中华流行病学杂志最新文献

To investigate the associations of serum gamma-glutamyl transferase (GGT) levels with the risk of cardiovascular disease (CVD) and its subtypes in patients with type 2 diabetes mellitus (T2DM) in Jiangsu Province. Methods: The participants were enrolled in the Comprehensive Research project regarding 'Prevention and Control of Diabetes' in Jiangsu Province. The baseline survey was conducted from 2013 to 2014, and follow-up until December 31, 2021. After excluding the participants who self-reported with chronic liver disease/stroke/coronary heart disease at baseline survey and those with incomplete information on GGT, a total of 16 147 T2DM patients were included in the final analysis. Cox proportional hazard regression models were used to calculate the hazard ratio (HR) and their 95%CI of GGT for CVD, myocardial infarction, and stroke. Restricted cubic spline models were applied to analyze the dose-response relationship between GGT and the risk of CVD and its subtypes. Results: During the median follow-up time of 8.02 years, 2 860 CVD cases were registered, including 196 cases of myocardial infarction and 2 730 cases of stroke. Multivariate Cox proportional risk regression model indicated that compared to the lowest serum GGT level group, the highest GGT level group had a 24% increased risk of CVD (HR=1.24, 95%CI: 1.09-1.41) and a 23% increased risk of stroke (HR=1.23, 95%CI: 1.08-1.40). The restricted cubic spline model showed a nonlinear dose-response relationship between GGT and the risk of CVD, myocardial infarction, and stroke in T2DM patients. Conclusions: High levels of GGT may be associated with an increased risk of cardiovascular disease in T2DM patients, which needs further exploration and validation in future clinical practice.

Standards are the technical support for economic activities and social development. The construction and standardization of the pathogenic microorganism preservation standard system is an important technical foundation for the high-quality development of preservation work. Establishing a pathogenic microorganism resource standard system is also important to the national biosafety standards. Through the standardization of pathogenic microbial resource preservation, we can ensure the effective management and sustainable utilization of pathogenic microbial resources, promote the transformation of resources, and serve as an important new element of new productivity to assist the innovative development of biosafety science and technology. This article elaborates and analyzes the establishment background, construction framework, standardization process, and application effects of the standard system for preserving pathogenic microbial resources, providing stronger support for further improving the standard system and promoting the standardization of pathogenic microbial resource preservation.

Objective: To analyze the acute poison epidemic and provide evidence for developing prevention and control strategies for acute poisoning. Methods: A retrospective analysis was conducted on acute poisoning cases collected from 2016 to 2022 in a health emergency information platform for acute poisoning accidents. The cases were grouped according to the distribution of poisoning occurrence time, geographic distribution, demographic distribution, types of toxicants, poisoning causes, and outcomes. Data were organized and analyzed using Excel 2016 and R 4.2.3. Results: A total of 95 754 acute poisoning cases were included in this study. The primary toxicants were pesticides, drugs, and industrial/household chemicals, accounting for 30.4%, 22.4%, and 20.4% of the total cases, respectively. Acute poisoning occurred throughout the year, with the highest frequency from June to August, accounting for 31.9%. The seasonal distribution varied among different types of toxicants. Except for plant poisoning, which showed a bimodal distribution, the other poisonings showed an unimodal distribution. There was a strong seasonality in fungal poisoning, which peaked in July. There was an obvious seasonality in animal poisoning, with a peak in August. The proportion of biological poisonings in the southwest region was higher than in other regions, including plants, animals, and fungi. There were more females than males, and their education level was mainly junior high school and below (35.2%). The main occupation was farmers (34.2%), and the main causes of poisoning were accidents and suicides. The case fatality rate of all poisoning cases was 1.24%. Pesticide poisoning was the most common type, and chlorfenapyr (11.68%), Diquat (7.23%), and paraquat (7.05%) ranked as the top three toxicants. Conclusions: The occurrence of acute poisoning has an obvious seasonal trend, and the toxicant spectrum of different regions and populations is different. A comprehensive poisoning surveillance system can provide a better understanding of the occurrence of poisonings, and facilitate the formulation of more scientifically precise poisoning prevention and control strategies.

Objective: To investigate the association between solid fuel use for heating, smoking, and respiratory diseases. Methods: This study is based on the Qingdao project of the China Kadoorie Biobank. After screening, 26 165 individuals were included in the study. We employed Cox proportional hazards regression models, stratified by risk age (in 5-year intervals) and sex while adjusting for confounding variables such as occupation and physical activity level to analyze the association between solid fuel use for heating, smoking, and increased risk of respiratory diseases. Results: Among the 26 165 participants, the average age of those using solid fuel for heating was (52.57±10.31) years, with females constituting 58.04% and former/current smokers accounting for 65.38%. The results indicated that both the solid fuel group and the former/current smoking group had a higher risk of respiratory diseases, with hazard ratios (HR) (95%CI) of 1.21 (1.04-1.41) and 1.41 (1.16-1.71), respectively. For the duration of solid fuel use, the HR (95%CI) for 20 years or more, it was 1.27 (1.07-1.51). The multiplicative interaction term between solid fuel use and smoking was statistically significant. Conclusions: The use of solid fuel for heating and smoking significantly increases the risk of respiratory diseases, and there may be a multiplicative interaction between solid fuel use and smoking.

