Fat Mass Estimation in Overweight and Obesity
Condition: Obesity
MeSH terms: Obesity; Overweight
Study type: Observational

Summary: 
      The prevalence of individuals who are classified as overweight or obese is increasing all
      over the world and it represents a primary health concern due to the relationship between
      obesity and a number of diseases, disabilities, comorbidities, and mortality. The definition
      of obesity should consider not only the increase of body weight but more precisely the
      increase in body fat mass. However, body composition evaluation is rarely performed in
      overweight and obese subjects and the diagnosis is almost always achieved just considering
      body mass index (BMI). In fact, whereas BMI can be considered an important tool in
      epidemiological surveys, different papers stated the limitations of the use of BMI in single
      individuals.

      Aim: to assess the determinants of body composition in a population of overweight and obese
      subjects and to propose a different model of estimation of fat mass (FM) in these subjects
      when more reliable equipments for the evaluation of body composition are not available.

      Methods: in 103 overweight or obese subjects ( 74 women, aged 41.5±10 years, and 29 men, aged
      43.8±8 years); a multidimensional evaluation was performed including the assessment of body
      composition using Dual Energy X-Ray Absorptiometry (DXA), anthropometry, bioimpedance
      analysis (BIA) and biochemical parameters (total cholesterol, triacylglycerol, HDL- and LDL-
      cholesterol, free fatty acids and glycerol, glucose, insulin, C-reactive protein, plasma
      acylated and unacetylated ghrelin, adiponectin and leptin serum levels).
    

Detailed description: 
      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.
    

Observational model: Cohort
Time perspective: Cross-Sectional
Sampling method: Probability Sample
Study population: 
        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.
      
Keywords: body composition; obesity