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Sleeping metabolic rate and body size in 12-week-old infants
1Infant and Child Nutrition Group, Dunn Nutrition Unit, Cambridge, UK.
Insights
Researchers sought to standardize sleeping metabolic rate (SMR) measurements. Adjusting SMR using exponents close to 0.50 for body weight and fat-free mass effectively accounts for body size and composition.
Area of Science:
- Human Physiology
- Metabolic Research
Background:
- Sleeping metabolic rate (SMR) is a key physiological indicator.
- SMR is significantly influenced by individual body size and composition.
- Standardized methods are needed for accurate SMR comparisons.
Purpose of the Study:
- To determine the optimal method for adjusting SMR.
- To account for body size and composition in SMR measurements.
- To enable reliable SMR comparisons across individuals and groups.
Main Methods:
- Studied 50 infants at 12 weeks of age.
- Measured SMR using indirect calorimetry.
- Assessed body size and composition via anthropometry and stable isotope techniques.
- Employed regression analysis to identify adjustment factors for SMR.
Main Results:
- Regression analysis indicated SMR adjustment powers of 0.41 for body weight and 0.44 for fat-free mass.
- Optimal powers for SMR adjustment at its minimum were found to be 0.41 for body weight and 0.45 for fat-free mass.
- These findings suggest exponents near 0.50 are effective.
Conclusions:
- SMR can be effectively adjusted using body weight or fat-free mass.
- Expressing SMR with exponents near 0.50 for weight or fat-free mass accounts for size and composition.
- This standardization facilitates more accurate SMR comparisons.
Objective:
Sleeping metabolic rate (SMR) is influenced by body size and body composition. In order to be able to compare SMR between individuals and groups, the best way to remove the effect of body size and body composition was sought.
Design:
A cohort of 50 infants was studied at 12 weeks. SMR was measured by indirect calorimetry, and body size and body composition by anthropometry and a stable isotope technique. Regression analysis was used to calculate the best way to remove the effect of body size and body composition on SMR.
Results:
Regression analysis showed that SMR was adjusted for body weight and fat-free mass by raising body weight to the power 0.41, and by raising fat-free mass to the power 0.44. When SMR was at its minimum level, the optimum powers were 0.41 for body weight and 0.45 for fat-free mass.
Conclusions:
SMR can be adjusted for body weight, or fat-free mass thereby taking both body size and body composition into account, by expressing SMR in terms of weight or fat free mass raised to powers close to 0.50.