Shinshu Med J, 67⑶：157～166, 2019

Associations of Autism Spectrum Quotient and Personality Profiles
with Eating Behaviors in Patients with Anorexia Nervosa andin a Non-clinical Population
Yuka Takahashi-Asai1）, Daimei Sasayama1）＊, Nobuhiro Sugiyama1）2）
Fumi Maruyama3） and Shinsuke Washizuka1）
1） Department of Psychiatry, Shinshu University School of Medicine
2） Department of Applied Occupational Therapy, Shinshu University School of Health Sciences
3） Department of Psychiatry, Japanese Red Cross Society Suwa Hospital

Several lines of evidence have revealed that patients with anorexia nervosa (AN) exhibit autistic featuresand characteristic personality profiles. The aims of the present study were : 1) to investigate whether AN is as­sociated with autistic features and a distinct personality profile, and 2) to examine the influence of personality
profiles and autistic features on body weight and eating attitudes in a non-clinical population. Participants were
22 women with AN and 94 healthy women. All participants were administered the Temperament and Character
Inventory (TCI), the Autism Spectrum Quotient (AQ), and the 26-item Eating Attitude Test (EAT-26). Consis­
tent with previous studies, the TCI revealed that patients with AN had significantly higher ‘harm avoidance’and lower ‘novelty seeking’, ‘reward dependence’, and ‘self-directedness’ dimension scores than healthy controls.
AQ scores were significantly higher in the patient group than in the healthy controls. Further, we found thatelevated AQ scores and a distinct personality profile were observed in AN patients. These were also associatedwith restrictive eating behaviors, as assessed by the EAT-26, in healthy individuals. Our findings suggest thathigh AQ scores and the characteristic TCI profile observed in patients with AN are associated with eating
behaviors prior to the onset of AN. Furthermore, high AQ scores and a distinct personality profile may be arisk factor for developing AN. Further studies using a longitudinal design may confirm these findings and help
to develop AN prevention methods for effectively targeting young individuals at risk. Shinshu Med J 67 : 157―
166, 2019(Received for publication December 10, 2018 ; accepted in revised form January 17, 2019)
Key words : anorexia nervosa, Temperament and Character Inventory, Autism Spectrum Quotient,
Eating Attitude Test

Ⅰ Introduction

prevalence of AN among young women is approxi­
mately 0.3 ％ 2）. Risk factors for AN include a first-

Anorexia nervosa (AN) is an eating disorder (ED)degree relative with AN3） and an anxiety disordercharacterized by the following three essential features :and/or obsessional traits in childhood4）. The semi-

persistent energy intake restriction ; intense fear of

starvation and purging associated with AN may also

gaining weight or of becoming fat, or persistent be­

result in life-threatening medical complications.

havior that interferes with weight gain ; and distur­Thus, a high mortality rate of 5.6 ％ per decade has

1）bances in body weight or shape . The 12-month＊

Corresponding author : Daimei Sasayama
Department of Psychiatry, Shinshu University School of
Medicine, 3-1-1 Asahi, Matsumoto, Nagano 390-8621, Japan
E-mail : sasayama@shinshu-u.ac.jp

No. 3, 2019

been reported in a prior meta-analysis of AN5）.
Previous studies have suggested that certain per­sonality traits are associated with the developmentof AN. Studies using the Temperament and Character
Inventory (TCI) assessment have found that patients
157

Takahashi-Asai・Sasayama・Sugiyama et al.

with AN have significantly lower ‘novelty seeking’gated the personality profiles and AQ scores of Jap­and ‘reward dependence’ but higher ‘harm avoidance’

anese women with and without AN. Further, we im­

6）than healthy controls . Additionally, Fassino et al.

