Future Guides for Biomarkers for Autism Spectrum Disorders

This review article explores the current landscape and future directions of biomarker research in autism spectrum disorders (ASDs), a complex neurodevelopmental condition characterized by diverse genetic, neurophysiological, metabolic, immune, and nutritional underpinnings. Despite the significant heritability of ASD, indicating a strong genetic component, the disorder’s etiology is multifactorial, with prenatal, perinatal, and early environmental exposures contributing to risk. The rising global prevalence of ASD underscores the urgent need for effective diagnostic tools and intervention strategies. Biomarkers offer a promising avenue for addressing these challenges, with the potential to predict ASD risk, aid in early diagnosis, monitor disease progression, and guide personalized treatment strategies. This review consolidates current knowledge on various types of ASD biomarkers, including genetic markers indicating specific gene mutations and epigenetic modifications, neurological markers identified through advanced imaging and neurophysiological techniques, metabolic disturbances captured through the analysis of specific pathways, immune markers reflecting maternal and fetal interactions, and nutritional markers highlighting dietary influences and deficiencies. Early therapeutic interventions are extremely helpful in treating ASD. However, the diagnosis of ASD is often delayed because identifying abnormal behaviors may not be possible until the disorder is well established. It would be a significant advancement to have biomarkers that can identify children at risk during the presymptomatic period to aid in early diagnosis, confirm behavioral observations, stratify patients into subgroups, and predict therapeutic response.

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-12-01
DOI
https://doi.org/10.4103/hjhs.hjhs_17_24
Citations
1
Primary Topic
Autism Spectrum Disorder Research
Type
article
Field-Weighted Citation Impact
0.34
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Future Guides for Biomarkers for Autism Spectrum Disorders

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SHILAP Revista de lepidopterología
Autism Spectrum Disorder Research
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Future Guides for Biomarkers for Autism Spectrum Disorders

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Abstract

This review article explores the current landscape and future directions of biomarker research in autism spectrum disorders (ASDs), a complex neurodevelopmental condition characterized by diverse genetic, neurophysiological, metabolic, immune, and nutritional underpinnings. Despite the significant heritability of ASD, indicating a strong genetic component, the disorder’s etiology is multifactorial, with prenatal, perinatal, and early environmental exposures contributing to risk. The rising global prevalence of ASD underscores the urgent need for effective diagnostic tools and intervention strategies. Biomarkers offer a promising avenue for addressing these challenges, with the potential to predict ASD risk, aid in early diagnosis, monitor disease progression, and guide personalized treatment strategies. This review consolidates current knowledge on various types of ASD biomarkers, including genetic markers indicating specific gene mutations and epigenetic modifications, neurological markers identified through advanced imaging and neurophysiological techniques, metabolic disturbances captured through the analysis of specific pathways, immune markers reflecting maternal and fetal interactions, and nutritional markers highlighting dietary influences and deficiencies. Early therapeutic interventions are extremely helpful in treating ASD. However, the diagnosis of ASD is often delayed because identifying abnormal behaviors may not be possible until the disorder is well established. It would be a significant advancement to have biomarkers that can identify children at risk during the presymptomatic period to aid in early diagnosis, confirm behavioral observations, stratify patients into subgroups, and predict therapeutic response.

SHILAP Revista de lepidopterología
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Openalex Percentile: Top 36%
Autism Spectrum Disorder Research
0.34
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