Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2025 Sep 9;67(2):282–295. doi: 10.1111/jcpp.70048

Mid‐childhood developmental and behavioural outcomes in infants with a family history of autism and/or attention deficit hyperactivity disorder

Tony Charman 1,✉, Tessel Bazelmans 1,2, Greg Pasco 1,2, Jannath Begum Ali 2, Mark H Johnson 2,3, Emily J H Jones 1,2; The BASIS/STAARS Team†
PMCID: PMC12812788  PMID: 40923413

Abstract

Background

Prospective studies of autism family history infants primarily report recurrence and predictors of autism at 3 years. Less is known about ADHD family history infants and later childhood outcomes. We characterise profiles of mid‐childhood developmental and behavioural outcomes in infants with a family history of autism and/or ADHD to identify potential support needs and patterns of co‐occurrence across domains.

Methods

Two hundred and sixty‐three infants (51% male; N = 198 autism/ADHD family history; N = 65 no family history) were assessed at 6–12 years. A latent profile analysis (LPA) with indicator variables measuring developmental abilities (IQ, adaptive function) and behavioural traits (autism, ADHD, anxiety) identified dimensional, data‐derived outcome classes.

Results

A seven‐class solution was the most robust and clinically meaningful. Two classes (27% and 23%) had typical development; two classes had high autism, ADHD, and anxiety traits—one with low IQ and adaptive function (10%) and one with average IQ but low adaptive function (13%); one class had elevated autism and ADHD but not anxiety traits (10%); and the final two classes had elevated ADHD (9%) and anxiety (8%) traits in isolation. Sex distribution was balanced across all classes. Children with autism were found in all classes but predominantly in the classes with low IQ/adaptive functioning and high behavioural traits, as well as in the class with elevated autism and ADHD traits. We found only partial continuity between membership of similarly derived 3‐year LPA classes and mid‐childhood LPA classes.

Conclusions

Many autism/ADHD family history infants develop typically. However, by mid‐childhood, in addition to those with autism, others show elevated neurodevelopmental (autism, ADHD) and neuropsychiatric (anxiety) behavioural traits. Lower developmental abilities (IQ and adaptive function) are primarily seen in children with an autism diagnosis. Family history infants should be monitored through childhood, and support provided should challenges emerge.

Keywords: Autism, ADHD, family history, infants, mid‐childhood outcomes, behaviour, development

Introduction

Autism and ADHD commonly co‐occur within individuals and family members and have shared heritability (Ghirardi et al., 2019; Hollingdale, Woodhouse, Young, Fridman, & Mandy, 2019; Jokiranta‐Olkoniemi et al., 2016, 2019; Miller et al., 2019). Other neurodevelopmental (intellectual disability) and emotional (anxiety, depression) and behavioural (oppositional defiant disorder, conduct disorder) neuropsychiatric conditions also commonly co‐occur with autism (Charman et al., 2011; Lai et al., 2019; Maenner et al., 2023; Simonoff et al., 2008) and ADHD (Elia, Ambrosini, & Berrettini, 2008; Kang et al., 2024; Pliszka, 2000) and within family members with both conditions (Ghirardi et al., 2021; Jokiranta‐Olkoniemi et al., 2016, 2019). Both within‐condition and cross‐condition autism and ADHD associations are also found between family members at the trait level (Ghirardi et al., 2019; Rommelse, Franke, Geurts, Hartman, & Buitelaar, 2010; Van Steijn et al., 2012). Common polygenic variation (autism and ADHD polygenic score (PGS)) is also associated with both early (Askeland et al., 2022) and later (Warrier et al., 2022) emerging neurodevelopmental and neuropsychiatric outcomes. Both within‐condition (e.g. ADHD PGS and ADHD traits) and cross‐condition (e.g. ADHD PGS and autism traits) associations are found, consistent with their shared heritability. In addition to psychiatric conditions, impairments in everyday adaptive functioning are common in both autism and ADHD (Clark, Prior, & Kinsella, 2002; Tillmann et al., 2019). Prospective studies of infants with a family history of neurodevelopmental conditions have the potential to illuminate the earliest emergence of these specific or co‐occurring symptom profiles before they are clinically ascertained, enabling us to inform caregivers and clinicians regarding early signs of atypicality that may require monitoring.

To date, we have a detailed understanding of clinical outcomes in infants with a family history of autism at age 3 (Dawson, Rieder, & Johnson, 2023; Jones, Gliga, Bedford, Charman, & Johnson, 2014; Szatmari et al., 2016). Autism sibling recurrence at 3 years in volunteer research cohorts is ~20% (Ozonoff et al., 2024). In addition to those who have autism, others have lower language, IQ, and adaptive functioning and higher behavioural dysregulation (Charman et al., 2017; Marrus et al., 2018; Messinger et al., 2013; Miller et al., 2019; Ozonoff et al., 2014). The outcomes of infants with a family history of ADHD have been less studied (Johnson, Gliga, Jones, & Charman, 2015; Miller et al., 2023). Miller et al. (2020) used latent profile analysis (LPA) of 3‐year autism and ADHD measures in infants with a family history of ASD and/or ADHD and identified classes with high levels of autism and ADHD scores, respectively, although both classes included children with an autism diagnosis and ‘ADHD concerns’. In a similar cohort, Charman et al. (2023) used LPA on a wider set of measures – also including IQ, adaptive behaviour, and anxiety – and found atypical outcome classes characterised by either low developmental level and high autism, ADHD, and anxiety traits or low adaptive functioning but not elevated behavioural traits. Thus, both a family history of autism and ADHD raise the likelihood of clinically relevant developmental difficulties in toddlerhood. However, we have a limited understanding of whether these early clinical profiles change or persist over later development.

Emerging evidence suggests that following children into mid‐childhood can reveal a range of additional clinical concerns in infant siblings that were not detected at age 3 (Brian et al., 2016; Landa, Reetzke, Holingue, Herman, & Hess, 2022; Ozonoff et al., 2018; Shephard et al., 2017). Bazelmans et al. (2024) reported a sibling autism recurrence rate of 37% in infants with a family history of autism re‐assessed at 9 years. Brian et al. (2016) found a similar mid‐childhood recurrence (34%) but Landa et al. (2022) a lower recurrence rate (20%) at age 6 years. Half of those given an autism diagnosis in mid‐childhood in Bazelmans et al. (2024) were not diagnosed with autism at the earlier 3‐year assessment. Later emerging or later recognised autism is consistent with the recognition in diagnostic manuals that symptoms ‘may not be manifest until social demands exceed limited capacities’ (DSM‐5; American Psychiatric Association, 2022) and is seen in clinical (Davidovitch, Gazit, Patalon, Leitner, & Rotem, 2023; Lord et al., 2006) and population cohorts (Hosozawa et al., 2020; Zhang et al., 2024).

Beyond autism diagnostic recurrence, Miller et al. (2016) found elevated rates of broader autism phenotype and ADHD traits, as well as language and learning difficulties in autism family history infants without autism at 7 years. Shephard et al. (2017) identified elevated anxiety in autism family history infants who themselves did not have an autism diagnosis at 7 years compared with infants with no autism family history. Salomone et al. (2018) reported that autism family history infants both with and without an autism diagnosis had lower IQ than infants with no family history at age 7, but that lower adaptive functioning was primarily found in those with autism. No previous studies have examined the continuity between profiles of preschool and later childhood developmental and behavioural outcomes in autism/ADHD family history infants.

Establishing mid‐childhood profiles of neurodevelopmental and neuropsychiatric outcomes in infants with a family history of autism and/or ADHD – and how they change between preschool and mid‐childhood – will be important both for identifying phenotypic outcomes associated with changes in brain and cognitive development in infant siblings beyond an autism diagnosis and to inform families and clinicians of the support infants may require as they grow up. To address this gap, the current study characterises mid‐childhood developmental and behavioural outcomes in a prospective longitudinal study of autism and/or ADHD family history infants. By design, and in contrast to much of the extant literature (Bazelmans et al., 2024; Brian et al., 2016; Landa et al., 2022), the current analysis does not primarily focus on autism diagnosis as a categorical outcome, although we did characterise autism diagnostic outcomes at both 3 years and mid‐childhood. Instead, we use dimensional measures to characterise developmental and behavioural trait outcomes. We include comparison infants with no family history of autism or ADHD, as well as infants with a family history of autism or ADHD (or both) 1 to fully utilise the prospective family history design and because these traits are continuously distributed through the population (Martin, Hamshere, Stergiakouli, O'Donovan, & Thapar, 2014; Robinson et al., 2016; Taylor et al., 2019). We conduct an LPA with indicator variables measuring IQ, adaptive function, and autism, ADHD, and anxiety traits to identify data‐derived phenotypic outcome classes and examine their association with family history, sex, and autism diagnosis. We also conduct a similar LPA on 3‐year developmental and behavioural traits to examine the continuity and discontinuity between preschool and mid‐childhood classes.

