Objective To systematically analyze the characteristics of the disease burden of hypertensive heart disease (HHD) in the elderly (≥60 years) globally and in China from 1990 to 2021, and to predict its future trends from 2022 to 2040, with the aim of providing data support for optimizing comprehensive prevention and control strategies for HHD. Methods Based on the Global Burden of Disease (GBD) 2021 database, the number of prevalent cases and disability-adjusted life years (DALYs) of HHD in the elderly were extracted for the world, China, and five regions categorized by sociodemographic index (SDI). Joinpoint regression was used to analyze the temporal trends of age-standardized prevalence rate and age-standardized DALYs rate of HHD in the elderly. A three-factor decomposition method was applied to evaluate the relative contributions of aging, population growth, and epidemiological changes to the variations in the elderly HHD burden. Additionally, a Bayesian age-period-cohort model was used to predict the elderly HHD burden from 2022 to 2040. Results In 2021, the number of prevalent elderly HHD cases reached 10 283 000 globally and 3 412 400 in China, representing increases of 179.20% and 159.20% respectively, compared with 1990. The DALYs of elderly HHD were 18 812 700 person-years globally and 4 731 400 person-years in China, rising by 76.08% and 29.45% respectively from 1990. Meanwhile, the growth rates of the number of prevalent cases and DALYs of elderly HHD varied across different SDI regions. From 1990 to 2021, the age-standardized prevalence rate of elderly HHD in China, as well as the age-standardized DALYs rate of elderly HHD both globally and in China, showed significant downward trends (all average annual percentage changes<0, all P<0.001). In 2021, the 70-74 years age group accounted for the highest proportion of prevalent cases and DALYs of elderly HHD, both globally and in China. Decomposition analysis revealed that population growth was the dominant factor driving the increase in the elderly HHD burden across all regions. The prediction model results indicated that the number of prevalent cases and DALYs of elderly HHD would continue to rise globally and in China from 2022 to 2040, with the growth rate of the elderly HHD burden in China between 2021 and 2040 expected to exceed the global average. Conclusion Over the past 32 years, although the age-standardized disease rates of elderly HHD have mainly shown a downward trend globally and in China, the absolute number of the disease burden has increased substantially. The projection model indicates a continued upward trajectory, with the growth rate in China higher than the global average. Therefore, there is an urgent need to implement precise prevention and control strategies to effectively mitigate the disease burden of elderly HHD.

Objective:To investigate the molecular mechanism of the 5-methylcytosine(m5C)reader Y-box binding protein 1(YBX1)in participating in the development and progression of colorectal cancer(CRC)by regulating the stability of the ferroptosis inhibitor membrane-spanning 4-domains subfamily A 15(MS4A15).Methods:Bioinformatics databases were used to investigate the mRNA and protein expression levels of YBX1 in CRC.RT-qPCR was used to measure the expression level of YBX1 in CRC cells.LC-MS was used to measure the level of m5C modification in CRC cells and nor-mal colorectal mucosal cells.CCK-8 assay was used to observe the effect of YBX1 on the proliferation of CRC cells,Transwell assay was used to observe its effect on the migration ability of CRC cells,and flow cytometry was sued to observe its effect on the apoptosis of CRC cells.Bioinformatics methods were used to identify the ferrop-tosis inhibitors that can interact with YBX1 and potential m5C modification sites.GEPIA2 was used to analyze the association between the expression of YBX1 and MS4A15.The expression of YBX1 was inhibited,and then the mRNA expression level and m5C modifica-tion level of MS4A15 were analyzed.The catRAPID database was used to find the binding sites between YBX1 protein and MS4A15 mRNA.CRC cells were treated with actinomycin D after inhibition of YBX1 expression,and RT-qPCR was used to measure the stabil-ity of MS4A15 mRNA.The expression of MS4A15 was inhibited,and then the proliferative activity,migration ability,and apoptosis rate of cells were measured,as well as the expression levels of the key indicators for ferroptosis,including MDA,ROS,and Fe2+.Results:High mRNA and protein expression levels of YBX1 were observed in CRC,and YBX1 was highly expressed in CRC cells.The m5C modification level of CRC cells was significantly higher than that of normal colorectal mucosal cells.YBX1 could promote the prolifera-tion and migration of CRC cells and inhibit the apoptosis of CRC cells.The bioinformatics analysis showed that YBX1 was positively correlated with the expression level of the ferroptosis inhibitor MS4A15,and there were multiple m5C modification sites on MS4A15.Inhibition of YBX1 expression could reduce the mRNA expression level and m5C modification level of MS4A15 and the stability of MS4A15 mRNA.There were significant reductions in the proliferative activity and migration ability of CRC cells and a significant in-crease in the apoptosis rate of CRC cells after inhibition of MS4A15 expression,with significant increases in the content of MDA,ROS,and Fe2+.Conclusion:These results show that YBX1 promotes the development and progression of CRC by stabilizing MS4A15 via m5C modification,which provides a promising targeted therapeutic strategy for CRC patients.

