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Impact of maternal dyslipidemia on infant neurodevelopment: The Japan Environment and Children’s Study

  • Noriko Motoki
    Affiliations
    Center for Perinatal, Pediatric, and Environmental Epidemiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
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  • Yuji Inaba
    Correspondence
    Corresponding author at: Center for Perinatal, Pediatric, and Environmental Epidemiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano 390-8621, Japan.
    Affiliations
    Center for Perinatal, Pediatric, and Environmental Epidemiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan

    Department of Neurology, Nagano Children’s Hospital, Azumino, Nagano, Japan

    Life Science Research Center, Nagano Children’s Hospital, Azumino, Nagano, Japan
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  • Takumi Shibazaki
    Affiliations
    Department of Pediatrics, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
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  • Yuka Misawa
    Affiliations
    Center for Perinatal, Pediatric, and Environmental Epidemiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan

    Department of Rehabilitation, Nagano Children’s Hospital, Azumino, Nagano, Japan
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  • Satoshi Ohira
    Affiliations
    Center for Perinatal, Pediatric, and Environmental Epidemiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
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  • Makoto Kanai
    Affiliations
    Center for Perinatal, Pediatric, and Environmental Epidemiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
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  • Hiroshi Kurita
    Affiliations
    Center for Perinatal, Pediatric, and Environmental Epidemiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
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  • Teruomi Tsukahara
    Affiliations
    Center for Perinatal, Pediatric, and Environmental Epidemiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan

    Department of Preventive Medicine and Public Health, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
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  • Tetsuo Nomiyama
    Affiliations
    Center for Perinatal, Pediatric, and Environmental Epidemiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan

    Department of Preventive Medicine and Public Health, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
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  • the Japan Environment, Children's Study (JECS) Group
Open AccessPublished:May 25, 2022DOI:https://doi.org/10.1016/j.braindev.2022.05.002

      Abstract

      Background

      Various genetic and environmental influences have been studied for developmental disorders; however, the precise cause remains unknown. This study assessed the impact of maternal serum total cholesterol (TC) level in early pregnancy on early childhood neurodevelopment.

      Methods

      The fixed data of 31,797 singleton births from a large national birth cohort study that commenced in 2011 were used to identify developmental disorders as estimated by Ages and Stages Questionnaire, third edition (ASQ-3) scores of less than −2 standard deviations at 12 months of age. Multiple logistic regression analysis was employed to search for correlations between possibility of developmental disorders and maternal TC levels in early pregnancy classified into 4 groups based on quartile (Q1–Q4) values.

      Results

      After controlling for potential confounding factors in 27,836 participants who ultimately underwent multivariate analysis, we observed that elevated TC levels were significantly associated with a higher risk of screen positive status for communication (Q4: adjusted odds ratio [aOR] 1.20, 95% confidence interval [CI] 1.05–1.37) and gross motor (aOR 1.13, 95% CI 1.03–1.25) ASQ-3 domain scores.

      Conclusion

      This large nationwide survey revealed a possible deleterious effect of hypercholesterolemia in early pregnancy on infant neurodevelopment and age-appropriate skill acquisition at 12 months age.

      Keywords

      1. Introduction

      Developmental disorder is defined as impaired development in the areas of speech and language, motor skills, social interaction, and cognition [
      • Oberklaid F.
      • Efron D.
      Developmental delay–identification and management.
      ]. The number of children with developmental disorder has increased dramatically in recent decades [
      • Blumberg S.J.
      • Bramlett M.D.
      • Kogan M.D.
      • Schieve L.A.
      • Jones J.R.
      • Lu M.C.
      • et al.
      Changes in prevalence of parent-reported autism spectrum disorder in school-aged U.S. children: 2007 to 2011-2012.
      ,
      • Boyle C.A.
      • Boulet S.
      • Schieve L.A.
      • Cohen R.A.
      • Blumberg S.J.
      • Yeargin-Allsopp M.
      • et al.
      Trends in the prevalence of developmental disabilities in US children, 1997–2008.
      ]. Although its estimated prevalence is generally 5–15% in pediatric populations [
      • Blumberg S.J.
      • Bramlett M.D.
      • Kogan M.D.
      • Schieve L.A.
      • Jones J.R.
      • Lu M.C.
      • et al.
      Changes in prevalence of parent-reported autism spectrum disorder in school-aged U.S. children: 2007 to 2011-2012.
      ,
      • Boyle C.A.
      • Boulet S.
      • Schieve L.A.
      • Cohen R.A.
      • Blumberg S.J.
      • Yeargin-Allsopp M.
      • et al.
      Trends in the prevalence of developmental disabilities in US children, 1997–2008.
      ,

      Simeonsson RJ, Sharp MC. Developmental delays. In: Hoekelman RA, Friedman SB, Nelson NM, Seidel HM eds. Primary pediatric care. St Louis; Mosby-Year Book; 1992. p 867-870.

      ], reported developmental disorder values vary with the socioeconomic characteristics of the study population, case definition, and age range [
      • Gottlieb C.A.
      • Maenner M.J.
      • Cappa C.
      • Durkin M.S.
      Child disability screening, nutrition, and early learning in 18 countries with low and middle incomes: data from the third round of UNICEF’s Multiple Indicator Cluster Survey (2005–06).
      ]. Various genetic and environmental influences have been studied for developmental disorders [
      • Deciphering Developmental Disorders Study
      Large-scale discovery of novel genetic causes of developmental disorders.
      ,
      • Tran N.Q.V.
      • Miyake K.
      Neurodevelopmental disorders and environmental toxicants: Epigenetics as an underlying mechanism.
      ,
      • Bölte S.
      • Girdler S.
      • Marschik P.B.
      The contribution of environmental exposure to the etiology of autism spectrum disorder.
      ]; however, the precise cause remains unknown.
      Fetal development is sustained by metabolites crossing the placenta from the maternal circulation [
      • Herrera E.
      Lipid metabolism in pregnancy and its consequences in the fetus and newborn.
      ]. Glucose and amino acids are the most important nutrients traversing the placenta [
      • Herrera E.
      • Palacin M.
      • Martin A.
      • Lasunción M.A.
      Relationship between maternal and fetal fuels and placental glucose transfer in rats with maternal diabetes of varying severity.
      ,
      • Knipp G.T.
      • Audus K.L.
      • Soares M.J.
      Nutrient transport across the placenta.
      ,
      • Sibley C.
      • Glazier J.
      • D'Souza S.
      Placental transporter activity and expression in relation to fetal growth.
      ]. Although the transfer of lipids is limited [
      • Herrera E.
      Implications of dietary fatty acids during pregnancy on placental, fetal and postnatal development–a review.
      ], these also play a major role in fetal growth. Cholesterol is important in embryonic development as an essential component of cell membranes [
      • Ohvo-Rekilä H.
      • Ramstedt B.
      • Leppimäki P.
      • Slotte J.P.
      Cholesterol interactions with phospholipids in membranes.
      ] and has functions in cell proliferation and differentiation [
      • Martínez‐Botas J.
      • Suárez Y.
      • Ferruelo A.J.
      • Gómez‐Coronado D.
      • Lasunció M.A.
      Cholesterol starvation decreases p34 (cdc2) kinase activity and arrests the cell cycle at G2.
      ,
      • Suárez Y.
      • Fernández C.
      • Ledo B.
      • Ferruelo A.J.
      • Martín M.
      • Vega M.A.
      • et al.
      Differential effects of ergosterol and cholesterol on Cdk1 activation and SRE-driven transcription.
      ] as well as cell-to-cell communication [
      • Mauch D.H.
      • Nägler K.
      • Schumacher S.
      • Göritz C.
      • Müller E.-C.
      • Otto A.
      • et al.
      CNS synaptogenesis promoted by glia-derived cholesterol.
      ]. The neuronal myelin membrane contains a high level of cholesterol, which is known to be important for myelin production and maintenance [
      • Chrast R.
      • Saher G.
      • Nave K.-A.
      • Verheijen M.H.G.
      Lipid metabolism in myelinating glial cells: lessons from human inherited disorders and mouse models.
      ]. In fetuses younger than 6 months, plasma cholesterol levels significantly correlate to maternal ones [
      • Napoli C.
      • D'Armiento F.P.
      • Mancini F.P.
      • Postiglione A.
      • Witztum J.L.
      • Palumbo G.
      • et al.
      Fatty streak formation occurs in human fetal aortas and is greatly enhanced by maternal hypercholesterolemia. Intimal accumulation of low density lipoprotein and its oxidation precede monocyte recruitment into early atherosclerotic lesions.
      ], suggesting that maternal cholesterol status influences fetal cholesterol in early gestation.
      Several studies have revealed that maternal dyslipidemia could be associated with obstetric complications such as hypertension, eclampsia, macrosomnia, and preterm birth [
      • Catov J.M.
      • Bodnar L.M.
      • Ness R.B.
      • Barron S.J.
      • Roberts J.M.
      Inflammation and dyslipidemia related to risk of spontaneous preterm birth.
      ,
      • Jin W.Y.
      • Lin S.L.
      • Hou R.L.
      • Chen X.Y.
      • Han T.
      • Jin Y.
      • et al.
      Associations between maternal lipid profile and pregnancy complications and perinatal outcomes: a population-based study from China.
      ,
      • Spracklen C.N.
      • Smith C.J.
      • Saftlas A.F.
      • Robinson J.G.
      • Ryckman K.K.
      Maternal hyperlipidemia and the risk of preeclampsia: a meta-analysis.
      ,
      • Wang X.
      • Guan Q.
      • Zhao J.
      • Yang F.
      • Yuan Z.
      • Yin Y.
      • et al.
      Association of maternal serum lipids at late gestation with the risk of neonatal macrosomia in women without diabetes mellitus.
      ]. Catov et al. [
      • Catov J.M.
      • Bodnar L.M.
      • Ness R.B.
      • Barron S.J.
      • Roberts J.M.
      Inflammation and dyslipidemia related to risk of spontaneous preterm birth.
      ] reported that elevated serum total cholesterol (TC) level in early pregnancy was related to an increased risk of spontaneous preterm birth. Although relationships between maternal dyslipidemia and pregnancy complications are well documented, less is known on the longer-term neurodevelopmental outcomes in the offspring. To our knowledge, no studies have investigated if excess or insufficient maternal TC level increases the risk of offspring developmental disorder in Japan. Accordingly, we conducted a large birth cohort study with the specific objective of examining the impact of maternal TC levels on offspring neurodevelopment.

