We recently published an article in Clinical Nutrition ESPEN (Czeck et al., 2023) on research we conducted to evaluate the validity of a total and regional dual X-ray absorptiometry (DXA) derived four-compartment model. This research built off our previous study (Czeck et al., 2022) that demonstrated it was possible to accurately determine total and regional volumes using DXA. You can read more about our research on using DXA to determine total and regional volume in our previous blog titled Dual X-Ray Absorptiometry-Derived Total and Regional Body Volume.
In the present study, we wanted to use the total and regional DXA-derived prediction equations from the original study to create a DXA-derived total and regional four-compartment model. The second objective of the present study was to compare our newly developed DXA-derived total and regional four-compartment model to a traditional regional four-compartment model. You may ask, “Why would I use a DXA-derived four-compartment model over the traditional four-compartment model?” Well,
using the traditional four-compartment model for the measurement of body composition takes a lot of equipment, and consequently a lot of time. Therefore, it is not practical to measure body composition using a four-compartment model. The idea of being able to use DXA to measure the majority of the compartments would result in less equipment being involved and would considerably reduce the time necessary to make these measures.
You may also ask, “What is the value of a regional DXA-derived four-compartment model?” One of the main advantages of a four-compartment model is an increase in accuracy. The addition of a fourth compartment improves the overall ability to determine body composition. So, instead of the traditional two-compartment model (i.e., fat mass and fat-free mass) and three-compartment model (i.e., fat mass, lean mass, and bone mass), a compartment of body water is added to create a four-compartment model. Another possible use of a four-compartment model is for the accurate tracking of regional changes in muscle, fat, or bone mass. These changes may be the result of injury, trauma, or disease/illness that can occur in both athletic and non-athletic populations.
So what did we find? The figure below demonstrates that there were no significant differences between the total as well as regional DXA-derived four-compartment model estimations for percent fat compared to the traditional four-compartment model. We also observed these trends for total and regional estimates of fat mass and fat-free mass.
Figure. DXA derived, traditional model, and DXA variables for percent fat for total body, arm, and leg.
You might be wondering, what does this research really mean? For one thing, the research described in this blog expands upon the use of DXA, by providing to those interested in body composition a methodology for the use of a regional DXA-derived four-compartment model. This is noteworthy as a DXA-derived four-compartment model is more convenient compared to the traditional method since it requires the use of less equipment and considerably less time to complete the measurements. In addition, a regional four-compartment model allows for tracking of regional changes due to injury, trauma (e.g., volumetric muscle loss), site-specific tumors, or cancers. While this study may seem to benefit a select number of individuals interested in DXA methodology, it also opens additional possibilities for the use of DXA.
Czeck MA, Juckett WT, Roelofs EJ, Dengel DR: Total and regional dual X-ray absorptiometry derived four-compartment model. Clinical Nutrition ESPEN 55;185-190, 2023.
Czeck MA, Roelofs EJ, Juckett WJ, Dengel DR: Dual X-ray absorptiometry-derived total and regional body volume. Clinical Nutrition ESPEN 52:100-104, 2022.
About the Author
Donald Dengel, Ph.D., is a Professor in the School of Kinesiology at the University of Minnesota and is a co-founder of Dexalytics. He serves as the Director of the Laboratory of Integrative Human Physiology, which provides clinical vascular, metabolic, exercise and body composition testing for researchers across the University of Minnesota.