Acta Diabetologica

, Volume 56, Issue 3, pp 373–375 | Cite as

Comparison of correlations of equation-derived body fat percentage and body mass index with carotid intima-media thickness

  • Manuel Fortún Landecho
  • Inmaculada Colina
  • Patricia Sunsundegui
  • Bruno Camarero
  • Jorge M. Núñez-CórdobaEmail author
  • Óscar Beloqui
Short Communication


Obesity is a critical cardiovascular disease (CVD) contributing risk factor. A proper overweight and obesity screening may help clinicians to better characterize patients’ CVD risk to counsel patients on health risks, lifestyle changes, treatment options, and decreasing the effect of the risk factor.

Body mass index (BMI) is an affordable anthropometric measure that can be used to define obesity in epidemiological research but may not represent a reliable indicator of body adiposity at an individual level.

Dual energy X-ray absorptiometry (DEXA) and air displacement plethysmography (ADP) are accepted methods to obtain accurate measures of body fat percentage (BF%), although these techniques are less available and more expensive than BMI, which may be an important limitation for screening purposes.

CUN-BAE (Clínica Universidad de Navarra-Body Adiposity Estimator) is an equation for estimating BF% affordably that has shown better association with several cardiometabolic risk...


Body fat percentage Body mass index Preclinical cardiovascular disease Carotid intima-media thickness Obesity 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Gómez-Ambrosi J, Silva C, Catalán V J et al (2012) Clinical usefulness of a new equation for estimating body fat. Diabetes Care 35:383–388CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Gómez-Ambrosi J, Silva C, Galofré JC et al (2012) Body mass index classification misses subjects with increased cardiometabolic risk factors related to elevated adiposity. Int J Obes (Lond) 36:286–294CrossRefGoogle Scholar
  3. 3.
    Martínez-Vila E, Páramo JA, Beloqui O et al (2003) Independent association of fibrinogen with carotid intima-media thickness in asymptomatic subjects. Cerebrovasc Dis 16:356–362CrossRefPubMedGoogle Scholar
  4. 4.
    Okorodudu DO, Jumean MF, Montori VM et al (2010) Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes (Lond) 34:791–799CrossRefGoogle Scholar
  5. 5.
    Bredella MA (2017) Sex differences in body composition. In: Mauvais-Jarvis F (ed) Sex and gender factors affecting metabolic homeostasis, diabetes and obesity. Springer International Publishing AG, Cham, pp 9–27CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2018

Authors and Affiliations

  • Manuel Fortún Landecho
    • 1
    • 2
  • Inmaculada Colina
    • 1
    • 2
  • Patricia Sunsundegui
    • 1
  • Bruno Camarero
    • 3
  • Jorge M. Núñez-Córdoba
    • 2
    • 4
    Email author
  • Óscar Beloqui
    • 1
    • 2
  1. 1.General Health Check-up Unit, Internal Medicine DepartmentClínica Universidad de NavarraPamplonaSpain
  2. 2.IdiSNA, Navarra Institute for Health ResearchPamplonaSpain
  3. 3.General Surgery and Digestive System ServiceComplejo Hospitalario de NavarraPamplonaSpain
  4. 4.Research Support Service, Central Clinical Trials UnitClínica Universidad de NavarraPamplonaSpain

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