Metabolic Brain Disease
https://doi.org/10.1007/s11011-020-00585-4

ORIGINAL ARTICLE

The role of lipid peroxidation in individuals with autism
spectrum disorders
Kunio Yui 1

2

1& George Imataka & Hitomi Sasaki & Ryoichi Shiroki

1

Received: 9 March 2020 / Accepted: 1 June 2020
# Springer Science+Business Media, LLC, part of Springer Nature 2020

pp

ro

va

l

Abstract
The role of malondialdehyde-modified low-density lipoprotein (MDA-LDL), an oxidized LDL, in the pathophysiology of autism
spectrum disorder (ASD) is unclear. We studied association between MDA-LDL and behavioral symptoms in 11 individuals
with ASD and 7 age -matched normal controls. Behavioral symptoms were assessed using the Aberrant Behavior Checklists(ABC). Because small sample size in this study, three measures were conducted: first, employment of adaptive Lasso forenhancing the accuracy of prediction and interpretability; second, calculation of coefficient of variation for an appropriate
selection of plasma variables; and third, selection of good candidates of plasma variables. Plasma levels of MDA-LDL,
eicosapentaenoic acid, docosahexaenoic acid (DHA) and DHA/arachidonic acid ratios were significantly higher, while plasma
superoxide dismutase (SOD) levels were significantly lower in the ASD group than in the control group. The total ABC scores
were significantly higher in the ASD group than in the control group. Multiple linear regression analysis and the adaptive Lasso
revealed association of increased plasma DHA levels with the ABC total scores and increased plasma MDA-LDL levels. Such
association between DHA and plasma MDA-LDL levels may contribute to behavior in individuals with ASD.

ra

Keywords Lipid peroxidation . Autistic behavior . Malondialdehyde-modified low-density lipoprotein . Docosahexaenoic acid
(DHA)

Introduction

Fo

Studying the mechanisms of action of lipid peroxidation is an
important research strategy to understand the pathophysiologyof autism spectrum disorder (ASD) (Meguid et al. 2011).
Membrane phospholipids in the brain are highly enriched inpolyunsaturated fatty acids (PUFAs), which are converted toother products including reactive aldehydes such as lipid peroxidation: production. Of reference, two main omega-6 fatty
acids lipid peroxidation products: malondialdehyde (MDA)
and 4-hydroxy-2-nonenal (4-HNE) have been reported(Ayala et al. 2014). MDA has been known as a useful biomarker for lipid peroxidation of omega-3 and omega-6 fatty
acids (Ayala et al. 2014). However, which of n-3 or n-6* Kunio Yui
yui16@bell.ocn.ne.jp
1

Department of Urology, Fujita Medical University School of
Medicine, Aichi 470-1192, Japan2

Department of Pediatrics, Dokkyo Medical University,
Tochigi 321-0293, Japan

PUFAs mainly contributes to the formation of MDA is still
under debate. A few studies have reported the elevation ofplasma MDA in 20 children with ASD compared to agematched 20 controls (Meguid et al. 2011), and that blood
MDA levels in 45 autistic children aged 3–11 years werehigher than age-matched 42 normal controls (GonzálezFraguela et al. 2013).
Accumulating evidence suggested a positive relationshipbetween the antioxidant protein superoxide dismutase (SOD)and MDA in erythrocytes; for example, results from a metaanalysis revealed higher MDA levels and lower SOD levels in
3961 subjects with major depression compared to 1484
healthy controls (Jiménez-Fernández et al. 2015). Other studies found higher serum MDA levels and decreased serum
SOD levels in 20 children with AD compared to 25 agematched normal controls (Meguid et al. 2011), indicating a
deficit of antioxidant defense mechanism in ASD subjects.
One of the lipid profiles, low-density lipoprotein (LDL) has
been well known to play an important role in brain development. A previous study reported significantly higher bloodlevels of LDL in 22 adult subjects with Asperger syndrome(mean age, 40.8 ± 10.8 years) than 22 age-matched normal

Metab Brain Dis

val

were matched with respect to feeding habits, age and full intelligent quotient (IQ) scores (Table 1). These individuals had thecore symptoms of the DSM-5 diagnostic criteria for ASD without any abnormal neurological symptoms. The 7 normal controls
were considered to be physically and mentally healthy based on
initial physical and mental screening tests. At the initial screening, physical (blood pressure and heart rate) and clinical laboratory examinations (hematology and plasma chemistry, including
plasma fatty acids, cholesterol and triglycerides) were performedon all 18 participants. These participants did not have any abnormalities in their physical examinations and laboratory findings.
The IQ of the individuals was estimated using the Wechsler
Intelligence Scale for children and adolescents aged 6–16 years
old (Wechsler intelligence scale for children-III, WISC-III) or therespective scale for adults (Wechsler Adult Intelligence Scale,
WAIS-R) (Table 1). None of the ASD or control individuals
had any history of neurological conditions, Attention-Deficit
Hyperactivity Disorder or learning disorders.
This study was performed with the approval of the Ethics
Committee of the Fujimoto Medical Clinic in Kobe City,
Japan. Written informed consent was obtained from the participants and/or their parents.

