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ObjectiveThe COVID-19 pandemic has become a major public health concern over the past 3 years, leading to adverse effects on front-line healthcare workers. This study aimed to develop a Body Mass Index (BMI) change prediction model among doctors and nurses in North China during the COVID-19 pandemic, and further identified the predicting effects of lifestyles, sleep quality, work-related conditions, and personality traits on BMI change.MethodsThe present study was a cross-sectional study conducted in North China, during May-August 2022. A total of 5,400 doctors and nurses were randomly recruited from 39 COVID-19 designated hospitals and 5,271 participants provided valid responses. Participants’ data related to social-demographics, dietary behavior, lifestyle, sleep, personality, and work-related conflicts were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a BMI change prediction model among doctors and nurses during the COVID-19 pandemic.ResultsOf participants, only 2,216 (42.0%) individuals kept a stable BMI. Results showed that personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions had effects on the BMI change among doctors and nurses. The prediction model for BMI change was developed with a 33-26-20-1 network framework. The DNN model achieved high prediction efficacy, and values of R2, MAE, MSE, and RMSE for the model were 0.940, 0.027, 0.002, and 0.038, respectively. Among doctors and nurses, the top five predictors in the BMI change prediction model were unbalanced nutritional diet, poor sleep quality, work-family conflict, lack of exercise, and soft drinks consumption.ConclusionDuring the COVID-19 pandemic, BMI change was highly prevalent among doctors and nurses in North China. Machine learning models can provide an automated identification mechanism for the prediction of BMI change. Personality traits, dietary behaviors, lifestyles, sleep quality, burnout, and work-related conditions have contributed to the BMI change prediction. Integrated treatment measures should be taken in the management of weight and BMI by policymakers, hospital administrators, and healthcare workers.

BACKGROUND Temporal trends and geographical variations in non-communicable diseases (NCDs) attributable to suboptimal diets in China have not been systematically investigated. OBJECTIVE This study aimed to estimate the disease burden related to major foods and nutrients at national and provincial levels in China from 1990 to 2019. METHODS Following the methods of the Global Burden of Disease Study (GBD) 2019, all-cause and cause-specific and province-specific mortality and disability-adjusted life-years (DALYs) burden attributable to diet were comprehensively evaluated across the 33 province-level administrative units in China between 1990 and 2019. RESULTS Nationally, in 2019, 2.0 million (95% uncertainty interval [UI]: 1.5, 2.6) deaths and 46.8 million (35.6, 60.0) DALYs were attributable to dietary risks in China, of which cardiovascular diseases accounted for the highest proportion among all cause outcomes (1.7 million [1.3, 2.3] deaths, 39.0 million [29.2, 49.7] DALYs). The age-standardized death and DALY rates associated with dietary risks were 162 (121, 211) deaths per 100 000 population in 1990 and 115 (85, 152) deaths per 100 000 population in 2019, and 3 570 (2 740, 4 544) DALYs per 100 000 population in 1990 and 2 394 (1 823, 3 071) DALYs per 100 000 population in 2019, with percentage change of -28.8% and -32.9% from the 1990’s estimates. High consumption of sodium (855 385 [320 611, 1 546 463] deaths; 21.1 million [35.6, 9.3] DALYs), low consumption of whole grains (383 478 [192 387, 502 816] deaths; 8.6 million [4.6, 11.3] DALYs), and high consumption of red meat (323 380 [204 050, 456 381] deaths; 8.7 million [6.1, 11.8] DALYs) were the leading dietary risk factors for deaths and DALYs of estimated burden in 2019. The burden of disease caused by dietary risks varied substantially across China, with the western and northeastern provinces demonstrating a comparatively high age-standardized rate linked to dietary risks. CONCLUSIONS Although the age-standardized death and DALY rates associated with dietary factors have declined since 1990, the impact of suboptimal diet on the burden of NCDs and regional disparities across China remains an important public health concern. The results from our study hold the potential for guiding the development of effective and localized dietary interventions that can enhance the quality of diets and alleviate the burden of dietary-related diseases.

The present study aimed to detect and analyze the concentrations of 12 trace elements in the sera and placental tissues of pregnant women with gestational diabetes mellitus (GDM) in Beijing, China using inductively coupled plasma mass spectrometry. Thirty pregnant women that participated in this study, and 20 matched normal controls were recruited in the study; the concentrations of trace elements were compared between these groups. The trace elements iron (Fe), copper (Cu), zinc (Zn), selenium (Se), chromium (Cr), manganese (Mn), nickel (Ni), strontium (Sr), lead (Pb), aluminum (Al), arsenic (As), and palladium (Pd) were detected in the sera and placental tissues of the pregnant women. Using a double antibody sandwich ELISA, the activity of glutathione peroxidases (GPXs), including GPX1-4, were assessed in the placental tissues. Compared with healthy pregnant women, the concentrations of Fe, Cu, Se, and Cr in the sera of pregnant women with GDM were significantly increased (P ˂ 0.05), whereas no significant differences in Al, Mn, Ni, As, Sr, Pd, and Pb sera concentrations were detected between these groups(P > 0.05); And Zn concentrations did not quite achieve acceptable levels of statistical significance (P = 0.047). Also in comparison to healthy pregnant women, Fe concentrations in the placental tissues of pregnant women with GDM were significantly increased (P ˂ 0.05), whereas Zn, Mn, Al, and As concentrations were significantly decreased (P ˂ 0.05); however, the concentrations of Cr, Ni, Se, Sr, Pd, Pb, and Cu in placental tissues did not differ between the groups. In addition, GPX1-4 activity did not differ between the GDM and healthy groups. The differences in trace element concentrations detected in pregnant patients with GDM and healthy pregnant patients suggest that some elements, such as Fe, Cu, Zn, and Se may play important roles in the disease and could be considered potential biomarkers.