Yu-Jiun Lin1,2, Chang-Hsien Lin3,4, Sen-Te Wang5,6, Shiyng-Yu Lin7,8, Shy-Shin Chang9,10. 1. Department of Family Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan. 101096105@hotmail.com. 2. Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan. 101096105@hotmail.com. 3. Department of Family Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan. poanlinch@msn.com. 4. Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan. poanlinch@msn.com. 5. Department of Family Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan. wangader@yahoo.com.tw. 6. Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan. wangader@yahoo.com.tw. 7. Department of Family Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan. daleslin@gmail.com. 8. Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan. daleslin@gmail.com. 9. Department of Family Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan. sschang0529@gmail.com. 10. Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan. sschang0529@gmail.com.
Abstract
BACKGROUND: There is a medical need for an easy, fast, and non-invasive method for metabolic syndrome (MetS) screening. This study aimed to assess the ability of FibroScan to detect MetS, in participants who underwent a self-paid health examination. METHODS: A retrospective cohort study was conducted on all adults who underwent a self-paid health examination comprising of an abdominal transient elastography inspection using FibroScan 502 Touch from March 2015 to February 2019. FibroScan can assess the level of liver fibrosis by using a liver stiffness score, and the level of liver steatosis by using the controlled attenuation parameter (CAP) score. The logistic regression analysis and receiver operating characteristic curve were applied to select significant predictors and assess their predictability. A final model that included all significant predictors that are found by univariate analysis, and a convenient model that excluded all invasive parameters were created. RESULTS: Of 1983 participants, 13.6% had a physical status that fulfilled MetS criteria. The results showed that the CAP score solely could achieve an area under the curve (AUC) of 0.79 (0.76-0.82) in predicting MetS, and the AUC can be improved to 0.88 (0.85-0.90) in the final model. An AUC of 0.85 (0.83-0.88) in predicting MetS was obtained in the convenient model, which includes only 4 parameters (CAP score, gender, age, and BMI). A panel of predictability indices (the ranges of sensitivity, specificity, positive and negative likelihood ratio: 0.78-0.89, 0.66-0.82, 2.64-4.47, and 0.17-0.26) concerning gender- and BMI-specific CAP cut-off values (range: 191.65-564.95) were presented for practical reference. CONCLUSIONS: Two prediction systems were proposed for identifying individuals with a physical status that fulfilled the MetS criteria, and a panel of predictability indices was presented for practical reference. Both systems had moderate predictive performance. The findings suggested that FibroScan evaluation is appropriate as a first-line MetS screening; however, the variation in prediction performance of such systems among groups with varying metabolic derangements warrants further studies in the future.
BACKGROUND: There is a medical need for an easy, fast, and non-invasive method for metabolic syndrome (MetS) screening. This study aimed to assess the ability of FibroScan to detect MetS, in participants who underwent a self-paid health examination. METHODS: A retrospective cohort study was conducted on all adults who underwent a self-paid health examination comprising of an abdominal transient elastography inspection using FibroScan 502 Touch from March 2015 to February 2019. FibroScan can assess the level of liver fibrosis by using a liver stiffness score, and the level of liver steatosis by using the controlled attenuation parameter (CAP) score. The logistic regression analysis and receiver operating characteristic curve were applied to select significant predictors and assess their predictability. A final model that included all significant predictors that are found by univariate analysis, and a convenient model that excluded all invasive parameters were created. RESULTS: Of 1983 participants, 13.6% had a physical status that fulfilled MetS criteria. The results showed that the CAP score solely could achieve an area under the curve (AUC) of 0.79 (0.76-0.82) in predicting MetS, and the AUC can be improved to 0.88 (0.85-0.90) in the final model. An AUC of 0.85 (0.83-0.88) in predicting MetS was obtained in the convenient model, which includes only 4 parameters (CAP score, gender, age, and BMI). A panel of predictability indices (the ranges of sensitivity, specificity, positive and negative likelihood ratio: 0.78-0.89, 0.66-0.82, 2.64-4.47, and 0.17-0.26) concerning gender- and BMI-specific CAP cut-off values (range: 191.65-564.95) were presented for practical reference. CONCLUSIONS: Two prediction systems were proposed for identifying individuals with a physical status that fulfilled the MetS criteria, and a panel of predictability indices was presented for practical reference. Both systems had moderate predictive performance. The findings suggested that FibroScan evaluation is appropriate as a first-line MetS screening; however, the variation in prediction performance of such systems among groups with varying metabolic derangements warrants further studies in the future.