{"paper_id":"2ff43328-20ae-49b9-8038-cd507b5f6249","body_text":"Clinical and genetic factors associated with regression in children with autism spectrum disorders | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical and genetic factors associated with regression in children with autism spectrum disorders Anna Maruani, Emma Delclaud, Paul Bruzeau, Richard Delorme, Anne-Claude Tabet, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6801394/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition with complex genetic and environmental underpinnings. A clinically significant subset of children with ASD experience developmental regression ( reg ASD), characterized by the acute loss of previously acquired skills. The mechanisms, predictors, and molecular basis of reg ASD remain poorly understood. Methods We retrospectively and prospectively analyzed a cohort of 505 children diagnosed with ASD at the Center of Excellence for Autism and Neurodevelopmental Disorders (Paris, France) between 2017 and 2023. Clinical, neurodevelopmental, and genetic data were collected, including detailed developmental histories, standardized diagnostic assessments (ADI-R, ADOS-2, VABS), and chromosomal microarray analysis (CMA). Statistical analyses included classification tree algorithms and principal component analysis to identify clinical predictors of regression, and gene ontology enrichment to explore molecular pathways. Results Developmental regression was detected in 74 children (15%). Prior to regression, reg ASD children exhibited more favorable neurodevelopmental profiles, including lower rates of prematurity, higher birth weight and height, and earlier acquisition of first words, compared with non-regressive ASD (non- reg ASD) peers. However, after regression, reg ASD children had significantly more severe neurodevelopmental impairments across cognitive, adaptive, and social domains (p < 0.001). Routine clinical variables did not reliably predict the onset of regression. CMA revealed that reg ASD is associated with distinct genetic deletions enriched in immune, inflammatory (particularly type I interferon), oxidative stress, angiogenesis, and synaptic pathways, with minimal overlap with non- reg ASD genetic profiles. Conclusions Our findings support the hypothesis that reg ASD represents a distinct clinical and molecular subgroup within ASD. The unique molecular signatures identified in reg ASD implicate immune and synaptic pathways, highlighting the need for targeted research and potential therapeutic interventions for this subgroup. Neurodevelopment neuroimmunology regression child and adolescent psychiatry Figures Figure 1 Figure 2 Introduction Autism spectrum disorder (ASD) represents a heterogeneous neurodevelopmental condition characterized by difficulties in social communication and interaction as well as restricted and repetitive behaviors/interests ( 1 , 2 ). ASD affects approximately 1–2% of children in the general population ( 3 ). Its exact pathophysiology remains unknown and is based on a complex interplay between genetic and environmental factors ( 4 , 5 ). The inherent heterogeneity of ASD makes it difficult to identify precise pathophysiological pathways. Recent studies have focused on homogeneous subgroups of children within the autistic spectrum based on shared genetic variant ( 6 , 7 ), a particular environmental risk factor ( 8 , 9 ), or a specific clinical presentation. Among the clinical presentations of interest, some children show an acute loss of previously acquired skills (typically language and/or social skills), defined here as regressive ASD ( reg ASD). reg ASD usually occurs before the age of 3 years, typically around 20 months ( 10 ) and is estimated to occur in 30% of patients with ASD ( 11 ). The exact mechanisms underlying regression are not yet understood, but some risk factors have been identified. For example, genetic syndromes associated with ASD, such as Rett syndrome and Phelan Mc Dermid syndrome, have higher rates of regression, and variants in genes involved in synapse regulation are preferentially found in reg ASD ( 12 – 15 ). Environmental factors suggest a higher prevalence of familial autoimmune disorders (e.g., thyroid disease, type 1 diabetes) and personal history of infectious episodes within six months prior to reg ASD, supporting the implication of immuno-inflammatory mechanisms in the regression ( 16 , 17 ). The clinical presentation of these patients after regression remains poorly defined. It has been proposed that many children exhibit subtle developmental differences prior to the onset of reg ASD but results are inconsistent. ( 18 , 19 ). Some authors find that children with reg ASD have a more severe autistic phenotype, whereas others find no difference compared to non- reg ASD ( 20 , 21 ).These clinical observations have led to the emergence of two major hypotheses regarding reg ASD: (i) reg ASD is a distinct clinical subgroup within ASD or (ii) reg ASD is a subclinical continuum that cannot be isolated from autism. To address this question, we studied a cohort of children with ASD to retrospectively determine the clinical presentation of the children before the onset of regression and to prospectively assess the severity of ASD symptoms in reg ASD compared to non- reg ASD. We then performed a molecular study to determine whether specific genetic variations are observed in reg ASD. We aim to identify predictive developmental markers and biological pathways associated with regression. Method Population We retrospectively collected data from children with ASD enrolled in the EXPECT study conducted by the Center of Excellence for Autism and Neurodevelopmental Disorders (InovAND-Robert Debré Hospital, Paris, France) between March 2017 and June 2023. This study was approved by the local ethics committee (2021-27 N° IDRCB: 2021-A00489-32). Informed consent was obtained from a parent and/or legal guardian prior to enrollment. The study was conducted in accordance with relevant guidelines/regulations and was conducted in accordance with the Declaration of Helsinki. ASD diagnosis and clinical characteristics Diagnosis was based on clinical evaluation by experienced clinicians, according to DSM-5 criteria ( 22 ) by summarizing the information from the Autism Diagnostic Interview-Revised (ADI-R) ( 23 , 24 ), the Autism Diagnostic Observation Schedule − 2nd edition (ADOS-2) ( 25 ). The ADI-R is a parent interview for the diagnosis of autism that can be administered starting at 24 months of age. It reviews the child's developmental history and examines communication, social development and play, restricted interests and behaviors, general behaviors, and age of onset of difficulties. reg ASD was determined using q11 of the ADI-R, which requires acquisition and use of skills for at least 3 months prior to loss to be classified as regression. All participants were screened by an expert child and adolescent psychiatrist with a semi-structured parental interview for pre- and perinatal history. Intellectual functioning was assessed with age-appropriate instruments such as the WISC-IV and V ( 26 – 28 ), Mullen Scales of Early Learning ( 29 ), and Raven’s Progressive Matrices ( 30 ). We also used the second edition of the Vineland Adaptive Behavior Scales (VABS) a benchmark heterogeneous questionnaire for assessing children's levels of autonomy and adjustment. It measures adaptive behavior in three main areas: communication, daily living skills, and socialization ( 31 , 32 ). Chromosomal Microarray Analysis (CMA) All patients underwent chromosomal microarray analysis. Genomic DNA was extracted from peripheral blood lymphocytes using standard protocols. Copy number variation (CNV) screening was performed using the Illumina OmniExpress SNP array (Illumina Inc., San Diego, CA), which contains over 700,000 markers with an average resolution of approximately 30 kb. Genotyping was performed using the Infinium assay, and data were analyzed using Illumina GenomeStudio software with the CNV partitioning algorithm. Copy number profiles were evaluated by analyzing the Log R Ratio—defined as the natural logarithm of the sample copy number relative to a reference—and the B Allele Frequency (BAF), allowing for the detection of both deletions and duplications, as well as regions of homozygosity or mosaicism. When a variant of uncertain significance (VUS), likely pathogenic or pathogenic CNV was identified, segregation analysis was performed in the parents using SNP array. For gene ontology enrichment analysis, we used the online tools provided by geneontology.org. Statistical analysis Before each analysis, we assessed the normality of the distributions of the variables using the Shapiro-Wilk test. For continuous data, depending on its normality, we used either Student's t-tests or Wilcoxon tests. For categorical data, we used the Fisher's test. To infer the presence or absence of regression based on multiple collected input variables, we used classification decision trees, which are supervised predictive