Patients with a history of cardiovascular disease are at high risk of developing secondary major adverse cardiac events (MACE). This study aimed to identify independent predictors of MACE after hospital admission which could be used to identify of high-risk patients who may benefit from preventive strategies. This study included 1,520 consecutive patients with coronary artery disease (CAD) (654 with acute coronary syndrome (ACS) and 866 with elective percutaneous coronary intervention (PCI) patients) who received PCI and/or stenting. MACE was defined as all-cause mortality or rehospitalization for a cardiovascular- related illness. Cardiovascular-related illnesses included heart failure, reinfarction (nonfatal), recurrence of angina pectoris and repeat PCI or coronary artery bypass graft. During a mean follow-up period of 32 months, 558 of the 1,520 patients developed at least one MACE. Cox regression analysis showed that the baseline clinical and biochemical variables which associated with MACE were age, being illiterate, a widow or widower, and/or economically dependent, having triple vessel disease, stent implantation, anemia, and/or diabetes mellitus, waist to hip ratio (WHR), diastolic blood pressure, fasting glucose, total cholesterol, high-density lipoprotein cholesterol (HDL-C), creatinine, estimated glomerular filtration rate (eGFR), red blood cell count, hemoglobin, hematocrit, and mean corpuscular-hemoglobin concentration (MCHC) in ACS patients, and age, malnourished, and/or economically dependent, taking hypoglycemic medication, having triple vessel disease, stent implantation, anemia, diabetes mellitus, and/or hypertension, WHR, fasting glucose, HDL-C, uric acid, creatinine, eGFR, high-sensitivity C-reactive protein, mean corpuscular volume, and MCHC in elective PCI patients. Using multivariate Cox regression analysis, we found the MACE’s independent factors are triple vessel disease, stent implantation, hypertension, and eGFR in ACS patients, and having triple vessel disease, stent implantation, hypertension, and uric acid in elective PCI patients. Having triple vessel disease, stent implantation, hypertension, and eGFR or uric acid independently predicted MACE in patients with CAD after long-term follow-up. Fortunately, these factors are modifiable and should be identified and monitored early.

There is increasing evidence linking a shift work schedule with various adverse health effects. The present study aimed to examine the relationship between shift work and the metabolic syndrome (MetS) in male steel workers, and also the possible mechanism of shift work-related metabolic derangements. A total of 1732 men aged 42 ± 8 years were enrolled in this cross-sectional study, including 862 day workers and 870 shift workers. Circulating levels of resistin were measured by ELISA using monoclonal specific antibodies. The shift workers had higher rates of MetS and its components (central obesity, hypertension, and hypertriglyceridemia) than the day workers. In multiple logistic regression analysis, shift work was independently associated with MetS. In further analysis, the shift workers had elevated circulating levels of resistin (13 ± 10 vs. 10 ± 7 ng/mL) and total white blood cell (WBC) count (6.865 ± 1.819 vs. 6.304 ± 1.547 109/L) than the day workers. In addition, both resistin level and total WBC count were significantly associated with shift work, MetS, and its components (body mass index, fasting glucose, triglyceride, and high-density lipoprotein-cholesterol levels), and plasma resistin levels were significantly associated with total WBC count (β = 0.34, p < 0.0001). Shift work was independently associated with MetS in male steel workers. Resistin and WBC count were associated with shift work-related metabolic derangements.

Background: Neutrophil gelatinase‑associated lipocalin (NGAL), also known as lipocalin 2, siderocalin, 24p3 or uterocalin, plays a key role in inflammation and in different types of cancer. In this study, we investigated whether plasma NGAL levels were altered in patients with breast cancer. The relationship between plasma NGAL levels and pretreatment hematologic profile was also explored.

A urine albumin/creatinine ratio (UACR) 60 ml/min/1.73 m² and UACR <30 mg/g enrolled in a diabetes disease management program between 2006 and 2020 were studied. The optimal cutoff point for baseline UACR as a predictor for progression to CKD according to the 2012 KDIGO definition was calculated using receiving operating characteristic curve analysis. After a mean of 4.9 years follow-up, the CKD risk progression increased in parallel with the quartiles of baseline UACR <30 mg/g (p for trend 8.44 mg/g, >10.9 mg/g, >8.15 mg/g in overall, male, and female patients, respectively) and the risk of CKD progression were significant. This study demonstrated that a cutoff UACR value of >10 mg/g could significantly predict the cumulative incidence and progression of CKD in patients with T2DM.

Purpose: This study aimed to investigate the common and unique risk factors and bidirectional relationship between chronic kidney disease (CKD) and nonalcoholic fatty liver disease (NAFLD) in patients with type 2 diabetes mellitus (T2DM). Patients and Methods: This was a cross-sectional study of patients with T2DM enrolled in a disease management program at two specialized diabetes outpatient clinics. Common and unique risk factors for CKD and NAFLD were examined using structural equation models (SEMs). SEMs were also used to examine direct and indirect effects of NAFLD on CKD and those of CKD on NAFLD. Results: A total of 1992 subjects with T2DM were enrolled in this study. In multivariate analysis, NAFLD was independently associated with the odds of CKD (adjusted odds ratio=1.59, 95% confidence interval=1.12– 2.25, P=0.009). SEMs showed that age, triglyceride, uric acid (UA), albumin, and HbA1c levels had statistically significant direct effects on CKD, and the final model could explain 22% of the variability in CKD. Age, triglycerides, body mass index (BMI), UA, white blood cell (WBC) count, serum glutamic pyruvic transaminase (SGPT) level, and smoking status had statistically significant direct effects on NAFLD, and the final model could explain 43% of the variability in NAFLD. The common risk factors contributing to both CKD and NAFLD were age, triglycerides, and UA. The unique risk factors were albumin and HbA1c for CKD, and BMI, WBC, SGPT, and smoking for NAFLD. In addition, SEM analysis also confirmed the bidirectional causal relationship between NAFLD and CKD. Conclusion: Common and unique risk factors and a bidirectional relationship existed between CKD and NAFLD in our patients with T2DM.