The utility of fat mass index vs. body mass index and percentage of body fat in the screening of metabolic syndrome

Abstract

BackgroundIt has been well documented that obesity is closely associated with metabolic syndrome (MetS). Although body mass index (BMI) is the most frequently used method to assess overweightness and obesity, this method has been criticized because BMI does not always reflect true body fatness, which may be better evaluated by assessment of body fat and fat-free mass. The objective of this study was to investigate the best indicator to predict the presence of MetS among fat mass index, BMI and percentage of body fat (BF %) and determine its optimal cut-off value in the screening of MetS in practice.MethodsA cross-sectional study of 1698 subjects (aged 20–79 years) who participated in the annual health check-ups was employed. Body composition was measured by bioelectrical impedance analysis (BIA). Fat mass index (FMI) was calculated. Sex-specific FMI quartiles were defined as follows: Q1: <4.39, Q2:4.39- < 5.65, Q3:5.65- < 7.03, Q4:≥7.03,in men; and Q1:<5.25, Q2:5.25- < 6.33, Q3:6.33- < 7.93,Q4:≥7.93, in women. MetS was defined by National Cholesterol Education Program/Adult Treatment Panel III criteria. The association between FMI quartiles and MetS was assessed using Binary logistic regression. Receiver operating curve(ROC) analysis was used to determine optimal cutoff points for BMI,BF% and FMI in relation to the area under the curve(AUC),sensitivity and specificity in men and women.ResultsThe adjusted odds ratios (95% CI) for the presence of MetS in the highest FMI quartile versus lowest quartile were 79.143(21.243-294.852) for men( P < 0.01) and 52.039(4.144-653.436) for women( P < 0.01) after adjusting age, BMI, BF%, TC, LDL, CRP, smoking status and exercise status, and the odds ratios were 9.166(2.157-38.952) for men( P < 0.01) and 25.574(1.945-336.228) for women( P < 0.05) when WC was also added into the adjustment. It was determined that BMI values of 27.45 and 23.85 kg/m2, BF% of 23.95% and 31.35% and FMI of 7.00 and 7.90 kg/m2 were the optimal cutoff values to predict the presence of MetS among men and women according to the ROC curve analysis. Among the indicators used to predict MetS, FMI was the index that showed the greatest area under the ROC curve in both sexes.ConclusionsHigher FMI levels appear to be independently and positively associated with the presence of MetS regardless of BMI and BF%. FMI seems to be a better screening tool in prediction of the presence of metabolic syndrome than BMI and percentage of body fat in men and women.

RETRACTED: Monocytes play different roles in stimulating T cells in obese diabetic individuals

“…[19][20][21] The body fat percentage (BF%) of study subjects was measured using a previously described method. 22 Diabetic subjects were diagnosed with type 2 diabetes but were not on medication at the time of sample collection. Age, sex, BMI, and other demographic characteristics are summarized in Table 1.…”

Predictors of Metabolic Syndrome in the Elderly: A Review

“…This index has been suggested to independently evaluate body fat from changes in fat free mass. In the study performed by Liu et al, 21 FMI was the parameter with the highest AUC, and high FMI showed significantly higher odds ratio for MetS than the low FMI in both sexes. These authors suggested that a FMI of 7.00 kg/m² for men and 7.90 kg/m² for women, evaluated by bioelectrical impedance analysis, could predict the presence of MetS.…”

“…FMI was only discussed in the study by Liu et al 21 Although the authors suggested a cut-off point, it is worth pointing out that there was no stratification by age group in the data analysis. In addition, the study was conducted in China, in a smaller population with brevilineal biotype, which limit the extrapolation of results to other populations.…”

Body Composition and BMI Growth Charts in Children With Down Syndrome

“…11 These BMI percentiles have excellent sensitivity for the identification of children most at risk for the health-related consequences of obesity and are easily applied in clinical practice. Among typically developing children, body fat distribution rather than the total body mass may have a greater impact on cardiovascular risk, 15,16 but body composition measurement by methods such as DXA cannot practically be applied as a screening measure in clinical practice. Currently, BMI is the easiest and best…”