      2. Materials and methods

      2.1 Study design and participants

      The data used in this study were obtained from the Japan Environment and Children’s Study (JECS), an ongoing cohort study that began in January 2011 to determine the effect of environmental factors on children’s health.
      In the JECS, pregnant women were enrolled between January 2011 and March 2014. The inclusion criteria were: 1) having residence in the study area at the time of recruitment, 2) expected delivery after August 1, 2011, and 3) capable of comprehending the Japanese language and completing the self-administered structured questionnaire in Japanese. This study was registered in the UMIN Clinical Trials Registry (number: UMIN000030786). Details of the JECS project have been described previously [
      • Michikawa T.
      • Nitta H.
      • Nakayama S.F.
      • Yamazaki S.
      • Isobe T.
      • Tamura K.
      • et al.
      Baseline profile of participants in the Japan Environment and Children’s Study (JECS).
      ,
      • Ishitsuka K.
      • Nakayama S.F.
      • Kishi R.
      • Mori C.
      • Yamagata Z.
      • Ohya Y.
      • et al.
      Japan Environment and Children’s Study: backgrounds, activities, and future directions in global perspectives.
      ,
      • 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).
      ]. The JECS protocol was reviewed and approved by the Institutional Review Board on Epidemiological Studies of the Ministry of the Environment (ethical number: No. 100910001) as well as by the Ethics Committees of all participating institutions. The JECS was conducted in accordance with the Helsinki Declaration and other nationally valid regulations and guidelines. Written informed consent was obtained from each participant.
      The present study was based on the “jecs-an-20180131” dataset released in March 2018 containing information on 104,065 pregnancies, which included prospectively collected data on infants up to 12 months of age as well as their mothers. We excluded 3,921 cases of miscarriage/stillbirth/missing data on pregnancy and 1,889 cases of multiple births, leaving 98,255 mothers who had a singleton live birth, including 50,563 with the father’s registration. Specifically, we focused on questionnaire data regarding the Ages and Stages Questionnaire, third edition (ASQ-3) developmental screening tool as self-described by mothers when their child was 12 months old [

      Squires J, Bricker D. Ages and Stages Questionnaires. Third Edition (ASQ-3TM). Baltimore: Paul H. Brookes Publishing Co.;2009.

      ]. Among the participants with the father’s registration, we excluded 9,053 cases of insufficient or missing ASQ-3 data and 3,350 cases missing maternal serum TC level data in early pregnancy. Preterm births with a gestational age of less than 37 weeks were excluded since they would have needed to be adjusted to the appropriate age for the ASQ-3 [

      Squires J, Bricker D. Ages and Stages Questionnaires. Third Edition (ASQ-3TM). Baltimore: Paul H. Brookes Publishing Co.;2009.

      ]. Participants with obvious risk factors for developmental disorders, such as neonatal asphyxia, physical abnormality at birth including infection, respiratory distress, congenital abnormality, hearing disability, and chromosomal abnormalities, were excluded. Participants with missing information regarding the above exclusion criteria were also excluded, leaving 31,797 participants for analysis. Those with missing covariate data were not included in multiple logistic regression testing, resulting in 27,836 participants in the final analysis (Fig. 1).

      2.2 Data collection

      Maternal non-fasting serum samples were collected in the first trimester to measure TC. Since the samples were obtained in a non-fasting state, triglyceride (TG) values were not included in the analysis.
      Information on socioeconomic status, parental smoking habit, and maternal alcohol consumption was collected during mid/late pregnancy by means of self-reported questionnaires. Details on parental history of neurodevelopmental disorders, epilepsy, and mental disease were also gathered from questionnaires described by the mothers and their partners. Maternal anthropometric data before and during pregnancy, complications and medication during pregnancy related to placental abnormalities, hypertensive disorders of pregnancy (HDP), and diabetes mellitus/gestational diabetes mellitus (DM/GDM), and a history of previous pregnancy were obtained from subject medical record transcriptions performed by physicians, midwives/nurses, and/or research coordinators. Pre-pregnancy body mass index (BMI) to evaluate maternal weight status was calculated according to World Health Organization Standards as body weight (kg)/height (m)2.

      2.3 Outcomes

      The main outcome of interest was ASQ-3 scores. We used the Japanese translation of the ASQ-3 [
      • Mezawa H.
      • Aoki S.
      • Nakayama S.F.
      • Nitta H.
      • Ikeda N.
      • Kato K.
      • et al.
      Psychometric profile of the Ages and Stages Questionnaires, Japanese translation.
      ]. The ASQ-3 is a parent-reported initial level developmental screening instrument for children aged 12 months with 30 items over 5 domains: communication, gross motor, fine motor, problem solving, and personal-social. Each item describes a skill, ability, or behavior to which a parent responds “yes” (10 points), “sometimes” (5 points), or “not yet” (0 points). Some parents omitted items when unsure of how to respond or if they had concerns about their child’s performance. ASQ-3 scores were not calculated for domains with 3 or more omitted items. In the case of 1 or 2 omitted items, an adjusted total domain score was calculated by adding the averaged item score either once for a single omission or twice for 2 omissions. The scores calculated for each domain were classified as normal development (above the cut-off, i.e., on track) or referral zone (less than 2 standard deviations below the mean). The manual for the original ASQ recommends that a child be considered “screen positive” if his/her score falls below the referral cut-off in any 1 of the 5 domains [

      Squires J, Bricker D. Ages and Stages Questionnaires. Third Edition (ASQ-3TM). Baltimore: Paul H. Brookes Publishing Co.;2009.

      ].