rocontrols, suggesting abnormal cholesterol metabolism(Dziobekm et al. 2007). Thus, blood LDL levels may contribute to the pathophysiology of ASD.
Lipid peroxidation markers such as glutathione peroxidase(GSH-Px) were significantly higher in autistic subjects compared to control children (Al-Gadani et al. 2009). Serum SOD,
and GSH-Px were significantly lower in autistic children compared with their controls, while MDA was significantly higher
among patients than controls in children less than six years ofage (Meguid et al. 2011). Additionally, the association between MDA and SOD was found in 90 children with ASD
who were treated with coenzyme Q10 (Mousavinejad et al.
2018). These studies indicated a close association between
lipid peroxidation markers and antioxidant enzyme SOD.
The MDA can further react with acetaldehyde to form
malondialdehyde-modified low-density lipoprotein (MDALDL). Recent evidence indicated that MDA-LDL is a goodmarker of oxidative stress (Ogawa et al. 2015) and lipid peroxidation. Several studies have reported that MDA-LDL significantly predicts mild traumatic brain injury (Sharma et al.
2017). However, there are a few studies on the role of MDALDL in the pathophysiology of neurodegenerative diseases
including ASD. This study for the first time examined the roleof MDA-LDA in ASD.
Taken together, the topics being studied are the role of both
lipid peroxidation marker and oxidative stress marker MDALDL and its relation to antioxidant SOD and major families of
PUFAs s in autistic behaviors of individuals. The purpose ofthis study specially addressed to examine which variable including major PUFAs and MDA-LDL may predict autistic
behaviors. For variable selection and regularization in orderto enhance the prediction accuracy and interpretability insmall sample size, we used adaptive Lasso (Wahid et al.
2017; Abdul-Razik Ismail 2015). To select reasonable variables selection and regularization, and enhance the accuracyof prediction and interpretability in small sample research,
adaptive Lasso was employed in this study.

Cautions for dealing with a small sample size

Fo

ra

pp

Small sample sizes in this study can be limitations of a study
design that can be affect the interpretation as they can affectthe interpretation of the effectiveness of a course; therefore an
approproate anakysis schould be considered (Morgan 2017).
There may be three measures for cautions in interpreting thesmall sample data; first, the outcome-adaptive lasso forselecting appropriate covariates for inclusion in propensity
score models to account for confounding bias and maintaining
statistical efficiency (Shortreed and Ertefaie 2017). To identify
the most effective variables on plasma levels of MDL-LDL,
DHA, eicosapentaenoic acid (EPA), and arachidonic acid
(ARA), omega 3/omega 6 ratio and SOD levels, a modified
least absolute shrinkage and selection operator (LASSO),
called adaptive Lasso was used; second, standard deviation(SD) are used interchangeably to express the variability because SD estimates the variability among sample observations(Curran-Everett 2008), and precise summarization of small
sample data (Nature Neuroscience, Rights and Permissions.
8, 123, 2005; https://doi.org/10.1038/nn0205-123). Of
reference, coefficient of variation (CV, %), which is definedas the SD divided by mean (SD/mean values, %)(Packiasabapathy et al. 2020) is widely used to measure therelative variation of a random variable to assess and compare
the performance of analytical techniques/equipments (Albert
and Zhang 2010). The CVs of PUFAs were 24.3–28.9%(Song et al. 2016) have been reported in blood PUFAs; andthird, identification of interesting target variables has beenreported to select good candidates for small sample size

Materials and methods
Participants
Subjects in this study comprised a total of 18 young, physically healthy individuals. Diagnoses were performed based on
the DSM-5 criteria and were additionally confirmed by theAutism Diagnostic Interview-Revised (ADI-R).
As shown in Table 1, among the 18 individuals, 11 had an
independent clinical diagnosis of ASD (8 males and 3 females,mean age: 12.3 ± 5.4 years old, age range: 6–21 years old), andthe remaining 7 were normal healthy controls (4 males and 3
females, mean age: 10.0 ± 4.1 years old, age range: 5–21 yearsold). The 11 individuals with ASD and the 7 normal controls

Metab Brain Dis
Table 1 Subject characteristics
and plasma levels of PUFAS,
MDA-LDL and antioxidant proteins, and the ABC subscalescores in the 11 individuals with
ASD and 7 normal controlsU

p value

10.0 ± 4.1

26.50

4/3

χ2 = 0.47

0.29
0.49

24.29 ± 7.25
262.29 ± 25.75

33.00
31.0

0.66
0.54

71.29 ± 17.87
5.69 ± 4.64
0.71 ± 1.25

15.00
9.00
0.00

0.035*
0.006**
0.000***

Variables

ASD(n = 11)

Controls(n = 7)Age (Year

12.3 ± 5.4

Sex (male/female)
Scores of Autism Diagnostic
Interview-Revised

3/8

Domain A (social)13.5 ± 4.3

N/A

Domain B (communication)
Domain C (stereotyped)

10.2 ± 4.4
3.7 ± 3.7

N/A
N/A

Plasma biomarkers levels
Cpa (mg/dl)
Tfb (mg/dl)

