While growing evidence suggests that circadian clock and obesity are intertwined, the underlying mechanism is poorly understood. Here, we investigate how circadian clock is linked to obesity.

Background: Liver cancer is one of the most common cancers and causes of cancer death worldwide. The objective was to elucidate novel hub genes which were benefit for diagnosis, prognosis, and targeted therapy in liver cancer via integrated analysis. Methods: GSE84402, GSE101685, and GSE112791 were filtered from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified by using the GEO2R. The GO and KEGG pathway of DEGs were analyzed in the DAVID. PPI and TF network of the DEGs were constructed by using the STRING, TRANSFAC, and Harmonizome. The relationship between hub genes and prognoses in liver cancer was analyzed in UALCAN based on The Cancer Genome Atlas (TCGA). The diagnostic value of hub genes was evaluated by ROC. The relationship between hub genes and tumor-infiltrate lymphocytes was analyzed in TIMER. The protein levels of hub genes were verified in HPA. The interaction between the hub genes and the drug were identified in DGIdb. Results: In total, 108 upregulated and 60 downregulated DEGs were enriched in 148 GO terms and 20 KEGG pathways. The mRNA levels and protein levels of CDK1, HMMR, PTTG1, and TTK were higher in liver cancer tissues compared to normal tissues, which showed excellent diagnostic and prognostic value. CDK1, HMMR, PTTG1, and TTK were positively correlated with tumor-infiltrate lymphocytes, which might involve tumor immune response. The CDK1, HMMR, and TTK had close interaction with anticancer agents. Conclusions: The CDK1, HMMR, PTTG1, and TTK were hub genes in liver cancer; hence, they might be potential biomarkers for diagnosis, prognosis, and targeted therapy of liver cancer.

Obesity may lead to cognitive impairment and psychiatric disorders, which are associated with alterations in the brain cortical structure. However, the exact causality remains inconclusive. We aimed to conduct two-sample Mendelian randomization (MR) analysis to identify the causal associations of obesity [body mass index (BMI), waist-hip ratio (WHR), and waist-hip ratio adjusted for BMI ((WHRadjBMI)) and brain cortical structure (cortical thickness and cortical surface area). Inverse-variance weighted (IVW) method was used as the main analysis, whereas a series of sensitivity analyses were employed to assess heterogeneity and pleiotropy. The main MR results showed that higher BMI significantly increased the cortical surface area of the transverse temporal (β = 5.13 mm², 95% confidence interval [CI]: 2.55–7.71, P = 9.9 × 10⁻⁵); higher WHR significantly decreased cortical surface area of the inferior temporal (β = −38.60, 95% CI: −56.67- -20.54, P = 1.2 × 10⁻⁵), but significantly increased cortical surface area of the isthmus cingulate (β = 14.25, 95% CI: 6.97–21.54, P = 1.2 × 10–4). No significant evidence of pleiotropy was found in the MR analyses. This study supports that obesity has a causal effect on the brain cortical structure. Further studies are warranted to understand the clinical outcomes caused by these effects.

Objective: Undoubtedly, the relationship between serum uric acid (SUA) and obesity is less data for Chinese patients with obesity. This study aimed to examine the prevalence of hyperuricemia (HUA) and the association between SUA and patients with obesity.Methods: All participants were categorized as overweight, obesity I, obesity II, and obesity III. In addition, based on SUA concentration, the participants were stratified into four quartiles. The authors used descriptive analysis, independent t-test, ANOVA, correlation analysis, and multiple linear regression models to verify the SUA level and obesity among Chinese adults.Results: Overall, the estimated prevalence of HUA was 69.8%. In the BMI categories, the prevalence of HUA was 5.1% in overweight, 15.2% in obesity I, 16.9% in obesity II, and 32.5% in obesity III. Correlation analysis shows that SUA is strongly correlated with BMI, waist circumference (WC), and hip circumference (HC). Multiple linear regression analysis shows that high density–lipoprotein cholesterol (HDL-C) is a protective predictor of serum uric acid levels in patients with obesity. Compared with the overweight, obesity I, obesity II, and obesity III were more likely to have higher levels in the SUA levels.Conclusion: We mainly showed that the serum uric acid levels in Chinese patients with severe obesity declined slightly as age increased.