Abstract

Bioelectrical impedance analysis (BIA)-derived phase angle (PhA) is a valuable parameter to assess physical health. However, the genetic and environmental aspects of PhA are not yet well understood. The present study aimed to estimate the heritability of PhA and investigate the relationships between PhA and anthropometric measurements. PhA and skeletal muscle mass index (SMI) were examined using multi-frequency BIA in 168 Japanese twin volunteers (54 males and 114 females; mean age = 61.0 ± 16.5 years). We estimated the narrow-sense heritability of these parameters and the genetic and environmental relationships between them using a genetic twin modeling. For the PhA, 51% (95% confidence interval: 0.33, 0.64) of the variance was explained by additive genetic effects, and 49% (95% confidence interval: 0.36, 0.67) was explained by unique environmental effects. The heritability of PhA was lower than the height, body weight, and body mass index. PhA shared almost no genetic variation with anthropometric measurements and SMI but shared an environmental variation (14%) with SMI. These findings suggest that the genes affecting PhA are different than those affecting anthropometric measurements and SMI. The correlation between PhA and SMI is caused by common environmental factors.

Highlights

  • Bioelectrical impedance analysis (BIA) has been widely used as a non-invasive, inexpensive, and quick technique to estimate body composition by sending a weak electric current [1,2]

  • Assessments of muscle quality are expected to guide treatment choices and monitor response to treatment [16]. Anthropometric measurements such as height, body weight, body mass index (BMI), and muscle mass phenotypes are influenced by genetic factors [17,18,19]

  • We hypothesized that the heritability of phase angle (PhA) would be lower than height, body weight, BMI, and muscle mass and have less genetic influence

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Summary

A Twin Study

Daisuke Matsumoto 1,2,3, * , Fujio Inui 2,3,4 , Chika Honda 3,5 , Rie Tomizawa 3 , Mikio Watanabe 3,6 , Karri Silventoinen 3,7 and Norio Sakai 3,8.

Introduction
Participants
Measurements
Statistical Analysis in behavioral genetics are as follows
Results
Discussion
Findings
Conclusions

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