ObjectiveTo study the pharmacological substances and mechanisms through which sesquiterpene ZH-13 from Aquilariae Lignum Resinatum improves neuroinflammation. MethodsBV-2 microglial cells were stimulated with lipopolysaccharide （LPS） to induce neuroinflammation. The cells were divided into the normal group， the model group， and the ZH-13 low- and high-dose treatment groups （10， 20 μmol·L^-1）. The model group was treated with 1 μmol·L^-1 LPS. Cell viability was assessed using the cell proliferation and activity assay （CCK-8 kit）. Nitric oxide （NO） release in the cell supernatant was measured using a nitric oxide kit （Griess method）. The mRNA expression levels of interleukin-1β （IL-1β）， tumor necrosis factor-α （TNF-α）， inducible nitric oxide synthase （iNOS）， and interleukin-6 （IL-6） were detected by real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）. The phosphorylation of mitogen-activated protein kinase （MAPK） pathway proteins was assessed by Western blot. ResultsCompared with the model group， ZH-13 dose-dependently reduced NO release from BV-2 cells under LPS stimulation （P<0.05， P<0.01）. In the 20 μmol·L^-1 ZH-13 treatment group， the mRNA expression levels of IL-1β， TNF-α， iNOS， and IL-6 were significantly reduced compared to the model group （P<0.05， P<0.01）. In both the low- and high-dose ZH-13 groups， the expression of the inflammatory factor TNF-α and the phosphorylation of c-Jun N-terminal kinase （JNK） in the upstream MAPK pathway were significantly reduced （P<0.05）. After stimulation with the JNK agonist anisomycin （Ani）， both low- and high-dose ZH-13 treatment groups showed reduced phosphorylation of JNK proteins compared to the Ani-treated group （P<0.01）. ConclusionThe sesquiterpene compound ZH-13 from Aquilariae Lignum Resinatum significantly ameliorates LPS-induced neuroinflammatory responses in BV-2 cells by inhibiting excessive JNK phosphorylation and reducing TNF-α expression. These findings elucidate the pharmacological substances and mechanisms underlying the sedative and calming effects of Aquilariae Lignum Resinatum.

This study aims to describe the population and regional distribution characteristics of smoking behavior among adult twins in the China Twin Registry (CNTR), as well as the concordance rates for smoking behavior in monozygotic and dizygotic twins, and estimate the heritability. The study population included adult twins in CNTR who had smoking questionnaire data. A random-effects regression model was used to describe the distribution of smoking behavior among different subgroups based on various characteristics. The concordance of smoking behavior between different zygosity groups was calculated, and heritability was estimated. A total of 28 444 twin pairs were included in this study, with an average age of (36.6±12.0) years. Among male twins, 41.2% were current smokers, while only 1.2% of females smoked. Higher smoking rates were observed among male smokers in the 50-59 age group ( z=23.0, P<0.001), northern regions ( z=2.9, P<0.01), rural areas ( z=-5.2, P<0.001), those who were divorced/widowed ( z=3.8, P<0.001), and first-born twins ( z=-4.3, P<0.001), while lower smoking rates were found in those with higher education ( z=-16.1, P<0.001) and unmarried individuals ( z=-16.0, P<0.001). The smoking concordance rate for male monozygotic twins was 69.6%, significantly higher than the 57.3% concordance rate for dizygotic twins ( χ 2=105.0, P<0.05). The heritability of smoking behavior in male twins was estimated at 28.9% (95% CI: 24.3%-33.4%). Stratified analyses showed differences in heritability across regions and age groups: the heritability in northern regions was 32.6% (95% CI: 27.3%-38.0%), higher than the 21.0% (95% CI: 12.4%-29.5%) observed in southern regions; the highest heritability of 35.1% (95% CI: 26.3%-43.9%) was found in the 18-29 age group, with heritability decreasing with age. In conclusion, the smoking rate and influencing factors in the twin population are similar to those in the general population, with unique characteristics, such as higher smoking rates in first-born twins. Genetic factors have a significant impact on smoking behavior.

