Clinical profiles in autism spectrum disorder: Enhancing diagnosis, treatment, and overall health outcomes

Medicine Advances Pub Date : 2024-12-17 DOI:10.1002/med4.89
Tielle Machado Cruz
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Abstract

Background

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by significant challenges in communication, behavior, and social interaction. The prevalence of ASD is rising, with current estimates indicating that it affects 1 in 36 children in the United States. Traditional diagnostic methods often fail to capture the full complexity of ASD, leading to incomplete treatment approaches. This study aimed to enhance diagnostic and therapeutic approaches to ASD by identifying and categorizing 10 clinical profiles using precision medicine.

Methods

This study presents a new stratification model for ASD that classifies individuals based on prevalent comorbidities and underlying biological mechanisms. The model is based on a synthesis of existing literature, clinical observations, and expert insights. Data were collected from detailed clinical evaluations, patient histories, and laboratory tests, and the model was refined through iterative analysis. It integrates findings from genetic, neurological, and psychiatric research to develop a comprehensive approach for diagnosing and treating ASD-related comorbidities.

Results

The study proposes 10 clinical profiles of ASD: syndromic, gastrointestinal, metabolic, mitochondrial, endocrine, bioaccumulative, infectious, immunological, inflammatory, and Neurological. Each profile is associated with specific symptoms and comorbidities influenced by genetic, environmental, and biological factors.

Conclusions

The stratification model proposed in this study enhances the diagnosis and treatment of ASD by recognizing its heterogeneity. By identifying specific clinical profiles, healthcare providers can develop personalized and integrated therapeutic strategies to improve the overall health outcomes of individuals with ASD.

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自闭症谱系障碍的临床概况:加强诊断、治疗和整体健康结果
自闭症谱系障碍(ASD)是一种以沟通、行为和社会互动方面的重大挑战为特征的神经发育疾病。自闭症谱系障碍的患病率正在上升,目前的估计表明,在美国,每36名儿童中就有1名患有自闭症。传统的诊断方法往往无法捕捉到自闭症谱系障碍的全部复杂性,导致治疗方法不完整。本研究旨在通过精准医学识别和分类10种临床特征,提高ASD的诊断和治疗方法。方法本研究提出了一种新的ASD分层模型,该模型基于常见的合并症和潜在的生物学机制对个体进行分类。该模型基于现有文献、临床观察和专家见解的综合。从详细的临床评估、患者病史和实验室测试中收集数据,并通过迭代分析改进模型。它整合了遗传学、神经学和精神病学的研究成果,开发了一种诊断和治疗自闭症相关合并症的综合方法。结果本研究提出了ASD的10个临床特征:综合征、胃肠道、代谢、线粒体、内分泌、生物蓄积、感染、免疫、炎症和神经学。每种特征都与受遗传、环境和生物因素影响的特定症状和合并症有关。结论本研究提出的分层模型通过识别ASD的异质性,提高了对ASD的诊断和治疗。通过识别特定的临床特征,医疗保健提供者可以制定个性化和综合的治疗策略,以改善ASD患者的整体健康状况。
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