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Adductor pollicis muscle thickness has a low association with lean mass in women.
Clinical Nutrition 2017 August 20
BACKGROUND & AIM: Lean mass (LM) is a functional component of body composition and is an important parameter of nutritional status assessment. The adductor pollicis muscle thickness (APMT) has been used as a predictor of LM, but it is not well known if this method presents a higher prediction of LM than simple anthropometric measurements, such as weight. Thus, we aimed to associate APMT (alone and plus weight) with LM in women.
METHODS: This cross-sectional study was conducted with 82 young and postmenopausal women. Body weight (Filizola®) and height (Welmy®) was quantified and APMT was measured by Lange® caliper. Body composition (LM and fat percentage) was estimated by dual-energy X-ray absorptiometry.
RESULTS: APMT was positively correlated with LM (r = 0.35; p = 0.001), however, weight was strongly correlated with LM (r = 0.81; p < 0.001). APMT showed a prediction of 12% of LM (β = 0.346, R2 = 0.120, p < 0.001), and weight explained the variations of LM by 65% (β = 0.808, R2 = 0.654, p < 0.001). When weight and APMT were evaluated together, there was an increase of only 0.06% in LM prediction (β = 0.820, R2 = 0.655, p < 0.001), compared to weight alone.
CONCLUSION: When compared to weight, APMT showed a low association with LM. These results suggest that a simpler anthropometric measurement, such as weight, can be a better predictor of LM than APMT.
METHODS: This cross-sectional study was conducted with 82 young and postmenopausal women. Body weight (Filizola®) and height (Welmy®) was quantified and APMT was measured by Lange® caliper. Body composition (LM and fat percentage) was estimated by dual-energy X-ray absorptiometry.
RESULTS: APMT was positively correlated with LM (r = 0.35; p = 0.001), however, weight was strongly correlated with LM (r = 0.81; p < 0.001). APMT showed a prediction of 12% of LM (β = 0.346, R2 = 0.120, p < 0.001), and weight explained the variations of LM by 65% (β = 0.808, R2 = 0.654, p < 0.001). When weight and APMT were evaluated together, there was an increase of only 0.06% in LM prediction (β = 0.820, R2 = 0.655, p < 0.001), compared to weight alone.
CONCLUSION: When compared to weight, APMT showed a low association with LM. These results suggest that a simpler anthropometric measurement, such as weight, can be a better predictor of LM than APMT.
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