Volume 7 • Issue 1

33

Research Article

Specific Composite Biomarker Profiles for β-Amyloid Accumulation in
Peripheral Blood in Healthy Subjects Relevant to Biological Factors:
Comparison with Patients with Dementia
Kouji Satoh1, Maremi Sato-Ueshima1, Hiroyo Kagami-Katsuyama1, Masakazu Nakamura2, Akihiko Ogata2, Mari MaedaYamamoto3, Jun Nishihira1*

Abstract
Measurement of amyloid concentration in blood using time-of-flightmass spectrometry was developed, and the combined normalized scoreof amyloid precursor proteins (composite biomarker: CM) in plasma wasfound to be related to the accumulation of β-amyloid (Aβ) in the brain.
Therefore, we measured the CM value in healthy subjects aged 30-79
years and investigated the association between the CM value and clinicalcharacteristics using multiple regression and decision tree analyses. The
results indicated that the CM value was associated with age, sex, ApoE
genotype, blood glucose, and Mg in healthy subjects. Factors affecting CM
values differed by age. The CM values of patients with central nervous
system disorders were higher than those of the healthy subjects. Moreover,
the relationship between CM and Mg concentrations differed between thepatients and healthy subjects. These results suggest that the CM value canbe used as a biomarker for the onset of dementia, including Alzheimer’s
disease. The combination of CM values and blood component results may
allow for more accurate diagnosis.

Keywords: β-amyloid; Aging; Sex; ApoE; Magnesium; Central nervous

disordersIntroduction
The World Health Organization reports that dementia affects more than 55
million people worldwide [1], 60–70% of whom have Alzheimer’s Disease(AD). AD can also be complicated by other central nervous system (CNS)
disorders; thus, early detection of AD is crucial for all CNS disorders [2,3].
AD induces the following neuropathological changes: (1) accumulation ofneurofibrillary tangles, amyloid plaques, and other deposits; (2) atrophycaused by neural and synaptic loss; and (3) neurodegeneration, such asneuroinflammation [2,4]. In the brain, an early symptom of AD is theaccumulation of β-amyloid (Aβ), which is produced from amyloid precursor
protein (APP) by β- and δ-secretase [2,4-6]. Aβ accumulation triggers
pathological changes in AD, including amyloid plaques and NFTs. Aβ,
composed of 42 amino acids (Aβ1-42), is more likely to form amyloid plaques,and Aβ accumulation causes neuropathological changes such as loss ofsynapses. NFTs are filaments of hyperphosphorylated tau proteins and theiraggregation causes the loss of cytoskeletal components, leading neuronalloss. Tau hyperphosphorylation occurs downstream of Aβ accumulation
and activates Aβ synthesis. Thus, AD causes accumulation of Aβ in theearly stages, which results in cognitive decline. The concentration of Aβ

Affiliation:
1
Department of Medical Management andInformatics, Hokkaido Information University,
Ebetsu, Hokkaido, Japan
Department of Neurology, Hokkaido Neurosurgical
Memorial Hospital, Sapporo, Hokkaido, Japan

2

National Agriculture and Food Research
Organization, Tsukuba, Ibaraki, Japan

3*Corresponding author:
Jun Nishihira, Department of Medical Management
and Informatics, Hokkaido Information University,
Ebetsu, Hokkaido, Japan.
Citation: Kouji Satoh, Maremi Sato-Ueshima,
Hiroyo Kagami-Katsuyama, Masakazu Nakamura,
Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles
for β-Amyloid Accumulation in Peripheral Bloodin Healthy Subjects Relevant to Biological Factors:
Comparison with Patients with Dementia. Journal of
Biotechnology and Biomedicine. 7 (2024): 33-46.
Received: November 16, 2023
Accepted: January 05, 2024
Published: January 25, 2024

