June 2021

Medical Mass Spectrometry Vol. 5 No. 1

Research Paper

Application of a UHPLC-MS/MS method to investigate the metabolic
pathways of Alzheimerʼs disease and dementia with Lewy bodies
using postmortem cerebrospinal ﬂuid and serum samples
1

1,2

3

3,4

1

Yoshio Muguruma , Haruhito Tsutsui , Yoshio Hashizume , Hiroyasu Akatsu , Koichi Inoue *1

Graduate School of Pharmaceutical Sciences, Ritsumeikan University, 1‒1‒1 Nojihigashi, Kusatsu, Shiga 525‒8577, Japan
2
ONO Pharmaceutical Co., Ltd, 3‒1‒1 Sakurai, Shimamoto-cho, Mishima-gun, Osaka 618‒8585, Japan3
Department of Neuropathology, Choju Medical Institute, Fukushimura Hospital, Toyohashi, Aichi 441‒8124, Japan
4
Department of Medicine for Community-Based Medical Education, Nagoya City University Graduate School of
Medical Sciences, Nagoya, Aichi 467‒0001, Japan

Abstract

Metabolic pathways are potential diagnostic markers for the early diagnosis of age-related cognitive decline

due to Alzheimer s disease (AD) and dementia with Lewy bodies (DLB). Investigating the minute ﬂuctuations of metabolite concentration or the ratios in cerebrospinal ﬂuid (CSF) and serum that are reﬂected in brain disorders could lead to
identify a useful biomarker candidate for the diﬀerential diagnosis between AD and DLB. Most metabolites are hydrophilic, so they were measured as 9-ﬂuorenylmethyl chloroformate (FMOC) derivatives simultaneously using a liquid chromatography based on C18 column. The aim of this study was to validate and apply a simple and eﬀective ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method for the simultaneous determination of
metabolites in CSF and serum samples from AD and DLB patients with conﬁrmed diagnoses based on brain pathology. To
evaluate the diversity of ninety-nine amine-mediated metabolic patterns and pathways, we identiﬁed possible metabolites
that contributed to and mutually inﬂuenced principal component analysis with integrated analytes. The concentrations ofseveral analytes diﬀered signiﬁcantly between the AD and DLB groups. The trends in the concentrations of intermediates
in the amine metabolic pathway indicated an increase or decrease in β-alanine metabolism. The common metabolic patterns in the CSF and serum of AD and DLB patients included elevated levels of cystathionine, 3-hydroxykynurenine, 3-methoxytyramine, N ,N -dimethyl-L-arginine, glutamate, uracil, and polyamines. The concentrations of cadaverine, arginine,GG

and acetyllysine were lower. The results of our metabolic pathway analysis indicated that neurodegeneration would be
involved peripheral β-alanine metabolites. Assessing comprehensively several metabolites identiﬁed using widely targeted
metabolomics as multi-biomarker candidates would be eﬀective technique to provide novel insights on neuroscience research.
Key words: widely targeted metabolomics, Alzheimer s disease, dementia with Lewy bodies, 9-ﬂuorenylmethyl chloroformate,
UHPLC-MS/MS* Corresponding author

1. Introduction

Koichi Inoue

Diseases that cause dementia are roughly divided into

Laboratory of Clinical & Analytical Chemistry, Graduate

four categories: Alzheimer s disease (AD), dementia with
Lewy bodies (DLB), cerebrovascular dementia, and fronto-

School of Pharmaceutical Sciences, Ritsumeikan University,1‒1‒1 Nojihigashi, Kusatsu, Shiga 525‒8577, Japan

temporal dementia. AD and DLB are the most common

Tel: ＋81‒77‒561‒5838, Fax: ＋81‒77‒561‒2564
E-mail: kinoue@fc.ritsumei.ac.jp

neurodegenerative diseases. The consensus guidelines

Epub March 3, 2021.

developed by the National Institute on Aging Alzheimer s
Association (NIA-AA) are currently the most widely used

DOI: 10.24508/mms.2021.06.002

clinical criteria for AD assessment

Received: October 7, 2020. Accepted: January 2, 2021.

