Association Between Night Shift and Hypertension: A Cross-Sectional Study in Chinese Adults

The correlation of shift work and CLOCK, BMAL1, and PER1 gene polymorphisms with hypertension in healthcare workers: A cross-sectional study

This study aimed to investigate the polymorphisms of circadian clock genes and the association of shift work and gene polymorphisms with hypertension in healthcare workers. This study recruited 222 healthcare workers, of whom 76 had primary hypertension (Hyp group) and 146 served as controls (Control group). General information and working hours were collected through questionnaires. Next, the identification of specific single nucleotide polymorphism (SNP) loci related to the Circadian locomotor output cycles kaput (CLOCK), brain and muscle arnt-like 1 (BMAL1), and PER1 genes was conducted by literature and PDGene database search. Venous blood samples were then collected for DNA extraction, and polymerase chain reaction-restriction fragment length polymorphism techniques were used to analyze the genotyping and allele frequency of the SNP sites. Finally, multivariate logistic regression was employed to analyze the association between various risk factors and hypertension in healthcare workers. Compared to the control group, the Hyp group had significantly higher proportions of alcohol consumption and family history of hypertension, while the average sleep duration and average exercise time were significantly lower. Shift work analysis showed that the Hyp group had a significantly lower average number of evening shifts per month while a much higher average number of night shifts per month compared to the control group. The GG genotype at the CLOCK rs1801260 locus was associated with a lower risk of hypertension (OR = 0.446), and the TT genotype of the BMAL1 rs11022775 locus also showed a similar protective effect (OR = 0.426). However, the genotype distribution of the PER1 rs2735611 locus was not significantly associated with the risk of hypertension. Multivariate regression analysis revealed that a family history of hypertension and insufficient sleep were significantly associated with the risk of hypertension, and the average number of night shifts per month was positively correlated with the risk of hypertension. Specific polymorphisms in the CLOCK and BMAL1 genes may have a protective effect against hypertension in healthcare workers, while polymorphisms in the PER1 gene are not significantly associated with the risk of hypertension. Additionally, a family history of hypertension, insufficient sleep, and shift work patterns may be significant risk factors for hypertension.

Prediction of positive pulmonary nodules based on machine learning algorithm combined with central carbon metabolism data

BACKGROUND

Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics.

METHODS

The study included 2248 participants at high risk for lung cancer from the Ma'anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, precision, recall, and F1 scores. In addition, SHapley Additive exPlanations (SHAP) was performed to assess the interpretability of the final selected model and to gain insight into the impact of features on the predicted results.

RESULTS

Finally, the two prediction models based on the random forest (RF) algorithm performed best, with AUC values of 0.87 and 0.83, respectively, better than other models. We found that homogentisic acid, fumaric acid, maleic acid, hippuric acid, gluconic acid, and succinic acid played a significant role in both PPNs prediction model and NPNs vs PPNs model, while 2-oxadipic acid only played a role in the former model and phosphopyruvate only played a role in the NPNs vs PPNs model. This model demonstrates the potential of central carbon metabolism for PPNs risk prediction and identification.

CONCLUSION

We developed a series of predictive models for PPNs, which can help in the early detection of PPNs and thus reduce the risk of lung cancer.