26.45 ± 6.74
276.64 ± 44.15
93.36 ± 18.54
2.45 ± 0.39
56.82 ± 22.31

MDA-LDLc (mg/dl)
SODd (U/ml)
Total scores of the ABCe
Values are mean ± SD

Cp: ceruloplasmin; b Tf: transferrin; c MDL-LDL low density: malondialdehyde modified

l

a

va

low-density lipoprotein; d SOD: superoxide dismutase; e ABC, Aberrant Behaviour Checklist* p < 0.05. ** p < 0.001, *** p < 0.001, versus normal controls (Mann-Whitney U test)
Subject characteristics

ro

abcd

ra

Assessment of behavioral symptoms

children for dyslipidemia (Kubo et al. 2017), and that nonfasting blood samples can be routinely used for the assessmentof plasma lipid profiles (Nordestgaard et al. 2016). Thus, five
hours of fasting time in this study may be reasonable.
The serum was obtained by centrifugation for 20 min at
3000 x g at room temperature (22 °C). The samples werefrozen at −80 °C until analysis. The specialists at SRL, Inc.
(Tokyo, Japan) measured the plasma levels of PUFAs, andantioxidant protein such as ceruloplasmin (Cp), transferrin(Tf), SOD and MDA-LDL.

pp

studies (Lichou et al. 2019). These three measures may be
useful for taking precautions for interpreting the data of thesmall sample size in this study.

Fo

The Aberrant Behavior Checklist (ABC) was used toassess the behavioral symptoms. ABC is an extensive
assessment that captures a wide variety of behavioral
problems (Karabekiroglu and Aman 2009). This test also appears to be capable of discriminating between several syndromes, such as disruptive behavior disorders
and the behavioral symptoms of ASD (Karabekiroglu
and Aman 2009).

Assays of plasma levels of PUFAs, Cp, SOD and Tf
Blood sampling procedures
Whole-blood samples were collected in the EDTA tubes byvenipuncture after five hours of fasting, and immediately
placed on ice in a refrigerator. Regarding fasting time, a previous review report on fasting in studies of triglycerides considered 9–12 h fasting (Simundic et al. 2014). Another study
reported that fasting for 8 h before assay of serum triglyceride
may be sufficient (Pongsuthana and Tivatunsakul 2016).
While, recent lipid studies commented that except for triglyceride, non-fasting lipid parameters are useful when screening

Plasma levels of PUFAs
The fatty acid composition of the PUFA fraction was determined as previously described (Yui et al. 2017). In summary,the total lipids were extracted from the plasma according topreviously described method. After transmethylation with
HCl-methanol, the PUFA composition was analyzed using
gas chromatography (GC2010 Shimazu Co., Japan). A totalof 24 long-chain fatty acids were identified. The plasma levelsof the fatty acids were expressed as the mean ± SD weight(percentage) of the total fatty acids.
Plasma levels of SOD
Human plasma was assayed using SOD Assay Kit (Takara
Bio, Tokyo) according to the cytochrome c method. The plasma SOD levels are expressed as units per milliliter.

Metab Brain Dis

Plasma levels of MDA-LDL
Venous blood sampling was performed during the fastingstate. An enzyme-linked immunosorbent assay used. The
plasma samples were enzyme-linked immunosorbent assay
method. The detection limit was 6.3 U/l, and intra- andinter-assay coefficients were < 5.6 and < 9.4% respectively(Kitano et al. 2004).

effective variables on plasma levels of MDL-LDL, DHA,
EPA, and ARA, omega 3/omega 6 ratio and SOD levels as ameasure for cautions in interpreting the small sample data, an
adaptive Lasso was used for statistical analysis on these plasma variables. The adaptive lasso is a model selection method
shown to be both consistent in variable selection and essential
in identifying important variables (Wahid et al. 2017) in small
sample size (Abdul-Razik Ismail 2015).

Controlling for dietary intake

Results
Results of the solution for dealing with a small sample
size in this study
Adaptive LASSO identified the most effective variables
on plasma levels of MDL-LDL, DHA, SOD concentrations, and the results were similar to those of a multiple
regression analysis. Therefore, the statistical analyses
applied in this study were proven as suitable procedures. The mean CVs of plasma lipid peroxidation related variables such as DHA, EPA and MDL-LDL
levels and plasma DHA/ARA ratio were 29.93% in the
ASD group and 33.25% in the control group. Finally,
we selected possibly sensitive lipid peroxidation markers
as candidates for small sample size studies (Lichou
et al. 2019).

Statistical analyses

Model

Fo

Table 2 Results of the multiple
linear regression

ra

pp

ro

As the data were not normally distributed, the non-parametricMann-Whitney U test for multiple comparisons was used todetermine the significant differences between the ASD andcontrol groups. Multiple regression analysis was used to confirm the relationship between the plasma oxidative stressrelated biomarkers and the other variables (two subject groups
and the ABC total scores) (Table 2). To identify the mostvalPlasma levels of PUFAS and MDA-LDL may be affected by prior dietary intake. In this study, all 18 participants received the “Japanese Food Guide” (Ministry of
Health Labour and Welfare and Ministry of Agriculture
Forestry and Fishers), which outlines the daily intake
guide of nutrients and food based on the “Overview
of Dietary Reference Intake for Japanese.

