RT Journal Article
T1 Predicting autistic traits, anxiety and depression symptoms using camouflaging autistic traits questionnaire (CAT-Q-ES): A machine learning study
A1 Conde-Pumpido Zubizarreta, Sabela
A1 Tubío Fungueiriño, María
A1 Pozo Rodríguez, Marta
A1 Carracedo Álvarez, Ángel
A1 Cernadas García, Eva
A1 Fernández Delgado, Manuel
A1 Fernández Prieto, Montserrat
K1 ASD
K1 Autism
K1 CAT-Q-ES
K1 Camouflaging
K1 Supervised machine learning
K1 Mental health
K1 Gender
AB Research has linked camouflaging with compensating and hiding autistic traits during social interactions. Furthermore, these strategies have been linked to increased anxiety and depression symptoms and to greater reliance on camouflaging behaviors among individuals with more autistic traits, even in non-autistic populations. This study evaluated the viability of a machine learning algorithm to predict autistic traits and symptoms of depression and anxiety using camouflaging behaviors. The sample included 601 participants: 102 autistic adults (72 women, 18 men, and 12 non-binary individuals) and 499 non-autistic adults (399 women, 92 men, and eight non-binary individuals). The study predicted autistic traits measured with the Broader Autism Phenotype Questionnaire (BAPQ) subscales - Aloofness, Pragmatics, and Rigidity - as well as the total score of depressive (Patient Health Questionnaire - PHQ-9) and anxious symptoms (General Anxiety Disorder - GAD-7) using the individual items from the Camouflaging Autistic Traits Questionnaire Spanish version (CAT-Q-ES) as predictors. We developed fifty supervised learning models, including support vector machines, neural networks, linear regressors, decision trees, random forests, and Gaussian processes, among others. Correlation coefficients between true and predicted scores were strong for Aloofness (R=.85), Pragmatics (R=.82), and Rigidity (R=.74), being only moderate for Depression (R=.60) and Anxiety (R=.54). Autism diagnosis or gender identity did not improve the prediction’s accuracy. These results show the viability of machine learning algorithms to predict autistic traits (Aloofness, Pragmatics and Rigidity) and anxiety-depression symptoms, using the CAT-Q-ES. This suggests potential for developing a tool that may improve autistic traits and emotional problems screening in individuals whose diagnosis is unclear or not yet established, regardless of gender identity.
PB Elsevier
SN 3050-6573
YR 2025
FD 2025-10
LK https://hdl.handle.net/10347/43881
UL https://hdl.handle.net/10347/43881
LA eng
NO Conde-Pumpido Zubizarreta, S., Tubío-Fungueiriño, M., Pozo-Rodríguez, M., Carracedo, A., Cernadas, E., Fernández-Delgado, M., & Fernández-Prieto, M. (2025). Predicting autistic traits, anxiety and depression symptoms using camouflaging autistic traits questionnaire (CAT-Q-ES): A machine learning study. Research in Autism, 128, 202712. 10.1016/j.reia.2025.202712
NO This work is supported by Fundación María José Jove. This investigation has received financial support from: Ministerio de Ciencia e Innovación, Instituto de Salud Carlos III (Spain) (PI22/00025), Reference Competitive Group accreditation 2022-2025, GRC (GI- 1636); European Union (European Regional Development Fund - ERDF); and Xunta de Galicia (Centro de investigación de Galicia accreditation 2024-2027 ED431G-2023/04 and ED431G-2023/02). S.C-P.Z and M.P-R are supported by a predoctoral fellowship by Xunta de Galicia.
DS Minerva
RD 23 abr 2026