ArticlePediatric research2025
A non-invasive tool for the early identification of children at risk of cardiometabolic dysfunction: data from the PODiaCar project.
Article in Pediatric research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence for Weight Management in Children: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly 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.
methodsA 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.
resultsA 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.
conclusionA 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.
Identifiers
41339771What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.