Normal values for body composition in adults are better represented by continuous reference ranges dependent on age...

Traditional methods for defining body composition reference values often rely on static groupings based on age and BMI. These groupings fail to capture the continuous and gradual changes in body composition that occur with aging and weight fluctuations. Prior research has shown that skeletal muscle mass index (SMI) and fat mass index (FMI) vary dynamically with age and BMI. However, the limitations of static stratification have remained unresolved. This gap motivated the need for a more nuanced approach to reference ranges. No prior work had resolved how age and BMI interact to influence body composition metrics. The field lacked continuous models that could adapt to individual variation. Existing models did not account for nonlinear relationships between age and body composition. This paper addresses these limitations by proposing a new method. The study aims to improve the accuracy of body composition evaluation in diverse populations.

The purpose of this study was to develop continuous reference ranges for body composition parameters that account for age and BMI. Traditional reference ranges often use static groupings, which may not reflect individual variation. The researchers aimed to improve the evaluation of body composition in adults. They focused on skeletal muscle mass index (SMI), fat mass index (FMI), and other related metrics. The motivation was to better understand how body composition changes with age and BMI. The study sought to provide a more accurate and adaptable framework for these parameters. By using continuous models, the researchers hoped to capture gradual changes more effectively. This approach could benefit clinical assessments of very overweight and very old individuals.

The study used cross-sectional data from 1958 healthy adults aged 18 to 97 years. Participants had a BMI range of 17.1 to 45.6 kg/m². The researchers conducted multiple regression analyses to predict body composition parameters. Independent variables included age, age squared, and BMI. The models predicted fat mass index (FMI), visceral adipose tissue (VAT), and others. The analysis was stratified by sex to account for biological differences. Regression models explained varying percentages of the variance in each parameter. The study used statistical techniques to assess the impact of age and BMI on each metric.

Regression models explained between 61% and 93% of the variance in body composition parameters. For FMI in women, the model explained 93% of the variance. Age had a minor impact, contributing 2-16% to the explained variance. BMI played a major role in FMI, VAT, and ALSTI, with total explained variance of 61-93%. In SMI, age was a major determinant, contributing 36% in men and 38% in women. BMI contributed equally to SMI variance, with total explained variance of 72-75%. For ECW/TBW ratio, age explained 79% in men and 74% in women. BMI added only 2-3% to the variance in ECW/TBW.

The derived continuous reference ranges are expected to improve body composition evaluation in very overweight and very old individuals. The study suggests that traditional static groupings oversimplify dynamic changes in body composition. The authors propose that these continuous models better reflect gradual changes with age and BMI. The findings indicate that BMI has a substantial impact on FMI, VAT, and ALSTI. Age is a major determinant of SMI and ECW/TBW ratio variance. The study highlights the importance of incorporating nonlinear relationships in reference models. Future studies need to validate these assumptions in clinical settings. The authors suggest that these models may improve diagnostic accuracy and individualized care.