Background: and Purpose Ruptured intracranial aneurysms (RIA) are associated with a mortality rate of up to 40% in the Chinese population, highlighting the critical need for targeted treatment interventions for at-risk individuals. Although the impact of aldehyde dehydrogenase 2 (ALDH2) gene mutations on susceptibility to intracranial aneurysms (IA) is well documented, the potential connection between ALDH2 rs671 single-nucleotide polymorphism (SNP) and RIA remains unexplored. Given the increased prevalence of ALDH2 gene mutations among Chinese Han individuals, it is clinically relevant to investigate the link between ALDH2 rs671 SNP and IA rupture. Methods: A prospective study was conducted on 546 patients diagnosed with IA to investigate the association between ALDH2 rs671 SNP and the risk of IA rupture. Results: The ALDH2 rs671 SNP (ALDH2*2) was significantly more prevalent in patients with unruptured IA (UIA) than in those with RIA (32.56% vs. 18.58%, P=0.004). Multivariate logistic regression analysis revealed that people with the ALDH2 mutation (ALDH2*1/*2 and ALDH2*2/*2 gene type) had a significantly reduced odds ratio (OR=0.49; 95% confidence level [CI] 0.27–0.88; P=0.018) for RIAs. Age-specific subgroup analysis indicated that the ALDH2 mutation provided a stronger protective effect in individuals aged 60 years and above with IA compared to those under 60 years old (OR=0.38 vs. OR=0.52, both P<0.05). Conclusion The incidence of RIA was significantly higher in individuals with a normal ALDH2 gene (ALDH2*1/*1) than in those with an ALDH2 rs671 SNP (ALDH2*1/*2 or ALDH2*2/*2). ALDH2 rs671 SNP may serve as a protective factor against RIA in the Chinese Han population.

Background: and Purpose Ruptured intracranial aneurysms (RIA) are associated with a mortality rate of up to 40% in the Chinese population, highlighting the critical need for targeted treatment interventions for at-risk individuals. Although the impact of aldehyde dehydrogenase 2 (ALDH2) gene mutations on susceptibility to intracranial aneurysms (IA) is well documented, the potential connection between ALDH2 rs671 single-nucleotide polymorphism (SNP) and RIA remains unexplored. Given the increased prevalence of ALDH2 gene mutations among Chinese Han individuals, it is clinically relevant to investigate the link between ALDH2 rs671 SNP and IA rupture. Methods: A prospective study was conducted on 546 patients diagnosed with IA to investigate the association between ALDH2 rs671 SNP and the risk of IA rupture. Results: The ALDH2 rs671 SNP (ALDH2*2) was significantly more prevalent in patients with unruptured IA (UIA) than in those with RIA (32.56% vs. 18.58%, P=0.004). Multivariate logistic regression analysis revealed that people with the ALDH2 mutation (ALDH2*1/*2 and ALDH2*2/*2 gene type) had a significantly reduced odds ratio (OR=0.49; 95% confidence level [CI] 0.27–0.88; P=0.018) for RIAs. Age-specific subgroup analysis indicated that the ALDH2 mutation provided a stronger protective effect in individuals aged 60 years and above with IA compared to those under 60 years old (OR=0.38 vs. OR=0.52, both P<0.05). Conclusion The incidence of RIA was significantly higher in individuals with a normal ALDH2 gene (ALDH2*1/*1) than in those with an ALDH2 rs671 SNP (ALDH2*1/*2 or ALDH2*2/*2). ALDH2 rs671 SNP may serve as a protective factor against RIA in the Chinese Han population.

