Comparison of body mass index (BMI) with the CUN-BAE body adiposity estimator in the prediction of hypertension and type 2 diabetes.

Vicente Martín,Ciberesp Cases And Controls In Pandemic Influenza Working Group, Spain ,José María Mayoral,Ady Castro,Angela Domínguez,Nuria Soldevila,Tania Fernandez-Villa,José María Quintana,Miguel Delgado-Rodríguez,Jesús Castilla,Verónica Dávila-Batista,Fernando González-Candelas,Antonio J Molina,Pere Godoy,Jenaro Astray

doi:10.1186/s12889-016-2728-3

Abstract

BackgroundObesity is a world-wide epidemic whose prevalence is underestimated by BMI measurements, but CUN-BAE (Clínica Universidad de Navarra - Body Adiposity Estimator) estimates the percentage of body fat (BF) while incorporating information on sex and age, thus giving a better match. Our aim is to compare the BMI and CUN-BAE in determining the population attributable fraction (AFp) for obesity as a cause of chronic diseases.MethodsWe calculated the Pearson correlation coefficient between BMI and CUN-BAE, the Kappa index and the internal validity of the BMI. The risks of arterial hypertension (AHT) and diabetes mellitus (DM) and the AFp for obesity were assessed using both the BMI and CUN-BAE.Results3888 white subjects were investigated. The overall correlation between BMI and CUN-BAE was R2 = 0.48, which improved when sex and age were taken into account (R2 > 0.90). The Kappa coefficient for diagnosis of obesity was low (28.7 %). The AFp was 50 % higher for DM and double for AHT when CUN-BAE was used.ConclusionsThe overall correlation between BMI and CUN-BAE was not good. The AFp of obesity for AHT and DM may be underestimated if assessed using the BMI, as may the prevalence of obesity when estimated from the percentage of BF.

Highlights

Obesity is a world-wide epidemic whose prevalence is underestimated by body mass index (BMI) measurements, but CUN-BAE (Clínica Universidad de Navarra - Body Adiposity Estimator) estimates the percentage of body fat (BF) while incorporating information on sex and age, giving a better match
This same fact explains the improvement in correlation when sex, age, or both are factored into the figures. This was aaOR: BMI; CUN-BAE Abbreviations: CI confidence interval, aOR adjusted odds ratio, AFp population attributable fraction, BMI body mass index, CUN-BAE Clínica Universidad de Navarra - Body Adiposity Estimator, BF body fat observed by Gallagher et al, who found an overall correlation R2 = 0.26, but when sex and age were taken into account the correlation was much better, with values for R2 of up to 0.67 [18]
Diabetes and arterial hypertension (AHT) are common ailments clearly related to obesity as a risk factor, which is why we studied their association with the two ways of assessing body fat, to find that estimates of BF according to CUN-BAE were more clearly related to AHT and diabetes mellitus (DM) than results from BMI, just as was noted by Dervaux et al in the assessment of body fat percentages [24]

Summary

Introduction

Obesity is a world-wide epidemic whose prevalence is underestimated by BMI measurements, but CUN-BAE (Clínica Universidad de Navarra - Body Adiposity Estimator) estimates the percentage of body fat (BF) while incorporating information on sex and age, giving a better match. Our aim is to compare the BMI and CUN-BAE in determining the population attributable fraction (AFp) for obesity as a cause of chronic diseases. Obesity is seen as an emerging epidemic around the world because it represents a growing threat to the health of the population It is a complex disease consisting of an excess or abnormal distribution or both of adipose tissue, giving rise to metabolic and endocrine alterations and changes in the immune system, resulting. The BMI underestimates the prevalence of obesity by 50 %, in comparison with direct measurement techniques of adipose; its relationship with adiposity is influenced by age, sex and race [1, 4,5,6,7] In this regard, an alternative for whites is the CUNBAE

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