Smart sensing the spectrum: cutting-edge biosensor and biomarker approaches for early autism detection.

Authors

Dhrubajyoti Ghosh, Samiran Sadhukhan, Mayank Attri, Tejinder Singh, Biplab Debnath, Pratyush Porel

Published in

Metabolic brain disease. Volume 41. Issue 1. Jun 22, 2026. Epub Jun 22, 2026.

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, often characterized by challenges in social interaction, repetitive behaviors, and restricted interests in work. Early diagnosis is critical for effective intervention, but current methods often rely on subjective behavioral assessments that can delay identification. This underscores the need for reliable biomarkers that can facilitate earlier detection of ASD. Biomarkers are primarily proteins in nature, found in blood, saliva, or other tissues, and have the potential to enhance diagnostic accuracy and speed. They can reveal underlying neurobiological changes associated with ASD, providing objective data to support clinical findings. The search for altered levels of certain amino acids and neurotransmitters and specific genetic biomarkers such as single-nucleotide polymorphisms (SNPs) and copy number variants (CNVs) has gained momentum, driven by the need for more definitive early diagnostic tools. Neuroinflammatory and neuroimaging biomarkers through MRI and fMRI have also shown promise for early detection of ASD. Recent advancements in biosensor technology have significantly improved the prospects for biomarker discovery in ASD. Innovations in nanotechnology and microfluidics have enabled the development of highly sensitive and specific biosensors that can trace minute quantities of biomarkers. These devices allow for non-invasive sampling and real-time monitoring, making early screening more feasible and accessible for young children. This review article mainly focuses on how the integration of these biosensor and biomarker technologies could transform the early detection of ASD, ultimately facilitating timely interventions and improving patients' outcomes.

PMID:
42329525
Bibliographic data and abstract were imported from PubMed on 22 Jun 2026.

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