An increasing body of evidence connects non-alcoholic fatty liver disease (NAFLD) to hypertension. The objective of this systematic review and meta-analysis was to estimate the nature and magnitude of the association between NAFLD and hypertension. We systematically searched PubMed, Embase, Cochrane Library, and Web of Science for observational studies published up to May 1, 2021. Cohort studies that reported data on the association between NAFLD and incident hypertension or between hypertension and incident NAFLD were included. We used random-effects models to conduct meta-analysis on the measures of association from individual studies. A total of 11 studies were eligible for inclusion, among which 4 studies including 25,260 participants reported the association between hypertension and new-onset NAFLD. The presence of hypertension was significantly associated with an increased risk of incident NAFLD (HR 1.63, 95% CI: 1.41–1.88; I² = 37.6%). On the other hand, 9 studies with data on 46,487 participants analyzed the effects of NAFLD on incident hypertension. Pooled analysis showed that the presence of NAFLD was significantly associated with an increased incidence of hypertension (HR 1.55, 95% CI: 1.29–1.87; I² = 80.5%). There was significant heterogeneity among the studies in this analysis ( < 0.01). Sensitivity analyses showed that the magnitude of the association was significantly different in subgroups stratified by a mean age of participants and geographical location, which explains part of the heterogeneity. In conclusion, this meta-analysis indicates the existence of a bidirectional relationship between NAFLD and hypertension independent of traditional cardiometabolic risk factors.

Background: Immune cells play an integral role in the development and progression of non-alcoholic fatty liver disease (NAFLD). This study was to identify immune-cell-related biomarkers for the diagnosis and treatment of NAFLD.Methods and findings: First, we introduced human liver transcriptome data from the GEO database (GSE48452 and GSE126848) and performed a weighted gene co-expression network analysis (WGCNA) to screen out the modules related to immune cell infiltration and to identify immune-cell-related differentially expressed genes (ICR-DEGs) associated with NAFLD progression. Further, the protein-protein interaction (PPI) network of ICR-DEGs was established to obtain hub genes and subsequently, the expression trend analysis was conducted to identify immune-cell-related biomarkers of NAFLD. Finally, the mRNA expression of biomarkers was validated in a NAFLD mouse model induced by high-fat diet (HFD) feeding. In total, we identified 66 ICR-DEGs and 13 hub genes associated with NAFLD. Among them, 9 hub genes (CD247, CD74, FCGR2B, IL2RB, INPP5D, MRPL16, RPL35, RPS3A, RPS8) were correlated with the infiltrating immune cells by the Pearson correlation analysis. Subsequently, 4 immune-cell-related biomarkers (RPL35, RPS3A, RPS8, and MRPL16) with the same expression trends in GSE48452 and GSE126848 datasets were identified. These biomarkers were enriched in immune-related pathways and had a good ability to distinguish between NASH and healthy samples. Moreover, we constructed a competing endogenous RNA (ceRNA) network of biomarkers and predicted twenty potential therapeutic drugs targeting RPS3A such as taxifolin and sitagliptin. Finally, experimental validation indicated that the hepatic mRNA expression of Rpl35, Rps3A, and Rps8 was significantly decreased in NAFLD mice.Conclusions: This study identified four ribosomal protein genes (RPL35, RPS3A, RPS8, and MRPL16) as immune-cell-related biomarkers of NAFLD, which may actively participate in the immune processes during NAFLD progression and could serve as potential targets for the diagnosis and treatment of NAFLD.