EPA

SODc
MDA-LDLd
ABCe total score
Group (1 = ASD, 2 = control)
DHA
SOD
MDA-LDL
ABCe total score
Group (1 = ASD, 2 = control)
DHA/ARASOD
MDA-LDLABC total score
Group (1 = ASD, 2 = control)

Model R2a

0.997

0.995

0.985

Model P value

Coefficients
Bb

Beta

p value

0.018 ± 0.008
0.002 ± 0.001
0.001 ± 0.001
0.132 ± 0.061

0.117
0.065
0.094
0.133

0.055
0.092
0.182
0.073

−0.035 ± 0.024- 0.004 ± 0.002- 0.002 ± 0.003- 0.228 ± 0.191- 0.102- 0.068- 0.054- 0.105

0.198
0.159
0.550
0.279

- 0.001 ± 0.008
0.000 ± 0.001
0.000 ± 0.001
0.008 ± 0.059

0.019
0.057
- 0.039
0.024

0.900
0.540
0.813
0.895

0.000**

0.000**

0.000**a

R2: R-squared values; b B: Unstandardized coefficients; c SOD: Superoxide dismutased

MDL-LDL: malondialdehyde-modified low-density lipoprotein; e ABC: Aberrant Behavior Checklist

cde

The multiple linear regression

Metab Brain Dis

Plasma levels of lipid peroxidation related biomarker
There was a significant increase in plasma MDA-LDL levels(p = 0.034) and a significant decrease in plasma SOD levels(p = 0.006) in the 11 individuals with ASD compared to the 7normal controls. Plasma EPA and DHA levels and the ratio ofplasma DHA/ARA were significantly higher in the ASD
group than in the control group (Table 1).

The adaptive Lasso was used to enhance the prediction accuracy and interpretability of the statistical model in small sample size (Wahid et al. 2017; Abdul-Razik Ismail 2015). For the
ABC total scores, plasma DHA levels (coefficient, 176.68)
and plasma DHA/ARA ratio (coefficient, −166.368) were selected by the adaptive Lasso (Table 3). Plasma EPA levels
were not selected as appropriate covariates for inclusion inpropensity score models. For plasma MDA-LDL levels, plasma levels of DHA (coefficient, −79.89) and EPA (coefficient,70.72) were selected (Table 3). Collectively, these statisticalfindings indicated that plasma DHA levels were more significantly associated with total ABC scores and plasma MDALDL levels.

DiscussionroPredictor variables: the multiple linear regression
analysis

Results of adaptive Lassol

There was no statistically significant difference be- tween thetwo groups with respect to age (p = 0.29) and sex (p = 0.49).
The 11 individuals with ASD were characterized by restricted,
repetitive and stereotyped patterns of behavior, (n = 8), irritability and crying (n = 3). Their mean total ABC score was
58.5 ± 19.6 (Table 1). Earlier studies reported a total ABC
score of 60.14 for children and adolescents with moderate tosevere ASD (Singh et al. 2014). Thus, the patients in this study
suffered from moderate behavioral autistic symptoms.

0.08, β = 0.117, p = 0.055).) (Table 2). As the plasma EPA
and DHA levels equally fit the models that distinguished the
ASD group from the control group.

vaStudy population

Because small sample sizes, this study employed three typesof measures: first, the adaptive lasso was used to identify themost effective variables on plasma levels of MDL-LDL,
DHA, eicosapentaenoic acid (EPA), and arachidonic acid
(ARA), omega 3/omega 6 ratio and SOD levels; second, CV
was well known to be useful in examining the effect of adjustment for variable cluster size (Eldridge et al. 2006); and third,
identification of useful targets for reducing target variables
were selected for small size study (Lichou et al. 2019).
Importantly, as the CVs of PUFAs were 24.3–28.9% in 305
men and women, aged 18–96 years, with immunodeficiency
virus (HIV), human herpesvirus and sarcoma (Song et al.

Table 3 Results of adaptive
Lasso

SE

Fo

ra

pp

The multiple linear regression analysis demonstrated that theplasma levels of EPA (R2 = 0.997, p = 0.000) and DHA (R2 =0.995, p = 0.000), and plasma ratios of DHA/ARA (R2 =0.985, p = 0.000) and EPA/ARA (R2 = 0.978, p = 0.000) weresignificantly associated with adjustment in the variables, SOD
and the total ABC scores in the two subject groups (Table 3).
These findings indicated that the plasma EPA and DHA levels
equally allowed for the prediction of these variables in the twogroups. The use of the plasma SOD levels as the dependent
variable showed a trend towards significant contribution to theplasma EPA levels (unstandardized coefficients, B = 0.18 ±Intercept

