Risk of stroke admission after long-term exposure to PM1: Evidence from a large cohort in South China.

BACKGROUND: Limited attention has been paid to the health effects of long-term PM1 exposure on stroke admission. Current investigations exploring the long-term PM exposure effect are largely based on observational studies, and PM generally is not allocated randomly to participants. Using traditional regression models might confuse messaging and hinder policy recommendations for pollution control and disease prevention policies. METHODS: We conducted a cohort study among 36,271 adults from one of the largest cities in China in 2015 and followed up through 2020. Hazard ratios of stroke admissions following long-term PM1 exposure were estimated via a causal inference approach, marginal structural time-varying Cox proportional hazard model, accounting for multiple confounders. Additionally, several sensitivity analyses and impact modification analyses were carried out. RESULTS AND DISCUSSION: Associations with 1 µg/m3 increase in long-term PM1 were identified for total (HR, 1.079; 95 %CI, 1.012-1.151) and ischemic stroke admissions (HR, 1.092; 95 %CI, 1.018-1.171). The harmful associations varied with exposure duration, initially increasing and then decreasing. The 2-3 years cumulative exposure was associated with a 3.3-5.4 % raised risk for total stroke. For every 1 µg/m³ increase in long-term PM1 exposure, females exhibited a higher risk of both total and ischemic stroke (13 % and 16 %) than men (4 % and 5 %). Low-exposure individuals (whose annual PM1 concentrations were under the third quartile among the annual concentrations for all the participants) exhibited greater sensitivity to PM1 effects (total stroke: 1.079 vs. 1.107; ischemic stroke: 1.092 vs. 1.116). The results underline the importance of safeguarding low-exposed people in highly polluted areas and suggest that long-term PM1 exposure may increase stroke admission risk, warranting attention to vulnerable groups.

The joint impact of PM2.5 constituents on the risk of cerebrovascular diseases hospitalization: A large community-based cohort study.

Air pollution poses significant health risks to urban areas, with limited focus on the chronic association of PM2.5 and its constituents on cerebrovascular diseases (CERs), especially regarding the joint associations. This study explores the individual and joint associations between PM2.5 constituents and CER hospitalization risks through a cohort analysis of 36,271 adults in the Pearl River Delta, South China, from 2015 to 2020. Cox proportional hazards regression and quantile-based g-computation models were used to quantify the individual and joint associations of annual mean concentrations of PM2.5 constituents with hospitalization for CERs. 1151 participants were hospitalized due to CERs during the five-year follow-up period. Joint associations analyses identified that one quartile increase in co-exposure may result in hazard ratios of 1.530 (1.441-1.623), 1.840 (1.710-1.980), and 1.609 (1.491-1.737) for CERs, total, and ischemic stroke hospitalization, respectively. The adverse effect was primarily driven by organic matter and chlorine. Men, those with a history of tobacco or alcohol use or with low residential greenness, were more susceptible to CERs hospitalization following PM2.5 constituents co-exposure. Upcoming strategies should focus on monitoring and regulating PM2.5 constituents, encouraging healthy lifestyles, and enhancing urban greenery.

How long-term PM exposure may affect all-site cancer mortality: Evidence from a large cohort in southern China.