. A high-probability

1,2)

11

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Medical Mass Spectrometry Vol. 5 No. 1

AD diagnosis is enabled by a combination of clinical characteristics and the veriﬁed presence of indicative biomark-and serum of AD and DLB patients can signiﬁcantly

ers. These include β-amyloid 1‒42 (Aβ1‒42) and/or phosphorylated tau (p-tau) in the brain, which are detected viaof our knowledge, no other metabolites in human biologicalamyloid positron emission tomography (PET) and magnetic

and DLB have been identiﬁed. Metabolomics technology
can thus be a powerful tool to detect altered low-molecu-

improve the accuracy of diﬀerential diagnosis. To the best
ﬂuids that would facilitate the diﬀerential diagnosis of AD

3‒5). Moreover, in the CSF, These
protein markers are quantitatively analyzed with a high-sen-resonance imaging (MRI)

lar-weight metabolites that are closely associated with AD

sitivity Enzyme-Linked Immunosorbent Assay or Immuno6,7)
precipitation-mass spectrometry . The diagnostic criteria

and DLB pathogenesis.
Metabolomics methods that employ ultra-high performance liquid chromatography and tandem mass spectrome-for DLB deﬁned by McKeith and the DLB consortium

try (UHPLC-MS/MS) in selected reaction monitoring(SRM) mode are used widely to rapidly and eﬀectively sep-include decreased uptake by dopamine transporters in thebasal ganglia, which is conﬁrmed via PET or single photon
123
emission computed tomography, and reduced [ I]-me-

arate a wide range of neurochemicals. However, reversed-

taiodobenzylguanidine (MIBG) uptake during myocardial

phase LC columns retain highly polar metabolites poorly,. However, these clinical tests are

and the sensitivity of MS detection is low. To overcome

not practical in the early stages of dementia, because they
are expensive, invasive, time-consuming, and lack the nec-

these limitations, chemical reagents have recently beendeveloped to derivatize ‒NH2, ‒COOH, and ‒SH functional

8‒10)

scintigraphy imaging

groups for the simultaneous quantitative analysis of biologi16‒19). 9-Fluorenylmethyl chloroformate (FMOC)
cal samples

essary sensitivity. It is thus important to develop bioanalytical techniques for the objective assessment of dementia

is a well-known labeling reagent that reacts immediately
with primary and secondary amines under alkaline condi-

type.
AD and DLB have heterogeneous clinical and pathologic

tions to form stable complexes. Sánchez-López et al. developed an LC-MS method that included FMOC derivatization

phenotypes that often overlap, so serious misdiagnosis
occasionally occurs. It is particularly diﬃcult to identify
patients who are confused because DLB-related pathology

to detect 35 polar amino acids and amines in plasma sam-

11‒13). Although these neurodegenera-

ples . We previously reported on FMOC derivatization astive diseases have similarities, the proteinopathies caused

an eﬀective strategy for the detection of a wide range ofamine metabolites. FMOC derivatization increased the sen-has been overlooked

20)

by AD and DLB diﬀer. The etiology of AD is associatedwith pathologic Aβ and tau protein aggregation, while
DLB is α -synuclein protein aggregation. The metabolic

sitivity of MS detection in positive electrospray ionization(ESI) mode and improved chromatographic features,
including peak shape and retention time on reversed-phasechanges in biological samples will be undoubtedly reﬂected

UHPLC columns . Our multi-analyte method provided a
21)

in the diﬀerence of these protein aggregates that cause AD
or DLB. Identifying altered metabolism in the central nervous system (CNS), including the brain and CSF, would

broad view of the metabolic pathways involving amines in
CSF and serum. Determining the comprehensive metabolo-

facilitate the direct association of observed premortem
brain pathology with related neurological AD and DLB dis-mic signatures of endogenous metabolites enabled us tobetter understand the pathological status and neurodegener-

orders. On the other hand, the practical candidate markers

ativein blood samples for simple clinical diagnosis is alsomechanisms caused by metabolic disorders.
High throughput UHPLC-MS/MS assays and derivatization

required.

are thus essential for the simultaneous quantitative analysis

The metabolite concentrations in biological ﬂuids ﬂuctu-

of target metabolites to diﬀerentiate between AD and DLB.

ate signiﬁcantly as neurodegenerative disease progresses,

Proﬁling metabolic pathways for the diﬀerential diagno-

which is due to changes in the activities and concentrations

sis of dementia must consider multiple combinations thatof metabolic enzymes. It has been reported that the brain
tissues of DLB patients contain lower levels of the nor-reﬂect the direct and indirect eﬀects of the CNS on brain
tissue. However, it is not possible to use biopsied brain tis-

adrenaline (NA) metabolite 3-methoxy-4-hydroxyphenyl-

sue for clinical diagnosis in the early stages of dementia. A

14,15).

combination of strategies, including neuropsychological

Measuring the amounts of MHPG and/or NA in the CSF

testing, imaging, and omics analysis, is recommended forglycol (MHPG) than the brain tissues of AD patients

12June 2021

Medical Mass Spectrometry Vol. 5 No. 1

the identiﬁcation of suitable and distinctive CNS metabo-(ELGA, London, UK).