7）plemented latent profile analysis (LPA) using TCI

reported that patients with AN had elevated ‘harm

dimensions and AQ scores as indicators to examine

avoidance’ and ‘persistence’ and low ‘self-directedness’

profiles associated with eating attitudes in non-ANlevels when compared to controls.

participants. The aims of the present study were : 1)

Although these previous studies have focused on

to investigate whether the AN group is associatedthe associations between AN and each TCI dimen­with a certain personality profile and autistic fea­sion, these personality dimensions do not exist in iso­tures, and 2) to examine the influence of personality

lation but rather interact dynamically with one an­

profiles and autistic features on body weight andother. When considering the complex interactions

eating attitudes in a non-clinical population.

that exist among these dimensions, a person-centered

Ⅱ Methods(rather than a variable-centered) statistical method
may be suitable for use in classifying individualsＡ

ParticipantsParticipants were 116 women aged 16 years orbased on their TCI dimensions. In light of this, Krug8）

used a latent profile analysis to identify per­older (22 patients with anorexia nervosa and 94

sonality profiles associated with EDs. They utilized ahealthy controls). All participants were Japanese

six-profile solution using seven subscales and foundwomen recruited from Shinshu University Hospital,

that the “inhibited” profile was characterized by

Matsumoto, Japan, or through poster or web-based

high levels of ‘harm avoidance’ and low levels ofadvertisements. All diagnoses of AN were made by‘novelty seeking’ and ‘persistence’. The “maladaptive”

a psychiatrist according to the Diagnostic and Statis­

profile was characterized by low levels of ‘reward

tical Manual of Mental Disorders Fifth Edition1）. The

dependence’, ‘self-directedness’, and ‘cooperative­

study protocol was approved by the Shinshu Univer­ness’, which were generally present with the great­sity ethics committee (Study no. 3375) and written

est levels of ED symptomatology and impulsive be­

informed consent was obtained from every partici­

haviors.

pant.

et al.

Several lines of evidence have demonstrated links

Ｂ

Clinical measures

9）
-12）

.

All participants were administered the 140-item

and a meta-analy­

version of the TCI-Revised (TCI-R-140)17）, the Au­

sis16）, patients with AN exhibited higher Autism

tism Spectrum Quotient (AQ)18）, and the 26-item

Spectrum Quotient (AQ) scores than healthy con­

Eating Attitude Test (EAT-26)19）. The TCI-R-140 istrols. However, the mean AQ scores for patients witha 140-item true/false self-report questionnaire thatbetween AN and autism spectrum disorders (ASDs)
13）
-15）

In previous systematic reviews

16）

AN did not exceed the clinical cut-off for ASD .

measures four dimensions of temperament and three

Most of the above studies were cross-sectional indimensions of character. The Japanese version ofdesign. Therefore, it is difficult to assess whether

the TCI-R-14020） was used in the present study.

characteristic TCI profiles and high AQ scores pre­The AQ is a self-administered questionnaire used tocede the onset of AN. No previous studies have ex­assess autistic spectrum characteristics in adults

amined whether non-anorexic individuals with highwith normal intelligence. The evaluation of autistic

AQ scores and TCI profiles characteristic of AN pa­

characteristics in the present study was performed

tients have a greater risk of developing AN. Fur­using the Japanese version of the AQ (AQ-J), which

thermore, no studies have examined whether AQ

has good internal consistency, reliability, test-retest

scores and TCI profiles in a non-clinical population

reliability, and discriminant validity21）. The EAT-26

are associated with anorexic eating behaviors.

is a 26-item self-administered questionnaire de­Given these gaps, in the present study, we investi­
158

signed to measure attitudes and behaviors associatedShinshu Med J

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Autism Spectrum Quotient and personality profiles associated with eating behaviors

with ED. It is comprised of three subscales : dieting,formation Criterion (AIC) and entropy poorly selectedbulimia, and oral control. Levels of ED psychopathol­the correct number of classes regardless of sample

ogy are assessed with a 5-point Likert scale systemsize. We determined the model containing the opti­

with answers ranging from “always” to “never.” A

mal number of profiles according to criteria outlined

total score above 20 indicates potential disorderedby previous studies24）27）. Specifically, we first evaluat­

eating.

ed the fit of a two-profile model and the number ofＣlatent profiles was increased until it was evident

Statistical analyses
Differences in demographic characteristics between

that the addition of latent profiles was unjustified.

patients with AN and healthy controls were examined

Each model was evaluated using the following fac­

using Mann-Whitney U test. Relationships between

tors : SABIC, number of cases in each profile, andEAT scores and TCI, AQ, and demographic charac­

posterior probabilities associated with each profile.

teristics were examined separately in AN and con­

Other fit indices [i.e. log likelihood (LL), AIC, CAIC,

trol groups using partial correlation coefficients andBIC, and integrated classification likelihood criterion

controlling for age. The above analyses were con­with Bayesian-type approximation (ICL)] were used

ducted using the Statistical Package for the Social

to further evaluate the validity of the model. Entro­

Sciences (SPSS) version 21.0 (IBM Corp, Armonk, Newpy was also used to assess the overall quality of theYork, U.S.).

final model. LPA was conducted by package tidyLPA

A stepwise logistic regression analysis was used
to determine the optimal model for prediction of AN.

using the R Project for Statistical Computing version
3.4.128）.