Objective:To investigate the relationship of several adiposity-related anthropometric parameters, including BMI, waist circumference (WC), waist-to-hip ratio (WHR), body fat percentage (BFP) and indoles in plasma with the incidence of atherosclerotic cardiovascular disease (ASCVD) in adults in China.Methods:In China Kadoorie Biobank (CKB) study, blood samples were collected from 2 183 participants in the first resurvey in 2008 to detect indoles. Participants' body weight, body height, WC, hip circumference, and BFP were measured at baseline survey in 2004 and resurvey in 2008, the BMI and WHR were calculated with standardized methods. The long-term follow-up of all participants started from the completion of the resurvey in 2008 until the occurrence of incident ASCVD, death, loss to follow-up or until December 31, 2018. CKB ascertained outcome status (incident ASCVD) through death and disease registries and national health insurance databases, supplemented by active follow-up. Multivariate linear regression model was used to estimate the associations of anthropometric measurements at baseline survey and the first resurvey, and changes in these measurements with 3 indoles [indole, indole-3-acetic acid (IAA), and indole-3-propionic acid (IPA)]. Cox proportional hazard regression model was used to estimate the associations between indoles and the risk for ASCVD.Results:Anthropometric measurements at baseline survey or the first resurvey were negatively associated with plasma IPA level. The regression coefficient ( β) of baseline BMI (per 1.0 kg/m 2) with 0.1 standard deviation ( SD) IPA was -0.23 (95% CI: -0.36 - -0.10) (false discovery rate=0.004). After adjusting for baseline BMI, the β of baseline WC, WHR and BFP with 0.1 SD IPA were -0.09 (95% CI: -0.18 - -0.01), -0.12 (95% CI: -0.19 - -0.05), and -0.20 (95% CI: -0.32 - -0.08), respectively. The annual change in BMI (difference between BMI in 2008 and 2004 divided by the time interval) was associated with indole and IAA, with β of 1.40 (95% CI: 0.58 - 2.21) and -1.07 (95% CI: -1.91 - -0.23), respectively, at each 0.1 increase of SD. Over a median ( Q1, Q3) follow-up of 10.46 (10.36, 10.53) years after 2008 resurvey, 236 cases of ASCVD were recorded. IAA and IPA levels were negatively associated with the risk for ASCVD, with hazard ratios for one SD increase of IAA and IPA of 0.87 (95% CI: 0.76 - 0.99) and 0.84 (95% CI: 0.73 - 0.96), respectively. Conclusions:Our results suggested that anthropometric measurements and their changing trends affect the levels of plasma imicrobial tryptophan metabolite levels, decreased levels of IAA and IPA levels are associated with increased risk of ASCVD and indoles in plasma including IPA and IAA might be the mediating factors for adiposity-induced ASCVD.

Objective:To analyze the prospective associations between liver biomarkers and mortality among Chinese middle-aged and elderly populations and to evaluate the mortality risk predictive value.Methods:A total of 22 758 participants from the 3 rd resurvey of the China Kadoorie Biobank were included. Cox proportional hazard models were used to analyze the prospective associations of 5 liver biomarkers with mortality. These liver biomarkers included two liver imaging biomarkers (liver fat attenuation parameter, liver stiffness measurement) and three serum liver enzyme biomarkers [gamma-glutamyl transferase (GGT), ALT, and AST]. Restricted cubic spline was used to assess the nonlinear associations between biomarkers and mortality. The area used the receiver operating characteristic curve (AUC) to evaluate the predictive ability of the models after incorporating liver biomarkers into traditional prediction models for mortality. Results:The mean age of the participants was (65.2±9.1) years, with a median follow-up of 1.5 years, during which 307 deaths occurred. Compared to individuals without hepatic steatosis, those with severe hepatic steatosis had a 79% higher risk of mortality, with a HR of 1.79 (95% CI: 1.06-3.03). Compared to individuals without hepatic fibrosis, those with advanced fibrosis and cirrhosis had higher mortality risks of 48% and 91%, respectively (both P<0.05). For each standard deviation increase in GGT, the mortality risk increased by 10% ( HR=1.10, 95% CI: 1.05-1.15), with the positive association plateauing at higher GGT levels. AST exhibited a U-shaped association with mortality risk. The AUC of the prediction model adding liver biomarkers into traditional prediction factors was 0.718 (95% CI: 0.679-0.757), with an increase of 0.030 ( P<0.001) compared with the traditional model. Conclusions:Severe hepatic steatosis, higher levels of hepatic fibrosis, and elevated GGT levels are significantly associated with higher mortality risk. AST shows a U-shaped nonlinear association with mortality risk. Incorporating liver biomarkers into traditional risk prediction models enhance the ability to predict mortality.

Objective:To investigate the prospective associations between plasma metabolites and the risks of all-cause and cause-specific mortality among Chinese adults.Methods:This study analyzed plasma metabolomics data from 2 183 healthy adults in the China Kadoorie Biobank (CKB), measured using targeted mass spectrometry. Cox proportional hazards regression models were used to examine the associations between 630 metabolites and the risk of all-cause mortality. Cause-specific hazard regression models evaluated the associations between metabolites and cardiovascular disease (CVD) risks, cancer, and other-cause mortality. Stepwise regression was used to identify key metabolites independently associated with all-cause mortality, and the area under the receiver operating characteristic curve (AUC) was calculated to assess the improvement in predictive performance when these metabolites were added to traditional risk prediction models.Results:The mean age of the participants was (53.2±9.8) years, 65.1% of whom were female. During a median follow-up of 14.5 years, 231 deaths occurred. A total of 44 metabolites were significantly associated with the risk of all-cause mortality [false discovery rate (FDR)-adjusted P<0.05], primarily including triglycerides, ceramides, and amino acids. Additionally, 29 and 15 metabolites were found to be associated with cancer and other-cause mortality, respectively, but no metabolites were significantly associated with CVD mortality after FDR corrections. Adding 14 metabolites independently associated with all-cause mortality into the traditional prediction model significantly improved its predictive performance. Specifically, incorporating metabolites into the traditional model, which already included laboratory biomarkers, increased the AUC to 0.798 (95% CI: 0.755-0.843), an improvement of 0.088 compared to the traditional model ( P<0.001). Conclusions:Multiple metabolites are significantly associated with mortality risk and can substantially improve the accuracy of mortality risk prediction models. These findings provide new insights into the physiological mechanisms of aging and offer valuable clues for personalized health risk assessment.