Volume 7 • Issue 1

Satoh K, J Biotechnol Biomed 2024
DOI:10.26502/jbb.2642-91280125

in cerebrospinal fluid (CSF) was positively correlated withwhole brain volume in non-demented individuals. Therefore,Aβ accumulation in brain may not be sufficient to induce
dementia or may be associated with brain atrophy during the
preclinical phase of AD [7,8]. Various factors influence AD
development, including sex and ApoE genotype [6,9]. Sex
differences are among the factors that affect the developmentof AD. Their effects are broad and include brain structure,stress response, and sex hormones. Serum and brain
testosterone concentrations were significantly lower in thepatients than in healthy individuals. In addition, endogenous
testosterone deficiency caused by orchiectomy leads to anincrease in soluble Aβ concentrations in the rat brains, andandrogen administration reduces Aβ concentrations [6,10].
The female hormone, estrogen, is also involved in the risk ofdeveloping AD. Ovariectomized mice show elevated levels ofsoluble Aβ in the brain, followed by accelerated accumulationof Aβ and rapid worsening of cognitive behaviors [6,10].
Decreased estrogen levels in adulthood increase the risk of
AD. Individuals with one copy of the E4-allele in the ApoE
gene have a three- to four-fold higher risk of developing
AD, and those with two copies have an 8-12 times higher
risk than those without the copy. Furthermore, with regard tosex differences, females with E4-allele have faster cognitive
decline than males [6,11].
Although Aβ is a typical biomarker of AD, its
accumulation has also been observed in other CNS disorders.
It has been reported that patients having vascular dementia(VaD) and dementia with Lewy bodies (DLB) with cognitive
decline have more Aβ accumulation in the brain than insubjects with normal cognitive function [12,13]. Patientswith Parkinson’s disease (PD) patients without cognitive
decline have Aβ concentrations in the CSF that are similar
to those in normal subjects; however, patients with PD withcognitive decline have significantly lower Aβ concentrationsin the CSF than normal subjects [13,14]. Thus, we speculatethat Aβ accumulation in the brain occur prior to the onsetof cognitive decline. Therefore, measuring Aβ accumulationin the brain using CSF or positron emission tomography(PET) is critical. However, these specialized techniques areunfavorable owing their high cost, invasiveness, and limited
availability in routine clinical practice.
In 2018, Nakamura et al. measured Aβ concentrationin plasma using mass spectrometry and showed a 90%
diagnostic accuracy for AD [15]. They demonstrated that
Aβ1-42 concentrations decreased in the plasma of patientswith AD, while Aβ1-40 and APP669-711 concentrations remained
unchanged. Furthermore, the combined normalized scoresof APP669-711/Aβ1-42 and Aβ1-40/Aβ1-42 (composite biomarker
value: CM value) positively correlated with Aβ accumulationin the brain and negatively correlated with Aβ1-42 concentration
in CSF [15,16]. Managing many samples in a limited period34with reduced invasiveness by measuring Aβ in peripheral
blood and predicting the accumulation of Aβ in the brain in
now possible.
In this study, we hypothesized that the transition fromhealthy individuals to patients with dementia, including
AD, is accompanied by a change in Aβ concentration in thebrain. We measured the change in CM value with clinicalcharacteristic factors in healthy subjects (n=603) using
multiple regression analysis and decision tree analysis. Basedon the regression equation obtained from the healthy subjects,
the estimated CM values of patients with CNS disorders(n=112) using the regression equation obtained from healthy
subjects were compared with the measured CM values. Here,
we showed that CM could be a valuable biomarker for thedetection of CNS disorders, particularly AD.

Materials and Methods
Healthy subjects
The participants were healthy volunteers registered at the
Health Information Science Research Center of Hokkaido
Information University (Ebetsu, Hokkaido, Japan). They
thoroughly understood the significance, contents, andpurposes of this study, “Accumulation and analysis ofbiomarkers involved in the prevention and improvement ofdementia in healthy subjects,” and provided written consent.
These participants had not been diagnosed or examined forcognitive decline or dementia in their clinical history as
confirmed by medical inversions at the Health Information
Science Research Center at Hokkaido Information University.
Healthy Japanese adult males and females aged 30-79 years(n = 603) participated in this study. The data included age,sex, family history of dementia, blood chemistry results, andother clinical data.

Patients with CNS disorders
We recruited patients who have been clinically diagnosed
as AD, PD with dementia (PDD), or DLB at the outpatient
clinic of the Hokkaido Neurosurgical Memorial Hospital(Sapporo, Hokkaido, Japan) and who consented to participate
in this study. Patients with AD, who satisfied the criteria ofprobable AD of the National Institute on Aging-Alzheimer’s
Association (NIA/AA) Workgroup 2011 [17] were includedin this study. Among patients with clinically diagnosed PDD
or DLB, we limited the objectives to those who satisfied thecriteria for clinically established or probable PD according
to the clinical diagnostic criteria for PD by the Movement
Disorder Society (MDS) 2015 [18] or the criteria for probable
DLB in the revised clinical diagnosis for DLB by the DLB
Consortium 2017 [19]. Furthermore, PDD or DLB was rediscriminated according to the one-year rule; we considereda patient to have DLB when dementia preceded more than
one year before the establishment of Parkinsonism [19].

Citation: Kouji Satoh, Maremi Sato-Ueshima, Hiroyo Kagami-Katsuyama, Masakazu Nakamura, Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

Volume 7 • Issue 1

Satoh K, J Biotechnol Biomed 2024
DOI:10.26502/jbb.2642-91280125

Finally, in these re-categorized patients with PDD or DLB,
we extracted those who showed atrophy of medial temporal
lobes on MRI scans [20,21] and hypoperfusion in the parietal
lobes, precuneus, and posterior cingulate gyrus on iodine123-labeled N-isopropyl-p-iodoamphetamine (123I-IMP)single-photon emission tomography (SPECT) scans [22,23],and defined them to be AD-PD or AD-DLB, combining theAD pathology.