Background: and Purpose Ruptured intracranial aneurysms (RIA) are associated with a mortality rate of up to 40% in the Chinese population, highlighting the critical need for targeted treatment interventions for at-risk individuals. Although the impact of aldehyde dehydrogenase 2 (ALDH2) gene mutations on susceptibility to intracranial aneurysms (IA) is well documented, the potential connection between ALDH2 rs671 single-nucleotide polymorphism (SNP) and RIA remains unexplored. Given the increased prevalence of ALDH2 gene mutations among Chinese Han individuals, it is clinically relevant to investigate the link between ALDH2 rs671 SNP and IA rupture. Methods: A prospective study was conducted on 546 patients diagnosed with IA to investigate the association between ALDH2 rs671 SNP and the risk of IA rupture. Results: The ALDH2 rs671 SNP (ALDH2*2) was significantly more prevalent in patients with unruptured IA (UIA) than in those with RIA (32.56% vs. 18.58%, P=0.004). Multivariate logistic regression analysis revealed that people with the ALDH2 mutation (ALDH2*1/*2 and ALDH2*2/*2 gene type) had a significantly reduced odds ratio (OR=0.49; 95% confidence level [CI] 0.27–0.88; P=0.018) for RIAs. Age-specific subgroup analysis indicated that the ALDH2 mutation provided a stronger protective effect in individuals aged 60 years and above with IA compared to those under 60 years old (OR=0.38 vs. OR=0.52, both P<0.05). Conclusion The incidence of RIA was significantly higher in individuals with a normal ALDH2 gene (ALDH2*1/*1) than in those with an ALDH2 rs671 SNP (ALDH2*1/*2 or ALDH2*2/*2). ALDH2 rs671 SNP may serve as a protective factor against RIA in the Chinese Han population.

Objective To analyze the levels of serum CTCs and ctDNA in NSCLC patients receiving first-line EGFR-TKI treatment, and to explore the clinical value of CTCs and ctDNA detection in assessing the efficacy of treatment for advanced lung cancer. Methods A total of 109 NSCLC patients receiving first-line EGFR-TKI treatment were enrolled. Serum tumor markers CEA, CTCs, and ctDNA were detected at baseline and after one month of treatment. Chest CT scans were performed, and treatment efficacy was evaluated based on RECIST1.1 criteria. CTCs were counted by enrichment-staining-computational algorithm to analyze malignant features, while ctDNA was assessed using digital PCR. Results Survival rate was low in patients with abnormal CEA and ctDNA tests at baseline and in patients with reduced serum CTCs after treatment. In the SD subgroup of patients with brain metastases and advanced stage, the PFS benefit was low. Conclusion Patients in the SD subgroup have significantly higher recurrence risks than those in the PR or CR subgroups. Therefore, CTC and ctDNA testing should be applied to patients in the SD subgroup to identify high-risk patients with poor response to EGFR-TKI treatment, intervene with additional treatment promptly, and obtain long progression-free survival.

