A clustering approach identifies an Autism Spectrum Disorder subtype more responsive to chronic oxytocin treatment

A clustering approach identifies an Autism Spectrum Disorder subtype more responsive to chronic oxytocin treatment | 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 Article A clustering approach identifies an Autism Spectrum Disorder subtype more responsive to chronic oxytocin treatment Keith Kendrick, Weihua Zhao, Jiao Le, Qi Liu, Siyu Zhu, Chunmei Lan, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3322690/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jul, 2024 Read the published version in Translational Psychiatry → Version 1 posted 11 You are reading this latest preprint version Abstract Over the last decade, a number of clinical trials have reported effects of chronic treatment with intranasal oxytocin on autistic symptoms but with inconsistent findings. Autism is a heterogeneous disorder and one factor which may influence treatment outcome is whether a subtype of individuals is more sensitive to oxytocin. In a recent cross-over trial on 41 young autistic children we reported that 44% showed a reliable improvement in clinical symptoms (Autism Diagnostic Observation Schedule, ADOS-2) after a 6-week intervention. In the current re-assessment of the data, we used an unsupervised data-driven cluster analysis approach to identify autism subtypes using 23 different demographic, social subtype, endocrine,eye-tracking and clinical symptom measures taken before treatment and this revealed an optimum of two different subtypes. We then assessed the proportion of identified responders to oxytocin and found that while 61.5% of one subtype included responders only 13.3% of the other did so. This oxytocin-sensitive subtype also showed overall significant post-treatment clinical and eye-tracking measure changes. The oxytocin-sensitive subtype was primarily characterized at baseline by lower initial clinical severity (ADOS-2) and greater interest in the eye-region of emotional faces. These features alone were nearly as efficient in identifying the two subtypes as all 23 baseline measures and this easy-to-conduct approach may help rapidly and objectively screen for oxytocin responders. Future clinical trials using oxytocin interventions may therefore achieve greater success by focusing on children with this specific autism subtype and help develop individualized oxytocin intervention. Health sciences/Diseases/Psychiatric disorders/Autism spectrum disorders Health sciences/Biomarkers/Prognostic markers Autistic subtype Oxytocin responder Clustering analysis Eye-tracking Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterized by two core symptoms including difficulties in social communication and interaction and restricted and repetitive behaviors (Diagnostic and Statistical Manual of Mental Disorders, DSM-V), with an estimated prevalence of around 2.76% 1 in the U.S. and around 1% in China 2 . Currently there is no approved pharmacological intervention for the core symptoms of ASD, especially in terms of social dysfunction 3 . While the hypothalamic neuropeptide oxytocin (OXT) has shown great translational promise in improving social function in animal models of autism 4 and from acute dosing in both typically developing and autistic individuals 5,6 , findings from a large number of clinical trials over the past decade using chronic intranasal treatment protocols have been inconsistent. Thus, while positive effects of repeated daily doses of intranasal OXT have been observed either on social responsivity 7–13 and/or on repetitive or adaptive behaviors 9,14,15 , other studies found no significant overall effects of OXT 16–20 . A number of factors may contribute to inconsistent findings in clinical trials using chronic intranasal OXT. To date there is some evidence for dose-magnitude 12,15 and dose-frequency dependency and treatment duration 9 . In terms of demographic and physiological measures one study on children reported stronger effects in younger children 20 and others have reported associations between improvement in the social responsivity scale and basal and/or treatment dependent changes in plasma OXT concentrations 9,10 . Treatment context may also play a role with several trials reporting positive effects when OXT is given prior to positive social interactions 9 or psychosocial training 8 . However, no study has adopted a data driven approach to assess whether there may be an autism sub-type where individuals are more likely to be responsive to chronic OXT treatment on the basis of multiple clinical, physiological and behavioral measures taken prior to treatment. Recently, a number of studies have used phenotypic/genetic- 21,22 , biological- 23,24 , neuronal- 25 and behavior assessments 26 to determine ASD subtypes to aid earlier and more accurate detection as well as inform treatment/intervention selection to provide more evidence for individualized precision medicine 27 . To date these different approaches come up with a variable number of subtypes and tend to only include one, or a few measures. A subtype approach using multiple measures and data-driven clustering analysis may offer a more robust approach to help both identify autism subtypes 28,29 and hopefully also provide a method to help predict which individuals may respond best to specific types of treatment. For example, a recent study used this approach to determine subgroups with migraine without aura who were more responsive to electroacupuncture treatment 30 . In the current study we therefore firstly aimed to classify autism subtypes using unsupervised data-driven clustering analysis of multiple, clinical, physiological, behavioral and eye-tracking measures and secondly to investigate whether individuals identified as exhibiting reliable reductions in symptom severity in our recent intranasal OXT clinical trial 9 were more likely to be from a specific subtype. 2. Methods 2.1 Participants A final sample of 41 children with ASD (mean age ± SD = 5.0 ± 1.3, 3 girls) before and after a course of 6-week OXT intranasal trial with 24IU, every other day, see Fig. 1 ) data from the published paper 9 have been re-analyzed to determine whether distinct subtypes could be identified and they were differentially responsive to the 6-week intranasal OXT intervention. The study used a computer randomized, double-blind, placebo-controlled, cross-over design but we only focused on the data before and after receiving 6-week intranasal OXT (24IU, every other day, Sichuan Meike Pharmaceutical Co, Sichuan, China). This study was conducted in the Chengdu Maternal and Children’s Central hospital (CMCCH) between June 2019 and July 2021 in accordance with the latest Declaration of Helsinki and has been approved by the Ethics Committee of CMCCH affiliated to the University of Electronic Science and Technology of China (number 201983) as well as the general ethics committee of the University (number 1420190601). The trial was pre-registered (Chinese Clinical Trial Registry: ChiCTR1900023774). All written informed consent was provided by their parents or legal guardians. Inclusion criteria were as follows: ( 1 ) age range 3–8 years; ( 2 ) diagnosed with ASD according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and Autism Diagnostic Observation Schedule-2 (ADOS-2), with scores of 5–10 on the ADOS-2 Comparison scale (i.e. moderate to severe autism) 31 ; ( 3 ) free of any chromosomal abnormalities or neurological diseases (e.g., epilepsy, Rett syndrome), or some other psychiatric disorders; ( 4 ) without any severe respiratory, hearing or visual impairments. 2.2 Assessment and questionnaires During the trial, severity of autistic symptoms was assessed using the gold-standard objective assessment (ADOS-2 Comparison, total scores and subscale scores including social affect-SA and restrictive and repetitive behavior -RRB) and the caregiver-based social responsivity scale-2 (SRS-2 total score). These were the primary outcome measures. Additionally, a wide range of secondary questionnaire-based measures were taken before and after treatment, including the adaptive behavior assessment system-II, global adaptive composite (ABAS-II GAC), social communication quotient (SCQ), repetitive behavior scale - revised (RBS-R), caregiver strain questionnaire (CSQ) and the Beijing Autism Subtype questionnaire (BASQ) was also given prior to treatment. Biological sampling including blood (6 ml – collected into EDTA tubes) and saliva samples (1 ml using salivettes) collected from each participant on the first day of the measurement of basal OXT concentrations. Saliva samples were also collected again at the end of treatment. Full details are given in Le et al 2022 9 . 2.3 Eye-tracking tasks All children were instructed to watch passively two eye-tracking tasks including a dynamic social interest task and a static face emotion expression where no responses were required. The social interest task displayed pairs of dynamic dancing Chinese human versus dynamic geometric patterns simultaneously over 40 seconds (20 x 2 s videos shown in two separate clips). The face emotion task consisted of 4 children’s faces and 2 adult faces expressing four different emotions (neutral, happy, sad and fear) in both genders. Following a 500ms interval, each face was randomly presented for 2s. In the social interest task, the primary measure was the percentage (%) of total time spent viewing the dynamic social stimuli relative to the dynamic geometric stimuli. In the face emotion task, the primary measures were the % time spent viewing the eye, nose and mouth regions. Full details are given in Le et al 2022 9 . 