Sanefuji et al. BMC Pediatrics(2021) 21:360
https://doi.org/10.1186/s12887-021-02815-9

RESEARCH ARTICLE

Open AccessPhysical growth and neurodevelopment
during the first year of life: a cohort studyof the Japan Environment and Children’sStudy
Masafumi Sanefuji1,2*, Yuri Sonoda1,2, Yoshiya Ito3, Masanobu Ogawa1, Vlad Tocan2, Hirosuke Inoue2,
Masayuki Ochiai2, Masayuki Shimono4,5, Reiko Suga5, Ayako Senju4,5, Satoshi Honjo6, Koichi Kusuhara4,5,
Shouichi Ohga1,2 and Japan Environment and Children’s Study Group

Abstract
Background: The association between a slower physical growth and poorer neurodevelopment has been
established in infants born preterm or small for gestational age. However, this association is inconsistent in termborn infants, and detailed investigations in infancy, when intervention is most beneficial for improving outcomes,
are lacking. We therefore examined this association separately by sex during the first year of life in term-born
infants.
Methods: Using data collected until children reached 12 months old in an ongoing prospective cohort of theJapan Environment and Children’s Study, we analyzed 44,264 boys and 42,541 girls with singleton term-birth. The
exposure variables were conditional variables that disentangle linear growth from weight gain relative to linear
growth, calculated from the length and weight at birth and 4, 7 and 10 months old. Neurodevelopmental delay
was identified using the Japanese-translated version of Ages & Stages Questionnaires, third edition.
Results: A reduced risk of neurodevelopmental delay at 6 months old was observed in children with a higher birthweight (adjusted relative risks [aRRs]: 0.91 and 0.93, 95 % confidence intervals [95 % CIs]: 0.87–0.96 and 0.88–0.98 inboys and girls, respectively) and increased linear growth between 0 and 4 months old (aRRs: 0.85 and 0.87, 95 %CIs: 0.82–0.88 and 0.83–0.91 in boys and girls, respectively). A reduced risk at 12 months was found in children withan increased linear growth between 0 and 4 months (aRRs: 0.92 and 0.90, 95 % CIs: 0.87–0.98 and 0.84–0.96 in boys
and girls, respectively), boys with an increased relative weight gain between 0 and 4 months (aRR: 0.90, 95 % CI:
0.84–0.97), and girls with a higher birth weight (aRR: 0.89, 95 % CI: 0.83–0.96).
Conclusions: These results suggest that a slow physical growth by four months old may be a predictor ofneurodevelopmental delay during infancy.
Keywords: Conditional growth modeling, Infant, Neurodevelopment, Physical growth* Correspondence: sanefuji39@gmail.com
1
Research Center for Environment and Developmental Medical Sciences,
Kyushu University, Fukuoka, Japan2
Department of Pediatrics, Graduate School of Medical Sciences, Kyushu
University, Fukuoka, Japan
Full list of author information is available at the end of the article© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

Sanefuji et al. BMC Pediatrics(2021) 21:360

Background
Prenatal and postnatal growth of the body has been
linked to later neurocognitive development. Growth restriction in utero is suggested to occur in infants born
small for gestational age while poor growth from birth
to term-equivalent age is often observed in infants born
preterm. Such growth-faltering infants are at high risk ofpoor neurodevelopmental outcomes later in life [1, 2]. In
these infants, a lack of catch-up growth is an important
indicator of a poor neurodevelopment [3–5]. The association between physical growth and neurodevelopment
has also been examined in general populations consisting mostly of term-born infants, but the findings are lessconsistent in such settings. Indeed, while several studies
with limited sample sizes have shown positive results[6–14], others have reported no association [15, 16].
Furthermore, as outcomes, a majority of these studies
investigated the neurocognitive function during preschool through adulthood [6–10, 13–16] with only a few
examining those in infancy [8, 11, 12], during whichintervention has the potential to improve the outcomesof both the parents and their children [17, 18].
Most of these previous studies have separately assessed
anthropometric gains in either length/height or weight
between two age points (e.g. birth to six months old). As
the length/height and weight are strongly correlated
within an individual, assessing these indices per se hampers the determination of which component is more influential. To dissociate the effects of length/height
growth from those of weight growth, Adair et al. andHorta et al. used a sophisticated method of ‘conditional
growth modeling’ [19, 20]. This modeling determines
conditional variables by regressing the current size (i.e.
length or weight) against all previous sizes [21, 22]. With
respect to each period, the variables comprise ‘linear
growth’ and ‘weight gain relative to linear growth’, which
represent length/height change and weight change separated from change in length/height, respectively. Theirstudies demonstrated a closer relationship of school
achievement and intelligence with linear growth thanwith relative weight again, noting some differences inpatterns between sexes.
Using a conditional growth model with a nationwide
prospective cohort from the Japan Environment andChildren’s Study (JECS) that includes over 100,000 children, the present study examined the relationship between physical growth and neurodevelopment separately
by sex during the first year of life in term-born infants.
To collect neurodevelopmental information from such alarge number of children, we used a parent-reported
screening tool rather than individualized face-to-facetests. During this period, neurodevelopment is difficult
to assess precisely, and important changes can manifest
in the individual developmental trajectory. However, aPage 2 of 11

detailed investigation of this relationship during infancy
is extremely important for detecting neurodevelopmental delay for early intervention.

Methods
DesignThe JECS is a nationwide, multicenter, prospective birthcohort study funded by the Ministry of Environment,
Japan. The study design details have been described elsewhere [23, 24]. In brief, pregnant participants were registered between January 2011 and March 2014 in 15
regional centers covering a wide geographical area inJapan. During pregnancy, data were obtained during thefirst and second/third trimesters using self-administeredquestionnaires. Detailed information regarding themother and child was obtained from medical records
transcripts during the first trimester, at the time of delivery, and when the child was 1 month old. After delivery,
data were collected at one and six months old and every
six months until the child was six years old, then twice ayear thereafter via self-reported questionnaires completed by the parents.
The JECS protocol was reviewed and approved by the
Ministry of Environment’s Institutional Review Board
for Epidemiological Studies and by the ethics committees of all participating institutions (#15000141). The
ethical approval for this study was an extension of the
ethical approval for the JECS protocol. Written informed
consent was obtained from all parents.
Participants

In this study, we used the fixed dataset “jecs-an20180131” released in March 2018. The dataset includes
all available data of 104,065 fetuses, linked to theirmothers’ data, collected until children were 12 months
old. As subjects, we selected 89,953 children with live
singleton term birth (≥ 37 and < 42 gestational weeks)
who had parents of Japanese nationality and complete
basic information on mother’s parity, child’s sex, length
and weight at birth (Fig. 1). After excluding 3,148 children who had malformation/severe diseases, we eventually analyzed 86,805 children, including 44,264 boys and42,541 girls.

