BACKGROUND: Previous evidence showed that ambient air pollution and cardiovascular mortality are related. However, there is a lack of evidence towards the modification effect of long-term lifestyle on the association between short-term ambient air pollution and death from cardiovascular events. METHOD: A total of 14,609 death from major adverse cardiovascular events (MACE) were identified among the China Kadoorie Biobank participants from 2013 to 2018. Ambient air pollution exposure including particulate matter 2.5 (PM_2.5), SO₂, NO₂, CO, and O₃ from the same period were obtained from space-time model reconstructions based on remote sensing data. Case-crossover design and conditional logistic regression was applied to estimate the effect of short-term exposure to air pollutants on MACE mortality. RESULTS: We found MACE mortality was significantly associated with PM_2.5 (relative percent increase 2.91% per 10 µg/m³ increase, 95% CI 1.32-4.53), NO₂ (5.37% per 10 µg/m³ increase, 95% CI 1.56-9.33), SO₂ (6.82% per 10 µg/m³ increase, 95% CI 2.99-10.80), and CO (2.24% per 0.1 mg/m³ increase, 95% CI 1.02-3.48). Stratified analyses indicated that drinking was associated with elevated risk of MACE mortality with NO₂ and SO₂ exposure; physical inactivity was associated with higher risk of death from MACE when exposed to PM_2.5; and people who had balanced diet had lower risk of MACE mortality when exposed to CO and NO₂. CONCLUSIONS The study results showed that short-term exposure to ambient PM_2.5, NO₂, SO₂, and CO would aggravate the risk of cardiovascular mortality, yet healthy lifestyle conduct might mitigate such negative impact to some extent.
Humans ; Cardiovascular Diseases/epidemiology* ; China/epidemiology* ; Male ; Female ; Air Pollution/adverse effects* ; Middle Aged ; Air Pollutants/analysis* ; Particulate Matter/analysis* ; Environmental Exposure/adverse effects* ; Life Style ; Aged ; Adult ; Risk Factors ; Cross-Over Studies ; East Asian People

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 between individual and combined exposure to ambient air pollutants and vitamin D, as well as the potential role of ultraviolet radiation (UVR) in mediating the relationships.Methods:This study included 6 967 participants from the China Kadoorie Biobank, whose baseline (2004-2008) blood 25-hydroxyvitamin D had been tested. The average monthly concentrations of pollutants (PM 2.5, PM 10, NO 2, O 3) and the UVR estimates in the month of blood sample collection were derived from the ChinaHighAirPollutants and ultraviolet datasets covering China, respectively. Linear models and multinomial logistic regression models were used to analyze the associations of each air pollutant with vitamin D concentrations and groups. Principal component analysis integrated with quantile-based g-computation was applied to evaluate the co-effects and relative contribution weights of air pollutants. Mediation analyses were performed to investigate the potential role of UVR. Results:PM 2.5, PM 10, NO 2, and O 3 were individually associated with vitamin D concentrations, as well as vitamin D groups: insufficiency (20-30 ng/ml) and deficiency (<20 ng/ml). For each 10 μg/m3 increase in monthly PM 2.5, PM 10, NO 2, and O 3, the percentage changes (95% CIs) in vitamin D concentrations were -1.71% (-2.16% - -1.26%), -1.30% (-1.60% - -1.00%), -3.77% (-4.60% - -2.93%), and 1.27% (0.91%-1.63%), respectively, with corresponding ORs (95% CIs) for vitamin D deficiency of 1.17 (1.06-1.29), 1.12 (1.05-1.19), 1.66 (1.38-2.00), and 0.81 (0.74-0.89). The mixture of the first principal component of PM 2.5 and PM 10 (PM), along with NO 2 and O 3, was negatively correlated with vitamin D. The percentage change (95% CI) in vitamin D concentrations for a one-quintile increase in the mixture was -2.20% (-3.56%- -0.82%), with NO 2 contributing the most (83%), followed by PM (17%). UVR-mediated association was 12.2% (95% CI：2.5%-23.0%) for PM 2.5 and 4.9% (95% CI：2.2%-8.0%) for PM 10 with vitamin D concentrations, respectively. Conclusion:Higher concentrations of particulate matter, NO 2, and a mixture of air pollutants were associated with lower vitamin D concentrations in Chinese adults, with reduced UVR acting as a partial mediator in the particulate matter-vitamin D relationship.

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.

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.