Background: Non-alcoholic fatty liver disease (NAFLD) and heart failure (HF) are related conditions with an increasing incidence. However, the mechanism underlying their association remains unclear. This study aimed to explore the shared pathogenic mechanisms and common biomarkers of NAFLD and HF through bioinformatics analyses and experimental validation.Methods: NAFLD and HF-related transcriptome data were extracted from the Gene Expression Omnibus (GEO) database (GSE126848 and GSE26887). Differential analysis was performed to identify common differentially expressed genes (co-DEGs) between NAFLD and HF. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were conducted to explore the functions and regulatory pathways of co-DEGs. Protein-protein interaction (PPI) network and support vector machine-recursive feature elimination (SVM-RFE) methods were used to screen common key DEGs. The diagnostic value of common key DEGs was assessed by receiver operating characteristic (ROC) curve and validated with external datasets (GSE89632 and GSE57345). Finally, the expression of biomarkers was validated in mouse models.Results: A total of 161 co-DEGs were screened out in NAFLD and HF patients. GO, KEGG, and GSEA analyses indicated that these co-DEGs were mainly enriched in immune-related pathways. PPI network revealed 14 key DEGs, and SVM-RFE model eventually identified two genes (CD163 and CCR1) as common key DEGs for NAFLD and HF. Expression analysis revealed that the expression levels of CD163 and CCR1 were significantly down-regulated in HF and NAFLD patients. ROC curve analysis showed that CD163 and CCR1 had good diagnostic values for HF and NAFLD. Single-gene GSEA suggested that CD163 and CCR1 were mainly engaged in immune responses and inflammation. Experimental validation indicated unbalanced macrophage polarization in HF and NAFLD mouse models, and the expression of CD163 and CCR1 were significantly down-regulated.Conclusion: This study identified M2 polarization impairment characterized by decreased expression of CD163 and CCR1 as a common pathogenic pathway in NAFLD and HF. The downregulation of CD163 and CCR1 may reflect key pathological changes in the development and progression of NAFLD and HF, suggesting their potential as diagnostic and therapeutic targets.

Acetaminophen (APAP), an antipyretic and analgesic commonly used during pregnancy, has been recognized as a novel environmental contaminant. Preliminary evidence suggests that prenatal acetaminophen exposure (PAcE) could adversely affect offspring's gonadal and neurologic development, but there is no systematic investigation on the characteristics of APAP's fetal developmental toxicity. Pregnant mice were treated with 100 or 400 mg/kg∙d APAP in the second-trimester, or 400 mg/kg∙d APAP in the second- or third-trimester, or different courses (single or multiple) of APAP, based on clinical regimen. The effects of PAcE on pregnancy outcomes, maternal/fetal blood phenotypes, and multi-organ morphological and functional development of fetal mice were analyzed. PAcE increased the incidence of adverse pregnancy outcomes and altered blood phenotypes including aminotransferases, lipids, and sex hormones in dams and fetuses. The expression of key functional genes in fetal organs indicated that PAcE inhibited hippocampal synaptic development, sex hormone synthesis, and osteogenic and chondrogenic development, but enhanced hepatic lipid synthesis and uptake, renal inflammatory hyperplasia, and adrenal steroid hormone synthesis. PAcE also induced marked pathological alterations in the fetal hippocampus, bone, kidney, and cartilage. The sensitivity rankings of fetal organs to PAcE might be hippocampus/bone > kidney > cartilage > liver > gonad > adrenal gland. Notably, PAcE-induced multi-organ developmental toxicity was more considerable under high-dose, second-trimester, and multi-course exposure and in male fetuses. This study confirmed PAcE-induced alterations in multi-organ development and function in fetal mice and elucidated its characteristics, which deepens the comprehensive understanding of APAP's developmental toxicity.

Background: This study aimed to develop and test an explainable machine learning (ML) predictive model based on lipid-related biomarkers to predict acute coronary syndrome (ACS) in hospitalized patients. Methods: A total of 10,127 consecutive hospitalized patients at three large hospitals were retrospectively studied between 2022 and 2024. ACS incidence was recorded as the primary outcome. Eight ML models were used to calculate the risk of ACS during hospitalization and to distribute patients into low-, intermediate-, and high-risk groups. Results: All patients were randomly divided into a 70% training set (n = 7088) and a 30% test set (n = 3039). ACS occurred in 1119 (15.8%) and 461 (15.2%) patients, respectively. The Light Gradient Boosting Machine (LightGBM) exhibited the best predictive performance (area under the curve, 0.829) for ACS in the training set. The final model, which included the top 10 features from the LightGBM model, including lipid-related markers and clinical features, achieved a C-index of 0.781 on the test set and demonstrated a significant ability to stratify patients into low-, intermediate-, and high-risk groups. Conclusion: We constructed a risk-stratification model based on lipid-related biomarkers derived from ML models to predict ACS in hospitalized patients, which could assist in identifying patients with high discriminatory capacity.