2.4 Data analyses 2.4.1 Characterizing autism subtypes based on basal individual clinical severity and eye-tracking performance A data-driven k-means clustering algorithm with Manhattan (L1) distance 32 was used to determine autism subtypes. Specifically, demographic information (age, gender), clinical variables (ADOS-2 comparison scores, ADOS-2 SA and RRB), assessment questionnaires (ABAS-II GAC, SCQ, RBS-R, CSQ, BASQ-aloof, passive, active but odd), eye-tracking performance (task 1: percentage of percentage (%) of total duration viewing social stimuli; task 2: percentage of percentage (%) of total duration viewing on eyes and nose for each emotion were included) and endocrine data (basal saliva OXT and basal plasma OXT concentrations) of each of the 41 children with autism were defined as the clustering features (in total 23 features). We varied the number of clusters (subtypes) from 2 to 10 and repeated the clustering algorithm 20 times with different random initial cluster centroids to help find a lower, local minimum total sum of distances. The optimal number of subtypes was determined by the variance ratio score (VRS), which is calculated as the ratio of the between-cluster variance to the within-cluster variance 30 where a higher VRS indicates better clustering performance. In addition, adjusted cosine similarity can remedy the potential drawback of two vectors with very different attribute values and can check whether there is a high or low similarity between clusters or subtypes 33 . This was calculated for each individual across the determined autism subtypes to validate the k-means clustering results. 2.4.2 Identifying OXT responders across autism subtypes Additionally, changes in ADOS-2 total scores between pre- and post OXT treatment were evaluated in individual participants using the Reliable Change Index 34 as a means of assessing the clinical significance (i.e. improve, no-change and deteriorate) of the 6-week OXT intervention. Individuals demonstrating a significant “improvement” using RCI criteria were considered as OXT+ (RCI-based OXT responders, n = 18) and those classified as exhibiting “no change” were defined as OXT- (RCI-based OXT non-responders, n = 23, response rate = 43.9%, Fig. 3A 9 ). There were no individuals classified by the RCI analysis as showing a deterioration. To identify OXT responders across subtypes based on clustering analyses, the responder rate was measured as a ratio between the number of responders and the total number of participants. The responder rate of different subtypes was compared with the null distributions of the permutation responder rate (10000 permutations). In addition, the difference in the responder rate across ASD subtypes was compared using a chi-square test. Given a lower frequency of improvement on the less objective caregiver completed SRS-2 following the 6-week OXT based on the RCI (27%) 9 , we did not include SRS-2 scores in the further analyses. 2.4.3 Evaluation of clustering-based subtypes on responses to OXT treatment In accordance with the clustering analysis, each individual with autism could be assigned to either subtype 1 or subtype 2 and paired t tests with p values FDR corrected were performed to evaluate differences between the two subtypes following the 6-week OXT treatment (i.e. pre- vs. post-treatment) on all primary and secondary outcome measures taken. 3. Results 3.1 Two autism subtypes identified by clustering analysis As shown in the Fig. 2 A, the VRS showed that the maximum value or optimal clustering performance was with two subtypes, and it monotonically decreased as the number of subtypes increased. We also calculated the inter-subject adjusted cosine similarity 33 across all clustering features, and the result revealed a high degree of similarity within each subtype (Fig. 2 B). Paired t-tests showed that a higher similarity within subtype (mean ± SD = 0.106 ± 0.095) than that between subtypes (mean ± SD= -0.150 ± 0.094) was observed (t = 9.30, 95% CI= [0.200, 0.312]). No differences across the two subtypes were observed in terms of age, gender ratio and oxytocin plasma or saliva concentrations (ps > 0.226). Importantly, the OXT responder rates for the two subtypes were 13.3% for subtype 1 (n = 15, 2 girls) and 61.5% (subtype 2, n = 26, 1 girl, Fig. 3 B -top ) and significantly lower (Fig. 2 C) or higher (Fig. 2 D) than the permutation responder rate, respectively. Chi-square test suggested that a significantly greater frequency of OXT responders were found in subtype 2 (chi-square = 32.01, p < 0.001). Analysis using t-tests with FDR correction showed that individuals in subtype 2 (higher OXT responder rate subtype) had significantly different scores on 11 different measures including greater interest in the eyes and reduced interest in the nose while viewing face emotions and less clinical severity (ADOS-2 SA subscale, SCQ and aloof social sub-scale scores) as compared with subtype 1 (lower OXT responder rate subtype, see Fig. 2 E). Additionally, the results of ablation analyses, where each single measure was removed systematically from the analysis, revealed that the performance of face eye-tracking performance (angry eyes, angry nose, fear eyes, neutral eyes and neutral nose) and ADOS-2 assessment (SA and RRB subscale scores) contribute more to subtype clustering since if any of them were removed, the maximum OXT responder rate was reduced (> 4%, details see Table 1 ). Notably, the OXT responder rates for the two subtypes were 18.8% (subtype 1, n = 16) and 60% (subtype 2, n = 25) if only these seven ablation-based outcomes involved in ADOS-2 and eye tracking for emotional faces were included as features (Fig. 3 B -bottom ). Table 1 Ablation protocol applied to clustering analysis Excluded Feature Max responder rate Δ responder rate Excluded Feature Max responder rate Δ responder rate None 61.5% - Without clustering 43.9% 17.6% Gender 61.5% 0% Basal saliva OXT 61.5% 0% Age 61.5% 0% Basal plasma OXT 61.5% 0% ADOS-CP 61.5% 0% Social 61.5% 0% ADOS-SA 55.6% -5.9% Angry eyes 57.1% -4.4% ADOS-RRB 55.6% -5.9% Angry nose 50.0% -11.5% ABAS-GAC 61.5% 0% Fear eyes 55.6% -5.9% SCQ 61.5% 0% Fear nose 61.5% 0% CSQ 61.5% 0% Happy eyes 61.5% 0% RBS 61.5% 0% Happy nose 61.5% 0% Aloof 61.5% 0% Neutral eyes 50.0% -11.5% Passive 61.5% 0% Neutral nose 50.0% -11.5% Active but odd 61.5% 0% Note: all 23 features were used for clustering analysis. None means that none of 23 features were excluded. ADOS-CP: Autism Diagnostic Observation Schedule-2- comparison score; SA: social affect; RRB: restrictive and repetitive behavior; ABAS-GAC: adaptive behavior assessment system-II, global adaptive composite; SCQ: Social communication quotient; CSQ: caregiver strain questionnaire; RBS: repetitive behavior scale - revised; Aloof/Passive/Active but odd: subscale of Beijing Autism Subtype Questionnaire; OXT: oxytocin. 3.2 Detailed evaluation of responses to OXT treatment in the two autism subtypes To confirm that individuals with autism subtype 2 (n = 26) were more sensitive to OXT, we compared all outcome measures before and after 6-week OXT treatment across two subtypes. Paired t-test with FDR correction suggested that OXT selectively improved individuals in subtype 2 in terms of autism severity (ADOS-2 total score, p = 0.009, comparison score, p = 0.009, SA, p = 0.017 and RRB, p = 0.017), adaptive behavior (ABAS-II GAC, p = 0.049 and RBS, p = 0.049) as well as increased the time spent on social stimuli (p = 0.049), angry eyes (p = 0.049) and neutral nose (p = 0.049) but not for subtype 1 following 6-weeks of OXT treatment (details see Table 2 , Figs. 3 C& 4 ). The results did not change across two autism subtypes (subtype 1, n = 16; subtype 2, n = 25) if only these seven measures were used for clustering (ps ≤ 0.05). Table 2 The comparisons between before and after 6-week OXT treatment in all outcomes. Subtype 1 (n = 15) Subtype 2 (n = 26) Variable T test (pre vs. post) P_FDR Variable T test (pre vs. post) P_FDR ADOS-total 1.964 0.198 ADOS-total 5.293 0.009 ** ADOS-CP 1.871 0.199 ADOS-CP 4.282 0.009 ** ADOS-SA 2.162 0.163 ADOS-SA 3.207 0.017 * ADOS-RRB -1.169 0.371 ADOS-RRB 3.202 0.017 * ABAS-GAC -2.23 0.261 ABAS-GAC -2.974 0.020 * SCQ -0.124 0.390 SCQ 1.497 0.185 CSQ 1.215 0.698 CSQ 1.858 0.116 RBS 1.416 0.698 RBS 2.396 0.049 * Social -0.923 0.452 Social -2.459 0.049 * Angry eyes -3.089 0.094 Angry eyes -2.361 0.049 * Angry nose 1.514 0.262 Angry nose -1.469 0.185 Fear eyes 2.474 0.115 Fear eyes 2.038 0.088 Fear nose 0.092 0.928 Fear nose -1.110 0.295 Happy eyes -2.633 0.115 Happy eyes -1.748 0.132 Happy nose 1.543 0.262 Happy nose -1.437 0.185 Neutral eyes -2.927 0.094 Neutral eyes -0.470 0.642 Neutral nose 1.332 0.315 Neutral nose -2.468 0.049 * ADOS-total: Autism Diagnostic Observation Schedule-2- total score; CP: comparison score; SA: social affect; RRB: restrictive and repetitive behavior; ABAS-GAC: adaptive behavior assessment system-II, global adaptive composite; SCQ: Social communication quotient; CSQ: caregiver strain questionnaire; RBS: repetitive behavior scale - revised; Aloof/Passive/Active but odd: subscale of Beijing Autism Subtype Questionnaire; OXT: oxytocin. * p < 0.5; ** p < 0.01; FDR-corrected. 