machine learning models. Classification decision trees provide a visual and intuitive approach to data analysis, allow for easy interpretation of complex data sets by segmenting the data into branches based on hierarchical decision rules, and facilitate the handling of both categorical and continuous variables without requiring extensive data preprocessing or assumptions about data distributions. Classification trees were generated using the R package 'Party'( 33 ). We then used Factoshiny to generate a primary component analysis with a correlation circle. We did not apply multiple testing corrections for p-values because many of our models, especially PCA and classification decision tree, did not produce p-value outputs. Statistical analysis was performed using R studio version 4.2.1. Ethical As part of the EXPECT study, this project was approved by the local ethics committee of the Robert Debré Hospital (2021-27 N° IDRCB: 2021-A00489-32). Informed consent was obtained from patients or a parent and/or legal guardian prior to enrollment. All procedures in both studies were performed in accordance with relevant guidelines/regulations and were conducted in accordance with the Declaration of Helsinki. Results Children with reg ASD have a better clinical pre-morbid phenotype A total of 505 patients were enrolled in the study, of which 74 (15%) showed developmental regression ( Table 1 and Supp Table 1 ). Table 1 Clinical characteristics of the study population : Non- reg ASD (ASD without regression); reg ASD (ASD with regression); AID: autoimmune disease Mean (SD) Non- reg ASD (n = 432) reg ASD (n = 74) p-value Sex ratio (M/F) 337/94 59/15 0.885 Maternal AID 13 (3.02%) 0(0.00%) 0.264 Pregnancy term (week) 38.53 (2.80) 39.23 (1.41) 0.001 Birth weight (g) 3209.17 (712.44) 3450.66 (550.19) 0.001 Birth height (cm) 49.12 (3.96) 49.98 (2.10) 0.008 Birth cranial perimeter (cm) 34.96 (3.68) 34.75 (1.68) 0.440 APGAR (1 minute) 9.40 (1.52) 9.38 (1.67) 0.907 APGAR (5 minutes) 9.81 (0.89) 9.89 (0.69) 0.455 First steps (months) 15.59 (7.49) 14.99 (5.01) 0.393 First words (months) 23.52 (14.47) 18.75 (12.00) 0.007 Early parental concerns (months) 23.68 (15.43) 20.13 (10.61) 0.022 We found no difference in maternal history of autoimmune disease or complications during pregnancy between the children who will become reg ASD and the non- reg ASD children (Table 1 and Supp Table 1 ). Regarding birth parameters, children with future reg ASD were born significantly less prematurely (p-value = 0.001), had a better birth weight (p-value = 0.001) and height (p-value = 0.008). For learning milestones, children with future reg ASD have their first words earlier (p = 0.007), but paradoxically their parents expressed concern earlier than parents of non- reg ASD (p = 0.022). Our data support the hypothesis that children who will develop reg ASD may have a better premorbid neurodevelopmental profile than non- reg ASD. Routine clinical observations are not predictive of regression. We then investigated whether we could predict which children would develop reg ASD from routine clinical data. Using a tree algorithm classification (Fig. 1 A), we failed to properly highlight children who will present reg ASD with a sensitivity of 6% ( Fig. 1 B ). Next, we used principal component analysis (PCA) and can discriminate children who will experience reg ASD from non- reg ASD with a first dimension explaining 30% of the variance and a second-dimension accounting for 16% of the variance (Fig. 1 C). However, using the correlation circle, none of the clinical data used allowed a clear explanation of this classification (Fig. 1 D ). We confirmed that the children who will present a reg ASD were different from non- reg ASD children but were not able to isolate the variables that explain such difference and therefore to highlight predictors of the onset of regression. Children with reg ASD have a more severe neurodevelopmental profile. Next, we investigated the clinical profile after regression of reg ASD compared to non − reg ASD (Table 2 ) Interestingly, using the D criteria of the ADI, we found, consistent with our previous findings of earlier concerns of parents of future reg ASD children, that reg ASD have more abnormalities before the 36 months than non- reg ASD (p-value = 0.023). Table 2 Clinical profile after regression . Mean (SD) Non- reg ASD (n = 432) reg ASD (n = 74) p_value Sex ratio (M/F) 337/94 59/15 0.885 Age (month) 217.92 (64.16) 212.33 (63.49) 0.496 Abnormality < 36 months (ADI) 4.09 (1.72) 4.60 (1.71) 0.023 Verbal language 325 (75.41%) 28 (37.84%) < 0.001 Non-verbal IQ 80.66 (25.29) 62.60 (25.24) < 0.001 Verbal IQ 86.81 (22.36) 74.31 (15.59) 0.007 Communication (VABS) 56.75 (25.37) 44.73 (25.02) 0.002 Social interaction (ADI) 16.62 (7.35) 19.87 (6.49) < 0.001 Social affect (ADOS) 12.77 (4.34) 14.98 (4.60) < 0.001 Socialisation (VABS) 55.35 (26.60) 47.04 (27.95) 0.050 Restricted repetitive behaviors (ADOS) 3.95 (2.14) 4.97 (2.10) < 0.001 Adaptive behaviors (VABS) 51.21 (23.89) 40.56 (25.49) 0.007 Clinical global impression (CGI) 5.61 (0.62) 6.03 (0.55) < 0.001 Global functioning 42.21 (10.79) 34.30 (9.12) < 0.001 After regression, 37.84% of reg ASD patients have functional language compared to 75.41% of non- r eg ASD (p-value < 0.001) and a lower nonverbal IQ (non- reg ASD: 80.66 (+/- 25.29); reg ASD: 62.60(+/-25.24); p-value < 0.001). Among the verbal subjects, we also found a lower verbal IQ in r eg ASD (non- reg ASD: 88.81 (+/- 22.36); reg ASD: 74.31(+/-15.59); p-value < 0.0001). Overall, reg ASD have more adaptive difficulties in communication (p-value = 0.002). Regarding autistic core symptoms, reg ASD have more difficulties in social interaction (p-value < 0.001), social affects (p-value < 0.001) and a tendency towards adaptive socialization (p-value = 0.05), but also more restricted and repetitive behaviors (p-value < 0.001). Overall, children with reg ASD have more difficulties in overall socio-adaptive behaviors (p-value = 0.007) with worse global functioning (p-value < 0.001) and more severe clinical global impression (p-value < 0.001). In conclusion, although children with reg ASD have a less severe premorbid clinical profile, after regression they exhibit global and more severe neurodevelopmental features compared to non- reg ASD. reg ASD is associated with a specific molecular background Among the 477 patients who underwent chromosomal microarray analysis, 68 showed with developmental regression and 409 did not ( Fig. 2 and Supp Table 2 ). To gain insight into the molecular mechanisms underlying regression, we next focused on the different genetic variations identified in children with reg ASD and non- reg ASD. We found pathogenic or likely pathogenic anomalies in 3 patients from the regression group (4.4%) and in 15 patients from the non-regression group (3.7%) (NS). Variants of uncertain significance (VUS) were found in 9.0% of the regression group and 11.0% of the non-regression group (NS). In the regression group, the detected abnormalities included an interstitial deletion of 2p16.3 involving NRXN1 , a complete trisomy 21 corresponding to 21q11.2q22.3 duplication, and an interstitial deletion of 22q13.31q13.33 involving SHANK3 . In the non-regression group, eight deletions were observed, including regions 1q31.3, 3q29, 9p24.3p23, 13q13.3, 19q13.32, 22q13.3, 22q13.32q13.33, and 2p25.3, affecting genes such as ASPM, NBEA, DOCK8, CALM3, BRPF1 , and SHANK3 . Five duplications were also found in this group, involving regions 4q21.23 ( WDFY3 ), 7q11.23 ( ELN ), 20q13.33, 22q13.33 ( SHANK3 ), and Yp11.31q12 in a 47, XYY context. Additionally, a triplication of the 15q11.2q13.2 region (including UBE3A and SNRPN ) and a complete trisomy 21 were identified. Most anomalies involved copy number changes across various genomic regions, with a significant proportion being de novo or of unknown inheritance. To assess whether the two groups share genetic mechanisms, we analyzed the genes affected by deletions. Of the 438 deleted genes identified, only 8 (1.8%) were shared between reg ASD and non- reg ASD, representing 12.5% of deleted genes in reg ASD and 2.1% in non- reg ASD (Fig. 2 C). This low overlap suggests distinct molecular pathways. To confirm this hypothesis, we performed gene ontology enrichment analysis on the deleted region ( Fig. 2 D and 2 E ) . In reg ASD, deleted region were enriched in pathways related to oxidative stress (phosphorylation of PGC-1alpha; p38 MAPK/RAS/erk pathways), angiogenesis (VEGFA-VEGFR/VEGF pathways), inflammation and especially type I interferon (NOD 1 2signaling pathways) and synaptic development (neuroxin/neuroligin) suggesting the involvement of the immune system in their pathophysiology compared to non- reg ASD, where mainly post-synaptic excitatory region were involved. Discussion Our findings are consistent with the hypothesis that regASD is a distinct clinical subgroup within ASD for several reasons. First, we found that regression occurs in children with a better premorbid neurodevelopmental profile. Our