Early Life Neurodevelopmental Indicators of ADHD and Autism at Age 10: A Prospective Cohort Study

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Despite the early signs, diagnoses are often delayed, limiting opportunities for timely interventions. Evidence suggest that deviations in these domains may be predictive of later ADHD and ASD. However, findings have been inconsistent, and further clarification is needed to improve early identification. Methods This prospective study followed 590 children from the COPSAC2010 birth cohort to examine associations between neurodevelopmental measurements from birth to age 3 years and later clinical diagnoses and traits manifestations of ADHD and ASD at age 10. Early life neurodevelopment data included motor milestones, language production measured at age 12 and 24 months, cognitive functioning and behavioral/emotional observations at 36 months, as well as a general development questionnaire at 3 years. Associations were analyzed using logistic and linear regression analyses adjusted for demographic and perinatal covariates. Results We found that lower word production, shorter sentence, lower cognitive functioning and more behavioral/emotional problems in early life were significantly associated with increased risk of ADHD and elevated ADHD and ASD traits at age 10. Conclusion Deficits in language, cognitive functioning and more behavioral and emotional problems in early life could serve as risk indicators for later neurodevelopmental disorders. Recognizing these risk indicators could inform timely detection and improve developmental outcomes. ADHD ASD Neurodevelopment Neurodevelopmental disorders Risk Indicators Figures Figure 1 Figure 2 Figure 3 Figure 4 What is Known – What is New ADHD and ASD are highly heritable neurodevelopmental disorders that may be preceded by early differences in language, cognition, and motor development. Evidence on early developmental markers, particularly motor milestones, remains inconsistent and few prospective cohorts combine developmental, clinical, and genetic data. In this population-based birth cohort, poorer early language, cognition, and behavioural regulation were associated with later ADHD diagnosis and with increased ADHD and autistic traits at age 10. INTRODUCTION Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) represent the two most prevalent neurodevelopmental disorders worldwide affecting around 7% and 1% of children, respectively 1 . They are characterized by impairments in developmental domains such as language, motor skills, communication and social interaction, behavioral flexibility, regulation of attention, activity and impulses 2 , 3 . The Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-V) classifies ADHD into three presentations: Inattentive, Hyperactive-Impulsive, and Combined 4 . ADHD is associated with long-term adverse outcomes including academic underachievement, addiction, psychiatric comorbidities and increased suicide risk 5 . Symptoms typically begin before age seven, but diagnosis often occurs at 10–11 years old, creating a diagnostic gap 6 . Early recognition and intervention may improve long-term outcomes 7 and quality of life 8 . ASD is characterized by impaired social communication, repetitive behaviors and hyperresponsiveness to sensory stimuli 9 . Symptom severity varies widely 10 . Although ASD can be diagnosed before age three, many children remain undiagnosed until school age or adolescence, and some reach adulthood without diagnosis 11 . Early interventions can improve functioning 12 . As with ADHD, diagnosis relies on a detailed developmental history and observations using standardized diagnostic tools 13 . ADHD and ASD show strong genetic components, with heritability estimates of about 70% and to 90%, respectively 3 , 14 . Genome-wide association studies (GWAS) identify risk variants that can be summarized into polygenic risk scores (PRS) 15 , reflecting an individual’s inherited susceptibility 14 . Existing studies show mixed findings on early neurodevelopment. Children later diagnosed with ADHD may walk early (before 11 months) or late (after 15 months) 16 , though systematic reviews report no consistent associations between early motor development and ADHD 17 . Lower cognition functioning between 15 months and four years have been linked to later ADHD diagnosis at age 9-11 18 . Higher ADHD PRS has been associated with enhanced motor development at 18 months 19 and language difficulties at ages 5 and 8 years 3 . For ASD, delays in motor skills, language and cognition at age 12–60 month, have been reported, and that motor delays becoming more pronounced with age 20 . Motor and communication (both verbal and non-verbal) skills may be interrelated, as motor skills shape children’s interaction with outher 21 , and early motor skill impairments may precede autism symptoms 21 . Higher ASD PRS has been linked to language difficulties at 18 months and motor difficulties at 36 months 3 . These inconsistencies, especially regarding motor skills, highlight the need to clarify early life manifestations of ADHD and ASD. COPSAC 2010 cohort 22 is a population-based, prospective cohort consisting of 700 mother-child pairs followed from pregnancy throughout childhood, with detailed neurodevelopmental data (milestones, language, cognition), and deep clinical neuropsychiatric assessment at age 10 (the COPSYCH study) 23 . Data also includes genome-wide data for PRS analyses. Few cohorts combine detailed developmental, clinical, and genetic data in the same children together with psychiatric diagnoses later in life. Using this dataset, we aimed to investigate associations between early neurodevelopmental measures from birth to age three and both categorical and dimensional ADHD and ASD outcomes at age 10. We hypothesized that i) deviations in early neurodevelopment are associated with increased risk of ADHD or ASD; ii) deviations are associated with more and ADHD or ASD traits; iii) higher ADHD or ASD PRSs is associated with early neurodevelopment deviations; and iv) higher ADHD or ASD PRS is associated with both and diagnosis and more traits. METHODS Participants The children participating in this study are part of the COPSAC 2010 cohort, an ongoing population-based mother-child cohort (n = 700). The families have been followed for scheduled visits at the COPSAC clinical research unit, with early childhood asthma as the primary outcome and neurodevelopment as secondary outcome, including an extensive neurocognitive and psychopathological evaluation at age 10 as part of the COPSYCH study 23 . Neurodevelopment measures in first years of life (0–3 years) We assessed early neurodevelopment from birth to age three by motor milestones, language development, cognitive performance, and general development. At age ten, categorical diagnoses of ADHD and autism were established through K-SADS-PL clinical interviews and assigned according to ICD-10. Dimensional traits were captured using validated parent-report scales (ADHD-RS and SRS-2). Polygenic risk scores for ADHD and autism were generated using large GWAS meta-analyses. Full descriptions of all instruments, demographic and perinatal factors are provided in Supplementary. Statistics Data analysis was conducted in R (R Foundation for Statistical Computing, Vienna, Austria). Developmental milestones were reduced using principal component analysis (PCA). ADHD/autism and non-ADHD/autism groups were compared using Student’s t-test for normally distributed continuous variables, Mann–Whitney U test for skewed continuous variables, and chi-square tests for categorical variables. Associations between early neurodevelopmental measures and ADHD/ASD traits were estimated with adjusted linear regression, and with ADHD/autism diagnosis (yes/no) at age 10 using adjusted logistic regression. Neurodevelopmental measures significantly associated with ADHD diagnosis were grouped into quartiles to assess linearity. Associations between ADHD/ASD PRS and diagnosis (yes/no) and traits were estimated with adjusted logistic and linear models. Covariates included sex, household income, maternal education, paternal education, gestational age, and maternal age at birth. As a second step, adjusted sensitivity analyses were performed: i) logistic regression between early neurodevelopment and ADHD diagnosis stratified by ADHD presentation, and ii) linear regression between early neurodevelopment and ADHD-RS subscales. Multiple comparisons were corrected using a 0.05 false discovery rate (FDR). RESULTS Out of 700 children, 604 (86%) participated in the COPSYCH study at age 10 with complete psychopathological data from 593 (85%) children. Three children were excluded from the analyses due to developmental coordination disorder, delayed psychomotor development and hydrocephalus, resulting in a final sample of 590 children. Of these, we diagnosed 65 children (11%, 49 boys) with ADHD and 16 (3%, 10 boys) with autism. Baseline characteristics regarding demography and perinatal factors among children with and without ADHD and with and without autism are presented in Table 1 . Pair-wise comparisons showed that the proportion of boys was significantly higher in the ADHD group and that mothers had a lower level of education in the ADHD compared to the no ADHD group. Mothers in the ADHD group were more likely to have smoked during pregnancy and had generally lower household income compared to the no ADHD group, although not statistically significant. There were no significant differences between the autism and no autism groups. Table 1 Baseline characteristics regarding demographic and perinatal factors of the ADHD, no ADHD, autism and no autism groups. The children in no ADHD or no autism group are healthy or have other psychiatric diagnosis except from ADHD or autism, respectively Abbreviations: ADHD: Attention Deficit /Hyperactivity Disorder, N (%): Number (Percentage), SD: Standard Deviation, IQR: Interquartile Range DEMOGRAPHIC ADHD NO ADHD P-VALUE AUTISM NO AUTISM P-VALUE N (%) 65 (11) 528 (89) 16 (2.69) 574 (97.3) Sex (male, N (%)) 49 (75.4) 256 (48.5) < 0.001 10 (62.5) 294 (51.2) 0.524 Maternal age at birth (mean (SD)) 32.2 (4.7) 31.4 (4.3) 0.745 33.1 (5.1) 32.3 (4.3) 0.490 Paternal age at birth (mean (AD)) 34.9 (5.7) 34.6 (5.2) 0.747 35.4 (6.3) 34.6 (5.2) 0.524 Household income 3 month before birth N (%) 0.093 0.926 high > 250,000 DKK 5 (7.7) 86 (16.3) 2 (12.5) 89 (15.5) medium > 150,000-250,000 DKK 33 (50.8) 274 (52.3) 9 (56.2) 298 (52.0) low 150,000 DKK 27 (41.5) 164 (31.3) 5 (31.2) 186 (32.5) Maternal level of education N (%) 0.006 0.296 university 9 (13.8) 160 (30.3) 2 (12.5) 166 (28.9) tradesman certificate 46 (70.8) 328 (62.1) 13 (81.2) 359 (62.5) elementary 10 (15.4) 40 (7.6) 1 (6.2) 49 (8.5) Paternal level of education N (%) 0.719 0.218 university 15 (23.1) 148 (29.0) 1 (6.7) 162 (28.9) tradesman certificate 44 (67.7) 310 (60.8) 11 (73.3) 343 (61.3) elementary 6 (9.2) 52 (10.2) 3 (20.0) 55 (9.8) Parents seperated (at any time up to age 10) N (%) 16 (24.6) 93 (17.8) 0.371 4 (25.0) 105 (18.5) 0.785 PREGNANCY AND BIRTH Gestational age (median [IQR]) 40 [39.1, 41.1] 40.1 [39.1, 41] 0.745 40.3 [39.3, 41.2] 40.1 [39.1, 41] 0.576 Delivery N (%) 0.750 0.320 acute sectio 8 (12.3) 62 (11.8) 1 (6.2) 69 (12.0) planned sectio 4 (6.2) 47 (9.0) 0 (0.0) 51 (8.9) vaginal birth 53 (81.5) 416 (79.2) 15 (93.8) 454 (79.1) Gestational diabetes N (%) 2 (3.1) 12 (2.5) 1.000 1 (6.2) 14 (2.4) 0.883 Preeclampsia N (%) 2 (3.1) 25 (4.7) 0.770 0 (0.0) 27 (4.7) 0.777 Smoking during pregnancy N (%) 5 (7.7) 14 (2.7) 0.071 0 (0.0) 19 (3.3) 0.983 Primary analyses Associations between early neurodevelopment and ADHD diagnosis at age 10 are shown in eTable 1 and Fig. 1 . Earlier achievement of early milestones PC (adjusted OR, [95%CI], p; 0.73 [0.57;0.93] 0.013), fewer total gestures (0.93 [0.87;0.93] 0.015), lower word production at 2 years (per SD) (0.69 [0.48;0.95] 0.028), shorter sentence length (0.71 [0.56;0.88] 0.003), lower cognitive functioning as measured by Bayley Scales (0.96 [0.93;0.99] 0.025), and more behavioral and emotional problems, measured by TOF (1.05 [1.00;1.09] 0.034) were associated with increased ADHD risk. After FDR correction, only sentence length remained significant. Moreover, lower ASQ social/personal scores (unadjusted OR [95%CI] p; 0.91 [0.86;0.96] < 0.001) and problem solving (0.97 [0.93;1.00] 0.049) were associated with increased risk of ADHD at age 10 in unadjusted models. Sensitivity analysis stratified by ADHD presentations, combined (N = 36) and inattentive (N = 29), Fig. 2 , showed significant associations between shorter sentence (adjusted OR [95%CI] 0.59 [0.40;0.84] 0.006), lower cognitive functioning 0.95 [0.89;1.00] 0.049) and the ADHD inattention presentation, though not significant after FDR correction. Unadjusted analyses also linked lower word production at 2 years (per SD) (OR [95%CI] p; 0.56 [0.32;0.92] 0.032), shorter sentence length (0.55 [0.38;0.77] 0.001), lower cognitive functioning (0.93 [0.88;0.98] 0.012), lower ASQ problem solving (0.94 [0.89;0.98] 0.008), and lower ASQ social/personal scores (0.9 [0.83;0.98] 0.01) to the inattentive presentation. For the combined presentation, earlier achievement of early milestones PC (OR [95%CI] p; 0.75 [0.55;0.99] 0.049), lower word production at 2 years (per SD) (0.66 [0.43;0.98] 0.046), and lower ASQ social/personal scores (0.93 [0.87;1.00] 0.041) were significant in unadjusted models. No neurodevelopmental measures remained significant for the combined presentation after adjustment. Results from the analysis investigating associations between neurodevelopment in first years of life and ADHD traits at age 10 are visualized in Fig. 3 and listed in eTable 2 . Shorter sentence at age 2 years (adjusted β [95% CI]; -0.63 [-1.12; -0.15], 0.011), more behavioral and emotional problems (0.16 [0.04; 0.28], 0.01), and lower ASQ social/personal scores (-0.22 [− 0.39; −0.06], 0.009) were associated with more severe ADHD traits at age 10. However, these findings were not significant after FDR correction. We also found significant associations between lower word production at 2 years (per SD) (-1.13 [-1.91;-0.34] 0.005), and worse score in ASQ fine motor (-0.11 [-0.20;-0.02] 0.015) and ASQ problem solving (-0.15 [-0.26;-0.05] 0.006) and more severe ADHD traits at age 10, but these findings were not significant after covariate adjustments. Sensitivity analyses of inattentive and hyperactive/impulsive ADHD traits showed that earlier achievement of late milestones PC was associated with higher burden of hyperactive/impulsive traits at age 10 (adjusted β -0.18 [-0.37;0.00] 0.049), Fig. 4 , a pattern not observed without stratifying ADHD presentations. After FDR correction, shorter sentence length (-0.49 [-0.79;-0.19], P FDR 0.015), lower cognitive functioning (-0.07 [-0.11;-0.02] P FDR 0.02), and lower ASQ social/personal scores (-0.19 [-0.29;-0.09], P FDR 0.005) were negatively associated with inattentive traits, and more behavioral and emotional problems were positively associated (0.12 [0.04;0.19] P FDR 0.02). No statistically significant associations were observed between neurodevelopmental measures and autism diagnosis, although borderline associations with total gestures at 1 year (adjusted OR [95%CI] p; 0.89 [0.78;0.99] 0.056) and word production at 2 years (per SD) (0.05 [0.23; 0.96] 0.053), eTable 3 . For autistic traits, lower word production at 2 years (adjusted β [95%CI] p; -2.83 [-4.45;-1.21] < 0.001), shorter sentence length (-1.99 [-3.01;-0.98] < 0.001), more behavioral problems (0.38 [0.13;0.62] 0.003), lower cognitive functioning (-0.2 [-0.34;-0.05] 0.008), and lower ASQ social/personal scores (-0.44 [-0.79;-0.10] 0.012) were significantly associated with more severe autistic traits at age 10, eTable 4 . Word production and sentence length at 2 years remained significant after FDR correction. In unadjusted analysis, poorer ASQ communication scores (-0.37 [-0.71;-0.04] 0.028) were also associated with more autistic traits. Secondary analyses Thereafter, we estimated associations between ADHD/ASD PRS and early neurodevelopmental measures, diagnoses, and traits to test genetic contributions. Earlier achievement of the late milestone PC was associated with higher ADHD PRS (unadjusted β [95% CI]; − 0.20 [–0.38; − 0.03], 0.024), with a similar trend after adjustment (–0.17 [–0.35; 0.01], 0.061). Higher ADHD PRS was significantly associated with ADHD diagnosis (adjusted OR 1.76 [1.31;2.38] < 0.001), the ADHD combined presentation (2.1 [1.43;3.17] < 0.001), and more ADHD traits at 10 years (adjusted β 1.69 [0.95;2.42] 0.001), but not the inattentive presentation, eTable 5 . Lower word production at 1 year (adjusted β − 0.12 [–0.24; 0.00], 0.043) and fewer total gestures (–0.86 [–1.59; − 0.13], 0.022) were associated with higher ASD PRS, though these findings did not remain after FDR correction. Higher ASD PRS (1.90 [0.45; 3.34], 0.01) was associated with more autistic traits, and showed a trend toward association with autism diagnosis (1.58 [0.93; 2.76], 0.097), eTable 6 . Sub-analysis dividing early neurodevelopmental measures into quartiles, eFigure 1 , showed that only the highest quartiles was associated with lower ADHD risk. For sentence length, the two highest quartiles predicted lower risk. Total gestures were associated with ADHD in all quartiles. Children achieving milestones later had lower ADHD risk. The highest quartile of cognitive functioning showed significantly lower risk, while more behavioral and emotional problems trended toward higher risk. Spearman correlations between cognitive functioning and language measures were weak: word production at 1 year (ρ = 0.15), at 2 years (ρ = 0.41), and sentence length (ρ = 0.35). Eight children in the cohort had both ADHD and autism diagnoses. We repeated the ADHD analysis excluding the children with autism in a sensitivity analysis, which did not change the findings. Due to low numbers, we could not make an analysis on solely autism, therefore, we excluded the ADHD children in the analysis of autistic traits, and the results remained consistent. DISCUSSION In this prospective mother-child cohort study, impairments in language development, early milestones, cognitive functioning, and behavior from birth to age 3 were associated with later neurodevelopmental outcomes. These included a higher risk of ADHD diagnosis, as well as more pronounced ADHD- and autistic traits at age 10. We did not observe significant associations with autism diagnosis, likely due to limited statistical power resulting. This aligns with existing evidence suggesting early neurodevelopmental vulnerabilities may precede psychopathology, though the precise nature of these connections remains complex 39 . Less gesture use at 1 year and lower word production and shorter sentence at 2 years were associated with higher ADHD risk. These results align with existing literature indicating early language difficulties as potential risk factor for ADHD traits 40 . Earlier acquisition of the late milestone component was associated with more hyperactive/impulsive traits, agreeing with a prior longitudinal study describing that children who later develop ADHD tend to start walking either at an earlier or later age than average 16 . However, systematic reviews have generally failed to establish consistent associations between atypical early motor development and ADHD 17 . While motor milestone deviations are present in some individuals with ADHD, a consistent directionality of such associations remain unclear, suggesting a substantial heterogeneity in developmental trajectories in ADHD. Notably, the inattentive presentation of ADHD appears to be the primary contributor to the significant association with word production, longest sentence, and cognitive functioning. While both the inattentive and combined ADHD presentation have deficits in attention, it has been suggested that the nature of the inattention symptoms differs between the two presentations 41 . Prior research indicates that deficits in working memory and processing speed