Exposure

The exposure variable was body growth from conception
to 10 months old. Length and weight at birth, which canbe viewed as growth from conception (length andweight = 0) to birth, were obtained from medical records.
Subsequent anthropometric data were all derived fromthe personal health record of the child. Nearly all Japanese children receive a public health checkup at fourmonths old. Their length and weight were thus directlySanefuji et al. BMC Pediatrics(2021) 21:360

Page 3 of 11

Fig. 1 Flowchart of participant selection

measured by a public health nurse or a hospital nurse
and officially documented in the record. They also receive similar checkups at 7 or 10 months old, occasionally at both 7 and 10 months old, depending on
residency. Parents were asked to transcribe these anthropometric data at the 4-month checkup in the 6month questionnaire and those at the 7- and 10-monthcheckups in the 12-month questionnaire. Because thedata were not measured at exactly 4, 7 and 10 months,
the values were extrapolated from the anthropometricdata at birth and those at the 4-, 7- and 10-monthcheckups [25]. The extrapolated data were referenced to
the Japanese growth standards separately by sex andthen converted into z-scores (i.e. length-for-age, weightfor-age) [26, 27]. In the standards, the mother’s parity
and child’s gestational age are additionally taken into
consideration when calculating z-scores at birth.
These z-scored length and weight values at different
ages were processed using conditional growth modeling,
as in the studies conducted by Adair et al. and Horta
et al. [19, 20] but additionally including birth length inour study. Conditional variables were obtained byregressing the current size (i.e. length or weight) against
all previous sizes [21, 22]. At each time point, a conditional variable represents physical growth during a timeinterval, and a positive value represents a linear growthor relative weight gain faster than predicted in that
period. For example, the conditional length at sevenmonths old indicates the linear growth from four toseven months old. To estimate the conditional length,
the current length was regressed against all previous
lengths and weights. The conditional length at sevenmonths old was derived by regressing the length at sevenmonths old against the length and weight at fourmonths old and birth. To estimate the conditional relative weight, the current weight was regressed against thecurrent length and all previous lengths and weights. For
instance, the conditional relative weight at seven months
old was calculated by regressing the weight at sevenmonths old against the length at seven months old andthe length and weight at four months old and birth.
Note that the length and weight at birth per se weretreated as conditional variables at birth, as they had no
previous anthropometric data to be regressed against.
Consequently, our conditional variables included length
and weight at birth and conditional length and relative
weight at 4, 7 and/or 10 months old.
Outcomes

The outcome measure was neurodevelopmental delay at
6 and 12 months old using the Japanese-translated version of the Ages & Stages Questionnaires: third edition(ASQ-3). This version was prepared through a backtranslation procedure and was approved by the publisherof the original English version [28]. The ASQ-3 canidentify infants or young children who require further
neurodevelopmental assessments to determine their eligibility for early intervention. The findings of theSanefuji et al. BMC Pediatrics(2021) 21:360

questionnaire basically agree with those of professionallyadministered developmental batteries [29, 30]. It has
been used in clinical and research settings and translated
into several languages [31–33].
The ASQ-3 assesses five developmental domains:
Communication, Gross Motor, Fine Motor, Problem
Solving, and Personal-Social domains. For each domain,
six skills are described to which parents answer “yes,”“sometimes,” or “not yet,” depending on whether or nottheir child has demonstrated the described skill. The responses are converted to points, with “yes” receiving 10
points; “sometimes”, 5 points; and “not yet”, 0 points.
The child’s score for each developmental domain is thesum of all points received for the items under that domain and ranges from 0 to 60 points. The cut-off scorefor each domain is defined as two standard deviations
below the mean score of large, standardized samples in
the United States of America. A child is defined as having a neurodevelopmental delay if a score is at or below
the cut-off level in any developmental domain. Althoughpreliminary cut-off scores of the Japanese translation
were recently proposed [34], these were not recommended to be used with confidence before 24 months
old because of very limited sample sizes. Therefore, thecut-off scores were determined using the same methodologies as the original version, based on available data at
6 (n = 82,994) and 12 months old (n = 78,947) (Fig. 1),
which represent the general Japanese population.

Statistical analyses

To examine which component and periods of physicalgrowth are specifically associated with infant neurodevelopment, we conducted multivariable regression analyses
using conditional variables at different ages. Because
these variables are not correlated with each other, thevariables and covariates were included together in thesame model without concerns about collinearity [20].
The covariates were (i) mother’s age; (ii) maternal smoking during pregnancy, as recorded in the first trimester;(iii) maternal and (iv) paternal education level (juniorhigh school, high school, and university or graduate
school); (v) annual family income (< 4 000 000, 4 000
000–5 999 999, ≥ 6 000 000 JPY); (vi) home speechstimulation at 1 month old (whether a mother did or did
not talk to her baby habitually). The ‘home speechstimulation’ covariate was used instead of the Home Observation for Measurement of the Environment scale[35], which is not employed in the JECS.
All analyses were performed using the R software program, version 4.0.3 (The R Foundation, Vienna, Austria).
Quasi-Poisson regression models were used to estimate
adjusted relative risks (aRRs) with 95 % confidence intervals (CIs).

Page 4 of 11

Results
Among the 44,264 boys and 42,541 girls eligible for the
analysis, anthropometric data were available at 4 monthsof age in 37,136 (83.9 %) boys and 35,732 (84.0 %) girls,
at 7 months in 16,231 (36.7 %) boys and 15,758 (37.0 %)girls, and at 10 months in 25,616 (57.9 %) boys and24,570 (57.8 %) girls, respectively. Ultimately, 10,016(22.6 %) boys and 9,801 (23.0 %) girls had complete dataat all timepoints of birth, 4, 7 and 10 months of age.
In these children, neurodevelopmental delay at 6
months old was observed at similar rates in boys (8.9 %)and girls (8.5 %), whereas that at 12 months old wasmore frequently identified in boys (16.9 %) than in girls(12.6 %). The baseline characteristics of the children
who did not have all data available were comparable
to those of children with complete data, but theirparents tended to more often be multipara, have asmoking habit and have less education and income
than those of complete-data subjects (Table 1). The
comparisons between children with and without anthropometric data using other combinations among 4,
7 and 10 months old showed similar tendencies(Table S1, S2, S3).
Multivariable regression models revealed that a reduced risk of neurodevelopmental delay at 6 months old
was found in boys with a higher birth weight (aRR: 0.91,
95 % CI: 0.87–0.96) and an increased conditional length
at 4 months old (aRR: 0.85, 95 % CI: 0.82–0.88) (Table 2),
and similarly in girls with a higher birth weight (aRR:
0.93, 95 % CI: 0.88–0.98) and an increased conditional
length at 4 months old (aRR: 0.87, 95 % CI: 0.83–0.91)(Table 3). On analyzing each neurodevelopmental domain of ASQ-3, the conditional length at four months
old was associated with all five domains, while the birthweight was associated with some domains, supporting
the stronger effect sizes of conditional length at fourmonths old than those of birth weight for both sexes.
With regard to neurodevelopmental delay at 12
months old, we first created a regression model using
the data of children who had complete anthropometricdata throughout the first year of age and all covariates(Tables 4 and 5, model 1). In boys, a reduced risk was
related to an increased conditional length at 4 months
old (aRR: 0.92, 95 % CI: 0.87–0.98) and an increased
conditional relative weight at 4 months old (aRR: 0.90,
95 % CI: 0.84–0.97) (Table 4). In girls, a reduced risk
was related to a higher birth weight (aRR: 0.89, 95 % CI:
0.83–0.96) and an increased conditional length at 4
months old (aRR: 0.90, 95 % CI: 0.84–0.96) (Table 5).
Analyses of each neurodevelopmental domain revealed
that, for both sexes, conditional length at four months
old was most consistently associated with several domains compared with other conditional variables, again
suggesting the importance of this variable. Conversely,Sanefuji et al. BMC Pediatrics(2021) 21:360