ABC total scores
DHAa
DHA/ARA
ARAb
SODc
MDA-LDLd
DHA
EPAe
SODf
Tfg
a

Estimate

p value

95% confidenceInterval

Lower boundUpper bound176.69−166.37−104.16−33.26

96.86
79.85
87.86
18.94

0.073
0.037
0.039
0.079

−17.08−322.87−203.71−70.37370.45−9.87−5.035
3.87−79.8970.73−47.3230.10

113.92
28.58
12,33
20.67

0.483
0.013
0.001
0.080

−506.86
14.72−71.50−4.43−250.94126.73−23.15
76.61

DHA: docosapentaenoic acid; b ARA: Arachidonic acid; c SOD: Superoxide dismutase; d low-density:malondialdehyde-modified low-density lopoprotein; e EPA: Eicosapentaenoic acid; f SOD; g Tf: Transferrin

Metab Brain Disval

DHA is most susceptible to oxidation among the PUFAs(Luchi et al. 2018). Therefore, plasma DHA levels might havea more potent influence on autistic behaviors. DHA changed theoxidant/antioxidant balance such as increased SOD and decreased MDA in the rat brain tissue (Avramovic et al. 2016) havebeen reported. Drawing these findings together, increased MDALDL levels may be closely related to decreased plasma SOD
levels. Therefore, plasma DHA levels primarily and preferentially influence autistic behaviors.
There is a trend toward significant correlation in plasma
levels between MDA-LDL and SOD. MDA is the end productof lipid peroxidation in the cell membrane, and its content canreflect the extent of lipid peroxidation. Plasma MDA levels
were consistently increased in children with ASD, while plasma SOD levels decreased, increased, or remained unchanged(Altun et al. 2018). Thus, the present finding that elevatedserum levels of MDA-LDL are associated with decreased serum levels of SOD may be reasonable.
MDA has been found to weaken cerebral function during
carbonyl stress (Itokawa et al. 2018) and MDA treatment
in vitro reduced cortical neuronal viability and induced cellular apoptosis and necrosis simultaneously (Cheng et al. 2011).
However, MDA is considered as an oxidative stress marker
and a fraction of metabolite of n-3 PUFAS. Therefore, plasma
MDA-LDL might have a little effect on autistic behaviors inindividuals with ASD.
In this study, plasma SOD levels in the ASD group weresignificantly lower than in the control groups. The increase ofserum DHA levels as well as serum DHA/ARA ratio might
counteract serum SOD levels, which might reduce serum
SOD levels. SOD did not afford any protection against theloss of barrier integrity induced by DHA (Roig-Pérez et al.
2014), and played a primary role in mediating the behavioral
plasticity (Horspool and Chang 2018). Particularly, SOD
plays a key role in the nervous system to mediate pathogen
avoidance behavior in response to the internal state of redox
homeostasis and avoidance behavior against dangerous situations (Horspool and Chang 2018). These observations suggestthat lowered plasma SOD levels might induce neuronal deficit
related to autistic behavior in the autism group. However,further precise studies are needed.
Our study had some limitations: the small sample size may
increase the likelihood of a false null hypothesis (type II andtype I errors), which skews the results and decreases the powerof the study. Previous studies have demonstrated that theadaptive Lasso used in this study is very competitive in termsof variable selection, estimation accuracy, and high efficiency
when small sample sizes are used (Wahid et al. 2017; AbdulRazik Ismail 2015). Using adaptive Lasso in statistical analyses may improve the reproducibility and sensitivity of thefindings and minimize the likelihood of type II as well as typeI errors due to small sample sizes. Additionally, the resultsfrom the linear regression analysis and adaptive Lasso were

Fo

ra

pp

ro

2016), the CVs of plasma variables in this study may indicate
an appropriate variable selection. These three measures may
contribute to taking cautions need by interpreting the small
sample size data in this study.
The multiple linear regression analysis and the adaptive
Lasso identified plasma DHA levels that equally fit themodels that distinguished the ASD group from the control
group. We conducted a control experiment for the dietaryintake of all 20 participants.
The present findings suggested that plasma DHA levels werethe most useful predictors for autistic behavior. Moreover, for
ABC total scores, the adaptive Lasso selected serum DHA levels.
Thus, plasma DHA levels may be associated with autistic behaviors. Of reference, a review article studied the association between n-3/n-6 PUFA ratio and the risk of breast cancer throughPubMed, Cochrane Library and EMBASE database on relevant
English-language studies concluded that a higher ratio of n-3/n-6
PUFA was associated with a lower risk of breast cancer due tolowered serum phospholipids (Yang et al. 2014). We have already reported that a higher plasma n-3/n-6 ratio may be related
to lower plasma levels of ARA-related signaling mediator suchas ceruloplasmin, and thus may contribute to the pathophysiology of ASD (Yui et al. 2017). These previous studies indicated thebeneficial effects of a higher ratio of n-3/n-6 PUFA as reflectedby increased DHA levels in human health or inflammatory
outcomes.
In this study, plasma DHA levels have more important effects
on autistic behaviors compared to plasma EPA levels or plasma
EPA/ARA ratio. These potential DHA effects on the neuronal
function may depend on the following neuronal membrane properties (Dyall 2015): a) DHA had a much greater capacity ofaccumulating into sphingomyelin/cholesterol-rich lipid rafts than
EPA, inducing a much greater effect on cell signaling; b) DHA is
more favorably converted into phosphatidylserine increasing
neuronal survival species in the brain; c) DHA has a uniqueand indispensable role in neuronal membranes, whereas, the rolefor EPA is only as a precursor to the eicosanoids and related
peroxy-fatty acids; d) DHA also promotes synaptogenesis andsynaptic expression of synapsin and glutamate receptors, producing more positive effects on neuronal plasticity. Collectively,
DHA has more important roles in neuronal function including
signaling and neuronal survival compared to EPA. Furthermore,a recent review article indicated that high DHA fish oils havemore excellent effects on various human health conditions including the brain and visual function, inflammation and immune
function (Ghasemi Fard et al. 2019). Thus, more potential effectsof DHA on the neuronal function might contribute to autistic
behaviors. These previous findings may support the present findings that plasma DHA levels a have more potent influence on
autistic behaviors.
DHA is a major component of the brain and a lipid essential
for its structure and function by providing a lipid domain and has
an important role in signaling and lipid mediator production.

