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Updated: Jun 9, 2025
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Validation of Fat Mass Metrics in Pediatric Obesity
Julia Lischka1,2, Thomas Pixner3,4, Katharina Mörwald5,3
1Department of Pediatrics, Paracelsus Medical University, Salzburg, Austria, lischka.julia@gmail.com.
Insights
The Hudda-Index predicts fat mass but Body Mass Index (BMI) is more reliable for assessing cardiometabolic risk in children. This study validated the Hudda-Index against MRI, finding BMI a superior anthropometric measure for risk assessment.
Area of Science:
- Pediatric Endocrinology
- Body Composition Analysis
- Metabolic Health
Background:
- Fat mass (FM) is linked to comorbidities like type 2 diabetes.
- The Hudda-Index estimates FM using anthropometry, aiding early risk identification in children.
- Independent validation of Hudda-Index against MRI, the gold standard for body composition, was needed.
Purpose of the Study:
- To validate fat mass (FM) calculated by the Hudda-Index against MRI measurements.
- To compare the Hudda-Index with other anthropometric measures like BMI, waist circumference, and skinfold thickness.
Main Methods:
- A cohort of 115 children aged 9-15 years was studied in Austria and Sweden.
- Standard anthropometry, blood sampling, and oral glucose tolerance tests were performed.
- Magnetic resonance imaging (MRI) was used to measure visceral adipose tissue (VAT) and subcutaneous adipose tissue.
Main Results:
- Body Mass Index (BMI) and waist circumference (WC) showed stronger associations with visceral adipose tissue (VAT) than Hudda-Index.
- BMI and Hudda-Index demonstrated a strong linear association and acceptable correlation with cardiometabolic parameters.
- VAT was significantly associated with liver status markers and insulin resistance, predicting metabolic dysfunction-associated steatotic liver disease.
Conclusions:
- Body Mass Index (BMI) remains the most reliable anthropometric tool for estimating cardiometabolic risk in children.
- Despite its imperfections, BMI offers more reliable cardiometabolic risk estimation than other anthropometry-based measures.
- The Hudda-Index, while correlating with BMI, did not surpass BMI's predictive capability for cardiometabolic risk.
Introduction:
Hudda-Index is a prediction model for fat mass (FM) based on simple anthropometric measures. FM is a crucial factor in the development of comorbidities, i.e., type 2 diabetes. Hence, Hudda-Index is a promising tool to facilitate the identification of children at risk for metabolic comorbidities. It has been validated against deuterium dilution assessments; however, independent validation against the gold standard for body composition analysis, magnetic resonance imaging (MRI), is lacking. The aim of this study was to validate FM calculated by Hudda-Index against FM measured by MRI. The secondary aim was to compare Hudda-Index to other anthropometric measures including body mass index (BMI), BMI-standard deviation score (BMI-SDS), waist/hip-ratio, waist circumference (WC), and skinfold thickness.
Methods:
The study cohort consists of 115 individuals between the age of 9 and 15 years, recruited at Paracelsus Medical University Hospital in Salzburg (Austria) and Uppsala University Children's Hospital (Sweden). Anthropometry, blood samples, and oral glucose tolerance tests followed standard procedures. MRI examinations were performed to determine visceral adipose tissue (VAT) and subcutaneous adipose tissue.
Results:
BMI and WC showed slightly stronger associations with the reference standard VAT (r = 0.72 and 0.70, p < 0.01, respectively) than Hudda-Index (r = 0.67, p < 0.01). There is an almost perfect linear association between BMI and Hudda-Index. Accordingly, BMI and Hudda-Index both showed an acceptable association with cardiometabolic parameters. VAT was strongly associated with markers of liver status (LFF r = 0.59, p < 0.01) and insulin resistance (HOMA-IR r = 0.71, p < 0.01) and predicted metabolic dysfunction-associated steatotic liver disease.
Conclusion:
BMI, although an imperfect measure, remains the most reliable tool and estimates cardiometabolic risk more reliably than other anthropometry-based measures.
