Objectives
Traditional evaluations of metabolic health may overlook underlying dysfunction in individuals who show no signs of insulin resistance or dyslipidemia. The purpose of this study was to characterize metabolic health in overweight and obese adults using traditional and non-traditional metabolic variables. A secondary purpose was to evaluate differences between overweight/obese and male/female cohorts, respectively.
Methods
Forty-nine overweight and obese adults (Mean ± SD; Age=35.0 ± 8.9 yrs; Body mass index=33.6 ± 5.2 kg·m−2; Percent body fat [%fat]=40.0 ± 7.3%) were characterized. Body composition (fat mass [FM], lean mass [LM], %fat) was calculated using a 4-compartment model; visceral adipose tissue (VAT) was quantified using B-mode ultrasound. Resting metabolic rate (RMR) and respiratory exchange ratio (RER) were evaluated using indirect calorimetry. Fasted blood and saliva samples were analyzed for total cholesterol (TC), high-density lipoproteins (HDL), low-density lipoproteins (LDL), triglycerides (TRG), glucose (GLUC), insulin, leptin, estradiol, and cortisol.
Results
The prevalence of individuals with two or more risk factors increased from 13% to 80% when non-traditional metabolic factors (list which factors) were considered in addition to traditional risk factors (GLUC, TRG, HDL). Between overweight and obese individuals, there were no significant differences in %fat (p=0.146), VAT (p=0.959), RER (p=0.493), blood lipids/GLUC (p>0.05), insulin (p=0.143), leptin (p=0.053), or cortisol (p=0.063); obese had higher FM, LM, RMR, and estradiol (p<0.001). Males had greater LM, RMR, and TRG (p<0.01); females had greater FM, %fat, HDL, and leptin (p<0.001). There were no significant sex differences in RER (p=0.638), estradiol (p=0.052), insulin (p=0.263), or cortisol (p=0.784).
Conclusions
Evaluating metabolic health beyond BMI and traditional cardio-metabolic risk factors can give significant insights into metabolic status. Due to high variability in metabolic health in overweight and obese adults and inherent sex differences, characterization based on body composition, metabolic factors, and hormonal profiles can improve early identification and approaches to disease prevention.