Search

Insulin resistance (IR) is the core for type 2 diabetes and metabolic syndrome. The homeostasis assessment model is a straightforward and practical tool for quantifying insulin resistance (HOMA-IR). Multiple adaptive regression spline (MARS) is a machine learning method used in many research fields but has yet to be applied to estimating HOMA-IR. This study uses MARS to build an equation to estimate HOMA-IR in pre-menopausal Chinese women based on a sample of 4,071 healthy women aged 20-50 with no major diseases and no medication use for blood pressure, blood glucose or blood lipids. Thirty variables were applied to build the HOMA-IR model, including demographic, laboratory, and lifestyle factors. MARS results in smaller prediction errors than traditional multiple linear regression (MLR) methods, and is thus more accurate. The model was established based on key impact factors including waist-hip ratio (WHR), C reactive protein (CRP), uric acid (UA), total bilirubin (TBIL), leukocyte (WBC), serum glutamic oxaloacetic transaminase (GOT), high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), serum glutamic pyruvic transaminase (GPT), and triglycerides (TG). The equation is as following:HOMA-IR = 6.634 - 1.448MAX(0, 0.833 - WHR) + 10.152MAX(0, WHR - 0.833) - 1.351MAX(0, 0.7 - CRP) - 0.449MAX(0, CRP - 0.7) + 1.062MAX(0, UA - 8.5) + +1.047(MAX(0, 0.83 - TBIL) + 0.681MAX(0, WBC - 11.53) - 0.071MAX(0, 11.53 - WBC) + 0.043MAX(0, 24 - GOT) - 0.017MAX(0, GOT - 24) + 0.021MAX(0, 59 - HDL) - 0.005MAX(0, HDL - 59) - 0.013MAX(0, 141 - SBP) - 0.033MAX(0, 100 - GPT) + 0.013MAX(0, GPT - 100) - 0.004MAX(303 - TG)Results indicate that MARS is a more precise tool than fasting plasma insulin (FPI) levels, and could be used in the daily practice, and further longitudinal studies are warranted.

Environmental antineoplastics such as sorafenib may pose a risk to humans through water recycling, and the increased risk of cardiotoxicity is a clinical issue in sorafenib users. Thus, developing strategies to prevent sorafenib cardiotoxicity is an urgent work. Empagliflozin, as a sodium-glucose co-transporter-2 (SGLT2) inhibitor for type 2 diabetes control, has been approved for heart failure therapy. Still, its cardioprotective effect in the experimental model of sorafenib cardiotoxicity has not yet been reported. Real-time quantitative RT-PCR (qRT-PCR), immunoblot, and immunohistochemical analyses were applied to study the effect of sorafenib exposure on cardiac SGLT2 expression. The impact of empagliflozin on cell viability was investigated in the sorafenib-treated cardiomyocytes using Alamar blue assay. Immunoblot analysis was employed to delineate the effect of sorafenib and empagliflozin on ferroptosis/proinflammatory signaling in cardiomyocytes. Ferroptosis/DNA damage/fibrosis/inflammation of myocardial tissues was studied in mice with a 28-day sorafenib ± empagliflozin treatment using histological analyses. Sorafenib exposure significantly promoted SGLT2 upregulation in cardiomyocytes and mouse hearts. Empagliflozin treatment significantly attenuated the sorafenib-induced cytotoxicity/DNA damage/fibrosis in cardiomyocytes and mouse hearts. Moreover, GPX4/xCT-dependent ferroptosis as an inducer for releasing high mobility group box 1 (HMGB1) was also blocked by empagliflozin administration in the sorafenib-treated cardiomyocytes and myocardial tissues. Furthermore, empagliflozin treatment significantly inhibited the sorafenib-promoted NFκB/HMGB1 axis in cardiomyocytes and myocardial tissues, and sorafenib-stimulated proinflammatory signaling (TNF-α/IL-1β/IL-6) was repressed by empagliflozin administration. Finally, empagliflozin treatment significantly attenuated the sorafenib-promoted macrophage recruitments in mouse hearts. In conclusion, empagliflozin may act as a cardioprotective agent for humans under sorafenib exposure by modulating ferroptosis/DNA damage/fibrosis/inflammation. However, further clinical evidence is required to support this preclinical finding., (© 2024 Wiley Periodicals LLC.)