Method

Participants

Four hundred and six infants (213 boys, 193 girls) were enrolled at either 5 or 10 months of age in a prospective family history study if they had a first‐degree relative with a community clinical diagnosis of autism, a community clinical diagnosis of ADHD, or elevated ADHD traits, or both. Parental report of an existing clinical diagnosis of autism and/or ADHD in an older sibling (proband) was the most common route. Some parents reported that they themselves had a diagnosis of either condition, or they or their older child had suspected ADHD, following which screening with a short version of one of the Conners suite of measures was employed to determine eligibility (see Appendix S1 and Table S1 for details). Comparison infants with no autism or ADHD family history and a typically developing older sibling were also recruited. This sample comprised three cohorts: Phase 1 (n = 104) reported in Shephard et al. (2017) and new Phase 2 (n = 143) and Phase 3 (n = 159) cohorts.

Research assessments were undertaken at 5, 10, and 14 months, 2 and 3 years, and mid‐childhood. Age of first visit was 12 months or younger 2 (M (SD) = 7.08 (2.21)). The total sample (N = 406) comprised 207 infants with a first‐degree relative (family history (FH)) with autism only (FH‐Autism), 30 infants with a first‐degree relative with ADHD only (FH‐ADHD), 67 infants with first‐degree relatives with both autism and ADHD (FH‐Autism + ADHD), and 102 infants with no first‐degree relative with either condition (No‐FH) (Table S1). Of these, 263 infants (64.8%; 133 boys, 130 girls) took part in the mid‐childhood follow‐up at 6–12 years (M (SD) = 8.65 (1.27)) between July 2014 and December 2023 and had at least one outcome measure used in the LPA and are included in the current analysis: 132 FH‐Autism, 15 FH‐ADHD, 51 FH‐Autism + ADHD, and 65 No‐FH (Table 1). Thirty‐four children did not attend in‐person assessments (parents completed questionnaires only) and an additional seven children attended but an autism diagnostic assessment was not completed, so analyses involving autism diagnostic outcome include 222 children.

Table 1.

Participant demographic characteristics by family history sampling frame

FH‐Autism FH‐Autism + ADHD FH‐ADHD No‐FH
N = 132 N = 51 N = 15 N = 65
N (%) N (%) N (%) N (%)
Sex
Male 64 (48%) 27 (53%) 8 (53%) 34 (52%)
Female 68 (52%) 24 (47%) 7 (47%) 31 (48%)

Age in years

Mean (SD)

 8.83 (1.31) 8.93 (1.35) 8.25 (0.69) 8.13 (1.05)
Child Ethnicity
White/European/Irish 104 (81%) 43 (86%) 14 (93%) 54 (84%)
Asian/African/African‐Caribbean/Mixed Heritage 25 (19%) 7 (14%) 1 (7%) 10 (16%)
Maternal highest education
Up to 16/GCSE 13 (10%) 2 (4%) 0 (0%) 2 (3%)
Up to 18/School/College 34 (27%) 17 (35%) 3 (20%) 11 (17%)
Degree level 50 (39%) 16 (33%) 5 (33%) 19 (30%)
Postgraduate/Professional 31 (24%) 13 (27%) 7 (47%) 31 (49%)
Annual household income
Up to £20,000 11 (9%) 3 (7%) 0 (0%) 4 (7%)
£20,000 to £40,000 32 (26%) 16 (35%) 1 (10%) 9 (15%)
£40,000 to £60,000 38 (31%) 10 (22%) 3 (30%) 13 (22%)
£60,000 to £80,000 19 (15%) 5 (11%) 1 (10%) 9 (15%)
Above £80,000 24 (19%) 12 (26%) 5 (50%) 25 (42%)
Open in a new tab

Data missing for some demographic variables. FH‐Autism = autism family history, FH‐Autism + ADHD = autism + ADHD family history, FH‐ADHD = ADHD family history, No‐FH = no family history of autism or ADHD.

Ethical approved by NHS RES London REC (14/LO/0170) and King's College London (RESCM‐18/19‐10556). Parents provided written informed consent and children written/verbal assent appropriate to developmental level.

Measures

Family demographic characteristics

Ethnicity

Infant sibling ethnicity was characterised as Asian/Black African/Black Caribbean/Mixed versus White/European/Irish.

Family sociodemographic information

Annual household income was coded on a 5‐point ordinal scale (<£20,000, £20,000–£40,000, £40,000–£60,000, £60,000–£80,000, >£80,000). Maternal highest education level was coded on a 4‐point ordinal scale (16/GCSE, 18/School/College, Degree level, Postgraduate/Professional).

Cognitive ability and adaptive functioning

The Wechsler Abbreviated Scale of Intelligence – Second Edition (WASI‐II; Wechsler, 2011) 3 was used to assess full‐scale IQ (FSIQ). 4 Vineland‐II (Phase 1) (Sparrow, Cicchetti, & Balla, 2005) and the Vineland‐3 Adaptive Behaviour Composite (ABC) (Phase 2/3) (Sparrow, Cicchetti, & Saulnier, 2016) were used to measure adaptive functioning.

Autism diagnostic and screening measures

Observational and parent‐report diagnostic instruments Autism Diagnostic Observation Schedule‐2 (ADOS‐2) (Lord et al., 2012) and Autism Diagnostic Interview‐Revised (ADI‐R) (Lord, Rutter, & Le Couteur, 1994) and parent‐report Social Communication Questionnaire (SCQ) (Rutter, Bailey, & Lord, 2003) and Social Responsiveness Scale‐2 (SRS‐2) (Constantino & Gruber, 2012) screening measures were completed.

ADHD and anxiety trait measures

Parent‐report Conners‐3 (Conners, 2008) was used to assess attention deficit hyperactivity disorder (ADHD) symptoms (raw and T‐scores for the DSM‐IV‐TR Inattentive and Hyperactive/Impulsive scales). Parent‐report Spence Children's Anxiety Scale – Parent (SCAS) (Spence, 1999) was used to assess anxiety symptoms (Total Anxiety raw and T‐scores).

Research diagnostic assessments

At both 3 years and mid‐childhood, a best estimate clinical diagnosis of autism spectrum disorder was made based on DSM‐5 criteria. 5 This was informed by, but not dependent on, scores on the ADOS‐2, ADI‐R, SCQ, and Vineland, researcher observations on the visit, and additional parent‐reported information, by experienced researchers and overseen by a senior clinical psychologist. Diagnosis in mid‐childhood involved review of all previous information, including 2‐ and 3‐year visits, and there was overlap in personnel involved, so decisions were not independent. We previously reported autism sibling recurrence for a subset of the current FH‐Autism infants 6 (N = 159) (Bazelmans et al., 2024). Children with a mid‐childhood autism diagnosis are characterised as those given an autism diagnosis at the 3‐year assessment (Earlier diagnosed) and those who were only given an autism diagnosis at the mid‐childhood assessment (Later diagnosed). 7

Statistical analysis

Retention from first visit to mid‐childhood assessment was examined both univariately using Chi‐squared tests for the binary and ordinal variables and also multivariately using logistic regression (with the Stata logit command) to determine unique predictors of retention accounting for potential collinearity between predictors. Phase, family history, and child (sex, ethnicity) and family (maternal education, family income) factors were tested as predictors of retention. In the univariate tests, we examined both across the four family history groups (FH‐Autism, FH‐Autism + ADHD, FH‐ADHD and No‐FH) and also by looking at a family history of autism (i.e. the FH‐Autism and FH‐Autism + ADHD groups combined) and a family history of ADHD (i.e. the FH‐ADHD and FH‐Autism + ADHD groups combined) separately. In the multivariate logistic regression model, we simultaneously entered all predictors of retention and entered autism family history and ADHD family history treated separately as dummy‐coded (0/1) variables and then their interaction to take account of single and dual family history status.

We conducted LPA using continuous indicator variables to identify homogeneous classes based on the following mid‐childhood indicator variables: WASI FSIQ, Vineland ABC, SRS‐2 total raw score, Conners‐3 Inattention and Hyperactivity/Impulsivity raw scores, and SCAS total anxiety raw score. Variables were modelled, conditional on latent class, using Poisson distributions. LPA was performed using the gsem command in Stata 18 (StataCorp., 2023) on the whole sample with at least one of the six outcome measures available (N = 263; 240 children had ≥4 measures, 11 ≥ 3, 10 ≥ 2, and 2 children 1 measure only). Models were estimated using maximum likelihood to account for data missing at random. To select the ‘best fitting’ solution, we examined conventional likelihood‐based (Bayesian information criterion (BIC)) and classification‐based (Integrated Classification Likelihood (ICL); entropy) fit statistics (Henson, Reise, & Kim, 2007); the proportion of participants represented in each class; and the extent to which classes captured clinically meaningful subgroups (Nylund, Asparouhov, & Muthén, 2007; Spurk, Hirschi, Wang, Valero, & Kauffeld, 2020). Individuals were assigned to classes based on the maximum aposterior probability of class membership (MAP). The high values of MAP we report made more complex multistep post‐assignment analysis methods unnecessary.

