Background With China's socioeconomic development, significant lifestyle changes have occurred among occupational groups, leading to alterations in cardiovascular metabolic risk factors. However, few studies have examined the secular trends of these risk factors in China's working population. Objective To analyze the trends in cardiovascular metabolic risk factors among the occupational population in nine provinces of China from 2009 to 2018, and to explore the associations between different occupational types and these risk factors, along with their clustering patterns, thereby providing evidence for targeted interventions. Methods This study utilized data from the China Health and Nutrition Survey (CHNS) in 2009, 2015, and 2018. The dataset covered 13274 employed individuals aged 18-59 years. Only participants with complete blood test results and physical measurement data were included in the final analysis. The average annual percentage change (AAPC) in the positive rates of risk factors was calculated using Joinpoint regression to analyze temporal trends. Multinomial logistic regression and binary logistic regression models were employed to examine the cross-sectional associations between occupational types (categorized as white-collar workers, agricultural workers, blue-collar workers, service workers, and others) and nine cardiovascular metabolic risk factors and their clustering (defined as having ≥2 risk factors) in 2018. Results A total of 8451 participants were included. From 2009 to 2018, significant increasing trends (AAPC>0, P<0.05) were observed for overweight, obesity, central obesity, borderline elevated triglycerides (TG), borderline elevated to elevated total cholesterol (TC), borderline elevated low-density lipoprotein cholesterol (LDL-C), elevated blood pressure and hypertension, diabetes. The most rapid increases were found for obesity (AAPC=6.07%, 95%CI: 3.42%, 8.88%), hypertension (AAPC=4.10%, 95%CI: 0.62%, 7.77%), and central obesity (AAPC=3.27%, 95%CI: 1.24%, 5.36%). The clustering rate of risk factors increased from 62.3% in 2009 to 73.2% in 2018 (AAPC=1.87%, 95%CI: 1.02%, 2.73%). The cross-sectional analysis in 2018, after adjusting for confounders, showed that compared with white-collar workers, blue-collar workers had a significantly lower risk of overweight (OR=0.67, 95%CI: 0.48, 0.95), central obesity (OR=0.65, 95%CI: 0.45, 0.92), and borderline high TG (OR=0.54, 95%CI: 0.35, 0.82). In contrast, agricultural workers had a higher risk of hypertension (OR=1.76, 95%CI: 1.14, 2.72). Conclusion Between 2009 and 2015, the burden of most cardiometabolic risk factors increases significantly among the Chinese occupational population, with the rate of increase slowing by 2018. Risks vary across occupational types, among which blue- collar workers report lower target outcomes. Targeted interventions focusing on the rapidly increasing risk factors and high-risk occupational groups are recommended.

Background With rising obesity rates and earlier hypertension onset among occupational populations, there is an urgent need to elucidate the long-term cardiovascular impacts of dynamic body weight patterns. Current evidence lacks trajectory modeling studies examining occupation-specific prevention strategies. Objective To investigate the association between long-term body mass index (BMI) trajectories and incident hypertension risk in Chinese working adults, and to examine occupation-specific heterogeneity in this relationship. Methods A dynamic sub-cohort of 4 413 occupational participants was constructed from ten survey waves (1991–2018) of the China Health and Nutrition Survey (CHNS). Eligible individuals had valid key BMI records at three or more independent follow-ups before the outcome event; the individual baseline was set as the year of their first participation in the survey. Group-based trajectory modeling (GBTM) was used to identify BMI change patterns. Cox proportional hazards regression was used to calculate hazard ratios (HRs) and 95% confidence interval (CI) for hypertension incidence across trajectory groups, with stratified analysis by occupational categories. Results Among 4413 occupational participants, 1441 incident hypertension cases were reported during the follow-up window. Three long-term BMI trajectories were identified: low-stable, moderate-slowly rising, and high-rising. In Model 3 with all selected confounders adjusted, compared with the low-stable group, the moderate-slowly rising group showed a significantly higher risk of incident hypertension (HR=1.28, 95%CI: 1.12, 1.45; P < 0.001), as did the high-increasing group (HR=1.80, 95%CI: 1.45, 2.23; P < 0.001). The stratified analyses revealed occupational differences in the association. Among agricultural workers, the moderate-slowly rising group (HR=1.35, 95%CI: 1.15, 1.57) and the high-rising group (HR=2.00, 95%CI: 1.50, 2.66) both exhibited significantly increased risks of hypertension (P<0.001). Conversely, among white-collar workers, an increased risk of hypertension was observed only in the high-rising group (HR=1.97, 95%CI: 1.23, 3.17; P<0.05). Conclusion Rapid BMI escalation constitutes a significant risk factor for incident hypertension in occupational populations. Targeted long-term weight monitoring and tailored interventions are recommended for high-risk occupations.

