Percentage of Body Fat and Tool flor Metabolice Síndrome

  

Mass (kg) by the square of his or her height (m), as previously described. In a sub-sample (48 adults, 54% women, range mean age = 20 to 40 years old), we verified the validity of BIA for predicting BF% in Colombian adults using DXA as a referencie.

Our analysis showed a strong agreement between the two methods, as reflected in the range of BF% (Lin’s concordance correlation coefficient = 0.943 (95% CI = 0.775 to 0.950, p = 0.041) and bias −0.6 (SD 2.2; 95% CI = −5.0 to 3.7).

In line with our findings, a previously study has shown a high correlation between fat mass determined by BIA and that obtained by a CT scan and DXA .

Thus, these results show that BIA and DXA are comparable methods for measuring body composition with lower/higher body fat percentages.

Metabolic Syndrome Diagnosis

After the subjects had fasted for 10–12 h, blood samples were obtained from capillary sampling between 6:00 a.m. and 9:00 a.m.Participants were asked not to engage in prolonged exercise in the 24 h prior to testing.

The biochemical profile included the following: (i) high-density lipoprotein cholesterol (HDL-C); (ii) triglycerides; (iii) low-density lipoprotein cholesterol (LDL-C); (iv) total cholesterol;(v) glucose fasting by enzymatic colorimetric methods, Polymer Technology Systems,PTS, Indianapolis, IN, USA).

The inter-assay reproducibility (coefficient of variation) was determined from 16 replicate analyses of 8 capillary blood pools over a period of 15 days.

The percentages obtained were 2.6% for triglycerides, 2.0% for total cholesterol, 3.2% for HDL-C, 3.6% for LDL-C, and 1.5% for fasting glucose.

Blood pressure was taken on the left arm at the heart level with an automatic device Omron M6 Comfort Healthcare Europe B.V., Hoofddorp, The Netherlands) while the participants were sitting still.

The blood pressure monitor cuff was placed two to three finger-widths above the bend of the arm and a two-minute pause was allowed between the first and second measurements with  a standard cuff for an arm circumference of 22–32 cm.MetS was defined in accordance with the updated harmonized criteria of the IDF .

Participants were considered to have MetS if they showed three or more of the following: (1) abdominal obesity for individuals (WC ≥ 80 cm in women and ≥ 90 cm in men); (2) hypertriglyceridemia (≥150 g/dL);(3) low HDL-C (<50 mg/dL in women and <40 mg/dL in men); (4) high blood pressure (systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg); (5) high fasting glucose (≥100 mg/dL).

Lifestyle Co-Variables

A standardized questionnaire, FANTASTIC lifestyle, was used to collect comprehensive information about substance use via a personal interview with participants.

Alcohol consumption and smoking status were defined as subjects who had consumed any alcoholic beverage ≥1 times per week, and those who had smoked ≥10 cigarettes per week, for at least six months, as previously described by Ramírez-Vélez et al. 

Participants who exercised three or more times a week      for >30 min were categorized as physically active (PA), and those who exercised less than three times a week were considered insufficiently physically active.

The accuracy of information about lifestyle co-variables obtained from the FANTASTIC questionnaire has been validated by different cross-sectional studies and described in detail elsewhere.

Ethics Statement

Informed consent was obtained from each participant. The protocol was based on the Helsinki Declaration Accord (World Medical Association for Human Subjects).

Moreover, ethical approval was obtained from the Universidad Manuela Beltrán (UMB N◦ 01-1802-2013).

Statistical Analysis

Participants’ characteristics obtained were given as mean values and standard deviation (SD). Histograms and Q–Q plots were used to verify the normality of the selected variables.

Independent two-tailed t-tests for continuous variables, and chi-square (χ²) tests for categorical variables, were usedto examine sex differences or MetS grouping.

The relationships between BF%, FMI, and MetS components were tested by means of partial correlation coefficients. This analysis was adjusted by age, sex, tobacco, and alcohol consumption, and PA levels.

To predict MetS with BF% and FMI, we used area under the curve (AUC), ranging between 0 and 1 (a worthless and a perfect test, respectively),

which is a global indicator of diagnostic performance, and represents the ability of the test to correctly classify participants with high risk MetS by p-values < 0.01 and an AUC > 0.80.

The positive likelihood ratio LR (+) and the negative likelihood ratio LR (−) were also determined. Cutoff points were chosen based on the highest Youden index, i.e., the point on the receiver operating characteristic curve (ROC) that is farthest from the line of equality.

Data analysis was performed using the Statistical Package for the Social Sciences for Windows SPSS, version 21.0 (SPSS Inc., Chicago, IL, USA). Statistical significance was set at p < 0.05.