Usefulness of relative fat mass in estimating body adiposity in Korean adult population

Comparison of Several Anthropometric Indices Related to Body Fat in Predicting Cardiorespiratory Fitness in School-Aged Children-A Single-Center Cross-Sectional Study

Body fat (BF) and cardiorespiratory fitness (CRF) are important health markers that ought to be considered in screening exams. The aim of this study was to assess the value of six indicators, i.e., tri-ponderal mass index (TMI), relative fat mass (RFM), waist-BMI ratio, waist-to-height ratio (WHtR), waist-to-hip ratio (WHR) and body mass index (BMI) in predicting CRF in school-aged children. The analysis was based on the data coming from the examination of 190 children participating in school physical education (PE) classes. Their body weight (BW) and height (BH), waist and hip circumference (WC; HC) and percentage of body fat (%BF) were measured; the CRF test was performed with the use of the 20 m shuttle run test (20 mSRT); peak heart rate (HRpeak) was measured; TMI, relative fat mass pediatric (RFMp), waist-BMI ratio, WHtR, BMI and WHR were calculated. Statistical analysis was mainly conducted using regression models. The developed regression models, with respect to the sex and age of the children, revealed RFMp as the strongest CRF indicator (R2 = 51.1%) and WHR as well as waist-BMI ratio as the weakest ones (R2 = 39.2% and R2 = 40.5%, respectively). In predicting CRF in school-aged children, RFMp turned out to be comparable to body fat percentage obtained by means of the bioimpedance analysis (BIA) (R2 = 50.3%), and as such it can be used as a simple screening measure in prophylactic exams of school children. All of these models were statistically significant (p < 0.001).

Lower Prevalence of Body Fat-Defined Obesity at Higher Altitudes in Peruvian Adults

Woolcott, Orison O., Till Seuring, and Oscar A. Castillo. Lower prevalence of body fat-defined obesity at higher altitudes in Peruvian adults. High Alt Med Biol. 24:214-222, 2023. Background: Previous studies have reported a lower prevalence of obesity (defined as a body mass index [BMI] ≥30 kg/m2) in populations from higher altitudes. Since BMI does not distinguish fat mass and fat-free mass, it is unclear whether there is an inverse association between altitude and body fat-defined obesity. Methods: We performed an analysis of cross-sectional data to examine the association between altitude and body fat-defined obesity (as opposed to BMI-defined obesity) using individual-level data from a nationally representative sample of the Peruvian adult population living between 0 and 5,400 m altitude. Body fat-defined obesity was diagnosed using the relative fat mass (RFM), an anthropometric index validated to estimate whole-body fat percentage. RFM cutoffs for obesity diagnosis were ≥40% for women and ≥30% for men. We utilized Poisson regression to estimate the prevalence ratio and confidence intervals (CIs) as the measure of the association, adjusting for age, cigarette use, and diabetes. Results: Analysis comprised 36,727 individuals (median age, 39 years; 50.1% women). In rural areas, for a one-km increase in altitude, the prevalence of body fat-defined obesity decreased by 12% among women (adjusted prevalence ratio: 0.88; 95% CI, 0.86 - 0.90; p < 0.001) and 19% among men (adjusted prevalence ratio: 0.81; 95% CI, 0.77 - 0.86; p < 0.001), on average, when all the other variables were held constant. The inverse association between altitude and obesity was less strong in urban areas than in rural areas but remained significant among women (p = 0.001) and men (p < 0.001). However, the relationship between altitude and obesity in women who live in urban areas appears to be nonlinear. Conclusions: In Peruvian adults, the prevalence of body fat-defined obesity was inversely associated with altitude. Whether this inverse association is explained by altitude per se or confounded by socioeconomic or other environmental factors, or differences in race/ethnicity or lifestyle, warrants further investigation.

Temporal trends in obesity defined by the relative fat mass (RFM) index among adults in the United States from 1999 to 2020: a population-based study

OBJECTIVES

The body mass index (BMI) largely underestimates excess body fat, suggesting that the prevalence of obesity could be underestimated. Biologically, women are known to have higher body fat than men. This study aimed to compare the temporal trends in general obesity by sex, ethnicity and age among adults in the USA using the relative fat mass (RFM), a validated surrogate for whole-body fat percentage and BMI.

DESIGN

Population-based study.

SETTING

US National Health and Nutrition Examination Survey, from 1999-2000 to 2017-March 2020.

PARTICIPANTS

A representative sample of adults 20-79 years in the USA.

