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Clinical usefulness of a new equation for estimating body fat.

ABSTRACT: To assess the predictive capacity of a recently described equation that we have termed CUN-BAE (Clínica Universidad de Navarra-Body Adiposity Estimator) based on BMI, sex, and age for estimating body fat percentage (BF%) and to study its clinical usefulness.We conducted a comparison study of the developed equation with many other anthropometric indices regarding its correlation with actual BF% in a large cohort of 6,510 white subjects from both sexes (67% female) representing a wide range of ages (18-80 years) and adiposity. Additionally, a validation study in a separate cohort (n = 1,149) and a further analysis of the clinical usefulness of this prediction equation regarding its association with cardiometabolic risk factors (n = 634) was carried out.The mean BF% in the cohort of 6,510 subjects determined by air displacement plethysmography was 39.9 ± 10.1%, and the mean BF% estimated by the CUN-BAE was 39.3 ± 8.9% (SE of the estimate, 4.66%). In this group, BF% calculated with the CUN-BAE showed the highest correlation with actual BF% (r = 0.89, P < 0.000001) compared with other anthropometric measures or BF% estimators. Similar agreement was found in the validation sample. Moreover, BF% estimated by the CUN-BAE exhibits, in general, better correlations with cardiometabolic risk factors than BMI as well as waist circumference in the subset of 634 subjects.CUN-BAE is an easy-to-apply predictive equation that may be used as a first screening tool in clinical practice. Furthermore, our equation may be a good tool for identifying patients at cardiovascular and type 2 diabetes risk.

SUBMITTER: Gomez-Ambrosi J

PROVIDER: S-EPMC3263863 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Clinical usefulness of a new equation for estimating body fat.

Gómez-Ambrosi Javier J Silva Camilo C Catalán Victoria V Rodríguez Amaia A Galofré Juan Carlos JC Escalada Javier J Valentí Victor V Rotellar Fernando F Romero Sonia S Ramírez Beatriz B Salvador Javier J Frühbeck Gema G

Diabetes care 20111216 2

<h4>Objective</h4>To assess the predictive capacity of a recently described equation that we have termed CUN-BAE (Clínica Universidad de Navarra-Body Adiposity Estimator) based on BMI, sex, and age for estimating body fat percentage (BF%) and to study its clinical usefulness.<h4>Research design and methods</h4>We conducted a comparison study of the developed equation with many other anthropometric indices regarding its correlation with actual BF% in a large cohort of 6,510 white subjects from bo ...[more]

PMID: 22179957

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Project description:BackgroundWe aimed to investigate the accuracy of different equations in evaluating estimated glomerular filtration rate (eGFR) in a Chinese population with different BMI levels.MethodsA total of 837 Chinese patients were enrolled, and the eGFRs were calculated by three Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations, three full-age spectrum (FAS) equations and two Modification of Diet in Renal Disease (MDRD) equations. Results of measured GFR (mGFR) by the 99Tcm-diathylenetriamine pentaacetic acid (99Tcm-DTPA) renal dynamic imaging method were the reference standards. According to BMI distribution, the patients were divided into three intervals: below 25th(BMIP25), 25th to 75th(BMIP25-75) and over 75th percentiles (BMIP75).ResultsThe medium BMI of the three BMI intervals were 20.9, 24.8 and 28.9 kg/m2, respectively. All deviations from mGFR (eGFR) were correlated with BMI (p < 0.05). The percentage of cases in which eGFR was within mGFR ±30% (P30) was used to represent the accuracy of each equation. Overall, eGFRFAS_Cr_CysC and eGFREPI_Cr_2009 performed similarly, showing the best agreement with mGFR among the eight equations in Bland-Altman analysis (biases: 4.1 and - 4.2 mL/min/1.73m2, respectively). In BMIP25 interval, eGFRFAS_Cr got - 0.7 of the biases with 74.2% of P30, the kappa value was 0.422 in classification of CKD stages and the AUC60 was 0.928 in predicting renal insufficiency, and eGFREPI_Cr_2009 got 2.3 of the biases with 71.8% of P30, the kappa value was 0.418 in classification of CKD stages and the AUC60 was 0.920 in predicting renal insufficiency. In BMIP25-75 interval, the bias of eGFRFAS_Cr_CysC was 4.0 with 85.0% of P30, the kappa value was 0.501 and the AUC60 was 0.941, and eGFRFAS_Cr_CysC showed balanced recognition ability of each stage of CKD (62.3, 63.7, 68.0, 71.4 and 83.3% respectively). In BMIP75 interval, the bias of eGFREPI_Cr_CysC_2012 was 3.8 with 78.9% of P30, the kappa value was 0.484 the AUC60 was 0.919, and eGFREPI_Cr_CysC_2012 equation showed balanced and accurate recognition ability of each stage (60.5, 60.0, 71.4, 57.1 and 100% respectively). In BMIP75 interval, the bias of eGFRFAS_Cr_CysC was - 1.8 with 78.5% of P30, the kappa value was 0.485, the AUC60 was 0.922. However, the recognition ability of each stage of eGFRFAS_Cr_CysC eq. (71.1, 61.2, 70.0, 42.9 and 50.0% respectively) was not as good as GFREPI_Cr_CysC_2012 equation.ConclusionFor a Chinese population, we tend to recommend choosing eGFRFAS_Cr and eGFREPI_Cr_2009 when BMI was around 20.9, eGFRFAS_Cr_CysC when BMI was near 24.8, and eGFREPI_Cr_CysC_2012 when BMI was about 28.9.