data confirm previous studies highlighting that children with a future regression have more favorable premorbid motor development and lower prematurity rates ( 34 ) a finding supported by retrospective video analyses ( 35 ). This hypothesis is further supported by our findings of earlier concern in parents of reg ASD, around 20 months, which typically coincides with the onset of regression. We hypothesize that in non- reg ASD, early and subtle neurodevelopmental abnormalities are less alarming to parents due to their gradual onset, whereas in reg ASD, there is an abrupt disruption in the developmental trajectory that causes greater concern. However, we have not been able to distinguish which children will become reg ASD. Some studies have supported that an immunoinflammatory event, such as familial autoimmune disease (e.g., thyroid disease, type 1 diabetes) or febrile episodes within the previous six months, may act as a trigger for regression ( 16 , 17 ). Thus, it is possible that our inability to predict the onset of regression is due to the absence of key variables in our clinical interview. Another hypothesis is that the trigger for regression may be due to a subclinical or biological event and therefore not observable by parents. Finally, the non- reg ASD group may be more heterogeneous than the reg ASD group, leading to significant noise in the data and masking key variables. Prospective studies with early neuropsychological assessment and identification of regression triggers as well as further characterization of the pre-morbid profiles of children with reg ASD are needed. Second, we showed that reg ASD has a more severe and global clinical profile after regression. While there is some consensus about more severe autistic symptoms in reg ASD( 36 ), the long-term clinical significance has been questioned ( 37 , 38 ). Some studies have reported only minor neurodevelopmental differences in communication skills despite equivalent age, raising doubts about the functional impact of regression ( 39 , 40 ). Our results support a severe global and functional impact of regression in reg ASD. Third, we found that reg ASD did not share the same genomic background as non- reg ASD, suggesting different molecular mechanisms involved in the disruption of brain development. Consistent with epidemiologic data between autoimmune disease and infections just before regression, we found that inflammation and oxidative stress pathways were more frequently affected in reg ASD. These data are supported by previous findings ( 41 ) and argue for further immunologic investigation of these patients, whose symptoms may be immune-mediated, as suggested by their good response to corticosteroid therapy ( 42 ). Our findings have several limitations. First, the prevalence of developmental regression in our sample was 17%, in contrast to the 32% reported in the literature. To limit bias, we used the ADI-R to assess regression (items 4 and 11), which requires prior acquisition of the affected skill and its use for a minimum of three months before its loss is considered regression. In fact, criteria for identifying regression vary across studies, and the DSM-5 does not provide consensus on quantitative thresholds ( 18 , 43 ) (e.g., whether skills were previously acquired, the duration of regression, the number and type of skills lost, and the age of onset). As such, observations of developmental regression are predominantly qualitative and retrospective, often relying on retrospective parental reports ( 44 ). As a result, more recent studies using more precise criteria and larger samples also report a prevalence of around 15% ( 45 ). New scales include additional questions and provide a more comprehensive assessment of regression, including loss of motor and social interaction skills ( 46 ). These tools also integrate an etiologic dimension by assessing the presence of infectious episodes in the months preceding regression. The refinement of standardized tools is essential to improve the definition and understanding of developmental regression, thereby helping to bridge the existing knowledge gap regarding its manifestations. Our findings also show a comparable frequency of pathogenic or likely pathogenic genomic anomalies between patients with and without regression, suggesting that regression is not systematically associated with a higher detection rate of anomalies via chromosomal microarray. Similarly, the proportion of variants of uncertain significance (VUS) did not differ significantly between the two groups. However, the anomalies identified in the regression group affect genes strongly associated with major neurodevelopmental disorders, such as NRXN1 and SHANK3 , highlighting the value of targeted genetic analyses in this population. The lack of statistically significant differences in the overall frequency of abnormalities between groups underscores the limitations of microarrays in detecting specific alterations potentially involved in regression and supports the use of higher resolution approaches such as whole genome sequencing ( 47 ). Finally, the broad range of genomic regions and genes involved further confirms the etiological heterogeneity of neurodevelopmental disorders and underscores the importance of integrating genomic data into the clinical context. Collectively, our findings suggest that regression represents a pivotal moment in the inflection of the neurodevelopmental curve of reg ASD. This underscores the critical need to better understand the etiopathogenic mechanisms underlying regression in order to improve intervention and care strategies. Declarations Ethics and Consent to Participate This study was approved by the local ethics committee (2021-27 N° IDRCB: 2021-A00489-32). Informed consent was obtained from a parent and/or legal guardian prior to enrollment. Consent for Publication All authors have reviewed the manuscript and agree to its publication Competing Interests The authors report there are no competing interests to declare. Author Contributions A.M., P.E. and R.D. conceived the study. 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Use of home videotapes to confirm parental reports of regression in autism. J Autism Dev Disord juill. 2008;38(6):1136–46. Goldberg WA, Osann K, Filipek PA, Laulhere T, Jarvis K, Modahl C, et al. Language and other regression: assessment and timing. J Autism Dev Disord déc. 2003;33(6):607–16. Hu C, Yang F, Yang T, Chen J, Dai Y, Jia F, et al. A Multi-Center Study on the Relationship Between Developmental Regression and Disease Severity in Children With Autism Spectrum Disorders. Front Psychiatry 1 janv. 2022;13:796554. Kim SH, Lord C. New autism diagnostic interview-revised algorithms for toddlers and young preschoolers from 12 to 47 months of age. J Autism Dev Disord janv. 2012;42(1):82–93. Furley K, Hunter MF, Fahey M, Williams K. Diagnostic findings and yield of investigations for children with developmental regression. Am J Med Genet août. 2024;194(8):e63607. Additional Declarations No competing interests reported. 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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-6801394\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":472115383,\"identity\":\"12f74089-ba5f-4b34-b82e-672fdca47180\",\"order_by\":0,\"name\":\"Anna Maruani\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"IHU Institut Robert Debré du cerveau de l’enfant, Université Paris- Cité\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Anna\",\"middleName\":\"\",\"lastName\":\"Maruani\",\"suffix\":\"\"},{\"id\":472115384,\"identity\":\"7d6d7ce4-3b98-47db-8f0a-df432d8c61fb\",\"order_by\":1,\"name\":\"Emma Delclaud\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"IHU Institut Robert Debré du cerveau de l’enfant, Université Paris- Cité\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Emma\",\"middleName\":\"\",\"lastName\":\"Delclaud\",\"suffix\":\"\"},{\"id\":472115385,\"identity\":\"aa35f1ce-2a71-4e51-af49-784e94bf78e6\",\"order_by\":2,\"name\":\"Paul Bruzeau\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"AP-HP, Robert-Debré University Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Paul\",\"middleName\":\"\",\"lastName\":\"Bruzeau\",\"suffix\":\"\"},{\"id\":472115386,\"identity\":\"b7023bd9-9c41-4651-a027-b065ccb848b6\",\"order_by\":3,\"name\":\"Richard Delorme\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"IHU Institut Robert Debré du cerveau de l’enfant, Université Paris- Cité\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Richard\",\"middleName\":\"\",\"lastName\":\"Delorme\",\"suffix\":\"\"},{\"id\":472115387,\"identity\":\"d2155547-d0cc-4fa9-a2fc-2c3b815d4364\",\"order_by\":4,\"name\":\"Anne-Claude Tabet\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Assistance publique-Hopitaux de Paris (AP-HP), Hopital Robert-Debré\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Anne-Claude\",\"middleName\":\"\",\"lastName\":\"Tabet\",\"suffix\":\"\"},{\"id\":472115388,\"identity\":\"7d8cad3d-4628-4bae-aa26-c6a8a99ef4b2\",\"order_by\":5,\"name\":\"Valérie