Page 5 of 11

Table 1 Baseline characteristics of the children with and without all anthropometric data at 4, 7 and 10 months old (n = 86,805)
Boy

Girl

With
data(n =10,016)

Missing Without
Missing Effectdata(n = 34,248)
sizea

With
data(n =9,801)

Missing Without
Missing Effectdata(n = 32,740)
sizea

Gestational age,mean (SD), wk

39.4 (1.1)

039.4 (1.1)

0

0.00

39.5 (1.1)

039.5 (1.1)

0

0.00

Birth length, mean(SD), cm

49.4 (2.0)

049.4 (1.9)

0

0.01

48.8 (1.9)

048.8 (1.9)

0

0.01

Birth weight, mean(SD), g3,088 (371)

03,117 (367)

0

0.03

2,993 (356)03,021 (357)

0

0.03

Multipara (%)

5,179 (51.7)

021,291 (62.2)

0

0.09

5,040 (51.4)

020,455 (62.5)

0

0.09

Maternal age, mean 31.4 (4.8)(SD), y1

31.1 (5.1)

4

0.03

31.4 (4.9)

0

31.0 (5.1)

1

0.03

Maternal smoking
(%)

112

6,418 (19.3)

948

0.05

1,429 (14.7)

109

6,167 (19.4)

891

0.05

856

0.05

799

0.06

1,010

0.04

2,882

0.03

901

0.00

1,435 (14.5)

Maternal education
(%)

83

88

Junior high school 321 (3.2)

1,798 (5.4)

282 (2.9)

1,690 (5.3)

High school

7,233 (72.8)

24,661 (73.9)

7,082 (72.9)

23,732 (74.3)

University/
graduate school

2,379 (24.0)

6,933 (20.8)

2,349 (24.2)

6,519 (20.4)

Paternal education
(%)

137

Junior high school 520 (5.3)

1,073

0.04

2,619 (7.9)

137545 (5.6)

2,527 (8.0)

High school

5,887 (59.6)

19,713 (59.4)

5,748 (59.5)

18,882 (59.5)

University/
graduate school

3,472 (35.1)

10,843 (32.7)

3,371 (34.9)

10,321 (32.5)

Family income (%)

723

3,207

0.04

687

Low

3,428 (36.9)

12,719 (41.0)

3,453 (37.9)

12,232 (41.0)

Middle3,162 (34.0)

10,242 (33.0)

2,998 (32.9)

9,837 (32.9)

High2,703 (29.1)

8,080 (26.0)

2,663 (29.2)

7,789 (26.1)

Home speechstimulation (%)

8,115 (81.4)

50

26,998 (81.1)

950

0.00

7,916 (81.4)

ND at 6 months (%) 841 (8.9)

599

2,439 (8.5)

5,636

0.01

782 (8.5)595

2,169 (7.9)

5,215

0.01

ND at 12 months
(%)

712

4,253 (15.8)

7,324

0.01

1,221 (13.6)

819

3,247 (12.6)

6,945

0.01

1,576 (16.9)

75

25,879 (81.3)

aEffect size indicates the strength of the difference between groups and is calculated as phi/Cramer’s V and r, using chi-square and Student’s t-tests for categoricaland numerical variables, respectively.
ND neurodevelopmental delay; SD standard deviation

Table 2 Association between conditional variables and neurodevelopmental delay at six months old in boys
Conditionalvariables at birth and4 months old

Total

Communication

Gross motor

Fine motor

Problem solving

Personal-Social

2,714/31,399(8.6 %)

578/31,595(1.8 %)

526/31,590(1.7 %)

823/31,506(2.6 %)

1,204/31,590(3.8 %)

492/31,539(1.6 %)

Length at birth

0.99 (0.95–1.04)

0.93 (0.83–1.03)

0.90 (0.80-1.00)

1.01 (0.92–1.10)

1.01 (0.94–1.09)

1.02 (0.91–1.15)

Weight at birth

0.91 (0.87–0.96)

1.02 (0.92–1.14)

0.97 (0.86–1.08)

0.88 (0.80–0.96)

0.88 (0.82–0.95)

0.87 (0.77–0.98)

cLength at 4 months
old0.85 (0.82–0.88)

0.84 (0.76–0.92)

0.83 (0.77–0.90)

0.82 (0.77–0.88)

0.84 (0.79–0.90)

0.84 (0.77–0.92)

crWeight at 4 months
old1.03 (0.98–1.08)

0.88 (0.79–0.99)

1.24 (1.11–1.38)

0.99 (0.90–1.09)

0.98 (0.90–1.06)

1.11 (0.98–1.25)

Data represent the adjusted relative risk (95 % confidence interval). All of the conditional variables and the following covariates were included together in themodel: maternal age and smoking, maternal and paternal education, family income, and home speech stimulation.
cLength conditional length; crWeight conditional relative weightSanefuji et al. BMC Pediatrics(2021) 21:360

Page 6 of 11

Table 3 Association between conditional variables and neurodevelopmental delay at six months old in girls
Conditionalvariables at birth and4 months old

Total

Communication

Gross motor

Fine motor

Problem solving

Personal-Social

2,432/30,358(8.0 %)

584/30,504(1.9 %)

468/30,505(1.5 %)

743/30,436(2.4 %)

1,113/30,510(3.6 %)

290/30,476(1.0 %)

Length at birth

0.99 (0.95–1.05)

1.04 (0.93–1.16)

1.12 (0.99–1.26)

0.98 (0.89–1.07)

0.96 (0.89–1.04)

0.90 (0.78–1.05)

Weight at birth0.93 (0.88–0.98)

0.93 (0.84–1.04)

0.83 (0.73–0.94)

0.96 (0.87–1.06)

0.95 (0.88–1.02)

0.94 (0.81–1.10)

cLength at 4 months
old0.87 (0.83–0.91)