Tuberculosis (TB) is still a major public health issue today. A novel TB vaccine is a key approach to reaching the targets of WHO's End-TB Strategy. In recent years, significant progress has been made in the research and development of TB vaccines both in China and abroad. A breakthrough has been made in both application scenarios and technological routes, and several vaccine candidates have entered phase Ⅲ clinical trials. This review summarizes information from clinical trials involving key TB vaccine candidates under development in China and abroad. It highlights six candidates that are most promising for marketing or inspiring future research and development, analyzes the current difficulties in the research and development of novel TB vaccines, and provides an outlook for the future.

With the rapid global population aging, multimorbidity is becoming a more serious public health problem, to which medical researchers have paid close attention. A standardized definition of multimorbidity is essential for relevant research. However, there is a lack of consensus on the definition of multimorbidity, which makes it difficult to compare results among studies and replicate important findings. Based on the conceptual comparison, this paper summarizes the key dimensions of the definition of multimorbidity, including the number and types of conditions, the granularity of classification, methods for obtaining outcomes, and settings and objectives of research. It also discusses the inherent relationships among the dimensions and their impact on research results, and introduces the characteristics of the commonly used condition lists in current multimorbidity definition research. By suggesting areas for improvement in the multimorbidity definition system, it aims to enhance the understanding of multimorbidity research and achieve consensus on the definition, thereby facilitating further multimorbidity research.

The human microbiome encompasses a diverse array of microorganisms and their functional interactions within the human body. It exhibits a vast diversity of species and complex roles across various body environments. Advanced sequencing technologies, such as 16S amplicon sequencing and metagenomic sequencing, facilitate in-depth analysis on this microbial community. Recent researches have suggested that characteristics of the human microbiome (such as diversity and composition of microbiome, involving metabolic pathways and metabolites) might be associated with the onset and progression of cardiovascular diseases. These findings provide valuable insights into the etiology of chronic diseases and might aid in the development of novel disease biomarkers and intervention strategies. This paper summarizes the designs, current status and key findings of current population-based research in this field, and introduce the future development and analyze the existing critical problems that need further investigations.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To analyze the prospective associations between liver biomarkers and mortality among Chinese middle-aged and elderly populations and to evaluate the mortality risk predictive value.Methods:A total of 22 758 participants from the 3 rd resurvey of the China Kadoorie Biobank were included. Cox proportional hazard models were used to analyze the prospective associations of 5 liver biomarkers with mortality. These liver biomarkers included two liver imaging biomarkers (liver fat attenuation parameter, liver stiffness measurement) and three serum liver enzyme biomarkers [gamma-glutamyl transferase (GGT), ALT, and AST]. Restricted cubic spline was used to assess the nonlinear associations between biomarkers and mortality. The area used the receiver operating characteristic curve (AUC) to evaluate the predictive ability of the models after incorporating liver biomarkers into traditional prediction models for mortality. Results:The mean age of the participants was (65.2±9.1) years, with a median follow-up of 1.5 years, during which 307 deaths occurred. Compared to individuals without hepatic steatosis, those with severe hepatic steatosis had a 79% higher risk of mortality, with a HR of 1.79 (95% CI: 1.06-3.03). Compared to individuals without hepatic fibrosis, those with advanced fibrosis and cirrhosis had higher mortality risks of 48% and 91%, respectively (both P<0.05). For each standard deviation increase in GGT, the mortality risk increased by 10% ( HR=1.10, 95% CI: 1.05-1.15), with the positive association plateauing at higher GGT levels. AST exhibited a U-shaped association with mortality risk. The AUC of the prediction model adding liver biomarkers into traditional prediction factors was 0.718 (95% CI: 0.679-0.757), with an increase of 0.030 ( P<0.001) compared with the traditional model. Conclusions:Severe hepatic steatosis, higher levels of hepatic fibrosis, and elevated GGT levels are significantly associated with higher mortality risk. AST shows a U-shaped nonlinear association with mortality risk. Incorporating liver biomarkers into traditional risk prediction models enhance the ability to predict mortality.