We compared the scores of the outcome classes using ANOVA with post hoc Tukey–Kramer corrections to account for unequal cell sizes and tested sex differences in class assignment using Chi‐square tests. We conducted an LPA analysis on comparable 3‐year measures to examine continuity between 3‐year and mid‐childhood profiles (Appendix S6).

Results

Retention from recruitment to mid‐childhood is shown in Table S2 for univariate tests and in Table S3 for the multivariate regression model. Univariately, retention was higher in Phases 1 and 2 than in Phase 3 (p < .001 and p < .05, respectively) and marginally higher in Phase 2 than Phase 3 (p = .05). Retention did not differ significantly by family history group (four group test p = .079; autism family history and ADHD family history tested separately both p > .22), infant sex, maternal education, or family income but was lower in Non‐White families (p < .01). In the multivariate model, only Phase significantly independently predicted retention, with the Stata margins pwcompare command indicating that the predicted marginal probabilities of retention were higher in Phase 1 than in Phase 2 (z = −2.40, p < .05) and in Phase 3 (z = −3.39, p < .01) taking account of the other predictors.

Table S4 shows mid‐childhood scores on the developmental and behavioural measures by family history group (see Appendix S2).

7‐class and 8‐class solutions had similar entropy values (0.86 and 0.85, respectively) and the best fit statistics (BIC = 11,284.69, ICL = 11,340.44 and BIC = 11,233.91, ICL = 11,287.43, respectively; Table S5). We chose the 7‐class solution as providing the most robust and clinically meaningful distribution of classes, with a minimum class size comprising 8.4% (N = 22) of the sample and average MAP values for all classes >0.90. Table S6 shows the correlations between the indicator variables and R 2 values from regressing each indicator onto the set of classes.

Scores of the 7 classes on the indicator measures used to derive the classes are shown in Table 2 and Figure 1. Based on the pattern across the measures and also the presence versus absence of a mid‐childhood autism diagnosis, we labelled the classes as follows. Class 1 (N = 71, 27%) = Typically Functioning + High IQ (TF + High IQ) (N = 3 (5%) mid‐childhood autism diagnosis 8 ); Class 2 (N = 61; 23%) = Typically Functioning (TF) (N = 6 (11%) autism); Class 3 (N = 22, 8%) = High Anxiety (N = 2 (10%) autism); Class 4 (N = 24, 9%) = High ADHD (N = 2 (10%) autism); Class 5 (N = 25, 10%) = Moderate Autism/ADHD (N = 10 (50%) autism) 9 ; Class 6 (N = 34, 13%) = Autism + Low Adaptive Functioning (Autism + LAF) (N = 23 (82%) autism); and Class 7 (N = 26, 10%) = Autism + Low IQ (N = 19 (90%) autism).

Table 2.

Mid‐childhood scores by LPA classes

TF + High IQ TF High anxiety High ADHD Mod autism/ADHD Autism + LAF Autism + Low IQ
Mean (SD) N Mean (SD) N Mean (SD) N Mean (SD) N Mean (SD) N Mean (SD) N Mean (SD) N
Age (months) 101.23 (13.45) 71 101.50 (15.07) 58 112.05 (15.49) 22 101.87 (14.95) 23 109.32 (15.88) 25 102.56 (14.36) 34 106.6 (18.19) 25
WASI FSIQ 115.19 (13.55)a 62 109.3 (15.58)a 53 112.90 (12.37)a 21 106.05 (13.06)a 21 117.57 (15.30)a 21 105.48 (16.59)a 27 91.50 (13.73)b 20
Vineland ABC 110.68 (9.45)a 63 102.51 (9.39)c 51 104.23 (11.61)a 22 96.10 (11.15)c 21 98.67 (12.50)c 21 80.50 (12.26)b 26 78.17 (13.04)b 18
SRS 41.03 (2.14)a 65 47.18 (2.82)c 56 50.62 (4.81)c 21 50.12 (3.59)c 24 60.75 (4.19)e 24 74.47 (8.13)d 32 87.09 (11.35)b 22
Inattention 43.32 (4.49)a 66 50.29 (6.72)c 59 49.10 (7.82)a 21 70.70 (9.53)b 23 58.71 (10.30)d 24 72.18 (12.16)b 33 77.48 (11.92)b 21
Hyper‐Imp 44.58 (5.64)a 66 48.83 (5.74)a 59 52.57 (6.97)c 21 75.13 (10.42)b 23 58.58 (10.73)c 24 77.39 (9.64)b 33 76.90 (13.04)b 21
Anxiety 46.00 (6.31)a 65 48.42 (5.01)a 52 63.52 (4.06)d 21 53.67 (7.37)c 18 49.81 (6.77)a 21 56.60 (6.36)c 25 68.81 (3.04)b 21
Open in a new tab

Groups marked with different subscript letters (a, b, c) differed significantly with Tukey–Kramer HSD correction applied (p < .05). TF + High IQ = typically functioning + High IQ; TF = typically functioning; High Anxiety = high anxiety traits; High ADHD = high ADHD traits; Mod Autism/ADHD = elevated autism + ADHD traits; Autism + LAF = autism + low adaptive Behaviour; Autism + Low IQ = autism + Low IQ. WASI = Wechsler Abbreviated Scale of Intelligence; FSIQ = full‐Scale IQ; ABC = Vineland Adaptive Behaviour Composite; SRS = Social Responsiveness Scale (T‐score); Inattention = Conners Inattention T‐score; Hyper‐Imp = Conners Hyperactivity/Impulsivity T‐score; Anxiety = Spence Children's Anxiety Scale‐Total T‐Score.

Figure 1.

Figure 1

Open in a new tab

Profile of indicator variables for LPA classes in mid‐childhood. y‐axis scale is z‐score derived separately for each measure from the current sample so all measures are similarly scaled to provide a profile across the measures. Anxiety, Spence children's anxiety scale‐total; Hyperactivity, Conners hyperactivity/impulsivity; Inattention, Conners inattention; IQ, full‐scale IQ; SRS, social responsiveness scale; Vineland, Vineland adaptive behaviour composite TF + High IQ = typically functioning + High IQ; TF = typically functioning; High Anxiety = high anxiety traits; High ADHD = high ADHD traits; Mod Autism/ADHD = elevated autism + ADHD traits; Autism + LAF = autism + low adaptive Behaviour; Autism + Low IQ = autism + Low IQ

As shown in Table 2, ANOVAs and post hocs for class differences are, as expected, in line with class identification (see Appendix S3 for statistical tests). The Autism + Low IQ class had lower FSIQ than the other classes, although the group mean was still in the (low) average range, well below average adaptive functioning, and elevated autism, ADHD, and anxiety trait scores. The Autism + LAF class had average IQ, well below average adaptive functioning, elevated autism and ADHD traits, and somewhat elevated anxiety traits. The Moderate Autism/ADHD class had average IQ and adaptive functioning, and elevated autism and ADHD but not anxiety traits. The High ADHD and High Anxiety classes had average IQ and adaptive functioning, did not have elevated autism traits but showed elevated ADHD and anxiety traits, respectively, in isolation. The TF + High IQ and TF classes both had typical behaviour and average adaptive function. When considered categorically in terms of the proportion of each class falling above or below elevated thresholds for the developmental and behavioural measures (>1SD) the pattern was similar (see Table 3).

Table 3.

Number and percentage of each LPA class in atypical range (±1 SD) on mid‐childhood indicator variables

Measure a TF + High IQ TF High Anxiety High ADHD Mod Autism/ADHD Autism + LAF Autism + Low IQ
N = 71 N = 61 N = 22 N = 24 N = 25 N = 34 N = 26
WASI < 85 0 (0%) 2 (4%) 1 (5%) 2 (10%) 0 (0%) 2 (7%) 8 (40%)
Vineland ABC < 85 1 (2%) 1 (2%) 0 (0%) 4 (19%) 3 (14%) 17 (65%) 16 (89%)
SRS T‐score ≥ 60 0 (0%) 0 (0%) 0 (0%) 2 (10%) 12 (50%) 32 (100%) 22 (100%)
Inattention T‐score ≥ 60 1 (2%) 5 (8%) 5 (8%) 3 (14%) 11 (46%) 28 (85%) 20 (95%)
Hyper‐Imp T‐score ≥ 60 2 (3%) 3 (5%) 3 (5%) 2 (10%) 11 (46%) 33 (100%) 18 (86%)
Anxiety T‐score ≥ 60 0 (0%) 0 (0%) 0 (0%) 16 (76%) 2 (23%) 9 (36%) 20 (95%)
Open in a new tab

TF + High IQ = typically functioning + High IQ; TF = typically functioning; High Anxiety = high anxiety traits; High ADHD = high ADHD traits; Moderate Autism/ADHD = elevated autism + ADHD traits; Autism + LAF = autism + low adaptive behaviour; Autism + Low IQ = autism + Low IQ. WASI = Wechsler Abbreviated Scale of Intelligence; FSIQ = Full‐Scale IQ; ABC = Vineland Adaptive Behaviour Composite; SRS = social Responsiveness Scale (T‐score); Inattention = Conners Inattention T‐Score; Hyper‐Imp = Conners Hyperactivity/Impulsivity T‐score; Anxiety = Spence Children's Anxiety Scale‐Total T‐score.

a

Measures available on N = 225 WASI, N = 222 Vineland, N = 244 SRS, N = 247 Conners, N = 223 SCAS. % are the proportion of those who completed each measure.

