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Body cell mass: model development and validation at the cellular level of body composition
ZiMian Wang1, Marie-Pierre St-Onge, Beatriz Lecumberri
1Weight Control Unit, Obesity Research Center, St. Luke's-Roosevelt Hospital, Columbia University College of Physicians and Surgeons, 1090 Amsterdam Avenue, 14th Floor, New York, NY 10025, USA. ZW28@Columbia.edu
American Journal of Physiology. Endocrinology and Metabolism
|October 9, 2003
Summary
New models improve body cell mass (BCM) estimation using total body potassium (TBK) and total body water (TBW). This validated formula offers more accurate BCM predictions for research.
Area of Science:
- Body composition analysis
- Human physiology
- Biomedical research
Background:
- Accurate estimation of metabolically active body cell mass (BCM) in vivo is crucial but current models are limited.
- The classic Moore model relies on an assumed potassium content in BCM, potentially leading to inaccuracies.
Purpose of the Study:
- To develop an improved, total body potassium (TBK)-independent BCM prediction model.
- To explore the sex and age dependence of the TBK/BCM ratio.
- To create a new TBK/BCM model based on physiological associations between TBK and total body water (TBW).
Main Methods:
- Measured TBW, extracellular water, total body nitrogen, bone mineral, and TBK in 112 healthy adults.
- Applied an improved earlier model (Cohn et al.) and developed a new TBK-TBW model.
- Utilized human reference data from published reports for validation.
Main Results:
- The improved Cohn model yielded a TBK/BCM ratio of 109.0 +/- 10.9 mmol/kg, independent of sex and age.
- A simplified TBK-TBW model produced a nearly identical TBK/BCM ratio of 109.1 mmol/kg.
- The new prediction formula [BCM = 0.0092 x TBK] provides ~11% higher BCM estimates than the classic Moore model.
Conclusions:
- A physiologically based, improved, and validated TBK-BCM prediction formula has been developed.
- This new formula offers more accurate BCM estimation compared to the classic Moore model.
- The findings are valuable for advancing body composition and metabolism research.