Visceral adipose tissue: Relations between single-slice areas and total volume

Wei Shen, Mark Punyanitya, Zi Mian Wang, Dympna Gallagher, Marie Pierre St-Onge, Jeanine Albu, Steven B. Heymsfield, Stanley Heshka

Research output: Contribution to journalArticlepeer-review

288 Scopus citations

Abstract

Background: Visceral adipose tissue (VAT), which is linked with the metabolic consequences of obesity, is usually characterized by measuring VAT area at the L4-L5 vertebral interspace. However, the location of the slice with the strongest relation to VAT volume is not established. Objective: We sought to investigate the relations between cross-sectional VAT areas at different anatomic locations and VAT volume in a large, diverse sample of healthy subjects. Design: VAT volume was derived from slice areas taken at 5-cm intervals from magnetic resonance images in 121 healthy men [x̄ ± SD age:41.9 ± 15.8 y; body mass index (BMI; in kg/m2): 26.0 ± 3.2; VAT: 2.7 ± 1.8 L] and 198 healthy women (age: 48.1 ± 18.7 y; BMI: 27.0 ± 5.4; VAT: 1.7 ± 1.2 L). Regression models were developed to identify the best single slice for estimating VAT volume. Results: The VAT area 10 cm above L4-L5 (A+10) in men (R2 = 0.932, P < 0.001) and 5 cm above L4-L5 (A+5) in women (R 2 = 0.945, P < 0.001) had the highest correlation with abdominal VAT. R2 increased by only 3.8% in men and 0.5% in women with adjustment for age, race, scanning position, BMI, and waist circumference. Studies using A+10 in men and A+5 in women will require 14% and 9% fewer subjects, respectively, than those using slices at L4-L5 and will have equivalent power. Conclusion: Measurement of slice areas at A +10 in men and A+5 in women provides greater power for the detection of VAT volume differences than does measurement at L4-L5.

Original languageEnglish
Pages (from-to)271-278
Number of pages8
JournalAmerican Journal of Clinical Nutrition
Volume80
Issue number2
DOIs
StatePublished - Aug 2004

Keywords

  • Body composition
  • Computed tomography
  • L4-L5
  • Magnetic resonance imaging
  • Volume prediction

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