22,23). The metabolomic analysis
of CSF and/or serum can provide insight into neurodegenlites to diagnose dementia

2.2. FMOC derivatized UHPLC-MS/MS analysis

erative processes and facilitate the identiﬁcation of poten-

The instrumental methodology was previously reported
21)

for the metabolic analysis of CSF samples . The detailed
LC-MS methodology including the column, the mobile

tial biomarkers to diﬀerentiate between AD and DLB. In
this study, we conducted the metabolomic analysis with afocus on endogenous aminergic metabolites using validated
a UHPLC-MS/MS method with FMOC derivatization. The

phase, the gradient condition, the derivatization process,method was applied to investigate amine-mediated meta-summarized in Supplementary information and Table S1.

bolic patterns in CSF and serum samples from patients with
conﬁrmed AD- and DLB-related pathologies. We also per-2.3. Biological informationand the SRM transitions of the 99 analytes and 33 ISs are

The bioanalysis included ﬁfteen individuals who underwent accurate, reliable, and detailed pathological evalua-formed bivariate and multivariate analysis to identify potential AD and DLB markers for diﬀerential diagnosis.

tions. Five of the subjects were diagnosed with AD, and

2. Materials and Methods

ﬁve were diagnosed with DLB. The study was conﬁned topatients who had been diagnosed with gradual premortem

2.1. Chemicals and reagents

memory loss, and plaques, tangles, and/or Lewy bodies

Ninety-nine target metabolites and thirty-three internal

were observed in the brain. Characteristic senile plaques

standards (ISs) were used for our comprehensive metabolic
analysis (Fig. 1). Detail analytical standards and abbrevia-(SPs) and neuroﬁbrillary tangles (NFTs) were evaluatedbased on their three-stage (A, B, C) and six-stage (I–VI)

tions were shown in Supplementary data. 9-Fluorenylmethyl chloroformate (FMOC), sodium hydrogen carbon-

Braak scores, respectively. Lewy body was classiﬁed aseither limbic type or neocortical type based on their distri-ate (NaHCO3), distilled water, and HPLC grade ethanol,
acetonitrile, and formic acid (FA) were obtained from Fujif-

butions. All human CSF and serum samples from AD

ilm Wako Pure Chemical Co. (Osaka, Japan) and used toyears of age were provided by the Fukushimura Brain Bank

derivatize the target amines. Puriﬁed water used throughout

at Fukushimura Hospital. We obtained informed consent inwriting from the patients guardians to have autopsies per-patients 76 to 92 years of age and DLB patients 81 to 94

the study was obtained from a PURELAB Flex 5 system

Fig. 1. Metabolic pathways for amine and amino acid metabolites based on the KEGG PATHWAY database.
13

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Medical Mass Spectrometry Vol. 5 No. 1

Table 1.
GroupsDLB

ADControlNo.

Biological information

Age

Sex

SP Braak stage NFT Braak stage Brain ﬁx (h)Pathological diagnosis

1

94

FemaleC

III

3

Limbic type

2

82

Male

B

II

3

Limbic type, CAA (medium)
Neocortical type3

85

Female

C

III

2

4

81

Male

B

II

2

Neocortical type5

86

FemaleB

IV

2

Neocortical type, CAA (medium)

1

88

Male

C

IV

No data2

92

Female

C

VI

12.5

CAA (light),α-synuclein (＋)
CAA (light),α-synuclein (−)

3

80

Male

C

VI

10

CAA (medium),α-synuclein (−)

4

87FemaleC

V

18

No data

5

76

Male

C

VI

2

CAA (medium),α-synuclein (−)

̶

85

Male

A

I

2.5

CAA (heavy), Multiple infarction,

̶

96

Female(−)

II‒III

3

Lewy body(−), Multiple infarction, Arteriosclerosis

̶

86

Male

A

I

3

̶

76

Male

(−)

I

3.5

Lewy body(−), CAA(−),α-synuclein (−), Multiple cerebral infarction

̶85

Female

(−)

(−)

4

Cervical spondylotic myelopathy, Tau deposition

Bottom note: CAA: cerebral amyloid angiopathy, SP: senile plague, NFT: neuroﬁbrillary tangle.

formed and permission to publish the diagnostic andresearch results. Detailed information about the partici-

2.5. Validation

pants, including age, gender, and the severity of SP and

derivatization, we evaluated speciﬁcity, selectivity, limits of

NFT progression is summarized in Table 1.

