Bioelectrical impedance analysis (BIA) is currently the most commonly used method in clinical practice to measure body composition. However, the bioelectrical impedance analyzer is not designed according to different countries, races, and elderly populations. Because different races may have different body compositions, a prediction model for the elderly population in Taiwan should be developed to avoid population bias, thereby improving the accuracy of community evaluation surveys. Dual energy X-ray absorptiometry (DXA) was used as a standard method for comparison, and impedance analysis was used for the development of a highly accurate predictive model that is suitable for assessing the body composition of elderly people. This study employed a cross-sectional design and recruited 438 elderly people who were undergoing health examinations at the health management center in the Tri-Service General Hospital as study subjects. Basic demographic variables and impedance analysis values were used in four predictive models, namely, linear regression, random forest, support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) models, to predict DXA body composition. The data from 354 study subjects were used to develop the predictive model, while the data from 84 study subjects were used to validate the accuracy of the predictive model. The body composition of elderly people as estimated by InBody 720 was highly correlated with that estimated by DXA. The correlation coefficient between InBody 720 and DXA for muscle mass was 0.969, and that for fat mass was 0.935. Consistency analysis results showed that InBody 720 tends to underestimate muscle mass and fat mass. A comparison of the accuracy of the linear regression, random forest, SVM, and XGBoost models showed that the linear regression has the highest accuracy. The correlation coefficient between the new model and DXA for muscle mass and fat mass were 0.977 and 0.978, respectively. The new predictive model can be used to monitor the nutrition status of elderly people and identify people with sarcopenia in the community.
The type 2 transmembrane serine protease matriptase is broadly expressed in human carcinomas and hematological cancers. The proteolytic activity of matriptase is a potential target of drugs and imaging probes. We assessed the fate of active matriptase following the induction of matriptase zymogen activation. Exposing eight human carcinoma cells to pH 6.0 buffer induced robust matriptase zymogen activation followed by rapid inhibition of the nascent active matriptase by hepatocyte growth factor activator inhibitor (HAI)-1. Consequently, no enzymatically active matriptase was detected in these cells. Some active matriptase is, however, rapidly shed to the extracellular milieu by these carcinoma cells. The lack of cell-associated active matriptase and the shedding of active matriptase were also observed in two hematological cancer lines. Matriptase shedding is correlated closely with the induction of matriptase activation, suggesting that matriptase activation and shedding are kinetically coupled. The coupling allows a proportion of active matriptase to survive HAI-1 inhibition by rapid shedding from cell surface. Our study suggests that cellular free, active matriptase is scarce and might not be an effective target for in vivo imaging and drug development.
Background Sarcopenia is considered to be a major factor in frailty, and early detection of sarcopenia is important to prevent frailty. Weakness of the lower abdominal region (WLAR) is one of the findings in Kampo (Traditional Japanese) medicine that indicates a condition similar to sarcopenia. We hypothesized that there may be a correlation between the iliopsoas (psoas) muscle and the rectus abdominis. In this study, we used measurements taken from computed tomography (CT) scans of the iliopsoas muscle and rectus abdominis, and investigated which measurements of abdominal muscle indices are relevant to a diagnosis of sarcopenia. Method The subjects were 100 consecutive patients (50 males and 50 females) who were treated in our department. We collected their age, height, weight, body mass index (BMI), and WLAR findings, which were divided into three levels: f(0): no WLAR, f(1): suspected WLAR and f(2): obvious WLAR. We also measured CT images of iliopsoas and rectus abdominis muscle-related indices and the psoas muscle index (PMI) was calculated. Results The correlation coefficient between the rectus abdominis data obtained from multiple regression analysis and the PMI was R2 = 0.36 or higher for both females and males, indicating that the size of the iliopsoas muscle can be predicted from measurements of the rectus abdominis muscle. In both females and males, this suggests that sarcopenia groupings determined by the PMI in the iliopsoas muscle are consistent with groupings based on WLAR. The length of the rectus abdominis muscle was measured, and the mean of each of the three WLAR groups showed significant differences in upper rectus abdominis muscle dehiscence and lower rectus abdominis muscle dehiscence in females. In males, there were significant differences in muscle dehiscence only of the upper rectus abdominis. Discussion It is known from previous reports that the iliopsoas muscle is an indicator of sarcopenia. This study suggests that the rectus abdominis muscle might also be an indicator of sarcopenia based on the relationship between the measured values of the iliopsoas and rectus abdominis muscles. When the cutoff values identified with the PMI were applied, significant differences were found among the groups based on PMI and WLAR values, which are currently used for diagnosis. These results suggest that WLAR may provide a useful way of screening case findings for sarcopenia. Conclusion In this study, we were able to identify positive correlations between the rectus abdominis and iliopsoas muscles by obtaining measurements from CT images. Our results indicate that the status of the rectus abdominis might be used in the diagnosis of sarcopenia.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.