Developments in estimating visceral fat area from medical examination data

Masato Nagai, Hideaki Komiya, Yutaka Mori, Teruo Ohta

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

Aim: The aim of this study is to develop a simple method for estimating visceral fat area (VFA) using medical examination data. Methods: The study subjects were 100 males who had undergone medical examihations and computed tomography (CT) at the umbilicus level. Multiple regression analysis was conducted to estimate VFA. The Bland & Altman Method was used to examine the tendency for mean difference between VFA observed by CT and WA estimated by medical examination data. We calculated cross-validation and sensitivity and specificity at VFA ≥ 100 cm2. Results: As a result of multiple regression analysis, the waist-height ratio (WHtR) and triglyceride (TG) were taken as independent variables (r=0.9 10). The Bland & Altman Method showed 0.00± 63.88 cm2. The cross-validation regression equation was r=0.889. Sensitivity was 0.833 and specificity was 0.900. Conclusion: WHtR is a simple index used to diagnose the accumulation of VFA. It has been reported that TG, which increases with accumulating VFA, is a factor in hyperlipidemia; therefore, we consider the obtained independent variables to be appropriate. In addition, the regression equation showed high correlation and good results by cross-validation, the Bland & Altman Method, sensitivity, and specificity. We assert that VFA can be estimated using this method.

Original languageEnglish
Pages (from-to)193-198
Number of pages6
JournalJournal of atherosclerosis and thrombosis
Volume15
Issue number4
DOIs
Publication statusPublished - 2008

Keywords

  • Computed tomography
  • Lifestyle-related disease
  • Metabolic syndrome
  • Waist circumference

ASJC Scopus subject areas

  • Internal Medicine
  • Cardiology and Cardiovascular Medicine
  • Biochemistry, medical

Fingerprint

Dive into the research topics of 'Developments in estimating visceral fat area from medical examination data'. Together they form a unique fingerprint.

Cite this