Project description:ImportanceBody mass index (BMI) is used to diagnose obesity in adolescents worldwide, despite evidence that weight does not scale with height squared in adolescents. To account for this, health care providers diagnose obesity using BMI percentiles for each age (BMI z scores), but this does not ensure that BMI is accurate in adolescents.ObjectiveTo compare the accuracy of BMI vs other body fat indices of the form body mass divided by heightn in estimating body fat levels in adolescents.Design, setting, and participantsCross-sectional data from the 1999 to 2006 US National Health and Nutrition Examination Survey were analyzed between September 2015 and December 2016.Main outcomes and measuresDual-energy x-ray absorptiometry and anthropometric data were used to determine changes in body fat levels, body proportions, and the scaling relationships among body mass, height, and percent body fat. To assess the merits of each adiposity index, 3 criteria were used: stability with age, accuracy in estimating percent body fat, and accuracy in classifying adolescents as overweight vs normal weight.ResultsParticipants included 2285 non-Hispanic white participants aged 8 to 29 years. Percent body fat varied with both age and height during adolescence, invalidating the standard weight-to-height regression as the way of finding the optimal body fat index. Because the correct regression model (percent body fat is proportional to mass divided by heightn) suggested that percent body fat scales to height with an exponent closer to 3, we therefore focused on the tri-ponderal mass index (TMI; mass divided by height cubed) as an alternative to BMI z scores. For ages 8 to 17 years, TMI yielded greater stability with age and estimated percent body fat better than BMI (R2?=?0.64 vs 0.38 in boys and R2?=?0.72 vs 0.66 in girls). Moreover, TMI misclassified adolescents as overweight vs normal weight less often than BMI z scores (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 19.4%; 95% CI, 17.8%-20.0%; P?<?.001) and performed equally as well as updated BMI percentiles derived from the same data set (TMI, 8.4%; 95% CI, 7.3%-9.5% vs BMI, 8.0%; 95% CI, 6.9%-9.1%; P?=?.62).Conclusions and relevanceThe tri-ponderal mass index estimates body fat levels more accurately than BMI in non-Hispanic white adolescents aged 8 to 17 years. Moreover, TMI diagnoses adolescents as overweight more accurately than BMI z scores and equally as well as updated BMI percentiles but is much simpler to use than either because it does not involve complicated percentiles. Taken together, it is worth considering replacing BMI z scores with TMI to estimate body fat levels in adolescents.

Project description:ImportanceProtein binding of valproate varies among ICU patients, altering the biologically active free valproate concentration (VPAC). Free VPAC is measured at few laboratories and is often discordant with total VPAC. Existing equations to predict free VPAC are either not validated or are inaccurate in ICU patients.ObjectivesWe designed this study to derive and validate a novel equation to predict free VPAC using data from ICU patients and to compare its performance to published equations.DesignRetrospective cohort study.SettingTwo academic medical centers.ParticipantsPatients older than 18 years old with concomitant free and total VPACs measured in the ICU were included in the derivation cohort if admitted from 2014 to 2018, and the validation cohort if admitted from 2019 to 2022.Main outcomes and measuresMultivariable linear regression was used to derive an equation to predict free VPAC. Modified Bland-Altman plots and the rate of therapeutic concordance between the measured and predicted free VPAC were compared.ResultsDemographics, median free and total VPACs, and valproate free fractions were similar among 115 patients in the derivation cohort and 147 patients in the validation cohort. The Bland-Altman plots showed the new equation performed better (bias, 0.3 [95% limits of agreement, -13.6 to 14.2]) than the Nasreddine (-9.2 [-26.5 to 8.2]), Kodama (-9.7 [-30.0 to 10.7]), Conde Giner (-7.9 [-24.9 to 9.1]), and Parent (-9.9 [-30.7 to 11.0]) equations, and similar to Doré (-2.0 [-16.0 to 11.9]). The Doré and new equations had the highest therapeutic concordance rate (73%).Conclusions and relevanceFor patients at risk of altered protein binding such as ICU patients, existing equations to predict free VPAC are discordant with measured free VPAC. A new equation had low bias but was imprecise. External validation should be performed to improve its precision and generalizability. Until then, monitoring free valproate is recommended during critical illness.

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Clinical usefulness of a new equation for estimating body fat.

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Clinical usefulness of a new equation for estimating body fat.

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