BACKGROUND: Evidence of a potential causal link between long-term exposure to particulate matter (PM) and all-site cancer mortality from large population cohorts remained limited and suffered from residual confounding issues with traditional statistical methods. AIMS: We aimed to examine the potential causal relationship between long-term PM exposure and all-site cancer mortality in South China using causal inference methods. METHODS: We used a cohort in southern China that recruited 580,757 participants from 2009 through 2015 and tracked until 2020. Annual averages of PM1, PM2.5, and PM10 concentrations were generated with validated spatiotemporal models. We employed a causal inference approach, the Marginal Structural Cox model, based on observational data to evaluate the association between long-term exposure to PM and all-site cancer mortality. RESULTS: With an increase of 1 µg/m³ in PM1, PM2.5, and PM10, the hazard ratios (HRs) and 95% confidence interval (CI) for all-site cancer were 1.033 (95% CI: 1.025-1.041), 1.032 (95% CI: 1.027-1.038), and 1.020 (95% CI: 1.016-1.025), respectively. The HRs (95% CI) for digestive system and respiratory system cancer mortality associated with each 1 µg/m³ increase in PM1 were 1.022 (1.009-1.035) and 1.053 (1.038-1.068), respectively. In addition, inactive participants, who never smoked, or who lived in areas of low surrounding greenness were more susceptible to the effects of PM exposure, the HRs (95% CI) for all-site cancer mortality were 1.042 (1.031-1.053), 1.041 (1.032-1.050), and 1.0473 (1.025-1.070) for every 1 µg/m³ increase in PM1, respectively. The effect of PM1 tended to be more pronounced in the low-exposure group than in the general population, and multiple sensitivity analyses confirmed the robustness of the results. CONCLUSION: This study provided evidence that long-term exposure to PM may elevate the risk of all-site cancer mortality, emphasizing the potential health benefits of improving air quality for cancer prevention.

m6A RNA Methylation and Implications for Hepatic Lipid Metabolism.

This review presents a summary of recent progress in research on the N6-methyladenosine (m6A) modification and regulatory roles in hepatic lipid metabolism. As the most abundant internal modification of eukaryotic RNA, the m6A modification is a dynamic and reversible process of the m6A enzyme system, which includes writers, erasers, and readers. m6A methylation depressed lipid synthesis and facilitated lipolysis in liver. The depletion of m6A methyltransferase Mettl14/Mettl3 raised fatty acid synthase (FAS), stearoyl-CoA desaturase-1 (SCD1), acetyl-CoA carboxylase (ACC), and elongase of very long chain fatty acids 6 (ELOVL6) in rodent liver, causing increases in liver weight, triglyceride (TG) production, and content in hepatocytes. FTO catalyzed m6A demethylation and the suppression m6A reader YTHDC2 promoted hepatocellular TG generation and hepatic steatosis in C57BL/6 mice through sterol regulatory element-binding protein 1c (SREBP-1c) signaling pathway, which upregulated the lipogenic genes FAS, SCD1, ACC, recombinant acetyl coenzyme a carboxylase alpha, and cell death-inducing DNA fragmentation factor-like effector C (CIDEC). Furthermore, FTO overexpression did not only enhance mitochondrial fusion to impair mitochondrial function and lipid oxidation but also promoted lipid peroxidation, accompanied by excessive TG in hepatocytes and rodent liver. Elevated m6A modification potently suppressed hepatic lipid accumulation, while the shrinkage of m6A modification arose hepatic lipid deposition. These findings have highlighted the beneficial role of m6A RNA methylation in hepatic lipid metabolism, potentially protecting liver from lipid metabolic disorders.

Unveiling causal connections: Long-term particulate matter exposure and type 2 diabetes mellitus mortality in Southern China.

Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1-2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment. A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders. During follow-up, a total of 717 subjects died from T2DM. For every 1 µg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019-1.053). Similarly, for every 1 µg/m3 increase in PM1 and PM1-2.5, the adjusted HRs and 95% CIs were 1.032 (1.003-1.062) and 1.085 (1.054-1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049). The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1-2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.

Long-term PM1 exposure and hypertension hospitalization: A causal inference study on a large community-based cohort in South China.