Table 4 shows the association between the derived LPA classes and the autism and ADHD family history sampling frame. All children in the Autism + Low IQ class were from the family history groups (all but one from the FH‐Autism or FH‐Autism + ADHD groups), as were most children from the Autism + LAF, Moderate Autism/ADHD, and High Anxiety classes. Three‐quarters (75%) of the No‐FH group were in the TF + High IQ and TF classes. Table 4 also shows the distribution of classes by child sex and presence/absence of an autism diagnosis in mid‐childhood; broken down by earlier versus later diagnosed children. The LPA outcome classes were balanced by sex (χ 2 (6, N = 263) = 1.56, p = .96). Children with an autism diagnosis were found in all classes but predominantly in the Autism + Low IQ and Autism + LAF classes and in the Moderate Autism/ADHD class.

Table 4.

LPA classes by family history group sex and early versus later autism diagnosis a

LPA class No‐FH FH‐Autism FH‐ADHD FH‐Autism + ADHD Total
N = 65 N = 132 N = 15 N = 51 N = 263
Row % Row % Row % Row % N
/Column % /Column % /Column % /Column %
TF + High IQ 32 (45%/49%) 28 (39%/21%) 3 (4%/20%) 8 (11%/16%) 71
TF 17 (28%/26%) 34 (56%/26%) 2 (3%/13%) 8 (13%/16%) 61
High Anxiety 3 (14%/5%) 13 (59%/10%) 0 (0%/0%) 6 (27%/12%) 22
High ADHD 7 (29%/11%) 10 (42%/8%) 2 (8%/13%) 5 (21%/10%) 24
Mod Autism/ADHD 3 (12%/5%) 13 (52%/10%) 6 (24%/40%) 3 (12%/6%) 25
Autism + LAF 3 (9%/5%) 15 (44%/11%) 1 (3%/7%) 15 (44%/29%) 34
Autism + Low IQ 0 (0%/0%) 19 (73%/14%) 1 (4%/7%) 6 (23%/12%) 26
LPA class Sex Female Earlier Autism Diagnosis Later Autism Diagnosis Never Autism
Male
N = 133 N = 130 N = 30 N = 35 N = 157
N (Row %) N (Row %) N (Row %) N (Row %) N (Row %)
TF + High IQ 34 (48%) 37 (52%) 2 (3%) 1 (2%) 58 (95%)
TF 31 (51%) 30 (49%) 1 (2%) 5 (10%) 45 (88%)
High Anxiety 12 (55%) 10 (45%) 1 (5%) 1 (5%) 19 (90%)
High ADHD 11 (46%) 33 (54%) 2 (10%) 0 (0%) 19 (90%)
Mod Autism/ADHD 12 (48%) 13 (52%) 3 (15%) 7 (35%) 10 (50%)
Autism + LAF 20 (59%) 14 (41%) 10 (37%) 13 (48%) 4 (15%)
Autism + Low IQ 13 (50%) 13 (50%) 11 (52%) 8 (38%) 2 (10%)
Open in a new tab

TF + High IQ = typically functioning + High IQ; TF = typically functioning; High Anxiety = high anxiety traits; High ADHD = high ADHD traits; Mod Autism/ADHD = elevated autism + ADHD traits; Autism + LAF = autism + low adaptive behaviour; Autism + Low IQ = autism + Low IQ. FH‐Autism = autism family history, FH‐Autism + ADHD = autism + ADHD family history, FH‐ADHD = ADHD family history, No‐FH = no‐family history of autism or ADHD.

a

Only for the N = 222 who underwent both a 3‐year and a mid‐childhood in‐person diagnostic assessment.

Developmental and behavioural scores of the mid‐childhood LPA classes at 3 years and findings from the 3‐year LPA analysis are reported in Appendices S4 and S5 and Tables S7–S10. In brief, the 6‐class solution provided the most robust and clinically meaningful distribution of 3‐year LPA classes. Based on the pattern across the measures and also the presence vs. absence of a 3‐year autism diagnosis, we labelled the classes as follows. Class 1 (N = 71, 28%) = Typically Developing + Very High IQ (TD + Very High IQ) (2% have autism); Class 2 (N = 86, 34%) = Typical Development + High IQ (TD + High IQ) (0% have autism); Class 3 (N = 29, 11%) = Typically Developing (TD) (8% have autism); Class 4 (N = 15, 6%) = Typical Development + Some Behavioural Concerns (TD + SBC) (15% have autism); Class 5 (N = 29; 11%) = Autism or Low IQ (28% have autism); Class 6 (N = 26, 10%) = Autism + Low Adaptive Functioning (Autism + LAF) (78% have autism). 3‐year and mid‐childhood LPA class agreement is shown in Figure 2 and Table S11. Continuity between class assignments was strongest for typically developing children and for children with an early (3‐year) autism diagnosis, although a number of children moved from broadly typical to atypical classes and vice versa.

Figure 2.

Figure 2

Open in a new tab

Continuity between 3‐year and mid‐childhood classes. 3‐Year LPA classes: TD + Very High IQ = typically developing + Very High IQ; TD + High IQ = typically developing + High IQ; TD = typically developing; TD + SBC = typical development + some behavioural concerns; Autism or Low IQ; Autism + LAF = autism + low adaptive functioning. Mid‐childhood (MC) LPA classes: TF + High IQ = typically functioning + High IQ; TF = typically functioning; High Anxiety = high anxiety traits; High ADHD = High ADHD traits; Moderate Autism/ADHD = elevated autism + ADHD traits; Autism + LAF = autism + low adaptive behaviour; Autism + Low IQ = autism + Low IQ

Discussion

We used a data‐driven approach using dimensional traits measures to examine developmental and behavioural profiles of mid‐childhood outcomes in infants with a family history of autism and/or ADHD. The 7‐class LPA solution we identified, in addition to two classes with high or typical developmental abilities and behaviour, found two classes with low IQ and/or low adaptive function and high autism, ADHD, and anxiety traits and a class with elevated autism and ADHD traits only (with typical IQ and adaptive behaviour and low anxiety traits). Children with a mid‐childhood autism diagnosis were found in all classes but predominantly in these three classes. The final two classes had, respectively, elevated levels of ADHD and anxiety traits in isolation. Sex distribution was balanced across all classes.

The pattern differs in relation to neurodevelopmental (autism, ADHD) and neuropsychiatric (anxiety) behavioural outcomes and developmental outcomes (IQ, adaptive function). For behavioural traits we identified classes with, respectively, elevated ADHD and anxiety traits in isolation in which very few children had autism and a class with moderately elevated autism and ADHD (but not anxiety) traits, only half of whom were autistic. By contrast, low IQ and/or low adaptive function were only present in the predominantly autistic outcome classes (Autism + Low IQ class: 90% autism; Autism + LAF class: 85% autism) with no other classes and few individual children having below‐average scores. These classes also had high levels not only of autism traits (consistent with their categorical autism diagnosis) but also high levels of ADHD traits, and in the Autism + Low IQ class, high levels of anxiety traits. Overall, our analysis suggests that around half of children with a family history of autism/ADHD might show clinical traits that warrant evaluation and that this extends beyond those who meet diagnostic criteria for autism.

Miller et al. (2016) previously reported elevated levels of autism, ADHD, and anxiety/mood traits in some 7‐year‐old autism family history infants without autism and also lower language abilities. In a sub‐sample of the current cohort, Shephard et al. (2017) found elevated anxiety traits at 7 years in family history infants without autism. The presence of elevated mid‐childhood neurodevelopmental and behavioural traits in autism and ADHD family history infants is consistent with the common co‐occurrence of these in individuals with an autism and ADHD diagnosis and in family members and their shared heritability (Ghirardi et al., 2019, 2021; Hollingdale et al., 2019; Jokiranta‐Olkoniemi et al., 2016, 2019; Lai et al., 2019; Miller, Musser, et al., 2019; Rommelse et al., 2010; Simonoff et al., 2008).

