BACKGROUND: Whether adherence to a healthy lifestyle is associated with a lower risk of developing pneumonia and a better long-term prognosis remains unclear. This study aimed to investigate associations of individual and combined lifestyle factors (LFs) with the incidence risk and long-term prognosis of pneumonia hospitalization. METHODS: Using data from the China Kadoorie Biobank study, we used the multistate models to investigate the role of five high-risk LFs, including smoking, excessive alcohol drinking, unhealthy dietary habits, physical inactivity, and unhealthy body shape, alone or in combination in the transitions from a generally healthy state at baseline to pneumonia hospitalization or cardiovascular disease (CVD, regarded as a reference outcome), and subsequently to mortality. RESULTS: Most of the five high-risk LFs were associated with increased risks of transitions from baseline to pneumonia and from pneumonia to death, but with different risk estimates. The greater the number of high-risk LFs, the higher the risk of developing pneumonia and long-term mortality risk after pneumonia, with the strength of associations comparable to that of LFs and CVD. Compared to participants with 0-1 high-risk LF, the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for transitions from baseline to pneumonia and from pneumonia to death in those with five high-risk LFs were 1.43 (1.28-1.60) and 1.98 (1.61-2.42), respectively. Correspondingly, the respective HRs (95% CIs) for transitions from baseline to CVD and from CVD to death were 2.00 (1.89-2.11) and 1.44 (1.30-1.59), respectively. The risk estimates changed slightly when further adjusting for the presence of major chronic diseases. CONCLUSION In this Chinese population, unhealthy LFs were associated with an increased incidence and long-term mortality risk of pneumonia.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Cardiovascular Diseases/etiology* ; China/epidemiology* ; Life Style ; Pneumonia/etiology* ; Prognosis ; Risk Factors ; Smoking

BACKGROUND: Prospective evidence on how offspring number influences morbidity and mortality remains limited. This study investigated the associations between number of offspring and morbidity and mortality risks among 0.5 million Chinese adults. METHODS: By using data from the China Kadoorie Biobank (CKB; n = 512,723, an approximately 12-year follow-up), sex-stratified phenome-wide association study (PheWAS) analyses were conducted to investigate associations between offspring number (without vs . with offspring; more than one vs . one offspring) and risks of ICD10-coded morbidity and mortality. Sex-specific adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were estimated by Cox proportional-hazards models. RESULTS: Among 210,129 men and 302,284 women aged 30-79 years, 1,338,837 incident events were recorded. PheWAS results revealed that offspring number was associated with disease risks across multiple systems. Cox models showed that childless men ( vs . one offspring) had higher risks for nine of 36 diseases, while childless women for five of 37. Each additional offspring was associated with reduced risks of mental and behavioral disorders in men (aHR [95% CI] = 0.93 [0.87-0.98]) and both mental and behavioral disorders (aHR [95% CI] = 0.93 [0.89-0.97]) and breast cancer (aHR [95% CI] = 0.82 [0.78-0.86]) in women. However, each additional offspring was associated with a 4% increase in the risk of cholelithiasis and cholecystitis in women (aHR [95% CI] = 1.04 [1.02-1.07]). Among 282,630 patients, 44,533 deaths were documented. Childless patients had higher mortality risk in both men (aHR [95% CI] = 1.37 [1.28-1.47]) and women (aHR [95% CI] = 1.27 [1.15-1.41]). For men, each additional offspring reduced mortality by 4% (aHR [95% CI] = 0.96 [0.95-0.98]), while for women, the lowest risk was observed among those with three to four offspring ( Pnonlinear <0.0001). CONCLUSIONS Offspring number is closely linked to morbidity and mortality risks. Further research is warranted to verify our findings and clarify the underlying mechanisms involved.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; China/epidemiology* ; Morbidity ; Proportional Hazards Models ; Prospective Studies ; Risk Factors ; Family Characteristics ; Mortality ; East Asian People

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.

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.