Vantalon\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"IHU Institut Robert Debré du cerveau de l’enfant, Université Paris- Cité\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Valérie\",\"middleName\":\"\",\"lastName\":\"Vantalon\",\"suffix\":\"\"},{\"id\":472115389,\"identity\":\"307fe5e0-d298-4f45-b1a0-bf2d88cde5b8\",\"order_by\":6,\"name\":\"Jonathan Levy\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Assistance publique-Hopitaux de Paris (AP-HP), Hopital Robert-Debré\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jonathan\",\"middleName\":\"\",\"lastName\":\"Levy\",\"suffix\":\"\"},{\"id\":472115390,\"identity\":\"0ecbc19b-0a79-4de5-beb8-bc9980dd6d4f\",\"order_by\":7,\"name\":\"Pierre Ellul\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3PMQrCMBTG8RcepEuxaye9QqWDCgWv0uAqIrg4CKYUuulcb6GTuFUCdfEIDoLQuSCIk1hDBae0biL5T9/y4yUAOt0P1g/L0TCAJDmAD0DOSuKIclAE2MeSoKMmyQcRZi1ywPQ6nUKToukIb3YadQBprv4LHayOR3AlGabZpMcRY+UVNF0SRMAiSahg68QSKlEQ60qCB8wl6T5eBLGCmEgCDr58GIlqEeoSntrtCOl4v1hmbBdWEUtcCJ95LcsIN+f77cS2RqgmZfZ7JMXdOuCjgnwpdDqd7v97Av0nQkaz4qR6AAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"IHU Institut Robert Debré du cerveau de l’enfant, Université Paris- Cité\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Pierre\",\"middleName\":\"\",\"lastName\":\"Ellul\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-06-02 10:53:11\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6801394/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6801394/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":84874860,\"identity\":\"425cd5dd-9faa-4ee2-9357-d36e173cb73a\",\"added_by\":\"auto\",\"created_at\":\"2025-06-18 09:35:32\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":91972,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003ePredictive analysis of developmental regression in children with autism spectrum disorder. \\u003c/strong\\u003e(A) Classification tree generated from routine clinical variables to identify children who will develop regressive ASD (\\u003csub\\u003ereg\\u003c/sub\\u003eASD). (B) Sensitivity analysis of the classification tree (C) Principal component analysis (PCA) plot discriminating \\u003csub\\u003ereg\\u003c/sub\\u003eASD from non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD. (D) Correlation circle from the PCA.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6801394/v1/2be49f61e1fab839424869d6.jpg\"},{\"id\":84873144,\"identity\":\"c75643a2-1dc8-421f-a042-b3c1242ce818\",\"added_by\":\"auto\",\"created_at\":\"2025-06-18 09:19:32\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":103052,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDistinct Genetic Deletion Profiles and Enriched Molecular Pathways in \\u003c/strong\\u003e\\u003csub\\u003e\\u003cstrong\\u003ereg\\u003c/strong\\u003e\\u003c/sub\\u003e\\u003cstrong\\u003eASD\\u003c/strong\\u003e (A) Overview of chromosomal microarray analysis (CMA) results in children with regressive ASD (\\u003csub\\u003ereg\\u003c/sub\\u003eASD, n=68) and non-regressive ASD (non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD, n=409), showing the distribution of pathogenic, likely pathogenic, and variants of uncertain significance (VUS) in each group.\\u003cbr\\u003e\\n(B) Genomic locations of identified deletions and duplications in \\u003csub\\u003ereg\\u003c/sub\\u003eASD and non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD\\u003cbr\\u003e\\n(C) Venn diagram illustrating minimal overlap of deleted genes between \\u003csub\\u003ereg\\u003c/sub\\u003eASD and non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD groups (D) Gene ontology enrichment analysis of deleted genes in \\u003csub\\u003ereg\\u003c/sub\\u003eASD, (E) Gene ontology enrichment in non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6801394/v1/7ad7d90436e30aa553b20d4d.jpg\"},{\"id\":87061283,\"identity\":\"933d6a25-1228-4b1c-af5b-0c66a2b9ee48\",\"added_by\":\"auto\",\"created_at\":\"2025-07-18 17:01:38\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1165111,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6801394/v1/cf5e4f2f-be26-483c-a703-731fc343005b.pdf\"},{\"id\":84874491,\"identity\":\"87a94731-8955-484e-a17f-88ae1d195586\",\"added_by\":\"auto\",\"created_at\":\"2025-06-18 09:27:32\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":13880,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SuppTable1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6801394/v1/5def519cadfcaf31226418c5.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Clinical and genetic factors associated with regression in children with autism spectrum disorders\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eAutism spectrum disorder (ASD) represents a heterogeneous neurodevelopmental condition characterized by difficulties in social communication and interaction as well as restricted and repetitive behaviors/interests (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). ASD affects approximately 1\\u0026ndash;2% of children in the general population (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). Its exact pathophysiology remains unknown and is based on a complex interplay between genetic and environmental factors (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe inherent heterogeneity of ASD makes it difficult to identify precise pathophysiological pathways. Recent studies have focused on homogeneous subgroups of children within the autistic spectrum based on shared genetic variant (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e), a particular environmental risk factor (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e), or a specific clinical presentation. Among the clinical presentations of interest, some children show an acute loss of previously acquired skills (typically language and/or social skills), defined here as regressive ASD (\\u003csub\\u003ereg\\u003c/sub\\u003eASD). \\u003csub\\u003ereg\\u003c/sub\\u003eASD usually occurs before the age of 3 years, typically around 20 months (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e) and is estimated to occur in 30% of patients with ASD (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe exact mechanisms underlying regression are not yet understood, but some risk factors have been identified. For example, genetic syndromes associated with ASD, such as Rett syndrome and Phelan Mc Dermid syndrome, have higher rates of regression, and variants in genes involved in synapse regulation are preferentially found in \\u003csub\\u003ereg\\u003c/sub\\u003eASD (\\u003cspan additionalcitationids=\\\"CR13 CR14\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). Environmental factors suggest a higher prevalence of familial autoimmune disorders (e.g., thyroid disease, type 1 diabetes) and personal history of infectious episodes within six months prior to \\u003csub\\u003ereg\\u003c/sub\\u003eASD, supporting the implication of immuno-inflammatory mechanisms in the regression (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe clinical presentation of these patients after regression remains poorly defined. It has been proposed that many children exhibit subtle developmental differences prior to the onset of \\u003csub\\u003ereg\\u003c/sub\\u003eASD but results are inconsistent. (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). Some authors find that children with \\u003csub\\u003ereg\\u003c/sub\\u003eASD have a more severe autistic phenotype, whereas others find no difference compared to non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e).These clinical observations have led to the emergence of two major hypotheses regarding \\u003csub\\u003ereg\\u003c/sub\\u003eASD: (i) \\u003csub\\u003ereg\\u003c/sub\\u003eASD is a distinct clinical subgroup within ASD or (ii) \\u003csub\\u003ereg\\u003c/sub\\u003eASD is a subclinical continuum that cannot be isolated from autism.\\u003c/p\\u003e \\u003cp\\u003eTo address this question, we studied a cohort of children with ASD to retrospectively determine the clinical presentation of the children before the onset of regression and to prospectively assess the severity of ASD symptoms in \\u003csub\\u003ereg\\u003c/sub\\u003eASD compared to non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD. We then performed a molecular study to determine whether specific genetic variations are observed in \\u003csub\\u003ereg\\u003c/sub\\u003eASD. We aim to identify predictive developmental markers and biological pathways associated with regression.\\u003c/p\\u003e\"},{\"header\":\"Method\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePopulation\\u003c/h2\\u003e \\u003cp\\u003eWe retrospectively collected data from children with ASD enrolled in the EXPECT study conducted by the Center of Excellence for Autism and Neurodevelopmental Disorders (InovAND-Robert Debr\\u0026eacute; Hospital, Paris, France) between March 2017 and June 2023. This study was approved by the local ethics committee (2021-27 N\\u0026deg; IDRCB: 2021-A00489-32). Informed consent was obtained from a parent and/or legal guardian prior to enrollment. The study was conducted in accordance with relevant guidelines/regulations and was conducted in accordance with the Declaration of Helsinki.