Limited evidence exists on the effect of submicronic particulate matter (PM1) on hypertension hospitalization. Evidence based on causal inference and large cohorts is even more scarce. In 2015, 36,271 participants were enrolled in South China and followed up through 2020. Each participant was assigned single-year, lag0-1, and lag0-2 moving average concentration of PM1 and fine inhalable particulate matter (PM2.5) simulated based on satellite data at a 1-km resolution. We used an inverse probability weighting approach to balance confounders and utilized a marginal structural Cox model to evaluate the underlying causal links between PM1 exposure and hypertension hospitalization, with PM2.5-hypertension association for comparison. Several sensitivity studies and the analyses of effect modification were also conducted. We found that a higher hospitalization risk from both overall (HR: 1.13, 95% CI: 1.05-1.22) and essential hypertension (HR: 1.15, 95% CI: 1.06-1.25) was linked to each 1 µg/m3 increase in the yearly average PM1 concentration. At lag0-1 and lag0-2, we observed a 17%-21% higher risk of hypertension associated with PM1. The effect of PM1 was 6%-11% higher compared with PM2.5. Linear concentration-exposure associations between PM1 exposure and hypertension were identified, without safety thresholds. Women and participants that engaged in physical exercise exhibited higher susceptibility, with 4%-22% greater risk than their counterparts. This large cohort study identified a detrimental relationship between chronic PM1 exposure and hypertension hospitalization, which was more pronounced compared with PM2.5 and among certain groups.

The joint effects of mixture exposure to multiple meteorological factors on step count: A panel study in China.

The public health burden of increasing extreme weather events has been well documented. However, the influence of meteorological factors on physical activity remains limited. Existing mixture effect methods cannot handle cumulative lag effects. Therefore, we developed quantile g-computation Distributed lag non-linear model (QG-DLNM) by embedding a DLNM into quantile g-computation to allow for the concurrent consideration of both cumulated lag effects and mixture effects. We gathered repeated measurement data from Henan Province in China to investigate both the individual impact of meteorological factor on step counts using a DLNM, and the joint effect using the QG-DLNM. We projected future step counts linked to changes in temperature and relative humidity driven by climate change under three scenarios from the sixth phase of the Coupled Model Intercomparison Project. Our findings indicate there are inversed U-shaped associations for temperature, wind speed, and mixture exposure with step counts, peaking at 11.6 °C in temperature, 2.7 m/s in wind speed, and 30th percentile in mixture exposure. However, there are negative associations between relative humidity and rainfall with step counts. Additionally, relative humidity possesses the highest weights in the joint effect (49% contribution). Compared to 2022s, future step counts are projected to decrease due to temperature changes, while increase due to relative humidity changes. However, when considering both future temperature and humidity changes driven by climate change, the projections indicate a decrease in step counts. Our findings may suggest Chinese physical activity will be negatively influenced by global warming.

L-GrAFT7 has High Accuracy in Predicting Early Allograft Failure after Liver Transplantation: A Multicenter Cohort Study in China.

Background and Aims: Increasing utilization of extended criteria donor leads to an increasing rate of early allograft failure after liver transplantation. However, consensus of definition of early allograft failure is lacking. Methods: A retrospective, multicenter study was performed to validate the Liver Graft Assessment Following Transplantation (L-GrAFT) risk model in a Chinese cohort of 942 adult patients undergoing primary liver transplantation at three Chinese centers. L-GrAFT (L-GrAFT7 and L-GrAFT10) was compared with existing models: the Early Allograft Failure Simplified Estimation (EASE) score, the model of early allograft function (MEAF), and the Early Allograft Dysfunction (EAD) model. Univariate and multivariate logistic regression were used to find risk factors of L-GrAFT high-risk group. Results: L-GrAFT7 had an area under the curve of 0.85 in predicting 90-day graft survival, significantly superior to MEAF [area under the curve (AUC=0.78, p=0.044)] and EAD (AUC=0.78, p=0.006), while there was no statistical significance between the predicting abilities of L-GrAFT7 and EASE (AUC=0.84, p>0.05). Furthermore, L-GrAFT7 maintains good predicting ability in the subgroup of high-donor risk index (DRI) cases (AUC=0.83 vs. MEAF, p=0.007 vs. EAD, p=0.014) and recipients of donors after cardiac death (AUC=0.92 vs. EAD, p<0.001). Through multivariate analysis, pretransplant bilirubin level, units of packed red blood cells, and the DRI score were selected as independent risk factors of a L-GrAFT7 high-risk group. Conclusions: The accuracy of L-GrAFT7 in predicting early allograft failure was validated in a Chinese multicenter cohort, indicating that it has the potential to become an accurate endpoint of clinical practice and transitional study of machine perfusion.