      2.4 Covariates

      Maternal serum TC levels were divided into four quartile levels. The range of each quartile was as follows: first quartile (Q1) ≤ 175 mg/dL, second quartile (Q2) = 176–196 mg/dL, third quartile (Q3) = 197–219 mg/dL, and fourth quartile (Q4) ≥ 220 mg/dL.
      The covariates in our models were selected a priori based on previously published literature [
      • Deciphering Developmental Disorders Study
      Large-scale discovery of novel genetic causes of developmental disorders.
      ,
      • Tran N.Q.V.
      • Miyake K.
      Neurodevelopmental disorders and environmental toxicants: Epigenetics as an underlying mechanism.
      ,
      • Bölte S.
      • Girdler S.
      • Marschik P.B.
      The contribution of environmental exposure to the etiology of autism spectrum disorder.
      ,
      • McDonald S.
      • Kehler H.
      • Bayrampour H.
      • Fraser-Lee N.
      • Tough S.
      Risk and protective factors in early child development: results from the All Our Babies (AOB) pregnancy cohort.
      ,
      • Demirci A.
      • Kartal M.
      Sociocultural risk factors for developmental delay in children aged 3–60 months: a nested case-control study.
      ,
      • Sandin S.
      • Lichtenstein P.
      • Kuja-Halkola R.
      • Larsson H.
      • Hultman C.M.
      • Reichenberg A.
      The familial risk of autism.
      ,
      • Colvert E.
      • Tick B.
      • McEwen F.
      • Stewart C.
      • Curran S.R.
      • Woodhouse E.
      • et al.
      Heritability of autism spectrum disorder in a UK population-based twin sample.
      ] and biologic plausibility. Maternal pre-pregnancy BMI was calculated using the mothers’ height and pre-pregnancy weight as listed in medical records and classified as underweight (<18.5 kg/m2), normal weight (18.5–24.9), overweight (25.0–29.9), or obese (≥30.0) [

      Morisaki N, Piedvache A, Morokuma S, Nakahara K, Ogawa M, Kato K, et al. Gestational weight gain growth charts adapted to Japanese pregnancies using a Bayesian approach in a longitudinal study: The Japan Environment and Children's Study. J Epidemiol 2021. doi: 10.2188/jea. JE20210049. Online ahead of print.

      ]. Gestational weight gain (GWG) was categorized as below, within, or above reference values based on the guidelines of the Institute of Medicine (now known as the National Academy of Medicine) of 2009 [

      Rasmussen K, Yaktine AL, editors. Institute of Medicine and National Research Council Committee to reexamine IOM pregnancy weight guidelines. Weight gain during pregnancy: reexamining the guidelines. Washington DC: National Academic Press; 2009.

      ]. Demographic covariates included maternal age, parental smoking habit, maternal alcohol consumption, socioeconomic status, and parental history of neurodevelopmental disorders, epilepsy, and mental disease. Socioeconomic status was evaluated by the highest level of education completed by the mother (junior high school, high school, vocational school/junior college, or university/graduate school) and annual household income (<4,000,000, 4,000,000–7,999,999, or ≥8,000,000 JPY) [
      • Mitsuda N.
      • N Awn J.P.
      • Eitoku M.
      • Maeda N.
      • Fujieda M.
      • Suganuma N.
      • et al.
      Association between maternal active smoking during pregnancy and placental weight: The Japan environment and Children's study.
      ]. Obstetric and medical variables, such as parity, means of pregnancy, maternal infection and other complications, and medications during pregnancy, were also evaluated. The history of epilepsy, neurodevelopmental disorders, or mental disease of parents was obtained from the questionnaire at registration in early pregnancy (yes or no). Neurodevelopmental disorders included attention deficit and hyperactivity disorder, learning disability, autism, Asperger’s syndrome, and pervasive developmental disorder. Mental disease included depression, schizophrenia, and anxiety disorder.

      2.5 Statistical analysis

      Distribution normality was confirmed by the Kolmogorov–Smirnov test. Data are expressed as the mean (standard deviation [SD]) or the median (interquartile range [IQR]) depending on whether they are normally distributed or not, respectively. We adopted multiple logistic regression models to investigate developmental disorder at 1 year as the dependent variable in association with maternal TC level in early pregnancy. Infants below and above the cut-off for each domain were judged as “screen positive” and “normal development”, respectively. Maternal TC level was subdivided as low, normal (reference), or high. Possible differences in the scores of each domain among the TC level groups were evaluated by one-way repeated measures of analysis of variance (ANOVA) followed by post hoc (Bonferroni) testing. We adopted logistic regression models to calculate adjusted odds ratios (aORs) and their 95% confidence intervals (CIs) while controlling for covariates, as described above. We excluded participants with missing information on any of the covariates used in the multiple logistic regression analysis. Spearman’s rank correlation coefficient was used to check for multicollinearity of covariates. Hosmer–Lemeshow testing was employed to assess the goodness-of-fit of the models. We also analyzed the subjects with incomplete ASQ-3 questionnaires as well as those without registered fathers to evaluate for possible selection bias. All statistical analyses were performed using SPSS statistical software version 24 (SPSS Inc., Chicago, Illinois). A P value of <0.05 was considered statistically significant.