Objective:To investigate the early predictive value of ECG PR interval for the risk of coronary heart disease(CHD) in patients with T2DM.Methods:A total of 7 886 participants from Guiyang who enrolled in the REACTION study between May and August 2011 were included. Baseline data were collected, and participants were followed for 10 years to monitor the occurrence of CHD. Logistic regression was used to identify risk factors for CHD, and a Cox proportional hazards model was employed to assess the predictive value of the PR interval for CHD incidence. Results:Over 10 years of follow-up, the overall incidence of CHD was 4.22%(245/5 812). The incidence was significantly higher in the T2DM group(7.57%, 96/1 268) than in the non-T2DM(3.28%, 149/4 544), and the shortened PR group(3.19%, 36/1 130; all P<0.05). Multiple logistic regression analysis identified both T2DM and prolonged PR interval as significant risk factors for CHD, with HR(95% CI) of 1.98(1.64-2.42) and 1.07(1.04-1.10), respectively(both P<0.01). The Cox proportional hazards model further revealed that the risk of CHD was significantly higher in the prolonged PR group than in the normal PR group, with an HR of 2.82(95% CI 2.34-3.12, P<0.01). Subgroup analysis showed that the risk of CHD was elevated in the non-T2DM with prolonged PR group, T2DM with normal PR group, and T2DM with prolonged PR group compared to the non-T2DM with normal PR group, with HRs(95% CI) of 1.43(1.14-1.82), 2.16(1.78-2.56), and 5.24(3.12-7.94), respectively. A significant interaction was observed between T2DM status and PR prolongation in predicting CHD risk(all P<0.01). Conclusions:Both T2DM and prolonged PR interval are independent risk factors for 10-year CHD incidence. Moreover, an interaction exists between T2DM and prolonged PR interval in predicting CHD risk. The PR interval may serve as an early predictor of CHD risk in patients with T2DM.

Objective:To investigate the 10-year incidence of reproductive system malignancies and their associated risk factors among overweight and obese women with different metabolic phenotypes aged 40 years and older in Guiyang City.Methods:A total of 6 444 female residents in Yunyan District, Guiyang City were included from the 2011 " Epidemiological Study on the Risk of Malignancy in Chinese Patients with Type 2 Diabetes Mellitus(REACTION)". Based on body mass index(BMI) and the presence or absence of metabolic syndrome(MetS), participants were categorized into four groups: metabolically healthy normal weight(MHNW, n=2 373), metabolically unhealthy normal weight(MUNW, n=1 098), metabolically healthy overweight/obese(MHO, n=2 229), and metabolically unhealthy overweight/obese(MUO, n=744). Baseline data were collected, and participants were followed up for 10 years to document the incidence of female reproductive system malignancies, including breast cancer, ovarian cancer, cervical cancer, and endometrial cancer. Logistic regression models were used to evaluate the association between metabolic phenotype and the 10-year risk of developing reproductive system malignancies. Results:Over a mean follow-up period of (9.89±0.77) years, the overall incidence of reproductive system malignancies was 1.15%(74/6, 444). Baseline characteristics such as age, BMI, high-density lipoprotein-cholesterol(HDL-C), and triglycerides(TG) differed significantly among the groups( P<0.05). The cumulative incidence of reproductive system malignancies in each group was: MHNW 0.93%(22/2 373), MUNW 0.73%(8/1 098), MHO 1.57%(35/2 229), and MUO 1.21%(9/744). There were no statistically significant differences in incidence across the four groups( P>0.05). However, multivariable logistic regression analysis revealed that, compared with the MHNW group, the adjusted HR (95% CI) were: MUNW 0.787(0.349-1.774, P>0.05), MHO 4.835(1.708-13.688, P<0.05), and MUO 3.719(1.144-12.089, P<0.05). Conclusion:Overweight and obesity are significant risk factors for reproductive system malignancies in women aged 40 and above. The presence of metabolic abnormalities in overweight or obese women further increases the risk of developing such malignancies.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.