Laboratory tests
Blood samples were collected from healthy subjects andpatients and sent to the Sapporo Clinical Laboratory, Inc.
(Sapporo, Hokkaido, Japan). Complete blood count (whiteblood cell [WBC], red blood cell [RBC], and platelet [Plt]),
liver function (aspartate aminotransferase [AST], alanine
aminotransferase [ALT], gamma-glutamyl transpeptidase[γGTP], total protein [TP], and albumin [Alb]), kidney
function (blood urea nitrogen [BUN], creatinine [Cre], anduric acid [UA]), lipid metabolism (low-density lipoproteincholesterol [LDL-C], high-density lipoprotein cholesterol[HDL-C], and triglyceride [TG]), glucose metabolism (blood
glucose [BG] and hemoglobin A1c [HbA1c]), thyroidstimulating hormone [TSH], serum electrolyte (sodium [Na],chloride [Cl], calcium [Ca], iron [Fe], magnesium [Mg],zinc [Zn]), and 25-hydroxyvitamin D (25OH.VD) was thenperformed.

Aβ measurement
Peripheral blood Aβ levels were measured as describedby Nakamura et al. [15]. Whole blood was collected in 7 mL
EDTA-2Na tubes (Venoject II, TERUMO, Shibuya, Tokyo,
Japan) and centrifuged at 2400×g for 5 min at 25°C within 60
min after blood collection. Plasma (500 µL) was transferred
to storage tubes (96 Jacket Tubes 0.7 mL, FCR&Bio, Kobe,
Hyogo, Japan) and frozen at a -80℃ freezer. After theisolation and enrichment of abundant plasma proteins byimmunoprecipitation, the specific affinity of the antibody
plasma Aβ levels was measured using immunoprecipitationmass spectrometry (IP–MS; Shimadzu Corporation, Kyoto,
Kyoto, Japan). Furthermore, they used an analyticaltechnique to quantify Aβ-related peptides of different masses
using matrix-assisted laser desorption/ionization TOF–MS(Shimadzu Corporation, Kyoto, Kyoto, Japan). Shimadzu
Corporation blindly performed the IP–MS analysis.

Genotyping of ApoE gene
Whole blood was collected in a 4 mL EDTA-2K tube
(Venoject II, TERUMO), and DNA was isolated from 400 μL
whole blood using a QIAamp DNA Blood Mini Kit (QIAGEN,
Hilden Germany). The DNA content of the concentrated
solution was quantified using NanoDrop spectrophotometer(Thermo Fisher, MA, USA). The genotype of the ApoE gene
was analyzed using Probe PCR with two TaqMan probe
sets (C___3084793_20, C____904973_10; ThermoFisher,35

MA, USA). The reaction solution was mixed with 1 ng/
μL DNA solution. ApoE was analyzed using predesigned
TaqMan genotyping assays (Applied Biosystems, CA, USA).
Approximately 1 ng of genomic DNA was amplified in a 5
μL reaction mixture in a 96-well plate containing 1 × Probe
PCR ExTaq master mix (Takara Bio, Shiga, Japan) and 1
× Taqman probe mix containing the respective primers andprobes. QuantStudio 3 was used with the standard endpoint
genotyping program (Thermo Fisher Scientific, MA,
USA). According to the manufacturer’s protocol, PCR was
performed with an initial denaturation at 94°C for 5 min,followed by 40 two-step cycles (denaturation for 30 s at
94°C, primer annealing, and extension for 60 s at 60°C).

Statistical analysis
All statistical analyses were performed using theStatistical Package for Social Sciences (SPSS® Statistics
version 25; IBM Corp, NY, USA) with a significancelevel of p<0.05. A three-step analysis was performed toinvestigate the relationship between CM values and clinicalcharacteristics in healthy subjects. First, a single regression
analysis was performed to select factors associated with
the CM value. Second, a multiple regression analysis was
performed to investigate the effects of these factors on the
CM value. Third, a classification and regression tree (CART)
was used for the analysis of CM-related clinical characteristic
factors and the classification of subject groups according tothese factors. Because the residuals of the multiple regression
analysis using the original blood test values were not normally
distributed, the blood test values and age were transformed tolog10 value, and the CM values were transformed to log10of (CM+3). Student’s t test (t-test), Mann-Whitney U test (Utest), or analysis of variance (ANOVA) were also used.

Results
Effects of biological factors (age, sex, and ApoE
genotype) on composite biomarker values in healthy
subjects
Healthy subjects were 603 Japanese individuals, ranging
in age from 30-79 years (average 54.5 years); 70% of theparticipants were female. Subjects were genotyped for ApoE,
and 20.4% were E4 carriers (Table 1). We then confirmed that
the CM values were associated with subject characteristics(age, sex, and ApoE genotype) using single regression analysis(Supplementary Table S1). Next, multiple regression analysis
was performed on the relationship between CM values andbiological factors (age, sex, and ApoE4 carrier status) (Table2). From the analysis, we found that the residuals with theregression equation were normally distributed (Shapiro-Wilk
test p=0.588) and confirmed that the multiple regression
equation was a significant equation with a significance of
p<0.001. With R2=0.140, we concluded that the prediction
accuracy of the multiple regression equation was satisfactory.