MAIN OUTCOME MEASURES

Age-adjusted prevalence of general obesity. RFM-defined obesity was diagnosed using validated cut-offs to predict all-cause mortality: RFM≥40% for women and ≥30% for men. BMI-defined obesity was diagnosed using a cut-off of 30 kg/m2.

RESULTS

Analysis included data from 47 667 adults. Among women, RFM-defined obesity prevalence was 64.7% (95% CI 62.1% to 67.3%) in 2017-2020, a linear increase of 13.9 percentage points (95% CI 9.0% to 18.9%; p<0.001) relative to 1999-2000. In contrast, the prevalence of BMI-defined obesity was 42.2% (95% CI 39.4% to 45.0%) in 2017-2020. Among men, the corresponding RFM-defined obesity prevalence was 45.8% (95% CI 42.0% to 49.7%), a linear increase of 12.0 percentage points (95% CI 6.6% to 17.3%; p<0.001). In contrast, the prevalence of BMI-defined obesity was 42.0 (95% CI 37.8% to 46.3%). The highest prevalence of RFM-defined obesity across years was observed in older adults (60-79 years) and Mexican Americans, in women and men. Conversely, the highest prevalence of BMI-defined obesity across years was observed in middle-age (40-59 years) and older adults, and in African American women.

CONCLUSIONS

The use of a surrogate for whole-body fat percentage revealed a much higher prevalence of general obesity in the USA from 1999 to 2020, particularly among women, than that estimated using BMI, and detected a disproportionate higher prevalence of general obesity in older adults and Mexican Americans.

Association between BMI, RFM and mortality and potential mediators: Prospective findings from the Moli-sani study

BACKGROUND

Body mass index (BMI) is the most frequently used adiposity measure, yet it is unable to differentiate fat mass from lean mass. Relative fat mass (RFM) has been proposed as an alternative. This paper aims to study RFM and BMI association with mortality in a general Italian population and potential mediators of such association.

METHODS

20,587 individuals from the Moli-sani cohort were analysed (mean age = 54 ± 11, women = 52%, median follow up = 11.2 years, interquartile range = 1.96 years). Cox regressions were used to assess BMI, RFM, and their interactive association with mortality. Dose-response relationships were computed with spline regression, mediation analysis was performed. All analyses were separated for men and women.

RESULTS

Men and women with BMI > 35 kg/m² and men in the 4th quartile of RFM showed an independent association with mortality (HR = 1.71, 95% CI = 1.30-2.26 BMI in men, HR = 1.37, 95%CI = 1.01-1.85 BMI in women, HR = 1.37 CI 95% = 1.11-1.68 RFM in men), that was lost once adjusted for potential mediators. Cubic splines showed a U-shaped association for BMI in men and women, and for RFM in men. Mediation analysis showed that 46.5% of the association of BMI with mortality in men was mediated by glucose, C reactive protein, forced expiratory volume in 1 s (FEV1), and cystatin C; 82.9% of the association of BMI in women was mediated by HOMA index, cystatin C and FEV1; lastly, 55% of RFM association with mortality was mediated by glucose, FEV1 and cystatin C. Regression models including BMI and RFM showed that RFM drives most of the risk in men, but is not predictive in women.

CONCLUSIONS

The association between anthropometric measures and mortality was U shaped and it was largely dependent on sex. Associations were mediated by glucose metabolism, renal and lung function. Public health interventions should mainly focus on people with severe obesity or impaired metabolic, renal, or respiratory function.

Performance of Body Adiposity Index and Relative Fat Mass in Predicting Bioelectric Impedance Analysis-Derived Body Fat Percentage: A Cross-Sectional Study among Patients with Type 2 Diabetes in the Ho Municipality, Ghana

Objective

The study sought to determine the diagnostic accuracy of body adiposity index (BAI) and relative fat mass (RFM) to predict BIA-derived BFP among patients with type 2 diabetes in the Ho municipality. Materials and Method. This hospital-based cross-sectional study involved 236 patients with type 2 diabetes. Demographic data, including age and gender were obtained. Height, waist circumference (WC), and hip circumference (HC) were measured using standard methods. BFP was estimated on a bioelectrical impedance analysis (BIA) scale. The validity of BAI and RFM as alternative estimates for BIA-derived BFP was evaluated based on mean absolute percentage error (MAPE), Passing-Bablok regression, Bland-Altman plots, receiver-operating characteristic curve (ROC), and kappa statistics analyses. A p value less than 0.05 was considered statistically significant.

