Scholarlink

Background <p>Type 1 Diabetes (T1D) exhibits considerable heterogeneity, impacting prediction, prevention, diagnosis, and treatment. Precision medicine aims to tailor treatments using ‘endotypes’—subtypes of disease with distinct pathophysiological mechanisms. However, proposed endotypes often lack mechanistic associations with clinical outcomes for accurately identifying T1D cases.</p> Methods <p>This study introduces an approach leveraging the multi-omics factor analysis (MOFA) strategy, a computational method for unsupervised integration analysis, to explore endotypes. Analyzing data from 146 new-onset children with T1D (54 females, 92 males; age range 5–18 years), including circulating immunome, transcriptome, and serum metabolic hormones, we identify 12 factors explaining variability across the three data sets.</p> Results <p>Here we find no associations, either direct or through clustering, between these 12 factors and clinical parameters, genetic predisposition, or disease outcome. These results suggest that a combination of clinical phenotypes might be responsible for the differences across T1D cases.</p> Conclusions <p>These findings challenge the assumption that T1D heterogeneity reflects diverse developmental mechanisms. These results add to the ongoing debate on endotypes and carry important implications for clinical trial design—particularly in how treatments are evaluated for their effectiveness across broad and diverse patient populations.</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

A multi-omics integration approach relying on circulating factors does not discern subtypes of childhood type 1 diabetes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月27日

查看全文
  • Valentina Codazzi,
  • Nicola Baldoni,
  • Giulia M. Scotti,
  • Anna Giovenzana,
  • Andrea Rigamonti,
  • Giulio Frontino,
  • Eugenia Bezzecchi,
  • Camillo Bechi Genzano,
  • Alessandra Mandelli,
  • Debora Carnovale,
  • Ilaria Marzinotto,
  • Vito Lampasona,
  • Paolo Fiorina,
  • Andrea Giustina,
  • Lorenzo Piemonti,
  • Manuela Battaglia,
  • Marco J. Morelli,
  • Riccardo Bonfanti,
  • Alessandra Petrelli

摘要

Background

Type 1 Diabetes (T1D) exhibits considerable heterogeneity, impacting prediction, prevention, diagnosis, and treatment. Precision medicine aims to tailor treatments using ‘endotypes’—subtypes of disease with distinct pathophysiological mechanisms. However, proposed endotypes often lack mechanistic associations with clinical outcomes for accurately identifying T1D cases.

Methods

This study introduces an approach leveraging the multi-omics factor analysis (MOFA) strategy, a computational method for unsupervised integration analysis, to explore endotypes. Analyzing data from 146 new-onset children with T1D (54 females, 92 males; age range 5–18 years), including circulating immunome, transcriptome, and serum metabolic hormones, we identify 12 factors explaining variability across the three data sets.

Results

Here we find no associations, either direct or through clustering, between these 12 factors and clinical parameters, genetic predisposition, or disease outcome. These results suggest that a combination of clinical phenotypes might be responsible for the differences across T1D cases.

Conclusions

These findings challenge the assumption that T1D heterogeneity reflects diverse developmental mechanisms. These results add to the ongoing debate on endotypes and carry important implications for clinical trial design—particularly in how treatments are evaluated for their effectiveness across broad and diverse patient populations.