Citation: Kouji Satoh, Maremi Sato-Ueshima, Hiroyo Kagami-Katsuyama, Masakazu Nakamura, Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

Volume 7 • Issue 1

Satoh K, J Biotechnol Biomed 2024
DOI:10.26502/jbb.2642-91280125

36

Table 1: Laboratory data of healthy subjects and patients with CNS disorders.
Healthy subjects (n=603)

Demographic data

Laboratory data

Patients (n=112)

Ave

Std

Ave

Std

U-test (p)*

Age

54.49

11.35

77.87

8.4

<0.001

Woman (%)

70

56.3

ApoE4 carrier (%)

20.4

18.8

CM

-0.467

0.509

0.647

0.842

<0.001

WBC

5.71

1.44

5.46

1.45

0.106

RBC

456.9

43.3

407.1

49.3

<0.001

Plt

25.43

5.44

22.4

7.22

<0.001

AST

22.56

7.24

20.59

6.23

<0.001

ALT

19.84

12.51

15.32

7.77

<0.001

γGTP

30.94

35.71

31.95

76.07

0.015

TP

7.39

0.35

7.49

0.85

<0.001

Alb

4.64

0.25

4.41

0.51

0.041

ChE

341.6

76.1

301.4

86

0.767

BUN

14.03

3.59

17.49

5.87

<0.001

CRE

0.77

0.14

0.77

0.25

<0.001

UA

4.94

1.27

4.93

1.35

<0.001

TG

95.69

78.6

110.9

62.24

<0.001

HDL-C

76.59

19.85

58.3

16.66

<0.001

LDL-C

128

32.8

100.8

33.4

0.023

BG

88.9

11.3

111.6

34.3

0.071

HbA1c

5.37

0.34

5.73

0.65

<0.001

TSH

1.58

0.96

2.16

2.18

<0.001

Na

142.2

1.7

141.1

3.1

0.003

Cl

102.9

1.9

105.6

4

<0.001

Mg

2.11

0.14

1.99

0.23

<0.001

Ca

9.47

0.38

9.14

0.46

<0.001

Fe

97.9

34.88

81.53

33.54

<0.001

Zn

86.74

12.56

65.32

10.72

<0.00125OH-VD

17.41

7.02

14.92

5.04

<0.001*: Significant difference between healthy subjects and patients was analyzed by Mann-Whitney U test.

Citation: Kouji Satoh, Maremi Sato-Ueshima, Hiroyo Kagami-Katsuyama, Masakazu Nakamura, Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

Volume 7 • Issue 1

Satoh K, J Biotechnol Biomed 2024
DOI:10.26502/jbb.2642-91280125

The coefficients of the regression equation showed that theCM value was positively correlated with age, and its influence
was strongest among the three characteristics. In brief, theCM values were lower in females, whereas they were higher
in both sexes with the E4 allele of ApoE (Table 2).
Decision tree analysis was used to investigate therelationship between CM values and biological factors. The
results showed that the CM value of healthy subjects could beclassified into six groups according to their biological factors.
However, the factors affecting the CM values varied by age

37(Figure 1). The groups were classified by age: ≤48 years,
48-62 years, and >62 years. The two groups, ≤48 years and48-62 years, were further classified into two groups by gender(node 4, 5, 6, 7). These >62 years were divided into twogroups according to the presence or absence of the E4 allele(node 8, 9). We further investigated the distribution of CM
in node 4-9 to study the influence of sex and ApoE genotype
on CM (Supplementary Figure S1). Nodes 4 (female subjects≤48 years of age) and 5 (male subjects ≤48 years of age)
had no difference in CM values due to ApoE genotype. In

Table 2: Multiple linear regression analysis for CM value for healthy subjects of different attributes
Coefficients

Standardized Coefficients

Significance (p)95.0% Confidence Interval
Lower

Upper

0.223

-0.1930.045(Constant)-0.074Sex-0.023-0.1250.001-0.037-0.009

ApoE4

0.024

0.115

0.003

0.008

0.04

logAge

0.278

0.314

<0.001

0.21

0.346

R =0.140 ANOVA p<0.001
The dependent variable was Log10 based (CM value+3) (logCM). Independent variables: sex: 0 = male, female = 1, E4: 0 = subject without
E4 allele, 1 = subject with E4 allele, logAge: log10 of age.
2

Figure 1: Decision tree model for predicting CM values based on demographic data of healthy subjects. The root and leaf nodes are
represented by squares, which contain the average logCM±standard division and the number of subjects in each group. Each branch represents
the decision rules and each leaf node represents the classification outcome. Node 0 (healthy subjects) was classified into three subgroups
according to age: node 1 (≤48 years), node 2 (48–62 years), and node 3 (>62 years). Node 1 was classified into two subgroups according togender: node 4 (female) and node 5 (male). Node 2 was classified into two subgroups according to sex: node 6 (female) and node 7 (male).
Node 3 was classified into two subgroups according to ApoE genotype: node 8 (non ApoE4 carrier) and node 9 (ApoE4 carrier). logCM, log10of (CM value+3); logAge, log10 of age; ApoE4 carrier, No = subject without E4 allele; Yes = subject with E4 allele.
Citation: Kouji Satoh, Maremi Sato-Ueshima, Hiroyo Kagami-Katsuyama, Masakazu Nakamura, Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

Satoh K, J Biotechnol Biomed 2024
DOI:10.26502/jbb.2642-91280125

subjects aged 48-62 years, node 7 (male subjects) showed
no difference in CM values by ApoE genotype, while node
6 (female subjects) showed significantly higher CM valuesin E4 carriers (p=0.039, Mann-Whitney U test). In subjects
aged 62 years, neither node 8 (subjects without E4 allele) nor
node 9 (subjects with E4 allele) differed in CM values by sex.