4. Discussion In the current study, we used data from our recent intranasal OXT intervention clinical trial 9 to identify two distinct autism subtypes by employing unsupervised data-driven clustering analysis of 23 different baseline measures. The proportion of individuals showing reliable improvements in clinical symptoms (ADOS-2 total score) after the 6-week OXT treatment was considerably greater in one subtype (61.5%) than in the other one (13.3%). Notably, ablation analysis revealed that the minimum number of included features for the two autism subtypes could be reduced to two main objective assessments (i.e. ADOS-2 scores and eye-tracking analysis of time spent viewing features of emotional faces) with 60% of OXT responders still being identified in one of the subtypes. Moreover, the autism subtype with the greater proportion of OXT responders was characterized by exhibiting greater interest in viewing the eyes or nose region of faces with different emotional expressions and a lower severity of social symptoms at baseline relative to the other subtype. In addition, individuals with the autism subtype with a more OXT responders showed significant overall improvement in autistic symptoms, adaptive behaviors and time spent on viewing the eyes of faces. Taken together our findings suggest that there may be a subtype of young children with autism who are more likely to show reduced symptoms in response to chronic OXT treatment and provide a promising approach to helping identify individuals most likely to show beneficial effects of OXT-based interventions in future studies. However, further validation of this clustering-based subtype established by combined clinical assessment and eye-tracking is required. Previous research primarily based on studies using single-dose administration of OXT have repeatedly shown that responses are often modulated by both context and personal characteristics 35,36 although less has been established concerning chronic treatment outcome responses. For chronic intervention studies some have reported associations between social symptom improvements and OXT concentrations 9,10 or age 20 but interestingly in our current analysis neither of these appeared to be important for sub-typing or predicting whether individuals exhibited improved symptoms. OXT receptor genotype has also been considered to be a factor modulating responses to both acute 37 and chronic doses of OXT 9 but again in the current analysis neither of these appeared to contribute greatly to either autism subtype or OXT responders. One previous study has reported greater improvements in autistic symptoms and increases in plasma OXT following a combined electroacupuncture and behavioral intervention in the aloof and passive social subtypes 38 and there was some indication in our current analysis that individuals scoring high on the aloof dimension were actually less likely to respond to OXT. Interestingly, two main objective assessments of autistic symptoms (ADOS-2 and eye-tracking face task) could still reliably detect 60% OXT responder rate in subtype 2 based on ablation analyses, indicating these two measures played the primary role in distinguishing subtypes. ADOS-2 has long been considered as a gold standard measure of autistic symptom severity with a high sensitivity and specificity 39 . In the current analysis subtype 1 individuals generally scored higher on both the social affect and repetitive and restrictive behavior scales that subtype 2 individuals suggesting that chronic OXT treatments are more likely to benefit individuals with lower initial overall symptom severity. However, scores on the social affect scale appear to be more informative in this respect suggesting that OXT may particularly benefit more individuals with less severe social symptoms. On the other hand, while eye tracking measures have revealed altered visual preferences in autism and have been proposed as an early biomarker of ASD 23,24,40,41 they have not been extensively included as treatment outcome measures. Of the two eye-tracking tasks included in our original study the one exhibiting the greatest utility for both subtyping and predicting OXT responders was the passive face emotion expression paradigm with individuals in the two subtypes typically exhibiting either very low interest in viewing the eye region of all face emotion types (subtype 1) or a greater interest in doing so (subtype 2). Thus, it would appear that individuals who are more likely to respond to OXT generally exhibit at least some initial interest in looking at the eye region of faces even though this is still generally less than that shown by typically developing children. The other eye-tracking task we used visual preference for dynamic social (dancing children) compared with dynamic geometric patterns. Autistic children generally spend more time looking at the geometric patterns while TD children spend more time looking at the dynamic social stimuli and was originally developed by Pierce et al 41 . We had previously found that this task was the most reliable in discriminating TD from ASD in Chinese children 40 and preference for the social stimuli can be facilitated by acute as well as chronic OXT treatment 9,42 . It has been proposed that this task may identify a specific ASD “GeoPref” subtype (dynamic geometric images vs. social images of children interacting and moving) with higher symptom severity and reduced resting state functional connectivity 23,24 , however we did not find any differences in this task across the two autism subtypes identified using multiple measures in our current study and the ablation analysis did not show that it contributed to identifying OXT responders. Although neuroimaging data, such as resting state functional connectivity, has also been used to identify autism subtypes to facilitate diagnosis and prediction of response to treatment 43,44 , it is challenging to collect neuroimaging data in a large population of individuals with ASD, especially in young children and has a relatively high cost. Administering an MRI scan on such a young ASD child under natural sleep or following extensive training can be difficult and use of sedative drugs may influence blood oxygen level dependent signals. Given our findings that only two objective, and easily administered, assessments including ADOS-2 and eye-tracking for emotional face task are needed for potential screening of OXT responders it may not be necessary to try and utilize either neuroimaging or more extensive genotyping to do so, although that does not of course mean that these measures could be informative in the context of other kinds of interventions. Some limitations of the current study should be acknowledged: ( 1 ) the current sample size is comparatively small due to strict inclusion criteria and the long-term intervention, although the observed differences between effects of OXT on the two subtypes identified is highly significant. ( 2 ) External and independent validation data would be further needed to validate our findings although that is currently difficult given that both ADOS-2 and eye-tracking measures have to date not been used in other clinical trials. ( 3 ) Among overall post-treatment improvements, only SA and not RRB sub-scale scores of ADOS-2 were observed in subtype 2 following the 6-week oxytocin intervention. This suggests that OXT may mainly improve social interaction and communication skills and that subtypes of restrictive and repetitive behavior should be investigated in the future studies. In summary, our approach of performing an unsupervised data-driven cluster analysis of a population of autistic children showing variable responses to chronic OXT treatment has revealed that out of 23 baseline measures included two different objective assessments (ADOS-2 and eye-tracking) which contributed 7 different measures were effective in identifying two different subtypes with markedly different responses to OXT. These two assessments may therefore represent a potential screening tool to identify individuals most likely to show improved symptoms following a chronic OXT treatment intervention. Declarations Conflict of interests The authors declare no competing interests. Acknowledgments This work was supported by Natural Science Foundation of Sichuan Province [grant number 2022NSFSC1375 - WHZ], Fundamental Research Funds for the Central Universities, UESTC [grant number ZYGX2020J027 - WHZ], National Natural Science Foundation of China (NSFC) [grant number 31530032 – KMK; grant number 82301732- JL] and Key Scientific and Technological projects of Guangdong Province [grant number 2018B030335001 - KMK]. Author contributions Authors contributions are as follows: conceptualization: W.Z., K.M.K.; methodology: W.Z., J.L., Qi L., L.Z, B.B., K.M.K.; investigation: J.L., S.Z., C.L., Q.Z., Y.Z., J.K., Qin L.; visualization: W.Z., J.L., Qi L.; funding acquisition: W.Z., K.M.K.; project administration: W.Z., J.L., L.Z., K.M.K.; supervision: W.Z., K.M.K., L.Z.; writing – original draft: W.Z., K.M.K.; writing –review and editing: W.Z., J.L., Qi L., L.Z., J.K., Y.Z., Qin L., C.L., R.Z., W.Y., B.B., K.M.K. Data availability The MATLAB codes for the clustering analysis are available on github (https://github.com/zhaolab205/ASD_responder). Additional data related to this study can be provided upon reasonable request. References Maenner MJ, Warren Z, Williams AR. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. MMWR Surveill Summ 2023; 72 : 1–14. Sun X, Allison C, Wei L, Matthews FE, Auyeung B, Wu YY et al. Autism prevalence in China is comparable to Western prevalence. Molecular Autism 2019; 10 : 7. Lord C, Elsabbagh M, Baird G, Veenstra-Vanderweele J. Autism spectrum disorder. Lancet 2018; 392 : 508–520. Wagner S, Harony-Nicolas H. Oxytocin and Animal Models for Autism Spectrum Disorder. In: Hurlemann R, Grinevich V (eds). Behavioral Pharmacology of Neuropeptides: Oxytocin . Springer International Publishing: Cham, 2018, pp 213–237. 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Zhang R, Jia M-X, Zhang J-S, Xu X-J, Shou X-J, Zhang X-T et al. Transcutaneous electrical acupoint stimulation in children with autism and its impact on plasma levels of arginine-vasopressin and oxytocin: A prospective single-blinded controlled study. Research in Developmental Disabilities 2012; 33 : 1136–1146. Gray KM, Tonge BJ, Sweeney DJ. Using the Autism Diagnostic Interview-Revised and the Autism Diagnostic Observation Schedule with Young Children with Developmental Delay: Evaluating Diagnostic Validity. J Autism Dev Disord 2008; 38 : 657–667. Kou J, Le J, Fu M, Lan C, Chen Z, Li Q et al. Comparison of three different eye-tracking tasks for distinguishing autistic from typically developing children and autistic symptom severity. Autism Research 2019; 12 : 1529–1540. Pierce K, Marinero S, Hazin R, McKenna B, Barnes CC, Malige A. 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Additional Declarations The authors have declared there is NO conflict of interest to disclose Cite Share Download PDF Status: Published Journal Publication published 29 Jul, 2024 Read the published version in Translational Psychiatry → Version 1 posted Editorial decision: revise 04 Mar, 2024 Review # 4 received at journal 21 Feb, 2024 Reviewer # 4 agreed at journal 29 Jan, 2024 Reviewer # 3 agreed at journal 15 Dec, 2023 Review # 1 received at journal 11 Oct, 2023 Reviewer # 2 agreed at journal 28 Sep, 2023 Reviewer # 1 agreed at journal 28 Sep, 2023 Reviewers invited by journal 28 Sep, 2023 Submission checks completed at journal 04 Sep, 2023 Editor assigned by journal 03 Sep, 2023 First submitted to journal 03 Sep, 2023 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-3322690","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":274159066,"identity":"65880b0a-f3df-4716-a367-018207ac3c45","order_by":0,"name":"Keith Kendrick","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBACCQY2IMnGwAOEEMAPIhIKiNdiwCDZANJiQFgLA1yLwQEIjRNINrAlPi4ouyfD33OATeLDnz/yxudXJ354YMAgzy92AKsWaQa2w8YzzhXzSJxtYJOc2WZguO3G280SQIcZzpydgFWLHAN7mzRvWwKPAT8DmzRvg0GC2Y2zG0BaEgxu49TS/huu5c8fgwTjGWc3/8CnBeiwY8xgLbwNbECOQYIBf+82vLZINrMlS/OcS+CROHOw2bK3zdhwxg3ebRYJBhI4/SJxvM3wM09Zgj1/T/LBGz/+yMnz95/dfPNHhY08vzR2LQzMcBZjA9QUsEoJ7MqxA/4DpKgeBaNgFIyCEQAABZdSlth93sgAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0371-5904","institution":"University of Electronic Science and Technology of China","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Keith","middleName":"","lastName":"Kendrick","suffix":""},{"id":274159067,"identity":"04ca13e0-e708-4207-b923-21e3c11c4a20","order_by":1,"name":"Weihua Zhao","email":"","orcid":"https://orcid.org/0000-0003-1780-4606","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weihua","middleName":"","lastName":"Zhao","suffix":""},{"id":274159068,"identity":"1af63bdf-092a-4d9c-b324-50c7db25f749","order_by":2,"name":"Jiao Le","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiao","middleName":"","lastName":"Le","suffix":""},{"id":274159069,"identity":"fed56df7-9609-4898-bbec-6321d954bbe7","order_by":3,"name":"Qi Liu","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Liu","suffix":""},{"id":274159070,"identity":"f0c62ce6-2cbe-42d4-bcfb-95ef07c2ca3e","order_by":4,"name":"Siyu Zhu","email":"","orcid":"","institution":"University of Electronic Science and Technology of China, Chengdu, China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Zhu","suffix":""},{"id":274159071,"identity":"2f6cdb87-d45c-41d2-9abf-b97b29f2144f","order_by":5,"name":"Chunmei Lan","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chunmei","middleName":"","lastName":"Lan","suffix":""},{"id":274159072,"identity":"71153fbe-d5c6-4dca-963c-7ac68c12ce23","order_by":6,"name":"Qianqian Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qianqian","middleName":"","lastName":"Zhang","suffix":""},{"id":274159073,"identity":"1ef7ddd4-db22-4ff2-b7e8-1fb09268afa7","order_by":7,"name":"Yingying 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Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenxu","middleName":"","lastName":"Yang","suffix":""},{"id":274159077,"identity":"ce481945-173b-4ba3-9060-8b25dc671ecf","order_by":11,"name":"Rong Zhang","email":"","orcid":"https://orcid.org/0000-0002-6889-5571","institution":"Peking University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Zhang","suffix":""},{"id":274159078,"identity":"793061cb-bad6-423c-b83e-7d8f246f0aae","order_by":12,"name":"Benjamin Becker","email":"","orcid":"https://orcid.org/0000-0002-9014-9671","institution":"The University of Hong Kong","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Becker","suffix":""},{"id":274159079,"identity":"05461408-a809-425a-a07b-ccb1df27d727","order_by":13,"name":"Lan Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-09-04 02:35:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3322690/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3322690/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41398-024-03025-4","type":"published","date":"2024-07-29T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52040025,"identity":"afff714b-504e-4518-b3ea-45b584bbe6f4","added_by":"auto","created_at":"2024-03-05 17:44:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":773308,"visible":true,"origin":"","legend":"\u003cp\u003eData inclusion. The schematic shows the included data (part of Le et al., 2022) for the clustering analyses of the cross-over trial from in total of 41 ASD. Subjects were required to complete a range of assessments, caregiver-based questionnaires and eye-tracking tasks as well as biological sample collection (saliva and blood) before and after receiving oxytocin (OXT) for 6 weeks (if treatment order is OXT first, the averaged outcome measures between W1 and W2 are defined as the baseline or pre-treatment data and the end of W8 is taken as the post-OXT; if treatment order is PLC first, the averaged measures between W11 and W12 are defined as the baseline or pre-treatment data and the end of W18 is regarded as the post-OXT). Outcome measures taken at each point are shown. Autism Diagnostic Observation Schedule – 2 (ADOS-2); Social Responsivity Scale-2 (SRS-2); Adaptive behavior assessment system-II global adaptive composite (ABAS-II GAC); Social communication quotient (SCQ); Repetitive behavior scale - revised (RBS-R); caregiver strain questionnaire (CSQ); Eye-tracking, Tasks 1 and 2. Points where blood or saliva samples were taken are also indicated.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3322690/v1/0e29cc3db750aaa085c5dbbc.jpg"},{"id":52040466,"identity":"45056997-a31e-485e-8e62-25fd7ff820ee","added_by":"auto","created_at":"2024-03-05 17:52:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":357386,"visible":true,"origin":"","legend":"\u003cp\u003eTwo ASD subtypes were determined and the corresponding basal characters. (A) Selection of the optimal number of subtypes in K-means clustering based on variance ratio score (VRS). (B) The inter-subjects adjusted cosine similarity across all clustering features. The responder rate of subtype 1 (C) and subtype 2 (D) was compared with the null distributions of the permutation responder rate (10000 permutations). (E) The significant differences between the two subtypes in the eleven outcome measures including clinical variables, questionnaires and eye-tracking performances. ADOS-SA: Autism Diagnostic Observation Schedule-2- Social affect; SCQ: Social communication quotient; Aloof: subscale of Beijing Autism Subtype Questionnaire. * p \u0026lt; 0.5; ** p \u0026lt; 0.01; *** p \u0026lt; 0.001; FDR-corrected.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3322690/v1/13342e988db7d48e823e9887.jpg"},{"id":52040027,"identity":"02d3d1f1-9af5-40f7-b44e-9fa33d8e1e35","added_by":"auto","created_at":"2024-03-05 17:44:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1104813,"visible":true,"origin":"","legend":"\u003cp\u003eResponders to oxytocin (OXT) identified using the reliable change index (RCI). A. In the original study (Le et al., 2022) without cluster analysis B. In the two identified subtypes of autism (ASD) based on all 23 baseline measures and following ablation analysis only using ADOS-2 (Autism diagnostic observation schedule – 2) and eye tracking measures for time spent looking at the eyes and nose of emotional faces. C. Box plots showing comparison of responses to OXT for the two autism subtypes showing greater combined improvements in ADOS-2, ABAS-GAC (adaptive behavior assessment system-II, global adaptive composite scale) and RBS (Repetitive behavior scale) scores and time spent looking at dynamic social stimuli and the eyes of angry faces and nose of neutral ones.