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.

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.

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 understand the current status of fear of missing out(FoMO)social interaction in adole-scent patients with severe accidental trauma,and to explore its potential categorical characteristics,and analyze the influencing factors of different categories.Methods A convenience sampling method was employed to select 301 adolescent patients with severe accidental trauma admitted to the surgical and trauma ICU of a tertiary hospital in Chengdu City from January to December 2024.Data were collected using a general information survey,the Fear of Missing Out Scale,the Adolescent Learning Stress Scale,and the Self-Perceived Burden Scale.Latent profile analysis and multiple logistic regression analysis were used to determine different profiles and their influencing factors.Results A total of 245 adolescent patients with severe accidental trauma were ultimately included,with a Fear of Missing Out Scale score of(21.55±8.17),being categorized into 4 potential profiles of"independent type"(28.16％),"normal type"(19.59％),"social dependence type"(22.04％),and"information deficiency type"(30.20％).The results of multiple logistic regression analysis showed that literacy,per capita monthly family income,level of academic stress,level of self-awareness of medical staff care,being an only child,level of self-perceived burden,and length of hospitalization were the factors influencing the potential profiles of FoMO social interaction in adolescent patients with unintentional trauma(all P＜0.05).Conclusion There is group heterogeneity in FoMO social interaction among adolescent patients with severe accidental trauma.Medical staff should develop targeted intervention measures based on the classification characteristics of different profiles of missing anxiety in adolescent accidental trauma patients to alleviate their levels of FoMO social interaction.

Objective::To examine the impact of hypertensive disorders of pregnancy (HDP) on offspring metabolomics.Methods::We searched five databases: PubMed, Ovid Embase, MEDLINE, Web of Science, and China National Knowledge Infrastructure, and included studies that reported metabolomics among human offspring born to HDP-complicated pregnancies.Results::Database search yielded 4054 articles, and after full-text screening, ten observational studies met inclusion criteria. Half of the studies had a sample size of less than 100 and were all observational studies in preeclampsia (PE) and gestational hypertension.Neonates were the most focused group in all included studies. Offspring born to HDP-complicated pregnancies exhibited statistically significant variations in blood metabolomics compared to their counterparts, characterized by amino acids, lipids, carnitine, and others (e.g., 1α,25-(OH) 2-D). Most studies reported a significant increase in differential metabolites of offspring born to HDP-complicated pregnancies. Four studies ( n = 1109) measured lipids-related metabolites, and all consistently showed that offspring born to PE-complicated pregnancies had significantly higher concentrations than non-PE exposed offspring. Conclusion::The existing evidence suggests an intergenerational effect of HDP on offspring metabolomics. Long-term follow-up studies are needed to advance the health effects of related adverse health outcomes and inform the prevention of offspring’s health.

Objective:To investigate the correlation among sex hormones, body mass index (BMI) , serum bone morphogenetic protein 15 (BMP-15) and bone metabolism parameters in adolescent patients with polycystic ovary syndrome (PCOS) .Methods:A total of 168 PCOS patients admitted from Aug. 2022 to Aug. 2023 were included in PCOS group, and 170 healthy adolescent women during the same period were included in the non-PCOS group. At the same time, the patients in PCOS group were divided into the obese group (n=81, BMI≥28 kg/m 2) and non-obese group (n=87, BMI < 25 kg/m 2) according to the BMI level. The levels of sex hormones, BMP-15 and bone metabolism indexes in all subjects were determined, and the correlation among sex hormones, BMI, BMP-15, bone metabolism indexes of PCOS patients was analyzed by Pearson correlation. Results:The levels of luteinizing hormone (LH) and testosterone (T) in PCOS group were higher ( t = 25.80, 27.99, P < 0.05) , and there was no statistically significant difference in follicle-stimulating hormone (FSH) levels between the two groups ( t = 1.73, P > 0.05) . The levels of LH and T in obese group were higher than those in the non-obese group ( t = 8.24, 23.90, P < 0.05) , and there was no statistically significant difference in FSH between the two groups ( t = 0.29, P > 0.05) . The BMP-15, osteocalcin and amino terminal propeptide (PINP) of type I collagen in PCOS group were lower than those in non-PCOS group ( t = 68.16, 65.89, 11.25, all P < 0.05) , and the cross-linked C-terminal peptide (CTX) of type I collagen was higher than those in non-PCOS group ( t = 13.01, P < 0.05) . BMP-15, osteocalcin and PINP in obese group were lower than those in non-obese group ( t = 14.74, 7.36, 7.90, all P < 0.05) , and CTX was higher ( t = 8.55, P < 0.05) . Pearson correlation analysis showed that LH was negatively correlated with BMP-15, osteocalcin and PINP in adolescent PCOS patients ( r= -0.42, -0.51, -0.42, P < 0.05) , and positively correlated with CTX ( r= 0.44, P < 0.05) . T was negatively correlated with BMP-15, osteocalcin and PINP ( r= -0.50, -0.49, -0.42, P < 0.05) , and positively correlated with CTX ( r= 0.48, P < 0.05) ; BMI was negatively correlated with BMP-15, osteocalcin and PINP ( r= -0.60, -0.66, -0.69, P < 0.05) , and positively correlated with CTX ( r= 0.70, P < 0.05) . Conclusions:In adolescent PCOS patients, LH, T and BMI is negatively correlated with BMP-15, osteocalcin and PINP, while positively correlated with CTX, indicating that PCOS and BMI can affect BMP-15 and bone metabolic markers, which has a certain correlation with ovarian function and abnormal bone metabolism.

