ABSTRACT
Purpose: The Hepatic Steatosis Index (HSI) is a reliable predictor of non-alcoholic fatty liver disease (NAFLD), which can increase the risk of type 2 diabetes mellitus (T2DM). However, limited research has directly predicted HSI's association with T2DM occurrence at normal blood glucose levels. Hence, this study aimed to assess the link between baseline HSI and T2DM development under euglycemic conditions while also exploring potential sex differences. Methods: Using data from the NAGALA cohort study, a Cox regression model analyzed the relationship between HSI and T2DM risk, calculating hazard ratios (HR) and 95% confidence intervals (CI). Subgroup analyses were conducted to investigate factors influencing HSI's prediction of incident T2DM. Results: During a mean 6.1-year follow-up, 238 individuals (1.65% of participants) developed T2DM. After adjusting for age, ethanol consumption, smoking status, SBP, DBP, TG, and TC, HSI showed a significant association with incident T2DM in individuals with normal glucose levels, consistent across sexes. Compared to the lowest quartile group (Q1), the HR and 95% CI for Q2, Q3, and Q4 were 1.09 (0.61, 1.93), 1.16 (0.68, 1.98), and 3.30 (2.04, 5.33), respectively (P for trend < 0.001). Subgroup analysis indicated that elevated HSI significantly increased the risk of incident T2DM in individuals with normal TG levels (P for interaction = 0.0170). Conclusion: This study highlights the significant association between elevated HSI levels and the likelihood of developing incident T2DM in individuals with normal glucose levels. Furthermore, it offers a simple and valuable screening tool for predicting T2DM.
ABSTRACT
Background: Non-alcoholic fatty liver disease (NAFLD) is increasingly observed in non-obese individuals. The ZJU (Zhejiang University) index has been established as a new and efficient tool for detecting NAFLD, but the relationship between the ZJU index and NAFLD within non-obese individuals still remains unclear. Methods: A post-hoc evaluation was undertaken using data from a health assessment database by the Wenzhou Medical Center. The participants were divided into four groups based on the quartile of the ZJU Index. Cox proportional hazards regression, Kaplan-Meier analysis and tests for linear trends were used to evaluate the relationship between the ZJU index and NAFLD incidence. Subgroup analysis was conducted to test the consistency of the correlation between ZJU and NAFLD in subsgroups. Receiver operative characteristic (ROC) curve analysis was performed to evaluate the predictive performance of the ZJU index, compared with the Atherogenic index of plasma (AIP) and Remnant lipoprotein cholesterol (RLP-C) index. Results: A total of 12,127 were included in this study, and 2,147 participants (17.7%) developed NAFLD in 5 years follow-up. Participants in higher ZJU quartiles tended to be female and have higher liver enzymes (including ALP, GGT, ALT, AST), GLU, TC, TG, LDL and higher NAFLD risk. Hazard Ratios (HR) and 95% confidence intervals (CI) for new-onset NAFLD in Q2, Q3, and Q4 were 3.67(2.43 to 5.55), 9.82(6.67 to 14.45), and 21.67(14.82 to 31.69) respectively in the fully adjusted model 3. With increased ZJU index, the cumulative new-onset NAFLD gradually increased. Significant linear associations were observed between the ZJU index and new-onset NAFLD (p for trend all<0.001). In the subgroup analysis, we noted a significant interaction in sex, with HRs of 3.27 (2.81, 3.80) in female and 2.41 (2.21, 2.63) in male (P for interaction<0.01). The ZJU index outperformed other indices with an area under the curve (AUC) of 0.823, followed by AIP (AUC=0.747) and RLP-C (AUC=0.668). Conclusion: The ZJU index emerges as a promising tool for predicting NAFLD risk in non-obese individuals, outperforming other existing parameters including AIP and RLP-C. This could potentially aid in early detection and intervention in this specific demographic.
Subject(s)
Non-alcoholic Fatty Liver Disease , Female , Humans , Male , Asian People , China/epidemiology , Incidence , Non-alcoholic Fatty Liver Disease/diagnosis , Non-alcoholic Fatty Liver Disease/epidemiology , Prospective Studies , Health Status IndicatorsABSTRACT
Zinc finger protein 281 (ZNF281) has been shown to promote tumor progression. However, the underlying mechanism remains to be further elucidated. In this study, ZNF281 knockdown increased the expression of mitochondrial transcription factor A (TFAM) in hepatocellular carcinoma (HCC) cells, accompanied with increment of mitochondrial content, oxygen consumption rate (OCR) and levels of TCA cycle intermetabolites. Mechanistic investigation revealed that ZNF281 suppressed the transcription of TFAM, nuclear respiratory factor 1 (NRF1) and peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α). Furthermore, ZNF281 interacted with NRF1 and PGC-1α, and was recruited onto the promoter regions of TFAM, TFB1M and TFB2M repressing their expression. Knockdown of TFAM reversed ZNF281 depletion induced up-regulation of mitochondrial biogenesis and function, as well as impaired epithelial mesenchymal transition, invasion and metastasis of HCC cells. Our research uncovered a novel suppressive function of ZNF281 on mitochondrial biogenesis through inhibition of the NRF1/PGC-1α-TFAM axis, which may hold therapeutic potentials for HCC.
ABSTRACT
Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.