This stepwise analysis was conducted as a forward

After determining the best profile solution, TCI,

stepping procedure and based on a likelihood ratio

AQ scores, BMI, and EAT scores were compared

test, with p＜0.05 indicating variable inclusion and p

between the latent profiles using analyses of covari­＞0.2 indicating exclusion from the model. Variables

ance (ANCOVAs), controlling for age, followed byused as potential predictors were scores from theBonferroni post-hoc tests for pairwise comparisons.

seven dimensions of the TCI and AQ scores. Nagelk­
erke’s R2 was used to approximate the proportion of

Ⅲ Results

variance explained by the model22）. The area under

Table 1 shows the clinical characteristics and TCI,

the receiver-operating characteristic (ROC) curve

EAT, and AQ scores of the participants. The age(AUC) was used to determine the predictive power

distribution did not significantly differ between theof the logistic model. The predicted probability with

AN and control groups. Patients with AN scoredthe highest Youden index was selected as the opti­

significantly higher on the ‘harm avoidance’ and sig­

mal cut-off point. Statistical significance was set at anificantly lower on ‘novelty seeking’, ‘reward depen­

two-tailed p-value ＜0.05. Analyses were performed

dence’, and ‘self-directedness’ than controls. EAT

using the SPSS version 11.0 (SPSS Japan, Tokyo).

and AQ scores were significantly higher in patients

LPA was performed in healthy controls to identify

with AN than in controls.

latent groups based on their total AQ score and theTable 2 and Table 3 show the partial correlation

scores on seven TCI dimensions. Simulation studies

coefficients, controlling for age, between BMI andrevealed that Bayesian Information Criteria (BIC),

EAT, TCI dimensions, and AQ scores in AN andsample-size adjusted BIC (SABIC), Consistent Akaike

control groups. BMI was significantly and negatively

Information Criterion (CAIC), and Bootstrap Likeli­

correlated with oral control scores in both AN and23）

were effective in selecting thecontrol groups. The AQ score was significantly cor­best model, especially when relying on smaller sam­related with BMI only in controls. The AQ scorehood Ratio Tests

24）
25）

. In contrast, a simula­was significantly correlated with ‘harm avoidance’tion study by Tein et al.26） reported that Akaike In­and ‘reward dependence’ and ‘cooperativeness’ di­ples as was the case here

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Takahashi-Asai・Sasayama・Sugiyama et al.
Table 1

Clinical characteristics and TCI, EAT, and AQ scores
Anorexia nervosa(n＝22)

Controls(n＝94)

Statistics

Clinical characteristics
Age (years)
Height (m)Weight (kg)BMI

28.2 (10.67)
1.54 (0.1)
36.6 (8.4)
15.3 (2.6)

24.4 (8.0)
1.58 (0.0)
50.6 (6.3)
20.3 (2.1)

U＝1150.5, P＝0.409
U＝745.0, P＝0.041
U＝186.5, P ＜ 0.001
U＝137.0, P ＜ 0.001

TCI scores
Novelty seeking
Harm avoidance
Reward dependence
Persistence
Self-directedness
CooperativenessSelf-transcendence

42.5 (6.7)
57.5 (7.1)
46.3 (7.8)
48.2 (9.6)
44.1 (7.3)
51.6 (8.6)
45.5 (5.7)

46.8 (6.5)
50.3 (7.7)
51.4 (6.4)
45.5 (6.6)
51.6 (7.7)
52.7 (6.1)
44.4 (7.3)

U＝642.5, P＝0.006
U＝1600.0, P ＜ 0.001
U＝662.0, P＝0.009
U＝1254.0, P＝0.121
U＝512.5, P ＜ 0.001
U＝951.5, P＝0.561
U＝1190.5, P＝0.269

EAT scores
Dieting
Bulimia and Food Preoccupation
Oral control
EAT total

16.32 (8.1)
7.8 (4.9)9.4 (5.9)
33.4 (14.7)

