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Exploring Sarcopenic Obesity in the Cancer Setting: Insights from the National Health and Nutrition Examination
Yinuo Jiang1,2, Wenjie Jiang3, Qun Wang4
1Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Bioengineering (Basel, Switzerland)
|September 27, 2025
Summary
Sarcopenic obesity (SO) significantly increases mortality risk in cancer patients. A machine learning model effectively predicts SO presence, aiding clinical practice and improving cancer patient prognosis.
Area of Science:
- Oncology
- Geriatrics
- Metabolic Disorders
Background:
- Sarcopenic obesity (SO) combines low muscle mass and obesity, posing diagnostic and prognostic challenges.
- Limited research exists on SO's impact within cancer patient populations.
- SO is associated with poor outcomes, necessitating further investigation in oncology.
Purpose of the Study:
- To explore the association between sarcopenic obesity and mortality in cancer patients.
- To identify predictors for SO development in cancer.
- To develop a predictive model for SO detection in cancer patients.
Main Methods:
- Utilized NHANES data (1999-2006, 2011-2016) from 1432 cancer patients.
- Employed Cox proportional hazard models for survival analysis.
- Applied six machine learning algorithms (logistic regression, LASSO, SVM, RF, XGBoost) to build predictive models for SO.
Main Results:
- Sarcopenic obesity was significantly associated with a higher risk of all-cause mortality in cancer patients (aHR 1.368).
- The LASSO machine learning model demonstrated superior predictive performance (AUC 0.891 training, 0.873 testing).
- This study is the first to use machine learning to predict SO in cancer patients.
Conclusions:
- Sarcopenic obesity is a significant prognostic factor for cancer patients.
- The developed LASSO model offers an effective tool for clinical SO prediction.
- Findings provide valuable insights for future research on SO in oncology.
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