Bioelectrical Impedance Analysis in Children: Between Necessity and Challenges

The year 2025 marks an unprecedented epidemiological turning point in the history of global child nutrition. For the first time, the prevalence of obesity in children exceeds that of underweight (9.4% vs 9.2%) [1]. Worldwide, one in five children aged 5 to 19 (391 million children) lives in an overweight situation, and 163 million live with obesity [2]. Faced with this reality, the study of body composition in children now constitutes a key step in pediatric monitoring. Bioelectrical impedance analysis (BIA) contributes to a more precise assessment of nutritional status, to the adaptation of management strategies, and to the longitudinal monitoring of their effectiveness.

Information Provided by BIA

BIA allows the estimation of fat mass (FM) and fat-free mass (FFM), as well as the indices associated with them: the FMI (Fat Mass Index) and the FFMI (Fat-Free Mass Index).

The FFMI is calculated as fat-free mass divided by height squared (FFM/height², in kg/m²), modeled on the body mass index (BMI). The FFMI isolates the non-fat component (muscle, bone, organs, water) which allows a more precise assessment of nutritional status and musculoskeletal growth status than BMI. Its interpretation must necessarily involve a Z-score or percentile, specific to age and sex. A non-referenced FFMI value provides no interpretable information. An FFMI of 14 kg/m² may be normal at 6 years and low at 16 years.

The FMI is calculated as fat mass divided by height squared (FM/height², in kg/m²). The FMI specifically isolates adiposity.

The combined interpretation of FMI and FFMI provides very useful information in pediatric practice. This pair of indices allows distinguishing an isolated excess of adiposity, an isolated deficit of fat-free mass, or the association of these two anomalies. The interpretation of each of these indices taken individually does not allow identifying all of these situations.

A study in children followed in pediatric outpatient care showed that a normal BMI may conceal a deficit of fat-free mass that BMI alone cannot detect [3]. This applies in both directions:

• Low FFMI + Normal BMI → masked undernutrition [3]
• High FFMI + High BMI → muscular child, not in real excess of adiposity (BMI false positive) [6
• Low FFMI + High FMI + High BMI → sarcopenic obesity, combining excess fat mass and deficit of fat-free mass, with a cumulative metabolic risk [4], [5]

Integration of BIA in Pediatric Monitoring

BIA presents numerous advantages for clinicians involved in the management of nutritional disorders as well as in monitoring child growth. The assessment of body composition offers a better view of nutritional status, highlighting both undernutrition and overnutrition situations. Technological advances and numerous studies conducted in recent years have improved the reliability of body composition estimation in children.

Childhood and adolescent obesity constitutes a major determinant of health in adulthood. It is associated with an increased risk of subsequently developing type 2 diabetes, coronary heart disease, arterial hypertension, strokes, certain cancers, as well as various respiratory and renal conditions, which are among the leading causes of morbidity and mortality worldwide. Several studies have shown that an increase in BMI during childhood is correlated with an increased risk of the appearance of these pathologies in adulthood [7]. Conversely, a study published in 2018 notably showed that a normalization of weight status before the end of adolescence was associated with a significant reduction in the risk of type 2 diabetes in adulthood [8].

The French National Authority for Health (Haute Autorité de Santé – HAS) published in 2022, then updated in 2023, a care pathway guide intended for children and adolescents in overweight or obesity situations [9]. This is based on five fundamental principles: early detection, grading of management according to the severity of the situation, development of a personalized care plan, preparation for the transition to adult care, and continuous coordination of the care pathway. BIA can be integrated into each of these steps by providing objective data complementary to clinical assessment. A relevant tool for the longitudinal monitoring of therapeutic interventions, BIA has the ability to follow changes in body composition in children with obesity during a lifestyle modification program [10].

Perspectives and Challenges

The recent evolution of pediatric BIA is moving towards a diversification of its clinical indications, beyond the sole screening of obesity and undernutrition. Several studies have been published in 2025–2026 using BIA in specific chronic pathologies: celiac disease [11], type 1 diabetes [12], nephrotic syndrome [13], and congenital heart diseases [14].

The main current challenges of BIA consist of expanding the studied populations to different ethnic groups and continuing comparative validation efforts against DXA. A recent systematic review including 28 studies in children aged 2 to 17 years shows that this methodological work is still ongoing and cannot be considered finalized [15].