Potential causal links and mediation pathway between urban greenness and lung cancer mortality: Result from a large cohort (2009 to 2020).

Urban greenness, as a vital component of the urban environment, plays a critical role in mitigating the adverse effects of rapid urbanization and supporting urban sustainability. However, the causal links between urban greenness and lung cancer mortality and its potential causal pathway remain poorly understood. Based on a prospective community-based cohort with 581,785 adult participants in southern China, we applied a doubly robust Cox proportional hazard model to estimate the causal associations between urban greenness exposure and lung cancer mortality. A general multiple mediation analysis method was utilized to further assess the potential mediating roles of various factors including particulate matter (PM1, PM2.5-1, and PM10-2.5), temperature, physical activity, and body mass index (BMI). We observed that each interquartile range (IQR: 0.06) increment in greenness exposure was inversely associated with lung cancer mortality, with a hazard ratio (HR) of 0.89 (95 % CI: 0.83, 0.96). The relationship between greenness and lung cancer mortality might be partially mediated by particulate matter, temperature, and physical activity, yielding a total indirect effect of 0.826 (95 % CI: 0.769, 0.887) for each IQR increase in greenness exposure. Notably, the protective effect of greenness against lung cancer mortality could be achieved primarily by reducing the particulate matter concentration.

The impact of long-term PM1 exposure on all-cause mortality and its interaction with BMI: A nationwide prospective cohort study in China.

BACKGROUND: China has a serious air pollution problem and a high prevalence of obesity. The interaction between the two and its impact on all-cause mortality is a public health issue of great concern. OBJECTIVES: This study aimed to investigate the association between long-term exposure to particulate matter with aerodynamic diameter ≤ 1 µm (PM1) and all-cause mortality, as well as the interaction effect of body mass index (BMI) in the association. METHODS: A total of 33,087 participants from 162 counties in 25 provinces in China were included, with annual average PM1 exposure being estimated based on the county address. The PM1-mortality relation was evaluated using the time-varying Cox proportional hazards models, with the dose-response relationship being fitted using the penalized splines. Besides, the potential interaction effect of BMI in the PM1-mortality relation was evaluated. RESULTS: The incidence of all-cause deaths was 76.99 per 10,000 person-years over a median of 8.2 years of follow-up. After controlling for potential confounders, the PM1-mortality relation was approximately J-shaped. The full-adjustment analysis observed the hazard ratio (HR) of all-cause mortality was 1.114 [95 % confidence interval (CI): 1.017-1.220] corresponding to a 10 µg/m3 rise in PM1 concentration. Further stratified analyses suggested the adverse effects of PM1 might be more pronounced among the underweight. DISCUSSION: Higher PM1 concentrations were associated with an increase in all-cause mortality. The BMI might further alter the relation, and the underweight population was the sensitive subgroup of the population that needed to be protected.

Sortilin-induced lipid accumulation and atherogenesis are suppressed by HNF1b SUMOylation promoted by flavone of Polygonatum odoratum. / çŽ‰ç«¹é»„é ®é€šè¿‡ä¿ƒè¿›HNF1b蛋白的SUMO化修饰抑制sortilinä»‹å¯¼çš„è„‚è´¨ç§¯ç´¯åŠåŠ¨è„‰ç²¥æ ·ç¡¬åŒ–.