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eASD diagnosis and clinical characteristics\\u003c/h3\\u003e\\n\\u003cp\\u003eDiagnosis was based on clinical evaluation by experienced clinicians, according to DSM-5 criteria (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e) by summarizing the information from the Autism Diagnostic Interview-Revised (ADI-R) (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e), the Autism Diagnostic Observation Schedule \\u0026minus;\\u0026thinsp;2nd edition (ADOS-2) (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e). The ADI-R is a parent interview for the diagnosis of autism that can be administered starting at 24 months of age. It reviews the child's developmental history and examines communication, social development and play, restricted interests and behaviors, general behaviors, and age of onset of difficulties. \\u003csub\\u003ereg\\u003c/sub\\u003eASD was determined using q11 of the ADI-R, which requires acquisition and use of skills for at least 3 months prior to loss to be classified as regression.\\u003c/p\\u003e \\u003cp\\u003eAll participants were screened by an expert child and adolescent psychiatrist with a semi-structured parental interview for pre- and perinatal history. Intellectual functioning was assessed with age-appropriate instruments such as the WISC-IV and V (\\u003cspan additionalcitationids=\\\"CR27\\\" citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e), Mullen Scales of Early Learning (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e), and Raven\\u0026rsquo;s Progressive Matrices (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e). We also used the second edition of the Vineland Adaptive Behavior Scales (VABS) a benchmark heterogeneous questionnaire for assessing children's levels of autonomy and adjustment. It measures adaptive behavior in three main areas: communication, daily living skills, and socialization (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003eChromosomal Microarray Analysis (CMA)\\u003c/h3\\u003e\\n\\u003cp\\u003eAll patients underwent chromosomal microarray analysis. Genomic DNA was extracted from peripheral blood lymphocytes using standard protocols. Copy number variation (CNV) screening was performed using the Illumina OmniExpress SNP array (Illumina Inc., San Diego, CA), which contains over 700,000 markers with an average resolution of approximately 30 kb. Genotyping was performed using the Infinium assay, and data were analyzed using Illumina GenomeStudio software with the CNV partitioning algorithm.\\u003c/p\\u003e \\u003cp\\u003eCopy number profiles were evaluated by analyzing the Log R Ratio\\u0026mdash;defined as the natural logarithm of the sample copy number relative to a reference\\u0026mdash;and the B Allele Frequency (BAF), allowing for the detection of both deletions and duplications, as well as regions of homozygosity or mosaicism. When a variant of uncertain significance (VUS), likely pathogenic or pathogenic CNV was identified, segregation analysis was performed in the parents using SNP array.\\u003c/p\\u003e \\u003cp\\u003e For gene ontology enrichment analysis, we used the online tools provided by geneontology.org.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eBefore each analysis, we assessed the normality of the distributions of the variables using the Shapiro-Wilk test. For continuous data, depending on its normality, we used either Student's t-tests or Wilcoxon tests. For categorical data, we used the Fisher's test. To infer the presence or absence of regression based on multiple collected input variables, we used classification decision trees, which are supervised predictive machine learning models. Classification decision trees provide a visual and intuitive approach to data analysis, allow for easy interpretation of complex data sets by segmenting the data into branches based on hierarchical decision rules, and facilitate the handling of both categorical and continuous variables without requiring extensive data preprocessing or assumptions about data distributions. Classification trees were generated using the R package 'Party'(\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e). We then used Factoshiny to generate a primary component analysis with a correlation circle. We did not apply multiple testing corrections for p-values because many of our models, especially PCA and classification decision tree, did not produce p-value outputs. Statistical analysis was performed using R studio version 4.2.1.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eEthical\\u003c/h3\\u003e\\n\\u003cp\\u003e As part of the EXPECT study, this project was approved by the local ethics committee of the Robert Debr\\u0026eacute; Hospital (2021-27 N\\u0026deg; IDRCB: 2021-A00489-32). Informed consent was obtained from patients or a parent and/or legal guardian prior to enrollment. All procedures in both studies were performed in accordance with relevant guidelines/regulations and were conducted in accordance with the Declaration of Helsinki.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eChildren with \\u003csub\\u003ereg\\u003c/sub\\u003eASD have a better clinical pre-morbid phenotype\\u003c/h2\\u003e \\u003cp\\u003eA total of 505 patients were enrolled in the study, of which 74 (15%) showed developmental regression \\u003cb\\u003e(\\u003c/b\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e \\u003cb\\u003eand Supp\\u003c/b\\u003e Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\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\\u003e\\u003cb\\u003eClinical characteristics of the study population\\u003c/b\\u003e: Non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD (ASD without regression); \\u003csub\\u003ereg\\u003c/sub\\u003eASD (ASD with regression); AID: autoimmune disease\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMean (SD)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNon-\\u003csub\\u003ereg\\u003c/sub\\u003eASD (n\\u0026thinsp;=\\u0026thinsp;432)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003csub\\u003ereg\\u003c/sub\\u003eASD (n\\u0026thinsp;=\\u0026thinsp;74)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\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\\u003e\\u003cb\\u003eSex ratio\\u003c/b\\u003e (M/F)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e337/94\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e59/15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.885\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMaternal AID\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13 (3.02%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0(0.00%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.264\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePregnancy term (week)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38.53 (2.80)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e39.23 (1.41)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBirth weight (g)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3209.17 (712.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3450.66 (550.19)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBirth height (cm)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e49.12 (3.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49.98 (2.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.008\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBirth cranial perimeter (cm)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e34.96 (3.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e34.75 (1.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.440\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAPGAR (1 minute)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.40 (1.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.38 (1.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.907\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAPGAR (5 minutes)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.81 (0.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.89 (0.69)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.455\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFirst steps (months)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15.59 (7.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.99 (5.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.393\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFirst words (months)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23.52 (14.47)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18.75 (12.