Objective: To analyze the dynamic development of physical, psychological, and cognitive degenerative multimorbidity among middle-aged and older Chinese adults (≥45 years old) while estimating the longitudinal association between socioeconomic status (SES) and the progression of multimorbidity. Methods: Based on data from the China Health and Retirement Longitudinal Study (2011-2020), the Sankey diagram was used to show the dynamic development of physical, psychological, and cognitive degenerative multimorbidity from 2011 to 2020. SES was constructed based on the level of education and total household wealth. Logistic regression was used to estimate OR and 95%CI to evaluate the association between SES and the progression of multimorbidity. Results: Of the 5 393 participants included, 4 484 (83.14%) of them developed new diseases, and the prevalence of physical, psychological, and cognitive degenerative multimorbidity increased from 38.04% to 74.23%. Compared to those with no reported disorders at baseline, participants with psychological disorder (for newly developed physical-cognitive multimorbidity: OR=4.59,95%CI: 2.89-7.29), cognitive disorder (for newly developed physical-psychological multimorbidity: OR=2.24,95%CI: 1.40-3.60), or their multimorbidity at baseline were more likely to progress to physical, psychological, and cognitive degenerative multimorbidity. After adjusting covariates, individuals with low SES were more likely to develop physical diseases (OR=1.45, 95%CI: 1.11-1.89), cognitive disorder (OR=1.84, 95%CI: 1.16-2.91), physical-psychological multimorbidity (OR=1.87, 95%CI: 1.37-2.56), physical-cognitive multimorbidity (OR=3.58, 95%CI: 2.54-5.06), psychological-cognitive multimorbidity (OR=5.66, 95%CI: 3.04-10.55), and physical-psychological-cognitive multimorbidity (OR=3.21, 95%CI: 2.06-5.01) in comparison to those with high SES. There is a dose-response relationship between SES and the multimorbidity progression (all trend P<0.001). Conclusions: The prevalence of physical, psychological, and cognitive degenerative multimorbidity increased significantly among middle-aged and older Chinese adults. Lower SES was associated with multiple patterns of physical, psychological, and cognitive disorders progression.

Objective: To analyze the dietary patterns of Chinese adults and explore the relationship with serum uric acid (SUA) and hyperuricemia (HUA). Methods: A total of 9 358 adults were selected in the 2018 China Health and Nutrition Survey. Dietary intake data were collected by three consecutive 24-hour dietary recalls and weighing method. The social demographic information of the survey subjects was obtained through questionnaire surveys. The dietary patterns were extracted using factor analysis, and the relationship between dietary patterns and SUA was analyzed using multiple linear regression analysis. The correlation between HUA and dietary patterns was analyzed using logistic regression analysis models. Results: Four dietary patterns were identified: northern (high intakes of wheat, other cereals,and tubers); modern (high intakes of fruit, dairy, eggs, and nuts); southern (high intakes of rice and vegetables);animal food-wine (high intake of organ meats, seafood, and wine). The multiple linear regression analysis results showed that the northern pattern was negatively correlated with SUA (β=-0.438, 95%CI: -0.500--0.376); the modern pattern was negatively correlated with SUA (β=-0.134, 95%CI: -0.219--0.049); the southern model was significantly correlated with higher SUA (β=0.146, 95%CI: 0.079-0.214); the animal food-wine pattern was positively correlated with SUA (β=0.188, 95%CI: 0.123-0.252). Logistic regression analysis showed that compared with the northern model score Q₁ group, the risk of developing HUA was reduced in Q₃ and Q₄ groups, with ORs values of 0.777 (95%CI: 0.650-0.929) and 0.509 (95%CI: 0.423-0.613), respectively; and compared with the modern model score Q₁ group, the higher the scores in Q₃ and Q₄ groups, the HUA was lower, with ORs of 0.793 (95%CI: 0.660-0.953) and 0.768 (95%CI: 0.631-0.934), respectively. Compared with the animal food-wine pattern score Q₁ group, the risk of developing HUA was increased in both Q₃ and Q₄ groups (Q₃ group: OR=1.224, 95%CI: 1.012-1.480; Q₄ group: OR=1.312, 95%CI: 1.086-1.584). Conclusions: Dietary patterns are associated with HUA. The northern and modern patterns are related to lower SUA levels and reduced risk of HUA, while the animal food-wine pattern increases the risk of HUA.