      3. Results

      The characteristics regarding maternal biography, socioeconomic background, parents’ past medication records, pregnancy and delivery history, feeding procedures, and perinatal records of the children are summarized according to maternal quartile serum TC levels in Table 1. There were significant differences among the quartile TC levels for maternal age, pre-pregnancy BMI, calorie intake in the first trimester, parental smoking habits, maternal history of mental disease, parity, means of pregnancy, diabetes mellitus, intrauterine growth retardation, mode of delivery, gestational age, birth weight, feeding procedure, and neonatal jaundice (all P < 0.05) (Table 1). Regarding the index of maternal and child physique, significant but weak correlations for maternal serum TC levels with maternal pre-pregnancy BMI (P < 0.001, r = 0.102) as well as with their child’s physical growth (Kaup’s index) at 12 months of age (P = 0.011, r = 0.016) were detected (Supplementary Fig. S1A and S1B). There were 11,266 screen positive participants (35.4%) who were outliers in at least 1 ASQ-3 domain (Table 1). We observed a significant difference among the maternal TC quartiles for the frequency of screen positives (P = 0.010). This incidence was greatest in the highest quartile (36.9% in the Q4 group).
      Table 1Characteristics of participants according to maternal serum TC level quartile in first trimester.
      VariableAllMaternal serum TC level quartileP value
      Q1 (≤ 175 mg/dL)Q2 (176–196 mg/dL)Q3 (197–219 mg/dL)Q4 (≥ 220 mg/dL)
      Participants, n31,7978,2887,8547,7437,912
      Maternal age at delivery (years)31 (28, 35)31 (28, 34)31 (28, 35)31 (28, 35)32 (28, 35)< 0.001 *
      Maternal age group, n (%)< 0.001
       < 35 years23,416 (73.6)6,320 (76.3)5,850 (74.5)5,655 (73.1)5,591 (70.7)
       ≥ 35 years8,379 (26.4)1,968 (23.7)2,004 (25.5)2,086 (26.9)2,321 (29.3)
      Pre-pregnancy BMI (kg/m2)20.5 (19.1, 22.5)20.2 (18.9, 21.9)20.5 (19.1, 22.4)20.7 (19.2, 22.8)20.8 (19.3, 23.0)< 0.001 *
      Pre-pregnancy BMI group, n (%)< 0.001
       Underweight (BMI < 18.5)4,975 (15.6)1,538 (18.6)1,249 (15.9)1,133 (14.6)1,055 (13.3)
       Normal weight (BMI 18.5–24.9)23,527 (74.0)6,125 (73.9)5,830 (74.2)5,740 (74.1)5,832 (73.7)
       Overweight (BMI 25.0–29.9)2,573 (8.1)489 (5.9)606 (7.7)678 (8.8)800 (10.1)
       Obese (BMI ≥ 30.0)720 (2.3)135 (1.6)168 (2.1)192 (2.5)225 (2.8)
      Body weight gain during pregnancy (kg)10.2 (8.0, 12.6)10.2 (8.0, 12.6)10.2 (8.0, 12.6)10.2 (8.0, 12.6)10.2 (8.0, 12.6)0.91 *
      Standard daily calorie intake (kcal/day)
       First trimester1,678 (1,366, 2,083)1,664 (1,353, 2,073)1,673 (1,369, 2,073)1,681 (1,359, 2,080)1,698 (1,379, 2,106)0.007 *
       Second/third trimester1,602 (1,366, 1,989)1,612 (1,306, 1,995)1,592 (1,301, 1,984)1,616 (1,309, 2,004)1,593 (1,298, 1,974)0.18 *
      Highest level of maternal education, n (%)0.054
       Junior high school1,066 (3.4)291 (3.5)240 (3.1)270 (3.5)265 (3.4)
       High school9,438 (29.9)2,369 (28.8)2,349 (30.1)2,296 (29.9)2,424 (30.9)
       Vocational school/Junior college13,756 (43.6)3,659 (44.4)3,340 (42.8)3,354 (43.7)3,403 (43.4)
       University/Graduate school7,293 (23.1)1,913 (23.2)1,870 (24.0)1,758 (24.1)1,752 (22.3)
      Annual household income during pregnancy, n (%)0.75
       < 4,000,000 JPY11,480 (38.5)3,038 (38.6)2,796 (38.0)2,792 (38.4)2,854 (38.7)
       4,000,000–7,999,999 JPY15,107 (50.7)3,924 (50.2)3,781 (51.4)3,659 (50.4)3,743 (50.7)
       ≥ 8,000,000 JPY3,273 (10.9)854 (10.9)782 (10.6)814 (11.2)787 (10.7)
      Maternal smoking during pregnancy, n (%)1,043 (3.3)330 (4.0)252 (3.2)225 (2.9)236 (3.0)< 0.001
      Partner's smoking during pregnancy, n (%)13,166 (42.0)3,403 (41.7)3,318 (42.8)3,261 (42.8)3,184 (40.9)0.045
      Maternal drinking during pregnancy, n (%)566 (1.8)161 (2.0)145 (1.9)143 (1.9)117 (1.5)0.13
      Maternal history of mental disease, n (%)1,634 (5.2)392 (4.7)418 (5.3)366 (4.7)458 (5.8)0.004
      Maternal history of developmental disorder, n (%)16 (0.1)3 (0.0)5 (0.1)4 (0.1)4 (0.1)0.89
      Maternal history of epilepsy, n (%)161 (0.5)38 (0.5)45 (0.6)35 (0.5)43 (0.5)0.63
      Partner's history of mental disease, n (%)775 (2.5)185 (2.3)184 (2.4)202 (2.6)204 (2.6)0.34
      Partner's history of developmental disorder, n (%)22 (0.1)8 (0.1)2 (0.0)4 (0.1)8 (0.1)0.21
      Partner's history of epilepsy, n (%)124 (0.4)45 (0.5)26 (0.3)29 (0.4)24 (0.3)0.064
      Parity, n (%)< 0.001
       Primiparous13,814 (44.5)3,827 (47.6)3,509, (45.7)3,334 (44.1)3,144 (40.6)
       Multiparous17,202 (55.5)4,211 (52.4)4,164 (54.3)4,220 (55.9)4,607 (59.4)
      Means of pregnancy for current birth, n (%)< 0.001
       Spontaneous29,514 (93.2)7,760 (94.0)7,311 (93.4)7,175 (93.0)7,268 (92.3)
       Ovulation induction through medication853 (2.7)211 (2.6)224 (2.9)205 (2.7)213 (2.7)
       Artificial insemination or in vitro fertilization1,316 (4.1)288 (3.5)296 (3.8)336 (4.4)396 (5.0)
      Maternal use of folic acid supplements, n (%)731 (2.3)176 (2.1)174 (2.2)178 (2.3)203 (2.6)0.27
      Diabetes mellitus/gestational diabetes mellitus, n (%)894 (2.8)197 (2.4)200 (2.5)231 (3.0)266 (3.4)< 0.001
      Hypertensive disorder of pregnancy, n (%)843 (2.7)217 (2.6)217 (2.8)207 (2.7)202 (2.6)0.87
      Intrauterine growth retardation, n (%)529 (1.7)167 (2.0)136 (1.7)122 (1.6)104 (1.3)0.005
      Mode of delivery for current birth, n (%)< 0.001
       Spontaneous delivery18,390 (57.9)4,681 (56.6)4,555 (58.1)4,534 (58.6)4,620 (58.5)
       Induced delivery5,854 (18.4)1,690 (20.4)1,441 (18.4)1,352 (17.5)1,371 (17.4)
       Vacuum extraction/Forceps delivery2,102 (6.6)545 (6.6)546 (7.0)504 (6.5)507 (6.4)
       Cesarean section5,409 (17.0)1,360 (16.4)1.302 (16.6)1,345 (17.4)1,402 (17.7)
      Gestational age (weeks)39 (38, 40)39 (38, 40)39 (38, 40)39 (38, 40)39 (38, 40)< 0.001 *
      Birth weight (g)3,050 (2,820, 3,294)3,018 (2,798, 3,270)3,046 (2,820, 3,290)3,060 (2,830, 3,300)3,078 (2,846, 3,324)< 0.001 *
      Birth weight categories, n (%)< 0.001
       < 2500 g1,609 (5.1)486 (5.9)407 (5.2)355 (4.6)361 (4.6)
       2500 to 3999 g29,926 (94.1)7,742 (93.4)7,394 (94.2)7,329 (94.7)7,461 (94.3)
       ≥ 4000 g257 (0.8)59 (0.7)51 (0.6)59 (0.8)88 (1.1)
      Gender (male), n (%)15,948 (50.2)4,249 (51.3)3,935 (50.1)3,842 (49.6)3,922 (49.6)0.11
      Method of feeding, n (%)< 0.001
       Breast feeding17,415 (54.8)4,683 (56.5)4,400 (56.1)4,133 (53.4)4,199 (53.1)
       Mixed feeding13,144 (41.3)3,316 (40.0)3,149 (40.1)3.301 (42.6)3,378 (42.7)
       Infant formula1,017 (3.2)241 (2.9)241 (3.1)253 (3.3)282 (3.6)
       Other221 (0.7)48 (0.6)64 (0.8)56 (0.7)53 (0.7)
      Neonatal jaundice, n (%)4,209 (13.4)1,152 (14.1)1,087 (14.0)1,064 (13.8)906 (11.6)< 0.001
      Kaup index at 12 months of age17.0 (16.1, 17.9)16.9 (16.1, 17.9)17.0 (16.1, 17.9)17.0 (16.2, 17.9)17.0 (16.1, 18.0)0.089 *
      Positive ASQ-3 screen ≥ 1 domain, n (%)11,266 (35.4)2,847 (34.4)2,764 (35.2)2,739 (35.4)2,916 (36.9)0.010
