Outdoor fine particulate matter exposure and telomere length in humans: A systematic review and meta-analysis.

Although the association between changes in human telomere length (TL) and ambient fine particulate matter (PM2.5) has been documented, there remains disagreement among the related literature. Our study conducted a systematic review and meta-analysis of epidemiological studies to investigate the health effects of outdoor PM2.5 exposure on human TL after a thorough database search. To quantify the overall effect estimates of TL changes associated with every 10 µg/m3 increase in PM2.5 exposure, we focused on two main topics, which were outdoor long-term exposure and prenatal exposure of PM2.5. Additionally, we included a summary of short-term PM2.5 exposure and its impact on TL due to limited data availability. Our qualitative analysis included 20 studies with 483,600 participants. The meta-analysis showed a statistically significant association between outdoor PM2.5 exposure and shorter human TL, with pooled impact estimates (ß) of -0.12 (95% CI: -0.20, -0.03, I2= 95.4%) for general long-term exposure and -0.07 (95% CI: -0.15, 0.00, I2= 74.3%) for prenatal exposure. In conclusion, our findings suggest that outdoor PM2.5 exposure may contribute to TL shortening, and noteworthy associations were observed in specific subgroups, suggesting the impact of various research variables. Larger, high-quality studies using standardized methodologies are necessary to strengthen these conclusions further.

Applications of digital health approaches for cardiometabolic diseases prevention and management in the Western Pacific region.

Cardiometabolic diseases (CMDs) are the major types of non-communicable diseases, contributing to huge disease burdens in the Western Pacific region (WPR). The use of digital health (dHealth) technologies, such as wearable gadgets, mobile apps, and artificial intelligence (AI), facilitates interventions for CMDs prevention and treatment. Currently, most studies on dHealth and CMDs in WPR were conducted in a few high- and middle-income countries like Australia, China, Japan, the Republic of Korea, and New Zealand. Evidence indicated that dHealth services promoted early prevention by behavior interventions, and AI-based innovation brought automated diagnosis and clinical decision-support. dHealth brought facilitators for the doctor-patient interplay in the effectiveness, experience, and communication skills during healthcare services, with rapidly development during the pandemic of coronavirus disease 2019. In the future, the improvement of dHealth services in WPR needs to gain more policy support, enhance technology innovation and privacy protection, and perform cost-effectiveness research.

Multimorbidity and mortality among older patients with coronary heart disease in Shenzhen, China.

BACKGROUND: The current understanding of the magnitude and consequences of multimorbidity in Chinese older adults with coronary heart disease (CHD) is insufficient. We aimed to assess the association and population-attributable fractions (PAFs) between multimorbidity and mortality among hospitalized older patients who were diagnosed with CHD in Shenzhen, China. METHODS: We conducted a retrospective cohort study of older Chinese patients (aged ≥ 65 years) who were diagnosed with CHD. Cox proportional hazards models were used to estimate the associations between multimorbidity and all-cause and cardiovascular disease (CVD) mortality. We also calculated the PAFs. RESULTS: The study comprised 76,455 older hospitalized patients who were diagnosed with CHD between January 1, 2016, and August 31, 2022. Among them, 70,217 (91.9%) had multimorbidity, defined as the presence of at least one of the predefined 14 chronic conditions. Those with cancer, hemorrhagic stroke and chronic liver disease had the worst overall death risk, with adjusted HRs (95% CIs) of 4.05 (3.77, 4.38), 2.22 (1.94, 2.53), and 1.85 (1.63, 2.11), respectively. For CVD mortality, the highest risk was observed for hemorrhagic stroke, ischemic stroke, and chronic kidney disease; the corresponding adjusted HRs (95% CIs) were 3.24 (2.77, 3.79), 1.91 (1.79, 2.04), and 1.81 (1.64, 1.99), respectively. All-cause mortality was mostly attributable to cancer, heart failure and ischemic stroke, with PAFs of 11.8, 10.2, and 9.1, respectively. As for CVD mortality, the leading PAFs were heart failure, ischemic stroke and diabetes; the corresponding PAFs were 18.0, 15.7, and 6.1, respectively. CONCLUSIONS: Multimorbidity was common and had a significant impact on mortality among older patients with CHD in Shenzhen, China. Cancer, heart failure, ischemic stroke and diabetes are the primary contributors to PAFs. Therefore, prioritizing improved treatment and management of these comorbidities is essential for the survival prognosis of CHD patients from a holistic public health perspective.

Physical activity, long-term fine particulate matter exposure and type 2 diabetes incidence: A prospective cohort study.