0.86 (0.78–0.94)

0.89 (0.81–0.99)

0.88 (0.81–0.96)

0.83 (0.78–0.89)

0.80 (0.72–0.91)

crWeight at 4 months
old1.05 (0.99–1.11)

0.92 (0.82–1.03)

1.44 (1.26–1.63)

0.99 (0.89–1.10)

0.99 (0.91–1.07)

1.14 (0.97–1.34)

Data represent the adjusted relative risk (95 % confidence interval). All of the conditional variables and the following covariates were included together in themodel: maternal age and smoking, maternal and paternal education, family income, and home speech stimulation.
cLength conditional length; crWeight conditional relative weight

the risk was largely unrelated to conditional variables at
7 and 10 months old, although an increased risk wasparadoxically observed in girls with an increased conditional length at 7 months old (aRR: 1.16, 95 % CI: 1.07–
1.26) (Table 5). We also built 2 other regression models
using the data of children with available data from 4 to 7
months old (model 2) and those with available data from4 to 10 months old (model 3). The results of the twomodels included some inconsistencies but yielded similar results concerning the importance of conditional variables in early infancy.

Discussion
The present study investigated the relationship between
physical growth and neurodevelopmental delay during
the first year of life in term-born infants. As indices ofphysical growth, we discriminated linear growth from
relative weight gain, represented as ‘conditional length’and ‘conditional relative weight’, respectively. A reduced
risk of neurodevelopmental delay at six months old wasobserved in boys and girls with a higher birth weight
and an increased conditional length at four months old.
A reduced risk of neurodevelopmental delay at 12
months old was found in boys and girls with a higher
conditional length at 4 months old, boys with a relatively
high conditional relative weight at 4 months old andgirls with a higher birth weight. These findings indicatethat children with a poor growth in early infancy are at
an increased risk for neurodevelopmental delay. In contrast, conditional variables at 7 and 10 months old did
not increase the risk.
This was one of the largest birth cohort studies yet
conducted to examine the association between physicalgrowth and the neurodevelopmental outcome. Our large
sample size had strong statistical power and revealed
subtle but significant associations. Furthermore, bodysizes were converted to z-scores using domestically standardized growth curves, and the sizes at birth were

further adjusted for the mother’s parity and child’s gestational age, enabling the precise estimation of the body
growth in utero. However, the present study included
several limitations. First, the information was mainly collected via self-administered questionnaires. Although theanthropometric data were based on official records, theidentified neurodevelopmental delay might be somewhat
equivocal, relying on the parent-reported screening testof the Japanese version of ASQ-3. Next, a considerable
proportion of children had missing information on physical growth at different time points and neurodevelopmental outcomes and were thus excluded from theanalysis, possibly producing selection bias. Finally, theresults of our regression analyses were not adjusted forother unmeasured confounders, including the maternal
intelligent quotient, which greatly influences the neurocognitive development of offspring.
We found that a higher conditional length at 4 months
old was consistently associated with a reduced risk ofneurodevelopmental delay at 6 and 12 months of age forboth sexes. The results agree with the findings from previous studies using conditional modeling, which reported
a greater impact of linear growth than relative weightgain on schooling and intelligence [19, 20]. The biological mechanisms underlying this association remains
unclear but is likely multifactorial. Prenatal and postnatal nutritional deficiency can induce growth failure of
the body and brain, as observed in low-income countries[36]. Although severe malnutrition is less likely to occur
in Japan than in other countries, placental dysfunction
and maternal excessive dieting may cause a poor nutrient supply to the fetus, while inappropriate feeding
habits might produce nutritional insufficiency during infancy. Another possible explanation is hormonal factors,
such as the growth hormone/insulin-like growth factor-I
pathway and the hypothalamic-pituitary-gonadal axis.
These endocrine systems are regulated by nutritionaland genetic factors [37–41] and play key roles in boththe physical and cerebral growth of the fetus and child[42–46]. Subtle differences in such hormonal levels

Sanefuji et al. BMC Pediatrics(2021) 21:360

Page 7 of 11

Table 4 Association between conditional variables and neurodevelopmental delay at 12 months old in boys
[Model 1] Conditional variables at birth, 4, 7
and 10 months old

Total

Communication Gross motor

Fine motor

Problemsolving

PersonalSocial

1,441/8,488(17.0 %)

396/8,534(4.6 %)

458/8,532(5.4 %)

372/8,526(4.4 %)

511/8,519(6.0 %)

303/8,506(3.6 %)

Length at birth

1.00 (0.94–1.07)

0.99 (0.87–1.12)

1.01 (0.90–1.13)

1.03 (0.90–
1.17)

0.97 (0.87–1.09)

1.00 (0.86–
1.16)

Weight at birth0.97 (0.91–1.03)

0.97 (0.85–1.10)

0.96 (0.86–1.08)

1.00 (0.88–
1.14)

0.97 (0.87–1.08)

0.92 (0.79–
1.06)

cLength at 4 months old

0.92 (0.87–0.98) 0.87 (0.78–0.98) 0.98 (0.89–1.09)

0.81 (0.73–
0.91)

0.86 (0.78–
0.95)

0.79 (0.70–
0.90)

crWeight at 4 months old

0.90 (0.84–0.97) 0.96 (0.83–1.10)

0.88 (0.77-1.00)

0.89 (0.78–
1.03)

0.86 (0.76–
0.97)

0.84 (0.72–
0.98)

cLength at 7 months old

1.07 (1.00-1.14)

1.14 (0.99–1.30)

1.13 (0.99–1.28)

1.08 (0.94–
1.24)

1.05 (0.95–1.18)

1.01 (0.89–
1.18)

crWeight at 7 months old

0.98 (0.89–1.07)

0.81 (0.65-1.00)

0.91 (0.74–1.10)

0.90 (0.72–
1.10)

1.00 (0.85–1.15)

1.03 (0.86–
1.22)

cLength at 10 months olda

0.98 (0.90–1.07)

0.91 (0.77–1.08)

1.13 (0.95–1.33)

1.00 (0.83–
1.20)

1.10 (0.94–1.27)

0.87 (0.73–
1.05)

crWeight at 10 months oldb

0.94 (0.87–1.03)

0.94 (0.83–1.16)

0.90 (0.81–1.05)

1.02 (0.85–
1.33)

0.96 (0.84–1.17)

0.97 (0.81–
1.26)[Model 2] Conditional variables at birth, 4 and7 months old

Total

Communication Gross motor

Fine motor

Problemsolving

PersonalSocial

2,281/13,689(16.7 %)

612/13,757(4.4 %)

698/13,759(5.1 %)

614/13,753(4.5 %)

814/13,742(5.9 %)

481/13,722(3.5 %)

Length at birth1.02 (0.97–1.07)

1.00 (0.90–1.10)

1.03 (0.94–1.14)

