Scholarlink

<p>Early diagnosis and intervention in children with autism spectrum disorder (ASD) is crucial. At present, diagnosis of ASD is primarily based on subjective tools. Identifying metabolic biomarkers will aid in early diagnosis of ASD complementing the assessment tools. The study aimed to conduct targeted metabolomic analysis and determine the plasma metabolites that can discriminate children with ASD from typically developing children (TD), and to determine the utility of machine learning in classifying ASD children based on the metabotypes. This was a multi-centric, analytical, case-control study conducted between April 2021–April 2023. Fasting plasma samples were obtained from seventy ASD and fifty-eight TD children, aged 2 to 12 years. Samples were quantitively analysed for 52 targeted metabolites (13 amino acids, 37 acylcarnitines, adenosine and 2-deoxyadenosine levels) using tandem mass spectrometry. An in-depth statistical analysis was performed. A total of 26 metabolites (11 amino acids, 14 acyl carnitines and adenosine) were found to be significantly (<i>p</i> &lt; 0.005) different between ASD and TD children. Adenosine and amino acid levels were significantly decreased in ASD children. Among acyl carnitines, short- and long-chain acyl carnitine levels were significantly decreased, while medium-chain acyl carnitine levels were significantly increased in ASD children. Octenoylcarnitine-C8:1 (Cut-off value- 0.025 mmol/L, AUC- 0.683) and adenosine (Cut-off value- 0.025 mmol/L, AUC- 0.673) were found to predict children with ASD at a sensitivity of 55.7% and 57.1%, specificity of 79.3% and 72.4% respectively. Based on the metabolites, machine learning models like Support Vector Machine (SVM) and Random Forest (RF) were able to discriminate ASD from TD children with the classification accuracy score being highest in RF (79.487%, AUC- 0.800). Significant abnormalities in plasma metabolites were observed leading to disturbances in the Krebs cycle, urea cycle and fatty acid oxidation, suggesting mitochondrial dysfunction that may possibly contribute in the pathobiology of ASD. Octenoylcarnitine-C8:1 and Adenosine may serve as potential metabolic biomarkers for ASD.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Targeted metabolomics in children with autism spectrum disorder with and without developmental regression

  • 期刊论文

  • 发表日期：

    2025年4月15日

查看全文
  • Chakkera Priyanka,
  • Rita Christopher,
  • Madhu Nagappa,
  • John Vijay Sagar Kommu,
  • Meghana Byalalu Krishnadevaraje,
  • Durai Murukan Gunasekaran,
  • Binu V. S. Nair,
  • Raghavendra Kenchaiah,
  • Nandakumar Dalavalaikodihalli Nanjaiah,
  • Mariamma Philip,
  • Sanjay K. Shivanna,
  • Pragalath Kumar Appadorai,
  • Hansashree Padmanabha

摘要

Early diagnosis and intervention in children with autism spectrum disorder (ASD) is crucial. At present, diagnosis of ASD is primarily based on subjective tools. Identifying metabolic biomarkers will aid in early diagnosis of ASD complementing the assessment tools. The study aimed to conduct targeted metabolomic analysis and determine the plasma metabolites that can discriminate children with ASD from typically developing children (TD), and to determine the utility of machine learning in classifying ASD children based on the metabotypes. This was a multi-centric, analytical, case-control study conducted between April 2021–April 2023. Fasting plasma samples were obtained from seventy ASD and fifty-eight TD children, aged 2 to 12 years. Samples were quantitively analysed for 52 targeted metabolites (13 amino acids, 37 acylcarnitines, adenosine and 2-deoxyadenosine levels) using tandem mass spectrometry. An in-depth statistical analysis was performed. A total of 26 metabolites (11 amino acids, 14 acyl carnitines and adenosine) were found to be significantly (p < 0.005) different between ASD and TD children. Adenosine and amino acid levels were significantly decreased in ASD children. Among acyl carnitines, short- and long-chain acyl carnitine levels were significantly decreased, while medium-chain acyl carnitine levels were significantly increased in ASD children. Octenoylcarnitine-C8:1 (Cut-off value- 0.025 mmol/L, AUC- 0.683) and adenosine (Cut-off value- 0.025 mmol/L, AUC- 0.673) were found to predict children with ASD at a sensitivity of 55.7% and 57.1%, specificity of 79.3% and 72.4% respectively. Based on the metabolites, machine learning models like Support Vector Machine (SVM) and Random Forest (RF) were able to discriminate ASD from TD children with the classification accuracy score being highest in RF (79.487%, AUC- 0.800). Significant abnormalities in plasma metabolites were observed leading to disturbances in the Krebs cycle, urea cycle and fatty acid oxidation, suggesting mitochondrial dysfunction that may possibly contribute in the pathobiology of ASD. Octenoylcarnitine-C8:1 and Adenosine may serve as potential metabolic biomarkers for ASD.