi

, Trabucchi

, Cattabeni

, Padovani

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

39.

Jelic

, Hagman

, Yamamoto

, Teranishi

, Nishimura

, Winblad

, Pavlov

(2013) Abnormal platelet amyloid-β protein precursor (AβPP) metabolism in Alzheimer’s disease: Identification and characterization of a new AβPP isoform as potential biomarker. J Alzheimers Dis 35, 285–295.

40.

Padovani

, Pastorino

, Borroni

, Colciaghi

, Rozzini

, Monastero

, Perez

, Pettenati

, Mussi

, Parrinello

, Cottini

, Lenzi

, Trabucchi

, Cattabeni

, Di Luca

(2001) Amyloid precursor protein in platelets: A peripheral marker for the diagnosis of sporadic AD. Neurology 57, 2243–2248.

41.

Sanchez-Gonzalez

, Ortiz

, Gallegos-Arreola

, Macias-Islas

, Arias-Merino

, Loera-Castaneda

, Martinez-Cano

, Velazquez-Brizuela

, Rosales-Corral

, Curiel-Ortega

, Pacheco-Moises

, Garcia

(2006) Altered beta-amyloid precursor protein isoforms in Mexican Alzheimer’s Disease patients. Dis Markers 22, 119–125.

42.

Srisawat

, Junnu

, Peerapittayamongkol

, Futrakul

, Soi-ampornkul

, Senanarong

, Praditsuwan

, Assantachai

, Neungton

(2013) The platelet amyloid precursor protein ratio as a diagnostic marker for Alzheimer’s disease in Thai patients. J Clin Neurosci 20, 644–648.

43.

Zimmermann

, Borroni

, Cattabeni

, Padovani

, Di Luca

(2005) Cholinesterase inhibitors influence APP metabolism in Alzheimer disease patients. Neurobiol Dis 19, 237–242.

44.

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.

45.

Liberati

, Altman

, Tetzlaff

, Mulrow

, Gotzsche

, Ioannidis

, Clarke

, Devereaux

, Kleijnen

, Moher

(2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ 339, b2700.

46.

, Mertz

, Loeb

(2014) Newcastle-Ottawa Scale: Comparing reviewers’ to authors’ assessments. BMC Med Res Methodol 14, 45.

47.

Nalivaeva

, Turner

(2013) The amyloid precursor protein: A biochemical enigma in brain develoment, function and disease. FEBS Lett 587, 2046–2054.

48.

Schlossmacher

, Ostaszewski

, Hecker

, Celi

, Haass

, Chin

, Lieberburg

, Furie

, Selkoe

(1992) Detection of distinct isoform patterns of the beta-amyloid precursor protein in human platelets and lymphocytes. Neurobiol Aging 13, 421–434.

49.

Davies

, Long

, Sgro

, Rathbun

, McMenamin

, Seetoo

, Tibbles

, Billingslea

, Fine

, Fishman

, Levesque

, Smith

, Wells

, Simons

(1997) Activated Alzheimer disease platelets retain more beta amyloid precursor protein. Neurobiol Aging 18, 147–153.

50.

Cole

, Galasko

, Shapiro

, Saitoh

(1990) Stimulated platelets release amyloid beta-protein precursor. Biochem Biophys Res Commun 170, 288–295.

51.

Bush

, Whyte

, Thomas

, Williamson

, Van Tiggelen

, Currie

, Small

, Moir

, Li

, Rumble

, et al. (1992) An abnormality of plasma amyloid protein precursor in Alzheimer’s disease. Ann Neurol 32, 57–65.

52.

Tang

, Hynan

, Baskin

, Rosenberg

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

53.

Sanchez-Gonzalez

, Ortiz

, Gallegos-Arreola

, Macias-Islas

, Arias-Merino

, Loera-Castaneda

, Martinez-Cano

, Velazquez-Brizuela

, Rosales-Corral

, Curiel-Ortega

, Pacheco-Moises

, Garcia

(2006) Altered beta-amyloid precursor protein isoforms in Mexican Alzheimer’s disease patients. Dis Markers 22, 119–125.

54.

Kozubski

, Swiderek

, Kloszewska

, Gwozdzinski

, Watala

(2002) Blood platelet membrane fluidity and the exposition ofmembrane protein receptors in Alzheimer disease (AD)patients–preliminary study. Alzheimer Dis Assoc Disord 16, 52–54.

55.

Zubenko

, Teply

, Winwood

, Huff

, Moossy

, Sunderland

, Martinez

(1996) Prospective study of increased platelet membrane fluidity as a risk factor for Alzheimer’s disease: Results at 5 years. Am J Psychiatry 153, 420–423.

56.

Tamaoka

, Fukushima

, Sawamura

, Ishikawa

, Oguni

, Komatsuzaki

, Shoji

(1996) Amyloid beta protein in plasma frompatients with sporadic Alzheimer’s disease. J NeurolSci 141, 65–68.

57.

Song

, Poljak

, Smythe

, Sachdev

(2009) Plasma biomarkers for mild cognitive impairment and Alzheimer’s disease. Brain Res Rev 61, 69–80.

58.

Lövheim

, Elgh

, Johansson

, Zetterberg

, Blennow

, Hallmans

, Eriksson

(2017) Plasma concentrations of free amyloid β cannot predict the development of Alzheimer’s disease. Alzheimers Dement 13, 778–782.

59.

Srisawat

, Junnu

, Peerapittayamongkol

, Futrakul

, Soi-ampornkul

, Senanarong

, Praditsuwan

, Assantachai

, Neungton

(2013) The platelet amyloid precursor protein ratio as a diagnostic marker for Alzheimer’s disease in Thai patients. J Clin Neurosci 20, 644–648.