Estimation of Appendicular Skeletal Muscle Mass in Studies Based on CHARLS May Cause Unreliable Conclusion

Abstract

We write to highlight a potential issue in the estimation of appendicular skeletal muscle mass (ASM) in studies based on the China Health and Retirement Longitudinal Study (CHARLS). In recent years, numerous studies based on the CHARLS database have been published, with a significant number focusing on sarcopenia. A PubMed search reveals that over 94 sarcopenia-related articles based on CHARLS data have been published to date, with the majority appearing in the past 3 years. Our journal has also contributed to this body of literature by publishing several of these studies [1, 2]. Although these studies have contributed significantly to the field, we argue that the estimation of ASM in many of these studies may be based on unreliable methods, potentially leading to questionable conclusions.

The accurate estimation of ASM is crucial for the diagnosis of sarcopenia and the assessment of its prevalence and impact. Although this formula has been widely used, we contend that its application in CHARLS-based studies may be inappropriate for several reasons.

First, the original study from which this formula was derived was conducted in a relatively young population with a mean age of only about 40 years (39.3 ± 14.5 years for males; 41.1 ± 14.1 years for females; 18–69 years old). In contrast, sarcopenia research primarily focuses on older adults, and the physiological characteristics of muscle mass in younger individuals may not be representative of those in the elderly. The sarcopenia research based on CHARLS usually excludes individuals younger than 60, with a mean age of more than 67 [1, 2]. This age discrepancy can significantly impact the applicability and accuracy of the derived formula in sarcopenia research.

Second, the original study was conducted in 2006, whereas many CHARLS data used in recent studies were collected around 2015 [1]. According to data from the General Administration of Sport of China (https://www.sport.gov.cn/n315/index.html), significant changes in the average height and weight of the Chinese population have occurred over the past two decades. These changes may affect the accuracy of ASM estimation using the original formula.

Another critical limitation of the widely used formula is its reliance on only a few anthropometric measurements—height, weight, sex and age—while neglecting other important body composition indicators such as waist circumference and calf circumference. This oversimplification can lead to substantial bias in the estimation of ASM. For example, using a fixed cut-off point based on the lowest 20th percentile of ASM, as seen in many CHARLS-based studies [1, 2], may incorrectly classify individuals with low stature and average weight as having low ASM. This approach essentially labels a fixed proportion of the population as having low muscle mass, regardless of their actual muscle status. The exclusion of other relevant anthropometric measurements and the use of a population-specific cut-off point without proper validation can result in misclassification and unreliable conclusions regarding sarcopenia prevalence and its associated health outcomes. However, the original CHARLS data collection did not include comprehensive anthropometric measurements such as calf circumference or other indicators that could provide a more nuanced understanding of body composition.

Lastly, the original formula has not been independently validated in other populations, particularly among older adults. Given the potential for population-specific differences in body composition and muscle mass distribution, the extrapolation of this formula to the CHARLS cohort may introduce significant bias. For example, studies have shown that the relationship between anthropometric measurements and muscle mass can vary by ethnicity, age and sex [4]. Therefore, the use of a formula derived from a different population and time period may not accurately reflect the ASM in the CHARLS cohort.

In conclusion, we urge caution in interpreting studies that estimate ASM using the aforementioned formula in CHARLS-based research. Future studies should consider validating or updating the ASM estimation methods to better reflect the characteristics of the CHARLS population. We believe that addressing this issue will enhance the reliability and validity of sarcopenia research based on CHARLS data.

The authors declare no conflicts of interest.