Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning
BackgroundMetabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic unhealthy (MU) phenotype in normal and obesity populat...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-08-01
|
| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1228300/full |
| _version_ | 1797732686532444160 |
|---|---|
| author | Xiujuan Deng Lin Qiu Xin Sun Hui Li Zejiao Chen Min Huang Fangxing Hu Zhenyi Zhang |
| author_facet | Xiujuan Deng Lin Qiu Xin Sun Hui Li Zejiao Chen Min Huang Fangxing Hu Zhenyi Zhang |
| author_sort | Xiujuan Deng |
| collection | DOAJ |
| description | BackgroundMetabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic unhealthy (MU) phenotype in normal and obesity population in China, and to explore the predictive ability of body composition indices to distinguish MU by generating machine learning algorithms.MethodsA cross-sectional study was conducted and the subjects who came to the hospital to receive a health examination were enrolled. Body composition was assessed using bioelectrical impedance analyser. A model generator with a gradient-boosting tree algorithm (LightGBM) combined with the SHapley Additive exPlanations method was adapted to train and interpret the model. Receiver-operating characteristic curves were used to analyze the predictive value.ResultsWe found the significant difference in body composition parameters between the metabolic healthy normal weight (MHNW), metabolic healthy obesity (MHO), metabolic unhealthy normal weight (MUNW) and metabolic unhealthy obesity (MUO) individuals, especially among the MHNW, MUNW and MUO phenotype. MHNW phenotype had significantly lower whole fat mass (FM), trunk FM and trunk free fat mass (FFM), and had significantly lower visceral fat areas compared to MUNW and MUO phenotype, respectively. The bioimpedance phase angle, waist-hip ratio (WHR) and free fat mass index (FFMI) were found to be remarkably lower in MHNW than in MUNW and MUO groups, and lower in MHO than in MUO group. For predictive analysis, the LightGBM-based model identified 32 status-predicting features for MUNW with MHNW group as the reference, MUO with MHO as the reference and MUO with MHNW as the reference, achieved high discriminative power, with area under the curve (AUC) values of 0.842 [0.658, 1.000] for MUNW vs. MHNW, 0.746 [0.599, 0.893] for MUO vs. MHO and 0.968 [0.968, 1.000] for MUO and MHNW, respectively. A 2-variable model was developed for more practical clinical applications. WHR > 0.92 and FFMI > 18.5 kg/m2 predict the increased risk of MU.ConclusionBody composition measurement and validation of this model could be a valuable approach for the early management and prevention of MU, whether in obese or normal population. |
| first_indexed | 2024-03-12T12:17:04Z |
| format | Article |
| id | doaj.art-d11033248ee343849c4efe932deb08f2 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-03-12T12:17:04Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-d11033248ee343849c4efe932deb08f22023-08-30T09:50:30ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-08-011410.3389/fendo.2023.12283001228300Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learningXiujuan DengLin QiuXin SunHui LiZejiao ChenMin HuangFangxing HuZhenyi ZhangBackgroundMetabolic syndrome (Mets) is considered a global epidemic of the 21st century, predisposing to cardiometabolic diseases. This study aims to describe and compare the body composition profiles between metabolic healthy (MH) and metabolic unhealthy (MU) phenotype in normal and obesity population in China, and to explore the predictive ability of body composition indices to distinguish MU by generating machine learning algorithms.MethodsA cross-sectional study was conducted and the subjects who came to the hospital to receive a health examination were enrolled. Body composition was assessed using bioelectrical impedance analyser. A model generator with a gradient-boosting tree algorithm (LightGBM) combined with the SHapley Additive exPlanations method was adapted to train and interpret the model. Receiver-operating characteristic curves were used to analyze the predictive value.ResultsWe found the significant difference in body composition parameters between the metabolic healthy normal weight (MHNW), metabolic healthy obesity (MHO), metabolic unhealthy normal weight (MUNW) and metabolic unhealthy obesity (MUO) individuals, especially among the MHNW, MUNW and MUO phenotype. MHNW phenotype had significantly lower whole fat mass (FM), trunk FM and trunk free fat mass (FFM), and had significantly lower visceral fat areas compared to MUNW and MUO phenotype, respectively. The bioimpedance phase angle, waist-hip ratio (WHR) and free fat mass index (FFMI) were found to be remarkably lower in MHNW than in MUNW and MUO groups, and lower in MHO than in MUO group. For predictive analysis, the LightGBM-based model identified 32 status-predicting features for MUNW with MHNW group as the reference, MUO with MHO as the reference and MUO with MHNW as the reference, achieved high discriminative power, with area under the curve (AUC) values of 0.842 [0.658, 1.000] for MUNW vs. MHNW, 0.746 [0.599, 0.893] for MUO vs. MHO and 0.968 [0.968, 1.000] for MUO and MHNW, respectively. A 2-variable model was developed for more practical clinical applications. WHR > 0.92 and FFMI > 18.5 kg/m2 predict the increased risk of MU.ConclusionBody composition measurement and validation of this model could be a valuable approach for the early management and prevention of MU, whether in obese or normal population.https://www.frontiersin.org/articles/10.3389/fendo.2023.1228300/fullmetabolic syndromemetabolic unhealthybody compositionmachine learningSHapley additive exPlanations |
| spellingShingle | Xiujuan Deng Lin Qiu Xin Sun Hui Li Zejiao Chen Min Huang Fangxing Hu Zhenyi Zhang Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning Frontiers in Endocrinology metabolic syndrome metabolic unhealthy body composition machine learning SHapley additive exPlanations |
| title | Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning |
| title_full | Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning |
| title_fullStr | Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning |
| title_full_unstemmed | Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning |
| title_short | Early prediction of body composition parameters on metabolically unhealthy in the Chinese population via advanced machine learning |
| title_sort | early prediction of body composition parameters on metabolically unhealthy in the chinese population via advanced machine learning |
| topic | metabolic syndrome metabolic unhealthy body composition machine learning SHapley additive exPlanations |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1228300/full |
| work_keys_str_mv | AT xiujuandeng earlypredictionofbodycompositionparametersonmetabolicallyunhealthyinthechinesepopulationviaadvancedmachinelearning AT linqiu earlypredictionofbodycompositionparametersonmetabolicallyunhealthyinthechinesepopulationviaadvancedmachinelearning AT xinsun earlypredictionofbodycompositionparametersonmetabolicallyunhealthyinthechinesepopulationviaadvancedmachinelearning AT huili earlypredictionofbodycompositionparametersonmetabolicallyunhealthyinthechinesepopulationviaadvancedmachinelearning AT zejiaochen earlypredictionofbodycompositionparametersonmetabolicallyunhealthyinthechinesepopulationviaadvancedmachinelearning AT minhuang earlypredictionofbodycompositionparametersonmetabolicallyunhealthyinthechinesepopulationviaadvancedmachinelearning AT fangxinghu earlypredictionofbodycompositionparametersonmetabolicallyunhealthyinthechinesepopulationviaadvancedmachinelearning AT zhenyizhang earlypredictionofbodycompositionparametersonmetabolicallyunhealthyinthechinesepopulationviaadvancedmachinelearning |