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Background <p>Early identification of children at risk for metabolic syndrome (MetS) can reveal traits linked to cardiometabolic disease. We aimed to develop a simple, user-friendly tool to detect pediatric cardiometabolic risk using clinical, nutritional, and lifestyle data.</p> Methods <p>A total of 317 patients (11.35 ± 3.62) were assessed using clinical, dietary, and biochemical data. Metabolic risk was defined by a MetS z-score &gt;0.75, and MetS diagnosis required at least three altered parameters (body composition, blood pressure, glucose, lipids). A 22-variable binary tool generated a cumulative risk score: ≥7 altered components indicated high risk; otherwise, low risk.</p> Results <p>A pathological MetS-score was found in 62.15% of subjects, while MetS was diagnosed in 39.4%. The MetS z-score was significantly correlated with MetS prevalence (<i>r</i> = 0.581). When considering a screening tool score ≥7, along with patients presenting at least 3 of 4 altered MetS parameters, the results demonstrated good sensitivity (0.768 [0.715, 0.835]), negative predictive value (0.775 [0.702, 0.848]), and accuracy (0.618 [0.564, 0.672]), though specificity (52.1% [0.420, 0.600]) and positive predictive value (0.511 [0.439, 0.582]) were moderate.</p> Conclusion <p>A score ≥7 reliably identifies children at cardiometabolic risk, providing a sensitive, non-invasive tool that supports early detection, prevention, and personalized care while reducing time and healthcare costs.</p> Impact <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Early detection of at-risk children can uncover cardio-metabolic traits.</p> </ItemContent> <ItemContent> <p>A 22-noninvasive variable tool was developed to identify pediatric cardio-metabolic risk.</p> </ItemContent> <ItemContent> <p>A score ≥7 effectively identifies children at cardiometabolic risk.</p> </ItemContent> <ItemContent> <p>The proposed non-invasive tool achieves good sensitivity (76.8%) and moderate specificity (52.1%).</p> </ItemContent> <ItemContent> <p>The tool supports clinicians in prevention, monitoring, and personalized care.</p> </ItemContent> </UnorderedList></p>

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A non-invasive tool for the early identification of children at risk of cardiometabolic dysfunction: data from the PODiaCar project

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月3日

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  • Valeria Calcaterra,
  • Lucia Labati,
  • Cristina Campoy,
  • Virginia Rossi,
  • Giulia Fiore,
  • Mireia Escudero-Marin,
  • Matteo Vandoni,
  • Elvira Verduci,
  • Luca Marin,
  • Valter Pagani,
  • Camilo Corbellini,
  • Savina Mannarino,
  • Rocio Bonillo Leon,
  • Inmaculada Guerrero,
  • Vittoria Carnevale Pellino,
  • Alessandro Gatti,
  • Umberto Ciriello,
  • Gianvincenzo Zuccotti

摘要

Background

Early identification of children at risk for metabolic syndrome (MetS) can reveal traits linked to cardiometabolic disease. We aimed to develop a simple, user-friendly tool to detect pediatric cardiometabolic risk using clinical, nutritional, and lifestyle data.

Methods

A total of 317 patients (11.35 ± 3.62) were assessed using clinical, dietary, and biochemical data. Metabolic risk was defined by a MetS z-score >0.75, and MetS diagnosis required at least three altered parameters (body composition, blood pressure, glucose, lipids). A 22-variable binary tool generated a cumulative risk score: ≥7 altered components indicated high risk; otherwise, low risk.

Results

A pathological MetS-score was found in 62.15% of subjects, while MetS was diagnosed in 39.4%. The MetS z-score was significantly correlated with MetS prevalence (r = 0.581). When considering a screening tool score ≥7, along with patients presenting at least 3 of 4 altered MetS parameters, the results demonstrated good sensitivity (0.768 [0.715, 0.835]), negative predictive value (0.775 [0.702, 0.848]), and accuracy (0.618 [0.564, 0.672]), though specificity (52.1% [0.420, 0.600]) and positive predictive value (0.511 [0.439, 0.582]) were moderate.

Conclusion

A score ≥7 reliably identifies children at cardiometabolic risk, providing a sensitive, non-invasive tool that supports early detection, prevention, and personalized care while reducing time and healthcare costs.

Impact

Early detection of at-risk children can uncover cardio-metabolic traits.

A 22-noninvasive variable tool was developed to identify pediatric cardio-metabolic risk.

A score ≥7 effectively identifies children at cardiometabolic risk.

The proposed non-invasive tool achieves good sensitivity (76.8%) and moderate specificity (52.1%).

The tool supports clinicians in prevention, monitoring, and personalized care.