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.007\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eEarly parental concerns (months)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23.68 (15.43)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20.13 (10.61)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.022\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eWe found no difference in maternal history of autoimmune disease or complications during pregnancy between the children who will become \\u003csub\\u003ereg\\u003c/sub\\u003eASD and the non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD children (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e\\u003cb\\u003eand Supp\\u003c/b\\u003e Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Regarding birth parameters, children with future \\u003csub\\u003ereg\\u003c/sub\\u003eASD were born significantly less prematurely (p-value\\u0026thinsp;=\\u0026thinsp;0.001), had a better birth weight (p-value\\u0026thinsp;=\\u0026thinsp;0.001) and height (p-value\\u0026thinsp;=\\u0026thinsp;0.008). For learning milestones, children with future \\u003csub\\u003ereg\\u003c/sub\\u003eASD have their first words earlier (p\\u0026thinsp;=\\u0026thinsp;0.007), but paradoxically their parents expressed concern earlier than parents of non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD (p\\u0026thinsp;=\\u0026thinsp;0.022).\\u003c/p\\u003e \\u003cp\\u003eOur data support the hypothesis that children who will develop \\u003csub\\u003ereg\\u003c/sub\\u003eASD may have a better premorbid neurodevelopmental profile than non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD.\\u003c/p\\u003e \\u003cp\\u003e \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eRoutine clinical observations are not predictive of regression.\\u003c/span\\u003e \\u003c/p\\u003e \\u003cp\\u003eWe then investigated whether we could predict which children would develop \\u003csub\\u003ereg\\u003c/sub\\u003eASD from routine clinical data. Using a tree algorithm classification (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA), we failed to properly highlight children who will present \\u003csub\\u003ereg\\u003c/sub\\u003eASD with a sensitivity of 6% \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB\\u003cb\\u003e).\\u003c/b\\u003e Next, we used principal component analysis (PCA) and can discriminate children who will experience \\u003csub\\u003ereg\\u003c/sub\\u003eASD from non- \\u003csub\\u003ereg\\u003c/sub\\u003eASD with a first dimension explaining 30% of the variance and a second-dimension accounting for 16% of the variance (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC). However, using the correlation circle, none of the clinical data used allowed a clear explanation of this classification (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eD\\u003cb\\u003e).\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eWe confirmed that the children who will present a \\u003csub\\u003ereg\\u003c/sub\\u003eASD were different from non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD children but were not able to isolate the variables that explain such difference and therefore to highlight predictors of the onset of regression.\\u003c/p\\u003e \\u003cp\\u003e \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eChildren with\\u003c/span\\u003e \\u003csub\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003ereg\\u003c/span\\u003e\\u003c/sub\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eASD have a more severe neurodevelopmental profile.\\u003c/span\\u003e\\u003c/p\\u003e \\u003cp\\u003eNext, we investigated the clinical profile after regression of \\u003csub\\u003ereg\\u003c/sub\\u003eASD compared to non\\u003csub\\u003e\\u0026minus;\\u0026thinsp;reg\\u003c/sub\\u003eASD (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eInterestingly, using the D criteria of the ADI, we found, consistent with our previous findings of earlier concerns of parents of future \\u003csub\\u003ereg\\u003c/sub\\u003eASD children, that \\u003csub\\u003ereg\\u003c/sub\\u003eASD have more abnormalities before the 36 months than non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD (p-value\\u0026thinsp;=\\u0026thinsp;0.023).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eClinical profile after regression\\u003c/b\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMean (SD)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNon-\\u003csub\\u003ereg\\u003c/sub\\u003eASD (n\\u0026thinsp;=\\u0026thinsp;432)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003csub\\u003ereg\\u003c/sub\\u003eASD (n\\u0026thinsp;=\\u0026thinsp;74)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\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\\u003e\\u003cb\\u003eSex ratio\\u003c/b\\u003e (M/F)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e337/94\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e59/15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.885\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e (month)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e217.92 (64.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e212.33 (63.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.496\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAbnormality\\u0026thinsp;\\u0026lt;\\u0026thinsp;36 months\\u003c/b\\u003e (ADI)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.09 (1.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.60 (1.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.023\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eVerbal language\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e325 (75.41%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28 (37.84%)\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNon-verbal IQ\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e80.66 (25.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e62.60 (25.24)\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eVerbal IQ\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e86.81 (22.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e74.31 (15.59)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.007\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCommunication\\u003c/b\\u003e (VABS)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e56.75 (25.37)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e44.73 (25.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSocial interaction\\u003c/b\\u003e (ADI)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16.62 (7.35)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19.87 (6.49)\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSocial affect\\u003c/b\\u003e (ADOS)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12.77 (4.34)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.98 (4.60)\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSocialisation\\u003c/b\\u003e (VABS)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e55.35 (26.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e47.04 (27.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.050\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRestricted repetitive behaviors\\u003c/b\\u003e (ADOS)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.95 (2.14)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.97 (2.10)\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAdaptive behaviors\\u003c/b\\u003e (VABS)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e51.21 (23.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e40.56 (25.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.007\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eClinical global impression (CGI)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.61 (0.62)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.03 (0.55)\\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 \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGlobal functioning\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e42.21 (10.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e34.30 (9.12)\\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 \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eAfter regression, 37.84% of \\u003csub\\u003ereg\\u003c/sub\\u003eASD patients have functional language compared to 75.41% of non- \\u003csub\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003er\\u003c/span\\u003eeg\\u003c/sub\\u003eASD (p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and a lower nonverbal IQ (non- \\u003csub\\u003ereg\\u003c/sub\\u003eASD: 80.66 (+/- 25.29); \\u003csub\\u003ereg\\u003c/sub\\u003eASD: 62.60(+/-25.24); p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Among the verbal subjects, we also found a lower verbal IQ in \\u003csub\\u003e\\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003er\\u003c/span\\u003eeg\\u003c/sub\\u003eASD (non- \\u003csub\\u003ereg\\u003c/sub\\u003eASD: 88.81 (+/- 22.36); \\u003csub\\u003ereg\\u003c/sub\\u003eASD: 74.31(+/-15.59); p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). Overall, \\u003csub\\u003ereg\\u003c/sub\\u003eASD have more adaptive difficulties in communication (p-value\\u0026thinsp;=\\u0026thinsp;0.002). Regarding autistic core symptoms, \\u003csub\\u003ereg\\u003c/sub\\u003eASD have more difficulties in social interaction (p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), social affects (p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and a tendency towards adaptive socialization (p-value\\u0026thinsp;=\\u0026thinsp;0.05), but also more restricted and repetitive behaviors (p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Overall, children with \\u003csub\\u003ereg\\u003c/sub\\u003eASD have more difficulties in overall socio-adaptive behaviors (p-value\\u0026thinsp;=\\u0026thinsp;0.007) with worse global functioning (p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and more severe clinical global impression (p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, although children with \\u003csub\\u003ereg\\u003c/sub\\u003eASD have a less severe premorbid clinical profile, after regression they exhibit global and more severe neurodevelopmental features compared to non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD.\\u003c/p\\u003e \\u003cp\\u003e \\u003csub\\u003e \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003ereg\\u003c/span\\u003e \\u003c/sub\\u003e \\u003cspan type=\\\"Underline\\\" class=\\\"Underline\\\" name=\\\"Emphasis\\\"\\u003eASD is associated with a specific molecular background\\u003c/span\\u003e \\u003c/p\\u003e \\u003cp\\u003eAmong the 477 patients who underwent chromosomal microarray analysis, 68 showed with developmental regression and 409 did not \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e \\u003cb\\u003eand Supp\\u003c/b\\u003e Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). To gain insight into the molecular mechanisms underlying regression, we next focused on the different genetic variations identified in children with \\u003csub\\u003ereg\\u003c/sub\\u003eASD and non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD. We found pathogenic or likely pathogenic anomalies in 3 patients from the regression group (4.4%) and in 15 patients from the non-regression group (3.7%) (NS). Variants of uncertain significance (VUS) were found in 9.0% of the regression group and 11.0% of the non-regression group (NS).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn the regression group, the detected abnormalities included an interstitial deletion of 2p16.3 involving \\u003cem\\u003eNRXN1\\u003c/em\\u003e, a complete trisomy 21 corresponding to 21q11.2q22.3 duplication, and an interstitial deletion of 22q13.31q13.33 involving \\u003cem\\u003eSHANK3\\u003c/em\\u003e. In the non-regression group, eight deletions were observed, including regions 1q31.3, 3q29, 9p24.3p23, 13q13.3, 19q13.32, 22q13.3, 22q13.32q13.33, and 2p25.3, affecting genes such as \\u003cem\\u003eASPM, NBEA, DOCK8, CALM3, BRPF1\\u003c/em\\u003e, and \\u003cem\\u003eSHANK3\\u003c/em\\u003e. Five duplications were also found in this group, involving regions 4q21.23 (\\u003cem\\u003eWDFY3\\u003c/em\\u003e), 7q11.23 (\\u003cem\\u003eELN\\u003c/em\\u003e), 20q13.33, 22q13.33 (\\u003cem\\u003eSHANK3\\u003c/em\\u003e), and Yp11.31q12 in a 47, XYY context. Additionally, a triplication of the 15q11.2q13.2 region (including \\u003cem\\u003eUBE3A\\u003c/em\\u003e and \\u003cem\\u003eSNRPN\\u003c/em\\u003e) and a complete trisomy 21 were identified. Most anomalies involved copy number changes across various genomic regions, with a significant proportion being \\u003cem\\u003ede novo\\u003c/em\\u003e or of unknown inheritance.\\u003c/p\\u003e \\u003cp\\u003eTo assess whether the two groups share genetic mechanisms, we analyzed the genes affected by deletions. Of the 438 deleted genes identified, only 8 (1.8%) were shared between \\u003csub\\u003ereg\\u003c/sub\\u003eASD and non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD, representing 12.5% of deleted genes in \\u003csub\\u003ereg\\u003c/sub\\u003eASD and 2.1% in non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC). This low overlap suggests distinct molecular pathways. To confirm this hypothesis, we performed gene ontology enrichment analysis on the deleted region \\u003cb\\u003e(\\u003c/b\\u003eFig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD and \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE\\u003cb\\u003e)\\u003c/b\\u003e. In \\u003csub\\u003ereg\\u003c/sub\\u003eASD, deleted region were enriched in pathways related to oxidative stress (phosphorylation of PGC-1alpha; p38 MAPK/RAS/erk pathways), angiogenesis (VEGFA-VEGFR/VEGF pathways), inflammation and especially type I interferon (NOD 1 2signaling pathways) and synaptic development (neuroxin/neuroligin) suggesting the involvement of the immune system in their pathophysiology compared to non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD, where mainly post-synaptic excitatory region were involved.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eOur findings are consistent with the hypothesis that regASD is a distinct clinical subgroup within ASD for several reasons. First, we found that regression occurs in children with a better premorbid neurodevelopmental profile. Our data confirm previous studies highlighting that children with a future regression have more favorable premorbid motor development and lower prematurity rates (\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e) a finding supported by retrospective video analyses (\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e). This hypothesis is further supported by our findings of earlier concern in parents of \\u003csub\\u003ereg\\u003c/sub\\u003eASD, around 20 months, which typically coincides with the onset of regression. We hypothesize that in non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD, early and subtle neurodevelopmental abnormalities are less alarming to parents due to their gradual onset, whereas in \\u003csub\\u003ereg\\u003c/sub\\u003eASD, there is an abrupt disruption in the developmental trajectory that causes greater concern. However, we have not been able to distinguish which children will become \\u003csub\\u003ereg\\u003c/sub\\u003eASD. Some studies have supported that an immunoinflammatory event, such as familial autoimmune disease (e.g., thyroid disease, type 1 diabetes) or febrile episodes within the previous six months, may act as a trigger for regression (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e). Thus, it is possible that our inability to predict the onset of regression is due to the absence of key variables in our clinical interview. Another hypothesis is that the trigger for regression may be due to a subclinical or biological event and therefore not observable by parents. Finally, the non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD group may be more heterogeneous than the \\u003csub\\u003ereg\\u003c/sub\\u003eASD group, leading to significant noise in the data and masking key variables.\\u003c/p\\u003e \\u003cp\\u003eProspective studies with early neuropsychological assessment and identification of regression triggers as well as further characterization of the pre-morbid profiles of children with \\u003csub\\u003ereg\\u003c/sub\\u003eASD are needed.\\u003c/p\\u003e \\u003cp\\u003eSecond, we showed that \\u003csub\\u003ereg\\u003c/sub\\u003eASD has a more severe and global clinical profile after regression. While there is some consensus about more severe autistic symptoms in \\u003csub\\u003ereg\\u003c/sub\\u003eASD(\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e), the long-term clinical significance has been questioned (\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e). Some studies have reported only minor neurodevelopmental differences in communication skills despite equivalent age, raising doubts about the functional impact of regression (\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e). Our results support a severe global and functional impact of regression in \\u003csub\\u003ereg\\u003c/sub\\u003eASD.