Effects of blood components on composite biomarker
values in healthy subjects
The relationship between CM values and blood
components was investigated using hierarchical multiple
regression analysis. Seventeen blood components weresignificantly correlated with the CM value in a single
regression analysis (Supplementary Table S1). Demographic
factors were analyzed using the forced entry method and 17
blood components were analyzed using the stepwise method.
The results showed that the residuals with the regression
equation were normally distributed (Shapiro-Wilk test

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p=0.428) and correlated with R2=0.172 (Table 3). Based on
the coefficients of the regression equation, multiple regression
analysis, even with the addition of blood constituents, also
showed correlations with age, sex, and ApoE genotype.
Furthermore, the blood components ALT, BG, HbA1c, andMg correlated well with CM values. Moreover, ALT, BG,
and Mg concentrations positively correlated with CM values,
whereas CM values negatively correlated with HbA1c levels.
Decision tree analysis was used to investigate the relationshipbetween CM values and the seven factors obtained from themultiple regression analysis. The results showed that HbA1c
and ALT levels did not result in decision trees. The subject
groups (nodes 4-9) were the same as those in Fig. 1, up to thesecond tree. The BG resulted in a decision tree for women
aged ≤48 years (Figure 2). Mg resulted in a decision tree forsubjects aged >62 years without E4 allele (Figure 2). Based onthese results, we concluded that CM values are significantly
related to BG and Mg in the blood components.

Figure 2: Decision tree model for predicting CM values based on data of healthy subjects. The root node and leaf nodes are represented
by squares, which contain the average logCM±standard division and the number of subjects in each group. Each branch represents the decisionrules, and each leaf node represents the classification outcome. node 0 (healthy subjects) was classified into three subgroups according to age:node 1 (≤48 years), node 2 (48–62 years), and node 3 (>62 years). Node 1 was classified into two subgroups according to gender: node 4(female) and node 5 (male). Node 4 was classified into two subgroups according to logBG: node10 (≤1.892) and node 11 (>1.892). Node 2 wasclassified into two subgroups according to gender: node 6 (female) and node 7 (male). Node 3 was classified into two subgroups according to
ApoE genotype: node 8 (non ApoE4 carrier) and node 9 (ApoE4 carrier). Node 8 was classified into two subgroups according to logMg: node
12 (≤1.892) and node 13 (>1.892). Abbreviation: logCM, log10 of (CM+3); logAge, log10 of age; logBG, log10 of BG; logMg, log10 of Mg.
Citation: Kouji Satoh, Maremi Sato-Ueshima, Hiroyo Kagami-Katsuyama, Masakazu Nakamura, Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

Volume 7 • Issue 1

Satoh K, J Biotechnol Biomed 2024
DOI:10.26502/jbb.2642-91280125

Comparison of CM values between healthy subjects
and patients with CNS disorder

39

using the regression equation obtained from the data of thehealthy subject, and the results were compared with themeasured values. The results showed that the residuals of
the patients were normally distributed but were significantly
higher than those of the healthy subjects (Figure 4). Moreover,
the residuals in patients with AD (n=13) were significantly
higher than those in patients with other neurological diseases,
although the sample size was small (Supplementary FigureS2). In addition, a decision tree created using data fromPatients with neurological diseases (n=112) showed higher
CM than healthy subjects (Figure 3); however, their age andblood concentration values were significantly different fromthose of healthy subjects (Table 1). These findings suggestthat clinical characteristics should be considered whencompared CM values between patients and healthy subjects.
Accordingly, the CM values of the patients were estimatedFigure 3: The distribution of CM value in healthy subjects and patients with CNS disorders. Thisgraph shows the relationship between CM value and age in healthy subjects and patients. Blackcross =healthy subject, red circle = patient. X-axis = age, Y-axis = CM value.
Table 3: Summary of hierarchical multiple linear regression analysis predicting CM value using data of healthy subjects.
Methodforcedstepwise

Coefficients

Standardized
Coefficients

Significance (p)95.0% Confidence Interval
Lower

Upper

0.002

-0.713-0.161(Constant)-0.437Sex-0.015-0.081

0.045

-0.03

0

ApoE4

0.021

0.103

0.007

0.006

0.037

logAge

0.25

0.283

<0.001

0.176

0.324

logBG

0.272

0.154

0.001

0.113

0.431

logALT

0.044

0.096

0.02

0.007

0.08

logHbA1c-0.344-0.1050.028-0.651-0.037

logMg

0.227

0.079

0.044

0.006

0.449

R2=0.172 ANOVA p<0.001
Dependent variable: logCM = log10 of (CM value+3). Independent variable: Sex: male = 0, female = 1, E4: subject without E4 allele = 0,
subject with E4 allele = 1, logAge = log10 of age, logBG = log10 of BG, logALT = log10 of ALT, logHbA1c = log10 of HbA1c, logMg = log10 ofMg.