1.01 (0.91–
1.12)

1.00 (0.91–1.09)

1.04 (0.93–
1.18)

Weight at birth0.96 (0.91–1.01)

0.96 (0.87–1.07)

0.94 (0.85–1.03)

0.97 (0.87–
1.07)

0.94 (0.86–1.03)

0.90 (0.80–
1.01)

cLength at 4 months old

0.93 (0.89–0.97) 0.88 (0.80–0.97) 0.97 (0.89–1.06)

0.85 (0.78–
0.93)

0.89 (0.82–
0.97)

0.81 (0.73–
0.90)

crWeight at 4 months old

0.90 (0.85–0.95) 0.88 (0.79–0.99) 0.89 (0.80–
0.99)

0.91 (0.81–
1.01)

0.85 (0.77–
0.93)

0.90 (0.79–
1.01)

cLength at 7 months old

1.08 (1.02–1.14) 1.09 (0.97–1.23)

1.14 (1.02–
1.27)

1.16 (1.03–
1.29)

1.11 (1.001.23)

1.05 (0.95–
1.19)

crWeight at 7 months old

1.00 (0.92–1.08)

0.87 (0.72–1.04)

0.99 (0.84–1.15)

0.94 (0.79–
1.10)

0.99 (0.86–1.11)

1.15 (0.99–
1.31)[Model 3] Conditional variables at birth, 4 and10 months old

Total

Communication Gross motor

Fine motor

Problemsolving

PersonalSocial

3,478/21,577(16.1 %)

910/21,687(4.2 %)

1086/21,684(5.0 %)

923/21,677(4.3 %)

1,232/21,656(5.7 %)

723/21,626(3.3 %)

Length at birth

1.00 (0.96–1.04)

0.93 (0.85–1.01)

0.98 (0.91–1.06)

0.99 (0.91–
1.08)

1.02 (0.95–1.10)

0.97 (0.88–
1.07)

Weight at birth0.97 (0.93–1.01)

0.98 (0.90–1.06)

0.98 (0.91–1.06)

0.99 (0.91–
1.08)

0.93 (0.87-1.00)

0.92 (0.83–
1.01)

cLength at 4 months old

0.92 (0.89–0.95) 0.85 (0.79–0.92) 0.97 (0.90–1.04)

0.85 (0.79–
0.91)

0.87 (0.82–
0.93)

0.83 (0.76–
0.90)

crWeight at 4 months old

0.92 (0.88–0.96) 0.94 (0.85–1.03)

0.89 (0.82–
0.98)

0.86 (0.80–
0.93)

0.87 (0.79–
0.96)

cLength at 10 months olda

1.00 (0.96–1.05)

0.91 (0.85–0.99) 1.00 (0.92–1.09)

1.00 (0.91–
1.09)

1.05 (0.97–1.14)

1.01 (0.92–
1.11)

crWeight at 10 months oldb

1.02 (0.97–1.07)

1.01 (0.92–1.11)

0.96 (0.88–
1.06)

1.01 (0.93–1.10)

1.08 (0.98–
1.17)

0.91 (0.841.00)

0.91 (0.85–0.99)

Data represent the adjusted relative risk (95 % confidence interval). All of the conditional variables and the following covariates were included together
in the model: maternal age and smoking, maternal and paternal education, family income, and home speech stimulation. a,bThe values of the variables
were different between the models because these variables were estimated from different combinations of the previous data ([model 1] at birth, 4 and7 months old; [model 3] at birth and 4 months old).
cLength conditional length; crWeight conditional relative weightSanefuji et al. BMC Pediatrics(2021) 21:360

Page 8 of 11

Table 5 Association between conditional variables and neurodevelopmental delay at 12 months old in girls[Model 1] Conditional variables at birth, 4, 7
and 10 months old

Total

Communication Gross motor

Fine motor

Problemsolving

PersonalSocial

1,117/8,211(13.6 %)

199/8,253(2.4 %)

540/8,254(6.5 %)

235/8,254(2.8 %)

348/8,244(4.2 %)

202/8,229(2.5 %)

Length at birth1.04 (0.97–1.12)

0.92 (0.77–1.11)

1.05 (0.94–1.17)

1.05 (0.88–
1.24)

1.01 (0.88–
1.16)

0.99 (0.82–
1.18)

Weight at birth0.89 (0.83–0.96) 0.96 (0.80–1.16)

0.87 (0.78–
0.97)

0.87 (0.73–
1.03)

0.86 (0.75–
0.99)

0.94 (0.78–
1.13)

cLength at 4 months old

0.90 (0.84–0.96) 0.85 (0.73–1.01)

1.02 (0.92–1.12)

0.77 (0.66–
0.90)

0.82 (0.73–
0.93)

0.78 (0.67–
0.92)

crWeight at 4 months old

0.96 (0.89–1.04)

0.80 (0.66–0.97) 0.94 (0.84–1.05)

0.97 (0.81–
1.16)

0.91 (0.79–
1.06)

0.88 (0.72–
1.06)

cLength at 7 months old

1.16 (1.07–1.26) 1.17 (0.95–1.43)

1.21 (1.08–1.37)

1.06 (0.91–
1.29)

1.14 (0.98–
1.33)

1.10 (0.91–
1.35)

crWeight at 7 months old

1.10 (0.98–1.23)

0.91 (0.67–1.20)

1.19 (1.00-1.39)

0.92 (0.70–
1.21)

1.01 (0.81–
1.25)

0.98 (0.73–
1.28)

cLength at 10 months olda

0.97 (0.88–1.08)

0.94 (0.73–1.21)

1.04 (0.89–1.21)

1.04 (0.82–
1.32)

1.07 (0.88–
1.29)

0.87 (0.69–
1.12)

crWeight at 10 months oldb

1.02 (0.91–1.18)

0.87 (0.73–1.17)

1.03 (0.87–1.27)

1.17 (0.86–
1.65)

1.23 (0.94–
1.63)

0.90 (0.75–
1.23)[Model 2] Conditional variables at birth, 4 and7 months old

Total

Communication Gross motor

Fine motor

Problemsolving

PersonalSocial

1,751/13,220(13.2 %)

313/13,289(2.4 %)

810/13,293(6.1 %)

371/13,292(2.8 %)

544/13,278(4.1 %)

317/13,250(2.4 %)

Length at birth1.01 (0.95–1.07)

0.93 (0.80–1.08)

1.06 (0.97–1.16)

1.01 (0.88–
1.16)

0.94 (0.84–
1.05)

0.98 (0.85–
1.14)

Weight at birth0.94 (0.88–0.99) 0.95 (0.82–1.10)

0.86 (0.78–
0.94)

0.96 (0.84–
1.10)

0.96 (0.86–
1.07)

0.95 (0.82–
1.10)

cLength at 4 months old

0.90 (0.86–0.95) 0.85 (0.75–0.97) 1.01 (0.94–1.10)