detection (LODs), limits of quantitation (LOQs), linearity,
and recovery. The use of 33 isotopically labeled ISs mini-

2.4. Pretreatment of CSF and serum samples

mized error due to analyte loss and the variability of MS
detection, which improved analytical performance. Speci-

To validate the UHPLC-MS/MS method with FMOC

Samples containing 2 mL each were obtained from theAD and DLB subjects. Following the ethical approval by
the Ritsumeikan University ethics panel (No. BKC-

ﬁcity and selectivity were evaluated using the retention
time and MS proﬁles of pure standards. The LODs andLOQs for the amine metabolites were based on the signal-

IRB-2015-012), the samples were gently divided into
‒1.0 mL aliquots in collection tubes and stored in a −80°Cfreezer until analyzed. Aliquots of CSF and serum (200μL)

to-noise (S/N) ratios obtained at the lowest detected concentrations. Calibration curves were constructed by sequen-from the AD and DLB patients were transferred into 2.0 mL

tially diluting the stock solutions to concentrations in a

microcentrifuge tubes and spiked with 20 μL of the mixed

ﬁxed range of 1 nM to 100μM and plotting the ratios of the

IS solution and 180μL of acetonitrile. The mixtures werestandard and IS peak areas (y) against the concentrations ofvigorously vortexed for 30 s and centrifuged in a CF15RN

the adjusted standard solutions (x, nM). The linear ranges

centrifuge (Hitachi, Tokyo, Japan) at 15,000 rpm (21,200 ×included a minimum of six concentrations. High and low
recoveries (%) were calculated according to F/(F0＋A) ×

g) for 5 min at 4°
C to remove proteins. After centrifugation,
300μL of each supernatant was subjected to FMOC deri-100. F and F0 are the analyte concentrations in the spiked
and unspiked samples, respectively, and A is the spike con-vatization as described in Section 2.3. The solvent was thenremoved from the reaction mixtures again using a centrifugal evaporator. The residues were dissolved in acetonitrile

centration. The same researcher analyzed the CSF andserum samples over a short period of time at room tempera-(75μL) and centrifuged at 15,000 rpm for 5 min to remove

ture to determine repeatability.

salts, and 50μL aliquots of the supernatants were mixed
with 50μL aliquots of 0.2% FA in water. The solutions

2.6. Data analysis

were vortexed, and 5μL of each sample was used for
UHPLC-MS/MS analysis.

MassLynx 4.1 (Waters Co., Milford, MA, USA), and Tar-

Data acquisition and analysis were performed using

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Medical Mass Spectrometry Vol. 5 No. 1

getLynx software was used for data processing to construct

our target compounds (Fig. 1). We then optimized thethe calibration curves and calculate sample concentrations.

method for a wide range of target metabolites. We aimed to

The quantitative data are reported as the mean±standard

expand on our previous work and develop a reliable anddeviation (SD) obtained from two biological samples. The

rapid analytical method to quantify the vast number ofamine metabolites in biological samples using UHPLC-MS/

means and SDs were determined using Microsoft Excel
software (version 1908, Microsoft) and compared by per-MS. To simultaneously quantify endogenous metabolites
with diﬀerent chemical properties, we employed amine-se-

forming Student s t-tests or Welch s t-tests. A calculated
p-value (＜0.05) was selected to indicate statistical signiﬁ-

lective FMOC derivatization and optimized the reaction

cance in every comparison. The measured β-Ala peripheral

conditions, reaction time, and temperature to obtain highmetabolite concentrations in DLB and AD samples werenormalized, exported, and compared by bivariate and multi-

yields . Optimal FMOC derivatization was achieved with21)

a reaction time of 10 min at room temperature. For sensitiveand selective detection of the target metabolites, we investi-

variate statistical analysis using MATLAB R2019b (MathWorks, Inc, Natick, MA). A two-dimensional clustering

gated their MS fragmentation patterns and determined theprecursor and product transitions by varying the cone volt-

heatmap was constructed to compare correlations in theor AD patients. The functional codes (pdist, linkage,age or collision energy. Most of analytes were detected as＋
mono-protonated derivatives ([M＋H] ). A product ion

cophenet, dendrogram, heatmap) were used to return andvisualize a matrix that encodes a tree that contains a hierar-

derived from the FMOC reagent moiety (m/z 179) was
selectivity generated during collision-induced dissociation

chical cluster of rows of input data matrices. Regression
analysis of the receiver operating characteristic (ROC)

in the tandem MS. We thus selected this SRM transition to
analyze most of the metabolites in positive ESI mode. Spe-curves was also performed for binary classiﬁcation based

ciﬁc product ions of Tau and cysteate were observed at m/z

on metabolic changes in the CSF and serum. The functional
codes (ﬁtglm, perfcurve) were used to return a generalized

