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Background &amp; aims <p>Pediatric metabolic dysfunction–associated steatotic liver disease (MASLD) is increasingly prevalent among children with overweight or obesity, yet its early diagnosis remains a major clinical challenge. This study aimed to identify circulating inflammatory proteins associated with MASLD and to develop a proteomic risk score (ProScore) to improve diagnostic accuracy.</p> Methods <p>In this cross-sectional study of 161 children (median age 8.5&#xa0;years) with overweight or obesity, MASLD was assessed by vibration-controlled transient elastography, with 42 cases identified. Plasma concentrations of 92 inflammation-related proteins were quantified using a high-throughput proximity extension assay. The ProScore was compared with eleven conventional anthropometric/metabolic indices (WHtR, METS-IR, SPISE, PNFI, VAI, LAP, TyG, TyG-ALT, TyG-WC, TyG-WHtR, and TyG-BMI) and a genetic risk score (GRS). Six machine learning algorithms were employed and diagnostic performance was assessed using area under the curve (AUC) with fivefold cross-validation.</p> Results <p>Fifteen proteins were significantly associated with MASLD. A six-protein panel (FGF-21, CDCP1, CD244, OPG, Flt3L, MCP-1) achieved the highest diagnostic accuracy (AUC = 0.84), exceeding that of all conventional indices (AUC = 0.65–0.78; all <i>P</i> &lt; 0.05). ProScore performance remained robust in school-based validation (AUC = 0.83), with no substantial improvement when combined with conventional indices. Diagnostic accuracy was higher in children with lower GRS (AUC = 0.92) than in those with higher GRS (AUC = 0.80; <i>P</i> = 0.003).</p> Conclusions <p>A proteomic signature of systemic inflammation provides accurate, non-invasive identification of MASLD in at-risk children, outperforming conventional metabolic and genetic tools, and may have utility in clinical and public health settings.</p>

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Plasma inflammatory proteome profiles identify MASLD among children with overweight or obesity

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Youxin Wang,
  • Daniel Q. Huang,
  • Pingping Zhang,
  • Mingyue Wang,
  • Yuying Wu,
  • Enkar Nur,
  • Li Li,
  • Hui Wang

摘要

Background & aims

Pediatric metabolic dysfunction–associated steatotic liver disease (MASLD) is increasingly prevalent among children with overweight or obesity, yet its early diagnosis remains a major clinical challenge. This study aimed to identify circulating inflammatory proteins associated with MASLD and to develop a proteomic risk score (ProScore) to improve diagnostic accuracy.

Methods

In this cross-sectional study of 161 children (median age 8.5 years) with overweight or obesity, MASLD was assessed by vibration-controlled transient elastography, with 42 cases identified. Plasma concentrations of 92 inflammation-related proteins were quantified using a high-throughput proximity extension assay. The ProScore was compared with eleven conventional anthropometric/metabolic indices (WHtR, METS-IR, SPISE, PNFI, VAI, LAP, TyG, TyG-ALT, TyG-WC, TyG-WHtR, and TyG-BMI) and a genetic risk score (GRS). Six machine learning algorithms were employed and diagnostic performance was assessed using area under the curve (AUC) with fivefold cross-validation.

Results

Fifteen proteins were significantly associated with MASLD. A six-protein panel (FGF-21, CDCP1, CD244, OPG, Flt3L, MCP-1) achieved the highest diagnostic accuracy (AUC = 0.84), exceeding that of all conventional indices (AUC = 0.65–0.78; all P < 0.05). ProScore performance remained robust in school-based validation (AUC = 0.83), with no substantial improvement when combined with conventional indices. Diagnostic accuracy was higher in children with lower GRS (AUC = 0.92) than in those with higher GRS (AUC = 0.80; P = 0.003).

Conclusions

A proteomic signature of systemic inflammation provides accurate, non-invasive identification of MASLD in at-risk children, outperforming conventional metabolic and genetic tools, and may have utility in clinical and public health settings.