Recent advancements have elucidated the multifaceted roles of the Schlafen (SLFN) family, including SLFN5, SLFN11, SLFN12, SLFN13, and SLFN14, which are implicated in immunological responses. However, little is known about the roles of this gene family in relation to malignancy development. The current study aimed to explore the diagnostic and prognostic potential of Schlafen family genes in colorectal adenocarcinoma (COAD) through bioinformatics analysis. Leveraging advanced bioinformatics tools of bulk RNA-sequencing and single-cell sequencing, we conducted in-depth analyses of gene expressions, functional enrichment, and survival patterns of patients with colorectal cancer compared to normal tissue. Among Schlafen family genes, the transcription levels of SLFN5 in COAD tissues were significantly elevated and correlated with poor survival outcomes. Furthermore, SLFN5 regulated the immune response via Janus kinase (JAK)/signal transduction and activator of transcription (STAT)/interferon (IFN)-alpha/beta signaling. These chemokines in inflammation are associated with diabetes and metabolism, suggesting their involvement in altered cellular energetics for COAD progress. In addition, an immune cell deconvolution analysis indicated a correlation between SLFN5 expression and immune-related cell populations, such as regulatory T cells (Tregs). These findings highlighted the potential clinical significance of SLFN5 in COAD and provided insights into its involvement in the tumor microenvironment and immune regulation. Meanwhile, the drug discovery data of SFLN5 with potential targeted small molecules suggested its therapeutic potential for COAD. Collectively, the current research demonstrated that SFLN5 play crucial roles in tumor development and serve as a prospective biomarker for COAD., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Background: The burden of asymptomatic left ventricular dysfunction (LVD) is greater than that of heart failure; however, a cost-effective tool for asymptomatic LVD screening has not been well validated. We aimed to prospectively validate an artificial intelligence (AI)-enabled electrocardiography (ECG) algorithm for asymptomatic LVD detection and evaluate its cost-effectiveness for opportunistic screening., Methods: In this prospective observational study, patients undergoing ECG at outpatient clinics or health check-ups were enrolled in 2 hospitals in Taiwan. Patients were stratified into LVD (left ventricular ejection fraction ≤ 40%) risk groups according to a previously developed ECG algorithm. The performance of AI-ECG was used to conduct a cost-effectiveness analysis of LVD screening compared with no screening. Incremental cost-effectiveness ratio (ICER) and sensitivity analyses were used to examine the cost-effectiveness and robustness of the results., Results: Among the 29,137 patients, the algorithm demonstrated areas under the receiver operating characteristic curves of 0.984 and 0.945 for detecting LVD within 28 days in the 2 hospital cohorts. For patients not initially scheduled for ECG, the algorithm predicted future echocardiograms (high-risk, 46.2%; medium-risk, 31.4%; low-risk, 14.6%) and LVD (high-risk, 26.2%; medium-risk, 3.4%; low-risk, 0.1%) at 12 months. Opportunistic screening with AI-ECG could result in a negative ICER of -$7,439 for patients aged 65 years, with consistent cost-savings across age groups and particularly in men. Approximately 91.5% of the cases were found to be cost-effective at the willingness-to-pay threshold of $30,000 in the probabilistic analysis., Conclusions: The use of AI-ECG for asymptomatic LVD risk stratification is promising, and opportunistic screening in outpatient clinics has the potential to reduce costs., (Copyright © 2023 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.)

The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPG base ), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPG base, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPG base , body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPG base was the most important factor to predict future prediabetes. However, after removing FPG base , WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years.