124 and m/z 112, respectively, in negative ESI mode. The
ESI-MS/MS conditions tested for the FMOC amine deriva-

linear model ﬁt to variables in the table or dataset array. We

tives are listed in Table S1. After identifying the optimal

used the areas under the ROC curves (AUCs) to evaluate

FMOC derivatization and MS conditions, we were able tothe diagnostic accuracy of the metabolic proﬁles. Principal
component analysis (PCA) of the target metabolite data

analyze 99 target metabolites in 10 minutes. Quantifying awide range of target metabolites enabled us to assess varia-

was conducted to obtain multivariate statistical models totions in the amine metabolic pathways in various humanclassify AD and DLB pathology statistically. The functional

body ﬂuids.

complex metabolic patterns of CSF and serum from DLB

codes (pca) was used to ﬁnd the principal components formissing values.

3.2. Method validation using CSF and serum
samples: LOQs, LODs, linearity, and recovery

3. Results and Discussion

biologically active metabolites in human CSF and serum in

the ingredients data. The mean value was substituted for

The quantiﬁcation assay was validated for the analysis ofterms of the LODs, LOQs, linearity, and recovery. The

3.1. Optimization of the highly sensitive and selective
targeted metabolomics method with FMOC derivatization

LOQs and LODs of the metabolites were in the nM range.

The simultaneous analysis of endogenous metabolites ina single run remains a major challenge in the ﬁeld of tar-results indicated that the assay was sensitive to detect trace

geted metabolomics, particularly for metabolites present incurves were prepared by plotting concentration (nM) on thex-axis and the derivative/IS peak area ratio on the y-axis.

The estimated LOQs (S/N＝10) ranged from 1 to 1,000 nM,
and the LODs (S/N＝3) ranged from 0.1 to 500 nM. Theselevels of the metabolites in CSF and serum. Calibration

low abundance or across a wide range of concentrations. To
analyze the proﬁles of amine metabolites in human biologi-

The linearity of the standard calibration curves in the quantiﬁcation range from 1 nM to 100μM was investigated byconstructing calibration curves that included at least six dif-

cal samples, we searched the Human Metabolome Database(HMDB)

24)and pathway databases, including the Kyoto

ferent concentrations. The linear ranges for most of the ana-

25)

Encyclopedia of Genes and Genomes (KEGG) , to select15

June 2021

Fig. 2.

Medical Mass Spectrometry Vol. 5 No. 1

Detailed metabolic variations in β -alanine metabolism observed in (A) CSF and (B) serum collected from AD and
DLB patients.

*P＜0.05, **P＜0.01, ***P＜0.005, ****P＜0.001. The color gradients indicate higher (red) or lower (blue) concentrations calculated by diving the median (AD/DLB).

lytes were spanned more than two orders of magnitude, andtwo types of dementia. Widely targeted metabolomics assay

all of the standard curves had high correlation coeﬃcients

revealed how metabolites in the DLB and AD groups werein the range of 0.990 to 1.000 when 1/x was used as aaﬀected by the respective pathologic phenotypes, whichweighting factor. 33 out of 99 analytes were measured after
diluting the CSF and serum samples ten-fold. However, thecould be useful for the diﬀerential diagnosis of DLB andconcentrations of Gln in CSF and Ala, ProOH, Asn, Glu,
Ac-Orn, uridine, Lys, and cystathionine in serum were

samples from patients with AD (n＝5), DLB (n＝5) andControl (n＝5). Typical standard and CSF UHPLC-ESI-MS/

exceeded the upper limit of the calibration range. Analyte

MS chromatograms are shown in our previous report

recovery and reproducibility were evaluated by spiking theChromatograms of all the target analytes in serum analyzed

standard solution in 180μL of pooled CSF and serum (n＝in this study are presented in Fig. S1. We quantiﬁed more

6). The validation results of human CSF samples werethan 70 of the 99 biologically active metabolites in bothAD. The method was used to analyze 15 CSF and serum

21)

21)

.

reported previously . Good recoveries from serum after
10-fold dilution were obtained for 57 of the 99 compounds

CSF and serum samples collected from AD and DLB(Supporting Information, Table S3). Recoveries from CSF(10‒10,000 nM) and serum (1,000‒10,000 nM) ranged frompossible to visualize the biochemical relationships between