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3322690/v1/3e9ea646b46e7f9c432ceadf.jpg"},{"id":52040467,"identity":"82e0a494-cb6a-43c4-9583-5a60cf0245f7","added_by":"auto","created_at":"2024-03-05 17:52:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":348886,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots showing the evaluation of pre vs post oxytocin treatment between the two autism subtypes. The differences for (A) ADOS-2 (Autism diagnostic observation schedule – 2) total score (B) proportion of time spent looking at the eyes of angry faces and (C) proportion of time spent looking at dynamic social compared to dynamic geometric stimuli are shown. * p \u0026lt; 0.5; ** p \u0026lt; 0.01; FDR-corrected.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3322690/v1/c8ce96eb9a948dacd0863d9f.jpg"},{"id":61386102,"identity":"fad1b399-0955-4986-bf99-2bd141fe64c1","added_by":"auto","created_at":"2024-07-30 07:08:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3325997,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3322690/v1/d5cbaa25-db15-4510-bc06-78db9ea57a21.pdf"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"A clustering approach identifies an Autism Spectrum Disorder subtype more responsive to chronic oxytocin treatment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAutism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterized by two core symptoms including difficulties in social communication and interaction and restricted and repetitive behaviors (Diagnostic and Statistical Manual of Mental Disorders, DSM-V), with an estimated prevalence of around 2.76% \u003csup\u003e1\u003c/sup\u003e in the U.S. and around 1% in China \u003csup\u003e2\u003c/sup\u003e. Currently there is no approved pharmacological intervention for the core symptoms of ASD, especially in terms of social dysfunction \u003csup\u003e3\u003c/sup\u003e. While the hypothalamic neuropeptide oxytocin (OXT) has shown great translational promise in improving social function in animal models of autism \u003csup\u003e4\u003c/sup\u003e and from acute dosing in both typically developing and autistic individuals \u003csup\u003e5,6\u003c/sup\u003e, findings from a large number of clinical trials over the past decade using chronic intranasal treatment protocols have been inconsistent. Thus, while positive effects of repeated daily doses of intranasal OXT have been observed either on social responsivity \u003csup\u003e7\u0026ndash;13\u003c/sup\u003e and/or on repetitive or adaptive behaviors \u003csup\u003e9,14,15\u003c/sup\u003e, other studies found no significant overall effects of OXT \u003csup\u003e16\u0026ndash;20\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA number of factors may contribute to inconsistent findings in clinical trials using chronic intranasal OXT. To date there is some evidence for dose-magnitude \u003csup\u003e12,15\u003c/sup\u003e and dose-frequency dependency and treatment duration \u003csup\u003e9\u003c/sup\u003e. In terms of demographic and physiological measures one study on children reported stronger effects in younger children \u003csup\u003e20\u003c/sup\u003eand others have reported associations between improvement in the social responsivity scale and basal and/or treatment dependent changes in plasma OXT concentrations \u003csup\u003e9,10\u003c/sup\u003e. Treatment context may also play a role with several trials reporting positive effects when OXT is given prior to positive social interactions \u003csup\u003e9\u003c/sup\u003e or psychosocial training \u003csup\u003e8\u003c/sup\u003e. However, no study has adopted a data driven approach to assess whether there may be an autism sub-type where individuals are more likely to be responsive to chronic OXT treatment on the basis of multiple clinical, physiological and behavioral measures taken prior to treatment.\u003c/p\u003e \u003cp\u003eRecently, a number of studies have used phenotypic/genetic-\u003csup\u003e21,22\u003c/sup\u003e, biological- \u003csup\u003e23,24\u003c/sup\u003e, neuronal-\u003csup\u003e25\u003c/sup\u003e and behavior assessments \u003csup\u003e26\u003c/sup\u003e to determine ASD subtypes to aid earlier and more accurate detection as well as inform treatment/intervention selection to provide more evidence for individualized precision medicine \u003csup\u003e27\u003c/sup\u003e. To date these different approaches come up with a variable number of subtypes and tend to only include one, or a few measures. A subtype approach using multiple measures and data-driven clustering analysis may offer a more robust approach to help both identify autism subtypes \u003csup\u003e28,29\u003c/sup\u003e and hopefully also provide a method to help predict which individuals may respond best to specific types of treatment. For example, a recent study used this approach to determine subgroups with migraine without aura who were more responsive to electroacupuncture treatment \u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the current study we therefore firstly aimed to classify autism subtypes using unsupervised data-driven clustering analysis of multiple, clinical, physiological, behavioral and eye-tracking measures and secondly to investigate whether individuals identified as exhibiting reliable reductions in symptom severity in our recent intranasal OXT clinical trial \u003csup\u003e9\u003c/sup\u003e were more likely to be from a specific subtype.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eA final sample of 41 children with ASD (mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u0026thinsp;=\u0026thinsp;5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3, 3 girls) before and after a course of 6-week OXT intranasal trial with 24IU, every other day, \u003cb\u003esee\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) data from the published paper \u003csup\u003e9\u003c/sup\u003e have been re-analyzed to determine whether distinct subtypes could be identified and they were differentially responsive to the 6-week intranasal OXT intervention. The study used a computer randomized, double-blind, placebo-controlled, cross-over design but we only focused on the data before and after receiving 6-week intranasal OXT (24IU, every other day, Sichuan Meike Pharmaceutical Co, Sichuan, China). This study was conducted in the Chengdu Maternal and Children\u0026rsquo;s Central hospital (CMCCH) between June 2019 and July 2021 in accordance with the latest Declaration of Helsinki and has been approved by the Ethics Committee of CMCCH affiliated to the University of Electronic Science and Technology of China (number 201983) as well as the general ethics committee of the University (number 1420190601). The trial was pre-registered (Chinese Clinical Trial Registry: ChiCTR1900023774). All written informed consent was provided by their parents or legal guardians. Inclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age range 3\u0026ndash;8 years; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) diagnosed with ASD according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and Autism Diagnostic Observation Schedule-2 (ADOS-2), with scores of 5\u0026ndash;10 on the ADOS-2 Comparison scale (i.e. moderate to severe autism)\u003csup\u003e31\u003c/sup\u003e; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) free of any chromosomal abnormalities or neurological diseases (e.g., epilepsy, Rett syndrome), or some other psychiatric disorders; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) without any severe respiratory, hearing or visual impairments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment and questionnaires\u003c/h2\u003e \u003cp\u003eDuring the trial, severity of autistic symptoms was assessed using the gold-standard objective assessment (ADOS-2 Comparison, total scores and subscale scores including social affect-SA and restrictive and repetitive behavior -RRB) and the caregiver-based social responsivity scale-2 (SRS-2 total score). These were the primary outcome measures. Additionally, a wide range of secondary questionnaire-based measures were taken before and after treatment, including the adaptive behavior assessment system-II, global adaptive composite (ABAS-II GAC), social communication quotient (SCQ), repetitive behavior scale - revised (RBS-R), caregiver strain questionnaire (CSQ) and the Beijing Autism Subtype questionnaire (BASQ) was also given prior to treatment. Biological sampling including blood (6 ml \u0026ndash; collected into EDTA tubes) and saliva samples (1 ml using salivettes) collected from each participant on the first day of the measurement of basal OXT concentrations. Saliva samples were also collected again at the end of treatment. Full details are given in Le et al 2022 \u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Eye-tracking tasks\u003c/h2\u003e \u003cp\u003eAll children were instructed to watch passively two eye-tracking tasks including a dynamic social interest task and a static face emotion expression where no responses were required. The social interest task displayed pairs of dynamic dancing Chinese human versus dynamic geometric patterns simultaneously over 40 seconds (20 x 2 s videos shown in two separate clips). The face emotion task consisted of 4 children\u0026rsquo;s faces and 2 adult faces expressing four different emotions (neutral, happy, sad and fear) in both