PubMed Health⌕ Search

Biomedical subjects

Zhao Liu

Publications and source records attributed to Zhao Liu.

2 recordsLinked to original sources
PubMed2026

Identification and expression validation of key genes of Xiaozhengtongluo formula in the treatment of diabetic nephropathy by Mendelian randomization.

Xiaozhengtongluo formula (XZTL) has a positive effect on the treatment of diabetic nephropathy (DN), but its mechanism is not fully understood. Therefore, it is important to explore the key genes of XZTL in the treatment of DN. Differentially expressed genes (DEGs) between DN and control obtained from GSE96804, drug target genes of XZTL, and disease target genes of DN obtained from public databases were intersected. Genes of intersection were defined as candidate genes. Next, Mendelian randomization (MR) analysis was used to ascertain the causal associations between candidate genes and DN. Afterwards, key genes were confirmed through receiver operating characteristic (ROC) curve analysis and expression validation. Subsequently, enrichment analysis, molecular regulatory network analysis, and molecular docking were conducted. Finally, experimental verification of the expression levels of key genes was performed through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Altogether, 29 candidate genes were screened via MR analysis, identifying APOD, IGFBP3, and LPL as significantly associated with DN. IGFBP3 and APOD were risk factors, whereas LPL was protective. Consistent expression trends across training and validation datasets defined them as key genes. All three were co-enriched in 26 pathways, including oxidative phosphorylation. Regulatory networks showed MIR497HG/hsa-miR-19a-3p regulated IGFBP3, and NEAT1/hsa-miR-29a-3p regulated LPL; IGFBP3 and LPL were co-targeted by SP3 and SP1. Molecular docking revealed APOD-baicalein, LPL-oleic acid, and IGFBP3-quercetin binding, suggesting therapeutic potential. RT-qPCR confirmed aberrant expression of these genes in DN, which was normalized by XZTL intervention. In this study, three key genes (APOD, IGFBP3, and LPL) of XZTL in the treatment of DN were finally obtained, providing mechanistic clues for understanding XZTL's multi-target mechanism and providing experimentally tractable candidate targets for DN molecular subtyping, targeted therapeutic development, and precision medicine approaches in TCM.

Diabetic Nephropathies↗
PubMed2026

Role of nicotine metabolite ratio in pharmacological interventions on smoking cessation: A systematic review and meta-analyses of randomized controlled trials.

BACKGROUND AND OBJECTIVES: Emerging evidence suggests that the nicotine metabolite ratio (NMR) may influence the efficacy of smoking cessation, yet its role across pharmacotherapies remains unclear. This study aims to investigate how NMR affects cessation outcomes under different medications to guide personalized treatment. METHODS: We searched PubMed, Medline, EMBASE, and the Cochrane Central Register of Controlled Trials (inception to September 30, 2024) for randomized controlled trials on pharmacotherapy for smoking cessation with NMR data. Data were synthesized using random-effects models, with heterogeneity assessment. The primary outcome was verified smoking cessation rate at the end of treatment or the closest time-point. RESULTS: Eleven RCTs with accessible full text were included in the qualitative analyses and nine were included in the quantitative synthesis. For non-titratable nicotine replacement therapy (NRT), normal/fast metabolizers demonstrated lower odds of smoking cessation than slow metabolizers (Odds Ratio, OR=0.81, 95% confidence interval, CI=0.68-0.96; 5 studies, I²=62.5%). No significant associations were shown between normal/fast and slow metabolizers using titratable NRT (OR=1.04, 95% CI=0.95-1.14; 2 studies, I²=0%), bupropion (OR=0.67, 95% CI=0.38-1.16; 2 studies, I²=51.4%), or varenicline (OR=1.17, 95% CI=0.79-1.74; 4 studies, I²=59.8%). CONCLUSION: Current evidence demonstrates that NMR moderates' treatment efficacy among those who smoke using non-titratable NRT, with slow metabolizers achieving significantly better cessation outcomes than normal/fast metabolizers. Substantial further research is needed to determine optimal medication hierarchies across metabolic profiles.

Humans↗