Intellectual disability commonly co‐occurs in autistic individuals (Charman et al., 2011; Maenner et al., 2023) and impairments in adaptive function are common in both autism and ADHD (Clark et al., 2002; Tillmann et al., 2019). In line with this, we found that lower dimensional or trait developmental outcomes aggregated almost exclusively in family history infants with a mid‐childhood autism diagnosis and not in those without. However, our findings differ from previous reports based on 3‐year outcomes that have reported low IQ and adaptive function in some non‐autistic family history infants (Charman et al., 2017, 2023; Marrus et al., 2018; Messinger et al., 2013). Consistent with this, we found only partial continuity between 3‐year and mid‐childhood LPA class membership. Continuity between class assignments was strongest for children with an early (3‐year) autism diagnosis, many of whom at that age also had low IQ and adaptive functioning. Importantly, we note that in our autism/ADHD family history sample very few children had low IQ, with only 15 (7%) scoring <85 (Table 3) and only 5 (2%) scoring <70. This is reflected in the below‐average mean score of the class labelled Autism + Low IQ, whose mean IQ was 91.50 (SD = 13.73). In line with this, only 3 children from 223 who completed ADOS‐2 assessments were not able to do Module 3's that require fluent verbal speech. 10 However, both the Autism + Low IQ class and the Autism + LAF class had mean adaptive scores well below average (M = 78.17 (SD = 13.04) and M = 80.50 (SD = 12.26), respectively – see Table 2), demonstrating the well‐established pattern that many individuals with autism have everyday adaptive functional skills lower than their scores on cognitive tests (Tillmann et al., 2019). A similar pattern with relatively lower IQ/DQ and language scores at 3 years but low average range IQ scores in mid‐childhood has also recently been reported in another study of autism family history infants (Gangi et al., 2025).

The most notable discontinuity was that a proportion of children identified in the TD + Very High IQ and TD + High IQ classes at 3 years went on by mid‐childhood to be identified in the Autism + LAF, Moderate Autism/ADHD, and High ADHD and High Anxiety classes. This pattern is consistent with our finding for categorical autism diagnostic outcome where a significant proportion of children who met criteria for autism at the mid‐childhood assessment were not given an autism diagnosis at 3 years (Bazelmans et al., 2024). The presence of neurodevelopmental and neuropsychiatric conditions changes as children develop. This is true both in the general population (Finsaas, Bufferd, Dougherty, Carlson, & Klein, 2018) and in autistic children (Hollocks et al., 2023; Wiggins et al., 2024). Our study confirms that this is also true for children with a family history of autism and ADHD, necessitating both characterising wider outcomes beyond autism and ADHD and continuing to clinically monitor children in this group beyond the preschool period.

All classes were balanced for sex. Previous autism family history studies have reported a more balanced 3‐year autism diagnosis sex ratio (~2:1) than population prevalence studies (Maenner et al., 2023; Ozonoff et al., 2024; Zwaigenbaum et al., 2012) and this was also true at mid‐childhood in our cohort (Bazelmans et al., 2024). One possibility is that our dimensional data‐driven approach to classifying outcomes identifies girls expressing similar levels of autism traits to boys who, despite this, would not meet the threshold for an autism diagnosis. This has previously been identified in both autism family history and in general population studies that have used data‐driven approaches to examine sex differences in autism traits (Burrows et al., 2022; Russell, Steer, & Golding, 2011). The classes with elevated ADHD and anxiety traits only were also balanced for sex. This contrasts with population studies that show higher rates of ADHD diagnosis in boys than girls prior to adolescence (13 years) but higher rates of anxiety disorders in girls only from 13 to 18 years (Dalsgaard et al., 2020) which may not have been seen at 6–12 years in this group.

Strengths and limitations

The study has a number of strengths. We followed a moderate‐to‐large sample of autism and ADHD family history infants recruited in the first year of life to mid‐childhood. Few studies to date have followed autism/ADHD family history infants beyond 3 years, and both neurodevelopmental and neuropsychiatric outcomes can be more reliably measured in mid‐childhood (Finsaas et al., 2018). Extending beyond previous studies primarily reporting autism recurrence, we characterise broader developmental and behavioural outcomes using a parsimonious person‐centred data‐driven modelling approach.

However, there are a number of limitations. Although we include both autism and ADHD family history infants (as well as no family history controls) the autism family history group (both those with and without an ADHD family history) comprises the largest group. We conducted autism diagnostic assessments but did not do so for ADHD and anxiety, reporting trait measures only. There is a reliance on parental report measures across all constructs except IQ, resulting in potential shared methods variance limitations and other potential biases, including the extent to which questionnaire measures can reliably distinguish between different psychopathology traits (Hus, Bishop, Gotham, Huerta, & Lord, 2013). Finally, our volunteer research sample had above‐average sociodemographic characteristics (family income and maternal education) and findings may not generalise to the wider population. We did test factors related to retention/attrition from first visit to mid‐childhood, and in a parsimonious multivariate model, only Phase (from our three sequential cohorts) was systematically associated with retention, while family income, maternal education, child ethnicity, and family history group were not. We do not have an obvious explanation for this cohort effect. It is important to recognise that research participants in family history studies may have particular motivations to volunteer and other hard‐to‐measure parent, family, and child factors that may impact representativeness and outcomes in addition to the above‐average sociodemographic characteristics we have identified.

Conclusions

Infant family history studies may provide unique insights into the structure of psychopathology. Their prospective nature allows us to test infant neurocognitive and behavioural predictors of later outcomes to reveal underlying mechanisms of neurodivergent versus typical development (Dawson et al., 2023; Johnson et al., 2015; Jones et al., 2014; Szatmari et al., 2016). Recruitment is based on family history and not clinical presentation and may reveal the natural history both of the timing of emergence of atypicality and of the covariation between neurodevelopmental and neuropsychiatric traits in autism and ADHD family members.

Many infants with a family history of autism and/or ADHD develop typically. However, by mid‐childhood, in addition to those who have autism (Bazelmans et al., 2024), around one‐third of infants with a family history of autism/ADHD have elevated neurodevelopmental (autism, ADHD) and neuropsychiatric (anxiety) traits. By contrast, lower developmental outcomes (IQ and adaptive function) primarily co‐occur in history infants with a family history of an autism diagnosis. Parents and clinicians need to be aware of this range of outcomes – beyond categorical autism diagnosis – as family history infants grow up, and support provided should challenges emerge. Measuring wider neurodevelopmental and behavioural outcomes also provides an opportunity for prospective autism and ADHD family history studies to examine the transdiagnostic specificity (Astle, Holmes, Kievit, & Gathercole, 2022; Michelini et al., 2024; Thapar, Cooper, & Rutter, 2017) of early neurobiological, neurocognitive and behavioural markers to later outcomes (Gui et al., 2020; Johnson et al., 2015; Shephard et al., 2019) which may identify novel translational insights and opportunities.

Ethical considerations

Parents provided written informed consent and children written/verbal assent appropriate to developmental level. Ethical approval from NHS Health Research Authority, National Research Ethics Service (NRES Committee London Central, 14/LO/0170, 13/05/2014) and Psychiatry, Nursing and Midwifery Research Ethics Subcommittee, King's College London (RESCM‐18/19‐10556, 28/03/2019).

Key points.

What's known?

  • Prospective studies of autism family history infants primarily report on autism recurrence and predictors of autism at 3 years but less is known about later childhood outcomes and ADHD family history infants.

What's new?

  • By mid‐childhood, in addition to those with an autism diagnosis, around one‐third of infants with a family history of autism and/or ADHD have elevated autism, ADHD, and anxiety traits.

  • However, low IQ and adaptive functioning primarily co‐occur in those with an autism diagnosis.

  • There was only partial continuity between 3‐year and mid‐childhood profiles, with some children being identified in typical classes at 3 years who went on to show elevated autism, ADHD, and anxiety traits in mid‐childhood.

What's relevant?

  • Family history infants should be monitored through childhood, and support provided when challenges emerge.

Supporting information

Appendix S1. Sample recruitment and characterization.

Table S1. Categorisation of family history group.

Table S2. Sample descriptives of retained vs. non‐retained from recruitment to mid‐childhood.

Table S3. Summary of multivariate logistic regression analysis for retention from recruitment to mid‐childhood.

Table S4. Mid‐childhood scores by family history group.

Appendix S2. Summary of mid‐childhood scores on the developmental and behavioural measures by family history group.

Table S5. Summary of the latent profile analysis (LPA) models.

Table S6. Correlations between indicator variables and regression of indictor variables on LPA 7‐class solution.

Appendix S3. Statistical analysis of mid‐childhood LPA classes on indicator variables.

Table S7. Three year scores by mid‐childhood LPA classes.

Appendix S4. Developmental and behavioural characterisation of the mid‐childhood LPA classes at 3‐years.

Appendix S5. Statistical analysis of 3‐year scores by mid‐childhood LPA classes.

Appendix S6. 3‐year LPA analysis.

Table S8. 3‐years scores by 3‐year LPA classes.

Table S9. 3‐year LPA class by family history group.

Table S10. 3‐year LPA classes by sex and mid‐childhood autism diagnosis (early vs. later).

Table S11. 3‐year LPA class by mid‐childhood LPA class.

Appendix S7. Mid‐childhood LPA repeated for autism and/or ADHD family history infants only.

Table S12. Mid‐childhood scores by LPA classes for family history infants only.

Table S13. Mid‐childhood classes for family history infants only LPA by family history group.

Table S14. Mid‐childhood classes for family history infants only LPA by autism diagnosis and by sex.