genders. Following a 500ms interval, each face was randomly presented for 2s. In the social interest task, the primary measure was the percentage (%) of total time spent viewing the dynamic social stimuli relative to the dynamic geometric stimuli. In the face emotion task, the primary measures were the % time spent viewing the eye, nose and mouth regions. Full details are given in Le et al 2022 \u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analyses\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Characterizing autism subtypes based on basal individual clinical severity and eye-tracking performance\u003c/h2\u003e \u003cp\u003eA data-driven k-means clustering algorithm with Manhattan (L1) distance \u003csup\u003e32\u003c/sup\u003e was used to determine autism subtypes. Specifically, demographic information (age, gender), clinical variables (ADOS-2 comparison scores, ADOS-2 SA and RRB), assessment questionnaires (ABAS-II GAC, SCQ, RBS-R, CSQ, BASQ-aloof, passive, active but odd), eye-tracking performance (task 1: percentage of percentage (%) of total duration viewing social stimuli; task 2: percentage of percentage (%) of total duration viewing on eyes and nose for each emotion were included) and endocrine data (basal saliva OXT and basal plasma OXT concentrations) of each of the 41 children with autism were defined as the clustering features (in total 23 features). We varied the number of clusters (subtypes) from 2 to 10 and repeated the clustering algorithm 20 times with different random initial cluster centroids to help find a lower, local minimum total sum of distances. The optimal number of subtypes was determined by the variance ratio score (VRS), which is calculated as the ratio of the between-cluster variance to the within-cluster variance \u003csup\u003e30\u003c/sup\u003e where a higher VRS indicates better clustering performance. In addition, adjusted cosine similarity can remedy the potential drawback of two vectors with very different attribute values and can check whether there is a high or low similarity between clusters or subtypes \u003csup\u003e33\u003c/sup\u003e. This was calculated for each individual across the determined autism subtypes to validate the k-means clustering results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Identifying OXT responders across autism subtypes\u003c/h2\u003e \u003cp\u003eAdditionally, changes in ADOS-2 total scores between pre- and post OXT treatment were evaluated in individual participants using the Reliable Change Index \u003csup\u003e34\u003c/sup\u003e as a means of assessing the clinical significance (i.e. improve, no-change and deteriorate) of the 6-week OXT intervention. Individuals demonstrating a significant \u0026ldquo;improvement\u0026rdquo; using RCI criteria were considered as OXT+ (RCI-based OXT responders, n\u0026thinsp;=\u0026thinsp;18) and those classified as exhibiting \u0026ldquo;no change\u0026rdquo; were defined as OXT- (RCI-based OXT non-responders, n\u0026thinsp;=\u0026thinsp;23, response rate\u0026thinsp;=\u0026thinsp;43.9%, \u003cb\u003eFig.\u0026nbsp;3A\u003c/b\u003e\u003csup\u003e9\u003c/sup\u003e). There were no individuals classified by the RCI analysis as showing a deterioration. To identify OXT responders across subtypes based on clustering analyses, the responder rate was measured as a ratio between the number of responders and the total number of participants. The responder rate of different subtypes was compared with the null distributions of the permutation responder rate (10000 permutations). In addition, the difference in the responder rate across ASD subtypes was compared using a chi-square test. Given a lower frequency of improvement on the less objective caregiver completed SRS-2 following the 6-week OXT based on the RCI (27%)\u003csup\u003e9\u003c/sup\u003e, we did not include SRS-2 scores in the further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Evaluation of clustering-based subtypes on responses to OXT treatment\u003c/h2\u003e \u003cp\u003eIn accordance with the clustering analysis, each individual with autism could be assigned to either subtype 1 or subtype 2 and paired t tests with p values FDR corrected were performed to evaluate differences between the two subtypes following the 6-week OXT treatment (i.e. pre- vs. post-treatment) on all primary and secondary outcome measures taken.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Two autism subtypes identified by clustering analysis\u003c/h2\u003e \u003cp\u003eAs shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, the VRS showed that the maximum value or optimal clustering performance was with two subtypes, and it monotonically decreased as the number of subtypes increased. We also calculated the inter-subject adjusted cosine similarity \u003csup\u003e33\u003c/sup\u003e across all clustering features, and the result revealed a high degree of similarity within each subtype (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Paired t-tests showed that a higher similarity within subtype (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u0026thinsp;=\u0026thinsp;0.106\u0026thinsp;\u0026plusmn;\u0026thinsp;0.095) than that between subtypes (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD= -0.150\u0026thinsp;\u0026plusmn;\u0026thinsp;0.094) was observed (t\u0026thinsp;=\u0026thinsp;9.30, 95% CI= [0.200, 0.312]).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNo differences across the two subtypes were observed in terms of age, gender ratio and oxytocin plasma or saliva concentrations (ps\u0026thinsp;\u0026gt;\u0026thinsp;0.226). Importantly, the OXT responder rates for the two subtypes were 13.3% for subtype 1 (n\u0026thinsp;=\u0026thinsp;15, 2 girls) and 61.5% (subtype 2, n\u0026thinsp;=\u0026thinsp;26, 1 girl, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e-top\u003c/b\u003e) and significantly lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) or higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) than the permutation responder rate, respectively. Chi-square test suggested that a significantly greater frequency of OXT responders were found in subtype 2 (chi-square\u0026thinsp;=\u0026thinsp;32.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Analysis using t-tests with FDR correction showed that individuals in subtype 2 (higher OXT responder rate subtype) had significantly different scores on 11 different measures including greater interest in the eyes and reduced interest in the nose while viewing face emotions and less clinical severity (ADOS-2 SA subscale, SCQ and aloof social sub-scale scores) as compared with subtype 1 (lower OXT responder rate subtype, see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Additionally, the results of ablation analyses, where each single measure was removed systematically from the analysis, revealed that the performance of face eye-tracking performance (angry eyes, angry nose, fear eyes, neutral eyes and neutral nose) and ADOS-2 assessment (SA and RRB subscale scores) contribute more to subtype clustering since if any of them were removed, the maximum OXT responder rate was reduced (\u0026gt;\u0026thinsp;4%, \u003cb\u003edetails see\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, the OXT responder rates for the two subtypes were 18.8% (subtype 1, n\u0026thinsp;=\u0026thinsp;16) and 60% (subtype 2, n\u0026thinsp;=\u0026thinsp;25) if only these seven ablation-based outcomes involved in ADOS-2 and eye tracking for emotional faces were included as features (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e-bottom\u003c/b\u003e).\u003c/p\u003e \u003cp\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\u003eAblation protocol applied to clustering analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcluded Feature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax responder rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔ responder rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcluded Feature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax responder rate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔ responder rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWithout clustering\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.9%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.6%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBasal saliva OXT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBasal plasma OXT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADOS-CP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADOS-SA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAngry eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADOS-RRB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAngry nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABAS-GAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFear eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFear nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHappy eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHappy nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAloof\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePassive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive but odd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: all 23 features were used for clustering analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNone means that none of 23 features were excluded. ADOS-CP: Autism Diagnostic Observation Schedule-2- comparison score; SA: social affect; RRB: restrictive and repetitive behavior; ABAS-GAC: adaptive behavior assessment system-II, global adaptive composite; SCQ: Social communication quotient; CSQ: caregiver strain questionnaire; RBS: repetitive behavior scale - revised; Aloof/Passive/Active but odd: subscale of Beijing Autism Subtype Questionnaire; OXT: oxytocin.