are more likely to be associated with the inattention presentation compared to the combined presentation of ADHD 42 . Our findings reinforce the distinction between ADHD presentations and could explain why we mostly see deviations in cognition and language in the inattentive presentation. We also observed trends between ADHD PRS and more behavioral and emotional problems, and earlier achievement of the late milestone component, consistent with prior findings of earlier walking in children with higher ADHD PRS 19 . Few studies have linked early cognitive development, assessed by the Bayley Scales, to later ADHD. One such study found lower Bayley scores at 15 and 24 months to be associated with greater ADHD severity, thus evaluated by The Disruptive Behavior Disorders Rating Scale 18 . Cognitive impairments in ADHD are frequently observed within the domain of executive functions 43 . The relationship between language and cognition has long been of interest in developmental research, though findings remain inconsistent and the nature of this association is not yet fully understood 44 . While some studies suggest that cognition and language mutually influence each other 45 , others have not found association between the two domains 46 . In our study, we observed a positive correlation between language measurements and cognitive functioning, suggesting they reflect overlapping developmental measures. The subgroup analysis, using quartile-based comparisons, showed that children with the highest word production and cognitive scores had significantly reduced odds of an ADHD diagnosis, and those achieving milestones earlier had lower risk. Gesture use did not differentiate ADHD risk, indicating that word production, sentence length and cognitive functioning may be effective for ruling out the risk of ADHD. Lower word production and shorter sentence at 2 years, lower cognitive functioning and more behavioral and emotional problems at age 2.5 years were associated with more autistic traits. Lower communication scores, as measured by the ASQ, were also associated with more autistic traits, consistent with evidence that communication delays are characteristic of ASD 10 , 47 . Another study evaluated whether ASQ could detect ASD and found that scores below the ‘monitor’ cutoff in the communication domain identified 95% of the children with ASD. The study emphasized that the ‘monitor’ cutoff on the communication domain only, is both sensitive and specific to ASD 48 . Together, these results emphasize the utility of the ASQ communication domain as a targeted screening tool for early ASD detection, in line with the findings in our study. Further, we found an association between ASD PRS and lower word production and less total gestures. Other studies have found that language difficulties were associated with ASD PRS 3 and that ASD PRS was associated with delays in cognition, language and motor skills 49 . A key strength of this study is the prospective design with children followed from birth with exhaustive clinical data collection and detailed psychopathological assessments at age 10. It is an unselected cohort with more than 86% follow-up rate, and the prospective and clinical design mitigates limitations such as recall and subjective biases. Our data on language acquisition are reported by parents, which has been demonstrated to be a reliable source to report the status of their child’s language, even when their children have developmental disorders such as ASD 50 . Further, we have objective markers regarding cognition and behavior at age 2.5 years. The expected associations we found between PRS and diagnoses and traits validate our psychopathology data. We found more children with ADHD than expected in the population, which could underpin the assumption of ADHD being under-diagnosed 7 or reflect that population-based register estimates are based on referred neurodevelopmental disorder cases. Limitations of our study include limited statistical power, especially related to children with autism, and interpreting our results should therefore be done with precaution, also taking into consideration that some estimates and OR are close to zero and one, respectively. However, we had the opportunity to substantiate the findings with our trait scores, where we found comparable significant results. Finally, few results remained significant after FDR correction, which we consider a minor issue as many of the early neurodevelopmental assessments are correlated. In conclusion, specific neurodevelopmental measures, including reduced cognitive functioning, delayed language development, and behavioral/emotional deviations were associated with increased risk of ADHD and higher ADHD and autistic traits at age 10. While these findings are not immediately applicable to clinical practice, they offer valuable theoretical insights into the early developmental trajectories of children with ADHD and autism and could potentially play a role in early detection of these traits. Future prospective research employing larger sample sizes is necessary to validate these findings and ultimately improve clinicians' ability to predict ADHD and autism and improve developmental outcomes. Abbreviations ADHD Attention-Deficit/Hyperactivity Disorder ASD Autism Spectrum Disorder NDD Neurodevelopmental Disorder DSM-V Diagnostic and Statistical Manual of Mental Disorders V COPSAC2010 COpenhagen Prospective Studies on Asthma in Childhood 2010 COPSYCH COpenhagen Prospective Study on Neuro-PSYCHiatric Development ADHD-RS Attention Deficit/Hyperactivity Disorder Rating Scale SRS-2 Social Responsiveness Scale-2 TOF Test Observation Form PRS Polygenic Risk Score GWAS Genome-wide Association Studies PCA Principal Component Analysis FDR False Discovery Rate Declarations Authors Contributions: Dr Camilla Grube, Dr Rebecca Kofod Vinding, Dr María Hernández-Lorca and Prof Bo Chawes conceptualized and designed the study and gave valuable input to the analysis. Dr María Hernández-Lorca helped with analysis, statistical support and figure design. Dr Julie B. Rosenberg, Dr Kristina Aagaard, MSc Michael Widdowson, MSc Tingting Wang, Dr Nilo Vahman, RN Hanne Lunn Nissen, Dr Jens Richardt Møllegaard Jepsen, Dr Klaus Bønnelykke, Dr Bjørn H. Ebdrup, Prof Bo Chawes contributed substantially to the acquisition, analyses, and interpretation of the data and provided important intellectual input to the manuscript. All authors critically reviewed and revised the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work. No honorarium, grant, or other form of payment was given to any of the authors to produce this manuscript. Ethics: The study was conducted in accordance with the guiding principles of the Declaration of Helsinki and was approved by the Local Ethics Committee (H-B-2008-093), and the Danish Data Protection Agency (2015-41-3696). Both parents gave written informed consent before enrolment. Source of Funding: All funding received by COPSAC is listed on www.copsac.com. The Lundbeck Foundation (Grant no R16-A1694); The Lundbeck grant for COPSYCH is (Grant no. R269-2017-5); The Danish Ministry of Health (Grant no 903516); Danish Council for Strategic Research (Grant no 0603-00280B) and The Capital Region Research Foundation have provided core support to the COPSAC research center. NOTE: The above-mentioned are the major funds supporting all of COPSAC. Funder/Sponsor: Beside the funding mentioned above the project was done with no other specific support. Acknowledgement Acknowledgements: We express our deepest gratitude to the children and families of the COPSAC2010 cohort study for all their support and commitment. We acknowledge and appreciate the unique efforts of the COPSAC research team. 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Atten Deficit Hyperact Disord 11(3):233–240. 10.1007/s12402-018-0280-y Arnett AB, MacDonald B, Pennington BF (2013) Cognitive and Behavioral Indicators of ADHD Symptoms Prior to School Age. J Child Psychol Psychiatry 54(12). 10.1111/jcpp.12104 Hannigan LJ, Askeland RB, Ask H et al Developmental milestones in early childhood and genetic liability to neurodevelopmental disorders. Psychol Med. 53(5):1750–1758. 10.1017/S0033291721003330 Mohd Nordin A, Ismail J, Kamal Nor N (2021) Motor Development in Children With Autism Spectrum Disorder. Front Pediatr 9:598276. 10.3389/fped.2021.598276 Posar A, Visconti P (2022) Early Motor Signs in Autism Spectrum Disorder. Children 9(2):294. 10.3390/children9020294 Bisgaard H, Vissing NH, Carson CG et al (2013) Deep phenotyping of the unselected COPSAC2010 birth cohort study. Clin Exp Allergy J Br Soc Allergy Clin Immunol 43(12):1384–1394. 10.1111/cea.12213 Mohammadzadeh P, Rosenberg JB, Vinding R et al (2022) Effects of prenatal nutrient supplementation and early life exposures on neurodevelopment at age 10: a randomised controlled trial - the COPSYCH study protocol. BMJ Open 12(2):e047706. 10.1136/bmjopen-2020-047706 Frankenburg W, Dodds J (1990) The Denver Developmental Assessment (Denver II). University of Colorado Medical School Wijnhoven TM, de Onis M, Onyango AW et al (2004) Assessment of gross motor development in the WHO Multicentre Growth Reference Study. Food Nutr Bull 25(1 Suppl):S37–45 Bjarnadóttir E, Stokholm J, Chawes B et al (2019) Determinants of neurodevelopment in early childhood – results from the Copenhagen prospective studies on asthma in childhood (COPSAC2010) mother–child cohort. Acta Paediatr 108(9):1632–1641. 10.1111/apa.14753 Bleses D, Vach W, Slott M et al (2008) The Danish Communicative Developmental Inventories: validity and main developmental trends. J Child Lang 35(3):651–669. 10.1017/S0305000907008574 Bayley N (2006) Bayley Scales of Infant and Toddler Development (Third Edition),Administration Manual. Harcourt Assessment. McConaughy S, Achenbach T (2004) Manual for the Test Observation Form for Ages 2–18. Center for Children, Youth & Families Singh A, Yeh CJ, Boone Blanchard S (2017) Ages and Stages Questionnaire: a global screening scale. Bol Med Hosp Infant Mex 74(1):5–12. 