This study aims to investigate the impact of hepatocyte nuclear factor 1ß (HNF1b) on macrophage sortilin-mediated lipid metabolism and aortic atherosclerosis and explore the role of the flavone of Polygonatum odoratum (PAOA-flavone)-promoted small ubiquitin-related modifier (SUMO) modification in the atheroprotective efficacy of HNF1b. HNF1b was predicted to be a transcriptional regulator of sortilin expression via bioinformatics, dual-luciferase reporter gene assay, and chromatin immunoprecipitation. HNF1b overexpression decreased sortilin expression and cellular lipid contents in THP-1 macrophages, leading to a depression in atherosclerotic plaque formation in low-density lipoprotein (LDL) receptor-deficient (LDLR-/-) mice. Multiple SUMO1-modified sites were identified on the HNF1b protein and co-immunoprecipitation confirmed its SUMO1 modification. The SUMOylation of HNF1b protein enhanced the HNF1b-inhibited effect on sortilin expression and reduced lipid contents in macrophages. PAOA-flavone treatment promoted SUMO-activating enzyme subunit 1 (SAE1) expression and SAE1-catalyzed SUMOylation of the HNF1b protein, which prevented sortilin-mediated lipid accumulation in macrophages and the formation of atherosclerotic plaques in apolipoprotein E-deficient (ApoE-/-) mice. Interference with SAE1 abrogated the improvement in lipid metabolism in macrophage cells and atheroprotective efficacy in vivo upon PAOA-flavone administration. In summary, HNF1b transcriptionally suppressed sortilin expression and macrophage lipid accumulation to inhibit aortic lipid deposition and the development of atherosclerosis. This anti-atherosclerotic effect was enhanced by PAOA-flavone-facilitated, SAE1-catalyzed SUMOylation of the HNF1b protein.

Long-term particulate matter exposure and the risk of neurological hospitalization: Evidence from causal inference of a large longitudinal cohort in South China.

With limited evidence on the neurological impact of particulate matter (PM) exposure in China, particularly for PM1 which is smaller but more toxic, we conducted a large Chinese cohort study using causal inference approaches to comprehensively clarify such impact. A total of 36,271 participants in southern China were recruited in 2015 and followed up through 2020. We obtained the neurological hospitalizations records by linking the cohort data to the electronic reports from 418 medical institutions across the study area. By using high-resolution PM concentrations from satellite-based spatiotemporal models and the cohort data, we performed marginal structural Cox models under causal assumptions to assess the potential causal links between time-varying PM exposure and neurological hospitalizations. Our findings indicated that increasing PM1, PM2.5, and PM10 concentrations by 1 µg/m³ were associated with higher overall neurological hospitalization risks, with hazard ratios (HRs) of 1.10 (95% confidence interval (CI) 1.04-1.16), 1.09 (95% CI 1.04-1.14), and 1.03 (95% CI 1.00-1.06), respectively. PM1 appeared to have a stronger effect on neurological hospitalization, with a 1% and 7% higher impact compared to PM2.5 and PM10, respectively. Additionally, each 1-µg/m3 increase in the annual PM1 concentration was associated with an elevated risk of hospitalizations for ischemic stroke (HR: 1.15; 95% CI, 1.06-1.26), which tended to be larger than the estimates for PM2.5 (HR: 1.13, 95% CI, 1.04-1.23) and PM10 (HR: 1.05, 95% CI, 1.00-1.09). Furthermore, never-married or female individuals tended be at a greater risk compared with their counterparts. Our study provides important insights into the health impact of particles, particularly smaller particles, on neurological hospitalization risk and highlights the need for clean-air policies that specifically target these particles.

Expressive probabilistic sampling in recurrent neural networks.

