Editorial: Body composition assessment techniques in clinical and epidemiological settings: Development, validation and use in dietary programs, physical training and sports

SVAT-sonographic thickness ratio of visceral adipose tissue to anterior thigh muscle: a novel approach for body composition analysis

Body composition analysis is essential for assessing health and fitness, providing insights into fat and muscle distribution in the body and serving as a vital indicator for various conditions. Traditional methods often lack precision, but ultrasound (US) has emerged as a noninvasive alternative. The intra-abdominal visceral adipose tissue to quadriceps muscle thickness ratio emerges as a significant indicator of metabolic health, offering clinicians a valuable tool for personalized interventions. With US, it represents a significant advancement in body composition analysis, promising detailed insights with its noninvasive and real-time imaging capabilities, likely expanding its role in improving human health assessment and interventions.

Development and cross-validation of predictive equations for fat-free mass estimation by bioelectrical impedance analysis in Brazilian subjects with overweight and obesity

Introduction

Obesity is a public health problem worldwide, and body composition assessment is a very important diagnostic tool. Bioelectrical Impedance Analysis (BIA) is a fast, non-invasive, relatively low-cost, and user-friendly technique; however, to obtain greater validity of the estimates, the predictive equations used must be population specific. Thus, the objectives of this study were: (1) to test the validity of four BIA equations used for fat-free mass (FFM) estimation and one model for fat mass (FM) estimation in adults with overweight or obesity; (2) develop and cross-validate new equations to estimate FFM to adults with overweight or obesity, and specific for those with obesity.

Methods

The non-probabilistic sample included 269 individuals, 53.2% with overweight and 46.8% with obesity, aged 18-79 years, randomly divided into two groups: development (n = 178) and cross-validation (n = 91), stratified by sex and classification as overweight or obese. The criterion technique was dual-energy-x-ray absorptiometry (DXA), whereas a tetrapolar single-frequency BIA equipment was used as the alternative method. Paired t-test, multiple regression, concordance correlation coefficient, and Bland-Altman analysis were used.

Results

Most existing equations were not valid and new equations were derived: (1) for individuals with overweight or obesity: CCC = 0.982; r2 = 0.95; standard error of estimate (SEE) = 2.50 kg; limits of agreement (LOA) = -5.0 to 4.8; and (2) specific for individuals with obesity: CCC = 0.968; r2 = 0.94; SEE = 2.53 kg; LOA = -5.3 to 5.2. No FFM differences were observed between the new models and the reference method (p > 0.05).

Conclusion

The new proposed models provide valid options to estimate FFM in an adult population with overweight/obesity.

Clustering by chemicals: A novel examination of chemical pollutants and social vulnerability in children and adolescents

BACKGROUND

Inhaled air pollutants are environmental determinants of health with negative impacts on human health. Air pollution has been linked to the incidence and progression of disease, with its effects unequally distributed across the population. Children compared to adults are a highly vulnerable group and suffer disproportionately from systemic environmental inequities exacerbated by social determinants.

OBJECTIVE

To explore air pollution cluster patterns among 6- to 19-year-olds from the 2015-2016 National Health and Nutrition Examination Survey (NHANES) and examine chemical cluster associations with social vulnerability.

METHODS

NHANES data was extracted for 697 children and adolescents. Social vulnerability characteristics from questionnaires were assembled to construct a modified social vulnerability index (SVI). Thirty-four air pollutant exposure chemicals were measured in urine and available from the laboratory sub-sample A data. K-means clustering classified the sample into three groups: low, medium, and high chemical exposure groups. Logistic regression was used to examine associations between high chemical group membership and SVI after adjusting for age, biological sex, and BMI. Complex survey analysis was conducted using SAS v9.4 to reflect population effects.

RESULTS

Air pollution clusters revealed significant differences in mean concentrations between groups for 31 analytes with minimal distinction in mixture profiles. SVI scores differed significantly between the three groups (P = .002), and with each point increase in their SVI, the odds of a child being assigned to the highest-chemical exposure group increased by 11.55% (95% CI: 1.02-1.31), after adjustment.

CONCLUSION

Unsupervised clustering of environmental sub-sample specimens from NHANES provides an innovative, multi-pollutant model that can be used to explore exposure patterns in this population. Utilizing the modified SVI allows for the identification of children that may be highly susceptible to air pollution. It is imperative to interpret the research findings in light of historical structural and discriminatory inequalities to develop beneficial and sustainable solutions.