Citation: Kouji Satoh, Maremi Sato-Ueshima, Hiroyo Kagami-Katsuyama, Masakazu Nakamura, Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

Satoh K, J Biotechnol Biomed 2024
DOI:10.26502/jbb.2642-91280125

Volume 7 • Issue 1

40

we hypothesized that the transition from healthy to a patientwith CNS disorders might be related to changes in CM values,
and analyzed the diversity of CM value between them.

Influence of clinical characteristic factors on CM
value in healthy subjects

Figure 4: The difference of residuals between healthy subjects
and patients with CNS disorders. The residuals of healthy subjects(n=603) and patients with CNS disorders (n=105) were calculatedby the regression equation in Table3. Both residuals were normally
distributed (healthy subjects: Shapiro-Wilk test p=0.428, patients:
p=0.718). the difference in residuals between healthy subjects andpatients was calculated with student t-test (p).

healthy subjects was used to classify patients. Patients with E4
allele (node 9) had significantly higher CM values than those
without E4 allele (node 8). These results were consistent withthose of healthy subjects. In patients without the E4 allele,
there was no significant difference in the CM values between
the group of patients with a low Mg concentration (node12) and a high Mg concentration (node 13) (node12=0.539
node13=0.532, t-test: p=0.814) (Table 4). This suggests thatMg plays an important role in regulation of the absence of
the E4 allele.

Discussions
In 2019, Nakamura et al. measured APP669-711, Aβ1-40,
and Aβ1-42 in peripheral blood and calculated the CM value
by combining normalized scores of APP669-711/ Aβ1-42 andAβ1-40/Aβ1-42. Because the CM value is 0.376 or higher andis present approximately 90% of patients with AD [15], it ispossible to detect the accumulation of Aβ in the brain withhigh sensitivity. Furthermore, this method uses plasma fromavailable blood collection, with a minimal burden on patients.
In this study, we performed CM measurements on a large
number of healthy subjects and investigated the correlation
between CM values and clinical characteristics. Furthermore,In this study, we showed that the CM values increased
with age; the value in females was lower than that in males,
and the value in ApoE4 carrier was higher by multiple
regression analysis (Table 2). In addition, using decision tree
analysis, we demonstrated that demographic factors affecting
CM values varied with age (Figure 1). Among the subjects
younger than 62 years, the male group had significantly
higher CM values than those of female group. In subjects >62
years of age, the CM value is higher in ApoE4 carriers. We
found that the effects of sex and ApoE on CM values variedwith age, which may be due to the opposing effects of sex
hormones and ApoE4 on Aβ accumulation. Sex hormones are
known to have inhibitory effects on Aβ formation [9,10,2427]. In males, blood testosterone concentrations and the riskof developing AD have been correlated based on a report thatmale patients with AD have lower serum testosterone levels[25,26]. In addition, there is an inverse correlation between
testosterone and Aβ concentrations in the brain [27]. In
females, reduced estrogen levels appear to be associated withan increased risk of AD [9,10]. Treatment with ovarian sex
steroid hormones significantly increased the expression of Aβ
clearance factors and decreased the soluble Aβ level in in rat
brain [10].
In contract, ApoE4 is known to increase Aβ oligomer
formation and Aβ deposition. Therefore, E4 allele is a
genetic risk factor for Aβ accumulation in the brain [4,6,11].
Taken together, the results of the current study indicate that
Aβ concentrations in the brain are largely influenced by sex
hormones at age 48 and younger, but by ApoE genotype
at age 62 and older. In the 48-62 age group, the influenceof ApoE genotype was also observed in female, which is avariable label in female sex hormone concentrations. In
males, testosterone levels decreased linearly with the age ≥20
years [28,29]. However, in females, estrogen levels decreased
sharply with the onset of menopause [29]. Therefore, theinfluence of ApoE genotype was also observed in females in
the 48-62 age group and may imply that the Aβ clearance
mechanisms of sex hormones in brain differ between males
and females. We used multiple regression and decision tree
analyses to analyze the blood components affecting the CM
values. These results suggest a relationship between the CM
values and ALT, BG, HbA1c, and Mg (Table 3). BG and Mg
also showed a relationship with CM values in both analyses(Table 3, Figure 2).
In this study, subjects with higher BG levels had higher
CM values. Cognitive decline is a complication of diabetes

Citation: Kouji Satoh, Maremi Sato-Ueshima, Hiroyo Kagami-Katsuyama, Masakazu Nakamura, Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