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Detailed evaluation of responses to OXT treatment in the two autism subtypes\u003c/h2\u003e \u003cp\u003eTo confirm that individuals with autism subtype 2 (n\u0026thinsp;=\u0026thinsp;26) were more sensitive to OXT, we compared all outcome measures before and after 6-week OXT treatment across two subtypes. Paired t-test with FDR correction suggested that OXT selectively improved individuals in subtype 2 in terms of autism severity (ADOS-2 total score, p\u0026thinsp;=\u0026thinsp;0.009, comparison score, p\u0026thinsp;=\u0026thinsp;0.009, SA, p\u0026thinsp;=\u0026thinsp;0.017 and RRB, p\u0026thinsp;=\u0026thinsp;0.017), adaptive behavior (ABAS-II GAC, p\u0026thinsp;=\u0026thinsp;0.049 and RBS, p\u0026thinsp;=\u0026thinsp;0.049) as well as increased the time spent on social stimuli (p\u0026thinsp;=\u0026thinsp;0.049), angry eyes (p\u0026thinsp;=\u0026thinsp;0.049) and neutral nose (p\u0026thinsp;=\u0026thinsp;0.049) but not for subtype 1 following 6-weeks of OXT treatment (details \u003cb\u003esee\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u0026amp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results did not change across two autism subtypes (subtype 1, n\u0026thinsp;=\u0026thinsp;16; subtype 2, n\u0026thinsp;=\u0026thinsp;25) if only these seven measures were used for clustering (ps\u0026thinsp;\u0026le;\u0026thinsp;0.05).\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\u003eThe comparisons between before and after 6-week OXT treatment in all outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSubtype 1 (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eSubtype 2 (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT test\u003c/p\u003e \u003cp\u003e(pre vs. post)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP_FDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT test\u003c/p\u003e \u003cp\u003e(pre vs. post)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP_FDR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADOS-total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADOS-total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADOS-CP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADOS-CP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADOS-SA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADOS-SA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADOS-RRB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADOS-RRB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABAS-GAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eABAS-GAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSCQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngry eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAngry eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngry nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAngry nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFear eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFear nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHappy eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHappy eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHappy nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHappy nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutral nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeutral nose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eADOS-total: Autism Diagnostic Observation Schedule-2- total score; CP: comparison score; SA: social affect; RRB: restrictive and repetitive behavior; ABAS-GAC: adaptive behavior assessment system-II, global adaptive composite; SCQ: Social communication quotient; CSQ: caregiver strain questionnaire; RBS: repetitive behavior scale - revised; Aloof/Passive/Active but odd: subscale of Beijing Autism Subtype Questionnaire; OXT: oxytocin. * p\u0026thinsp;\u0026lt;\u0026thinsp;0.5; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; FDR-corrected.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the current study, we used data from our recent intranasal OXT intervention clinical trial \u003csup\u003e9\u003c/sup\u003e to identify two distinct autism subtypes by employing unsupervised data-driven clustering analysis of 23 different baseline measures. The proportion of individuals showing reliable improvements in clinical symptoms (ADOS-2 total score) after the 6-week OXT treatment was considerably greater in one subtype (61.5%) than in the other one (13.3%). Notably, ablation analysis revealed that the minimum number of included features for the two autism subtypes could be reduced to two main objective assessments (i.e. ADOS-2 scores and eye-tracking analysis of time spent viewing features of emotional faces) with 60% of OXT responders still being identified in one of the subtypes. Moreover, the autism subtype with the greater proportion of OXT responders was characterized by exhibiting greater interest in viewing the eyes or nose region of faces with different emotional expressions and a lower severity of social symptoms at baseline relative to the other subtype. In addition, individuals with the autism subtype with a more OXT responders showed significant overall improvement in autistic symptoms, adaptive behaviors and time spent on viewing the eyes of faces. Taken together our findings suggest that there may be a subtype of young children with autism who are more likely to show reduced symptoms in response to chronic OXT treatment and provide a promising approach to helping identify individuals most likely to show beneficial effects of OXT-based interventions in future studies. However, further validation of this clustering-based subtype established by combined clinical assessment and eye-tracking is required.\u003c/p\u003e \u003cp\u003ePrevious research primarily based on studies using single-dose administration of OXT have repeatedly shown that responses are often modulated by both context and personal characteristics \u003csup\u003e35,36\u003c/sup\u003e although less has been established concerning chronic treatment outcome responses. For chronic intervention studies some have reported associations between social symptom improvements and OXT concentrations \u003csup\u003e9,10\u003c/sup\u003e or age \u003csup\u003e20\u003c/sup\u003e but interestingly in our current analysis neither of these appeared to be important for sub-typing or predicting whether individuals exhibited improved symptoms. OXT receptor genotype has also been considered to be a factor modulating responses to both acute \u003csup\u003e37\u003c/sup\u003e and chronic doses of OXT \u003csup\u003e9\u003c/sup\u003e but again in the current analysis neither of these appeared to contribute greatly to either autism subtype or OXT responders. One previous study has reported greater improvements in autistic symptoms and increases in plasma OXT following a combined electroacupuncture and behavioral intervention in the aloof and passive social subtypes \u003csup\u003e38\u003c/sup\u003e and there was some indication in our current analysis that individuals scoring high on the aloof dimension were actually less likely to respond to OXT.\u003c/p\u003e \u003cp\u003eInterestingly, two main objective assessments of autistic symptoms (ADOS-2 and eye-tracking face task) could still reliably detect 60% OXT responder rate in subtype 2 based on ablation analyses, indicating these two measures played the primary role in distinguishing subtypes. ADOS-2 has long been considered as a gold standard measure of autistic symptom severity with a high sensitivity and specificity \u003csup\u003e39\u003c/sup\u003e. In the current analysis subtype 1 individuals generally scored higher on both the social affect and repetitive and restrictive behavior scales that subtype 2 individuals suggesting that chronic OXT treatments are more likely to benefit individuals with lower initial overall symptom severity. However, scores on the social affect scale appear to be more informative in this respect suggesting that OXT may particularly benefit more individuals with less severe social symptoms. On the other hand, while eye tracking measures have revealed altered visual preferences in autism and have been proposed as an early biomarker of ASD \u003csup\u003e23,24,40,41\u003c/sup\u003e they have not been extensively included as treatment outcome measures. Of the two eye-tracking tasks included in our original study the one exhibiting the greatest utility for both subtyping and predicting OXT responders was the passive face emotion expression paradigm with individuals in the two subtypes typically exhibiting either very low interest in viewing the eye region of all face emotion types (subtype 1) or a greater interest in doing so (subtype 2). Thus, it would appear that individuals who are more likely to respond to OXT generally exhibit at least some initial interest in looking at the eye region of faces even though this is still generally less than that shown by typically developing children. The other eye-tracking task we used visual preference for dynamic social (dancing children) compared with dynamic geometric patterns. Autistic children generally spend more time looking at the geometric patterns while TD children spend more time looking at the dynamic social stimuli and was originally developed by Pierce et al \u003csup\u003e41\u003c/sup\u003e. We had previously found that this task was the most reliable in discriminating TD from ASD in Chinese children\u003csup\u003e40\u003c/sup\u003e and preference for the social stimuli can be facilitated by acute as well as chronic OXT treatment \u003csup\u003e9,42\u003c/sup\u003e. It has been proposed that this task may identify a specific ASD \u0026ldquo;GeoPref\u0026rdquo; subtype (dynamic geometric images vs. social images of children interacting and moving) with higher symptom severity and reduced resting state functional connectivity \u003csup\u003e23,24\u003c/sup\u003e, however we did not find any differences in this task across the two autism subtypes identified using multiple measures in our current study and the ablation analysis did not show that it contributed to identifying OXT responders.