BIA allows a more complete assessment of body composition in children and can improve prevention as well as nutritional management from an early age. Healthy beginnings lay solid foundations for the future.

References

1. UNICEF. Feeding Profit: How Food Environments are Failing Children. Child Nutrition Report 2025. New York : UNICEF ; septembre 2025.
2. World Obesity Federation. Feeding Profit: New UNICEF Report Exposes How Food Environments are Failing Children. Communiqué de presse, septembre 2025
3. Zhu Y et al. Assessment of nutritional status in paediatric outpatients using bioelectrical impedance analysis and anthropometric z-scores. J Paediatr Child Health. 2021. doi:10.1111/jpc.15450. PMID:33749969
4. Sack C, Ferrari N, Friesen D, Haas F, Klaudius M, Schmidt L, Torbahn G, Wulff H, Joisten C. Health Risks of Sarcopenic Obesity in Overweight Children and Adolescents: Data from the CHILT III Programme (Cologne). J Clin Med. 2022 Jan 5;11(1):277. doi: 10.3390/jcm11010277. PMID: 35012017; PMCID: PMC8746104.
5. Zembura M, Matusik P. Sarcopenic Obesity in Children and Adolescents: A Systematic Review. Front Endocrinol (Lausanne). 2022 Jun 1;13:914740. doi: 10.3389/fendo.2022.914740. Erratum in: Front Endocrinol (Lausanne). 2023 Aug 15;14:1269546. doi: 10.3389/fendo.2023.1269546. PMID: 35721709; PMCID: PMC9198401.
6. Etchison WC, Bloodgood EA, Minton CP, Thompson NJ, Collins MA, Hunter SC, Dai H. Body mass index and percentage of body fat as indicators for obesity in an adolescent athletic population. Sports Health. 2011 May;3(3):249-52. doi: 10.1177/1941738111404655. PMID: 23016014; PMCID: PMC3445161.
7. Horesh A, Tsur AM, Bardugo A, Twig G. Adolescent and Childhood Obesity and Excess Morbidity and Mortality in Young Adulthood-a Systematic Review. Curr Obes Rep. 2021 Sep;10(3):301-310. doi: 10.1007/s13679-021-00439-9. Epub 2021 May 5. PMID: 33950400.
8. Bjerregaard LG, Jensen BW, Ängquist L, Osler M, Sørensen TIA, Baker JL. Change in Overweight from Childhood to Early Adulthood and Risk of Type 2 Diabetes. N Engl J Med. 2018 Apr 5;378(14):1302-1312. doi: 10.1056/NEJMoa1713231. PMID: 29617589.
9. Haute Autorité de Santé. Guide du parcours de soins : surpoids et obésité chez l’enfant et l’adolescent(e) [Internet]. Saint-Denis : HAS ; 2022 [mis à jour 2023].
10. Benjaminsen CR, Jørgensen RM, Vestergaard ET, Bruun JM. Compared to dual-energy X-ray absorptiometry, bioelectrical impedance effectively monitors longitudinal changes in body composition in children and adolescents with obesity during a lifestyle intervention. Nutr Res. 2025;133:1-12.
11. Gülseren et al. Bioelectrical Impedance Analysis to Identify the Affected Body Component in Girls With Celiac Disease. Pediatrics International. 2026.
12. Bioelectrical impedance analysis of body composition in children and adolescents with type 1 diabetes: a prospective case-control study. European Journal of Pediatrics. 2025.
13. Quist JR, Ward LC, Jødal L, Andersen RF, Hvas CL, Brantlov S. Integrating machine learning for advanced analysis of bioelectrical impedance parameters in children with nephrotic syndrome. Front Pediatr. 2026;14:1714324.
14. Katz DA et al. Bioelectrical Impedance Analysis and How it Correlates to Intracardiac Hemodynamics in Patients with Congenital Heart Disease. Pediatr Cardiol. 2026;47(2):777-783.
15. Bioelectrical Impedance Analysis Versus Dual X-Ray Absorptiometry for Obesity Assessment in Pediatric Populations: A Systematic Review. Diagnostics. 2025;15(12):1505.