Background Unhealthy lifestyle behaviors may be associated with an increased risk of cardiometabolic risk factor aggregation (CMRF≥ 2), and few studies have focused on the correlation between the two in occupational populations. Objective To investigate the current status of CMRF≥2 and the compliance of healthy lifestyle in male occupational personnel, explore the effect of lifestyle on cardiometabolic risk, and provide reference for formulating healthy behavior promotion strategies and reducing cardiometabolic risk in occupational populations. Methods The study subjects were selected from male workers who completed occupational health examinations at an occupational disease prevention and control hospital in Shanxi Province from May to December 2023, and 15125 study subjects aged 18−60 years were finally included according to pre-determined inclusion and exclusion criteria. All subjects received a series of assessments including questionnaires (basic information, lifestyle habits, and occupational factors), physical examination, laboratory tests, and six cardiometabolic risk factors (central obesity, hypertension, type 2 diabetes, hypertriglyceridemia, low-density lipoprotein cholesterolemia, and hyperuricemia). A healthy lifestyle score was calculated based on six behavioral factors (smoking, drinking, physical activity, diet, sleep, sedentary behavior) and 1 point for one positive healthy behavior. The enrolled workers were then divided into three groups according to their total scores: 0−1 points (poor group), 2−3 points (moderate group), and 4−6 points (good group). Logistic regression models were used to analyze the statistical associations between lifestyle and CMRF≥2. Results The median age of the 15125 male study participants was 40 years, and the positive rate of CMRF≥2 was 53.5%. Adherence to moderate alcohol consumption was the most compliant healthy behavior (79.4%), followed by current non-smoking (41.7%), while adherence to adequate sedentary behavior (23.6%) and healthy eating (21.1%) were relatively low, and only 17.4% were able to adhere to four or more healthy lifestyle behaviors. After adjusting for confounders, when compared with the poor group high lifestyle score was found to be a protective factor for CMRF≥2 (moderate group: OR=0.70, 95%CI: 0.63, 0.77; good group: OR=0.66, 95%CI: 0.58, 0.75). In stratified analyses across different job types (coal miners, auxiliary workers, ground workers, and others), occupational stress (with or without), shift patterns (day shift, night shift, or rotating shift), dust exposure (with or without), and noise exposure (with or without), individuals with higher lifestyle scores exhibited a consistently lower risk of CMRF≥2. There was an interaction between shift pattern and lifestyle on CMRF≥2 (P_interaction<0.05). Conclusion This occupational group has a high positive rate of CMRF≥2 and a high prevalence of poor lifestyles with low adherence to some healthy behaviors. High healthy lifestyle scores are associated with a lower risk of aggregation of cardiometabolic risk factors, which may be reduced through lifestyle interventions.

ObjectiveTo investigate the mechanism of action of Huangqi Chifengtang （HQCFT） on rats with atherosclerosis （AS） by regulating the gut microbiota and their metabolites. MethodsA rat model of AS was induced through high-fat diet feeding and vitamin D₃ injection， and the modeling lasted for 12 weeks. Fifty eight-week-old male SD rats were randomly divided into five groups： A blank group， a model group， a group receiving a low dose of HQCFT at 1.53 g·kg^-1 （HQCFT-L group）， a group receiving a high dose of HQCFT at 3.06 g·kg^-1 （HQCFT-H group）， and a group receiving atorvastatin calcium tablets at 1.8 mg·kg^-1 （Ato group）， with 10 rats in each group. Oral gavage administration started on the day after model establishment， once daily for four weeks. The efficacy of HQCFT was verified using aortic hematoxylin-eosin （HE） staining and determination of lipid levels and hemorrheology. The real-time polymerase chain reaction （Real-time PCR） was used for detecting inflammatory factor levels in the aorta， high-throughput sequencing for analyzing the gut microbiota composition in intestinal contents， targeted metabolomics for detecting short-chain fatty acid （SCFA） levels， and non-targeted metabolomics for identifying metabolomic profiles of intestinal contents. ResultsCompared with that in the blank group， the aortic tissue of rats in the model group showed significant AS lesions， including endothelial damage， inflammatory infiltration， and formation of fibrous plaques and calcified foci. Moreover， serum triacylglycerol （TG）， total cholesterol （TC）， and low-density lipoprotein cholesterol （LDL-C） levels were significantly elevated （P<0.05）， while high-density lipoprotein cholesterol （HDL-C） levels were significantly reduced （P<0.05）. Significant increases were observed in whole blood viscosity， plasma viscosity， and the mRNA expression levels of NOD-like receptor pyrin domain containing 3 （NLRP3）， Caspase-1， interleukin （IL）-β， IL-6， and tumor necrosis factor-α （TNF-α） in aortic tissue （P<0.05）. Additionally， gut microbiota composition， SCFA levels， and metabolomic profiles were significantly altered. Compared with those in the model group， serum TC， TG， and LDL-C levels， as well as the whole blood viscosity and plasma viscosity， were significantly reduced in all groups treated with HQCFT （P<0.05）. Significant decreases were observed in NLRP3 mRNA expression levels in all groups treated with HQCFT， Caspase-1， IL-β， and IL-6 mRNA expression levels in the HQCFT-H group， and TNF-α mRNA expression levels in the HQCFT-L group （P<0.05）. HQCFT reversed the increase in the F/B ratio and dialled back the decrease in the relative abundance of Blautia and the increase in that of Desulfovibrio. HQCFT promoted the production of acetic acid， valeric acid， and propionic acid. Non-targeted metabolomics identified 39 differential metabolites， which were mainly enriched in metabolic pathways such as arachidonic acid metabolism and primary bile acid biosynthesis. ConclusionThe mechanism by which HQCFT ameliorates AS injury may be related to the improvement of dyslipidemia and body inflammatory responses by altering gut microbiota composition， promoting SCFA production， and regulating the levels of metabolites in intestinal contents.