Volume 7 • Issue 1

Satoh K, J Biotechnol Biomed 2024
DOI:10.26502/jbb.2642-91280125

41

of the healthy subjects (Table 4). These results may be linked
to the effect of the disease onset on clinical characteristicsof the patients. Lower blood Mg concentrations are linked
to lower cognitive function (Alzheimer-like disease)[35-37]. It has also known that patients with CNS disorders
have lower Mg concentrations [36]. In the current study,
the Mg concentrations in patients of CNS disorders weresignificantly lower than those in healthy subjects (Table 1).
In addition, some blood component concentrations weresignificantly different between the patients and healthy
subjects. This indicates that the relationship between clinicalcharacteristics and CM values may differ between healthy
subjects and patients due to pathophysiological factors,
regardless of the type of CNS disorders, and that patients
may have a greater accumulation of Aβ in the brain thanin healthy individuals. PD and DLB are categorized intoa group of diseases called α-synucleinopathy, in which
toxic oligomeric α-synuclein accumulates in nerve cells.
The concentration of α-synuclein in the CSF of patientswith PD is lower than that in healthy individuals [38]. It is
positively correlated with the concentration of Aβ1-42 in the
CSF [39]. These reports imply that the concentration of
Aβ1-42 in the blood is lower in patients with PD than in healthy
individuals. Aβ accumulation in brain has also been detectedin patients with VaD and PDD [12-14]. Therefore, patientswith CNS disorders may exhibit higher CM values than thoseof healthy individuals. Since patients with CNS disorders
have higher CM values, which are significantly higher inpatients with AD (Table 4, Supplementary Figure S2), CM
values would be a useful marker for the diagnosis of CNS
disorders, including AD. However, as CM values are affectedassociated with hyperglycemia [30-33]. These reports are
consistent with the results of this study in that the BG and
HbA1c levels of patients with CNS disorders were significantly
higher than those of healthy subjects (Table 1). In the present
study, HbA1c was inversely related to CM values associatedwith Aβ accumulation in brain, which differ from previous
reports. This difference may be due to the fact that the subjects
in this analysis are healthy subjects and the HbA1c changes
are at the level of healthy subjects, which may be different
from that of patients with hyperglycemia. The current study
showed that CM values were positively correlated with Mg
concentrations, which was expected because Mg is related
to the accumulation of Aβ. In healthy individuals, the serum
Mg concentration is physiologically constant, independent ofage, and depends on Mg intake [34,35]. However, serum Mg
concentrations are decreased in diabetes, cardiac diseases,
and cognitive decline, including AD. Mg is known to inhibit
Aβ formation and tau phosphorylation [35-37]. The role ofMg may vary depending on health and disease conditions.
However, these reports are inconsistent with the results ofthe present study. This inconsistency should be discussed
from various perspectives, including age, gender, and clinicalhistory of healthy individuals and patients.

Difference in CM values between healthy subjects
and patients with CNS disorder.
We showed that the regression equation for CM values
estimated from data of healthy subjects could not be adapted
for patients with CNS disorder (Figure 3). Moreover, thedecision tree created from the data of the healthy subjects wasapplied to the patients and yielded results different from those

Table 4: The difference of logCM values between healthy subjects and patients with CNS disorders in each subgroup classified by decision
tree analysis.
v

Healthy subjectsnmeanPatients
std▪node 1

190

0.362

0.067

▫node 4

136

0.351

0.067

n

mean

std

node 10

24

0.31

0.084

node 11

112

0.36

0.06

▫node 5

54

0.389

0.059

▪node 2

237

0.391

0.08

7

0.524

0.073

▫node 6

187

0.385

0.081

4

0.509

0.077

▫node 7

50

0.413

0.071

3

0.546

0.078

▪node 3

176

0.438

0.089

105

0.552

0.102

▫node 8

138

0.427

0.085

85

0.538

0.105

node 12

110

0.416

0.082

66

0.539

0.102

node 13

28

0.472

0.086

19

0.532

0.119

▫node 9

38

0.476

0.094

20

0.613

0.053

Distribution of healthy subjects and patients in each subgroup. N = number of subjects; mean = average logCM value in each subgroup;
std = standard deviation of the logCM value in each subgroup. The subgroups were classified using decision tree analysis, as shown in Figure 2.
Citation: Kouji Satoh, Maremi Sato-Ueshima, Hiroyo Kagami-Katsuyama, Masakazu Nakamura, Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

Volume 7 • Issue 1

Satoh K, J Biotechnol Biomed 2024
DOI:10.26502/jbb.2642-91280125

by clinical characteristics, a combination of CM values andother clinical characteristics may be desirable for diagnosis of
CNS disorders. In this study, we identified and characterized
patients with AD by decision tree analysis using the clinicalcharacteristics of subjects aged ≥70 years. The first decision
rule was the CM, followed by the Mg concentration(Supplementary Figure S3). This result suggests that CM isan effective biomarker for AD determination and Mg as abiomarker makes it a more accurate for diagnosis of AD.