JCPP-67-282-s001.docx (86.3KB, docx)

Acknowledgements

TC, MHJ, and EJHJ developed the study concept and design and obtained funding. TB, GP, JBA, and The BASIS/STAARS Teams collected and curated the data. TC performed the data analysis. TC drafted the manuscript, and all authors provided critical revisions and approved the final version of the manuscript.

The authors are grateful to all the families who took part in this study. The BASIS/STAARS team consists of: Rowan Arthur, Jeni Baykoca, Anna Blasi, Patrick Bolton, Celeste Cheung, Kenny Chiu, Leila Dafner, Kim Davies, Mayada Elsabbagh, Janice Fernandes, Laurel Fish, Isobel Gammer, Teodora Gliga, Jeanne Guiraud, Rianne Haartsen, Sarah Kalwarowsky, Anna Kolesnik, Michelle Liew, Sarah Lloyd‐Fox, Helen Maris, Luke Mason, Mia Medas, Bosiljka Milosavljevic, Louise O'Hara, Andrew Pickles, Laura Pirazzoli, Helena Ribeiro, Erica Salomone, Elizabeth Shephard, Chloë Taylor, Leslie Tucker. For the purposes of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Accepted Author Manuscript version arising from this submission.

Supported by awards from the Medical Research Council (MR/R011427/1, G0701484, MR/K021389/1, MR/T003057/1), Autistica, Autism Speaks. The results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS‐2‐TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. Any views expressed are those of the authors and not necessarily those of the funders (IHI‐JU2). European Union Horizon Europe grant no. 101057385 (R2D2‐MH) and UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee [grant no. 10039383] and by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 22.00277. However, views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union. TB is supported by a grant from the UK Medical Research Council (MR/X010716/1). Capital equipment funding from the Maudsley Charity (980) and Guy's and St Thomas' Charity (STR130505). The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

TC consultancy fees from F. Hoffmann‐La Roche Ltd. and royalties from Sage Publishing and Guilford Press. MHJ royalties from Wiley‐Blackwell, OUP, and MIT Press. The remaining authors have declared that they have no competing or potential conflicts of interest.

Conflict of interest statement: See Acknowledgements for full disclosures.

Endnotes

1

We report results a similar LPA in the autism and/or ADHD family history infants only (excluding the No‐FH infants) in the Supplementary Materials.

2

Six children's first research visit was after 12 months (13–15 months).

3

4 subtest version (Block design, Vocabulary, Matrix Reasoning, Similarities) in Phase 1 and 2‐subtest (Vocabulary, Matrix Reasoning) version in Phases 2 and 3.

4

On the N = 229 children seen for in‐person assessments WASIs were completed on N = 225. Only two children were unable to access the WASI due to limited language ability.

5

In the Phase 1 cohort 3‐year diagnostic was assigned using ICD‐10 but on subsequent review all cases who met criteria for pervasive developmental disorder met DSM‐5 criteria for autism spectrum disorder.

6

Including those who also had an ADHD Family History (FH‐Autism + ADHD).

7

Excluding the N = 3 who ‘lost diagnosis’ between the 3‐year and mid‐childhood diagnostic assessment (see Bazelmans et al., 2024; Shephard et al., 2017).

8

N (%) with a mid‐childhood autism diagnosis relates to the subset (N = 222) on whom full autism diagnostic assessments were completed (see Method).

9

The LPA including autism and/or ADHD family history infants only produced a similar 6‐class solution with two typically developing/functioning classes, two predominantly autism classes (one with low IQ and low adaptive functioning, one low adaptive functioning), and two classes with, respectively, predominantly isolated ADHD and anxiety traits in isolation. Children with an autism diagnosis were found in all classes (see Appendix S7 and Tables S12–S14).

10

One child completed a Module 1 (preverbal/single words) and two children a Module 2 (short phrase speech).

Data availability statement

Data available following review as indicated here: https://www.basisnetwork.org/collaboration‐and‐project‐affiliation/.