Objective:To construct a prediction model for postoperative poor in-hospital prognosis in patients with traumatic brain injury (TBI) and evaluate its predictive performance.Methods:A retrospective case control study was conducted to analyze the clinical data of 1 120 TBI patients admitted to Changde Hospital Affiliated to Xiangya Medical College of Central South University from May 2019 to December 2024. The patients were divided into the training set ( n=784) and verification set ( n=336) at a ratio of 7∶3. Based on the Glasgow outcome scale-extended (GOS-E) at discharge, the training set was stratified into favorable prognosis group ( n=335, GOS-E 5-8 points) and poor prognosis group ( n=449, GOS-E 1-4 points). The two groups in the training set were compared in terms of general baseline indicators, TBI-related clinical indicators, and admission laboratory blood test results. Univariate analysis and Lasso regression analysis were employed to screen risk factors associated with postoperative poor in-hospital prognosis in TBI patients. Multivariate Logistic regression analysis was used to determine independent risk factors and construct a regression equation. The regression equation was presented using R language to create a visual nomogram for predicting postoperative poor in-hospital prognosis in TBI patients. In both the training set and verification set, the predictive performance of the model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve (AUC), plotting calibration curves, and performing decision curve analysis (DCA). Results:The results of the univariate analysis indicated that the age, Charlson complication index (CCI), time from trauma to admission, time from trauma to operation, cause of injury, abbreviated injury scale (AIS) (head and neck), injury severity score (ISS), admission Glasgow coma scale (GCS), admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraventricular hemorrhage, subarachnoid hemorrhage, decompressive craniotomy, intraoperative blood loss, intraoperative blood transfusion, traumatic cerebral infarction, postoperative delayed bleeding, epilepsy seizures, as well as the following admission tested results including red blood cell count, white blood cell count, platelet count, neutrophil percentage, percentage of lymphocytes, albumin, total bilirubin, urea nitrogen, thrombin time (TT), prothrombin time (PT), international standardized ratio (INR), glutamic aminotransferase, alanine aminotransferase, creatinine, and blood glucose were statistically different between the two groups in the training set ( P<0.05). Lasso regression analysis suggested 14 risk factors of age, CCI, cause of injury, head and neck AIS, ISS, admission GCS, admission pupil responsiveness, multiple craniocerebral injuries, subdural hematoma, intracerebral hematoma, intraoperative blood loss, admission platelet count, admission albumin, admission blood glucose for postoperative poor in-hospital prognosis. The results of the multivariate Logistic regression analysis showed that age ( OR=1.02, 95% CI 1.00, 1.03, P<0.01), CCI ( OR=1.46, 95% CI 1.02, 2.09, P<0.05), head and neck AIS ( OR=1.43, 95% CI 1.11, 1.85, P<0.01), ISS ( OR=2.16, 95% CI 1.39, 3.35, P<0.01), admission GCS ( OR=1.59, 95% CI 1.19, 2.13, P<0.01), intracerebral hematoma ( OR=4.41, 95% CI 2.15, 9.44, P<0.01), intraoperative blood loss ( OR=1.05, 95% CI 1.00, 1.09, P<0.05), admission platelet count ( OR=0.98, 95% CI 0.97, 0.99, P<0.01), admission blood glucose ( OR=1.08, 95% CI 1.02, 1.15, P<0.05) could be the main risk factors to construct a prediction model for postoperative poor in-hospital prognosis in TBI patients. Meanwhile, a regression equation was constructed: Logit[ P/(1- P)]=-2.4+ 0.02×"age"+0.38×"CCI"+0.36×"head and neck AIS"+0.77×"ISS"+0.47×"admission GCS"+1.48×"intracerebral hematoma"+0.05×intraoperative blood loss-0.02×admission platelet count+0.08×admission blood glucose. In the training set, the predictive model for poor postoperative in-hospital prognosis in TBI patients achieved an AUC of 0.87 (95% CI 0.84, 0.89), with a Youden′s index of 0.57, sensitivity of 73.70%, and specificity of 83.00%. In the verification set, the model showed an AUC of 0.80 (95% CI 0.76, 0.85), with a Youden′s index of 0.63, sensitivity of 65.20%, and specificity of 77.90%. In the training set, the Brier score for the calibration curve was 0.14 (95% CI 0.13, 0.16). In the verification set, the Brier score for the calibration curve was 0.18 (95% CI 0.15, 0.20). The DCA diagram indicated that the nomogram prediction model provided high clinical net benefit for predicting postoperative poor in-hospital prognosis in TBI patients. Conclusion:The prediction model for postoperative poor in-hospital prognosis in TBI patients, constructed based on age, CCI, head and neck AIS, ISS, admission GCS, intracerebral hematoma, intraoperative blood loss, admission platelet count, and admission blood glucose, exhibits good predictive performance.