0.83 (0.74–
0.94)

0.82 (0.75–
0.91)

0.81 (0.71–
0.92)

crWeight at 4 months old

0.93 (0.88–0.99) 0.78 (0.67–0.92) 0.92 (0.83–1.01)

0.96 (0.84–
1.11)

0.89 (0.79–
0.99)

0.94 (0.81–
1.09)

cLength at 7 months old

1.17 (1.10–1.26) 1.13 (0.96–1.35)

1.22 (1.10–
1.35)

1.15 (0.98–
1.34)

1.14 (1.01–
1.30)

1.07 (0.92–
1.27)

crWeight at 7 months old

1.10 (0.99–1.21)

0.89 (0.72–1.13)

1.19 (1.03–1.37)

0.94 (0.76–
1.18)

1.07 (0.89–
1.27)

0.98 (0.78–
1.24)[Model 3] Conditional variables at birth, 4 and10 months old

Total

Communication Gross motor

Fine motor

Problemsolving

PersonalSocial

2,669/20,756(12.9 %)

441/20,857(2.1 %)

1,263/20,864(6.1 %)

537/20,858(2.6 %)

827/20,837(4.0 %)

463/20,799(2.2 %)

Length at birth1.02 (0.97–1.07)

0.95 (0.84–1.07)

1.03 (0.95–1.10)

1.03 (0.92–
1.15)

1.03 (0.94–
1.12)

1.04 (0.92–
1.17)

Weight at birth

0.92 (0.88–0.97) 0.95 (0.84–1.08)

0.92 (0.85–
0.99)

0.89 (0.79–
0.99)

0.91 (0.83–
0.99)

0.87 (0.77–
0.98)

cLength at 4 months old

0.91 (0.88–0.95) 0.85 (0.77–0.95) 0.95 (0.89–1.01)

0.88 (0.80–
0.97)

0.86 (0.80–
0.93)

0.87 (0.78–
0.97)

crWeight at 4 months old

0.98 (0.93–1.03)

0.94 (0.83–
1.06)

0.96 (0.87–
1.05)

0.88 (0.78–
1.01)

cLength at 10 months olda

1.06 (1.01–1.12) 0.99 (0.88–1.13)

1.06 (0.98–1.15)

1.08 (0.96–
1.22)

1.15 (1.04–
1.26)

1.02 (0.90–
1.16)

crWeight at 10 months oldb

1.05 (0.99–1.13)

1.04 (0.94–1.14)

0.96 (0.85–
1.11)

1.05 (0.93–
1.18)

0.95 (0.83–
1.11)

0.86 (0.76–0.99) 0.98 (0.91–1.06)

0.98 (0.84–1.15)

Data represent the adjusted relative risk (95 % confidence interval). All of the conditional variables and the following covariates were included together
in the model: maternal age and smoking, maternal and paternal education, family income, and home speech stimulation. a,bThe values of the variables
were different between the models because these variables were estimated from different combinations of the previous data ([model 1] at birth, 4 and7 months old; [model 3] at birth and 4 months old).
cLength conditional length; crWeight conditional relative weightSanefuji et al. BMC Pediatrics(2021) 21:360

might be responsible for the association between physical growth and neurodevelopment.
We also found other associations between physicalgrowth and neurodevelopmental delay during infancy.
The association between a higher birth weight and a reduced risk of neurodevelopmental delay at 6 and 12
months old is concordant with the well-known findingthat birth weight is a significant determinant of laterneurodevelopment [1, 47–49]. The boy-specific association between the conditional relative weight at 4
months and neurodevelopmental delay at 12 months
was in line with the association between conditional
relative weight and school achievement reported by
Adair et al. [20]. The aforementioned endocrine factors
have differential effects on somatic and cognitive development between sexes [46, 50] and might account atleast partially for this association. Conversely, higher
conditional variables at 7 and 10 months old did not reduce the risk of neurodevelopmental delay at 12 months,
suggesting less susceptibility of the developing brain inlate infancy than in early infancy. An increased risk at 12
months was also paradoxically observed in girls with anincreased conditional length at either 7 or 10 months
old in all the regression models. When an infant shows
slow growth in early infancy, they may be inclined to be
fed more by their parents and thus may grow fasterthereafter. Such modification of parents’ feeding behavior might yield this paradoxical association, although thisinformation was not collected in the JECS.

Conclusions
Using appropriate conditional modeling, we showed thatpoor early physical growth was associated with an increased risk of neurodevelopment delay during the firstyear of life in term-born infants partly in a sexdependent manner. If a child presents with growth failure in early infancy, the infant should be monitored
carefully to detect neurodevelopmental delay for earlyintervention. Ongoing investigations in the JECS cohort
may reveal later-term outcomes.
Abbreviations
aRR: Adjusted relative risk; ASQ-3: Ages & Stages Questionnaires, third
edition; CI: Confidence interval; JECS: The Japan Environment and Children’s
Study; JPY: Japanese Yen

Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s12887-021-02815-9.
Additional file 1:
Acknowledgements
We thank all participants of the JECS and all staff members involved in datacollection. The findings and conclusions of this article are the sole

Page 9 of 11

responsibility of the authors and do not represent the official views of theJapanese government. Members of the JECS Group as of 2020: Michihiro
Kamijima (principal investigator; Nagoya City University, Nagoya, Japan), ShinYamazaki (National Institute for Environmental Studies, Tsukuba, Japan),
Yukihiro Ohya (National Center for Child Health and Development, Tokyo,
Japan), Reiko Kishi (Hokkaido University, Sapporo, Japan), Nobuo Yaegashi(Tohoku University, Sendai, Japan), Koichi Hashimoto (Fukushima Medical
University, Fukushima, Japan), Chisato Mori (Chiba University, Chiba, Japan),
Shuichi Ito (Yokohama City University, Yokohama, Japan), Zentaro Yamagata(University of Yamanashi, Chuo, Japan), Hidekuni Inadera (University of
Toyama, Toyama, Japan), Takeo Nakayama (Kyoto University, Kyoto, Japan),
Hiroyasu Iso (Osaka University, Suita, Japan), Masayuki Shima (Hyogo Collegeof Medicine, Nishinomiya, Japan), Youichi Kurozawa (Tottori University,
Yonago, Japan), Narufumi Suganuma (Kochi University, Nankoku, Japan),
Koichi Kusuhara (University of Occupational and Environmental Health,
Kitakyushu, Japan), and Takahiko Katoh (Kumamoto University, Kumamoto,Japan).
Authors' contributions
M. Sanefuji conceived and designed the study, analyzed the data,interpreted the results, and wrote the manuscript. YS and M. Ogawa
interpreted the results and critically reviewed the manuscript. YI and SH
analyzed the data, interpreted the results and critically reviewed themanuscript. VT, HI, M. Ochiai, AS, M. Shimono and RS critically reviewed themanuscript. KK and SO directed the study and critically reviewed themanuscript. The author(s) read and approved the final manuscript.
Funding
The Japan Environment and Children’s Study and the present study were
funded by the Ministry of the Environment of Japan.
Availability of data and materials
Data are unsuitable for public deposition due to ethical restrictions and thelegal framework of Japan. It is prohibited by the Act on the Protection of
Personal Information (Act No. 57 of 30 May 2003, amendment on 9
September 2015) to publicly deposit data containing personal information.
The Ethical Guidelines for Medical and Health Research Involving Human
Subjects enforced by the Japan Ministry of Education, Culture, Sports,
Science and Technology and the Ministry of Health, Labour and Welfare also
restricts the open sharing of the epidemiologic data. All inquiries about
access to data should be sent to: jecs-en@nies.go.jp. The person responsible
for handling enquiries sent to this e-mail address is Dr Shoji F. Nakayama,
JECS Programme Office, National Institute for Environmental Studies.

