ABSTRACT
Objective: To evaluate the prevalence of excessive adiposity among normal-weight individuals, and their cardiometabolic risk. Methods: This cross-sectional study included 3,001 participants (ages 20-95, 52% men, BMI 28.0 ± 5.5 kg/m2) who completed an anthropometric evaluation, dual x-ray absorptiometry (DXA) scan to measure body composition, and cardiometabolic blood markers. Excess adiposity was defined as ≥25% for men and ≥ 35% for women. Results: Of the entire study participants, 967 were in normal BMI (18.5-24.9 kg/m2) with a wide body fat distribution (4-49%). Of them, 26% of men and 38% of women were classified with excess adiposity. As compared to normal-weight lean participants, normal-weight obese men and women had higher triglycerides (76.5 ± 37.3 vs. 101.2 ± 50.3 mg/dL, p = 0.004 and 84 ± 44.2 vs. 101.4 ± 91.1 mg/dL, p = 0.030; respectively) and elevated low-density lipoprotein cholesterol (103.3 ± 31.7 vs. 119.6 ± 45.5 mg/dL, p = 0.011) and total cholesterol (171.5 ± 40.3 vs. 190.2 ± 39 mg/dL, p = 0.007) for men only. Among NWO, abdominal circumference was prevalent in 60% of the females with NWO (≥88 cm), but only in 4% of males (≥102 cm). Conclusion: Higher adiposity, even within normal weight, increases cardiometabolic risk, and abdominal waist circumference misclassified obesity in normal-weight individuals. This study highlights the need for a body composition evaluation to determine cardiometabolic risk for adults with normal body weight.
ABSTRACT
Current prediction equations for resting metabolic rate (RMR) were validated in a relatively small sample with high-individual variance. This study determined the accuracy of five common RMR equations and proposed a novel prediction equation, including body composition. A total of 3001 participants (41 ± 13 years; BMI 28.5 ± 5.5 kg/m2; 48% males) from nutrition clinics in Israel were measured by indirect calorimetry to assess RMR. Dual-energy X-ray absorptiometry were used to evaluate fat mass (FM) and free-fat mass (FFM). Accuracy and mean bias were compared between the measured RMR and the prediction equations. A random training set (75%, n = 2251) and a validation set (25%, n = 750) were used to develop a new prediction model. All the prediction equations underestimated RMR. The Cunningham equation obtained the largest mean deviation [-16.6%; 95% level of agreement (LOA) 1.9, -35.1], followed by the Owen (-15.4%; 95% LOA 4.2, -22.6), Mifflin-St. Jeor (-12.6; 95% LOA 5.8, -26.5), Harris-Benedict (-8.2; 95% LOA 11.1, -27.7), and the WHO/FAO/UAU (-2.1; 95% LOA 22.3, -26.5) equations. Our new proposed model includes sex, age, FM, and FFM and successfully predicted 73.5% of the explained variation, with a bias of 0.7% (95% LOA -18.6, 19.7). This study demonstrates a large discrepancy between the common prediction equations and measured RMR and suggests a new accurate equation that includes both FM and FFM.
