NAs are extracted from blood using a pneumatic centrifugal platform.

Background It is well established that body composition influences metabolic health, but emerging data are conflicting with the largely purported idea that a large fat‐free mass (FFM) has a protective effect on health. A potential explanation for these discrepancies is the way FFM is represented. The first objective is to determine the association between the metabolic syndrome (MetS) and FFM when the latter was represented in three different ways: 1—absolute FFM; 2—relative to squared height (FFMi); and 3—relative to body weight (FFM%). The second objective is to assess the impact of FFM on the relative risk of having the MetS after taking fat mass, physical activity, and sociodemographic variables into account. Methods A total of 5274 individuals from the National Health and Nutrition Examination Survey database were studied. Age‐specific and sex‐specific quartiles of the three representations of FFM were defined, and the prevalence of MetS was determined in each of them. Quartiles of FFMi (kg/m2) were used to calculate the odds ratios of having the MetS independently of FM, physical activity levels, and sociodemographic variables. Results The prevalence of MetS decreased with increasing quartiles of whole‐body FFM% (Q1: 40%; Q4: 10%) but grew with increasing quartiles of absolute FFM (Q1: 13%; Q4: 40%) and FFMi (Q1: 10%; Q4: 44%). Similar results were observed for appendicular and truncal FFM. The odds ratios of having the MetS, independently of fat mass, physical activity, and sociodemographic variables, were significantly greater in the fourth quartile of FFMi when compared with the first quartiles of each specific subgroup [Q4 vs. Q1: younger men: 4.16 (1.99–8.68); younger women: 5.74 (2.46–13.39); older men: 1.98 (1.22–3.22); older women: 2.88 (1.69–4.90); all P ≤ 0.01]. Conclusions These results support the notion that the representation of FFM significantly influences its association with MetS and that a larger FFM, whether absolute or relative to height, is associated with alterations in cardiometabolic health.

Objectives: First, to establish the respective ability of body mass index (BMI), waist circumference (WC), and relative fat mass index (RFM), to estimate body fat (BF%) measured by DXA (DXA-BF%) and correctly identify postmenopausal women living with obesity (BF% > 35). Second, to identify the best indicator of successful weight-loss intervention in postmenopausal women living with obesity. Methods: A total of 277 women (age: 59.8 ± 5.3 y; BF%: 43.4 ± 5.3) from five weight-loss studies with complete data for anthropometric measurements [BMI = weight/height (kg/m2); WC (cm)] and BF% were pooled together. Statistical performance indicators were determined to assess ability of RFM [64−(20 × height/waist circumference) + (12 × sex)], BMI and WC to estimate BF% before and after weight-loss intervention and to correctly identify postmenopausal women living with obesity. Results: Compared with RFM (r = 0.51; r 2 = 0.27; RMSE = 4.4%; Lin's CCC = 0.46) and WC (r = 0.49; r 2 = 0.25; RMSE = 4.8%; Lin's CCC = 0.41), BMI (r = 0.73; r 2 = 0.52; RMSE = 3.7%; Lin's CCC = 0.71) was the best anthropometric index to estimate DXA-BF% and correctly identify postmenopausal women living with obesity (sensitivity + specificity: BMI = 193; RFM = 152; WC = 158), with lower misclassification error, before weight-loss intervention. After weight-loss, the change in BMI was strongly correlated with change in DXA-BF%, indicating that the BMI is the best indicator of success weight-loss intervention. Conclusion: In the absence of more objective measures of adiposity, BMI is a suitable proxy measure for BF% in postmenopausal women, for whom a lifestyle intervention is relevant. Furthermore, BMI can be used as an indicator to assess success of weight-loss intervention in this subpopulation.

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