AIM:To analyze the influencing factors of postoperative malignant glaucoma(MG)in patients with primary angle-closure glaucoma(PACG)using logistic regression and decision tree models.METHODS:A retrospective study was conducted on PACG patients who underwent surgery at Eye Hospital of Handan City from March 2020 to March 2025. Patients were divided into two groups: the MG group, who developed MG within 6 mo postoperatively, and the non-MG group. Data were collected from the electronic medical record system. Univariate analysis was performed, followed by multivariate logistic regression to identify independent risk factors. A classification and regression tree model was constructed to visualize the hierarchical relationships among predictors. The predictive performance of the two models was evaluated and compared using receiver operating characteristic(ROC)curve analysis.RESULTS:Totally 182 cases(182 eyes)with PACG were enrolled in this study, including 91 cases(91 eyes)in the MG group and 91 cases(91 eyes)in the non-MG group. In the MG group, there were 53 males and 38 females; 69 cases were aged ≥60 y and 22 cases were aged <60 y. In the non-MG group, there were 47 males and 44 females; 33 cases were aged ≥60 y and 58 cases were aged <60 y. The non-MG group comprised 91 patients, including 47 males and 44 females. Among them, 33 cases were aged ≥60 y, and 58 cases were aged<60 y. The MG group had significantly higher proportions of patients aged ≥60 y, diabetes, moderate-stage PACG, persistent high intraocular pressure(IOP), complete anterior chamber angle closure, lens thickness <4.5 mm, axial length <22 mm, and severe postoperative inflammation compared to the non-MG group(all P<0.01). Multivariate Logistic regression identified the following as independent influencing factors for postoperative MG: age [OR (95%CI)=2.136(1.401-3.255)], PACG stage [OR (95%CI)=2.996(2.044-4.391)], IOP [OR (95%CI)=3.527(1.604-7.755)],anterior chamber angle [OR (95%CI)=4.826(2.498-9.324)], axial length [OR (95%CI)=5.125(1.265-20.771)], and severe postoperative inflammation [OR (95%CI)=2.338(1.478-3.699)](all P<0.05). The decision tree model selected six explanatory variables: age, PACG stage, IOP, anterior chamber angle status, axial length, and severe postoperative inflammation. Axial length was the primary splitting factor at the root node. The areas under the ROC curve(AUC)for the logistic regression and decision tree models were 0.913(0.863-0.950)and 0.921(0.872-0.956), respectively, with no significant difference between them(Z=0.561, P=0.575).CONCLUSION:Both the logistic regression and decision tree models effectively identify key influencing factors for postoperative MG in PACG patients, including age, PACG stage, IOP, anterior chamber angle status, axial length, and severe postoperative inflammation. The decision tree model offers an intuitive, visual representation of risk stratification, facilitating clinical decision-making. Both models are applicable for clinical risk assessment.

Over the past two decades, genome-wide association study(GWAS) has identified numerous genetic variants and loci associated with heritable diseases. With the gradual maturation and saturation of GWAS methodologies, transcriptome-wide association study(TWAS) offers a novel perspective by linkinggenetic phenotypes to gene expression levels. By integrating TWAS with other multi-omics analyses, researchers can gain a deeper understanding of heritable diseases. This article provides an overview of recent groundbreaking and representative TWAS methods and tools, analyzes their strengths and limitations, and discusses future trends in TWAS development.

ObjectiveTo investigate the status and influencing factors of latent tuberculosis infection (LTBI) among close contacts of patients with bacteriologically confirmed pulmonary tuberculosis in Jing’an District, Shanghai, and to provide scientific evidence for the development of tuberculosis infection testing and preventive intervention strategies for close contacts of bacteriologically positive patients. MethodsFrom October 2022 to October 2023, patients with bacteriological confirmed pulmonary tuberculosis registered in Jing’an District, Shanghai, and their close contacts were enrolled as the research subjects. The close contacts were screened for tuberculosis infection using the interferon-γ release assay (IGRA). The chi-square test and binary logistic regression model were used to analyze factors influencing LTBI. ResultsA total of 112 close contacts were included, of whom 30 (26.79%) were male and 82 (73.21%) were female. The age ranged from 1 to 86 years, with a median (25th, 75th percentiles) of 47.50 (32.25, 61.00) years. The infection rate of LTBI was 21.43% (24/112), and the incidence of active pulmonary tuberculosis was 2.68% (3/112). Univariate analyses showed that a positive sputum smear of the index case, being a direct relative of the index case, co-living, and not wearing a mask were significantly associated with LTBI (all P<0.05). Multivariate logistic regression analyses showed that the risk of latent infection in close contacts of the index case with a positive sputum smear was 6.50 times higher than in those with a negative sputum smear (95%CI: 1.82‒23.25, P=0.004). ConclusionThe infection rate of LTBI in close contacts of patients with bacteriologically confirmed pulmonary tuberculosis in Jing’an District, Shanghai, is relatively high, and close contacts of sputum smear-positive patients face an even higher risk of infection. Therefore, tuberculosis infection screening and follow-up management should be strengthened for this population.

Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

The aging trend is intensifying currently,but there is still a lack of standardized diagnosis and treat-ment schemes for severe infections in elderly people.This paper focuses on the recommendations for immune-related clinical diagnosis and treatment routes as well as the idea of risk stratified diagnosis and treatment for elderly peo-ple,aiming to effectively prevent infectious diseases in elderly people and perform stratified management through systematic and scientific means of immune function monitoring and regulation,so as to enhance the standardized level of diagnosis and treatment as well as clinical treatment effect of infection in elderly people.

Objective To investigate the impact of intergenerational caregiving on the brain structure and function of grandparents,and to analyze its correlation with caregiving factors.Methods Healthy adults(66 with grandchildren,24 without grandchildren)were recruited as study subjects,and clinical and MRI data were collected.Resting-state brain functional degree centrality(DC)and surface-based morphometry(SBM)methods were used to compare the differences in brain structure and function between the groups with and without grandchildren.The correlation between the differences in brain regions and △ values with grandchild's age,number,and time spent in childcare were assessed,respectively.Results Compared to the group without grandchildren,the group with grandchildren showed reduced surface area and cortical volume in the left middle temporal gyrus,as well as decreased DC values in the left medial superior frontal gyrus,bilateral orbital superior frontal gyrus,and left anterior cingulate and paracingulate gyrus(P＜0.05),respectively.In the grandchildren group,DC values and △ values in the left orbital superior frontal gyrus,left anterior cingulate and paracingulate gyrus were significantly positively correlated with time spent in childcare.Conclusion The brain structures and functions of grandparents related to empathy and motivation are changed in intergenerational caregiving,which may reveal the neuroplasticity after caring for their grandchildren.

Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

Objective:To investigate the interactive effects of loss of the only child and childhood trauma on brain structure, function, and structure-function coupling, and to analyze their association with clinical symptom.Methods:A total of 112 parents who lost their only child and participated in the psychological aid project organized by Local Civil Affairs Department in Sunan aear of Jiangsu Province in China from April 2021 to July 2021 and 36 healthy controls recruited from the community during the same period were selected. Based on childhood trauma questionnaire scores, parents who had lost their only child were divided into those with childhood trauma (group A, n=55) and those without childhood trauma (group B, n=57); similarly, the healthy controls were divided into a group with childhood trauma (group C, n=12) and a group without childhood trauma (group D, n=24). All participants were evaluated by clinical scales such as Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), and Mini-Mental State Examination (MMSE). MRI 3D-T1 structural images and resting-state functional magnetic resonance imaging data were collected; gray matter volume (GMV) and degree centrality (DC) were calculated by standardized image preprocessing procedure, and ratio of DC to GMV within each voxel was computed to obtain the structure-function coupling map. A two-factor analysis of variance was used to analyze the independent effect and interactive effect of loss of the only child and childhood trauma on GMV, DC, and DC/GMV coupling value. Spearman rank correlation analysis was used to evaluate the associations of above indicators in brain regions with significant difference in independent effect and interactive effect with clinical scale scores. Results:(1) Compared with the participants without childhood trauma (group B+group D), the participants with childhood trauma (group A+group C) showed significantly reduced GMV in the left middle temporal gyrus and right dorsolateral superior frontal gyrus (voxel-level P<0.01, cluster-level P<0.05, Gaussian random field [GRF] corrected). A significant interactive effect of loss of the only child and childhood trauma on GMV in the right precuneus was observed (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). (2) Compared with the healthy controls, parents who had lost their only child exhibited significantly increased DC in the left middle frontal gyrus (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). Compared with participants without childhood trauma, participants with childhood trauma showed significantly increased DC in the right thalamus (voxel-level P<0.01, cluster-level P< 0.05, GRF corrected). A significant interactive effect of loss of the only child and childhood trauma on DC in the left dorsolateral superior frontal gyrus was observed (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). (3) Compared with the healthy controls, parents who had lost their only child showed significantly decreased DC/GMV coupling value in the left middle frontal gyrus (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). Compared with participants without childhood trauma, participants with childhood trauma showed significantly increased DC/GMV coupling value in the right thalamus (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). A significant interactive effect of loss of the only child and childhood trauma on DC/GMV coupling value in the left dorsolateral superior frontal gyrus was observed (voxel-level P<0.01, cluster-level P<0.05, GRF corrected). (4) Correlation analysis revealed that GMV in the right precuneus with significant interactive effect of loss of the only child and childhood trauma was positively correlated with MMSE score ( r s=0.317, P=0.010, Bonferroni corrected). GMV in the left middle temporal gyrus with significant independent effect of childhood trauma was positively correlated with both HAMD score and HAMA score ( r s=0.362, P=0.006; r s= 0.349, P=0.008, Bonferroni corrected). Conclusion:Loss of the only child and childhood trauma can interact to jointly affect the brain structure, function, and structure-function coupling; and some of these brain structure alterations are closely associated with clinical symptoms.