      Total number of screen positive domains in ASQ-3 at 12 months of age, n (%)0.19
       1 domain6,312 (19.9)1,582 (19.1)1,567 (20.0)1,551 (20.0)1,612 (20.4)
       2 domains2,801 (8.8)731 (8.8)677 (8.6)652 (8.4)741 (9.4)
       3 domains1,339 (4.2)320 (3.9)331 (4.2)333 (4.3)35 (4.5)
       4 domains600 (1.9)163 (2.0)136 (1.7)154 (2.0)147 (1.9)
       5 domains214 (0.7)51 (0.6)53 (0.7)49 (0.6)61 (0.8)
      TC, total cholesterol; BMI, body mass index; ASQ-3, Ages and Stages Questionnaire, third edition.
      Continuous variables are expressed as the median (interquartile range).
      * Kruskal–Wallis test among TC level categories.
      Data were missing on maternal age (n = 2), pre-pregnancy BMI (n = 2), maternal education level (n = 244), household income (n = 1,973), maternal smoking habit (n = 304), partner's smoking habit (n = 470), maternal drinking habit (n = 267), maternal history of neurodevelopmental disorders (n = 69), partner's history of neurodevelopmental disorders (n = 357), parity (n = 781), mode of delivery (n = 42), birth weight (n = 5), and neonatal jaundice (n = 283).
      The ASQ-3 domain classifications and proportions that were scored as a risk of referral zone at 12 months by maternal TC level are shown in Table 2. In chi-square analysis, there was a significant difference in the prevalence of referral zone in the gross motor domain among the maternal TC level groups. The distribution of prevalence in the referral zone for the communication domain was similar to that for the gross motor domain, although no significant difference was observed (P = 0.26). ANOVA showed that the score for the gross motor domain was significantly lower in the highest TC group (Q4) than in the Q1 and Q2 groups (both P < 0.01).
      Table 2ASQ-3 domain scores at 12 months of age and proportions at risk of delay according to maternal serum TC level quartile in first trimester (n = 31,797).
      ASQ-3 domain (cut-off score)Maternal serum TC level quartileP value
      Q1 (≤ 175 mg/dL)Q2 (176–196 mg/dL)Q3 (197–219 mg/dL)Q4 (≥ 220 mg/dL)
      n = 8,288n = 7,854n = 7,743n = 7,912
      Communication (15.64 points)
       Score (points)38.0 ± 13.238.2 ± 13.238.4 ± 13.438.0 ± 13.50.22
       On track, n (%)7,752 (93.5)7,357 (93.7)7,238 (93.5)7,353 (92.9)
       Referral, n (%)536 (6.5)497 (6.3)505 (6.5)559 (7.1)0.26
      Gross motor (21.49 points)
       Score (points)43.5 ± 17.1 *43.4 ± 17.1 *43.1 ± 17.142.5 ± 17.70.001
       On track, n (%)7,211 (87.0)6,839, (87.1)6,708 (86.6)6,744 (85.2)
       Referral, n (%)1,077 (13.0)1,015 (12.9)1,035 (13.4)1,168 (14.8)0.002
      Fine motor (34.50 points)
       Score (points)48.5 ± 11.348.5 ± 11.248.6 ± 11.248.3 ± 11.40.59
       On track, n (%)7,507 (90.6)7,093 (90.3)7,023 (90.7)7,122 (90.0)
       Referral, n (%)781 (9.4)761 (9.7)720 (9.3)790 (10.0)0.47
      Problem solving (27.32 points)
       Score (points)42.7 ± 13.242.5 ± 13.442.8 ± 13.442.5 ± 13.40.38
       On track, n (%)7,098 (85.6)6,646 (84.6)6,565 (84.8)6,701 (84.7)
       Referral, n (%)1,190 (14.4)1,208 (15.4)1,178 (15.2)1,211 (15.3)0.23
      Personal-social (21.73 points)
       Score (points)37.6 ± 14.237.6 ± 14.337.5 ± 14.437.3 ± 14.40.48
       On track, n (%)6,961 (84.0)6,612 (84.2)6,466 (83.5)6,588 (83.3)
       Referral, n (%)1,327 (16.0)1,242 (15.8)1,277 (16.5)1,324 (16.7)0.37
      ASQ-3, Ages and Stages Questionnaire, third edition; TC, total cholesterol. Plus-minus variables are the mean ± standard deviation.
      Differences in scores of ASQ-3 domains were assessed with one-way repeated measures of variance followed by post hoc (Bonferroni) testing. * P < 0.01 versus the Q4 group.
      In multiple logistic regression analysis after adjustment for covariates, higher maternal serum TC levels were significantly associated with an increased risk of screen positive in the communication (aOR for Q4 serum TC level vs. Q1 1.20, 95% CI 1.05–1.37; P for trend = 0.010) and gross motor (aOR for Q4 serum TC level vs. Q1 1.13, 95% CI 1.03–1.25; P for trend = 0.014) ASQ-3 domains (Table 3).
      Table 3Odds ratio and 95% confidence intervals for the association between maternal serum TC level quartile in first trimester and screen positive in ASQ-3 domains (n = 27,836).
      ASQ-3 domainMaternal serum TC level quartileP for trend
      Q1 (≤ 175 mg/dL)Q2 (176–196 mg/dL)Q3 (197–219 mg/dL)Q4 (≥ 220 mg/dL)
      Communication
       Crude OR (95% CI)1.00 (Reference)0.99 (0.87–1.13)0.90 (0.90–1.16)1.14 (1.01–1.29)0.040
       Adjusted* OR (95% CI)1.00 (Reference)1.01 (0.88–1.16)1.03 (0.90–1.08)1.20 (1.05–1.37)0.010
      Gross motor
       Crude OR (95% CI)1.00 (Reference)1.01 (0.92–1.11)1.05 (0.95–1.15)1.18 (1.08–1.29)< 0.001
       Adjusted* OR (95% CI)1.00 (Reference)0.98 (0.89–1.09)1.00 (0.91–1.11)1.13 (1.03–1.25)0.014
      Fine motor
       Crude OR (95% CI)1.00 (Reference)1.04 (0.94–1.16)1.00 (0.90–1.12)1.10 (0.99–1.23)0.14
       Adjusted* OR (95% CI)1.00 (Reference)1.08 (0.96–1.21)0.99 (0.89–1.12)1.09 (0.97–1.22)0.38
      Problem solving
       Crude OR (95% CI)1.00 (Reference)1.09 (0.99–1.19)1.08 (0.98–1.18)1.11 (1.01–1.21)0.035
       Adjusted* OR (95% CI)1.00 (Reference)1.10 (0.99–1.21)1.07 (0.97–1.18)1.09 (0.99–1.21)0.14
      Personal-social
       Crude OR (95% CI)1.00 (Reference)1.00 (0.92–1.09)1.05 (0.96–1.14)1.09 (0.99–1.18)0.034
       Adjusted* OR (95% CI)1.00 (Reference)0.99 (0.90–1.09)1.01 (0.92–1.10)1.06 (0.96–1.16)0.24
      ASQ-3, Ages and Stages Questionnaire, third edition; TC, total cholesterol; OR, odds ratio; CI, confidence interval. *Adjusted for maternal age, pre-pregnancy body mass index, parental smoking habit, maternal drinking habit, maternal highest level of education, annual household income, parental history of developmental disorders, epilepsy, and mental disease, means of pregnancy, use of folic acid supplements, maternal gestational weight gain, complications during pregnancy (including diabetes mellitus/gestational diabetes mellitus and hypertensive disorder of pregnancy), intrauterine growth restriction, gender, birth weight, method of feeding, and neonatal jaundice.
      Lastly, we evaluated the characteristics of 9,053 participants with missing ASQ-3 data (Supplementary Table S1) and 47,692 participants without registered fathers (Supplementary Table S2) to test for selection bias. Significant differences were detected for several maternal as well as offspring characteristics in both analyses. Moreover, participants with missing ASQ-3 data or without father registration showed significantly higher maternal TC levels (P = 0.001 and P < 0.001, respectively) (Supplementary Tables S1 and S2). Finally, the 38,160 participants with TC data were divided into those who were analyzed (n = 31,797) and those who were excluded by the exclusion criteria (n = 6,363). The distribution of maternal TC values was similar (Supplemental Fig. S2).