10.1016/j.bmhimx.2016.07.008 Kaufman J, Birmaher B, Brent D et al (1997) Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data. J Am Acad Child Adolesc Psychiatry 36(7):980–988. 10.1097/00004583-199707000-00021 World Health Organization. The ICD-10 Classification of Mental and Behavioural Disorders: Diagnostic Criteria for Research. World Health Organization (1993) Accessed May 11, 2023. https://apps.who.int/iris/handle/10665/37108 Makransky G, Bilenberg N (2014) Psychometric properties of the parent and teacher ADHD Rating Scale (ADHD-RS): measurement invariance across gender, age, and informant. Assessment 21(6):694–705. 10.1177/1073191114535242 Bruni TP (2014) Test Review: Social Responsiveness Scale–Second Edition (SRS-2). J Psychoeduc Assess 32(4):365–369. 10.1177/0734282913517525 Demontis D, Walters GB, Athanasiadis G et al (2023) Genome-wide analyses of ADHD identify 27 risk loci, refine the genetic architecture and implicate several cognitive domains. Nat Genet 55(2):198–208. 10.1038/s41588-022-01285-8 Grove J, Ripke S, Als TD et al (2019) Identification of common genetic risk variants for autism spectrum disorder. Nat Genet 51(3):431–444. 10.1038/s41588-019-0344-8 Ge T, Chen CY, Ni Y, Feng YCA, Smoller JW (2019) Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun 10(1):1776. 10.1038/s41467-019-09718-5 Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. Am J Hum Genet 81(3):559–575. 10.1086/519795 Miller M, Arnett AB, Shephard E et al (2023) Delineating early developmental pathways to ADHD: Setting an international research agenda. JCPP Adv 3(2):e12144. 10.1002/jcv2.12144 Peyre H, Galera C, van der Waerden J et al (2016) Relationship between early language skills and the development of inattention/hyperactivity symptoms during the preschool period: Results of the EDEN mother-child cohort. BMC Psychiatry 16(1):380. 10.1186/s12888-016-1091-3 de la Peña IC, Pan MC, Thai CG, Alisso T (2020) Attention-Deficit/Hyperactivity Disorder Predominantly Inattentive Subtype/Presentation: Research Progress and Translational Studies. Brain Sci 10(5):292. 10.3390/brainsci10050292 Simone AN, Bédard ACV, Marks DJ, Halperin JM (2016) Good Holders, Bad Shufflers: An Examination of Working Memory Processes and Modalities in Children with and without Attention-Deficit/Hyperactivity Disorder. J Int Neuropsychol Soc 22(1):1–11. 10.1017/S1355617715001010 Coghill DR, Seth S, Matthews K (2014) A comprehensive assessment of memory, delay aversion, timing, inhibition, decision making and variability in attention deficit hyperactivity disorder: advancing beyond the three-pathway models. Psychol Med 44(9):1989–2001. 10.1017/S0033291713002547 Perszyk DR, Waxman SR (2018) Linking Language and Cognition in Infancy. Annu Rev Psychol . ;69(Volume 69, 2018):231–250. 10.1146/annurev-psych-122216-011701 Cahyaningsih T, Gunarsih I, Abdurrauf NLR, Rahmadhani YSN, Islamiyah N, Saodi S (2024) The Influence Of Cognition On Children’s Language Development: The Relationship Between Comprehension, Memory and Language. Social Science Research Network . Preprint posted online November 26. 10.2139/ssrn.5035251 Webster RI, Erdos C, Evans K et al (2006) The Clinical Spectrum of Developmental Language Impairment in School-Aged Children: Language, Cognitive, and Motor Findings. Pediatrics 118(5):e1541–e1549. 10.1542/peds.2005-2761 Iverson JM, Northrup JB, Leezenbaum NB, Parlade MV, Koterba EA, West KL (2018) Early Gesture and Vocabulary Development in Infant Siblings of Children with Autism Spectrum Disorder. J Autism Dev Disord 48(1):55–71. 10.1007/s10803-017-3297-8 Hardy S, Haisley L, Manning C, Fein D (2015) Can Screening With the Ages and Stages Questionnaire Detect Autism? J Dev Behav Pediatr JDBP 36(7):536–543. 10.1097/DBP.0000000000000201 Takahashi N, Harada T, Nishimura T et al (2020) Association of Genetic Risks With Autism Spectrum Disorder and Early Neurodevelopmental Delays Among Children Without Intellectual Disability. JAMA Netw Open 3(2):e1921644. 10.1001/jamanetworkopen.2019.21644 Miller LE, Perkins KA, Dai YG, Fein DA (2017) Comparison of Parent Report and Direct Assessment of Child Skills in Toddlers. Res Autism Spectr Disord 41–42:57–65. 10.1016/j.rasd.2017.08.002 Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9105614","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612053961,"identity":"ac5276cb-3ae8-4b0d-a7b7-d154ef12ef11","order_by":0,"name":"Camilla Grube","email":"","orcid":"","institution":"Copenhagen Prospective Studies on Asthma in Childhood","correspondingAuthor":false,"prefix":"","firstName":"Camilla","middleName":"","lastName":"Grube","suffix":""},{"id":612053962,"identity":"44fffbc7-e54e-4b02-9f31-3163363721b0","order_by":1,"name":"María Hernández-Lorca","email":"","orcid":"","institution":"Copenhagen Prospective Studies on Asthma in Childhood","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Hernández-Lorca","suffix":""},{"id":612053963,"identity":"8011c59e-3f80-4269-acc6-bfdff1fbac4e","order_by":2,"name":"Rebecca K Vinding","email":"","orcid":"","institution":"Copenhagen Prospective Studies on Asthma in Childhood","correspondingAuthor":false,"prefix":"","firstName":"Rebecca","middleName":"K","lastName":"Vinding","suffix":""},{"id":612053964,"identity":"26142863-8fc4-4bf1-aaea-24a3142a09cf","order_by":3,"name":"Julie Rosenberg","email":"","orcid":"","institution":"Copenhagen Prospective Studies on Asthma in Childhood","correspondingAuthor":false,"prefix":"","firstName":"Julie","middleName":"","lastName":"Rosenberg","suffix":""},{"id":612053965,"identity":"2d7092ee-32f4-4169-a523-b72b76ae345c","order_by":4,"name":"Kristina Aagaard","email":"","orcid":"","institution":"Copenhagen Prospective Studies on Asthma in Childhood","correspondingAuthor":false,"prefix":"","firstName":"Kristina","middleName":"","lastName":"Aagaard","suffix":""},{"id":612053966,"identity":"1fc2c92d-b10e-4e38-ac64-73ff4979e5f7","order_by":5,"name":"Michael Widdowson","email":"","orcid":"","institution":"Copenhagen Prospective Studies on Asthma in Childhood","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Widdowson","suffix":""},{"id":612053967,"identity":"70058e5b-2585-4e19-9b02-4fe87c1488ae","order_by":6,"name":"Tingting Wang","email":"","orcid":"","institution":"Copenhagen Prospective Studies on Asthma in Childhood","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Wang","suffix":""},{"id":612053968,"identity":"962655f1-0918-4f04-9566-e1a3d288fb4d","order_by":7,"name":"Nilo Vahman","email":"","orcid":"","institution":"Copenhagen Prospective Studies on Asthma in Childhood","correspondingAuthor":false,"prefix":"","firstName":"Nilo","middleName":"","lastName":"Vahman","suffix":""},{"id":612053969,"identity":"05c8feda-5a16-43b6-a332-ba58a2416667","order_by":8,"name":"Hanne Lunn Nissen","email":"","orcid":"","institution":"Copenhagen Prospective Studies on Asthma in Childhood","correspondingAuthor":false,"prefix":"","firstName":"Hanne","middleName":"Lunn","lastName":"Nissen","suffix":""},{"id":612053970,"identity":"bd704402-15eb-4570-bd20-6c2bf9698c62","order_by":9,"name":"Jens Richardt Møllegaard Jepsen","email":"","orcid":"","institution":"Mental Health Services","correspondingAuthor":false,"prefix":"","firstName":"Jens","middleName":"Richardt Møllegaard","lastName":"Jepsen","suffix":""},{"id":612053971,"identity":"7651c160-aef9-41a1-89ae-87dce9b6ddd9","order_by":10,"name":"Bjørn H Ebdrup","email":"","orcid":"","institution":"Mental Health Services","correspondingAuthor":false,"prefix":"","firstName":"Bjørn","middleName":"H","lastName":"Ebdrup","suffix":""},{"id":612053972,"identity":"6723323a-f1d4-41b3-b53c-24b1cddba745","order_by":11,"name":"Klaus Bønnelykke","email":"","orcid":"","institution":"Copenhagen Prospective Studies on Asthma in Childhood","correspondingAuthor":false,"prefix":"","firstName":"Klaus","middleName":"","lastName":"Bønnelykke","suffix":""},{"id":612053973,"identity":"77850d57-6110-4ece-b21b-96147b42a540","order_by":12,"name":"Bo Chawes","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYDACCTBpQ7qWNNK1HCZBB//s5mcSH/6cl+eXbn/44QeDnZxuAyFL7hwzk5zZdttw5pwzxpI9DMnGZgcIaDGQSDC7zdtwO8HgRg6DNAPDgcRthLWkf7v95885oJb0x7+J1JJjdpuB7QBQS4IZcbZI3Mgp/9nblgzyi5lljwERfuGfkb7Z4McfO1CIPb7xo8JOjqAWJPvA7iRaOVzLKBgFo2AUjAIsAAAXLkE2P3m8JQAAAABJRU5ErkJggg==","orcid":"","institution":"Copenhagen Prospective Studies on Asthma in Childhood","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Chawes","suffix":""}],"badges":[],"createdAt":"2026-03-12 13:54:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9105614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9105614/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105728394,"identity":"69e55254-6869-413d-8fe2-b88ee1672e06","added_by":"auto","created_at":"2026-03-30 11:11:42","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":297985,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot illustrating results from logistic regression analyses between early life neurodevelopmental measures and ADHD diagnosis at age 10. \u003cstrong\u003eAbbreviations\u003c/strong\u003e: PC: Principal Component, ASQ: Ages and Stages Questionnaire, TOF: Test Observation Form (Behavioral and emotional problems), Bayley Score (Cognitive functioning). *: p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9105614/v1/a753d57cde1887de9e2860fe.jpeg"},{"id":105621238,"identity":"d608ac5d-4e07-4ae2-91a4-acdcbff05f14","added_by":"auto","created_at":"2026-03-28 08:28:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20441,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot illustrating results from a) unadjusted and b) adjusted logistic regression stratifying between early life neurodevelopmental measured and ADHD diagnosis, stratified by ADHD presentations: combined presentation (green line) and inattentive presentation (orange line). \u003cstrong\u003eAbbreviations\u003c/strong\u003e: PC: Principal Component, ASQ: Ages and Stages Questionnaire, TOF: Test Observation Form (Behavioral and emotional problems), Bayley Score (Cognitive functioning). *: p\u0026lt;0.05\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9105614/v1/7ddc3555d850b86065b32b61.png"},{"id":105728601,"identity":"af9f6c4b-3918-4d8f-9279-290334c87b91","added_by":"auto","created_at":"2026-03-30 11:12:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":9400,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot illustrating associations between early life neurodevelopmental measures and ADHD traits (ADHD-RS) at age 10. \u003cstrong\u003eAbbreviations\u003c/strong\u003e: PC: Principal Component, ASQ: Ages and Stages Questionnaire, TOF: Test Observation Form (Behavioral and emotional problems), Bayley Score (Cognitive functioning). *: p\u0026lt;0.05\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9105614/v1/f92cb4c4368d4bc004b76d85.png"},{"id":105752094,"identity":"b3b090fd-33ad-4843-b4df-d3d00355a3b8","added_by":"auto","created_at":"2026-03-30 15:54:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21756,"visible":true,"origin":"","legend":"\u003cp\u003eResults from a) unadjusted and b) adjusted regression analysis between early life neurodevelopment measures and inattentive and hyperactive traits as measured with the ADHD-RS subscales: inattention subscale (orange) and Hyperactivity/impulsivity (green). \u003cstrong\u003eAbbreviations:\u003c/strong\u003e PC: Principal Component, ASQ: Ages and Stages Questionnaire, TOF: Test Observation Form (Behavioral and emotional problems), Bayley Score (Cognitive functioning). *: p\u0026lt;0.05\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9105614/v1/69cf66a88825c73de9c67bc5.png"},{"id":107468163,"identity":"ea5ec240-2cc2-442a-b8b3-437c58b51b4d","added_by":"auto","created_at":"2026-04-21 19:09:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":802009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9105614/v1/f41790ac-3fdc-41ca-ab5b-ab5dc6a57fa3.pdf"},{"id":105621244,"identity":"2e64f877-7de5-4881-a43c-4af7e56dccfc","added_by":"auto","created_at":"2026-03-28 08:28:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":75228,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-9105614/v1/a203df0127860e447c0f9e6f.docx"},{"id":105621240,"identity":"60f8b352-a455-4bb5-a53b-40fc32e9aef9","added_by":"auto","created_at":"2026-03-28 08:28:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20753,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9105614/v1/8d17e9a0dacca2427166cdce.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Life Neurodevelopmental Indicators of ADHD and Autism at Age 10: A Prospective Cohort Study","fulltext":[{"header":"What is Known – What is New","content":"\u003cul\u003e\n \u003cli\u003eADHD and ASD are highly heritable neurodevelopmental disorders that may be preceded by early differences in language, cognition, and motor development. Evidence on early developmental markers, particularly motor milestones, remains inconsistent and few prospective cohorts combine developmental, clinical, and genetic data.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIn this population-based birth cohort, poorer early language, cognition, and behavioural regulation were associated with later ADHD diagnosis and with increased ADHD and autistic traits at age 10.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eAttention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) represent the two most prevalent neurodevelopmental disorders worldwide affecting around 7% and 1% of children, respectively\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. They are characterized by impairments in developmental domains such as language, motor skills, communication and social interaction, behavioral flexibility, regulation of attention, activity and impulses\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-V) classifies ADHD into three presentations: Inattentive, Hyperactive-Impulsive, and Combined\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. ADHD is associated with long-term adverse outcomes including academic underachievement, addiction, psychiatric comorbidities and increased suicide risk\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Symptoms typically begin before age seven, but diagnosis often occurs at 10\u0026ndash;11 years old, creating a diagnostic gap\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Early recognition and intervention may improve long-term outcomes\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and quality of life\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eASD is characterized by impaired social communication, repetitive behaviors and hyperresponsiveness to sensory stimuli\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Symptom severity varies widely\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Although ASD can be diagnosed before age three, many children remain undiagnosed until school age or adolescence, and some reach adulthood without diagnosis\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Early interventions can improve functioning\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. As with ADHD, diagnosis relies on a detailed developmental history and observations using standardized diagnostic tools\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eADHD and ASD show strong genetic components, with heritability estimates of about 70% and to 90%, respectively\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Genome-wide association studies (GWAS) identify risk variants that can be summarized into polygenic risk scores (PRS)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, reflecting an individual\u0026rsquo;s inherited susceptibility\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExisting studies show mixed findings on early neurodevelopment. Children later diagnosed with ADHD may walk early (before 11 months) or late (after 15 months)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, though systematic reviews report no consistent associations between early motor development and ADHD\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Lower cognition functioning between 15 months and four years have been linked to later ADHD diagnosis at age 9-11\u003csup\u003e18\u003c/sup\u003e. Higher ADHD PRS has been associated with enhanced motor development at 18 months\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and language difficulties at ages 5 and 8 years\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. For ASD, delays in motor skills, language and cognition at age 12\u0026ndash;60 month, have been reported, and that motor delays becoming more pronounced with age\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Motor and communication (both verbal and non-verbal) skills may be interrelated, as motor skills shape children\u0026rsquo;s interaction with outher\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and early motor skill impairments may precede autism symptoms\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Higher ASD PRS has been linked to language difficulties at 18 months and motor difficulties at 36 months\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These inconsistencies, especially regarding motor skills, highlight the need to clarify early life manifestations of ADHD and ASD.\u003c/p\u003e \u003cp\u003eCOPSAC\u003csub\u003e2010\u003c/sub\u003e cohort\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e is a population-based, prospective cohort consisting of 700 mother-child pairs followed from pregnancy throughout childhood, with detailed neurodevelopmental data (milestones, language, cognition), and deep clinical neuropsychiatric assessment at age 10 (the COPSYCH study)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Data also includes genome-wide data for PRS analyses. Few cohorts combine detailed developmental, clinical, and genetic data in the same children together with psychiatric diagnoses later in life. Using this dataset, we aimed to investigate associations between early neurodevelopmental measures from birth to age three and both categorical and dimensional ADHD and ASD outcomes at age 10. We hypothesized that i) deviations in early neurodevelopment are associated with increased risk of ADHD or ASD; ii) deviations are associated with more and ADHD or ASD traits; iii) higher ADHD or ASD PRSs is associated with early neurodevelopment deviations; and iv) higher ADHD or ASD PRS is associated with both and diagnosis and more traits.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe children participating in this study are part of the COPSAC\u003csub\u003e2010\u003c/sub\u003e cohort, an ongoing population-based mother-child cohort (n\u0026thinsp;=\u0026thinsp;700). The families have been followed for scheduled visits at the COPSAC clinical research unit, with early childhood asthma as the primary outcome and neurodevelopment as secondary outcome, including an extensive neurocognitive and psychopathological evaluation at age 10 as part of the COPSYCH study\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNeurodevelopment measures in first years of life (0–3 years)\u003c/h3\u003e\n\u003cp\u003eWe assessed early neurodevelopment from birth to age three by motor milestones, language development, cognitive performance, and general development. At age ten, categorical diagnoses of ADHD and autism were established through K-SADS-PL clinical interviews and assigned according to ICD-10. Dimensional traits were captured using validated parent-report scales (ADHD-RS and SRS-2). Polygenic risk scores for ADHD and autism were generated using large GWAS meta-analyses. Full descriptions of all instruments, demographic and perinatal factors are provided in Supplementary.