44.9 to 121.3% and from 67.7 to 117.8%, respectively. The

Fig. S3). Statistically signiﬁcant diﬀerences between theconcentrations of seven peripheral β-Ala metabolites were

patients (Table S4). Metabolic pathway analysis made it
amine metabolism in the CSF and serum samples (Fig. S2,

precision of the recovery is reported as the coeﬃcient ofvariation, CV (%). Precision was within 11.5% at each con-indicated by p-values below 0.05 in Student s t-tests or
Welch s t-tests. (Fig. 2). Similar metabolic patterns

centration. We prepared 33 isotopically labeled ISs for the
study, although this was an insuﬃcient number for a robust
analysis. Therefore, we are currently investigating addi-

observed in the CSF and serum of AD and DLB patients
included increased concentrations of cystathionine,
3-KynOH, 3-MeO-tyramine, ADMA, Glu, uracil, and poly-tional ISs. The detailed method validation data are shownin Table S2 and Table S3.

amines. The concentrations of CAD, Arg, and Ac-Lys werereduced. The median ratios and p-values of the metabolites

3.3. Widely targeted metabolomic analysis of CSF andserum from DLB and AD patientsin the CSF and serum samples that reﬂected similar metabolic patterns are listed in order in Table 2. The signiﬁcanceBased on diﬀerences between the pathological character-of diﬀerences between the seven compounds was evaluatedistics of DLB and AD, metabolic variations could bein subsequent statistical analyses.
To assess the seven metabolites as CSF and serum possi-

expected in biological samples to discriminate between the16June 2021

Medical Mass Spectrometry Vol. 5 No. 1

Table 2.

List of signiﬁcant changes in metabolites in CSF or serum from AD vs DLB

Analytes

CSF

Serum
p-value

Ratio
0.37

̶

0.88

0.37

̶

1.05

0.42

EA
β -Ala0.97

Alap-value

Ratio
0.04

*

2.97

0.0002

****

1.76

0.04

*
̶
*

3.54

Uracil

2.86

0.01

̶
*

2.81

0.90

Glu
3-MeO-tyramine

1.82

0.36

̶

1.84

0.04

2.17

0.06

̶

3.56

0.30

Arg
Ac-Lys

0.82

0.69

̶

0.66

0.05

̶
*

0.66

0.53

̶

0.51

0.27

̶

ADMA

1.67

0.68

2.09

0.49

̶

DAP

1.96

0.03

̶
*

1.72

0.38

̶

CAD

0.32

0.62

̶

0.56

0.65

̶

2.19

0.32

̶

Cystathionine
3-KynOH

1.88

0.38

̶

2.31

0.57

̶

9.12

0.10

̶

SPD
Ac-SPM

1.60

0.41

̶

1.30

0.58

̶

2.88

0.36

̶

1.70

0.90

̶

SPM

1.72

0.38

̶

4.08

0.15

̶

Ratio is the comparative value of the median (AD/DLB). The p-value was calculated using Student t-test or
Welch s t test (*p＜0.05, **p＜0.01, ***p＜0.005, ****p＜0.001).

ble biomarker candidates, we performed bivariate and mul-ter outliers. The PCA results for the seven metabolites aretivariate statistical analysis and built models to diﬀerentiate

shown in Fig. 3C and 3D. In the ﬁrst principal component
plot, the horizontal (x) axis eﬀectively and distinctly sepa-

between the AD and DLB groups. The concentration ofeach compound in CSF and serum was normalized by sub-rated the two groups according to the identiﬁed metabo-tracting the mean concentration from the absolute concentration and dividing the resulting value by the SD. The heat-lites. This result was signiﬁcant, because it indicated thatmaps of the DLB and AD groups based on the normalized

patients diﬀered. The seven metabolites could thus serve as

metabolite concentrations in CSF (Fig. 3A) and serum (Fig.

possible biomarker candidates for diﬀerential diagnosis.