Metab Brain Dis

Authorship clarified All authors adhere to the guidelines for authorshipthat are applicable in their specific research field and the following
guidelines:
1)
2)
3)

Kunio Yui developed the study project.
All authors whose names appear on the submission.
Kunio Yui, George Imataka and Hitomi Sasaki made substantial
contributions to the conception, design of the work, the acquisition,
analysis, or interpretation of data; Hitomi Sasaki and or Ryoichi
Shiroki reviewed the paper. All authors drafted the work or revised
it critically for important intellectual content and agree to be accountable for all aspects of the work in ensuring that questions related tothe accuracy or integrity of any part of the work are appropriately
investigated and resolved.

Data transparency All authors made sure that all data and materials aswell as software application or custom code support their publishedclaims and comply with field standards. All authors have agreed thatjournals may have individual policies on (sharing) research data in concordance with disciplinary norms and expectations.

roCompliance with ethical standards

The corresponding author, and the order of authors are all correct at
submission. We understood that change to authorship cannot bemade after acceptance of a manuscript.

lFunding information This study was funded by a Grant-in-Aid for
Scientific Research (C) (2014–2016; No 26461777) from the Ministryof Education, Culture, Sports, Science and Technology, Japan.

&vaconsistent with previous reports, which indicated two important aspects of lipid peroxidation: the association between increased plasma MDA-LDL levels and decreased plasma SOD
levels (Meguid et al. 2011; Jiménez-Fernández et al. 2015)
and the role of plasma DHA levels (Dyall 2015; Luchi et al.
2018; Ghasemi Fard et al. 2019). Although the sample size issmall, this paper contributes to growing evidence that plasma
DHA levels may play important roles in lipid peroxidationand consequently in autistic behavior. However, further studies with larger sample sizes are needed.
In conclusion, the present findings might provide useful
information that increases in plasma DHA levels might increase plasma MDA-LDL levels, which counteracts plasma
SOD levels, thus reducing plasma SOD levels. This neurobiological phenomenon may induce neuronal deficit related to
autistic behavior in individuals with ASD. Three measures forsmall sample size may contribute to taking cautions need byinterpreting the small sample size data in this study.

Referencespp

Conflict of interest The authors have no conflict of interests concerning
the materials or methods used in the present study or findings presented inthis study.

Ethical responsibilities of authors

&
&
&

&
&
&

&

The manuscript was not be submitted to more than one journal for
simultaneous consideration.
The submitted work was original and should not have been publishedelsewhere in any form or language (partially or in full).
A single study was not be split up into several parts to increase thequantity of submissions and submitted to various journals or to one
journal over time (i.e. ‘salami-slicing/publishing’).
Results were presented clearly, honestly, and without fabrication,
falsification or inappropriate data manipulation (including image
based manipulation). Authors adhered to discipline-specific rules
for acquiring, selecting and processing data.
No data, text, or theories by others are presented as if they were theauthor’s own (‘plagiarism’).
This research do not misapply to pose a threat to public health ornational security.
We avoided untrue statements about an entity (who can be an individual person or a company) or descriptions of their behavior or
actions that could potentially be seen as personal attacks or allegations about that person.
Research that did not misapply to pose a threat to public health ornational security.

Fo

&

ra

Informed consent Informed consent was obtained by all participants inthis study. This study was performed with the approval of the Ethics
Committee of the Fujimoto Medical Clinic in Kobe City, Japan.
Written informed consent was obtained from the patients or their parents.