In sampling-based Bayesian models of brain function, neural activities are assumed to be samples from probability distributions that the brain uses for probabilistic computation. However, a comprehensive understanding of how mechanistic models of neural dynamics can sample from arbitrary distributions is still lacking. We use tools from functional analysis and stochastic differential equations to explore the minimum architectural requirements for $\textit{recurrent}$ neural circuits to sample from complex distributions. We first consider the traditional sampling model consisting of a network of neurons whose outputs directly represent the samples (sampler-only network). We argue that synaptic current and firing-rate dynamics in the traditional model have limited capacity to sample from a complex probability distribution. We show that the firing rate dynamics of a recurrent neural circuit with a separate set of output units can sample from an arbitrary probability distribution. We call such circuits reservoir-sampler networks (RSNs). We propose an efficient training procedure based on denoising score matching that finds recurrent and output weights such that the RSN implements Langevin sampling. We empirically demonstrate our model's ability to sample from several complex data distributions using the proposed neural dynamics and discuss its applicability to developing the next generation of sampling-based brain models.

Addition of a liver to the normothermic perfusion circuit reduces renal pro-inflammatory factors.

BACKGROUND: Normothermic machine perfusion (NMP) provides a novel platform to preserve isolated organs in an artificial condition. Our study aimed to explore the interaction between the liver and kidney at an ex vivo organ level by adding a liver to the kidney NMP circuit. METHODS: Porcine kidney and liver obtained from abattoir were subjected to 9 h NMP after suffering 30-min warm ischemia time and 90-min cold ischemia time. The liver-kidney NMP group (n = 5) and the single-kidney NMP group (n = 5) were designed. During the NMP, perfusion parameters, blood gas analysis, and tissue samples were compared. RESULTS: The perfusate of both groups remained stable, and continuous urine production was observed during NMP. In the liver-kidney NMP group, the lactate level was low, while blood urea nitrogen increased and glucose levels decreased. After the NMP, the renal tissue in the liver-kidney group exhibited fewer histological changes such as tubular epithelium vacuolization, along with reduced expression of IL-6, IL-8, IL-1ß, NLRP3, and GSDMD. CONCLUSIONS: Our results indicated that the expression of renal pro-inflammatory factors was reduced in the liver-kidney NMP system.

The complex role of air pollution on the association between greenness and respiratory mortality: Insight from a large cohort, 2009-2020.

BACKGROUND: Although emerging studies have illuminated the protective association between greenness and respiratory mortality, efforts to quantify the potentially complex role of air pollution in the causal pathway are still limited. We aimed to examine the potential roles of air pollution in the causal pathway between greenness and respiratory mortality in China. METHODS: We used data from a community-based prospective cohort of 654,115 participants in southern China (Jan 2009-Dec 2020). We evaluated the greenness exposure as a three-year moving average Normalized Difference Vegetation Index (NDVI) within the 500 m buffer around the residence. Cox proportional hazards model was applied to estimate the association between greenness and respiratory mortality. Causal mediation analysis combined with a four-way dimensional decomposition method was utilized to simultaneously quantify the interaction and mediation role of air pollution including PM2.5, PM10, or NO2 on the greenness-respiratory mortality relationship. FINDINGS: We observed 6954 respiratory deaths during 12 years of follow-up. Increasing NDVI level from the lowest to the highest quartile is associated with a 19 % (95%CI: 13-25 %) reduction in the respiratory mortality risk. For the total protective effect, the proportion attributable to the overall negative interaction between greenness and air pollution (PM2.5, PM10, or NO2) was 2.2 % (1.7-3.2 %), 3.5 % (0.4-3.7 %), or 25.0 % (22.8-27.1 %), respectively. Simultaneously, we estimated 25.5 % (20.1-32.0 %), 49.5 % (32.5-71.9 %), or 1.0 % (0.8-1.2 %) of the total protective association was mediated through a reduction in PM2.5, PM10, or NO2, respectively. INTERPRETATION: Increased greenness exposure mitigated respiratory mortality through both the antagonistic interaction and mediation pathway of air pollution (PM2.5, PM10, or NO2).

The impact of monthly air pollution exposure and its interaction with individual factors: Insight from a large cohort study of comprehensive hospitalizations in Guangzhou area.

