Fat Mass Estimation in Overweight and Obesity

Participants This study was based on the baseline data from a randomised controlled trial aimed at the evaluation of the effects of 2- month consumption of a combination of bioactive food ingredients on changes in body composition, satiety control, thermogenesis, serum markers of lipolysis .

The study was performed under the approval of the Ethics Committee of the Department of Internal Medicine and Medical Therapy at University of Pavia. The informed consent to the study was obtained by all the participants or their legal representatives. Healthy males and females aged from 25 to 45 years, with a BMI greater than 25 kg/m2 and less than 35 kg/m2, were eligible for the study. All subjects underwent physical examination, anthropometric assessment and routine laboratory tests. The complete medical history was collected for all the subjects. Individuals who were pregnant or lactating, or had any disease potentially affecting body composition and laboratory evaluation were excluded from the study, especially severe hepatic or renal disease, unstable cardiovascular disease, uncontrolled hypertension, active cancer or surgery for weight loss were the main exclusion criteria.

Multidimensional evaluation After a 12- hour fasting, and abstinence from water since midnight, the subjects arrived at around 8:00 a.m., using motorised transportation, at the Endocrinology and Clinical Nutrition Unit of the University of Pavia (Italy) and at the Dietetic and Metabolic Unit, "Villa delle Querce" Clinical Rehabilitation Institute in Rome (Italy).

Blood sampling for routine blood analysis and for the measurements of leptin, adiponectin, ghrelin, insulin, glycerol, free fatty acid levels, as well as the assessment of body composition by dual energy X-ray absorptiometry (DXA) and anthropometry were performed in the fasting state at baseline.

Body composition was measured using DXA (Lunar Prodigy DXA, General Electric Medical Systems, Wisconsin). The in vivo coefficients of variation were 4.2% and 0.48% for fat and lean mass, respectively. Central fat, defined as the approximation of the visceral fat, was assessed with DXA, measuring the fat percentage corresponding to an ideal rectangle defined from the upper edge of the second lumbar vertebra to the lower edge of the fourth lumbar vertebra. The vertical sides of this area were the continuation of the lateral sides of the rib cage . All measurements for each parameter were gathered by the same investigator.

The following anthropometric measurements were performed in all subjects:

body weight and height; biceps (BSF), triceps (TSF), suprailiac (SISF), and subscapular (SSSF) skinfold thicknesses, waist circumference (W), hip circumference (H), arm circumference (AC) and calf circumference (CC) In order to avoid the inter- assessor variability, anthropometric variables were measured by a unique investigator following a standardized technique .

Using the abovementioned anthropometric parameters, the following variables were calculated:

Body mass index (BMI): weight (kg) / height2 (m2) Waist to hip ratio (WHR) arm muscle area arm fat area muscle arm circumference Bioimpedance analysis (BIA): whole-body impedance vector components, resistance (R) and reactance (Xc), were measured with a single-frequency 50- KHz analyzer from AKERN Bioresearch Italy). Measurements were obtained following standardized procedures . The external calibration of the instrument was checked with a calibration circuit of known impedance value. Estimations of fat-free mass (FFM) and FM by BIA were obtained using gender- specific, BIA prediction equations recently developed by Sun et al in a large population that included extremes of BMI values. The fat mass index (FMI) was calculated through the normalisation of FM, obtained by the BIA, for height: FMI = FM (kg)/ height (m)2.

Biochemical analyses

Subjects were instructed to fast over 12 hours, and to refrain from any form of exercise for 48 hours, before blood collection. Female subjects were tested during the early follicular phase of their menstrual cycles (days 3-10). Fasting venous blood samples were drawn between 08.00 and 10.00 a.m.. Blood collection and handling were carried out under strictly standardized conditions and clinical chemistry parameters were detected with dedicated commercial kits. In particular total cholesterol, triacylglycerol, HDL- and LDL- cholesterol, free fatty acid (FFA), glycerol, glucose, insulin, C- reactive protein (CRP), plasma acylated and unacetylated ghrelin, adiponectin and leptin serum levels were measured. Leptin/ adiponectin ratio (LAR) was calculated. Insulin resistance was evaluated using the Homeostasis Model Assessment (HOMA) and Quantitative Insulin sensitivity Check Index (QUICKI) using the following formulas:

HOMA-Insulin Resistance = [(fasting insulin, mcU/ml) x (plasma glucose, mmol/l)]/22.5 QUICKI = 1/ [log (glucose, mg/dl) + log (insulin, µU/ml)] Statistical analysis Data were described as mean and standard deviation (SD) if continuous and as percentage if categorical.

We considered FM from DXA as the outcome variable and all the anthropometric, bioimpedance and laboratory data as potential explicative variables.

The predictive value of BMI and FM from BIA were compared to the FM from DXA (overall predictive value, sensibility, specificity, positive and negative predictive values). Therefore we considered the following cut-off values for the definition of obesity :

FM ≥ 25 % for men and ≥ 35 % for women (at DXA and BIA) BMI ≥ 30 kg/m2 6 The variance analysis and the Student t-test were used to assess the significance of differences in the averages; the χ2 to compare the frequencies observed with those expected; the Pearson's to evaluate the correlation existing between two continuous variables.

Variables univariately proven correlated with the outcome variable were entered a pool of potential contributors in multiple regression analysis.

We estimated models using a forward likelihood stepwise method (cut-off probability for entry: 0.05). With each added variable the discriminant function was recalculated and any variable that no longer met the significance level was removed from the equation (cut-off probability for removal: 0.1).

Some variables with similar biological significance were excluded from the logistic analysis, in order to avoid the confounding effect of collinearity (verified with Pearson's r, t-test or χ2). The best fitting model was chosen according to the value of the correlation coefficients R2, (comparing the explained variance of the model's predictions with the total variance of the data), the adjusted R2 (R2 adj), considering a correction for inclusion of variables.

We considered a significance level equal to a 5% probability of error. Data were analysed using the SPSS for Windows 10.0 (SPSS Inc 1989-1999) and the Win Episcope 2.0 [Wageningen University (N), University of Edinburgh (GB)] statistical software packages.