Evaluation of body fat changes during weight loss by using improved anthropometric predictive equations

BACKGROUND/AIM: Skinfold-based equations are widely used to evaluate body fat (BF), but over-/underestimation is often reported. We evaluate the capacity of improved skinfold-based equations to estimate BF changes during weight reduction and compare them against well-established equations. METHODS: Overweight adults (n = 44) participated in a 4-month weight reduction intervention. Dual-energy X-ray absorptiometry (DXA) and anthropometric measurements were taken at baseline and after intervention. The BF% was calculated using García, Peterson, and Durnin and Womersley (DW) equations. RESULTS: Baseline and postintervention BF% measured by DXA correlated highest with BF% predicted according to García (r = 0.934 and r = 0.948, respectively), followed by Peterson (r = 0.941 and r = 0.932, respectively) and DW (r = 0.557 and r = 0.402, respectively); only a slight systematic error in overestimating the BF% was observed in estimates according to García (r = 0.147 and r = 0.104, respectively; p < 0.001), while increasing errors occurred using the Peterson (r = 0.624 and r = 0.712, respectively; p < 0.001) and DW (r = 0.767 and r = 0.769, respectively; p < 0.001) equations. Moderate correlations between BF changes (kg) measured by DXA and predicted by DW (r = 0.7211), Peterson (r = 0.697), and García (r = 0.645) were observed

Improved prediction of body fat by measuring skinfold thickness, circumferences, and bone breadths

OBJECTIVE: To develop improved predictive regression equations for body fat content derived from common anthropometric measurements. RESEARCH METHODS AND PROCEDURES: 117 healthy German subjects, 46 men and 71 women, 26 to 67 years of age, from two different studies were assigned to a validation and a cross-validation group. Common anthropometric measurements and body composition by DXA were obtained. Equations using anthropometric measurements predicting body fat mass (BFM) with DXA as a reference method were developed using regression models. RESULTS: The final best predictive sex-specific equations combining skinfold thicknesses (SF), circumferences, and bone breadth measurements were as follows: BFM(New) (kg) for men = -40.750 + {(0.397 x waist circumference) + [6.568 x (log triceps SF + log subscapular SF + log abdominal SF)]} and BFM(New) (kg) for women = -75.231 + {(0.512 x hip circumference) + [8.889 x (log chin SF + log triceps SF + log subscapular SF)] + (1.905 x knee breadth)}. The estimates of BFM from both validation and cross-validation had an excellent correlation, showed excellent correspondence to the DXA estimates, and showed a negligible tendency to underestimate percent body fat in subjects with higher BFM compared with equations using a two-compartment (Durnin and Womersley) or a four-compartment (Peterson) model as the reference method. DISCUSSION: Combining skinfold thicknesses with circumference and/or bone breadth measures provide a more precise prediction of percent body fat in comparison with established SF equations. Our equations are recommended for use in clinical or epidemiological settings in populations with similar ethnic background

Improved prediction of body fat by measuring skinfold thickness, circumferences, and bone breadths

OBJECTIVE: To develop improved predictive regression equations for body fat content derived from common anthropometric measurements. RESEARCH METHODS AND PROCEDURES: 117 healthy German subjects, 46 men and 71 women, 26 to 67 years of age, from two different studies were assigned to a validation and a cross-validation group. Common anthropometric measurements and body composition by DXA were obtained. Equations using anthropometric measurements predicting body fat mass (BFM) with DXA as a reference method were developed using regression models. RESULTS: The final best predictive sex-specific equations combining skinfold thicknesses (SF), circumferences, and bone breadth measurements were as follows: BFM(New) (kg) for men = -40.750 + {(0.397 x waist circumference) + [6.568 x (log triceps SF + log subscapular SF + log abdominal SF)]} and BFM(New) (kg) for women = -75.231 + {(0.512 x hip circumference) + [8.889 x (log chin SF + log triceps SF + log subscapular SF)] + (1.905 x knee breadth)}. The estimates of BFM from both validation and cross-validation had an excellent correlation, showed excellent correspondence to the DXA estimates, and showed a negligible tendency to underestimate percent body fat in subjects with higher BFM compared with equations using a two-compartment (Durnin and Womersley) or a four-compartment (Peterson) model as the reference method. DISCUSSION: Combining skinfold thicknesses with circumference and/or bone breadth measures provide a more precise prediction of percent body fat in comparison with established SF equations. Our equations are recommended for use in clinical or epidemiological settings in populations with similar ethnic background