Background: Although the association between short-term air pollution exposure and certain hospitalizations has been well documented, evidence on the effect of longer-term (e. g., monthly) air pollution on a comprehensive set of outcomes is still limited. Method: A total of 68,416 people in South China were enrolled and followed up during 2019-2020. Monthly air pollution level was estimated using a validated ordinary Kriging method and assigned to individuals. Time-dependent Cox models were developed to estimate the relationship between monthly PM10 and O3 exposures and the all-cause and cause-specific hospitalizations after adjusting for confounders. The interaction between air pollution and individual factors was also investigated. Results: Overall, each 10 µg/m3 increase in PM10 concentration was associated with a 3.1% (95%CI: 1.3%-4.9%) increment in the risk of all-cause hospitalization. The estimate was even greater following O3 exposure (6.8%, 5.5%-8.2%). Furthermore, each 10 µg/m3 increase in PM10 was associated with a 2.3%-9.1% elevation in all the cause-specific hospitalizations except for those related to respiratory and digestive diseases. The same increment in O3 was relevant to a 4.7%-22.8% elevation in the risk except for respiratory diseases. Additionally, the older individuals tended to be more vulnerable to PM10 exposure (P interaction: 0.002), while the alcohol abused and those with an abnormal BMI were more vulnerable to the impact of O3 (P interaction: 0.052 and 0.011). However, the heavy smokers were less vulnerable to O3 exposure (P interaction: 0.032). Conclusion: We provide comprehensive evidence on the hospitalization hazard of monthly PM10 and O3 exposure and their interaction with individual factors.

The Added Value of Sarcopenia on Existing Risk Scores to Predict Mortality after TIPS Placement: A Multicenter Study.

RATIONALE AND OBJECTIVES: Multiple prognostic scores have been applied for predicting survival after TIPS placement. The aim was to evaluate the added value of sarcopenia on existing risk scores and develop a sarcopenia-based scoring system for survival prediction and risk stratification. MATERIALS AND METHODS: In the derivation cohort of 386 cirrhotic patients undergoing TIPS, five risk scores (Child-Pugh, MELD, MELD-Na, MELD 3.0, and FIPS) were compared for prediction of short- and long-term mortality after TIPS. Sarcopenia was diagnosed based on the L3 skeletal muscle index and was incorporated into existing scores to assess its added value. A novel sarcopenia-based score was developed and externally validated in an independent cohort of 198 patients undergoing TIPS. RESULTS: Among existing scores, the FIPS score showed the highest discrimination (c-index: 0.756-0.783) and calibration (Brier score: 0.059-0.127). Besides, the FIPS score was significantly associated with the severity of baseline sarcopenia and reversal of sarcopenia after TIPS. The inclusion of sarcopenia improved discrimination of existing scores in different degrees and sarcopenia could stratify the low-risk categories deemed by these scores. A FIPS-sarcopenia score was developed, showing superior discrimination over existing scores (c-index: 0.777-0.804 in the derivation cohort, 0.738-0.788 in the validation cohort). With a determined cutoff of 0.8, this score allowed for the identification of two prognostic subgroups with distinct prognoses. CONCLUSION: FIPS score was highly correlated with the severity of sarcopenia and sarcopenia reversal after TIPS, and sarcopenia could improve the prognostic ability of existing scores. A FIPS-sarcopenia score was developed and validated, showing improved survival prediction and risk stratification.

Interactions between long-term ambient particle exposures and lifestyle on the prevalence of hypertension and diabetes: insight from a large community-based survey.