      4. Discussion

      We herein describe the first large-scale nationwide birth cohort study in Japan to determine the relationship of maternal TC in early pregnancy with offspring development. Our results indicate that maternal hypercholesterolemia may increase the risk of a screen positive ASQ-3 result at 12 months and offer a way to detect children who should be referred for further assessment.
      Maternal cholesterol levels are physiologically elevated during early gestation, but it remains controversial whether maternal hypercholesterolemia can induce unfavorable effects on the fetus and pregnancy outcome [
      • Bartels Ä.
      • O'Donoghue K.
      Cholesterol in pregnancy: a review of knowns and unknowns.
      ,
      • Vrijkotte T.G.
      • Krukziener N.
      • Hutten B.A.
      • Vollebregt K.C.
      • van Eijsden M.
      • Twickler M.B.
      Maternal lipid profile during early pregnancy and pregnancy complications and outcomes: the ABCD study.
      ]. However, high maternal TC levels during pregnancy have been linked to increased risks of preterm delivery, gestational diabetes, and preeclampsia [
      • Edison R.J.
      • Berg K.
      • Remaley A.
      • Kelley R.
      • Rotimi C.
      • Stevenson R.E.
      • et al.
      Adverse birth outcome among mothers with low serum cholesterol.
      ,
      • Gunderson E.P.
      • Quesenberry Jr, C.P.
      • Jacobs Jr, D.R.
      • Feng J.
      • Lewis C.E.
      • Sidney S.
      Longitudinal study of prepregnancy cardiometabolic risk factors and subsequent risk of gestational diabetes mellitus: The CARDIA study.
      ,
      • Gratacós E.
      • Casals E.
      • Gómez O.
      • Llurba E.
      • Mercader I.
      • Cararach V.
      • et al.
      Increased susceptibility to low density lipoprotein oxidation in women with a history of pre-eclampsia.
      ]. Kaneko et al. [
      • Kaneko K
      • Ito Y
      • Ebara T
      • Kato S
      • Matsuki T
      • Tamada H
      • et al.
      Association of Maternal Total Cholesterol With SGA or LGA Birth at Term: the Japan Environment and Children’s Study.
      ] also demonstrated that higher maternal TC in mid-pregnancy was significantly associated with the development of large for gestational age among Japanese mothers in a JECS cohort. However, the long-term outcome of offspring of mothers with hypercholesterolemia during pregnancy is still unknown.
      It is uncertain why maternal hypercholesterolemia may affect offspring neurodevelopment. Among lipids, cholesterol plays a key role in embryo and fetal development as an essential component of cell membrane, and is also associated with cell proliferation, cell differentiation, and cell-to-cell communication in the embryo and fetus [
      • Ohvo-Rekilä H.
      • Ramstedt B.
      • Leppimäki P.
      • Slotte J.P.
      Cholesterol interactions with phospholipids in membranes.
      ,
      • Martínez‐Botas J.
      • Suárez Y.
      • Ferruelo A.J.
      • Gómez‐Coronado D.
      • Lasunció M.A.
      Cholesterol starvation decreases p34 (cdc2) kinase activity and arrests the cell cycle at G2.
      ,
      • Suárez Y.
      • Fernández C.
      • Ledo B.
      • Ferruelo A.J.
      • Martín M.
      • Vega M.A.
      • et al.
      Differential effects of ergosterol and cholesterol on Cdk1 activation and SRE-driven transcription.
      ,
      • Mauch D.H.
      • Nägler K.
      • Schumacher S.
      • Göritz C.
      • Müller E.-C.
      • Otto A.
      • et al.
      CNS synaptogenesis promoted by glia-derived cholesterol.
      ]. Cholesterol is also contained in neuronal myelin membranes at a level as high as 26% by weight and is known to be important for myelin production and maintenance during brain maturation [
      • Chrast R.
      • Saher G.
      • Nave K.-A.
      • Verheijen M.H.G.
      Lipid metabolism in myelinating glial cells: lessons from human inherited disorders and mouse models.
      ]. Excess energy intake and insufficient energy expenditure are linked to an increased prevalence of dyslipidemia [
      • Jung U.J.
      • Choi M.S.
      Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease.
      ]. Several animal models have revealed relationships between a maternal high-fat diet and offspring neurodevelopment, whereby high-fat diet exposure during the perinatal period induced such mental disorders as anxiety behavior [
      • Janthakhin Y.
      • Rincel M.
      • Costa A.M.
      • Darnaudéry M.
      • Ferreira G.
      Maternal high-fat diet leads to hippocampal and amygdala dendritic remodeling in adult male offspring.
      ,
      • Winther G.
      • Elfving B.
      • Müller H.K.
      • Lund S.
      • Wegener G.
      Maternal high-fat diet programs offspring emotional behavior in adulthood.
      ,
      • Sasaki A.
      • de Vega W.C.
      • St-Cyr S.
      • Pan P.
      • McGowan P.O.
      Perinatal high fat diet alters glucocorticoid signaling and anxiety behavior in adulthood.
      ] and spatial cognitive function [
      • Tozuka Y.
      • Kumon M.
      • Wada E.
      • Onodera M.
      • Mochizuki H.
      • Wada K.
      Maternal obesity impairs hippocampal BDNF production and spatial learning performance in young mouse offspring.
      ]. Maternal obesity has been associated with neurodevelopmental disorders, such as attention-deficit/hyperactivity disorder [
      • Rodriguez A.
      • Miettunen J.
      • Henriksen T.B.
      • Olsen J.
      • Obel C.
      • Taanila A.
      • et al.
      Maternal adiposity prior to pregnancy is associated with ADHD symptoms in offspring: Evidence from three prospective pregnancy cohorts.
      ] and autism spectrum disorder [
      • Krakowiak P.
      • Walker C.K.
      • Bremer A.A.
      • Baker A.S.
      • Ozonoff S.
      • Hansen R.L.
      • et al.
      Maternal metabolic conditions and risk for autism and other neurodevelopmental disorders.
      ]. However, the above studies used extremely high-fat diets for animals or did not measure serum TC levels among obese mothers. Further validation of the current investigation is desired.
      It is important to ascertain whether neurodevelopmental evaluations at 12 months are clinically valid for subsequent diagnosis. In one study longitudinally comparing child ASQ-3 domain screening results based on cut-off scores, the vast majority (88.9–96.7%) received the same categorization results at 9, 18, and 24 months of age [
      • Agarwal P.K.
      • Xie H.
      • Sathyapalan Rema A.S.
      • Rajadurai V.S.
      • Lim S.B.
      • Meaney M.
      • et al.
      Evaluation of Ages and Stages Questionnaire (ASQ 3) as a developmental screener at 9, 18, and 24 months.
      ]. Other reports have provided evidence on the concurrent validity of the ASQ-3 and clinical diagnosis of developmental disorders, as well as on the reliability of the ASQ-3 in a multi-ethnic population [
      • Fauls J.R.
      • Thompson B.L.
      • Johnston L.M.
      Validity of the Ages and Stages Questionnaire to identify young children with gross motor difficulties who require physiotherapy assessment.
      ,
      • Ga H.Y.
      • Kwon J.Y.
      A comparison of the Korean ages and stages questionnaires and Denver developmental delay screening test.
      ,
      • Romero Otalvaro A.M.
      • Grañana N.
      • Gaeto N.
      • Torres M.L.Á.
      • Zamblera M.N.
      • Vasconez M.A.
      • et al.
      ASQ-3: Validation of the Ages and Stages Questionnaire for the detection of neurodevelopmental disorders in Argentine children.
      ]. The number of children who tested screen positive (i.e., failed at least 1 of the 5 domains) in this investigation was high at 35.4%. The majority of screen-positive children in the present study had a failure in 1 domain. One study evaluating the validity of the ASQ-3 in Japan suggested a revised deficit criterion of failure in at least 2 domains [
      • Mezawa H.
      • Aoki S.
      • Nakayama S.F.
      • Nitta H.
      • Ikeda N.
      • Kato K.
      • et al.
      Psychometric profile of the Ages and Stages Questionnaires, Japanese translation.
      ]. In addition, it was revealed that only communication and gross motor among the 5 domains were associated with maternal hypercholesterolemia in this study. They also reported that for the communication, the gross motor and personal-social domains of the 12 months questionnaire, the Japanese ASQ-3 cutoff score was lower than original ASQ-3 cutoff score by more than 10 points [
      • Mezawa H.
      • Aoki S.
      • Nakayama S.F.
      • Nitta H.
      • Ikeda N.
      • Kato K.
      • et al.
      Psychometric profile of the Ages and Stages Questionnaires, Japanese translation.
      ]. Compared with US children, Japanese children acquire the several skills including communication and gross motor skills >2 months later. The differences in score distribution between the Japanese and original ASQ-3 results found might reflect differences of lifestyle and culture, rather than a lack of validity. Although the results of this investigation could have been overestimated, analysis by TC quartile categories showed a dose–response pattern in 2 domains. Fetal exposure to hypercholesterolemia may have adversely affected neurodevelopment and age-appropriate skill acquisition in the highest quartile group; the cohort will be followed until 13 years of age to verify this possibility.
      A strength of this report was that both maternal and paternal history of neurodevelopmental problems were adjusted for as covariates. Indeed, genetic influences may outweigh those of a shared environment on the incidence of neurodevelopmental disorders [
      • Demirci A.
      • Kartal M.
      Sociocultural risk factors for developmental delay in children aged 3–60 months: a nested case-control study.
      ,
      • Sandin S.
      • Lichtenstein P.
      • Kuja-Halkola R.
      • Larsson H.
      • Hultman C.M.
      • Reichenberg A.
      The familial risk of autism.
      ]. Since selection bias might have been created by excluding participants without father registration, we performed an additional sub-analysis on the group without registered fathers to rule this possibility out.
      This study had several limitations. First, the developmental score data measured by the ASQ-3 were self-reported and therefore subjective, and the diagnosis and severity of any potential developmental disorder could not be verified. Second, the data on neurodevelopment were collected between 1 month before and 1 month after 12 months of age; therefore, any disorders diagnosed afterwards were not considered. Third, the large attrition rate of unpaired participants or those not fully completing the ASQ-3 questionnaire might have been a source of selection bias (Supplementary Tables S1, S2). Thus, we could not conclusively discount the possibility of under-reporting the incidence of developmental disorders. Fourth, since the parental history of neurodevelopmental disorders, epilepsy, and mental disease were also obtained from self-reported questionnaires, those results might not have conformed to established diagnostic criteria or ICD coding. Another limitation was the non-fasting blood sampling in this study. Although cholesterol level is less affected by diet than that of TG, the maximum mean changes are reportedly +0.3 mmol/L (26 mg/dL) for TG and −0.2 mmol/L (8 mg/dL) for TC in non-fasting versus fasting blood samples [
      • Nordestgaard B.G.
      A test in context: lipid profile, fasting versus nonfasting.
      ]. Hence, there was a possibility that cholesterol values were slightly underestimated.
      Despite the above limitations, this is the first study employing a large dataset from a Japanese nationwide birth cohort study to analyze the influence of maternal TC on apparently normal-born children after controlling for confounders identified by earlier reports. It provides important information on a possible adverse effect of maternal hypercholesterolemia on offspring neurodevelopment, particularly communication and gross motor ability, to suggest dietary consultation and monitoring for Japanese women desiring pregnancy.