Conclusions
We demonstrated that the CM value calculated from theAβ concentration in the blood was influenced by clinicalcharacteristic factors. Moreover, the difference of CM value
between healthy subjects and patients has been suggested tobe caused by the onset of CNS disorders included AD. The
measurement of Aβ in the peripheral blood used in this study
has a significant advantage, with little burden on the subjects,
and it is possible to screen and check a number of patientswith CNS disorders and healthy individuals. In future studies,measurements over time can be used to assess changes in
the rate of Aβ accumulation, which will help determine theseverity and stage of dementia in various cognitive disorders,
including AD.

Acknowledgments
This work was supported by Bio-oriented Technology
Research Advancement Institution [grant number SIP2B];
Ministry of Health, Labour and Welfare [grant numbers
JPMH19AC5003, JPMH20AC5002, JPMH21AC5002]. We
thank members of the Health information Science ResearchCenter of Hokkaido Information University and Hokkaido
Neurosurgical Memorial Hospital for their technicalassistance with the clinical trial and data treatment.

Authors contributions
KS: Investigation, Formal analysis, Visualization,
Writing- original draft. MSU: Data curation. Formal
analysis. HKK: Formal analysis. Writing - Original Draft.
MN: Investigation, Acquisition and interpretation of thedata. AO: Conceptualization, Supervision. MMY: Project
administration, Funding acquisition. JN: Conceptualization,
Project administration, Funding acquisition.

Conflicts of interest statements
The authors have no actual or potential conflicts ofinterest.

42

Ethics approval
This study was conducted in accordance with the
Declaration of Helsinki. The Ethics Committee approved
all procedures involving human subjects at Hokkaido
Information University (approved on May 26, 2021; approval
number: 2021-22). This study complied with the ethicalguidelines for human medical research of the Ministry of
Education, Culture, Sports, Science, and Technology and theMinistry of Health, Labour, and Welfare of Japan.

Funding
Mari Maeda-Yamamoto and Jun Nishihira received
funding from the cross-ministerial Strategic Innovation
Promotion Program (SIP), “Technologies for Smart Bioindustry and Agriculture” (funding agency: Bio-oriented
Technology Research Advancement Institution, Japan),grant number: SIP2B, and MHLW KAKENHI Program
Grant Number JPMH19AC5003, JPMH20AC5002, andJPMH21AC5002. These funders had no role in the study
design, data collection and analysis, decision to publish, ormanuscript preparation.

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Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

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Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

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Citation: Kouji Satoh, Maremi Sato-Ueshima, Hiroyo Kagami-Katsuyama, Masakazu Nakamura, Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

Satoh K, J Biotechnol Biomed 2024
DOI:10.26502/jbb.2642-91280125

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SUPPLEMENTARY FILES

Figure S1: Frequency of logCM value. a) As shown in Fig 1, nodes 4 and 6 are female subject groups, and nodes 5 and 7 are malesubject groups. The subjects in each group were divided into subgroups according to whether they were ApoE4 carriers. b) Node 8 is the non
ApoE4 carrier group, and node 9 is the ApoE4 carrier group. The participants in each group were divided into subgroups according to sex.
Histograms show the distribution of logCM in each subgroup. The Mann–Whitney U test was used to compare subgroups derived from thesame node. Abbreviation: logCM = log10 of (CM+3).

Figure S2: Characteristics of patients with Alzheimer’s disease without complications. Patients with AD uncomplicated by other CNS
disorders were categorized into “AD patients.” a) We compared the residuals between patients with AD (n=13) and other patients (n=92). The
difference in residuals was calculated using the Mann–Whitney U test. b) Classification of AD patients based on decision trees using healthy
subject data in Figure 2.
Citation: Kouji Satoh, Maremi Sato-Ueshima, Hiroyo Kagami-Katsuyama, Masakazu Nakamura, Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.

Satoh K, J Biotechnol Biomed 2024
DOI:10.26502/jbb.2642-91280125

Volume 7 • Issue 1

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Figure S3: Decision tree model for predicting CM values based on data of subjects aged ≥70 years. The root and leaf nodes are represented
by squares that indicate the number of subjects with probable AD. Each branch represents decision rules, and each leaf node represents aclassification outcome. Node 0 (subjects aged ≥70 years including healthy subjects and patients) was classified into two subgroups according
to logCM: node 1 (≤0.592) and node 2 (>0.592). Node 1 was classified into two subgroups according to logMg: node 3 (≤0.230) and node 4(>0.230). Node 2 was classified into two subgroups according to logMg: node 5 (≤0.322) and node 6 (>0.332).

Single regression analysis was performed to select the characteristic clinical factors correlated with logCM values. logCM: log10 (CM value+3).
*logxx = log10 transformed value.
Table S1: Results of single regression analysis.
Citation: Kouji Satoh, Maremi Sato-Ueshima, Hiroyo Kagami-Katsuyama, Masakazu Nakamura, Akihiko Ogata, Mari Maeda-Yamamoto, Jun
Nishihira. Specific Composite Biomarker Profiles for β-Amyloid Accumulation in Peripheral Blood in Healthy Subjects Relevant toBiological Factors: Comparison with Patients with Dementia. Journal of Biotechnology and Biomedicine. 7 (2024): 33-46.