\u003c/p\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eData analysis was conducted in R (R Foundation for Statistical Computing, Vienna, Austria). Developmental milestones were reduced using principal component analysis (PCA). ADHD/autism and non-ADHD/autism groups were compared using Student\u0026rsquo;s t-test for normally distributed continuous variables, Mann\u0026ndash;Whitney U test for skewed continuous variables, and chi-square tests for categorical variables. Associations between early neurodevelopmental measures and ADHD/ASD traits were estimated with adjusted linear regression, and with ADHD/autism diagnosis (yes/no) at age 10 using adjusted logistic regression. Neurodevelopmental measures significantly associated with ADHD diagnosis were grouped into quartiles to assess linearity. Associations between ADHD/ASD PRS and diagnosis (yes/no) and traits were estimated with adjusted logistic and linear models. Covariates included sex, household income, maternal education, paternal education, gestational age, and maternal age at birth. As a second step, adjusted sensitivity analyses were performed: i) logistic regression between early neurodevelopment and ADHD diagnosis stratified by ADHD presentation, and ii) linear regression between early neurodevelopment and ADHD-RS subscales. Multiple comparisons were corrected using a 0.05 false discovery rate (FDR).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e Out of 700 children, 604 (86%) participated in the COPSYCH study at age 10 with complete psychopathological data from 593 (85%) children. Three children were excluded from the analyses due to developmental coordination disorder, delayed psychomotor development and hydrocephalus, resulting in a final sample of 590 children. Of these, we diagnosed 65 children (11%, 49 boys) with ADHD and 16 (3%, 10 boys) with autism.\u003c/p\u003e \u003cp\u003eBaseline characteristics regarding demography and perinatal factors among children with and without ADHD and with and without autism are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Pair-wise comparisons showed that the proportion of boys was significantly higher in the ADHD group and that mothers had a lower level of education in the ADHD compared to the no ADHD group. Mothers in the ADHD group were more likely to have smoked during pregnancy and had generally lower household income compared to the no ADHD group, although not statistically significant. There were no significant differences between the autism and no autism groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics regarding demographic and perinatal factors of the ADHD, no ADHD, autism and no autism groups. The children in no ADHD or no autism group are healthy or have other psychiatric diagnosis except from ADHD or autism, respectively Abbreviations: ADHD: Attention Deficit /Hyperactivity Disorder, N (%): Number (Percentage), SD: Standard Deviation, IQR: Interquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDEMOGRAPHIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADHD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO ADHD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-VALUE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUTISM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO AUTISM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-VALUE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e528 (89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e574 (97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male, N (%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e294 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age at birth (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.2 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.4 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.1 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.3 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaternal age at birth (mean (AD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.9 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.6 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.4 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.6 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income 3 month before birth N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh\u0026thinsp;\u0026gt;\u0026thinsp;250,000 DKK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emedium\u0026thinsp;\u0026gt;\u0026thinsp;150,000-250,000 DKK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e298 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow 150,000 DKK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5 (31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e186 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal level of education N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e166 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etradesman certificate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e328 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e359 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eelementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaternal level of education N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003euniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e162 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etradesman certificate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310 (60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11 (73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e343 (61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eelementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents seperated (at any time up to age 10) N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e105 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePREGNANCY AND BIRTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 [39.1, 41.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.1 [39.1, 41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.3 [39.3, 41.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.1 [39.1, 41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelivery N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eacute sectio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplanned sectio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evaginal birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (81.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416 (79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (93.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e454 (79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational diabetes N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreeclampsia N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking during pregnancy N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePrimary analyses\u003c/h3\u003e\n\u003cp\u003eAssociations between early neurodevelopment and ADHD diagnosis at age 10 are shown in \u003cb\u003eeTable 1\u003c/b\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Earlier achievement of early milestones PC (adjusted OR, [95%CI], p; 0.73 [0.57;0.93] 0.013), fewer total gestures (0.93 [0.87;0.93] 0.015), lower word production at 2 years (per SD) (0.69 [0.48;0.95] 0.028), shorter sentence length (0.71 [0.56;0.88] 0.003), lower cognitive functioning as measured by Bayley Scales (0.96 [0.93;0.99] 0.025), and more behavioral and emotional problems, measured by TOF (1.05 [1.00;1.09] 0.034) were associated with increased ADHD risk. After FDR correction, only sentence length remained significant. Moreover, lower ASQ social/personal scores (unadjusted OR [95%CI] p; 0.91 [0.86;0.96]\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and problem solving (0.97 [0.93;1.00] 0.049) were associated with increased risk of ADHD at age 10 in unadjusted models.\u003c/p\u003e \u003cp\u003eSensitivity analysis stratified by ADHD presentations, combined (N\u0026thinsp;=\u0026thinsp;36) and inattentive (N\u0026thinsp;=\u0026thinsp;29), Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, showed significant associations between shorter sentence (adjusted OR [95%CI] 0.59 [0.40;0.84] 0.006), lower cognitive functioning 0.95 [0.89;1.00] 0.049) and the ADHD inattention presentation, though not significant after FDR correction. Unadjusted analyses also linked lower word production at 2 years (per SD) (OR [95%CI] p; 0.56 [0.32;0.92] 0.032), shorter sentence length (0.55 [0.38;0.77] 0.001), lower cognitive functioning (0.93 [0.88;0.98] 0.012), lower ASQ problem solving (0.94 [0.89;0.98] 0.008), and lower ASQ social/personal scores (0.9 [0.83;0.98] 0.01) to the inattentive presentation. For the combined presentation, earlier achievement of early milestones PC (OR [95%CI] p; 0.75 [0.55;0.99] 0.049), lower word production at 2 years (per SD) (0.66 [0.43;0.98] 0.046), and lower ASQ social/personal scores (0.93 [0.87;1.00] 0.041) were significant in unadjusted models. No neurodevelopmental measures remained significant for the combined presentation after adjustment.\u003c/p\u003e \u003cp\u003eResults from the analysis investigating associations between neurodevelopment in first years of life and ADHD traits at age 10 are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and listed in \u003cb\u003eeTable 2\u003c/b\u003e. Shorter sentence at age 2 years (adjusted β [95% CI]; -0.63 [-1.12; -0.15], 0.011), more behavioral and emotional problems (0.16 [0.04; 0.28], 0.01), and lower ASQ social/personal scores (-0.22 [\u0026minus;\u0026thinsp;0.39; \u0026minus;0.06], 0.009) were associated with more severe ADHD traits at age 10. However, these findings were not significant after FDR correction. We also found significant associations between lower word production at 2 years (per SD) (-1.13 [-1.91;-0.34] 0.005), and worse score in ASQ fine motor (-0.11 [-0.20;-0.02] 0.015) and ASQ problem solving (-0.15 [-0.26;-0.05] 0.006) and more severe ADHD traits at age 10, but these findings were not significant after covariate adjustments.\u003c/p\u003e \u003cp\u003eSensitivity analyses of inattentive and hyperactive/impulsive ADHD traits showed that earlier achievement of late milestones PC was associated with higher burden of hyperactive/impulsive traits at age 10 (adjusted β -0.18 [-0.37;0.00] 0.049), Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a pattern not observed without stratifying ADHD presentations. After FDR correction, shorter sentence length (-0.49 [-0.79;-0.19], P\u003csub\u003eFDR\u003c/sub\u003e 0.015), lower cognitive functioning (-0.07 [-0.11;-0.02] P\u003csub\u003eFDR\u003c/sub\u003e 0.02), and lower ASQ social/personal scores (-0.19 [-0.29;-0.09], P\u003csub\u003eFDR\u003c/sub\u003e 0.005) were negatively associated with inattentive traits, and more behavioral and emotional problems were positively associated (0.12 [0.04;0.19] P\u003csub\u003eFDR\u003c/sub\u003e 0.02).