      Acknowledgments

      The authors would like to thank all the participants of this study and all individuals involved in data collection, as well as Ms. Tomoko Kamijo for her assistance in data analysis and Mr. Trevor Ralph for his English editorial support.
      Members of the JECS Group as of 2019: Michihiro Kamijima (principal investigator, Nagoya City University, Nagoya, Japan), Shin Yamazaki (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 College of 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).

      Author contributions

      Study concept and design: NM and YI. Analysis of data: NM. Interpretation of data: NM, YI, TT, and TN. Drafting the manuscript: NM. Critical revision of the manuscript: YI, TS, YM, SO, MK, HK, TT, TN, and the JECS group.

      Statement of financial support

      The Japan Environment and Children’s Study was funded by the Ministry of the Environment of the government of Japan . The finding and conclusions of this study are solely the response of the authors and do not represent the official views of the above government.

      Disclosure

      The authors declare no conflict of interest.

      Appendix A. Supplementary data

      The following are the Supplementary data to this article:

      References

        • Oberklaid F.
        • Efron D.
        Developmental delay–identification and management.
        Aust Fam Physician. 2005; 34: 739-742
        • Blumberg S.J.
        • Bramlett M.D.
        • Kogan M.D.
        • Schieve L.A.
        • Jones J.R.
        • Lu M.C.
        • et al.
        Changes in prevalence of parent-reported autism spectrum disorder in school-aged U.S. children: 2007 to 2011-2012.
        Natl Health Stat Report 2013. 2007; 65: 1-11
        • Boyle C.A.
        • Boulet S.
        • Schieve L.A.
        • Cohen R.A.
        • Blumberg S.J.
        • Yeargin-Allsopp M.
        • et al.
        Trends in the prevalence of developmental disabilities in US children, 1997–2008.
        Pediatrics. 2011; 127: 1034-1042
      1. Simeonsson RJ, Sharp MC. Developmental delays. In: Hoekelman RA, Friedman SB, Nelson NM, Seidel HM eds. Primary pediatric care. St Louis; Mosby-Year Book; 1992. p 867-870.

        • Gottlieb C.A.
        • Maenner M.J.
        • Cappa C.
        • Durkin M.S.
        Child disability screening, nutrition, and early learning in 18 countries with low and middle incomes: data from the third round of UNICEF’s Multiple Indicator Cluster Survey (2005–06).
        Lancet. 2009; 374: 1831-1839
        • Deciphering Developmental Disorders Study
        Large-scale discovery of novel genetic causes of developmental disorders.
        Nature. 2015; 519: 223-228
        • Tran N.Q.V.
        • Miyake K.
        Neurodevelopmental disorders and environmental toxicants: Epigenetics as an underlying mechanism.
        Int J Genomics. 2017; 2017: 7526592
        • Bölte S.
        • Girdler S.
        • Marschik P.B.
        The contribution of environmental exposure to the etiology of autism spectrum disorder.
        Cell Mol Life Sci. 2019; 76: 1275-1297
        • Herrera E.
        Lipid metabolism in pregnancy and its consequences in the fetus and newborn.
        Endocrine. 2002; 19: 32-55
        • Herrera E.
        • Palacin M.
        • Martin A.
        • Lasunción M.A.
        Relationship between maternal and fetal fuels and placental glucose transfer in rats with maternal diabetes of varying severity.
        Diabetes. 1985; 34: 42-46
        • Knipp G.T.
        • Audus K.L.
        • Soares M.J.
        Nutrient transport across the placenta.
        Adv Drug Deliv Rev. 1999; 38: 41-58
        • Sibley C.
        • Glazier J.
        • D'Souza S.
        Placental transporter activity and expression in relation to fetal growth.
        Exp Physiol. 1997; 82: 389-402
        • Herrera E.
        Implications of dietary fatty acids during pregnancy on placental, fetal and postnatal development–a review.
        Placenta. 2002; 23: S9-S19
        • Ohvo-Rekilä H.
        • Ramstedt B.
        • Leppimäki P.
        • Slotte J.P.
        Cholesterol interactions with phospholipids in membranes.
        Prog Lipid Res. 2002; 41: 66-97
        • Martínez‐Botas J.
        • Suárez Y.
        • Ferruelo A.J.
        • Gómez‐Coronado D.
        • Lasunció M.A.
        Cholesterol starvation decreases p34 (cdc2) kinase activity and arrests the cell cycle at G2.
        FASEB J. 1999; 13: 1359-1370
        • Suárez Y.
        • Fernández C.
        • Ledo B.
        • Ferruelo A.J.
        • Martín M.
        • Vega M.A.
        • et al.
        Differential effects of ergosterol and cholesterol on Cdk1 activation and SRE-driven transcription.
        Eur J Biochem. 2002; 269: 1761-1771
        • Mauch D.H.
        • Nägler K.
        • Schumacher S.
        • Göritz C.
        • Müller E.-C.
        • Otto A.
        • et al.
        CNS synaptogenesis promoted by glia-derived cholesterol.
        Science. 2001; 294: 1354-1357
        • Chrast R.
        • Saher G.
        • Nave K.-A.
        • Verheijen M.H.G.
        Lipid metabolism in myelinating glial cells: lessons from human inherited disorders and mouse models.
        J Lipid Res. 2011; 52: 419-434
        • Napoli C.
        • D'Armiento F.P.
        • Mancini F.P.
        • Postiglione A.
        • Witztum J.L.
        • Palumbo G.
        • et al.
        Fatty streak formation occurs in human fetal aortas and is greatly enhanced by maternal hypercholesterolemia. Intimal accumulation of low density lipoprotein and its oxidation precede monocyte recruitment into early atherosclerotic lesions.
        J Clin Invest. 1997; 100: 2680-2690
        • Catov J.M.
        • Bodnar L.M.
        • Ness R.B.
        • Barron S.J.
        • Roberts J.M.
        Inflammation and dyslipidemia related to risk of spontaneous preterm birth.
        Am J Epidemiol. 2007; 166: 1312-1319
        • Jin W.Y.
        • Lin S.L.
        • Hou R.L.
        • Chen X.Y.
        • Han T.
        • Jin Y.
        • et al.
        Associations between maternal lipid profile and pregnancy complications and perinatal outcomes: a population-based study from China.
        BMC Pregnancy Childbirth. 2016; 16: 60
        • Spracklen C.N.
        • Smith C.J.
        • Saftlas A.F.
        • Robinson J.G.
        • Ryckman K.K.
        Maternal hyperlipidemia and the risk of preeclampsia: a meta-analysis.
        Am J Epidemiol. 2014; 180: 346-358
        • Wang X.
        • Guan Q.
        • Zhao J.
        • Yang F.
        • Yuan Z.
        • Yin Y.
        • et al.
        Association of maternal serum lipids at late gestation with the risk of neonatal macrosomia in women without diabetes mellitus.
        Lipids Health Dis. 2018; 17: 78
        • Michikawa T.
        • Nitta H.
        • Nakayama S.F.
        • 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
        • Ishitsuka K.
        • Nakayama S.F.
        • Kishi R.
        • Mori C.
        • Yamagata Z.
        • Ohya Y.
        • et al.
        Japan Environment and Children’s Study: backgrounds, activities, and future directions in global perspectives.
        Environ Health Prev Med. 2017; 22: 61
        • 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
      2. Squires J, Bricker D. Ages and Stages Questionnaires. Third Edition (ASQ-3TM). Baltimore: Paul H. Brookes Publishing Co.;2009.