5.3 (5.0)
0.8 (1.8)
1.4 (2.3)
7.5 (7.3)

U＝1819.0, P ＜ 0.001
U＝1839.5, P ＜ 0.001
U＝1938.5, P ＜ 0.001
U＝1957.5, P ＜ 0.001

AQ scores

28.4 (8.3)

17.5 (7.9)

U＝1713.0, P ＜ 0.001

Data are shown as mean (standard deviation).
BMI : body mass index, TCI : Temperament and Character Inventory, EAT : Eating Attitude Test,
AQ : Autism Spectrum Quotient
Table 2

Partial correlation coefficients controlling for age between BMI, and EAT, TCI dimension, and AQ
scores in the patient group
NS

HA

RD

P

SD

C

Harm avoidance (HA)
Reward dependence (RD)
Persistence (P)

-0.18
-0.08
-0.28

-0.45＊-0.64＊＊

0.48＊

Self-directedness (SD)

-0.25

-0.31

-0.20

0.27

Cooperativeness (C)Self-transcendence (ST)EAT
Dieting (EAT-1)

-0.43
-0.39

-0.27
-1.67

0.40
0.17

0.48＊＊
0.54＊＊

0.39
-0.06

0.20

0.46＊

ST

EAT-1

EAT-2

EAT-3

EAT
total

AQ

BMI

TCI
Novelty seeking (NS)

Bulimia and Food
Preoccupation (EAT-2)
Oral control (EAT-3)

0.56＊＊

-0.19
0.35

-0.28
-0.36

0.15
-0.30

-0.11
-0.06
-0.68＊＊＊ -0.41

0.14
-0.16

0.58＊＊

-0.26

0.00

-0.10

0.12

-0.08

-0.04

0.26

0.44＊

0.12

EAT total

0.34

0.01

-0.31

0.03

-0.32

-0.18

0.13

0.91＊＊＊

0.70＊＊＊

＊＊

AQ

0.03

0.59

BMI

0.29

-0.04

-0.90
-0.11

＊＊＊

＊

＊

0.68＊＊＊

-0.51

-0.04

-0.51

-0.15

0.10

0.34

0.14

0.22

0.03

0.02

-0.02

-0.25

-0.06

0.13

-0.66＊

-0.25

-0.10

＊＊＊

p ＜ 0.001, ＊＊p ＜ 0.01, ＊p ＜ 0.05

TCI : Temperament and Character Inventory
NS : novelty seeking, HA : harm avoidance, RD : reward dependence, P : persistence, SD : self-directedness, C : cooperativeness,
ST : self-transcendence
EAT : Eating Attitude Test
AQ : Autism Spectrum Quotient
BMI : body mass index
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Autism Spectrum Quotient and personality profiles associated with eating behaviors
Table 3

Partial correlation coefficients controlling for age between BMI, and EAT, TCI dimension, and AQ
scores in the control group
NS

HA

RD

P

SD

C

ST

EAT-1

EAT-2

EAT-3

EAT
total

AQ

BMI

TCI
Novelty seeking (NS)Harm avoidance (HA)

-0.28＊＊

Reward dependence (RD)0.33＊＊ -0.23＊

Persistence (P)Self-directedness (SD)-0.22＊
0.02-0.19-0.49＊＊＊

-0.12
0.20

0.27＊＊＊

Cooperativeness (C)

-0.18

-0.14

0.24

0.39＊＊＊

0.51＊＊＊

Self-transcendence (ST)

0.16

-0.16

0.04

0.25＊

-0.03

0.08

Dieting (EAT-1)
Bulimia and Food Pre­occupation (EAT-2)

