PURPOSE: The purpose of this study was to understand the situation of diabetes patients receiving examinations for diabetes complications and to explore the factors influencing their intention to receive examinations for diabetes complications. METHODS: A cross-sectional study was performed that included 251 diabetes patients who visited outpatient clinics in Southern Taiwan. A survey using a self-administered questionnaire was conducted from October 2015 to January 2016. The questionnaire included items on demographic characteristics, perceived susceptibility to diabetes complications, perceived seriousness of diabetes complications, perceived benefits of taking action to receive diabetes complication examinations, perceived barriers to taking action to receive diabetes complication examinations, and the intention to receive diabetes complication examinations. The data were analyzed using regression analysis. RESULTS: The percentage of participants who received fundus, foot, and kidney examinations was 67.7%, 61.4%, and 73.3%, respectively. Every point increase on the perceived barriers to taking action to receive diabetes complication examinations scale increased the intention to receive a foot examination in the following year by 0.91 times (p = .002), and every point increase on the perceived susceptibility to diabetes complications scale increased the intention to receive a kidney examination in the following year by 1.19 times (p = .045). CONCLUSIONS: Nurses should shoulder the responsibility to increase patients' intention to receive examination of diabetes complications. The results of this study can be used to promote nurses' care efficacy in preventing diabetes complications. They can also provide medical institutions with information to establish prevention and control policies for diabetes complications.
Ambulatory Care/utilization ; Cross-Sectional Studies ; Diabetic Angiopathies/nursing/*prevention & control/psychology ; Diabetic Nephropathies/nursing/*prevention & control/psychology ; Disease Susceptibility/psychology ; Early Diagnosis ; Female ; Humans ; Intention ; Kidney Function Tests ; Male ; Middle Aged ; Nurse-Patient Relations ; Ophthalmoscopy ; Patient Acceptance of Health Care/*psychology ; Perception ; Physical Examination/nursing/*psychology/utilization ; Taiwan

The objectives of this cohort analysis were to explore the relationship between insulin resistance (IR) and the criteria for metabolic syndrome (MetS) and to evaluate the ability to detect IR in subjects fulfilling those criteria. We enrolled 511 healthy subjects (218 men and 283 women) and measured their blood pressure (BP), body mass index, high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), and fasting plasma glucose levels. Insulin suppression testing was done to measure insulin sensitivity as the steady-state plasma glucose (SSPG) value. Subjects with an SSPG value within the top 25% were considered to have IR. The commonest abnormality was a low HDL-C level, followed by high BP. The sensitivity to detect IR in subjects with MetS was about 47%, with a positive predictive value of about 64.8%, which has higher in men than in women. In general, the addition of components to the criteria for MetS increased the predictive value for IR. The most common combination of components in subjects with MetS and IR were obesity, high BP, and low HDL-C levels. All of the components were positive except for HDL-C, which was negatively correlated with SSPG. The correlation was strongest for obesity, followed by high TG values. In subjects with MetS, sensitivity for IR was low. However, body mass index and TG values were associated with IR and may be important markers for IR in subjects with MetS.
Adult ; Aged ; *Biological Markers ; Blood Glucose/metabolism ; Blood Pressure ; Body Mass Index ; Cholesterol, HDL/blood ; Female ; Humans ; *Insulin Resistance ; Male ; Metabolic Syndrome X/*diagnosis/*epidemiology ; Middle Aged ; Obesity, Morbid/diagnosis/epidemiology ; Predictive Value of Tests ; Prevalence ; Risk Factors ; Sensitivity and Specificity ; Triglycerides/blood

Mounting evidence indicates that melatonin has possible activity against different tumors. Pazopanib is an anticancer drug used to treat renal cell carcinoma (RCC). This study tested the anticancer activity of melatonin combined with pazopanib on RCC cells and explored the underlying mechanistic pathways of its action. Methods: The 786-O and A-498 human RCC cell lines were used as cell models. Cell viability and tumorigenesis were detected with the MTT and colony formation assays, respectively. Apoptosis and autophagy were assessed using TUNEL, annexin V/propidium iodide, and acridine orange staining with flow cytometry. The expression of cellular signaling proteins was investigated with western blotting. The in vivo growth of tumors derived from RCC cells was evaluated using a xenograft mouse model. Results: Together, melatonin and pazopanib reduced cell viability and colony formation and promoted the apoptosis of RCC cells. Furthermore, the combination of melatonin and pazopanib triggered more mitochondrial, caspase-mediated, and LC3-II-mediated autophagic apoptosis than melatonin or pazopanib alone. The combination also induced higher activation of the p38 mitogen-activated protein kinase (p38MAPK) in the promotion of autophagy and apoptosis by RCC cells than melatonin or pazopanib alone. Finally, tumor xenograft experiments confirmed that melatonin and pazopanib cooperatively inhibited RCC growth in vivo and predicted a possible interaction between melatonin/pazopanib and LC3-II. Conclusion: The combination of melatonin and pazopanib inhibits the growth of RCC cells by inducing p38MAPK-mediated mitochondrial and autophagic apoptosis. Therefore, melatonin might be a potential adjuvant that could act synergistically with pazopanib for RCC treatment.

The impact the metabolic syndrome (MetS) components on the severity of insulin resistance (IR) has not been reported. We enrolled 564 subjects with MetS and they were divided into quartiles according to the level of each component; and an insulin suppression test was performed to measure IR. In males, steady state plasma glucose (SSPG) levels in the highest quartiles, corresponding to body mass index (BMI) and fasting plasma glucose (FPG), were higher than the other three quartiles and the highest quartiles, corresponding to the diastolic blood pressure and triglycerides, were higher than in the lowest two quartiles. In females, SSPG levels in the highest quartiles, corresponding to the BMI and triglycerides, were higher than in all other quartiles. No significant differences existed between genders, other than the mean SSPG levels in males were greater in the highest quartile corresponding to BMI than that in the highest quartile corresponding to HDL-cholesterol levels. The factor analysis identified two underlying factors (IR and blood pressure factors) among the MetS variables. The clustering of the SSPG, BMI, triglyceride and HDLcholesterol was noted. Our data suggest that adiposity, higher FPG and triglyceride levels have stronger correlation with IR and subjects with the highest BMI have the highest IR.
Waist-Hip Ratio ; Triglycerides/blood ; Middle Aged ; Metabolic Syndrome X/*metabolism ; Male ; *Insulin Resistance ; Humans ; Female ; Fasting/blood ; Cholesterol, HDL/blood ; Body Mass Index ; Blood Glucose/analysis ; Aged ; 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.