Association of possible sarcopenia with all-cause mortality in patients with solid cancer: A nationwide multicenter cohort study

Yin, Liangyu; Song, Chunhua; Cui, Jiuwei; Lin, Xin; Li, Na; Fan, Yang; Zhang, Ling; Liu, Jie; Chong, Feifei; Cong, Minghua; Li, Zengning; Li, Suyi; Guo, Zengqing; Li, Wei; Shi, Hanping; Xu, Hongxia

doi:10.1016/j.jnha.2023.100023

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…Epidemiological studies have indicated that individuals with different types of cancers have different rates of sarcopenia. A nationwide multicenter cohort study from China reported that the frequency of sarcopenia in individuals with ovarian, bladder, lung, prostate, and some gastrointestinal malignancies was over 25%, which is slightly higher than that in other cancers [ 27 ]. According to an analysis of the prevalence of sarcopenia in individuals with various medical conditions, sarcopenia is more common in those with gastrointestinal, lung, gynecological, and urinary system cancers [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and sarcopenia in individuals with cancer: a cross-sectional study

He,

Ye,

Zhu

et al. 2024

Lipids Health Dis

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Background Cancer and sarcopenia are both closely related to lipid metabolism, but the relationship between lipid metabolism and patients with cancer and sarcopenia has not been thoroughly studied. The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) is a reliable measure of lipid metabolism. The purpose of this study was to determine the possible relationship between the NHHR and sarcopenia in individuals with cancer. Methods Data from the National Health and Nutrition Examination Survey (NHANES) database for individuals with cancer, with and without sarcopenia was analyzed using weighted multiple regression equations, weighted regression cubic spline (RCS) analysis, and weighted subgroup analysis. Results In total, 1,602 individuals with cancer were included, of whom 17.1% had sarcopenia. In Adjusted Model 2, the occurrence of sarcopenia was found to be significantly associated with a higher NHHR in cancer (95% confidence interval [CI]:1.01–1.39, P = 0.036). Individuals with high a NHHR had a 2.09-fold higher risk of developing sarcopenia in comparison to those with a low NHHR (95% CI:1.12–3.92, P = 0.022). RCS analysis further identified a U-shaped non-linear relationship between females with cancer and the muscle index. Subgroup analysis indicated that sex was a significant stratifying factor, whereas age, race, marital status, smoking and drinking habits, and history of cardiovascular disease, arthritis, hypertension, and diabetes had no significant impact. Conclusion From the perspective of lipid metabolism, the NHHR may serve as an indicator for monitoring and preventing the occurrence of sarcopenia in individuals with cancer, particularly for females with cancer who appear to have greater sensitivity.

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Section: Discussionmentioning

confidence: 99%

Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and sarcopenia in individuals with cancer: a cross-sectional study

He,

Ye,

Zhu

et al. 2024

Lipids Health Dis

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An Artificial Intelligence Approach for Test‐Free Identification of Sarcopenia

Yin,

Zhao

2024

J cachexia sarcopenia muscle

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BackgroundThe diagnosis of sarcopenia relies extensively on human and equipment resources and requires individuals to personally visit medical institutions. The objective of this study was to develop a test‐free, self‐assessable approach to identify sarcopenia by utilizing artificial intelligence techniques and representative real‐world data.MethodsThis multicentre study enrolled 11 661 middle‐aged and older adults from a national survey initialized in 2011. Follow‐up data from the baseline cohort collected in 2013 (n = 9403) and 2015 (n = 10 356) were used for validation. Sarcopenia was retrospectively diagnosed using the Asian Working Group for Sarcopenia 2019 framework. Baseline age, sex, height, weight and 20 functional capacity (FC)–related binary indices (activities of daily living = 6, instrumental activities of daily living = 5 and other FC indices = 9) were considered as predictors. Multiple machine learning (ML) models were trained and cross‐validated using 70% of the baseline data to predict sarcopenia. The remaining 30% of the baseline data, along with two follow‐up datasets (n = 9403 and n = 10 356, respectively), were used to assess model performance.ResultsThe study included 5634 men and 6027 women (median age = 57.0 years). Sarcopenia was identified in 1288 (11.0%) individuals. Among the 20 FC indices, the running/jogging 1 km item showed the highest predictive value for sarcopenia (AUC [95%CI] = 0.633 [0.620–0.647]). From the various ML models assessed, a 24‐variable gradient boosting classifier (GBC) model was selected. This GBC model demonstrated favourable performance in predicting sarcopenia in the holdout data (AUC [95%CI] = 0.831 [0.808–0.853], accuracy = 0.889, recall = 0.441, precision = 0.475, F1 score = 0.458, Kappa = 0.396 and Matthews correlation coefficient = 0.396). Further model validation on the temporal scale using two longitudinal datasets also demonstrated good performance (AUC [95%CI]: 0.833 [0.818–0.848] and 0.852 [0.840–0.865], respectively). The model's built‐in feature importance ranking and the SHapley Additive exPlanations method revealed that lifting 5 kg and running/jogging 1 km were relatively important variables among the 20 FC items contributing to the model's predictive capacity, respectively. The calibration curve of the model indicated good agreement between predictions and actual observations (Hosmer and Lemeshow p = 0.501, 0.451 and 0.374 for the three test sets, respectively), and decision curve analysis supported its clinical usefulness. The model was implemented as an online web application and exported as a deployable binary file, allowing for flexible, individualized risk assessment.ConclusionsWe developed an artificial intelligence model that can assist in the identification of sarcopenia, particularly in settings lacking the necessary resources for a comprehensive diagnosis. These findings offer potential for improving decision‐making and facilitating the development of novel management strategies of sarcopenia.

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Clinical outcomes and associated factors with mortality in systemic sclerosis patients with sarcopenia

Hongkanjanapong,

Pongkulkiat,

Mahakkanukrauh

et al. 2024

The American Journal of the Medical Sciences

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Association of possible sarcopenia with all-cause mortality in patients with solid cancer: A nationwide multicenter cohort study

Cited by 4 publications

References 42 publications

Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and sarcopenia in individuals with cancer: a cross-sectional study

Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and sarcopenia in individuals with cancer: a cross-sectional study

An Artificial Intelligence Approach for Test‐Free Identification of Sarcopenia

Clinical outcomes and associated factors with mortality in systemic sclerosis patients with sarcopenia

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