\\u003c/p\\u003e \\u003cp\\u003eThird, we found that \\u003csub\\u003ereg\\u003c/sub\\u003eASD did not share the same genomic background as non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD, suggesting different molecular mechanisms involved in the disruption of brain development. Consistent with epidemiologic data between autoimmune disease and infections just before regression, we found that inflammation and oxidative stress pathways were more frequently affected in \\u003csub\\u003ereg\\u003c/sub\\u003eASD. These data are supported by previous findings (\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e) and argue for further immunologic investigation of these patients, whose symptoms may be immune-mediated, as suggested by their good response to corticosteroid therapy (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eOur findings have several limitations. First, the prevalence of developmental regression in our sample was 17%, in contrast to the 32% reported in the literature. To limit bias, we used the ADI-R to assess regression (items 4 and 11), which requires prior acquisition of the affected skill and its use for a minimum of three months before its loss is considered regression. In fact, criteria for identifying regression vary across studies, and the DSM-5 does not provide consensus on quantitative thresholds (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e) (e.g., whether skills were previously acquired, the duration of regression, the number and type of skills lost, and the age of onset). As such, observations of developmental regression are predominantly qualitative and retrospective, often relying on retrospective parental reports (\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e). As a result, more recent studies using more precise criteria and larger samples also report a prevalence of around 15% (\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eNew scales include additional questions and provide a more comprehensive assessment of regression, including loss of motor and social interaction skills (\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e). These tools also integrate an etiologic dimension by assessing the presence of infectious episodes in the months preceding regression. The refinement of standardized tools is essential to improve the definition and understanding of developmental regression, thereby helping to bridge the existing knowledge gap regarding its manifestations.\\u003c/p\\u003e \\u003cp\\u003eOur findings also show a comparable frequency of pathogenic or likely pathogenic genomic anomalies between patients with and without regression, suggesting that regression is not systematically associated with a higher detection rate of anomalies via chromosomal microarray. Similarly, the proportion of variants of uncertain significance (VUS) did not differ significantly between the two groups. However, the anomalies identified in the regression group affect genes strongly associated with major neurodevelopmental disorders, such as \\u003cem\\u003eNRXN1\\u003c/em\\u003e and \\u003cem\\u003eSHANK3\\u003c/em\\u003e, highlighting the value of targeted genetic analyses in this population. The lack of statistically significant differences in the overall frequency of abnormalities between groups underscores the limitations of microarrays in detecting specific alterations potentially involved in regression and supports the use of higher resolution approaches such as whole genome sequencing (\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e). Finally, the broad range of genomic regions and genes involved further confirms the etiological heterogeneity of neurodevelopmental disorders and underscores the importance of integrating genomic data into the clinical context.\\u003c/p\\u003e \\u003cp\\u003eCollectively, our findings suggest that regression represents a pivotal moment in the inflection of the neurodevelopmental curve of \\u003csub\\u003ereg\\u003c/sub\\u003eASD. This underscores the critical need to better understand the etiopathogenic mechanisms underlying regression in order to improve intervention and care strategies.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eEthics and Consent to Participate\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was approved by the local ethics committee (2021-27 N\\u0026deg; IDRCB: 2021-A00489-32). Informed consent was obtained from a parent and/or legal guardian prior to enrollment.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cbr\\u003e\\u0026nbsp;Consent for Publication\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors have reviewed the manuscript and agree to its publication\\u003cbr\\u003e\\u0026nbsp;Competing Interests\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors report there are no competing interests to declare.\\u003cbr\\u003e\\u0026nbsp;Author Contributions\\u003c/p\\u003e\\n\\u003cp\\u003eA.M., P.E. and R.D. conceived the study. E.D. and V.V contributed to implementation of the research. P.E., E.D., J.L. and P.B. analysed the data. P.E. and A.M. supervised the project. A.M., P.E., R.D., AC.T., J.L. contributed to the interpretation of the results. All authors discussed the results and contributed to the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cbr\\u003e\\u0026nbsp;Funding\\u003c/p\\u003e\\n\\u003cp\\u003eThis work benefited from State aid managed by the Agence Nationale de la Recherche under the France 2030 programme, under the reference ANR-23-IAHU-0010.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cbr\\u003e\\u0026nbsp;Availability of Data and Materials\\u003c/p\\u003e\\n\\u003cp\\u003eData are available on request from corresponding author\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eLord C, Brugha TS, Charman T, Cusack J, Dumas G, Frazier T et al. Autism spectrum disorder. 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Front Psychiatry 1 janv. 2022;13:796554.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKim SH, Lord C. New autism diagnostic interview-revised algorithms for toddlers and young preschoolers from 12 to 47 months of age. J Autism Dev Disord janv. 2012;42(1):82\\u0026ndash;93.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFurley K, Hunter MF, Fahey M, Williams K. Diagnostic findings and yield of investigations for children with developmental regression. Am J Med Genet ao\\u0026ucirc;t. 2024;194(8):e63607.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\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\":\"info@researchsquare.com\",\"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\":\"Neurodevelopment, neuroimmunology, regression, child and adolescent psychiatry\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6801394/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6801394/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eAutism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition with complex genetic and environmental underpinnings. A clinically significant subset of children with ASD experience developmental regression (\\u003csub\\u003ereg\\u003c/sub\\u003eASD), characterized by the acute loss of previously acquired skills. The mechanisms, predictors, and molecular basis of \\u003csub\\u003ereg\\u003c/sub\\u003eASD remain poorly understood.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe retrospectively and prospectively analyzed a cohort of 505 children diagnosed with ASD at the Center of Excellence for Autism and Neurodevelopmental Disorders (Paris, France) between 2017 and 2023. Clinical, neurodevelopmental, and genetic data were collected, including detailed developmental histories, standardized diagnostic assessments (ADI-R, ADOS-2, VABS), and chromosomal microarray analysis (CMA). Statistical analyses included classification tree algorithms and principal component analysis to identify clinical predictors of regression, and gene ontology enrichment to explore molecular pathways.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eDevelopmental regression was detected in 74 children (15%). Prior to regression, \\u003csub\\u003ereg\\u003c/sub\\u003eASD children exhibited more favorable neurodevelopmental profiles, including lower rates of prematurity, higher birth weight and height, and earlier acquisition of first words, compared with non-regressive ASD (non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD) peers. However, after regression, \\u003csub\\u003ereg\\u003c/sub\\u003eASD children had significantly more severe neurodevelopmental impairments across cognitive, adaptive, and social domains (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Routine clinical variables did not reliably predict the onset of regression. CMA revealed that \\u003csub\\u003ereg\\u003c/sub\\u003eASD is associated with distinct genetic deletions enriched in immune, inflammatory (particularly type I interferon), oxidative stress, angiogenesis, and synaptic pathways, with minimal overlap with non-\\u003csub\\u003ereg\\u003c/sub\\u003eASD genetic profiles.\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eOur findings support the hypothesis that \\u003csub\\u003ereg\\u003c/sub\\u003eASD represents a distinct clinical and molecular subgroup within ASD. The unique molecular signatures identified in \\u003csub\\u003ereg\\u003c/sub\\u003eASD implicate immune and synaptic pathways, highlighting the need for targeted research and potential therapeutic interventions for this subgroup.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Clinical and genetic factors associated with regression in children with autism spectrum disorders\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-06-18 09:19:27\",\"doi\":\"10.21203/rs.3.rs-6801394/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"2422ff34-a567-436f-a5ba-1c7af7456d32\",\"owner\":[],\"postedDate\":\"June 18th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-07-18T16:53:31+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-06-18 09:19:27\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6801394\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6801394\",\"identity\":\"rs-6801394\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}