Identification of neurodevelopmental trajectories in infancy and of risk factors affecting deviant development: A longitudinal birth cohort study

International Journal of Epidemiology

Volumn 45, Issue 2, 2016, Pages 543-553

  Nishimura, Tomoko ^a   Takei, Nori ^a,b   Tsuchiya, Kenji J ^a   Asano, Ryosuke ^a   Mori, Norio ^a  

^a HAMAMATSU UNIVERSITY SCHOOL OF MEDICINE   (Japan)

^b KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

Birth cohort; Delayed growth; Neurodevelopment; Risk factor; Small placenta; Trajectory

Indexed keywords

AGE CLASS; CHILD HEALTH; COHORT ANALYSIS; DEMOGRAPHY; DEVELOPMENTAL BIOLOGY; GROWTH RESPONSE; IDENTIFICATION METHOD; NEUROLOGY; PARTICIPATORY APPROACH; PREGNANCY; RISK FACTOR; SEX RATIO;

ADULT; ARTICLE; BRAIN DEVELOPMENT; CHILD DEVELOPMENT; CHILDBIRTH; COHORT ANALYSIS; DEVELOPMENTAL STAGE; FEMALE; FIRST TRIMESTER PREGNANCY; FOLLOW UP; GESTATIONAL AGE; HUMAN; INFANT; JAPANESE (PEOPLE); LANGUAGE DELAY; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MALE; MATERNAL AGE; MOTHER; PATERNAL AGE; PATIENT EDUCATION; PREGNANT WOMAN; PREMATURE LABOR; PRIORITY JOURNAL; RISK FACTOR; SECOND TRIMESTER PREGNANCY; SMALL FOR DATE INFANT; DEVELOPMENTAL DISABILITIES; GROWTH, DEVELOPMENT AND AGING; MENTAL DISEASE; NEWBORN; ODDS RATIO; PATHOPHYSIOLOGY; PHYSIOLOGY; PREGNANCY; PRENATAL EXPOSURE; SEX DIFFERENCE; SEX RATIO; STATISTICAL MODEL; VERY LOW BIRTH WEIGHT;

CHILD DEVELOPMENT; COHORT STUDIES; DEVELOPMENTAL DISABILITIES; FEMALE; HUMANS; INFANT; INFANT, NEWBORN; INFANT, SMALL FOR GESTATIONAL AGE; INFANT, VERY LOW BIRTH WEIGHT; LOGISTIC MODELS; LONGITUDINAL STUDIES; MALE; NEURODEVELOPMENTAL DISORDERS; ODDS RATIO; PREGNANCY; PRENATAL EXPOSURE DELAYED EFFECTS; RISK FACTORS; SEX DISTRIBUTION; SEX FACTORS;

EID: 84971628895     PISSN: 03005771     EISSN: 14643685     Source Type: Journal    
DOI: 10.1093/ije/dyv363     Document Type: Article

References (49)