ObjectiveTo investigate the mechanism of action of Huangqi Chifengtang （HQCFT） on rats with atherosclerosis （AS） by regulating the gut microbiota and their metabolites. MethodsA rat model of AS was induced through high-fat diet feeding and vitamin D₃ injection， and the modeling lasted for 12 weeks. Fifty eight-week-old male SD rats were randomly divided into five groups： A blank group， a model group， a group receiving a low dose of HQCFT at 1.53 g·kg^-1 （HQCFT-L group）， a group receiving a high dose of HQCFT at 3.06 g·kg^-1 （HQCFT-H group）， and a group receiving atorvastatin calcium tablets at 1.8 mg·kg^-1 （Ato group）， with 10 rats in each group. Oral gavage administration started on the day after model establishment， once daily for four weeks. The efficacy of HQCFT was verified using aortic hematoxylin-eosin （HE） staining and determination of lipid levels and hemorrheology. The real-time polymerase chain reaction （Real-time PCR） was used for detecting inflammatory factor levels in the aorta， high-throughput sequencing for analyzing the gut microbiota composition in intestinal contents， targeted metabolomics for detecting short-chain fatty acid （SCFA） levels， and non-targeted metabolomics for identifying metabolomic profiles of intestinal contents. ResultsCompared with that in the blank group， the aortic tissue of rats in the model group showed significant AS lesions， including endothelial damage， inflammatory infiltration， and formation of fibrous plaques and calcified foci. Moreover， serum triacylglycerol （TG）， total cholesterol （TC）， and low-density lipoprotein cholesterol （LDL-C） levels were significantly elevated （P<0.05）， while high-density lipoprotein cholesterol （HDL-C） levels were significantly reduced （P<0.05）. Significant increases were observed in whole blood viscosity， plasma viscosity， and the mRNA expression levels of NOD-like receptor pyrin domain containing 3 （NLRP3）， Caspase-1， interleukin （IL）-β， IL-6， and tumor necrosis factor-α （TNF-α） in aortic tissue （P<0.05）. Additionally， gut microbiota composition， SCFA levels， and metabolomic profiles were significantly altered. Compared with those in the model group， serum TC， TG， and LDL-C levels， as well as the whole blood viscosity and plasma viscosity， were significantly reduced in all groups treated with HQCFT （P<0.05）. Significant decreases were observed in NLRP3 mRNA expression levels in all groups treated with HQCFT， Caspase-1， IL-β， and IL-6 mRNA expression levels in the HQCFT-H group， and TNF-α mRNA expression levels in the HQCFT-L group （P<0.05）. HQCFT reversed the increase in the F/B ratio and dialled back the decrease in the relative abundance of Blautia and the increase in that of Desulfovibrio. HQCFT promoted the production of acetic acid， valeric acid， and propionic acid. Non-targeted metabolomics identified 39 differential metabolites， which were mainly enriched in metabolic pathways such as arachidonic acid metabolism and primary bile acid biosynthesis. ConclusionThe mechanism by which HQCFT ameliorates AS injury may be related to the improvement of dyslipidemia and body inflammatory responses by altering gut microbiota composition， promoting SCFA production， and regulating the levels of metabolites in intestinal contents.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Previous studies have discovered a correlation between cigarette smoking and cortical thickness and surface area, but the causal relationship remains unclear. The objective of this investigation is to scrutinize the causal association between them. Methods: To derive summary statistics from a genome-wide association study (GWAS) on cortical thickness, surface area, and four smoking behaviors: 1) age of initiation of regular smoking (AgeSmk); 2) smoking initiation (SmkInit); 3) smoking cessation (SmkCes); 4) cigarettes per day (CigDay). Linkage disequilibrium score regression (LDSC) was employed to examine genetic association analysis. Furthermore, for traits with significant genetic associations, Mendelian randomization (MR) analyses were conducted. Results: The LDSC analysis revealed nominal genetic correlations between AgeSmk and right precentral surface area, left caudal anterior cingulate surface area, left cuneus surface area, left inferior parietal surface area, and right caudal anterior cingulate thickness, as well as between CigDay and left caudal middle frontal surface area, between SmkCes and left entorhinal thickness, and between SmkInit and left rostral anterior cingulate surface area, right rostral anterior cingulate thickness, and right superior frontal thickness (rg=-0.36–0.29, p<0.05). MR analysis showed a unidirectional causal association between left caudal middle frontal surface area and CigDay (βIVW=0.056, pBonferroni=2×10-4). Conclusion Left caudal middle frontal surface area has the potential to serve as a significant predictor of smoking behavior.