3B) were created. The estimated amounts of peripheral
β-Ala metabolites in the CSF and serum of AD patients

ROC curves and AUC values are widely used to evaluate

were higher than those estimated for DLB patients. The

thus performed ROC analysis for binary classiﬁcation using

concentrations of uracil and DAP in CSF were 2.9 and 2.0
times higher, respectively, and the diﬀerences were statisti-

the signiﬁcant biomarker candidates to discriminate

the metabolic proﬁles of bioﬂuids from AD and DLB

the performance of diagnostic and prognostic assays. We

between DLB and AD (Fig. 4A and 4B). Uracil in CSF and
β-Ala in serum had the best AUC values, which reachedcally signiﬁcant. The β-Ala concentration in serum wasthree times higher. Hierarchical clustering analyses was
performed to obtain respective proﬁles between the sam-

1.00. The AUC values of the others were also above 0.84.

ples and between the seven analytes. With the exception ofincluding the heat maps, hierarchical clustering, ROC
curves, and PCA collectively indicated that β-Ala metabo-

The results of bivariate and multivariate statistical analysis,

AD sample No. 2 (CSF), the samples were divided between
an AD cluster and a DLB cluster. Although we suspectedthat the EA, Ala, and β -Ala concentrations were slightly

lites in CSF and serum could be used as possible biomarker
candidates for the diﬀerential diagnosis of DLB and AD.

correlated, there was no obvious relationship between the

3.4. β-alanine metabolism

seven analytes based on the metabolic pathway (Fig. 3A,
3B). We chose to perform PCA analysis, because it reduced
the dimensionality of the data. This allowed us to discrimi-

β-Ala is largely present in the brain and muscles. It is an
endogenous β -amino acid and a core metabolic precursor

nate between the variances of the samples and identify clus-

for various metabolic pathways such as pyrimidine, β-Ala,17

June 2021

Fig. 3.

Medical Mass Spectrometry Vol. 5 No. 1

Multivariate statistical analysis to discriminate between dementia types.

Heatmap and hierarchical clustering analyses to compare peripheral β-Ala metabolic diﬀerences between (A) CSF and (B) serumfrom AD and DLB patients. Values in the heatmap were calculated by subtracting the mean concentration from the absolute concentration and dividing the resulting value by the SD. Samples listed on the vertical axis correspond to the samples in Table 1 (DLB
No. 1 to DLB No. 5; blue, AD No. 1 to No. 5; red and controls; green). PCA score plots based on peripheral β-Ala metabolitesin (C) CSF (2 kinds) and (D) serum (5 kinds) obtained using MATLAB R2019b software. The DLB samples, AD samples wereclearly distinguished by the ﬁrst principal component (x-axis). The contribution ratios of principal component 1 (PC1) and principal
component 2 (PC2) on the score plot of CSF were 51.57% and 48.43% respectively. The contribution ratios of PC1, PC2 and PC3
on the score plot of serum were 54.38%, 19.25% and 14.85% respectively.

propanoate, and pantothenate metabolism according to the
KEGG database. β-Ala is assumed to be present in bioﬂu-

β-Ala is important for primary inhibitory neurotransmit-

ids, because it has a low molecular weight and can passthrough the blood-brain barrier. As expected, β-Ala and theadult mice, including hypoxia, hypoglycemia, ischemia,

ters, including GABA and Gly. Neural cell damages inoxidative stress, and the presence of free radicals, promoted
28)
the release of β-Ala . We thus conclude that elevatedmetabolites in the CSF and serum samples were detected
and quantiﬁed accurately. Tsuruoka et al. showed that theconcentration of β-Ala was 2.0-fold higher in the serum of

β-Ala concentrations in CSF and serum might indicate neu-dementia patients . On the other hand, high serum β-Ala

than DLB patients.

ral cell damage that occurs more frequently in AD patients

26)

Imidazole dipeptide, a carnosine generated from β-Alalevels were signiﬁcantly associated with a lower risk of
27)

AD . The uracil and DAP concentrations in the CSF of
AD patients and the β -Ala concentration in the serum of

by carnosine synthase, has important roles as an antioxidant
and free radical scavenger. It also has an anti-aging eﬀect inthese patients were 2.9-fold, 2.0-fold, and 3.0-fold higher,
respectively, than DLB patients. An elevated β-Ala concen-

human ﬁbroblasts. In the anti-aging mechanism suggested

tration was consistent with the results of another study.

aging in vivo and reduces the activity of the protein .

by Hipkiss et al., carnosine binds to a protein that causes29)

18June 2021

Fig. 4.