1. Bolton PF, Golding J, Emond A, Steer CD. Autism spectrum disorder and autistic traits in the Avon Longitudinal Study of Parents and Children: precursors and early signs. J Am Acad Child Adolesc Psychiatry 2012;51:249-11.
2. Lemcke S, Juul S, Parner ET, Lauritsen MB, Thorsen P. Early signs of autism in toddlers: a follow-up study in the Danish National Birth Cohort. J Autism Dev Disord 2013;43:2366-75.
3. Brian AJ, Roncadin C, Duku E et al. Emerging cognitive profiles in high-risk infants with and without autism spectrum disorder. Res Autism Spectr Disord 2014;8:1557-66.
4. Mullen EM. Mullen Scales of Early Learning. AGS edn. Minneapolis, MN: Pearson Assessments, 1995.
5. Barbaro J, Dissanayake C. Developmental profiles of infants and toddlers with autism spectrum disorders identified prospectively in a community-based setting. J AutismDevDisord 2012;42:1939-48.
6. Landa RJ, Gross AL, Stuart EA, Bauman M. Latent class analysis of early developmental trajectory in baby siblings of children with autism. J Child Psychol Psychiatry 2012;53:986-96.
7. Savchev S, Sanz-Cortes M, Cruz-Martinez R et al. Neurodevelopmental outcome of full-term small-for-gestationalage infants with normal placental function. Ultrasound Obstet Gynecol 2013;42:201-06.
8. Sutton PS, Darmstadt GL. Preterm birth and neurodevelopment: a review of outcomes and recommendations for early identification and cost-effective interventions. J TropPediatr 2013;59:258-65.
9. Idring S, Magnusson C, Lundberg M et al. Parental age and the risk of autism spectrum disorders: findings from a Swedish population-based cohort. Int J Epidemiol 2014;43:107-15.
10. Hanscombe KB, Trzaskowski M, Haworth CMA, Davis OSP, Dale PS, Plomin R. Socioeconomic status (SES) and children's intelligence (IQ): in a UK-representative sample: SES moderates the environmental, not genetic, effect on IQ. PLoS One 2012;7:e30320.
11. Burns TG, King TZ, Spencer KS. Mullen Scales of Early Learning: The utility in assessing children diagnosed with autism spectrum disorders, cerebral palsy, and epilepsy. Appl Neuropsychol Child 2013;2:33-42.
12. Tsuchiya KJ, Matsumoto K, Suda S et al. Searching for very early precursors of autism spectrum disorders: the Hamamatsu Birth Cohort for Mothers and Children (HBC). J Dev Orig Health Dis 2010;1:158-73.
13. Takagai S, Tsuchiya KJ, Itoh H et al. Cohort Profile: Hamamatsu Birth Cohort for Mothers and Children (HBC Study). Int J Epidemiol 2016;45:333-42.
14. Ministry of Health, Labour and Welfare. Vital, Health and Social Statistics Division, Statistics and Information Department. 2013. Vital Statistics in JAPAN-The latest trends. http://www.mhlw.go.jp/english/database/db-hw/dl/81-1a2en.pdf (14 February 2015, date last accessed).
15. Yoshida K, Yamashita H, Ueda M, Tashiro N. Postnatal depression in Japanese mothers and the reconsideration of 'Satogaeri bunben'. Pediatr Int 2001;43:189-93.
16. Muthén B, Muthén L. Integrating person-centered and variablecentered analysis: Growth mixture modeling with latent trajectory classes. Alcohol Clin Exp Res 2000;24:882-91.
17. Andruff H, Carraro N, Thompson A, Gaudreau P, Louvet B. Latent class growth modelling: a tutorial. Tutor Quant Methods Psychol 2009;5:11-24.
18. Asparouhov T, Muthén B. Multivariate statistical modeling with survey data. Proceedings of the Federal Committee on Statistical Methodology (FCSM) Research Conference, Washington DC, 16-16 November 2005. Washington DC: FCSM, 2005.
19. Masyn KE. Latent class analysis and finite mixture modeling. In: Nathan P, Little T (eds). The Oxford Handbook of Quantitative Methods, Vol. 2: Statistical Analysis. New York, NY: Oxford University Press, 2013.
20. Lo Y, Mendell NR, Rubin DB. Testing the number of components in a normal mixture. Biometrika 2001;88:767-78.
21. Nylund KL, Asparouhov T, Muthén B. Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Struct Equ Modeling 2007;14:535-69.
22. Jung T, Wickrama KAS. An introduction to Latent Class Growth Analysis and Growth Mixture Modeling. Soc Pers Psychol Compass 2008;2:302-17.
23. Vermunt JK. Latent class modeling with covariates: Two improved three-step approaches. Polit Anal 2010;18:450-69.
