INTRODUCTIONThere is no single method of measuring insulin resistance that is both accurate and can be easily performed by general researchers. We validate the accuracy of oral glucose insulin sensitivity (OGIS) in the Chinese by comparing the OGIS120 and OGIS180, homeostasis model assessment of insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (OUICKI) with steady-state plasma glucose (SSPG) in different glucose tolerance subjects.

MATERIALS AND METHODSWe enrolled 515 subjects, aged between 20 and 75 years old, during routine health evaluations. All subjects were divided into normal, obese, impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and type 2 diabetes (T2D) groups. Participants had a 3-hour oral glucose tolerance test (OGTT) and SSPG with an insulin suppression test. The relationships between SSPG and OGIS120, OGIS180, HOMA-IR, and QUICKI were evaluated.

RESULTSThe normal group had the highest OGIS120, OGIS180 and lowest SSPG as compared with the other 4 groups. OGIS180, HOMA-IR and QUICKI in all 5 groups were significantly related to SSPG (r = 0.397-0.621, all P <0.05). OGIS120 in all 5 groups was not significantly related to SSPG (r = 0.003-0.226). Additionally, the r value of OGIS180 against SSPG was not higher than the other 2 insulin sensitivity surrogates from OGTT.

CONCLUSIONSAlthough OGIS180 was more accurate in estimating insulin sensitivity than OGIS120 in the Chinese, it was not superior to the traditional surrogates such as HOMA-IR or QUICKI.

Adult ; Aged ; Case-Control Studies ; China ; Female ; Glucose Tolerance Test ; methods ; Humans ; Insulin Resistance ; Male ; Middle Aged ; Prediabetic State ; diagnosis ; Young Adult

Background/Aims: Chronic hepatitis C (CHC) patients who failed antiviral therapy are at increased risk for hepatocellular carcinoma (HCC). This study assessed the potential role of metformin and statins, medications for diabetes mellitus (DM) and hyperlipidemia (HLP), in reducing HCC risk among these patients. Methods: We included CHC patients from the T-COACH study who failed antiviral therapy. We tracked the onset of HCC 1.5 years post-therapy by linking to Taiwan’s cancer registry data from 2003 to 2019. We accounted for death and liver transplantation as competing risks and employed Gray’s cumulative incidence and Cox subdistribution hazards models to analyze HCC development. Results: Out of 2,779 patients, 480 (17.3%) developed HCC post-therapy. DM patients not using metformin had a 51% increased risk of HCC compared to non-DM patients, while HLP patients on statins had a 50% reduced risk compared to those without HLP. The 5-year HCC incidence was significantly higher for metformin non-users (16.5%) versus non-DM patients (11.3%; adjusted sub-distribution hazard ratio [aSHR]=1.51; P=0.007) and metformin users (3.1%; aSHR=1.59; P=0.022). Statin use in HLP patients correlated with a lower HCC risk (3.8%) compared to non-HLP patients (12.5%; aSHR=0.50; P<0.001). Notably, the increased HCC risk associated with non-use of metformin was primarily seen in non-cirrhotic patients, whereas statins decreased HCC risk in both cirrhotic and non-cirrhotic patients. Conclusions Metformin and statins may have a chemopreventive effect against HCC in CHC patients who failed antiviral therapy. These results support the need for personalized preventive strategies in managing HCC risk.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.