Assessment of body composition: Intrinsic methodological limitations and statistical pitfalls

An Overview of Sarcopenia: Focusing on Nutritional Treatment Approaches

Sarcopenia is a syndrome characterized by the progressive and generalized loss of skeletal muscle mass and strength. This condition is associated with physical disability, decreased quality of life, and increased mortality. Therefore, reducing the prevalence of sarcopenia could significantly lower healthcare costs. Sarcopenia can be classified into primary and secondary sarcopenia. The former is related to aging and begins after the fourth decade of life; after that, there is a muscle loss of around 8% per decade until age 70 years, which subsequently increases to 15% per decade. On the other hand, secondary sarcopenia can affect all individuals and may result from various factors including physical inactivity, malnutrition, endocrine disorders, neurodegenerative diseases, inflammation, and cachexia. Understanding the multiple mechanisms involved in the onset and progression of sarcopenia allows for us to develop strategies that can prevent, treat, or at least mitigate muscle loss caused by increased protein breakdown. One potential treatment of sarcopenia is based on nutritional interventions, including adequate caloric and protein intake and specific nutrients that support muscle health. Such nutrients include natural food rich in whey protein and omega-3 fatty acids as well as nutritional supplements like branched-chain amino acids, β-hydroxy-β-methylbutyrate, and vitamin D along with food for special medical purposes. It is important to emphasize that physical exercises, especially resistance training, not only promote muscle protein synthesis on their own but also work synergistically with nutritional strategies to enhance their effectiveness.

Development and clinical application of bioelectrical impedance analysis method for body composition assessment

Obesity, which is characterized by excessive body fat, increases the risk of chronic diseases, such as type 2 diabetes, cardiovascular diseases, and certain cancers. Sarcopenia, a decline in muscle mass, is also associated with many chronic disorders and is therefore a major concern in aging populations. Body composition analysis is important in the evaluation of obesity and sarcopenia because it provides information about the distribution of body fat and muscle mass. It is also useful for monitoring nutritional status, disease severity, and the effectiveness of interventions, such as exercise, diet, and drugs, and thus helps assess overall health and longevity. Computed tomography, magnetic resonance imaging, and dual-energy X-ray absorptiometry are commonly used for this purpose. However, they have limitations, such as high cost, long measurement time, and radiation exposure. Instead, bioelectrical impedance analysis (BIA), which was introduced several decades ago and has undergone significant technological advancements, can be used. It is easily accessible, affordable, and importantly, poses no radiation risk, making it suitable for use in hospitals, fitness centers, and even at home. Herein, we review the recent technological developments and clinical applications of BIA to provide an updated understanding of BIA technology and its strengths and limitations.

L-shaped association between lean body mass to visceral fat mass ratio with hyperuricemia: a cross-sectional study

BACKGROUND

Insufficient attention has been given to examining the correlation between body composition and hyperuricemia, leading to inconsistent findings. The primary objective of this research is to explore the association between lean body mass index (LMI), visceral fat mass index (VFMI), and hyperuricemia. A specific emphasis will be placed on assessing the link between the ratio of lean body mass to visceral fat mass (LMI/VFMI) and hyperuricemia.

METHODS

The present study employed a cross-sectional design and involved a total of 9,646 individuals who participated in the National Health and Nutrition Examination Survey (NHANES). To explore the associations among the variables, logistic and linear regressions were employed. Additionally, subgroup analyses and sensitivity analyses were conducted based on various characteristics.

RESULTS

The results showed that LMI was positively associated with hyperuricemia (for Per-SD: OR = 1.88, 95%CI: 1.75, 2.01; for quartiles [Q4:Q1]: OR = 5.37, 95%CI: 4.31, 6.69). Meanwhile, VFMI showed a positive association with hyperuricemia (for Per-SD: OR = 2.02, 95%CI: 1.88, 2.16; for quartiles [Q4:Q1]: OR =8.37, 95%CI: 6.70, 10.47). When considering the effects of In LMI/VFMI, an L-shaped negative association with hyperuricemia was observed (for Per-SD: OR = 0.45, 95%CI: 0.42, 0.49; for quartiles [Q4:Q1]: OR = 0.16, 95%CI: 0.13, 0.20). Subgroup and sensitivity analyses demonstrated the robustness of this association across different subgroups. Additionally, the segmented regression analysis indicated a saturation effect of 5.64 for the In LMI/VFMI with hyperuricemia (OR = 0.20, 95%CI: 0.17, 0.24). For every 2.72-fold increase of In LMI/VFMI, the risk of hyperuricemia was reduced by 80%.

CONCLUSION

The LMI/VFMI ratio is non-linearly associated with serum uric acid. Whether this association is causal needs to be confirmed in further longitudinal studies or Mendelian randomization.