24. Asparouhov T, Muthén B. Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Struct Equ Modeling 2014:21;329-41.
25. Ogawa Y, Iwamura T, Kuriya N et al. Birth size standards by gestational age for Japanese neonates. ActaNeonat Jpn 1998;34:624-32.
26. Almog B, Shehata F, Aljabri S, Levin I, Shalom-Paz E, Shrim A. Placenta weight percentile curves for singleton and twins deliveries. Placenta 2011;32:58-62.
27. Law J, Boyle J, Harris F, Harkness A, Nye C. Screening for speech and language delay: a systematic review of the literature. Health Technol Assess 1998;2:1-184.
28. Boyle CA, Boulet S, Schieve LA et al. Trends in the prevalence of developmental disabilities in US children, 1997-2008. Pediatrics 2011;127:2010-989.
29. van Batenburg-Eddes T, Henrichs J, Schenk JJ et al. Early infant neuromotor assessment is associated with language and nonverbal cognitive function in toddlers: the Generation R Study. J Dev Behav Pediatr 2013;34:326-334.
30. Landa RJ, Gross AL, Stuart EA, Faherty A. Developmental trajectories in children with and without autism spectrum disorders: the first 3 years. Child Dev 2013;84:429-42.
31. Kraemer S. The fragile male. BMJ 2000;321:1609-12.
32. Collaer ML, Hines M. Human behavioral sex differences: a role for gonadal hormones during early development? Psychol Bull 1995;118:55-107.
33. Kang HJ, Kawasawa YI, Cheng F et al. Spatio-temporal transcriptome of the human brain. Nature 2011;478;483-89.
34. Lenz KM, Nugent BM, Haliyur R, McCarthy MM. Microglia are essential to masculinization of brain and behavior. J Neurosci 2013;33:2761-72.
35. Bale TL, Baram TZ, Brown AS et al. Early life programming and neurodevelopmental disorders. Biol Psychiatry 2010;68:314-19.
36. Ruigrok AN, Salimi-Khorshidi G, Lai MC et al. A meta-analysis of sex differences in human brain structure. Neurosci Biobehav Rev 2014;39:34-50.
37. Walker SP, Wachs TD, Grantham-McGregor S et al. Inequality in early childhood: risk and protective factors for early child development. Lancet 2011;378:1325-38.
38. Suda S, Takei N. Disturbed growth in early life and later neurocognitive development related especially to psychiatric disorders. In: Preedy VR, Watson RR, Martin CR (eds). Handbook of Behavior, Diet and Nutrition. London: Springer, 2011.
39. Mallard C, Loeliger M, Copolov D, Rees S. Reduced number of neurons in the hippocampus and the cerebellum in the postnatal guinea-pig following intrauterine growth-restriction. Neuroscience 2000;100:327-33.
40. Mackay DF, Smith GC, Dobbie R, Cooper SA, Pell JP. Obstetric factors and different causes of special educational need: retrospective cohort study of 407, 503 schoolchildren. Int J Gynaecol Obstet 2013;120:297-308.
41. Saigal S, Doyle LW. An overview of mortality and sequelae of preterm birth from infancy to adulthood. Lancet 2008;371:261-69.
42. Heinonen S, Taipale P, Saarikoski S. Weights of placentae from small-for-gestational age infants revisited. Placenta 2001;22: 399-404.
43. Lahti J, Raïkkönen K, Sovio U et al. Early-life origins of schizotypal traits in adulthood. Br J Psychiatry 2009;195:132-37.
44. Khalife N, Glover V, Hartikainen AL et al. Placental size is associated with mental health in children and adolescents. PLoS One 2012;7:e40534.
45. Girardi G, Fraser J, Lennen R, Vontell R, Jansen M 3rd, Hutchison G. Imaging of activated complement using ultrasmall superparamagnetic iron oxide particles (USPIO)-conjugated vectors: an in vivo in utero non-invasive method to predict placental insufficiency and abnormal fetal brain development. Mol Psychiatry 2014;110:1-10.
46. Zeltser LM, Leibel RL. Roles of the placenta in fetal brain development. Proc Natl Acad Sci U S A 2011;108:15667-68.
47. D'Onofrio BM, Rickert ME, Frans E et al. Paternal age at childbearing and offspring psychiatric and academic morbidity. JAMA Psychiatry 2014;71:432-38.
48. Idring S, Magnusson C, Lundberg M et al. Parental age and the risk of autism spectrum disorders-findings from a Swedish population-based cohort. Int J Epidemiol 2014;43:107-15.
49. Kong A, Frigge ML, Masson G et al. Rate of de novo mutations and the importance of father's age to disease risk. Nature 2012;488:471-75.