Results

BAI showed systematic bias in estimating BIA-derived BFP in both genders, but this was not evident between RFM and BFP among females (t = -0.62; p = 0.534). While BAI showed "good" predictive accuracy in both genders, RFM exhibited "high" predictive accuracy for BFP (MAPE: 7.13%; 95% CI: 6.27-8.78) among females according to MAPE analysis. From the Bland-Altman plot analysis, the mean difference between RFM and BFP was acceptable among females [0.3 (95% LOA: -10.9 to 11.5)], but both BAI and RFM recorded large limits of agreement and low Lin's concordance correlation coefficient with BFP (Pc < 0.90) in the two gender populations. The optimal cut-off, sensitivity, specificity, and Youden index for RFM were >27.2, 75%, 93.75%, and 0.69, respectively, while those of BAI were >25.65, 80%, 84.37%, and 0.64, respectively, among males. Among females, the values for RFM were >27.26, 92.57%, 72.73%, and 0.65, whereas those of BAI were >29.4, 90.74%, 70.83%, and 0.62, respectively. The accuracy of discriminating between BFP levels was higher among females [BAI (AUC: 0.93) and RFM (AUC: 0.90)] compared to males [BAI (AUC: 0.86) and RFM (AUC: 0.88)].

Conclusion

RFM had a better predictive accuracy of BIA-derived BFP in females. However, both RFM and BAI failed as valid estimates for BFP. Furthermore, gender-specific performance in the discrimination of BFP levels for RFM and BAI was observed.

Obesity among Medical Students of a Medical College: A Descriptive Cross-sectional Study

Introduction

The prevalence of overweight and obesity is increasing these days. The adverse effect of obesity can be seen in different physiological functions. Relative fat mass is a newly identified parameter to estimate whole body fat. This study aimed to find out the prevalence of obesity among medical students of a medical college.

Methods

A descriptive cross-sectional study was carried out among medical students of a medical college from 1 September 2021 to 30 January 2022. Ethical approval was taken from the Institutional Review Committee (Reference number: FNMC/539/078/79). Simple random sampling was done. Height was measured using a stadiometer and waist circumference was measured using non-stretchable tape. Relative fat mass was calculated using the relative fat mass equation. The data was categorised according to the distribution of fat mass. Point estimate and 95% Confidence Interval were calculated.

Results

Out of 180 medical students, 57 (31.67%) (24.87-38.47, 95% Confidence Interval) were obese according to relative fat mass cut-off. The mean fat mass among male and female participants with high relative fat mass was 27.057±1.42 and 35.674±2.63 respectively.

Conclusions

The prevalence of obesity was lower than in other studies done in similar settings.

Keywords

body fat; obesity; medical students.

Is relative fat mass (RFM) a better indicator of high blood pressure levels when compared to other anthropometric indexes?

BACKGROUND

relative fat mass (RFM) has been proposed recently, and the effectiveness in relation to other anthropometric indexes already consolidated regarding the predictive capacity of high blood pressure levels (HBPL) has not been investigated yet.

OBJECTIVES

the objective was to analyze the predictive capacity of RFM for HBPL, and to compare it with others indexes.

METHODS

a cross-sectional study was conducted with 896 individuals. Weight, height, hip, waist circunference (WC), and neck circunference measurements were evaluated, and RFM, body mass index (BMI), body adiposity index, waist/hip ratio, and waist/height ratio were calculated. Systolic (SBP) and diastolic (DBP) blood pressures were measured on one occasion. Descriptive statistics, Pearson's correlation, a logistic regression model, and the analysis of the receiver operating characteristic (ROC) curves were used.

RESULTS

HBPL proportion was higher in men (34.68 %, p < 0.01). There was a positive correlation (p < 0.01) between all anthropometric measurements and SBP and DBP. WC in males (OR, 3.66; p < 0.01) and BMI in females (OR, 5.06; p < 0.01) showed the greatest associations with HBPL. There was no statistical difference (p > 0.05) in the area under the curve.

CONCLUSIONS

the findings of our study suggest that RFM is not the best index for predicting HBPL, although it has shown positive associations.

Estimating trunk fat in children according to sex using basic somatic readings: an opportunity for improving evaluation among girls

BACKGROUND

The fat mass estimators waist-to-height ratio (WHtR) and relative fat mass-pediatric (RFMp) complement the widely accepted body mass index (BMI) in obesity evaluation.

AIMS OF THE STUDY

Conduct an easy appraisal of trunk fat and the cardiometabolic risk associated with pediatric obesity.

