Enhancing metabolic syndrome prediction using fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography data and machine learning: a comprehensive analysis

Abstract

Background: Metabolic syndrome (MetS) is a complex health concern and the incidence of MetS is rising, even among the general population, necessitating effective identification and management strategies. This study aimed to determine if a predictive model using variables from fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) and machine learning (ML) could enhance the prediction of MetS.

Methods: We retrospectively reviewed the medical records of 1,250 adults who underwent FDG PET/CT for cancer screening between 2014 and 2020. MetS was diagnosed according to the National Cholesterol Education Program Adult Treatment Panel III criteria. The study analyzed standardized uptake values (SUVs), area, and Hounsfield unit (HU) of various body organs from FDG PET/CT and developed a multivariable predictive model for MetS integrating FDG PET/CT variables using least absolute shrinkage and selection operator (LASSO) regression. The performance of a predictive model was assessed using the area under the receiver operating characteristic curve (AUC).

Results: The study population comprised 720 men and 530 women with a median age of 54 years, and MetS was present in 26.3% of the subjects. The LASSO regression identified the area of visceral adipose tissue (VAT), mean HU of VAT, mean SUV of VAT, mean HU of skeletal muscle, mean SUV of blood pool, and body mass index as meaningful variables. Our multivariable LASSO model effectively predicted MetS with similar performance in both training and test sets (AUC, 0.792 and 0.828, respectively; P=0.173) and demonstrated superior predictive performance compared to univariable models in the test set (AUC, 0.828)-body mass index (0.794; P=0.017), the area of VAT (0.788; P<0.001), and the mean HU of VAT (0.777; P<0.001).

Conclusions: Our findings established the potential of FDG PET/CT, enhanced with ML, in predicting MetS.