Estimate of body composition by Hume's equation: validation with DXA

Comparison of ten predictive equations for estimating lean body mass with dual-energy X-ray absorptiometry in older patients

OBJECTIVES

White fat contributes to body weight (BW) but accumulates very little [18F]fluorodeoxyglucose ([18F]FDG) in the fasting state. As a result, higher standardised uptake values normalised to BW (SUV) are observed in non-fatty tissue in obese patients compared to those in non-obese patients. Therefore, SUV normalised to lean body mass (SUL) that makes tumour uptake values less dependent on patients' body habitus is considered more appropriate. This study aimed to assess ten mathematical equations to predict lean body mass (LBM) by comparison with dual-energy X-ray absorptiometry (DXA) as the reference method.

METHODS

DXA-based LBM was compared with ten equation-based estimates of LBM in terms of the slope, bias and 95% limits of agreement (LOA) of Bland-Altman plots, and Pearson correlation coefficients (r). Data from 747 men and 811 women aged 60-65 years were included.

RESULTS

Gallagher's equation was optimal in males (slope = 0.13, bias = -2.4 kg, LOA = 12.8 kg and r = 0.900) while Janmahasatian's equation was optimal in females (slope = 0.14, bias = -0.9 kg, LOA = 10.7 kg and r = 0.876). Janmahasatian's equation performed slightly better than Gallagher's in the pooled male and female data (slope = 0.00, bias = -1.6 kg, LOA = 12.3 kg and r = 0.959).

CONCLUSIONS

The Gallagher and Janmahasatian equations were optimal and almost indistinguishable in predicting LBM in subjects aged 60-65 years.

ADVANCES IN KNOWLEDGE

Determination of the optimum equation for predicting lean body mass to improve the calculation of SUL for [18F]FDG PET quantification.

Urinary creatinine based equations for estimation of fat free mass in patients with intestinal insufficiency or intestinal failure

BACKGROUND & AIMS

Assessment of body composition is an important aspect of disease management in patients with intestinal insufficiency (INS) or intestinal failure (IF). However, in daily clinical settings most body composition methods are too expensive or impractical, leaving body composition to be assessed by less reliable methods such as skin fold thickness. The aim of this study was to investigate and validate the use of an equation for the estimation of fat-free mass (FFM) with bioelectrical impedance analysis (BIA) as reference method.

METHODS

A literature search for identification of urinary creatinine-based FFM-prediction equations was carried out a long side the creation of an equation by multiple linear regression. The correlation of each equation with FFM (measured by BIA in 277 patients with either INS or IF) was done by Pearson's correlation. Further investigation and validation of performance was done for the equations with the strongest correlation by Bland-Altman analysis, determination of root mean square error (RMSE), and intraclass correlation (ICC). The validation was carried out in a new group of 37 patients with either INS or IF.

RESULTS

A total of 11 prediction equations were correlated with FFM measured by BIA. The equation called FFMmultiple and FFM-5 had the strongest correlation (r = 0.969, p < 0.01 and r = 0.950, p < 0.01, respectively). FFMmultiple was superior to FFM-5 regarding Bland-Altman analysis, RMSE, and ICC in the study group (Mean bias ± Standard Deviation = 0.042 ± 2.352 versus 0.309 ± 3.196; 95% limits of agreement = [-4.568; 4.651] versus [-5.955; 6.578]; RMSE = 0.158 versus 0.236; ICC = 0.969 versus 0.948). Cross-validation resulted in a Bland-Altman analysis with a statistically significant difference between FFMmultiple and FFM by BIA. FFM-5 showed wide 95% limits of agreement ([-6.977; 6.421]).

CONCLUSIONS

Two urinary creatinine-based equations (FFMmultiple and FFM-5) showed promising results as possible substitutes to BIA, however further investigation and cross validation revealed inauspicious results. Thus, the present study cannot recommend the use of a prediction equation instead of BIA for the assessment of FFM in patients with INS and IF.

Dynamics of Anthropometric Indices in a Large Paired Cohort With 10 Years of Follow-Up: Paving the Way to Sarcopenic Obesity

Introduction: Paired cohort investigations assessing the evolution of anthropometric indices are scarce. Here we assessed the 10-year evolution of BMI, total body fat, and lean body mass in 50,019 participants aged 18-90 years at the time of first assessment. Material and Methods: A retrospective cohort study using an electronic database that contains anonymized, longitudinal data from Primary Care medical records covering the 2007-2008 and 2017-2018 periods. Total body fat was estimated using the Clínica Universidad de Navarra-Body Adiposity Estimator formula, and the Hume formula was applied to estimate lean body mass. Results: The mean BMI of participants <60 years old in the 2007-2008 period increased significantly, from 27.5 to 28.3 kg/m² (p < 0.001). However, the BMI of older subjects decreased during the next decade, from 28.9 to 28.3 kg/m² (p < 0.001). The estimated total body fat showed a continuous progressive increase over all ages. Finally, lean body mass showed a progressive increase until the 40s, with a plateau between 40-45 years old and an uninterrupted decrease until older ages. Also, subjects who increased their BMI by 2 kg/m² during the 10-year period were mainly women and younger at baseline, with a lower initial BMI and total body fat in comparison with those who experienced a BMI decrease of ≥2.0 kg/m². Conclusion: The evolutions of BMI and the estimated body compositions reported here confirm that the adverse decrease in lean body mass begins in middle age. The proportion of older subjects is important when evaluating overweight and obesity prevalence in cross-sectional studies.