Degree of Accuracy of the BMI Z-Score to Determine Excess Fat Mass Using DXA in Children and Adolescents

Abstract

Obesity is caused by fat accumulation. BMI Z-score is used to classify the different degrees of weight status in children and adolescents. However, this parameter does not always express the true percentage of body fat. Our objective was to determine the degree of agreement between the fat mass percentage measured by DXA and the stratification of weight according to BMI Z-score in the pediatric age group. We designed a descriptive cross-sectional study. The patients were classified as underweight/normal weight with Z-scores between −2 and +0.99, overweight from 1 to 1.99, obese from 2 to 2.99, and very obese ≥3. We included 551 patients (47% girls), with a mean age of 11.5 ± 2.8 years (3.7–18 years). Higher BMI Z-scores were associated with a higher percentage of total fat (p < 0.001). However, there were important overlaps between both parameters, such that the BMI Z-score classified patients with the same percentage of total fat mass as having a different nutritional status classification. In conclusion, the stratification of weight status according to BMI Z-score revealed that 46.7% of patients had a fat percentage that did not correspond to their classification. For a more accurate weight assessment in clinical practice, we recommend combining anthropometric indices with diagnostic tools that better correlate with DXA, such as electrical bioimpedance.

Estimation of Salt Intake in Normotensive and Hypertensive Children: The Role of Body Weight

“…Regarding BMI z-scores, the participants were classified as underweight/normal weight if their z-scores were between −2 and +0.99. Those with BMI z-scores between 1 to 1.99 were considered overweight, those from 2 to 2.99 obese, and those from ≥3 very obese children [ 57 ]. Our study results indicate that hypertensive children are mostly overweight or obese, while most normotensive children have a normal body weight.…”

Childhood overweight and obesity: age stratification contributes to the differences in metabolic characteristics

“…BMI z score in children was found to be associated with total fat percentages ( p < 0.001) [14]; therefore, it is increasingly used to assess childhood obesity [15], but the detailed metabolites mapping for BMI z score is lacking in children with overweight and obesity, especially its metabolic relations with age stratifications. The results of OPLS indicated the prominent metabolites that contributed to BMI z score were different between the younger and older children.…”

“…Amino acid metabolism and ketone body metabolism disturbance were involved in younger children with overweight and obesity, whereas glycerophospholipid metabolism, glycolysis/gluconeogenesis, and citric acid cycle (TCA cycle) disturbance were involved in older children with overweight and obesity. BMI z score in children was found to be associated with total fat percentages (p < 0.001) [14]; therefore, it is increasingly used to assess childhood obesity [15], but the detailed metabolites mapping for BMI It may be a manifestation of the body's inhibition of excessive autophagy and maladapted unfolded protein response and improving insulin sensitivity [18]. What is worth noting the lower level of glutamine, implying the lower energy expenditure and lean mass construction, which subsequently lowers the metabolic rate at rest and thus excess energy tends to be stored as ectopic fat deposits, especially in the intraabdominal part.…”

Body composition and nutritional status of school‐aged Haitian children living in urban and peri‐urban areas of Puerto Plata, Dominican Republic

“…Measurements were compared to international references to determine age and sex‐specific Z ‐scores. Although the World Health Organization (WHO) recommends the use of BMI Z ‐scores as a simple and effective way to assess nutritional status, BMI does not always provide accurate estimates of body fat mass and some children and adolescents may be misclassified due to its inability to distinguish between fat and fat‐free mass (Monasor‐Ortolá et al, 2021). As such, skinfolds, MUAC, UAMA, and UAFA were also used to examine patterns in the sample based on body composition.…”