-0.08
0.01

0.27＊＊
0.25＊

-0.10
-0.06

-0.04
-0.18

-0.22＊
-0.12

-0.13
-0.16

0.01
0.08

0.58＊＊＊

Oral control (EAT-3)
EAT total-0.26＊-0.13

0.35＊＊
0.36＊＊＊-0.28＊＊
-0.17

0.05
-0.06

-0.12
-0.21＊

-0.02
-0.14

-0.15
-0.02

0.32＊＊
0.93＊＊＊

0.24
0.73＊＊＊

0.59＊＊＊

-0.18
0.10

0.41＊＊＊
0.11-0.59＊＊＊ -0.09
0.22＊
-0.10

-0.03
0.04

0.22
0.09

0.30＊＊-0.00

0.35＊＊＊ 0.33＊＊-0.26＊
-0.02

EAT

AQ
BMI
＊＊＊

p ＜ 0.001,＊＊-0.38＊＊＊ -0.37＊＊＊
-0.70
-0.04

-0.26＊＊

p ＜ 0.01, p ＜ 0.05

TCI : Temperament and Character Inventory
NS : novelty seeking, HA : harm avoidance, RD : reward dependence, P : persistence, SD : self-directedness, C : cooperativeness,
ST : self-transcendence
EAT : Eating Attitude Test
AQ : Autism Spectrum Quotient
BMI : body mass index

mensions of the TCI in both AN and control groups.

higher in the four-profile model than in the three-

Table 4 shows the results of the stepwise logis­

profile model. Furthermore, posterior probabilities

tic regression analysis. The Nagelkerke R2 values

and entropy were highest in the 3-profile model.

showed that 49.5 ％ of the variance was explained

Therefore, we rejected the four-profile model andby the model. The AUC of the ROC was 0.897 (95 ％adopted the three-profile model as best fitting.

confidence interval : 0.839, 0.954). The following pre­diction model for AN was derived :The three profiles determined by the LPA are
shown in Fig. 1. Profile 1, the most prevalent, had

Predicted Probability＝exp (－8.084＋0.173×(AQclose to average scores on the AQ and all of the TCI

score)＋0.081×(‘persistence’ score)＋0.114×(‘coopera­

dimensions (n＝57 ; balanced profile). Profile 2 hadtiveness’ score)－0.150×(‘self-directedness’ score))/the highest ‘novelty seeking’, ‘cooperativeness’, and{1＋exp (－8.084＋0.173×(AQ score)＋0.081×(‘per­‘self-transcendence’ scores and the lowest ‘harm

sistence’ score)＋0.114×(‘cooperativeness’ score)－

avoidance’ score (n＝14 ; impulsive profile). Profile 30.150×(‘self-directedness’ score))}.

had the highest AQ score and ‘harm avoidance’ andAt the optimal cut-off point of 0.14, as determined

the lowest ‘novelty seeking’ dimension score (n＝by the Youden index, the sensitivity and specificity

23 ; high-AQ/inhibited profile). No significant differ­of detecting AN were 95.5 ％ and 73.4 ％, respectively.

ence in age was observed between profiles (F＝1.594,

We next performed LPA in control participants

df＝2, p＝0.209). Fig. 2 shows the mean BMI andwith the seven TCI dimensions and the total AQ

EAT scores for each profile. An ANCOVA revealed

score as indicator variables. Fit indices of the com­significant differences in EAT scores among thepeting latent profile models are shown in Table 5.

three groups (F＝8.95, df＝2, p＜0.001). The total

The three-profile model demonstrated better fit

EAT score was significantly higher in the high-AQ/

than the two-profile model according to SABIC, LL,

inhibited profile compared to the other profiles

AIC, CAIC, BIC, and ICL. These fit indices were(post-hoc test with Bonferroni correction : P＜0.001

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Takahashi-Asai・Sasayama・Sugiyama et al.
Table 4

Stepwise logistic regression analysis in patients with anorexia nervosa and controls
Stepwise analysis

Step and variable
Step 1
AQ
Constant
Step 2
AQ
P
Constant
Step 3AQ
P
SD
Constant
Step 4AQ
P
SD
CooperativenessConstant

Nagelkerke R2

Beta

SE

Wald

P valueOR

95 ％CI

0.151－4.902

0.034
0.908

19.42329.133＜0.001＜0.001

1.164
0.007

1.088, 1.245

0.17
0.088
－9.481

0.038
0.04
2.425

19.9624.798
15.284＜0.001
0.028＜0.0011.186
1.092＜0.001

1.1, 1.278
1.009, 1.182

0.389

0.144
0.113
－0.114－4.727

0.041
0.043
0.047
3.082

12.383
7.003
5.873
2.352

＜0.001
0.008
0.015
0.125

1.154
1.12
0.892
0.009

1.066, 1.251
1.03, 1.218
0.814, 0.978

0.456

0.173
0.081
－0.15
0.114
－8.084

0.046
0.045
0.054
0.06
2.331

13.863
3.164
7.805
3.59
4.729

＜0.001
0.075
0.005
0.058
0.03

1.189
1.084
0.861
1.12
＜0.0011.085, 1.302
0.992, 1.185
0.775, 0.956
0.996, 1.26

0.332

0.495

SE : standard error ; OR : odds ratio : 95 ％CI : 95 ％ confidence interval ; AQ : autism spectrum quotient, P : persistence,
SD : self-directedness
Table 5Fit indices for the latent profile analysis