\u003c/p\u003e \u003cp\u003eAlthough neuroimaging data, such as resting state functional connectivity, has also been used to identify autism subtypes to facilitate diagnosis and prediction of response to treatment \u003csup\u003e43,44\u003c/sup\u003e, it is challenging to collect neuroimaging data in a large population of individuals with ASD, especially in young children and has a relatively high cost. Administering an MRI scan on such a young ASD child under natural sleep or following extensive training can be difficult and use of sedative drugs may influence blood oxygen level dependent signals. Given our findings that only two objective, and easily administered, assessments including ADOS-2 and eye-tracking for emotional face task are needed for potential screening of OXT responders it may not be necessary to try and utilize either neuroimaging or more extensive genotyping to do so, although that does not of course mean that these measures could be informative in the context of other kinds of interventions.\u003c/p\u003e \u003cp\u003eSome limitations of the current study should be acknowledged: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the current sample size is comparatively small due to strict inclusion criteria and the long-term intervention, although the observed differences between effects of OXT on the two subtypes identified is highly significant. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) External and independent validation data would be further needed to validate our findings although that is currently difficult given that both ADOS-2 and eye-tracking measures have to date not been used in other clinical trials. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Among overall post-treatment improvements, only SA and not RRB sub-scale scores of ADOS-2 were observed in subtype 2 following the 6-week oxytocin intervention. This suggests that OXT may mainly improve social interaction and communication skills and that subtypes of restrictive and repetitive behavior should be investigated in the future studies.\u003c/p\u003e \u003cp\u003eIn summary, our approach of performing an unsupervised data-driven cluster analysis of a population of autistic children showing variable responses to chronic OXT treatment has revealed that out of 23 baseline measures included two different objective assessments (ADOS-2 and eye-tracking) which contributed 7 different measures were effective in identifying two different subtypes with markedly different responses to OXT. These two assessments may therefore represent a potential screening tool to identify individuals most likely to show improved symptoms following a chronic OXT treatment intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Foundation of Sichuan Province [grant number 2022NSFSC1375 - WHZ], Fundamental Research Funds for the Central Universities, UESTC [grant number ZYGX2020J027 - WHZ], National Natural Science Foundation of China (NSFC) [grant number 31530032 \u0026ndash; KMK; grant number 82301732- JL] and Key Scientific and Technological projects of Guangdong Province [grant number 2018B030335001 - KMK].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors contributions are as follows: conceptualization: W.Z., K.M.K.; methodology: W.Z., J.L., Qi L., L.Z, B.B., K.M.K.; investigation: J.L., S.Z., C.L., Q.Z., Y.Z., J.K., Qin L.; visualization: W.Z., J.L., Qi L.; funding acquisition: W.Z., K.M.K.; project administration: W.Z., J.L., L.Z., K.M.K.; supervision: W.Z., K.M.K., L.Z.; writing \u0026ndash; original draft: W.Z., K.M.K.; writing \u0026ndash;review and editing: W.Z., J.L., Qi L., L.Z., J.K., Y.Z., Qin L., C.L., R.Z., W.Y., B.B., K.M.K.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MATLAB codes for the clustering analysis are available on github (https://github.com/zhaolab205/ASD_responder). Additional data related to this study can be provided upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMaenner MJ, Warren Z, Williams AR. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years \u0026mdash; Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. \u003cem\u003eMMWR Surveill Summ\u003c/em\u003e 2023; \u003cstrong\u003e72\u003c/strong\u003e: 1\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eSun X, Allison C, Wei L, Matthews FE, Auyeung B, Wu YY \u003cem\u003eet al.\u003c/em\u003e Autism prevalence in China is comparable to Western prevalence. \u003cem\u003eMolecular Autism\u003c/em\u003e 2019; \u003cstrong\u003e10\u003c/strong\u003e: 7.\u003c/li\u003e\n\u003cli\u003eLord C, Elsabbagh M, Baird G, Veenstra-Vanderweele J. 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Autism is a heterogeneous disorder and one factor which may influence treatment outcome is whether a subtype of individuals is more sensitive to oxytocin. In a recent cross-over trial on 41 young autistic children we reported that 44% showed a reliable improvement in clinical symptoms (Autism Diagnostic Observation Schedule, ADOS-2) after a 6-week intervention. In the current re-assessment of the data, we used an unsupervised data-driven cluster analysis approach to identify autism subtypes using 23 different demographic, social subtype, endocrine,eye-tracking and clinical symptom measures taken before treatment and this revealed an optimum of two different subtypes. We then assessed the proportion of identified responders to oxytocin and found that while 61.5% of one subtype included responders only 13.3% of the other did so. This oxytocin-sensitive subtype also showed overall significant post-treatment clinical and eye-tracking measure changes. The oxytocin-sensitive subtype was primarily characterized at baseline by lower initial clinical severity (ADOS-2) and greater interest in the eye-region of emotional faces. These features alone were nearly as efficient in identifying the two subtypes as all 23 baseline measures and this easy-to-conduct approach may help rapidly and objectively screen for oxytocin responders. Future clinical trials using oxytocin interventions may therefore achieve greater success by focusing on children with this specific autism subtype and help develop individualized oxytocin intervention.\u003c/p\u003e","manuscriptTitle":"A clustering approach identifies an Autism Spectrum Disorder subtype more responsive to chronic oxytocin treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 17:44:52","doi":"10.21203/rs.3.rs-3322690/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-03-04T15:11:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-02-21T18:44:56+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-01-29T07:41:49+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2023-12-15T17:17:54+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2023-10-11T23:11:31+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2023-09-28T12:18:15+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2023-09-28T09:04:51+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2023-09-28T07:59:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-09-04T12:36:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-09-04T02:30:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2023-09-04T02:30:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5581813f-54f0-4a00-97f8-eee2e8d54ec2","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28898992,"name":"Health sciences/Diseases/Psychiatric disorders/Autism spectrum disorders"},{"id":28898993,"name":"Health sciences/Biomarkers/Prognostic markers"}],"tags":[],"updatedAt":"2024-07-30T07:08:02+00:00","versionOfRecord":{"articleIdentity":"rs-3322690","link":"https://doi.org/10.1038/s41398-024-03025-4","journal":{"identity":"translational-psychiatry","isVorOnly":false,"title":"Translational Psychiatry"},"publishedOn":"2024-07-29 04:00:00","publishedOnDateReadable":"July 29th, 2024"},"versionCreatedAt":"2024-03-05 17:44:52","video":"","vorDoi":"10.1038/s41398-024-03025-4","vorDoiUrl":"https://doi.org/10.1038/s41398-024-03025-4","workflowStages":[]},"version":"v1","identity":"rs-3322690","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3322690","identity":"rs-3322690","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}