Mapping the knowledge structure and emerging trends of blood-based biomarkers in autism spectrum disorder: A bibliometric analysis (2016-2026).

Background: Autism spectrum disorder (ASD) is a clinically heterogeneous neurodevelopmental condition, and its diagnosis still relies mainly on behavioral assessment. Blood-based biomarkers may provide objective support for early detection, biological subgrouping, clinical monitoring, and individualized intervention. However, findings in this field remain dispersed across multiple biological pathways. This study aimed to map the knowledge structure and emerging trends of ASD blood-based biomarker research. Methods: Publications on ASD blood-based biomarkers from 2016 to 2026 were retrieved from the Web of Science Core Collection. After duplicate removal and eligibility screening, 1785 English-language articles and reviews were included. CiteSpace, VOSviewer, and Microsoft Excel were used to examine publication trends, collaboration networks, influential journals and authors, co-cited references, keyword clusters, and citation bursts. Results: Research output increased steadily, with the United States and China contributing the largest number of publications and the United States showing stronger international connectivity. Co-cited reference and keyword analyses identified oxidative stress, inflammatory cytokines, gut microbiota, metabolomics, mitochondrial respiration, and gastrointestinal symptoms as major knowledge clusters. Recent burst terms, including machine learning, Mendelian randomization, gut-brain axis, innate immunity, sex differences, and neurological disorders, indicate a shift toward causal inference, multi-omics integration, and data-driven biomarker prediction. Conclusions: ASD blood-based biomarker research has grown steadily over the past decade and is moving from descriptive clinical observations toward mechanistic, multi-omics, and computational approaches. Oxidative stress, mitochondrial dysfunction, gut-brain interactions, inflammatory responses, and machine learning-based prediction now represent major directions in this field. Future studies should focus on multicenter longitudinal cohorts, standardized analytical workflows, detailed clinical phenotyping, and independent validation to improve reproducibility and support clinical translation.