ProfilesNumber of cases

PP (range)

SABIC

LL

AIC

CAIC

BIC

ICL

Entropy

2
3
4
5

21, 7314, 23, 57
11, 16, 21, 46
9, 14, 15, 20, 36(0.919, 0.966)(0.959, 0.993)(0.940, 0.991)(0.911, 0.998)

4898.047
4851.251
4871.077
4840.095

2412.287
2382.650
2386.325
2364.596

4930.573
4889.300
4914.650
4889.191

5118.368
5108.985
5166.224
5172.655

5065.368
5046.985
5095.224
5092.655

5075.050
5053.630
5104.215
5104.227

0.955
0.969
0.958
0.949

PP : posterior probability, SABIC : sample-size adjusted Bayesian Information Criteria, LL : log likelihood
AIC : Akaike Information Criterion, CAIC : Consistent Akaike Information Criterion, BIC : Bayesian Information Criteria,
ICL : integrated classification likelihood criterion with Bayesian-type approximation

versus balanced profile and P＜0.01 versus impulsive
profile).

avoidance’ and lower on ‘self-directedness’29）.
We also found that the AQ scores of patients withⅣ

AN were significantly higher than those of controls.

Discussion

These findings also agree with previous studies that

The present study revealed that TCI-R profiles

found that autistic characteristics were higher in pa­

significantly differ between women with and without

tients with AN13）30）31）. However, the cross-sectional

AN. High ‘harm avoidance’ and low ‘novelty seeking’,

design of the present study, as well as those of pre­‘reward dependence’, and ‘self-directedness’ scoresvious studies10）32）, does not allow us to infer whether

were observed in patients with AN, results that are

autistic characteristics were premorbidly present in

6）

consistent with the existing literature . Our results

patients with AN. The observed autistic characteris­also agreed with a previous study comparing TCI intics in patients with AN may partly be due to theJapanese women with and without ED, which foundinefficiencies in set-shifting and central coherence

that ED patients scored significantly higher on ‘harm

caused by acquiring AN33）34）.

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Autism Spectrum Quotient and personality profiles associated with eating behaviors

Autism Spectrum Quotient
CooperativenessHarm avoidanceNovelty seeking
Persistence
Reward dependence
Self‐directedness
Self‐transcendence

AQ: Autism Spectrum Quotient
TCI: Temperament andCharacter Inventory
balanced profile (n = 57)

Fig. 1

impulsive profile (n = 14)

high‐AQ/inhibited profile (n = 23)

AQ and TCI dimension scores for each profile in non-clinical participants

determned eaby latent
analysisinin thecontrol groupusingparthe sevendThe three
AT profiles
scoresforh profilep ofilenon‐cinical
ic TCIpdimensions and total AQ scores as indicator variables are shown.

25

20

15

10
***
*

5

***

*

***

***

0
BMI

Dietingbalanced profile(n = 57)

Bulimia and Food
Preoccupation

impulsive profile(n = 14)

Oral control

high‐AQ/inhibited profile(n = 23)

Total EAT score

BMI: Body mass index
EAT: Eating Attitude Test

Fig. 2 BMI and EAT scores for each profile in non-clinical participants
Mean BMI and EAT scores for each profile, determined via latent profile analysis in the control group,
are shown. An ANCOVA, controlling for age, revealed significant EAT score difference among profiles.
Error bars show the standard errors of the means. Pairwise Bonferroni-corrected post hoc tests revealed
significantly higher EAT scores in the high-AQ/inhibited profile than in other profiles.
＊

p ＜ 0.05 when compared with profile 3＊＊

P ＜ 0.01 when compared with profile 3＊＊＊

P ＜ 0.001 when compared with profile 3

No. 3, 2019163

Takahashi-Asai・Sasayama・Sugiyama et al.