\u003c/p\u003e \u003cp\u003eNo statistically significant associations were observed between neurodevelopmental measures and autism diagnosis, although borderline associations with total gestures at 1 year (adjusted OR [95%CI] p; 0.89 [0.78;0.99] 0.056) and word production at 2 years (per SD) (0.05 [0.23; 0.96] 0.053), \u003cb\u003eeTable 3\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eFor autistic traits, lower word production at 2 years (adjusted β [95%CI] p; -2.83 [-4.45;-1.21]\u0026thinsp;\u0026lt;\u0026thinsp;0.001), shorter sentence length (-1.99 [-3.01;-0.98]\u0026thinsp;\u0026lt;\u0026thinsp;0.001), more behavioral problems (0.38 [0.13;0.62] 0.003), lower cognitive functioning (-0.2 [-0.34;-0.05] 0.008), and lower ASQ social/personal scores (-0.44 [-0.79;-0.10] 0.012) were significantly associated with more severe autistic traits at age 10, \u003cb\u003eeTable 4\u003c/b\u003e. Word production and sentence length at 2 years remained significant after FDR correction. In unadjusted analysis, poorer ASQ communication scores (-0.37 [-0.71;-0.04] 0.028) were also associated with more autistic traits.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSecondary analyses\u003c/h2\u003e \u003cp\u003eThereafter, we estimated associations between ADHD/ASD PRS and early neurodevelopmental measures, diagnoses, and traits to test genetic contributions.\u003c/p\u003e \u003cp\u003eEarlier achievement of the late milestone PC was associated with higher ADHD PRS (unadjusted β [95% CI]; \u0026minus;\u0026thinsp;0.20 [\u0026ndash;0.38; \u0026minus;\u0026thinsp;0.03], 0.024), with a similar trend after adjustment (\u0026ndash;0.17 [\u0026ndash;0.35; 0.01], 0.061). Higher ADHD PRS was significantly associated with ADHD diagnosis (adjusted OR 1.76 [1.31;2.38]\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the ADHD combined presentation (2.1 [1.43;3.17]\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and more ADHD traits at 10 years (adjusted β 1.69 [0.95;2.42] 0.001), but not the inattentive presentation, \u003cb\u003eeTable 5\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eLower word production at 1 year (adjusted β \u0026minus;\u0026thinsp;0.12 [\u0026ndash;0.24; 0.00], 0.043) and fewer total gestures (\u0026ndash;0.86 [\u0026ndash;1.59; \u0026minus;\u0026thinsp;0.13], 0.022) were associated with higher ASD PRS, though these findings did not remain after FDR correction. Higher ASD PRS (1.90 [0.45; 3.34], 0.01) was associated with more autistic traits, and showed a trend toward association with autism diagnosis (1.58 [0.93; 2.76], 0.097), \u003cb\u003eeTable 6\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eSub-analysis dividing early neurodevelopmental measures into quartiles, \u003cb\u003eeFigure 1\u003c/b\u003e, showed that only the highest quartiles was associated with lower ADHD risk. For sentence length, the two highest quartiles predicted lower risk. Total gestures were associated with ADHD in all quartiles. Children achieving milestones later had lower ADHD risk. The highest quartile of cognitive functioning showed significantly lower risk, while more behavioral and emotional problems trended toward higher risk. Spearman correlations between cognitive functioning and language measures were weak: word production at 1 year (ρ\u0026thinsp;=\u0026thinsp;0.15), at 2 years (ρ\u0026thinsp;=\u0026thinsp;0.41), and sentence length (ρ\u0026thinsp;=\u0026thinsp;0.35).\u003c/p\u003e \u003cp\u003eEight children in the cohort had both ADHD and autism diagnoses. We repeated the ADHD analysis excluding the children with autism in a sensitivity analysis, which did not change the findings. Due to low numbers, we could not make an analysis on solely autism, therefore, we excluded the ADHD children in the analysis of autistic traits, and the results remained consistent.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this prospective mother-child cohort study, impairments in language development, early milestones, cognitive functioning, and behavior from birth to age 3 were associated with later neurodevelopmental outcomes. These included a higher risk of ADHD diagnosis, as well as more pronounced ADHD- and autistic traits at age 10. We did not observe significant associations with autism diagnosis, likely due to limited statistical power resulting. This aligns with existing evidence suggesting early neurodevelopmental vulnerabilities may precede psychopathology, though the precise nature of these connections remains complex\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLess gesture use at 1 year and lower word production and shorter sentence at 2 years were associated with higher ADHD risk. These results align with existing literature indicating early language difficulties as potential risk factor for ADHD traits\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Earlier acquisition of the late milestone component was associated with more hyperactive/impulsive traits, agreeing with a prior longitudinal study describing that children who later develop ADHD tend to start walking either at an earlier or later age than average\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, systematic reviews have generally failed to establish consistent associations between atypical early motor development and ADHD\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. While motor milestone deviations are present in some individuals with ADHD, a consistent directionality of such associations remain unclear, suggesting a substantial heterogeneity in developmental trajectories in ADHD. Notably, the inattentive presentation of ADHD appears to be the primary contributor to the significant association with word production, longest sentence, and cognitive functioning. While both the inattentive and combined ADHD presentation have deficits in attention, it has been suggested that the nature of the inattention symptoms differs between the two presentations\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Prior research indicates that deficits in working memory and processing speed are more likely to be associated with the inattention presentation compared to the combined presentation of ADHD\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Our findings reinforce the distinction between ADHD presentations and could explain why we mostly see deviations in cognition and language in the inattentive presentation.\u003c/p\u003e \u003cp\u003eWe also observed trends between ADHD PRS and more behavioral and emotional problems, and earlier achievement of the late milestone component, consistent with prior findings of earlier walking in children with higher ADHD PRS\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFew studies have linked early cognitive development, assessed by the Bayley Scales, to later ADHD. One such study found lower Bayley scores at 15 and 24 months to be associated with greater ADHD severity, thus evaluated by The Disruptive Behavior Disorders Rating Scale\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Cognitive impairments in ADHD are frequently observed within the domain of executive functions\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The relationship between language and cognition has long been of interest in developmental research, though findings remain inconsistent and the nature of this association is not yet fully understood\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. While some studies suggest that cognition and language mutually influence each other\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, others have not found association between the two domains\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In our study, we observed a positive correlation between language measurements and cognitive functioning, suggesting they reflect overlapping developmental measures. The subgroup analysis, using quartile-based comparisons, showed that children with the highest word production and cognitive scores had significantly reduced odds of an ADHD diagnosis, and those achieving milestones earlier had lower risk. Gesture use did not differentiate ADHD risk, indicating that word production, sentence length and cognitive functioning may be effective for ruling out the risk of ADHD.