Declarations
Ethics approval and consent to participate
The JECS protocol was reviewed and approved by the Ministry ofEnvironment’s Institutional Review Board for Epidemiological Studies and by
the ethics committees of all participating institutions (#15000141). The
ethical approval for this study was an extension of the ethical approval for
the JECS protocol. Written informed consent was obtained from all parents.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
Research Center for Environment and Developmental Medical Sciences,
Kyushu University, Fukuoka, Japan. 2Department of Pediatrics, Graduate
School of Medical Sciences, Kyushu University, Fukuoka, Japan. 3Japanese
Red Cross Hokkaido College of Nursing, Kitami, Japan. 4Department ofPediatrics, University of Occupational and Environmental Health, Kitakyushu,
Japan. 5Regional Center for Japan Environment and Children’s Study,
University of Occupational and Environmental Health, Kitakyushu, Japan.
6
Department of Pediatrics, National Hospital Organization Fukuoka National
Hospital, Fukuoka, Japan.
1Sanefuji et al. BMC Pediatrics(2021) 21:360

Received: 8 April 2021 Accepted: 19 July 2021

References
1. Walker DM, Marlow N. Neurocognitive outcome following fetal growth
restriction. Arch Dis Child Fetal Neonatal Ed. 2008;93:F322-5.
2. Sutton PS, Darmstadt GL. Preterm birth and neurodevelopment: a review ofoutcomes and recommendations for early identification and cost-effective
interventions. J Trop Pediatr. 2013;59:258–65.
3. Guellec I, Lapillonne A, Marret S, Picaud JC, Mitanchez D, Charkaluk ML,
et al. Effect of intra- and extrauterine growth on long-term neurologic
outcomes of very preterm infants. J Pediatr. 2016;175:93–9 e91.
4. Taine M, Charles MA, Beltrand J, Roze JC, Leger J, Botton J, et al. Early
postnatal growth and neurodevelopment in children born moderately
preterm or small for gestational age at term: a systematic review. Paediatr
Perinat Epidemiol. 2018;32:268–80.
5. Ruys CA, Hollanders JJ, Broring T, van Schie PEM, van der Pal SM, van deLagemaat M, et al. Early-life growth of preterm infants and its impact onneurodevelopment. Pediatr Res. 2019;85:283–92.
6. Martorell R, Horta BL, Adair LS, Stein AD, Richter L, Fall CH, et al. Weightgain in the first two years of life is an important predictor of schooling
outcomes in pooled analyses from five birth cohorts from low- and middleincome countries. J Nutr. 2010;140:348–54.
7. Heinonen K, Raikkonen K, Pesonen AK, Kajantie E, Andersson S, Eriksson JG,
et al. Prenatal and postnatal growth and cognitive abilities at 56 months ofage: a longitudinal study of infants born at term. Pediatrics. 2008;121:e1325-33.
8. Hamadani JD, Tofail F, Huda SN, Alam DS, Ridout DA, Attanasio O, et al.
Cognitive deficit and poverty in the first 5 years of childhood in
Bangladesh. Pediatrics. 2014;134:e1001-8.
9. Pongcharoen T, Ramakrishnan U, DiGirolamo AM, Winichagoon P, Flores R,
Singkhornard J, et al. Influence of prenatal and postnatal growth on
intellectual functioning in school-aged children. Arch Pediatr Adolesc Med.
2012;166:411–6.
10. Emond AM, Blair PS, Emmett PM, Drewett RF. Weight faltering in infancy
and IQ levels at 8 years in the Avon Longitudinal Study of Parents andChildren. Pediatrics. 2007;120:e1051-8.
11. Lasky RE, Klein RE, Yarbrough C, Engle PL, Lechtig A, Martorell R. The
relationship between physical growth and infant behavioral development inrural Guatemala. Child Dev. 1981;52:219–26.
12. Skuse D, Pickles A, Wolke D, Reilly S. Postnatal growth and mental
development: evidence for a “sensitive period”. J Child Psychol Psychiatry.
1994;35:521–45.
13. Horta BL, Sibbritt DW, Lima RC, Victora CG. Weight catch-up and achieved
schooling at 18 years of age in Brazilian males. Eur J Clin Nutr. 2009;63:369–74.
14. Raikkonen K, Forsen T, Henriksson M, Kajantie E, Heinonen K, Pesonen AK,
et al. Growth trajectories and intellectual abilities in young adulthood: theHelsinki Birth Cohort study. Am J Epidemiol. 2009;170:447–55.
15. Belfort MB, Rifas-Shiman SL, Rich-Edwards JW, Kleinman KP, Oken E, Gillman
MW. Infant growth and child cognition at 3 years of age. Pediatrics. 2008;
122:e689-95.
16. Beyerlein A, Ness AR, Streuling I, Hadders-Algra M, von Kries R. Early rapid
growth: no association with later cognitive functions in children born not
small for gestational age. Am J Clin Nutr. 2010;92:585–93.
17. Benzies KM, Magill-Evans JE, Hayden KA, Ballantyne M. Key components ofearly intervention programs for preterm infants and their parents: asystematic review and meta-analysis. BMC Pregnancy Childbirth. 2013;
13(Suppl 1):10.
18. Cioni G, Inguaggiato E, Sgandurra G. Early intervention inneurodevelopmental disorders: underlying neural mechanisms. Dev Med
Child Neurol. 2016;58(Suppl 4):61–6.
19. Horta BL, Victora CG, de Mola CL, Quevedo L, Pinheiro RT, Gigante DP, et al.
Associations of linear growth and relative weight gain in early life withhuman capital at 30 years of age. J Pediatr. 2017;182:85–91 e83.
20. Adair LS, Fall CH, Osmond C, Stein AD, Martorell R, Ramirez-Zea M, et al.
Associations of linear growth and relative weight gain during early life withadult health and human capital in countries of low and middle income:findings from five birth cohort studies. Lancet. 2013;382:525–34.
21. Keijzer-Veen MG, Euser AM, van Montfoort N, Dekker FW, Vandenbroucke
JP, Van Houwelingen HC. A regression model with unexplained residuals
was preferred in the analysis of the fetal origins of adult diseases
hypothesis. J Clin Epidemiol. 2005;58:1320–4.