References

  1. American Psychiatric Association . (2022). Diagnostic and statistical manual of mental disorders (5th Ed., text rev). Arlington, VA: Author. [Google Scholar]
  2. Askeland, R.B. , Hannigan, L.J. , Ask, H. , Ayorech, Z. , Tesli, M. , Corfield, E. , … & Havdahl, A. (2022). Early manifestations of genetic risk for neurodevelopmental disorders. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 63, 810–819. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Astle, D.E. , Holmes, J. , Kievit, R. , & Gathercole, S.E. (2022). Annual Research Review: The transdiagnostic revolution in neurodevelopmental disorders. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 63, 397–417. [DOI] [PubMed] [Google Scholar]
  4. Bazelmans, T. , Arthur, R. , Pasco, G. , Shephard, E. , Milosavljevic, B. , Ali, J.B. , … & =BASIS/STAARS Team . (2024). Mid‐childhood autism sibling recurrence in infants with a family history of autism. Research, 17, 1501–1514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Brian, J. , Bryson, S.E. , Smith, I.M. , Roberts, W. , Roncadin, C. , Szatmari, P. , & Zwaigenbaum, L. (2016). Stability and change in autism spectrum disorder diagnosis from age 3 to middle childhood in a high‐risk sibling cohort. Autism, 20, 888–892. [DOI] [PubMed] [Google Scholar]
  6. Burrows, C.A. , Grzadzinski, R.L. , Donovan, K. , Stallworthy, I.C. , Rutsohn, J. , St John, T. , … & IBIS Network . (2022). A data‐driven approach in an unbiased sample reveals equivalent sex ratio of autism spectrum disorder‐associated impairment in early childhood. Biological Psychiatry, 92, 654–662. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Charman, T. , Pasco, G. , Hendry, A. , Bazelmans, T. , Narvekar, N. , Goodwin, A. , … & =STAARS Team . (2023). Three year outcomes in infants with a family history of autism and/or attention deficit hyperactivity disorder. JCPP Advances, 3, e12189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Charman, T. , Pickles, A. , Simonoff, E. , Chandler, S. , Loucas, T. , & Baird, G. (2011). IQ in children with autism spectrum disorders: Data from the Special Needs and Autism Project (SNAP). Psychological Medicine, 41, 619–627. [DOI] [PubMed] [Google Scholar]
  9. Charman, T. , Young, G.S. , Brian, J. , Carter, A. , Carver, L.J. , Chawarska, K. , … & Zwaigenbaum, L. (2017). Non‐ASD outcomes at 36 months in siblings at familial risk for autism spectrum disorder (ASD): A Baby Siblings Research Consortium (BSRC) study. Autism Research: Official Journal of the International Society for Autism Research, 10, 169–178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Clark, C. , Prior, M. , & Kinsella, G. (2002). The relationship between executive function abilities, adaptive behaviour, and academic achievement in children with externalising behaviour problems. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 43, 785–796. [DOI] [PubMed] [Google Scholar]
  11. Conners, C. (2008). Conners 3rd edition manual. Toronto, ON, Canada: Multi‐Health Systems. [Google Scholar]
  12. Constantino, J. , & Gruber, C. (2012). Social responsiveness scale, 2nd Edn (SRS‐2). Torrance, CA: Western Psychological Services. [Google Scholar]
  13. Dalsgaard, S. , Thorsteinsson, E. , Trabjerg, B.B. , Schullehner, J. , Plana‐Ripoll, O. , Brikell, I. , … & Pedersen, C.B. (2020). Incidence rates and cumulative incidences of the full Spectrum of diagnosed mental disorders in childhood and adolescence. JAMA Psychiatry, 77, 155–164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Davidovitch, M. , Gazit, S. , Patalon, T. , Leitner, Y. , & Rotem, R.S. (2023). Late diagnosis of autism spectrum disorder‐journey, parents' concerns, and sex influences. Autism Research: Official Journal of the International Society for Autism Research, 16, 294–301. [DOI] [PubMed] [Google Scholar]
  15. Dawson, G. , Rieder, A.D. , & Johnson, M.H. (2023). Prediction of autism in infants: Progress and challenges. The Lancet. Neurology, 22, 244–254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Elia, J. , Ambrosini, P. , & Berrettini, W. (2008). ADHD characteristics: I. Concurrent co‐morbidity patterns in children & adolescents. Child and Adolescent Psychiatry and Mental Health, 2, 15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Finsaas, M.C. , Bufferd, S.J. , Dougherty, L.R. , Carlson, G.A. , & Klein, D.N. (2018). Preschool psychiatric disorders: Homotypic and heterotypic continuity through middle childhood and early adolescence. Psychological Medicine, 48, 2159–2168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gangi, D.N. , Iosif, A.‐M. , Maqbool, S. , Hill, M.M. , Parikh, C. , Young, G.S. , & Ozonoff, S. (2025). Continuity in social communication development among school‐aged siblings of autistic children. Developmental Psychology. online ahead of print. 10.1037/dev0002025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Ghirardi, L. , Kuja‐Halkola, R. , Butwicka, A. , Martin, J. , Larsson, H. , D'Onofrio, B.M. , … & Taylor, M.J. (2021). Familial and genetic associations between autism spectrum disorder and other neurodevelopmental and psychiatric disorders. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 62, 1274–1284. [DOI] [PubMed] [Google Scholar]
  20. Ghirardi, L. , Pettersson, E. , Taylor, M.J. , Freitag, C.M. , Franke, B. , Asherson, P. , … & Kuja‐Halkola, R. (2019). Genetic and environmental contribution to the overlap between ADHD and ASD trait dimensions in young adults: A twin study. Psychological Medicine, 49, 1713–1721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Gui, A. , Mason, L. , Gliga, T. , Hendry, A. , Begum Ali, J. , Pasco, G. , … & the BASIS‐STAARS team . (2020). Look duration at the face as a developmental endophenotype: Elucidating pathways to autism and ADHD. Development and Psychopathology, 32, 1303–1322. [DOI] [PubMed] [Google Scholar]
  22. Henson, J.M. , Reise, S.P. , & Kim, K.H. (2007). Detecting mixtures from structural model differences using latent variable mixture modeling: A comparison of relative model fit statistics. Structural Equation Modeling: A Multidisciplinary Journal, 14, 202–226. [Google Scholar]
  23. Hollingdale, J. , Woodhouse, E. , Young, S. , Fridman, A. , & Mandy, W. (2019). Autistic spectrum disorder symptoms in children and adolescents with attention‐deficit/hyperactivity disorder: A meta‐analytical review. Psychological Medicine, 50, 2240–2253. [DOI] [PubMed] [Google Scholar]
  24. Hollocks, M.J. , Leno, V.C. , Chandler, S. , White, P. , Yorke, I. , Charman, T. , … & Simonoff, E. (2023). Psychiatric conditions in autistic adolescents: Longitudinal stability from childhood and associated risk factors. European Child & Adolescent Psychiatry, 32, 2197–2208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hosozawa, M. , Sacker, A. , Mandy, W. , Midouhas, E. , Flouri, E. , & Cable, N. (2020). Determinants of an autism spectrum disorder diagnosis in childhood and adolescence: Evidence from the UK Millennium Cohort Study. Autism: The International Journal of Research and Practice, 24, 1557–1565. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hus, V. , Bishop, S. , Gotham, K. , Huerta, M. , & Lord, C. (2013). Factors influencing scores on the social responsiveness scale. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 54, 216–224. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Johnson, M.H. , Gliga, T. , Jones, E. , & Charman, T. (2015). Annual research review: Infant development, autism, and adhd – Early pathways to emerging disorders. Journal of Child Psychology and Psychiatry, 56, 228–247. [DOI] [PubMed] [Google Scholar]
  28. Jokiranta‐Olkoniemi, E. , Cheslack‐Postava, K. , Joelsson, P. , Suominen, A. , Brown, A.S. , & Sourander, A. (2019). Attention‐deficit/hyperactivity disorder and risk for psychiatric and neurodevelopmental disorders in siblings. Psychological Medicine, 49, 84–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Jokiranta‐Olkoniemi, E. , Cheslack‐Postava, K. , Sucksdorff, D. , Suominen, A. , Gyllenberg, D. , Chudal, R. , … & Sourander, A. (2016). Risk of psychiatric and neurodevelopmental disorders among siblings of Probands with autism Spectrum disorders. JAMA Psychiatry, 73, 622–629. [DOI] [PubMed] [Google Scholar]
  30. Jones, E.J.H. , Gliga, T. , Bedford, R. , Charman, T. , & Johnson, M.H. (2014). Developmental pathways to autism: A review of prospective studies of infants at risk. Neuroscience and Biobehavioral Reviews, 39, 1–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kang, J. , Lee, H. , Kim, S. , Kim, H.J. , Lee, H. , Kwon, R. , … & Cortese, S. (2024). Comorbid health conditions in people with attention‐deficit/hyperactivity disorders: An umbrella review of systematic reviews and meta‐analyses. Asian Journal of Psychiatry, 99, 104135. [DOI] [PubMed] [Google Scholar]
  32. Lai, M.‐C. , Kassee, C. , Besney, R. , Bonato, S. , Hull, L. , Mandy, W. , … & Ameis, S.H. (2019). Prevalence of co‐occurring mental health diagnoses in the autism population: A systematic review and meta‐analysis. The Lancet. Psychiatry, 6, 819–829. [DOI] [PubMed] [Google Scholar]
  33. Landa, R.J. , Reetzke, R. , Holingue, C.B. , Herman, D. , & Hess, C.R. (2022). Diagnostic stability and phenotypic differences among school‐age children diagnosed with ASD before age 2. Frontiers in Psychiatry, 13, 805686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Lord, C. , Risi, S. , DiLavore, P.S. , Shulman, C. , Thurm, A. , & Pickles, A. (2006). Autism from 2 to 9 years of age. Archives of General Psychiatry, 63, 694–701. [DOI] [PubMed] [Google Scholar]
  35. Lord, C. , Rutter, M. , DiLavore, P. , Risi, S. , Gotham, K. , & Bishop, S. (2012). Autism diagnostic observation schedule, second edition (ADOS‐2). Los Angeles, CA: Western Psychological Services. [Google Scholar]
  36. Lord, C. , Rutter, M. , & Le Couteur, A. (1994). Autism diagnostic interview‐revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24, 659–685. [DOI] [PubMed] [Google Scholar]
  37. Maenner, M.J. , Warren, Z. , Williams, A.R. , Amoakohene, E. , Bakian, A.V. , Bilder, D.A. , … & Shaw, K.A. (2023). Prevalence and characteristics of autism spectrum disorder among children aged 8 years—Autism and developmental disabilities monitoring network, 11 sites, United States, 2020. Morbidity and Mortality Weekly Report. Surveillance Summaries, 72, 1–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Marrus, N. , Hall, L.P. , Paterson, S.J. , Elison, J.T. , Wolff, J.J. , Swanson, M.R. , … & IBIS Network . (2018). Language delay aggregates in toddler siblings of children with autism spectrum disorder. Journal of Neurodevelopmental Disorders, 10, 29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Martin, J. , Hamshere, M.L. , Stergiakouli, E. , O'Donovan, M.C. , & Thapar, A. (2014). Genetic risk for attention‐deficit/hyperactivity disorder contributes to neurodevelopmental traits in the general population. Biological Psychiatry, 76, 664–671. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Messinger, D. , Young, G.S. , Ozonoff, S. , Dobkins, K. , Carter, A. , Zwaigenbaum, L. , … & Sigman, M. (2013). Beyond autism: A baby siblings research consortium study of high‐risk children at three years of age. Journal of the American Academy of Child and Adolescent Psychiatry, 52, 300–308.e1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Michelini, G. , Carlisi, C.O. , Eaton, N.R. , Elison, J.T. , Haltigan, J.D. , Kotov, R. , … & Wilson, S. (2024). Where do neurodevelopmental conditions fit in transdiagnostic psychiatric frameworks? Incorporating a new neurodevelopmental spectrum. World Psychiatry: Official Journal of the World Psychiatric Association (WPA), 23, 333–357. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Miller, M. , Arnett, A.B. , Shephard, E. , Charman, T. , Gustafsson, H.C. , Joseph, H.M. , … & Jones, E.J.H. (2023). Delineating early developmental pathways to ADHD: Setting an international research agenda. JCPP Advances, 3, e12144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Miller, M. , Austin, S. , Iosif, A.‐M. , de la Paz, L. , Chuang, A. , Hatch, B. , & Ozonoff, S. (2020). Shared and distinct developmental pathways to ASD and ADHD phenotypes among infants at familial risk. Development and Psychopathology, 32, 1323–1334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Miller, M. , Iosif, A.‐M. , Young, G.S. , Bell, L.J. , Schwichtenberg, A.J. , Hutman, T. , & Ozonoff, S. (2019). The dysregulation profile in preschoolers with and without a family history of autism spectrum disorder. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 60, 516–523. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Miller, M. , Iosif, A.‐M. , Young, G.S. , Hill, M. , Phelps Hanzel, E. , Hutman, T. , … & Ozonoff, S. (2016). School‐age outcomes of infants at risk for autism spectrum disorder. Autism Research: Official Journal of the International Society for Autism Research, 9, 632–642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Miller, M. , Musser, E.D. , Young, G.S. , Olson, B. , Steiner, R.D. , & Nigg, J.T. (2019). Sibling recurrence risk and cross‐aggregation of attention‐deficit/hyperactivity disorder and autism spectrum disorder. JAMA Pediatrics, 173, 147–152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Nylund, K.L. , Asparouhov, T. , & Muthén, B.O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14, 535–569. [Google Scholar]
  48. Ozonoff, S. , Young, G.S. , Belding, A. , Hill, M. , Hill, A. , Hutman, T. , … & Iosif, A.‐M. (2014). The broader autism phenotype in infancy: When does it emerge? Journal of the American Academy of Child and Adolescent Psychiatry, 53, 398–407.e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Ozonoff, S. , Young, G.S. , Bradshaw, J. , Charman, T. , Chawarska, K. , Iverson, J.M. , … & Zwaigenbaum, L. (2024). Familial recurrence of autism: Updates from the Baby Siblings Research Consortium. Pediatrics, 154, e2023065297. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Ozonoff, S. , Young, G.S. , Brian, J. , Charman, T. , Shephard, E. , Solish, A. , & Zwaigenbaum, L. (2018). Diagnosis of autism Spectrum disorder after age 5 in children evaluated longitudinally since infancy. Journal of the American Academy of Child and Adolescent Psychiatry, 57, 849–857.e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Pliszka, S.R. (2000). Patterns of psychiatric comorbidity with attention‐deficit/hyperactivity disorder. Child and Adolescent Psychiatric Clinics of North America, 9, 525–540. [PubMed] [Google Scholar]
  52. Robinson, E.B. , St Pourcain, B. , Anttila, V. , Kosmicki, J.A. , Bulik‐Sullivan, B. , Grove, J. , … & Daly, M.J. (2016). Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nature Genetics, 48, 552–555. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Rommelse, N.N.J. , Franke, B. , Geurts, H.M. , Hartman, C.A. , & Buitelaar, J.K. (2010). Shared heritability of attention‐deficit/hyperactivity disorder and autism spectrum disorder. European Child & Adolescent Psychiatry, 19, 281–295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Russell, G. , Steer, C. , & Golding, J. (2011). Social and demographic factors that influence the diagnosis of autistic spectrum disorders. Social Psychiatry and Psychiatric Epidemiology, 46, 1283–1293. [DOI] [PubMed] [Google Scholar]
  55. Rutter, M. , Bailey, A. , & Lord, C. (2003). The Social Communication Questionnaire (SCQ). Los Angeles, CA: Western Psychological Services. [Google Scholar]
  56. Salomone, E. , Shephard, E. , Milosavljevic, B. , Johnson, M.H. , Charman, T. , & =BASIS Team . (2018). Adaptive behaviour and cognitive skills: Stability and change from 7 months to 7 years in siblings at high familial risk of autism Spectrum disorder. Journal of Autism and Developmental Disorders, 48, 2901–2911. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Shephard, E. , Bedford, R. , Milosavljevic, B. , Gliga, T. , Jones, E.J.H. , Pickles, A. , … & The BASIS Team . (2019). Early developmental pathways to childhood symptoms of attention‐deficit hyperactivity disorder, anxiety and autism spectrum disorder. Journal of Child Psychology and Psychiatry, 60, 963–974. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Shephard, E. , Milosavljevic, B. , Pasco, G. , Jones, E. , Gliga, T. , Happe, F. , … & BASIS Team . (2017). Mid‐childhood outcomes of infant siblings at familial high‐risk of autism spectrum disorder. Autism Research, 10, 546–557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Simonoff, E. , Pickles, A. , Charman, T. , Chandler, S. , Loucas, T. , & Baird, G. (2008). Psychiatric disorders in children with autism spectrum disorders: Prevalence, comorbidity, and associated factors in a population‐derived sample. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 921–929. [DOI] [PubMed] [Google Scholar]
  60. Sparrow, S. , Cicchetti, D. , & Balla, D. (2005). Vineland adaptive behavior scales, second edition (Vineland‐II). Circle Pines, MN: America Guidance Service. [Google Scholar]
  61. Sparrow, S. , Cicchetti, D. , & Saulnier, C. (2016). Vineland adaptive behavior scales, third edition (Vineland‐3). San Antonio, TX: Pearson. [Google Scholar]
  62. Spence, S.H. (1999). Spence Children's Anxiety Scale (Parent version). Brisbane: University of Queensland. [Google Scholar]
  63. Spurk, D. , Hirschi, A. , Wang, M. , Valero, D. , & Kauffeld, S. (2020). Latent profile analysis: A review and “how to” guide of its application within vocational behavior research. Journal of Vocational Behavior, 120, 103445. [Google Scholar]
  64. StataCorp . (2023). Stata Statistical Software: Release 18. College Station, TX: StataCorp LLC. [Google Scholar]
  65. Szatmari, P. , Chawarska, K. , Dawson, G. , Georgiades, S. , Landa, R. , Lord, C. , … & Halladay, A. (2016). Prospective longitudinal studies of infant siblings of children with autism: Lessons learned and future directions. Journal of the American Academy of Child and Adolescent Psychiatry, 55, 179–187. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Taylor, M.J. , Martin, J. , Lu, Y. , Brikell, I. , Lundström, S. , Larsson, H. , & Lichtenstein, P. (2019). association of genetic risk factors for psychiatric disorders and traits of these disorders in a Swedish population twin sample. JAMA Psychiatry, 76, 280–289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Thapar, A. , Cooper, M. , & Rutter, M. (2017). Neurodevelopmental disorders. The Lancet Psychiatry, 4, 339–346. [DOI] [PubMed] [Google Scholar]
  68. Tillmann, J. , San José Cáceres, A. , Chatham, C.H. , Crawley, D. , Holt, R. , Oakley, B. , … & & EU‐AIMS LEAP group . (2019). Investigating the factors underlying adaptive functioning in autism in the EU‐AIMS longitudinal European autism project. Autism Research: Official Journal of the International Society for Autism Research, 12, 645–657. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Van Steijn, D.J. , Richards, J.S. , Oerlemans, A.M. , De Ruiter, S.W. , Van Aken, M.A.G. , Franke, B. , … & Rommelse, N.N. (2012). The co‐occurrence of autism spectrum disorder and attention‐deficit/hyperactivity disorder symptoms in parents of children with ASD or ASD with ADHD. Journal of Child Psychology and Psychiatry, 53, 954–963. [DOI] [PubMed] [Google Scholar]
  70. Warrier, V. , Zhang, X. , Reed, P. , Havdahl, A. , Moore, T.M. , Cliquet, F. , … & Baron‐Cohen, S. (2022). Genetic correlates of phenotypic heterogeneity in autism. Nature Genetics, 54, 1293–1304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Wechsler, D. (2011). WASI‐II: Wechsler abbreviated scale of intelligence. New York: Psychological Corporation. [Google Scholar]
  72. Wiggins, L.D. , Overwyk, K. , Powell, P. , Daniels, J. , DiGuiseppi, C. , Nadler, C. , … & Pazol, K. (2024). Changes in autism and Co‐occurring conditions from preschool to adolescence: Considerations for precision monitoring and treatment planning. Journal of Autism and Developmental Disorders. Online ahead of print. 10.1007/s10803-024-06550-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Zhang, X. , Grove, J. , Gu, Y. , Buus, C.K. , Nielsen, L.K. , Neufeld, S.A.S. , … & Warrier, V. (2024). An axis of genetic heterogeneity in autism is indexed by age at diagnosis and is associated with varying developmental and mental health profiles. medRxiv: The Preprint Server for Health Sciences, 2024.07.31.24311279. 10.1101/2024.07.31.24311279 [DOI]
  74. Zwaigenbaum, L. , Bryson, S.E. , Szatmari, P. , Brian, J. , Smith, I.M. , Roberts, W. , … & Roncadin, C. (2012). Sex differences in children with autism spectrum disorder identified within a high‐risk infant cohort. Journal of Autism and Developmental Disorders, 42, 2585–2596. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix S1. Sample recruitment and characterization.