Abdul-Razik Ismail E (2015) Behavior of lasso quantile regression withsmall sample sizes. J Multidiscip Eng Sci Technol 2:388–394 ISNN,
3159–0040
Albert A, Zhang L (2010) A novel definition of the multivariate coefficient ofvariation. Biom J 52:667–675. https://doi.org/10.1002/bimj.201000030
Al-Gadani Y, El-Ansary A, Attas O, Al-Ayadhi L (2009) Metabolic
biomarkers related to oxidative stress and antioxidant status in
Saudi autistic children. Clin Biochem 42:1032–1040. https://doi.
org/10.1016/j.clinbiochem.2009.03.011
Altun H, Şahin N, Kurutaş EB, Karaaslan U, Sevgen FH, Fındıklı E(2018) Assessment of malondialdehyde levels, superoxide dismutase, and catalase activity in children with autism spectrum disorders.
Psychiatry Clin Psychopharmacol 28:408–415. https://doi.org/10.
1080/24750573.2018.1470360
Avramovic N, DragutinovicV KD, Colovic M, Trbovic A, de Luka S,
Milovanovic I, Popovic T (2016) The effects of omega 3 fatty acidsupplementation on brain tissue oxidative status in aged Wistar rats.
Hippokratia 16(3):241–245
Ayala A, Muñoz MF, Argüelles S (2014) Lipid peroxidation: production,
metabolism, and signaling mechanisms of malondialdehyde and 4hydroxy-2-nonenal. Oxidative Med Cell Longev 2014:360438–
360431. https://doi.org/10.1155/2014/360438
Cheng J, Wang F, Yu DF, Wu PF, Chen J (2011) The cytotoxic mechanism of malondialdehyde and protective effect of carnosine via protein cross-linking/mitochondrial dysfunction/reactive oxygen
species/MAPK pathway in neurons. Eur J Pharmacol 650:
650184–650194. https://doi.org/10.1016/j.ejphar.2010.09.033
Curran-Everett D (2008) Explorations in statistics: standard deviations
and standard errors. Adv Physiol Educ 32(3):203–208. https://doi.
org/10.1152/advan.90123.2008

Metab Brain Disval

Kronenberg F, Remaley AT, Rifai N, Ros E, Langlois M, European
Atherosclerosis Society (EAS) and the European Federation of Clinical
Chemistry and Laboratory Medicine (EFLM) Joint Consensus Initiative(2016) Fasting is not routinely required for determination of a lipid
profile: clinical and laboratory implications including flagging at desirable concentration cutpoints-a joint consensus statement from theEuropean atherosclerosis society and European Federation of Clinical
Chemistry and Laboratory Medicine. Clin Chem 62:930–946. https://doi.org/10.1373/clinchem.2016.258897
Ogawa K, Tanaka T, Nagoshi T, Sekiyama H, Arase S, Minai K, Ogawa
T, Yoshimura M (2015) Increase in the oxidised low-density lipoprotein level by smoking and the possible inhibitory effect of statin
therapy in patients with cardiovascular disease: a retrospective
study. BMJ Open. https://doi.org/10.1136/bmjopen-2014-005455
Packiasabapathy S, Prasad V, Rangasamy V, Popok D, Xu X, Novack V,
Subramaniam B (2020) Cardiac surgical outcome prediction byblood pressure variability indices Poincaré plot and coefficient ofvariation: a retrospective study. BMC Anesthesiol 20:56. https://doi.
org/10.1186/s12871-020-00972-5
Pongsuthana S, Tivatunsakul N (2016) Optimal fasting time before measurement of serum triglyceride levels in healthy volunteers. J Med
Assoc Thail 99(Suppl 2):S42–S46
Roig-Pérez S, Guardiola F, Moretó M, Ferrer R (2014) Lipid peroxidationinduced by DHA enrichment modifies paracellular permeability in
Caco-2 cells: protective role of taurine. J Lipid Res 45(8):1418–1428
Sharma R, Rosenberg A, Bennett ER, Laskowitz DT, Acheson SK (2017) A
blood-based biomarker panel to risk-stratify mild traumatic brain injury.
PLoS One 12:e0173798. https://doi.org/10.1371/journal.pone.0173798
Shortreed SM, Ertefaie A (2017) Outcome-adaptive lasso: variable selection for causal inference. Biometrics 73:1111–1122. https://doi.org/
10.1111/biom.12679
Simundic AM, Cornes M, Grankvist K, Lippi G, Nybo M (2014)
Standardization of collection requirements for fasting samples: for
the Working Group on Preanalytical Phase (WG-PA) of theEuropean Federation of Clinical Chemistry and Laboratory
Medicine (EFLM). Clin Chim Acta 432:33–337. https://doi.org/
10.1016/j.cca.2013.11.008
Singh K, Connors SL, Macklin EA, Smith KD, Fahey JW, Talalay P,
Zimmerman AW (2014) Sulforaphane treatment of autism spectrum
disorder (ASD). Proc Natl Acad Sci U S A 111:15550–11555.
https://doi.org/10.1073/pnas.1416940111
Song X, Schenk JM, Diep P, Murphy RA, Harris TB, Eiriksdottir G,
Gudnason V, Casper C, Lampe JW, Neuhouser ML (2016)
Measurement of circulating phospholipid fatty acids: association
between relative weight percentage and absolute concentrations. J
Am Coll Nutr 35:647–656
Wahid A, Khan DM, Hussain I (2017) Robust adaptive lasso method forparameter's estimation and variable selection in high-dimensional
sparse models. PLoS One 12(8):e0183518. https://doi.org/10.1371/
journal.pone.0183518 eCollection 2017
Yang B, Ren XL, Fu YQ, Gao JL, Li D (2014) Ratio of n-3/n-6 PUFAs
and risk of breast cancer: a meta-analysis of 274135 adult females
from 11 independent prospective studies. BMC Cancer 4:105.
https://doi.org/10.1186/1471-2407-14-105
Yui K, Imataka G, Kawasak Y, Yamada H (2017) Increased ω-3 polyunsaturated fatty acid/arachidonic acid ratios and upregulation of
signaling mediator in individuals with autism spectrum disorders.
Life Sci 145:205–212. https://doi.org/10.1016/j.lfs.2015.12.039