Page 10 of 11

22. Li H, Stein AD, Barnhart HX, Ramakrishnan U, Martorell R. Associations
between prenatal and postnatal growth and adult body size andcomposition. Am J Clin Nutr. 2003;77:1498–505.
23. Kawamoto T, Nitta H, Murata K, Toda E, Tsukamoto N, Hasegawa M, et al.
Rationale and study design of the Japan environment and children’s study(JECS). BMC Public Health. 2014;14:25.
24. Michikawa T, Nitta H, Nakayama SF, Yamazaki S, Isobe T, Tamura K, et al.
Baseline profile of participants in the Japan Environment and Children’s
Study (JECS). J Epidemiol. 2018;28:99–104.
25. Zhang X, Tilling K, Martin RM, Oken E, Naimi AI, Aris IM, et al. Analysis of‘sensitive’ periods of fetal and child growth. Int J Epidemiol. 2019;48:116–23.
26. Tanaka T, Yokoya S, Kato N, Ito Y, Tachibana K, Sugihara S, et al. Basic viewfor assessment of physical size in Japanese children. J Jpn Pediatr Soc. 2011;
115:1705–9.
27. Japanese Society for Pediatric Endocrinology. Excel®-based Clinical Tools for
Growth Evaluation of Children. Available from http://jspe.umin.jp/medical/
chart_dl.html Accessed 20 Jan 2019.
28. Squires J, Elizabeth T, Bricker D, Potter L. ASQ-3 User’s Guide. Baltimore:
Brookes Publishing; 2009.
29. Limbos MM, Joyce DP. Comparison of the ASQ and PEDS in screening fordevelopmental delay in children presenting for primary care. J Dev Behav
Pediatr. 2011;32:499–511.
30. Schonhaut L, Armijo I, Schonstedt M, Alvarez J, Cordero M. Validity of theAges and Stages Questionnaires in term and preterm infants. Pediatrics.
2013;131:e1468-74.
31. Lopes S, Graca P, Teixeira S, Serrano AM, Squires J. Psychometric properties
and validation of Portuguese version of Ages & Stages Questionnaires (3rdedition): 9, 18 and 30 Questionnaires. Early Hum Dev. 2015;91:527–33.
32. Bernard JY, De Agostini M, Forhan A, Alfaiate T, Bonet M, Champion V, et al.
Breastfeeding duration and cognitive development at 2 and 3 years of age
in the EDEN mother-child cohort. J Pediatr 2013;163:36–42 e31.
33. Filgueiras A, Pires P, Maissonette S, Landeira-Fernandez J. Psychometricproperties of the Brazilian-adapted version of the Ages and Stages
Questionnaire in public child daycare centers. Early Hum Dev. 2013;89:561–76.
34. Mezawa H, Aoki S, Nakayama SF, Nitta H, Ikeda N, Kato K, et al.
Psychometric profile of the Ages and Stages Questionnaires, Japanesetranslation. Pediatr Int. 2019;61:1086–95.
35. Caldwell BM, Bradley RH. Home observation for measurement of theenvironment. Little Rock: University of Arkansas; 1984.
36. Martorell R. Improved nutrition in the first 1000 days and adult humancapital and health. Am J Hum Biol. 2017;29:e22952.
37. Xia K, Yu Y, Ahn M, Zhu H, Zou F, Gilmore JH, et al. Environmental andgenetic contributors to salivary testosterone levels in infants. Front
Endocrinol (Lausanne). 2014;5:187.
38. Hawkes CP, Grimberg A. Insulin-like growth factor-I is a marker for thenutritional state. Pediatr Endocrinol Rev. 2015;13:499–511.
39. Gluckman PD, Gunn AJ, Wray A, Cutfield WS, Chatelain PG, Guilbaud O,
et al. Congenital idiopathic growth hormone deficiency associated withprenatal and early postnatal growth failure. The International Board of the
Kabi Pharmacia International Growth Study. J Pediatr. 1992;121:920–3.
40. Woods KA, Camacho-Hubner C, Savage MO, Clark AJ. Intrauterine growth
retardation and postnatal growth failure associated with deletion of theinsulin-like growth factor I gene. N Engl J Med. 1996;335:1363–7.
41. Abuzzahab MJ, Schneider A, Goddard A, Grigorescu F, Lautier C, Keller E,
et al. IGF-I receptor mutations resulting in intrauterine and postnatal growth
retardation. N Engl J Med. 2003;349:2211–22.
42. de Bie HM, Oostrom KJ, Delemarre-van de Waal HA. Brain development,
intelligence and cognitive outcome in children born small for gestational
age. Horm Res Paediatr. 2010;73:6–14.
43. van Pareren YK, Duivenvoorden HJ, Slijper FS, Koot HM, Hokken-Koelega AC.
Intelligence and psychosocial functioning during long-term growth
hormone therapy in children born small for gestational age. J Clin
Endocrinol Metab. 2004;89:5295–302.
44. Chaplin JE, Kristrom B, Jonsson B, Tuvemo T, Albertsson-Wikland K.
Growth hormone treatment improves cognitive function in shortchildren with growth hormone deficiency. Horm Res Paediatr. 2015;83:
390–9.
45. Gunnell D, Miller LL, Rogers I, Holly JM, Team AS. Association of insulin-likegrowth factor I and insulin-like growth factor-binding protein-3 withintelligence quotient among 8- to 9-year-old children in the Avon
Longitudinal Study of Parents and Children. Pediatrics. 2005;116:e681-6.

Sanefuji et al. BMC Pediatrics(2021) 21:360

46. Becker M, Hesse V. Minipuberty: why does it happen? Horm Res Paediatr.
2020;93:76–84.
47. Pryor J, Silva PA, Brooke M. Growth, development and behaviour inadolescents born small-for-gestational-age. J Paediatr Child Health. 1995;31:
403–7.
48. Geva R, Eshel R, Leitner Y, Valevski AF, Harel S. Neuropsychological outcomeof children with intrauterine growth restriction: a 9-year prospective study.
Pediatrics. 2006;118:91–100.
49. O’Keeffe MJ, O’Callaghan M, Williams GM, Najman JM, Bor W. Learning,
cognitive, and attentional problems in adolescents born small for
gestational age. Pediatrics. 2003;112:301–7.
50. Mayer M, Schmitt K, Kapelari K, Frisch H, Kostl G, Voigt M. Spontaneous
growth in growth hormone deficiency from birth until 7 years of age:development of disease-specific growth curves. Horm Res Paediatr. 2010;74:
136–44.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.

Page 11 of 11