Table S1. Categorisation of family history group.

Table S2. Sample descriptives of retained vs. non‐retained from recruitment to mid‐childhood.

Table S3. Summary of multivariate logistic regression analysis for retention from recruitment to mid‐childhood.

Table S4. Mid‐childhood scores by family history group.

Appendix S2. Summary of mid‐childhood scores on the developmental and behavioural measures by family history group.

Table S5. Summary of the latent profile analysis (LPA) models.

Table S6. Correlations between indicator variables and regression of indictor variables on LPA 7‐class solution.

Appendix S3. Statistical analysis of mid‐childhood LPA classes on indicator variables.

Table S7. Three year scores by mid‐childhood LPA classes.

Appendix S4. Developmental and behavioural characterisation of the mid‐childhood LPA classes at 3‐years.

Appendix S5. Statistical analysis of 3‐year scores by mid‐childhood LPA classes.

Appendix S6. 3‐year LPA analysis.

Table S8. 3‐years scores by 3‐year LPA classes.

Table S9. 3‐year LPA class by family history group.

Table S10. 3‐year LPA classes by sex and mid‐childhood autism diagnosis (early vs. later).

Table S11. 3‐year LPA class by mid‐childhood LPA class.

Appendix S7. Mid‐childhood LPA repeated for autism and/or ADHD family history infants only.

Table S12. Mid‐childhood scores by LPA classes for family history infants only.

Table S13. Mid‐childhood classes for family history infants only LPA by family history group.

Table S14. Mid‐childhood classes for family history infants only LPA by autism diagnosis and by sex.

JCPP-67-282-s001.docx (86.3KB, docx)

Data Availability Statement

Data available following review as indicated here: https://www.basisnetwork.org/collaboration‐and‐project‐affiliation/.

Articles from Journal of Child Psychology and Psychiatry, and Allied Disciplines are provided here courtesy of Wiley

RESOURCES