Lenvatinib Exacerbates the Decrease in Skeletal Muscle Mass in Patients with Hepatocellular Carcinoma, Whereas Atezolizumab Plus Bevacizumab Does Not

This study aimed to evaluate chronological changes in skeletal muscle index (SMI), subcutaneous and visceral adipose tissue indices (SATI and VATI), AFP, PIVKA-II, and ALBI scores during atezolizumab plus bevacizumab (AB) or lenvatinib (LEN) treatment for hepatocellular carcinoma (HCC) and the effect of these changes on survival. A total of 94 patients with HCC (37 were on AB and 57 on LEN) were enrolled. SMI, SATI, VATI, AFP, PIVKA-II, and ALBI scores were analyzed at the time of the treatment introduction (Intro), 3 months after the introduction (3M), at drug discontinuation (End), and the last observational time (Last). The differences between chronological changes were analyzed using the Wilcoxon paired test. The independent predictors for survival and the changes in SMI during AB or LEN (c-SMI%) were analyzed using the Cox proportional hazards model treating all these factors as time-varying covariates and the analysis of covariance, respectively. SMI in the AB group was maintained over time (42.9-44.0-40.6-44.2 cm2/m2), whereas that in the LEN group significantly decreased during the Intro-3M (p < 0.05) and 3M-End (p < 0.05) period (46.5-45.1-42.8-42.1 cm2/m2). SMI (p < 0.001) was an independent predictor for survival together with AFP (p = 0.004) and ALBI score (p < 0.001). Drug choice (AB or LEN; p = 0.038) and PIVKA-II (p < 0.001) were extracted as independent predictors for c-SMI%. AB treatment was significantly superior to LEN in terms of maintaining skeletal muscle, which is an independent predictor for survival.

Imaging modalities for measuring body composition in patients with cancer: opportunities and challenges

Body composition assessment (ie, the measurement of muscle and adiposity) impacts several cancer-related outcomes including treatment-related toxicities, treatment responses, complications, and prognosis. Traditional modalities for body composition measurement include body mass index, body circumference, skinfold thickness, and bioelectrical impedance analysis; advanced imaging modalities include dual energy x-ray absorptiometry, computerized tomography, magnetic resonance imaging, and positron emission tomography. Each modality has its advantages and disadvantages, thus requiring an individualized approach in identifying the most appropriate measure for specific clinical or research situations. Advancements in imaging approaches have led to an abundance of available data, however, the lack of standardized thresholds for classification of abnormal muscle mass or adiposity has been a barrier to adopting these measurements widely in research and clinical care. In this review, we discuss the different modalities in detail and provide guidance on their unique opportunities and challenges.

Premature Death in Bodybuilders: What Do We Know?

Premature deaths in bodybuilders regularly make headlines and are cited as evidence that bodybuilding is a dangerous activity. A wealth of research has revealed elite athletes typically enjoy lower mortality rates than non-athletes, but research on bodybuilder lifespan is surprisingly limited. Anabolic androgenic steroid (AAS) use is commonly cited as a key contributor to morbidity and premature mortality in bodybuilders, but this area of research is highly nuanced and influenced by numerous confounders unique to bodybuilding. It is quite possible that bodybuilders are at elevated risk and that AAS use is the primary reason for this, but there remains much unknown in this realm. As global participation in bodybuilding increases, and healthcare providers play a more active role in monitoring bodybuilder health, there is a need to identify how numerous factors associated with bodybuilding ultimately influence short- and long-term health and mortality rate. In this Current Opinion, we discuss what is currently known about the bodybuilder lifespan, identify the nuances of the literature regarding bodybuilder health and AAS use, and provide recommendations for future research on this topic.

Assessing reliability and validity of different stiffness measurement tools on a multi-layered phantom tissue model

Changes in the mechanical properties (i.e., stiffness) of soft tissues have been linked to musculoskeletal disorders, pain conditions, and cancer biology, leading to a rising demand for diagnostic methods. Despite the general availability of different stiffness measurement tools, it is unclear as to which are best suited for different tissue types and the related measurement depths. The study aimed to compare different stiffness measurement tools' (SMT) reliability on a multi-layered phantom tissue model (MPTM). A polyurethane MPTM simulated the four layers of the thoracolumbar region: cutis (CUT), subcutaneous connective tissue (SCT), fascia profunda (FPR), and erector spinae (ERS), with varying stiffness parameters. Evaluated stiffness measurement tools included Shore Durometer, Semi-Electronic Tissue Compliance Meter (STCM), IndentoPRO, MyotonPRO, and ultrasound imaging. Measurements were made by two independent, blinded examiners. Shore Durometer, STCM, IndentoPRO, and MyotonPRO reliably detected stiffness changes in three of the four MPTM layers, but not in the thin (1 mm thick) layer simulating FPR. With ultrasound imaging, only stiffness changes in layers thicker than 3 mm could be measured reliably. Significant correlations ranging from 0.70 to 0.98 (all p < 0.01) were found. The interrater reliability ranged from good to excellent (ICC(2,2) = 0.75-0.98). The results are encouraging for researchers and clinical practitioners as the investigated stiffness measurement tools are easy-to-use and comparatively affordable.