        • Mezawa H.
        • Aoki S.
        • Nakayama S.F.
        • Nitta H.
        • Ikeda N.
        • Kato K.
        • et al.
        Psychometric profile of the Ages and Stages Questionnaires, Japanese translation.
        Pediatr Int. 2019; 61: 1086-1095
        • McDonald S.
        • Kehler H.
        • Bayrampour H.
        • Fraser-Lee N.
        • Tough S.
        Risk and protective factors in early child development: results from the All Our Babies (AOB) pregnancy cohort.
        Res Dev Disabil. 2016; 58: 20-30
        • Demirci A.
        • Kartal M.
        Sociocultural risk factors for developmental delay in children aged 3–60 months: a nested case-control study.
        Eur J Pediatr. 2018; 177: 691-697
        • Sandin S.
        • Lichtenstein P.
        • Kuja-Halkola R.
        • Larsson H.
        • Hultman C.M.
        • Reichenberg A.
        The familial risk of autism.
        JAMA. 2014; 311: 1770-1777
        • Colvert E.
        • Tick B.
        • McEwen F.
        • Stewart C.
        • Curran S.R.
        • Woodhouse E.
        • et al.
        Heritability of autism spectrum disorder in a UK population-based twin sample.
        Psychiatry. 2015; 72: 415
      3. Morisaki N, Piedvache A, Morokuma S, Nakahara K, Ogawa M, Kato K, et al. Gestational weight gain growth charts adapted to Japanese pregnancies using a Bayesian approach in a longitudinal study: The Japan Environment and Children's Study. J Epidemiol 2021. doi: 10.2188/jea. JE20210049. Online ahead of print.

      4. Rasmussen K, Yaktine AL, editors. Institute of Medicine and National Research Council Committee to reexamine IOM pregnancy weight guidelines. Weight gain during pregnancy: reexamining the guidelines. Washington DC: National Academic Press; 2009.

        • Mitsuda N.
        • N Awn J.P.
        • Eitoku M.
        • Maeda N.
        • Fujieda M.
        • Suganuma N.
        • et al.
        Association between maternal active smoking during pregnancy and placental weight: The Japan environment and Children's study.
        Placenta. 2020; 94: 48-53
        • Bartels Ä.
        • O'Donoghue K.
        Cholesterol in pregnancy: a review of knowns and unknowns.
        Obstet Med. 2011; 4: 147-151
        • Vrijkotte T.G.
        • Krukziener N.
        • Hutten B.A.
        • Vollebregt K.C.
        • van Eijsden M.
        • Twickler M.B.
        Maternal lipid profile during early pregnancy and pregnancy complications and outcomes: the ABCD study.
        J Clin Endocrinol Metab. 2012; 97: 3917-3925
        • Edison R.J.
        • Berg K.
        • Remaley A.
        • Kelley R.
        • Rotimi C.
        • Stevenson R.E.
        • et al.
        Adverse birth outcome among mothers with low serum cholesterol.
        Pediatrics. 2007; 120: 723-733
        • Gunderson E.P.
        • Quesenberry Jr, C.P.
        • Jacobs Jr, D.R.
        • Feng J.
        • Lewis C.E.
        • Sidney S.
        Longitudinal study of prepregnancy cardiometabolic risk factors and subsequent risk of gestational diabetes mellitus: The CARDIA study.
        Am J Epidemiol. 2010; 172: 1131-1143
        • Gratacós E.
        • Casals E.
        • Gómez O.
        • Llurba E.
        • Mercader I.
        • Cararach V.
        • et al.
        Increased susceptibility to low density lipoprotein oxidation in women with a history of pre-eclampsia.
        BJOG. 2003; 110: 400-404
        • Kaneko K
        • Ito Y
        • Ebara T
        • Kato S
        • Matsuki T
        • Tamada H
        • et al.
        Association of Maternal Total Cholesterol With SGA or LGA Birth at Term: the Japan Environment and Children’s Study.
        J Clin Endocrinol Metab. 2022; 107: e118-e129
        • Jung U.J.
        • Choi M.S.
        Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease.
        Int J Mol Sci. 2014; 15: 6184-6223
        • Janthakhin Y.
        • Rincel M.
        • Costa A.M.
        • Darnaudéry M.
        • Ferreira G.
        Maternal high-fat diet leads to hippocampal and amygdala dendritic remodeling in adult male offspring.
        Psychoneuroendocrinology. 2017; 83: 49-57
        • Winther G.
        • Elfving B.
        • Müller H.K.
        • Lund S.
        • Wegener G.
        Maternal high-fat diet programs offspring emotional behavior in adulthood.
        Neuroscience. 2018; 388: 87-101
        • Sasaki A.
        • de Vega W.C.
        • St-Cyr S.
        • Pan P.
        • McGowan P.O.
        Perinatal high fat diet alters glucocorticoid signaling and anxiety behavior in adulthood.
        Neuroscience. 2013; 240: 1-12
        • Tozuka Y.
        • Kumon M.
        • Wada E.
        • Onodera M.
        • Mochizuki H.
        • Wada K.
        Maternal obesity impairs hippocampal BDNF production and spatial learning performance in young mouse offspring.
        Neurochem Int. 2010; 57: 235-247
        • Rodriguez A.
        • Miettunen J.
        • Henriksen T.B.
        • Olsen J.
        • Obel C.
        • Taanila A.
        • et al.
        Maternal adiposity prior to pregnancy is associated with ADHD symptoms in offspring: Evidence from three prospective pregnancy cohorts.
        Int J Obes (Lond). 2008; 32: 550-557
        • Krakowiak P.
        • Walker C.K.
        • Bremer A.A.
        • Baker A.S.
        • Ozonoff S.
        • Hansen R.L.
        • et al.
        Maternal metabolic conditions and risk for autism and other neurodevelopmental disorders.
        Pediatrics. 2012; 129: e1121-e1128
        • Agarwal P.K.
        • Xie H.
        • Sathyapalan Rema A.S.
        • Rajadurai V.S.
        • Lim S.B.
        • Meaney M.
        • et al.
        Evaluation of Ages and Stages Questionnaire (ASQ 3) as a developmental screener at 9, 18, and 24 months.
        Early Hum Dev. 2020; 147105081
        • Fauls J.R.
        • Thompson B.L.
        • Johnston L.M.
        Validity of the Ages and Stages Questionnaire to identify young children with gross motor difficulties who require physiotherapy assessment.
        Dev Med Child Neurol. 2020; 62: 837-844
        • Ga H.Y.
        • Kwon J.Y.
        A comparison of the Korean ages and stages questionnaires and Denver developmental delay screening test.
        Ann Rehabil Med. 2011; 35: 369-374
        • Romero Otalvaro A.M.
        • Grañana N.
        • Gaeto N.
        • Torres M.L.Á.
        • Zamblera M.N.
        • Vasconez M.A.
        • et al.
        ASQ-3: Validation of the Ages and Stages Questionnaire for the detection of neurodevelopmental disorders in Argentine children.
        Arch Argent Pediatr. 2018; 116: 7-13
        • Nordestgaard B.G.
        A test in context: lipid profile, fasting versus nonfasting.
        J Am Coll Cardiol. 2017; 70: 1637-1646