INTRODUCTION: Evidence on the interaction of lifestyle and long-term ambient particle (PM) exposure on the prevalence of hypertension, diabetes, particularly their combined condition is limited. We investigate the associations between PM and these outcomes and whether the associations were modified by various lifestyles. METHODS: This was a large population-based survey during 2019-2021 in Southern China. The concentrations of PM were interpolated and assigned to participants by the residential address. Hypertension and diabetes status were from questionnaires and confirmed with the community health centres. Logistic regression was applied to examine the associations, followed by a comprehensive set of stratified analyses by the lifestyles including diet, smoking, drinking, sleeping and exercise. RESULTS: A total of 82 345 residents were included in the final analyses. For each 1 µg/m3 increase in PM2.5, the adjusted OR for the prevalence of hypertension, diabetes and their combined condition were 1.05 (95% CI 1.05 to 1.06), 1.07 (95% CI 1.06 to 1.08) and 1.05 (95% CI 1.04 to 1.06), respectively. We observed that the association between PM2.5 and the combined condition was greatest in the group with 4-8 unhealthy lifestyles (OR=1.09, 95% CI 1.06 to 1.13) followed by the group with 2-3 and those with 0-1 unhealthy lifestyle (P interaction=0.026). Similar results and trends were observed in PM10 and/or in those with hypertension or diabetes. Individuals who consumed alcohol, had inadequate sleep duration or had poor quality sleep were more vulnerable. CONCLUSION: Long-term PM exposure was associated with increased prevalence of hypertension, diabetes and their combined condition, and those with unhealthy lifestyles suffered greater risks of these conditions.

Cysteine carboxyethylation generates neoantigens to induce HLA-restricted autoimmunity.

Autoimmune diseases such as ankylosing spondylitis (AS) can be driven by emerging neoantigens that disrupt immune tolerance. Here, we developed a workflow to profile posttranslational modifications involved in neoantigen formation. Using mass spectrometry, we identified a panel of cysteine residues differentially modified by carboxyethylation that required 3-hydroxypropionic acid to generate neoantigens in patients with AS. The lysosomal degradation of integrin αIIb [ITGA2B (CD41)] carboxyethylated at Cys96 (ITGA2B-ceC96) generated carboxyethylated peptides that were presented by HLA-DRB1*04 to stimulate CD4+ T cell responses and induce autoantibody production. Immunization of HLA-DR4 transgenic mice with the ITGA2B-ceC96 peptide promoted colitis and vertebral bone erosion. Thus, metabolite-induced cysteine carboxyethylation can give rise to pathogenic neoantigens that lead to autoreactive CD4+ T cell responses and autoantibody production in autoimmune diseases.

Potential causal links between long-term ambient particulate matter exposure and cardiovascular mortality: New evidence from a large community-based cohort in South China.

BACKGROUND: Cardiovascular disease (CVD) mortality is associated with long-term particulate matter (PM) exposure. However, evidence from large, highly-exposed population cohort and observational-data-based causal inference approaches remains limited. AIMS: We examined the potential causal links between PM exposure and the CVD mortality in South China. METHODS: 580,757 participants were recruited during 2009-2015 and followed up through 2020. Satellite-based annual concentrations of PM2.5, PM10, and PMcoarse (i.e., PM10 - PM2.5) at 1 km2 spatial resolution were estimated and assigned to each participant. Marginal structural Cox models with time-varying covariates, adjusted using inverse probability weighting, were developed to evaluate the association between prolonged PM exposure and CVD mortality. RESULTS: For overall CVD mortality, the hazard ratios and 95% confidence interval for each 1 µg/m3 increase in the annual average concentration of PM2.5, PM10, and PMcoarse were 1.033 (1.028-1.037), 1.028 (1.024-1.032), and 1.022 (1.012-1.033), respectively. All three PMs were linked to a higher mortality risk for myocardial infarction and ischemic heart disease (IHD). The mortality risk of chronic IHD and hypertension was linked to PM2.5 and PM10. Significant association between PMcoarse and other heart disease mortality was also observed. The older, women, less-educated participants, or inactive participants exhibited particularly higher susceptibility. Participants who were generally exposed to PM10 concentrations below 70 µg/m3 were more vulnerable to PM2.5-, PM10- and PMcoarse-CVD mortality risks. CONCLUSION: This large cohort study provides evidence for the potential causal links between increased CVD mortality and ambient PM exposure, as well as socio-demographics linked to the highest vulnerability.