Purpose: To analyze the safety of combination treatment comprising drug-eluting bead transarterial chemoembolization (DEB-TACE) and immune checkpoint inhibitors (ICIs) in hepatocellular carcinoma (HCC)., Method: In total, 208 HCC patients receiving DEB-TACE were enrolled for this retrospective single-institution study. Among them, 50 patients who received ICIs at an interval less than one month from DEB-TACE were categorized into the DEB-ICI group; the remaining 158 patients were categorized into the DEB group. Albumin-bilirubin (ALBI) score before and at three months after DEB-TACE were recorded to evaluate liver function changes. Adverse events within three months after DEB-TACE were considered TACE-related and were compared between the two groups., Results: The DEB-ICI group had significantly higher incidence of liver abscess than the DEB group (14.0 % versus 5.1 %, p-value = 0.0337). No significant difference in the other TACE-related adverse events and change of ALBI score between the groups. Univariate logistic regression confirmed that combination with ICIs was an independent risk factor for liver abscess after DEB-TACE (odds ratio = 3.0523, 95 % confidence interval: 1.0474-8.8947, p-value = 0.0409); other parameters including subjective angiographic chemoembolization endpoint scale and combined targeted therapy were nonsignificant risk factors in this study population. In the DEB-ICI group, patients who received ICIs before DEB-TACE exhibited a trend toward liver abscess formation compared with those who received DEB-TACE before ICIs (23.8 % versus 6.9 %, p-value = 0.0922)., Conclusions: Combination treatment involving DEB-TACE and ICIs at an interval less than one month increased the risk of liver abscess after DEB-TACE. Greater caution is therefore warranted for HCC patients who receive ICIs and DEB-TACE with this short interval., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background: Splenectomy has previously been found to increase the risk of cancer development, including lung, non-melanoma skin cancer, leukemia, lymphoma, Hodgkin's lymphoma, and ovarian cancer. The risk of cancer development in liver transplantation (LT) with simultaneous splenectomy remains unclear., Aim: To compare hepatocellular carcinoma (HCC) recurrence and de novo malignancy between patients undergoing LT with and without simultaneous splenectomy., Methods: We retrospectively analyzed the outcomes of 120 patients with HCC within the University of California San Francisco criteria who received LT with ( n = 35) and without ( n = 85) simultaneous splenectomy in the Tri-Service General Hospital. Univariate and multivariate Cox regression analyses for cancer-free survival and mortality were established. The comparison of the group survival status and group cancer-free status was done by generating Kaplan-Meier survival curves and log-rank tests., Results: The splenectomy group had more hepatitis C virus infection, lower platelet count, higher -fetoprotein level, and longer operating time. Splenectomy and age were both positive independent factors for prediction of cancer development [hazard ratio (HR): 2.560 and 1.057, respectively, P < 0.05]. Splenectomy and hypertension were positive independent factors for prediction of mortality. (HR: 2.791 and 2.813 respectively, P < 0.05). The splenectomy group had a significantly worse cancer-free survival (CFS) and overall survival (OS) curve compared to the non-splenectomy group (5-year CFS rates: 53.4% vs 76.5%, P = 0.003; 5-year OS rate: 68.1 vs 89.3, P = 0.002)., Conclusion: Our study suggests that simultaneous splenectomy should be avoided as much as possible in HCC patients who have undergone LT., Competing Interests: Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article., (©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.)

Background: The COVID-19 outbreak has not only changed the lifestyles of people globally but has also resulted in other challenges, such as the requirement of self-isolation and distance learning. Moreover, people are unable to venture out to exercise, leading to reduced movement, and therefore, the demand for exercise at home has increased., Objective: We intended to investigate the relationships between a Nintendo Ring Fit Adventure (RFA) intervention and improvements in running time, cardiac force index (CFI), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index score), and mood disorders (5-item Brief Symptom Rating Scale score)., Methods: This was a randomized prospective study and included 80 students who were required to complete a 1600-meter outdoor run before and after the intervention, the completion times of which were recorded in seconds. They were also required to fill out a lifestyle questionnaire. During the study, 40 participants (16 males and 24 females, with an average age of 23.75 years) were assigned to the RFA group and were required to exercise for 30 minutes 3 times per week (in the adventure mode) over 4 weeks. The exercise intensity was set according to the instructions given by the virtual coach during the first game. The remaining 40 participants (30 males and 10 females, with an average age of 22.65 years) were assigned to the control group and maintained their regular habits during the study period., Results: The study was completed by 80 participants aged 20 to 36 years (mean 23.20, SD 2.96 years). The results showed that the running time in the RFA group was significantly reduced. After 4 weeks of physical training, it took females in the RFA group 19.79 seconds (P=.03) and males 22.56 seconds (P=.03) less than the baseline to complete the 1600-meter run. In contrast, there were no significant differences in the performance of the control group in the run before and after the fourth week of intervention. In terms of mood disorders, the average score of the RFA group increased from 1.81 to 3.31 for males (difference=1.50, P=.04) and from 3.17 to 4.54 for females (difference=1.38, P=.06). In addition, no significant differences between the RFA and control groups were observed for the CFI peak acceleration (CFIPA)&#95;walk, CFIPA&#95;run, or sleep quality., Conclusions: RFA could either maintain or improve an individual's physical fitness, thereby providing a good solution for people involved in distance learning or those who have not exercised for an extended period., Trial Registration: ClinicalTrials.gov NCT05227040; https://clinicaltrials.gov/ct2/show/NCT05227040., (©Yi-Syuan Wu, Wei-Yun Wang, Ta-Chien Chan, Yu-Lung Chiu, Hung-Che Lin, Yu-Tien Chang, Hao-Yi Wu, Tzu-Chi Liu, Yu-Cheng Chuang, Jonan Wu, Wen-Yen Chang, Chien-An Sun, Meng-Chiung Lin, Vincent S Tseng, Je-Ming Hu, Yuan-Kuei Li, Po-Jen Hsiao, Chao-Wen Chen, Hao-Yun Kao, Chia-Cheng Lee, Chung-Bao Hsieh, Chih-Hung Wang, Chi-Ming Chu. Originally published in JMIR Serious Games (https://games.jmir.org), 22.03.2022.)