Objective:To explore the RNA expression and alterations in brain structure in individuals who have experienced stressful life events (SLE), as well as the correlation patterns between them and their association with the occurrence of depression.Methods:Prospectively, a total of 80 SLE subjects were recruited from the psychiatry and psychology clinic of the Jiangsu University Affiliated Yixing Hospital between January 2021 and December 2022, with 16 normal controls (NC) enrolled concurrently. The 17 items Hamilton depression scale (HAMD-17) and social readjustment rating scale (SRRS) were used to assess depressive symptoms and stress levels. RNA sequencing information of peripheral blood and imaging data at baseline were collected. Based on whether depression occurred during the 2-year follow-up period, SLE subjects were divided into the SLE-depression group ( n=15) and the SLE-non-depression group ( n=65). Differentially expressed genes (DEGs) were screened using differential analysis and protein-protein interaction (PPI) networks. Fractional anisotropy (FA) of white matter tracts and gray matter volume (GMV) were extracted using tract-based spatial statistics and voxel-based morphometry.Using analysis of variance compared inter-group differences in gene expression, GMV and white matter FA values. Partial correlation analysis was used to explore correlations between DEGs, altered GMV and white matter microstructure. Gene set enrichment analysis (GSEA) was performed on key genes to identify potential biological pathways. Propensity score matching constructed sensitivity subgroups to verify result robustness. Results:The SLE-depression group showed significantly higher SRRS and HAMD-17 scores at baseline and at the end of follow-up compared to the SLE-non-depression group and the NC group ( H=47.773, 35.427, 41.114, all P<0.05). Expression levels of IL-10 (2.12±0.28, 2.43±0.44), EZH2 (2.11±0.43, 2.45±0.51), NCAM1 (3.60±0.30, 3.03±0.39), CD3E (4.95±0.37, 4.57±0.48), CCK (3.29±0.28, 3.02±0.42), and CX3CR1 (5.55±0.40, 5.91±0.34) were significantly different between the SLE-depression group and SLE-non-depression group( F=5.549~28.371, all P<0.05). Compared with the SLE-non-depression group, the SLE-depression group exhibited significantly lower FA values in the genu of the corpus callosum (0.29±0.04, 0.31±0.04) and the left uncinate fasciculus (0.31±0.02, 0.33±0.02), as well as significantly smaller GMV in the right hippocampus (0.29±0.07, 0.33±0.06), bilateral middle frontal gyrus (left: 0.27±0.05, 0.31±0.05; right: 0.28±0.06, 0.32±0.06), right insula (0.36±0.03, 0.38±0.04), and left precentral gyrus (0.19±0.04, 0.24±0.05) ( F=4.593-12.064, all P<0.05, FDR correction). GMV in the right anterior cingulate and paracingulate gyri was significantly larger than that in the SLE-non-depression group (0.34±0.05, 0.29±0.06) ( F=6.704, P=0.034, FDR correction). Partial correlation analysis revealed significantly stronger correlations between hub DEGs and altered brain regions in the SLE-depression group ( r=0.017-0.801) compared to the SLE-non-depression group ( r=0.002-0.382), with a statistically significant difference ( U=629, P<0.001; Cliff's Delta=0.454). GSEA indicated that the aforementioned genes were primarily involved in pathways including the ribosome, spliceosome, ribosome biogenesis in eukaryotes, and neuroactive ligand-receptor interaction. Sensitivity analysis confirmed that the above results remained statistically significant after balancing sample sizes (all P<0.05). Conclusion:The SLE-depression group showed specific RNA expression and brain structure alterations compared to the SLE-non-depression group, and the correlation between RNA and brain structure was significantly enhanced in the SLE-depression group. This suggests that the correlation between genes and brain structure in the SLE population may be related to their susceptibility to depression.

Objective:To investigate the predictive value of multimodal magnetic resonance imaging (MRI) techniques in assessing symptom remission of post-traumatic stress disorder (PTSD) of bereaved parents who lost their only child.Methods:In this prospective study, 34 parents with PTSD resulting from the loss of the only child were followed-up for 2 years. Based on the PTSD diagnostic status at the end of the follow-up, participants were divided into the remission group and the persistent group.R 3.6.1 and SPSS 20.0 software were used for statistical analysis.Baseline clinical data and neuroimaging findings were compared between the two groups. Logistic regression and LASSO regression analyses were used to identify independent predictors of PTSD symptom remission. The predictive performance of these factors was evaluated by receiver operating characteristic (ROC) curve analysis.Results:Initial screening with univariate Logistic regression and LASSO regression revealed that regional homogeneity (ReHo) in the left middle temporal gyrus, the combined predictive value based on ReHo, and the integrated predictive value combining gray matter volume (GMV) and ReHo (GMV-ReHo predictor) were significant factors influencing symptom remission (all P<0.05). Multivariate Logistic regression further demonstrated that the GMV-ReHo predictor retained independent predictive significance ( P<0.05), with ROC curve analysis showing an area under the curve (AUC) of 0.979 (95% CI=0.935-0.996, P<0.001) for its ability to predict PTSD remission. Notably, a combined model incorporating both the scores of the clinician administered PTSD scale (CAPS) and the GMV-ReHo predictor achieved an enhanced predictive performance, yielding an AUC of 0.984 (95% CI=0.952-0.998, P<0.001). Conclusion:The GMV-ReHo predictor effectively identifies symptom remission in PTSD resulting from the loss of the only child.