METHODS

A total of 472 children (39% boys in the total sample) were classified as underweight, normal weight, overweight or obese (nutritional groups, NGs) according to BMI Z-score after initial anthropometric data were obtained and ad hoc exclusion criteria were applied. WHtR and RFMp (% of total fat) were calculated for each group, associations were assessed through multiple linear regression (MLR), and differences between sexes were evaluated (medians, IQR).

RESULTS

The mean age (mean (95% CI)) was 10.8 y (10.1-11.1). The values in the total sample were as follows: WHtR, 0.5 (0.49-0.51) and RFMp%, 32.3 (31.7-33.0). In the overweight group, the values were as follows: WHtR, 0.51 (0.50-0.52) and RFMp(%), 34.2 (33.3-35.1). In the obese group, the values were as follows: WHtR, 0.56 (0.55-0.57) and RFMp(%), 37.8 (36.9-38.6). The associations were as follows (NG; independent variables): In the NG, adjusted R2 values were between 0.74 and 0.78. In the total sample, the beta coefficient was 3.36 (P < 0.001) for RFMp for girls; for waist circumference (WC), the beta coefficient was 2.97 (P < 0.001), and for WHtR the beta coefficients were - 0.01 (p < 0.001) and 0.03 (p < 0.001),for girls and for WC respectively. The sex differences were as follows: BMI exhibited no differences in the NG (Mann-Whitney U). WHtR (median (IQR)) differed (M vs. F) in the total sample (0.49 (0.45-0.54) vs. 0.52 (0.45-0.56), p < 0.004); in the overweight group (0.51 (0.48-0.53) vs. 0.54 (0.51-0.55), p < 0.001); and in the obese group (0.55 (0.52-0.57) vs. 0.57 (0.54-0.60), p < 0.004). RFMp (%) differed in the total group (29.21 (24.27-32.92) vs. 36.63 (30.2-39.51), p < 0.001); in the overweight group (31.24 (28.35-32.35) vs. 37.95 (35.75-38.82), p < 0.001) and in the obese group (35.89 (32.05-36.15) vs. 40.63 (38.27-42.42), p < 0.001).

CONCLUSIONS

WHtR and RFMp are simple and reliable indices that do not require centile charts. Their values, including waist circumference, can be used to estimate the different trunk fat components in boys and girls better than BMI, especially if individuals are overweight or obese. RFMp proved to be more reliable as it considers sex. Both should be included in routine anthropometric readings.

Validity and accuracy of body fat prediction equations using anthropometrics measurements in adolescents

BACKGROUND

The pediatric relative fat mass (RFM) has been recently presented and validated as an index for estimating percentage body fat (%BF) in North American children and adolescents. Similar to body mass index (BMI) and tri-ponderal mass index (TMI), RFM uses anthropometric measures (i.e., weight, height and waist circumference) to estimate body composition. The primary purpose of this study was to validate the newly developed RFM equation for %BF prediction in Southern Brazilian adolescents; as secondary objective, we compared %BF estimation from BMI- and TMI-derived equations.

METHODS

A total of 631 individuals (434 boys) aged 11 to 18 were analyzed. Bland-Altman analyses were used to determine concordance between predicted equations and %BF measured by DXA; results are presented using mean difference (i.e., bias) and standard deviation. Sensitivity and specificity were calculated for %BF percentile classifications.

RESULTS

RFM underestimated %BF in 65.2% of boys (- 4.3 ± 2.8%) and 84.8% of girls (- 5.3 ± 2.7%). In contrast, TMI overestimated %BF in 62.9% of boys (4.0 ± 2.9%) and 56.3% (3.5 ± 3.0%) of girls. The performance of BMI showed mixed results; %BF was overestimated in 68.4% of boys (5.0 ± 4.0%) and underestimated in 67.5% of girls (- 3.9 ± 2.6%), all p < 0.001. Although, RFM had the highest specificity for %BF percentile classifications, sensitivity was low and inferior to BMI and TMI.

CONCLUSION

TMI was superior to RFM and BMI in predicting %BF in Southern Brazilian adolescents. Using RFM, BMI or TMI equations for %BF prediction without a population-specific correction factor may lead to incorrect interpretations. We suggest that correction factors should be investigated to improve the accuracy of these surrogate indices for body composition assessment.

LEVEL OF EVIDENCE

Level V, cross sectional descriptive study.

Obesity among postmenopausal women: what is the best anthropometric index to assess adiposity and success of weight-loss intervention?