It might also be speculated, based on our findings

supports our finding that high AQ and ‘harm avoid­in the control group, that autistic characteristics areance’ and low ‘novelty seeking’, ‘reward dependence’,

present before the onset of AN. AQ scores in theand ‘self-directedness’ are associated with elevatedcontrol group here were significantly and negatively

risk for AN. Although no significant differences incorrelated with BMI and significantly and positively

the persistence dimension was observed between

correlated with EAT scores. These findings suggest

patients with AN and controls, our LPA results showthat autistic characteristics are related to restrictivethat high ‘persistence’ dimension scores were as­eating behaviors, even in those who have not yetsociated with the individual’s personality profile

been diagnosed with AN. The correlation between

and thus related to problematic eating behaviors. A

AQ scores and BMI or EAT scores in patients withlogistic regression analysis also revealed that highAN was smaller and nonsignificant, suggesting thatlevels of ‘persistence’ allowed for improved discrimi­

the strong presence of autistic characteristics is anation between ANs and controls. The AQ scoresrisk factor for developing AN but may not necessar­

and personality profiles in the high-AQ/inhibitedily be associated with the severity of AN.

group resembled those of AN patients, suggesting

The oral control subscale score was significantly

that non-AN individuals with autistic features andand negatively correlated with BMI in both the AN

the personality profiles characteristic of AN are

and control groups. These findings in the patient

likely to exhibit anorexic eating behaviors and are atgroup are consistent with a previous study by Gar­an elevated risk for developing AN.

19）

ner et al. . These correlations were also observed in

While it offers significant benefits, the present

the control group, which indicates that the oral con­study also has several limitations that warrant sometrol subscale score may be particularly useful in

discussion. First, a self-report questionnaire was usedscreening women for risk of emaciation and AN. We

to collect participant data. Therefore, data on indi­found that high ‘harm avoidance’ and low ‘self-

viduals’ height and weight may not be accurate. Sec­

directedness’ scores were associated not only with

ondly, we did not collect medical history information

AN but also with problematic eating behaviors in afrom participants in the control group. Therefore, we

non-clinical population. A recent study found thatare unable to correct for diseases that might haveobese individuals had higher ‘self-directedness’

confounded our data. Thirdly, the present study hadscores than non-obese individuals, and that higha limited sample size. A limited number of AN par­‘harm avoidance’ and low ‘self-directedness’ scores

ticipants did not allow us to perform LPA in thewere associated with binge eating and night eating

patient group. Future studies with larger sample

35）

in obese individuals . Taken together, high ‘harm

sizes are necessary to confirm the present findings

avoidance’ and low ‘self-directedness’ may lead toand to further elucidate the role of personality profiles

various eating problems in non-AN individuals andin AN pathology. Finally, only Japanese women were

may be another risk factor for developing AN.

included in the present study, limiting the potential

Low ‘novelty seeking’ was also associated withfor generalization of our findings to other populations.

high oral control subscale in the control group. In

In conclusion, the present study replicated previ­

9）

contrast, Dalle Grave et al. reported that high ‘nov­

ous studies by finding that patients with AN hadelty seeking’ was associated with binge eating inhigher ‘harm avoidance’ and AQ scores and lower

obese individuals. Therefore, high ‘novelty seeking’‘novelty seeking’, ‘reward dependence’, and ‘self-

may be a protective factor against restrictive eating

directedness’ dimension scores than did healthy

but may increase the risk of uncontrolled eating.

controls. Further, we report, for the first time to our

The LPA in control individuals revealed that prob­

knowledge, that a distinct personality profile andlematic eating behaviors were most likely to behigh AQ scores were associated with restrictive

observed in the high-AQ/inhibited group. This result

eating behaviors in a non-clinical population. Our

164

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Autism Spectrum Quotient and personality profiles associated with eating behaviorsfindings reveal that patients with AN have high AQtures may be a risk factor for developing AN. Fur­and characteristic TCI profiles (i.e. high ‘harm avoid­

ther studies using a longitudinal study design may

ance’ and ‘persistence’ and low ‘novelty seeking’ andconfirm these findings and may help to develop AN‘reward dependence’) and further suggest the possi­

prevention methods that effectively target young in­bility that such personality profile and autistic fea­

dividuals at greatest risk.

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