Fo

ra

pp

ro

Dyall SC (2015) Long-chain omega-3 fatty acids and the brain: a reviewof the independent and shared effects of EPA, DPA and DHA. Front
Aging Neurosci 7:52. https://doi.org/10.3389/fnagi.2015.00052
Dziobekm I, Gold SM, Wolf OT, Convit A (2007) Hypercholesterolemia inAsperger syndrome: independence from lifestyle, obsessive-compulsive
behavior, and social anxiety. Psychiatry Res 149(1–3):321–324
Eldridge SM, Ashby D, Kerry S (2006) Sample size for cluster randomized trials: effect of coefficient of variation of cluster size and analysis method. Int J Epidemiol 35:1292–1300
Ghasemi Fard S, Wang F, Sinclair AJ, Elliott G, Turchini GM (2019)
How does high DHA fish oil affect health? A systematic review of
evidence. Crit Rev Food Sci Nutr 59:1684–1727. https://doi.org/10.
1080/10408398.2018.1425978
González-Fraguela ME, Hung MD, Vera H, Maragoto C, Noris E, Blanco
L, Galvizu R, Robindon M (2013) Oxidative stress markers in children with autism spectrum disorders. Br J Med Med Res 3:307–317
Horspool AM, Chang HC (2018) Neuron-specific regulation of superoxide dismutase amid pathogen-induced gut dysbiosis. Redox Biol 17:
377–385. https://doi.org/10.1016/j.redox.2018.05.007
Itokawa M, Miyashita M, Arai M, DanT, Takahashi K, Tokunaga T et al(2018) Pyridoxamine: A novel treatment for schizophrenia with enhanced carbonyl stress. Psychiatry Clin Neurosci 72:35–44. https://doi.org/10.1111/pcn.12613
Jiménez-Fernández S, Gurpegui M, Díaz-Atienza F, Pérez-Costillas L,
Gerstenberg M, Correll CU (2015) Oxidative stress and antioxidant
parameters in patients with major depressive disorder compared tohealthy controls before and after antidepressant treatment: resultsfrom a meta-analysis. J Clin Psychiatry 76:1658–1667. https://doi.
org/10.4088/JCP.14r09179
Karabekiroglu K, Aman MG (2009) Validity of the aberrant behavior
checklist in a clinical sample of toddlers. Child Psychiatry Hum
Dev 40:99–110. https://doi.org/10.1007/s10578-008-0108-7
Kitano S, Kanno T, Maekawa M, Sakiurabayashi I, Kotani K, Hisatomi H(2004) Improved method for the immunological detection ofmalondialdehyde-modified low-density lipoproteins in human serum. Anal Chim Acta 509:229–235
Kubo T, Takahashi K, Furujo M, Hyodo Y, Tsuchiya H, Hattori M,
Fujinaga S, Urayama K (2017) Usefulness of non-fasting lipid parameters in children. J Pediatr Endocrinol Metab 30:77–83. https://doi.org/10.1515/jpem-2016-0271
Lichou F, Orazio S, Dulucq S, Etienne G, Longy M, Hubert C, Groppi A,
Monnereau A, Mahon FX, Turcq B (2019) Novel analyticalmethods to interpret large sequencing data from small sample sizes.
Hum Genomics 13:41. https://doi.org/10.1186/s40246-019-0235-1
Luchi K, Ema M, Suzuki M, Yokoyama C, Hisatomi H (2018) Oxidized
unsaturated fatty acids induce apoptotic cell death in cultured cells. Mol
Med Rep 19:2767–2773. https://doi.org/10.3892/mmr.2019.9940
Meguid N, Dardir AA, Abdel-Raouf ER, Hashish A (2011) Evaluation ofoxidative stress in autism: defective antioxidant enzymes and increased lipid peroxidation. Biol Trace Elem Res 143:58–65.
https://doi.org/10.1007/s12011-010-8840-9
Morgan CJ (2017) Use of proper statistical techniques for research studies
with small samples. Am J Phys Lung Cell Mol Phys 313:L873–
L877. https://doi.org/10.1152/ajplung.00238.2017
Mousavinejad E, Ghaffari MA, Riahi F, Hajmohammadi M, Tiznobeyk
Z, Mousavinejad M (2018) Coenzyme Q10 supplementation reduces
oxidative stress and decreases antioxidant enzyme activity in children with autism spectrum disorders. Psychiatry Res 265:62–69.
https://doi.org/10.1016/j.psychres.2018.03.061
Nordestgaard BG, Langsted A, Mora S, Kolovou G, Baum H, Bruckert E,
Watts GF, Sypniewska G, Wiklund O, Borén J, Chapman MJ, Cobbaert
C, Descamps OS, von Eckardstein A, Kamstrup PR, Pulkki K,

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.