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; r2 = 0.27; RMSE = 4.4%; Lin's CCC = 0.46) and WC (r = 0.49; r2 = 0.25; RMSE = 4.8%; Lin's CCC = 0.41), BMI (r = 0.73; r2 = 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.

A Comparison of Equation Córdoba for Estimation of Body Fat (ECORE-BF) with Other Prediction Equations

There are multiple formulas for estimating the percentage of body fat (BF%). Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) is one of the most used formulas because of its accuracy and its association with cardiovascular pathologies. Equation Córdoba for Estimation of Body Fat (ECORE-BF) was developed to simplify the calculation of BF% while maintaining a similar level of accuracy. The objective was to compare ECORE-BF in a large sample of Spanish workers using CUN-BAE as a reference. A cross-sectional study was carried out on 196,844 participants. The BF% was estimated using different formulas: relative fat mass (RFM), Palafolls, Deurenberg, and ECORE-BF. The accuracy of the estimation was determined using Lin’s concordance correlation coefficient (CCC) and the Bland–Altman method, using CUN-BAE as the reference method. ECORE-BF reached the highest concordance (CCC = 0.998). It also showed the lowest mean difference (−0.0077) and the tightest agreement limits (−0.9723, 0.9569) in the Bland–Altman test. In both analyses, it remained robust even when separating the analyses by sex, nutritional status, or age. ECORE-BF presented as the most straightforward and most accurate equation for the estimation of BF%, remaining robust regardless of population characteristics.

Predictive value of relative fat mass algorithm for incident hypertension: a 6-year prospective study in Chinese population

OBJECTIVES

Individuals with obesity especially excessive visceral adiposity have high risk for incident hypertension. Recently, a new algorithm named relative fat mass (RFM) was introduced to define obesity. Our aim was to investigate whether it can predict hypertension in Chinese population and to compare its predictive power with traditional indices including body mass index (BMI), waist circumference (WC) and waist-to-height ratio (WHtR).

DESIGN

A 6-year prospective study.

SETTING

Nine provinces (Hei Long Jiang, Liao Ning, Jiang Su, Shan Dong, He Nan, Hu Bei, Hu Nan, Guang Xi and Gui Zhou) in China.

PARTICIPANTS

Those without hypertension in 2009 survey and respond in 2015 survey.

INTERVENTION

Logistic regression were performed to investigate the association between RFM and incident hypertension. Receiver operating characteristic (ROC) analysis was performed to compare the predictive ability of these indices and define their optimal cut-off values.

MAIN OUTCOME MEASURES

Incident hypertension in 2015.

RESULTS

The prevalence of incident hypertension in 2015 based on RFM quartiles were 14.8%, 21.2%, 26.8% and 35.2%, respectively (p for trend <0.001). In overall population, the OR for the highest quartile compared with the lowest quartile for RFM was 2.032 (1.567-2.634) in the fully adjusted model. In ROC analysis, RFM and WHtR had the highest area under the curve (AUC) value in both sexes but did not show statistical significance when compared with AUC value of BMI and WC in men and AUC value of WC in women. The performance of the prediction model based on RFM was comparable to that of BMI, WC or WHtR.

CONCLUSIONS

RFM can be a powerful indictor for predicting incident hypertension in Chinese population, but it does not show superiority over BMI, WC and WHtR in predictive power.

Association between Basal Metabolic Rate and Handgrip Strength in Older Koreans

We investigated the relationship between the basal metabolic rate (BMR) and muscle strength through measurement of handgrip strength. We conducted a cross-sectional study of a population representative of older Korean from the 2014–2016 Korean National Health and Nutrition Examination Survey. A total of 2512 community-dwelling men and women aged 65 years and older were included. The BMR was calculated with the Singapore equation and handgrip strength was measured using a digital dynamometer. The patients were categorized into handgrip strength quartiles and a weighted one-way analysis of variance (ANOVA) for continuous variables and a weighted chi-squared test for categorical variables were performed. Pearson, Spearman correlation analysis, univariate, and multivariate linear regression were performed. Analysis of covariance (ANCOVA) was also performed to determine the association between basal metabolic rate and handgrip strength quartiles after adjusting for confounding factors. The BMR increased according to handgrip strength quartile after adjusting for age, BMI, relative fat mass, comorbidity number, resistance exercise, aerobic physical activity, household income, educational level, smoking status, and alcohol ingestion in both sexes (p < 0.001). Handgrip strength has a positive association with the BMR in older Korean people. Therefore, muscle strength exercises should be considered for regulating the BMR in the older people.