Objective: To understand the prevalence and its influencing factors of metabolic syndrome (MS) among employees in petrochemical enterprises, so as to provide insights into the prevention and control of MS among employees in petrochemical enterprises. Methods: The employees in petrochemical enterprises who underwent health examinations at the Fourth Affiliated Hospital of Gansu University of Traditional Chinese Medicine from May 2021 to December 2022 were selected as the survey subjects. Demographic information, lifestyle behaviors and occupational exposure were collected using questionnaires, and the blood biochemical indicators were measured through laboratory testing. Factors affecting MS were identified using a multivariable logistic regression model. Results: A total of 2 479 individuals were included, with a mean age of (44.84±7.87) years. There were 1 684 males (67.93%) and 795 females (32.07%). There were 905 cases of MS, with a detection rate of 36.51%. Multivariable logistic regression analysis showed that gender (male, OR=2.246, 95%CI: 1.353-3.728), age (≥40 years, OR=3.523, 95%CI: 2.003-6.194), noise exposure (OR=1.894, 95%CI: 1.272-2.821), smoking index (>0~200 cigarette-years, OR=1.907, 95%CI: 1.155-3.149; >200 cigarette-years, OR=2.257, 95%CI: 1.320-3.859), hyperuricemia (OR=3.013, 95%CI: 1.852-4.900) and γ-glutamyltransferase (abnormal, OR=2.691, 95%CI: 1.589-4.559) were the influencing factors of MS among employees in petrochemical enterprises. Conclusion The risk of MS occurrence among employees in petrochemical enterprises is related to gender, age, noise exposure, smoking index, hyperuricemia and γ-glutamyltransferase level.

Objective:To study the current status of longitudinal extrauterine growth restriction (EUGR) in extremely preterm infants (EPIs) and to develop a prediction model based on clinical data from multiple NICUs.Methods:From January 2017 to December 2018, EPIs admitted to 32 NICUs in North China were retrospectively studied. Their general conditions, nutritional support, complications during hospitalization and weight changes were reviewed. Weight loss between birth and discharge > 1SD was defined as longitudinal EUGR. The EPIs were assigned into longitudinal EUGR group and non-EUGR group and their nutritional support and weight changes were compared. The EPIs were randomly assigned into the training dataset and the validation dataset with a ratio of 7∶3. Univariate Cox regression analysis and multiple regression analysis were used in the training dataset to select the independent predictive factors. The best-fitting Nomogram model predicting longitudinal EUGR was established based on Akaike Information Criterion. The model was evaluated for discrimination efficacy, calibration and clinical decision curve analysis.Results:A total of 436 EPIs were included in this study, with a mean gestational age of (26.9±0.9) weeks and a birth weight of (989±171) g. The incidence of longitudinal EUGR was 82.3%(359/436). Seven variables (birth weight Z-score, weight loss, weight growth velocity, the proportion of breast milk ≥75% within 3 d before discharge, invasive mechanical ventilation ≥7 d, maternal antenatal corticosteroids use and bronchopulmonary dysplasia) were selected to establish the prediction model. The area under the receiver operating characteristic curve of the training dataset and the validation dataset were 0.870 (95% CI 0.820-0.920) and 0.879 (95% CI 0.815-0.942), suggesting good discrimination efficacy. The calibration curve indicated a good fit of the model ( P>0.05). The decision curve analysis showed positive net benefits at all thresholds. Conclusions:Currently, EPIs have a high incidence of longitudinal EUGR. The prediction model is helpful for early identification and intervention for EPIs with higher risks of longitudinal EUGR. It is necessary to expand the sample size and conduct prospective studies to optimize and validate the prediction model in the future.