The disease risk prediction model is the basis of precision prevention and an essential reference for clinical treatment decisions. The development of risk prediction models requires the support of a large amount of high-quality data. A large population cohort study is an important basis for this study. The United Kingdom Biobank (UKB), as a mega-population cohort and biobank, has played an essential role in the exploration of disease etiology and research related to disease prevention and control, with its rich baseline and follow-up data and concepts and mechanisms shared globally. This study followed PRISMA guidelines and included 210 articles with corresponding authors from 18 countries, of which 58 (27.62%) were from the UKB. A total of 491 disease risk prediction models were extracted for cancer, cardiovascular and cerebrovascular diseases, endocrine and metabolic diseases, respiratory diseases, and other diseases and their subgroups, of which 132 were developed by UKB without validation, 183 were developed by UKB with internal validation, 17 were developed by UKB with external validation, and 159 were developed by external development with UKB validation. A total of 188 models used only macro variables (38.29%), and 303 models combined macro and micro variables (61.71%). Model construction methods included survival outcome models, logistic regression, and machine learning. Survival outcome models were dominated by Cox proportional risk regression models and a few models considering competitive risk, accelerated failure models, or different baseline risk functions. Machine learning models included random forest, XGBoost, CatBoost, support vector machine, convolutional neural network, and other methods. The UKB is an essential resource for multiple disease risk prediction modeling studies.

Objective: To examine the associations of pre-pregnancy body mass index (BMI), gestational weight gain, and gestational diabetes mellitus (GDM) with early childhood BMI trajectories. Methods: A total of 1 227 mother-child pairs from the Peking University Birth Cohort in Tongzhou were included in this study. In the cohort, maternal pre-pregnancy weight, height, gestational weight gain, and GDM diagnosis were collected. The children were followed up at birth and at 1, 3, 6, 9, 12, 18, 24, 30, and 36 months of age to obtain height/length and weight data. The longitudinal data-based k-means clustering algorithm was used to identify early childhood BMI trajectory groups. The associations of maternal pre-pregnancy BMI, gestational weight gain, and GDM with early childhood BMI trajectories were analyzed using the logistic regression model. We further explored whether there is an interaction effect between pre-pregnancy overweight/obesity and excessive gestational weight gain on the risk of the high BMI trajectory in early childhood through multiplicative and additive interaction analyses. Results: The prevalence rates of overweight and obesity before pregnancy were 21.2% (260 cases) and 6.6% (81 cases) respectively. The prevalence of excessive gestational weight gain and GDM was 57.7% (708 cases) and 30.9% (379 cases). The early childhood BMI trajectories were named low, medium, and high trajectories, accounting for 30.5%, 45.4% and 24.1%, respectively. After controlling potential confounding factors, it was found that pre-pregnancy overweight (OR=1.54, 95%CI: 1.12-2.12), obesity (OR=2.33, 95%CI: 1.41-3.85), and excessive gestational weight gain (OR=1.47, 95%CI: 1.10-1.97) were risk factors for being in the high BMI trajectory in early childhood. GDM was not significantly associated with early childhood BMI trajectories (P>0.05). Compared with the independent effects of pre-pregnancy overweight/obesity (OR=1.90, 95%CI: 1.17-3.09) and excessive gestational weight gain (OR=1.45, 95%CI: 1.03-2.04), the risk of being in the high BMI trajectory in early childhood was greater when the two factors coexisted (OR=2.38, 95%CI: 1.60-3.54). However, both the multiplicative and additive models showed no interaction effect between pre-pregnancy overweight/obesity and excessive gestational weight gain. Conclusions: Maternal pre-pregnancy overweight/obesity and excessive gestational weight gain are independent risk factors for children being in the high BMI trajectory in early childhood, providing scientific evidence for obesity prevention.

Anthropometric indexes play a crucial role in identifying obesity. However, as an internationally commonly used indicator of obesity diagnosis, BMI has limitations in distinguishing lean body mass from fat mass. The weight-adjusted waist index (WWI), a novel anthropometric index, assesses adiposity through standardized waist circumference for weight, which presents an excellent capacity to predict the morbidity and mortality risks of various diseases. However, research on WWI is still in the exploratory stage, and there is a lack of unified standards for using this indicator to determine obesity. In addition, its potential application in children and adolescents also urgently needs to be explored. Therefore, this article comprehensively summarizes and compares the distinctive characteristics between WWI and other obesity-related anthropometric indexes. Bibliometric methods are used to analyze the hotspots and trends of WWI-related research, and we focus on discussing the value of WWI in predicting adverse health outcomes, including cardiovascular disease, diabetes, liver and kidney diseases. We aim to promote the application of WWI in a broader field and fully demonstrate its important public health significance and broad application prospects.