Influence of hormone replacement therapy on proteomic pattern in serum of postmenopausal women

OBJECTIVES: Proteomics approaches to cardiovascular biology and disease hold the promise of identifying specific proteins and peptides or modification thereof to assist in the identification of novel biomarkers. METHOD: By using surface-enhanced laser desorption and ionization time of flight mass spectroscopy (SELDI-TOF-MS) serum peptide and protein patterns were detected enabling to discriminate between postmenopausal women with and without hormone replacement therapy (HRT). RESULTS: Serum of 13 HRT and 27 control subjects was analyzed and 42 peptides and proteins could be tentatively identified based on their molecular weight and binding characteristics on the chip surface. By using decision tree-based Biomarker Patternstrade mark Software classification and regression analysis a discriminatory function was developed allowing to distinguish between HRT women and controls correctly and, thus, yielding a sensitivity of 100% and a specificity of 100%. The results show that peptide and protein patterns have the potential to deliver novel biomarkers as well as pinpointing targets for improved treatment. The biomarkers obtained represent a promising tool to discriminate between HRT users and non-users. CONCLUSION: According to a tentative identification of the markers by their molecular weight and binding characteristics, most of them appear to be part of the inflammation induced acute-phase respons

The effect of polyphenols in olive oil on heart disease risk factors: a randomized trial.

Background: Virgin olive oils are richer in phenolic content than refined olive oil. Small, randomized, crossover, controlled trials on the antioxidant effect of phenolic compounds from real-life daily doses of olive oil in humans have yielded conflicting results. Little information is available on the effect of the phenolic compounds of olive oil on plasma lipid levels. No international study with a large sample size has been done. Objective: To evaluate whether the phenolic content of olive oil further benefits plasma lipid levels and lipid oxidative damage compared with monounsaturated acid content. Design: Randomized, crossover, controlled trial. Setting: 6 research centers from 5 European countries. Participants: 200 healthy male volunteers. Measurements: Glucose levels, plasma lipid levels, oxidative damage to lipid levels, and endogenous and exogenous antioxidants at baseline and before and after each intervention. Intervention: In a crossover study, participants were randomly assigned to 3 sequences of daily administration of 25 mL of 3 olive oils. Olive oils had low (2.7 mg/kg of olive oil), medium (164 mg/kg), or high (366 mg/kg) phenolic content but were otherwise similar. Intervention periods were 3 weeks preceded by 2-week washout periods. Results: A linear increase in high-density lipoprotein (HDL) cholesterol levels was observed for low-, medium-, and high-polyphenol olive oil: mean change, 0.025 mmol/L (95% CI, 0.003 to 0.05 mmol/L), 0.032 mmol/L (CI, 0.005 to 0.05 mmol/L), and 0.045 mmol/L (CI, 0.02 to 0.06 mmol/L), respectively. Total cholesterol-HDL cholesterol ratio decreased linearly with the phenolic content of the olive oil. Triglyceride levels decreased by an average of 0.05 mmol/L for all olive oils. Oxidative stress markers decreased linearly with increasing phenolic content. Mean changes for oxidized lowdensity lipoprotein levels were 1.21 U/L (CI, -0.8 to 3.6 U/L), -1.48 U/L (-3.6 to 0.6 U/L), and -3.21 U/L (-5.1 to -0.8 U/L) for the low-, medium-, and high-polyphenol olive oil, respectively. Limitations: The olive oil may have interacted with other dietary components, participants' dietary intake was self-reported, and the intervention periods were short. Conclusions: Olive oil is more than a monounsaturated fat. Its phenolic content can also provide benefits for plasma lipid levels and oxidative damage