Population‐based cohort imaging: skeletal muscle mass by magnetic resonance imaging in correlation to bioelectrical‐impedance analysis. Issue 2 (25th January 2022)

• Record Type: Journal Article
• Authors: Kiefer, Lena S.; Fabian, Jana; Rospleszcz, Susanne; Lorbeer, Roberto; Machann, Jürgen; Kraus, Mareen S.; Fischer, Marc; Roemer, Frank; Rathmann, Wolfgang; Meisinger, Christa; Heier, Margit; Nikolaou, Konstantin; Peters, Annette; Storz, Corinna; Schlett, Christopher L.; Bamberg, Fabian
• Is Part Of: Journal of cachexia, sarcopenia and muscle. Volume 13:Issue 2(2022)
• Page Start: 976
• Page End: 986
• Publication Date: 2022-01-25
• Subjects: Skeletal muscle mass -- Fat‐free skeletal muscle mass -- Skeletal muscle segmentation -- Magnetic resonance imaging -- Quantitative imaging biomarker; Cachexia -- Periodicals; Muscles -- Aging -- Periodicals; Muscles -- Periodicals; Cachexia; Sarcopenia; Muscles; Cachexia; Muscles; Muscles -- Aging; Periodicals; Periodicals; 616
• DOI: 10.1002/jcsm.12913
• Languages: English
• ISSNs: 2190-5991

Abstract

Abstract: Background: Skeletal muscle mass is subjected to constant changes and is considered a good predictor for outcome in various diseases. Bioelectrical‐impedance analysis (BIA) and magnetic resonance imaging (MRI) are approved methodologies for its assessment. However, muscle mass estimations by BIA may be influenced by excess intramuscular lipids and adipose tissue in obesity. The objective of this study was to evaluate the feasibility of quantitative assessment of skeletal muscle mass by MRI as compared with BIA. Methods: Subjects from a population‐based cohort underwent BIA (50 kHz, 0.8 mA) and whole‐body MRI including chemical‐shift encoded MRI (six echo times). Abdominal muscle mass by MRI was quantified as total and fat‐free cross‐sectional area by a standardized manual segmentation‐algorithm and normalized to subjects' body height 2 (abdominal muscle mass indices: AMMIMRI ). Results: Among 335 included subjects (56.3 ± 9.1 years, 56.1% male), 95 (28.4%) were obese (BMI ≥ 30 kg/m 2 ). MRI‐based and BIA‐based measures of muscle mass were strongly correlated, particularly in non‐obese subjects [ r < 0.74 in non‐obese ( P < 0.001) vs. r < 0.56 in obese ( P < 0.001)]. Median AMMITotal(MRI) was significantly higher in obese as compared with non‐obese subjects (3246.7 ± 606.1 mm 2 /m 2 vs. 2839.0 ± 535.8 mm 2 /m 2, P < 0.001, respectively), whereas the ratio AMMIFat‐free /AMMITotal (by MRI) was significantly higher in non‐obese individuals (59.3 ± 10.1% vs. 53.5 ± 10.6%, P < 0.001, respectively). No significant difference was found regarding AMMIFat‐free(MRI) ( P = 0.424). In analyses adjusted for age and sex, impaired glucose tolerance and measures of obesity were significantly and positively associated with AMMITotal(MRI) and significantly and inversely with the ratio AMMIFat‐free(MRI) /AMMITotal(MRI) ( P < 0.001). Conclusions: MRI‐based assessment of muscle mass is feasible in population‐based imaging and strongly correlated with BIA. However, the observed weaker correlation in obese subjects may explain the known limitation of BIA in obesity and promote MRI‐based assessments. Thus, skeletal muscle mass parameters by MRI may serve as practical imaging biomarkers independent of subjects' body weight.