Medical Mass Spectrometry Vol. 5 No. 1

Diﬀerential diagnostic performance based on the ROC curves of signiﬁcantly diﬀerent β -Ala metabolites.
(A) The CSF markers DAP (AUC＝0.96) and uracil (AUC＝1.00). (B) The serum markers Ala (AUC＝0.88), EA (AUC＝0.88), Arg(AUC＝0.88), β-Ala (AUC＝1.00), and Glu (AUC＝0.84).

peripheral β-Ala metabolites would lead to identify novel
multi-biomarker candidates for diﬀerential diagnosis

Although they found no signiﬁcant diﬀerence between the
carnosine concentrations in the CSF and serum of AD and
DLB patients, interestingly, the levels of β-Ala and car-between dementia types.

nosine in CSF and serum samples from DLB patients werelower than they were in samples from AD patients. β-Ala

4. Conclusions

and carnosine are also thought to have roles in neuromuscu-

In this study, we developed a sensitive and rapid
UHPLC-MS/MS assay to quantify 99 amine metabolites inlar protection based on changes in calcium sensitivity,
which is involved in exercise duration, capacity, and performance . Saunders et al. reported that β-Ala supplementa-

CSF and serum samples from AD and DLB patients with
conﬁrmed diagnoses based on brain pathology. Derivatiza-tion had a signiﬁcantly positive eﬀect on exercise, and ameta-analysis indicated that the eﬀect was due to antion with FMOC enabled us to simultaneously separate themetabolites in 10 min and evaluate diverse amine-mediated30)

metabolic patterns and pathways. This method will be useful for identifying neurometabolites that are mirrored in cir-

31)

increase in the carnosine concentration in muscle . In
short, DLB pathology may cause peripheral neuropathy,
which is the one of Parkinsonism that is characterized bybradykinesia, tremor, and muscular rigidity because β-Ala

culating blood and peripheral biomarker candidates thatmay provide insight into the metabolic behavior of the
CNS. This study showed that β-Ala metabolism was asso-and carnosine concentrations decrease. It is necessary forthe detection of carnosine in serum to improve LC-MS sen-ciated with dementia based on the results of a UHPLC-MS/

sitivity in a future investigation. Accurate quantiﬁcation
using essential or native IS and a large-scale survey ofMS assay. The concentrations of uracil and DAP in the CSF
of AD patients and the β-Ala concentration in their serum

human bioﬂuids from patients with diﬀerent types ofwere 2.9 times, 2.0 times, and 3.0 times higher, respec-

dementia is also needed.

tively, than they were in samples from DLB patients. These

Intriguingly, it has been reported that β-Ala-substituted
peptides inhibit disease-linked protein aggregation by act-

diﬀerences were statistically signiﬁcant, and they indicateding as β-sheet breaker, which can reduce the aggregation of
α-synuclein and other polypeptides associated with dis-

speciﬁc to AD and DLB. The data obtained in this study
may be highly informative for both widely targeted metabo-eases due to misfolding. The trimeric β-Ala-substituted
peptide β-Ala-Gln-Lys has also been reported to inhibit A

lomics assays and interpreting metabolomic patterns inthat certain metabolic patterns were aﬀected by pathologies

human samples. In the future, we hope to overcome the32)

β1‒40 aggregation . Therefore, bioanalysis to evaluate theassociation between β-Ala-substituted peptides and abnor-

limitation of sample size and the challenges using blood

mal brain proteins that cause dementia and identify enzyme

integrate the identiﬁcation of metabolic patterns using ourdeﬁciencies responsible for metabolic disorders using ourwidely targeted metabolomics are interesting goals for

widely targeted metabolomics method with conventional
statistical analyses and artiﬁcial intelligence-based technol-

future work. Also, further metabolomic research of theogies.

samples to diﬀerentiate between AD and DLB. We plan to19June 2021

Medical Mass Spectrometry Vol. 5 No. 1

Acknowledgments

65: 1863‒1872, 2005.

This study was supported by the grants from the Japa-

9) McKeith IG, Boeve BF, Dickson DW, Halliday G, Taylor

nese Brain Bank Network for Neuroscience Research, theMinistry of Education, Culture, Sports, Science, and Tech-JP, et al: Diagnosis and management of dementia withnology of Japan and JSPS KAKENHI Grant Numbers JP

sortium. Neurology 89: 88‒100, 2017.

Lewy bodies: Fourth consensus report of the DLB Con-

20J22464. We would like to thank Norihiro Ogawa,10) McCleery J, Morgan S, Bradley KM, Noel-Storr AH, An-

Takeshi Kanesaka and Chie Taniguchi for their technicalsorge O, et al: Dopamine transporter imaging for the diag-assistance in the clinical and pathological study, and all

nosis of dementia with Lewy bodies. Cochrane Databasepatients and their guardians for donating the patient organs.

Syst Rev 1: CD010633, 2015.
11) Oinas M, Polvikoski T, Sulkava R, Myllykangas L, Juva

Conﬂict of Interest

K, et al: Neuropathologic ﬁndings of dementia with lewy
bodies (DLB) in a population-based Vantaa 85＋study. J

The authors report no conﬂict of interest.

Alzheimers Dis 18: 677‒689, 2009.

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