\u003c/p\u003e \u003cp\u003eLower word production and shorter sentence at 2 years, lower cognitive functioning and more behavioral and emotional problems at age 2.5 years were associated with more autistic traits. Lower communication scores, as measured by the ASQ, were also associated with more autistic traits, consistent with evidence that communication delays are characteristic of ASD\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Another study evaluated whether ASQ could detect ASD and found that scores below the \u0026lsquo;monitor\u0026rsquo; cutoff in the communication domain identified 95% of the children with ASD. The study emphasized that the \u0026lsquo;monitor\u0026rsquo; cutoff on the communication domain only, is both sensitive and specific to ASD\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Together, these results emphasize the utility of the ASQ communication domain as a targeted screening tool for early ASD detection, in line with the findings in our study. Further, we found an association between ASD PRS and lower word production and less total gestures. Other studies have found that language difficulties were associated with ASD PRS\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e and that ASD PRS was associated with delays in cognition, language and motor skills\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA key strength of this study is the prospective design with children followed from birth with exhaustive clinical data collection and detailed psychopathological assessments at age 10. It is an unselected cohort with more than 86% follow-up rate, and the prospective and clinical design mitigates limitations such as recall and subjective biases. Our data on language acquisition are reported by parents, which has been demonstrated to be a reliable source to report the status of their child\u0026rsquo;s language, even when their children have developmental disorders such as ASD\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Further, we have objective markers regarding cognition and behavior at age 2.5 years. The expected associations we found between PRS and diagnoses and traits validate our psychopathology data. We found more children with ADHD than expected in the population, which could underpin the assumption of ADHD being under-diagnosed\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e or reflect that population-based register estimates are based on referred neurodevelopmental disorder cases. Limitations of our study include limited statistical power, especially related to children with autism, and interpreting our results should therefore be done with precaution, also taking into consideration that some estimates and OR are close to zero and one, respectively. However, we had the opportunity to substantiate the findings with our trait scores, where we found comparable significant results. Finally, few results remained significant after FDR correction, which we consider a minor issue as many of the early neurodevelopmental assessments are correlated.\u003c/p\u003e \u003cp\u003eIn conclusion, specific neurodevelopmental measures, including reduced cognitive functioning, delayed language development, and behavioral/emotional deviations were associated with increased risk of ADHD and higher ADHD and autistic traits at age 10. While these findings are not immediately applicable to clinical practice, they offer valuable theoretical insights into the early developmental trajectories of children with ADHD and autism and could potentially play a role in early detection of these traits. Future prospective research employing larger sample sizes is necessary to validate these findings and ultimately improve clinicians' ability to predict ADHD and autism and improve developmental outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAttention-Deficit/Hyperactivity Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAutism Spectrum Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeurodevelopmental Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSM-V\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiagnostic and Statistical Manual of Mental Disorders V\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPSAC2010\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCOpenhagen Prospective Studies on Asthma in Childhood 2010\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPSYCH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCOpenhagen Prospective Study on Neuro-PSYCHiatric Development\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADHD-RS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAttention Deficit/Hyperactivity Disorder Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRS-2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocial Responsiveness Scale-2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTOF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTest Observation Form\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolygenic Risk Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-wide Association Studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAuthors Contributions:\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr Camilla Grube, Dr Rebecca Kofod Vinding, Dr María Hernández-Lorca and Prof Bo Chawes conceptualized and designed the study and gave valuable input to the analysis. Dr María Hernández-Lorca helped with analysis, statistical support and figure design.\u003c/p\u003e\n\u003cp\u003eDr Julie B. Rosenberg, Dr Kristina Aagaard, MSc Michael Widdowson, MSc Tingting Wang, Dr Nilo Vahman, RN Hanne Lunn Nissen, Dr Jens Richardt Møllegaard Jepsen, Dr Klaus Bønnelykke, Dr Bjørn H. Ebdrup, Prof Bo Chawes contributed substantially to the acquisition, analyses, and interpretation of the data and provided important intellectual input to the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors critically reviewed and revised the manuscript for important intellectual content.\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript as submitted and agreed to be accountable for all\u003c/p\u003e\n\u003cp\u003easpects of the work. No honorarium, grant, or other form of payment was given to any of the\u003c/p\u003e\n\u003cp\u003eauthors to produce this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eEthics:\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the guiding principles of the Declaration of Helsinki and was approved by the Local Ethics Committee (H-B-2008-093), and the Danish Data Protection Agency (2015-41-3696). Both parents gave written informed consent before enrolment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eSource of Funding:\u003c/u\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll funding received by COPSAC is listed on www.copsac.com. The\u003c/p\u003e\n\u003cp\u003eLundbeck Foundation (Grant no R16-A1694); The Lundbeck grant for COPSYCH is (Grant\u003c/p\u003e\n\u003cp\u003eno. R269-2017-5); The Danish Ministry of Health (Grant no 903516); Danish Council for\u003c/p\u003e\n\u003cp\u003eStrategic Research (Grant no 0603-00280B) and The Capital Region Research Foundation\u003c/p\u003e\n\u003cp\u003ehave provided core support to the COPSAC research center.\u003c/p\u003e\n\u003cp\u003eNOTE: The above-mentioned are the major funds supporting all of COPSAC.\u003c/p\u003e\n\u003cp\u003eFunder/Sponsor: Beside the funding mentioned above the project was done with no other\u003c/p\u003e\n\u003cp\u003especific support.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgements: We express our deepest gratitude to the children and families of the COPSAC2010 cohort study for all their support and commitment. We acknowledge and appreciate the unique efforts of the COPSAC research team. We also like to acknowledge the huge work of late professor Hans Bisgaard, who was the founder of COPSAC and was head of the clinical research center for more than 25 years.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFast K, Wentz E, Roswall J, Strandberg M, Bergman S, Dahlgren J (2024) Prevalence of attention-deficit/hyperactivity disorder and autism in 12-year-old children: A population-based cohort. Dev Med Child Neurol 66(4):493\u0026ndash;500. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/dmcn.15757\u003c/span\u003e\u003cspan address=\"10.1111/dmcn.15757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThapar A, Cooper M, Rutter M (2017) Neurodevelopmental disorders. 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Res Autism Spectr Disord 41\u0026ndash;42:57\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.rasd.2017.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.rasd.2017.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ADHD, ASD, Neurodevelopment, Neurodevelopmental disorders, Risk Indicators ","lastPublishedDoi":"10.21203/rs.3.rs-9105614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9105614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAttention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are the two most prevalent neurodevelopmental disorders, characterized by early-onset impairments across multiple developmental domains, including language, motor coordination, cognition and behavioral regulation. Despite the early signs, diagnoses are often delayed, limiting opportunities for timely interventions. Evidence suggest that deviations in these domains may be predictive of later ADHD and ASD. However, findings have been inconsistent, and further clarification is needed to improve early identification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective study followed 590 children from the COPSAC2010 birth cohort to examine associations between neurodevelopmental measurements from birth to age 3 years and later clinical diagnoses and traits manifestations of ADHD and ASD at age 10. Early life neurodevelopment data included motor milestones, language production measured at age 12 and 24 months, cognitive functioning and behavioral/emotional observations at 36 months, as well as a general development questionnaire at 3 years. Associations were analyzed using logistic and linear regression analyses adjusted for demographic and perinatal covariates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found that lower word production, shorter sentence, lower cognitive functioning and more behavioral/emotional problems in early life were significantly associated with increased risk of ADHD and elevated ADHD and ASD traits at age 10.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeficits in language, cognitive functioning and more behavioral and emotional problems in early life could serve as risk indicators for later neurodevelopmental disorders. Recognizing these risk indicators could inform timely detection and improve developmental outcomes.\u003c/p\u003e","manuscriptTitle":"Early Life Neurodevelopmental Indicators of ADHD and Autism at Age 10: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-28 08:28:33","doi":"10.21203/rs.3.rs-9105614/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0efa30ea-f4f4-48c3-9c03-d369d1c2d0c4","owner":[],"postedDate":"March 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T19:09:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-28 08:28:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9105614","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9105614","identity":"rs-9105614","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}