Estimation of Skeletal Muscle Mass and Visceral Adipose Tissue Volume by a Single Magnetic Resonance Imaging Slice in Healthy Elderly Adults. Issue 10 (31st August 2016)

• Record Type: Journal Article
• Authors: Schweitzer, Lisa; Geisler, Corinna; Pourhassan, Maryam; Braun, Wiebke; Glüer, Claus-Christian; Bosy-Westphal, Anja; Müller, Manfred J
• Is Part Of: Journal of nutrition. Volume 146:Issue 10(2016)
• Page Start: 2143
• Page End: 2148
• Publication Date: 2016-08-31
• Subjects: assessment of body composition -- age-related changes in body composition -- sarcopenia -- sarcopenic obesity -- advanced age -- imaging techniques -- skeletal muscle -- visceral adipose tissue -- simplification of the MRI protocol; Nutrition -- Periodicals; Diet -- Periodicals; 613.205
• DOI: 10.3945/jn.116.236844
• Languages: English
• ISSNs: 0022-3166

Abstract

Abstract: Background: Assessing skeletal muscle (SM) and visceral adipose tissue (VAT) by a single MRI slice at lumbar vertebra (L) 3 can replace whole-body MRI in young and middle-aged adults. However, this technique has not been proven in older adults. Objective: The aim of this analysis was to reinvestigate the best estimate for SM and VAT in an independent population of healthy elderly people. Methods: SM and VAT were assessed by whole-body MRI in 84 subjects ≥60 y [45 men; mean ± SD age: 68.4 ± 5.4 y, mean ± SD body mass index (in kg/m 2 ): 25.5 ± 3.5]. SM and VAT areas of 9 slices at the lumbar spine were analyzed. The best estimate was investigated by Pearson correlations. Total volumes (in liters) were predicted by the area at lumbar vertebra 3 (AL3 ). Besides Bland-Altman analysis, linear regressions were performed to explain the variance of the bias by age, height, and percentage of fat mass (%FM). In a mixed population (healthy elderly plus reference population), linear regression with total SM and VAT volume as dependent variables and AL3, age, and height as independent variables was applied. Results: When comparing the correlation coefficients between the tissue areas and total volumes, L3 was identified as the best estimate ( r range: 0.71–0.94; all P < 0.05). However, Bland-Altman analysis showed a positive SM bias in men (mean ± SD: –1.0% ± 9.0%; P < 0.05) and a negative SM bias in women (mean ± SD: 3.7% ± 9.6%; P < 0.05). Contrary to SM, no significant bias was observed for VAT. In the elderly, stepwise linear regression showed height as a predictor for SM bias ( R 2 = 0.21, SEE = 2.07 L; P < 0.05) and %FM and age as predictors of the nonsignificant VAT bias ( R 2 = 0.26, SEE = 0.22L, P < 0.05), in men only. In the mixed population, AL3 and height were predictors for total SM, and AL3 for total VAT, independent of sex. Conclusions: AL3 was confirmed as the best estimate for SM and VAT volumes in healthy elderly adults. Contrary to VAT, there is a bias for SM, and height has to be added to the algorithm.