Clinical nutrition ESPEN · 2025
A nomogram model combining sarcopenic obesity and biomarkers to predict the risk of vascular stiffness.
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Liu Wenwen, Zhu Mingyu, Wei Ziyi, Chen Ningxin, Han Tingting, Zhang Ting, Weng Yurong, Fan Yiling, Hu Yaomin
Аннотация
BACKGROUND & AIMS: Accumulating evidence reveals that sarcopenia and obesity display a close association with vascular aging. However, comprehensive analysis of the clinical model for estimating the risk of arterial stiffness based on the co-existence of sarcopenia and obesity has not yet been performed.
METHODS: Here, we curated anthropometric, serological, clinical and computerized tomography (CT) variables from 1136 patients and applied univariate analysis (to eliminate irrelevant predictors) and logistic regression analysis (p < 0.05) in the development cohort to establish the clinical model. The precision of the model was evaluated through area under the curve (AUC) of receiver operator characteristic (ROC), calibration plot and decision curve analysis (DCA), for its discriminative power, calibration consistency and clinical usefulness.
RESULTS: Logistic regression analysis identified that body mass index (BMI), total triglyceride (TG), interleukin-6 (IL-6), previous diabetes and hypertension, skeletal muscle fat index (SMFI) and skeletal muscle index (SMI) served as independent predictors for arterial stiffness and three clinical models based on these variables were constructed. The Model incorporating SMI and SMFI simultaneously (model SMI + SMFI), exhibited superior performance as compared to the models with only SMI (model SMI) or only SMFI (model SMFI), with reference to the discriminative power (ROC SMI + SMFI 0.795), calibrative ability (Eavg SMI + SMFI 0.022, Emax SMI + SMFI 0.041) and clinical utility in the validation cohort.
CONCLUSIONS: This research presents a model for the estimation of pulse wave velocity (PWV), which incorporates BMI, TG, IL-6, previous diabetes and hypertension, SMI and SMFI. The model incorporating sarcopenia and obesity simultaneously instead individually, predicts arterial stiffness more accurately. This advancement could enhance our understanding of the role of sarcopenic obesity in vascular dysfunction.
METHODS: Here, we curated anthropometric, serological, clinical and computerized tomography (CT) variables from 1136 patients and applied univariate analysis (to eliminate irrelevant predictors) and logistic regression analysis (p < 0.05) in the development cohort to establish the clinical model. The precision of the model was evaluated through area under the curve (AUC) of receiver operator characteristic (ROC), calibration plot and decision curve analysis (DCA), for its discriminative power, calibration consistency and clinical usefulness.
RESULTS: Logistic regression analysis identified that body mass index (BMI), total triglyceride (TG), interleukin-6 (IL-6), previous diabetes and hypertension, skeletal muscle fat index (SMFI) and skeletal muscle index (SMI) served as independent predictors for arterial stiffness and three clinical models based on these variables were constructed. The Model incorporating SMI and SMFI simultaneously (model SMI + SMFI), exhibited superior performance as compared to the models with only SMI (model SMI) or only SMFI (model SMFI), with reference to the discriminative power (ROC SMI + SMFI 0.795), calibrative ability (Eavg SMI + SMFI 0.022, Emax SMI + SMFI 0.041) and clinical utility in the validation cohort.
CONCLUSIONS: This research presents a model for the estimation of pulse wave velocity (PWV), which incorporates BMI, TG, IL-6, previous diabetes and hypertension, SMI and SMFI. The model incorporating sarcopenia and obesity simultaneously instead individually, predicts arterial stiffness more accurately. This advancement could enhance our understanding of the role of sarcopenic obesity in vascular dysfunction.