Background: Despite the adverse effects of ambient fine particulate matter (PM2.5) on type 2 diabetes and the beneficial role of physical activity (PA), the influence of PM2.5 on the relationship between PA and type 2 diabetes remains unclear. Methods: In this prospective study with 71,689 participants, PA was assessed by a questionnaire and was categorized into quartiles for volume and three groups for intensity. Long-term PM2.5 exposure was calculated using 1-km resolution satellite-based PM2.5 estimates. PM2.5 exposure and PA's effect on type 2 diabetes were assessed by cohort-stratified Cox proportional hazards models, individually and in combination. Results: In 488,166 person-years of follow-up, 5487 incident type 2 diabetes cases were observed. The association between PA and type 2 diabetes was modified by PM2.5. Compared with the lowest quartile of PA volume, the highest quartile was associated with reduced type 2 diabetes risk in low PM2.5 stratification (≤65.02 µg/m3) other than in high PM2.5 stratification (>65.02 µg/m3), with the hazard ratio (HR) of 0.75 (95% confidence interval [CI]: 0.66-0.85) and 1.10 (95% CI: 0.99-1.22), respectively. Similar results were observed for PA intensity. High PM2.5 exposure combined with the highest PA levels increased the risk of type 2 diabetes the most (HR = 1.79, 95% CI: 1.59-2.01 for PA volume; HR = 1.82, 95% CI: 1.64-2.02 for PA intensity). Conclusion: PA could reduce type 2 diabetes risk in low-pollution areas, but high PM2.5 exposure may weaken or even reverse the protective effects of PA. Safety and health benefits of PA should be thoroughly assessed for long-term polluted residents.

Dietary intake changes the associations between long-term exposure to fine particulate matter and the surrogate indicators of insulin resistance.

The relationship of fine particulate matter (PM2.5) exposure and insulin resistance remains inclusive. Our study aimed to investigate this association in the project of Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR). Specifically, we examined the associations between long-term PM2.5 exposure and three surrogate indicators of insulin resistance: the triglyceride-glucose index (TyG), TyG with waist circumference (TyG-WC) and metabolic score for insulin resistance (METS-IR). Additionally, we explored potential effect modification of dietary intake and components. Generalized estimating equations were used to evaluate the associations between PM2.5 and the indicators with an unbalanced repeated measurement design. Our analysis incorporated a total of 162,060 observations from 99,329 participants. Each 10 µg/m3 increment of PM2.5 was associated with an increase of 0.22 % [95 % confidence interval (CI): 0.20 %, 0.25 %], 1.60 % (95 % CI: 1.53 %, 1.67 %), and 2.05 % (95 % CI: 1.96 %, 2.14 %) in TyG, TyG-WC, and METS-IR, respectively. These associations were attenuated among participants with a healthy diet, particularly those with sufficient intake of fruit and vegetable, fish or tea (pinteraction < 0.0028). For instance, among participants with a healthy diet, TyG increased by 0.11 % (95 % CI: 0.08 %, 0.15 %) per 10 µg/m3 PM2.5 increment, significantly lower than the association observed in those with an unhealthy diet. The findings of this study emphasize the potential of a healthy diet to mitigate these associations, highlighting the urgency for improving air quality and implementing dietary interventions among susceptible populations in China.

Urinary Albumin-to-Creatinine Ratio in Normal Range, Cardiovascular Health, and All-Cause Mortality.

Importance: Although cumulative evidence suggests that elevated urinary albumin-to-creatinine ratio (UACR) in the normal range (<30 mg/g) may be associated with an increased risk of mortality, few studies have investigated whether cardiovascular health (CVH) modifies the harmful outcomes of high-normal UACR. Objective: To investigate associations of traditionally normal UACR and CVH with all-cause mortality. Design, Setting, and Participants: This cohort study used National Health and Nutrition Examination Survey data from 2005 through 2018 and linked mortality information until 2019. Data were analyzed from March 1 through October 31, 2023. The study included adult participants aged 20 to 79 years with a normal UACR (<30 mg/g) based on Kidney Disease: Improving Global Outcomes criteria. Exposures: The UACR was treated as a continuous variable and categorized into tertiles delineated as low (<4.67 mg/g), medium (4.67-7.67 mg/g), and high (7.68 to <30 mg/g). Cardiovascular health was assessed using Life's Essential 8 scores and grouped as poor (0-49 points), moderate (50-79 points), and ideal (80-100 points). Main Outcomes and Measures: Multivariable Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs for associations of UACR with all-cause mortality in total participants and as stratified by CVH groups. Results: The study included 23 697 participants (mean [SD] age, 45.58 [15.44] years; 11 806 women [49.7%] and 11 891 men [50.3%]). During the median 7.8 years (range, 4.5-11.1 years) of follow-up, 1403 deaths were recorded. Near-linear associations were observed for continuous UACR and CVH with all-cause mortality. Compared with the low UACR group, high UACR in the normal range showed an increased mortality risk in the moderate and poor CVH groups (CVH [50-79]: HR, 1.54 [95% CI, 1.26-1.89]; CVH [0-49]: HR, 1.56 [95% CI, 1.10-2.20]), with a significant multiplicative interaction of UACR and CVH (P < .001). Conclusions and Relevance: The findings suggest that high UACR within the normal range is associated with a significantly increased risk of all-cause mortality, with the association more pronounced in adults with poor CVH status. These findings highlight the importance of risk management for early kidney dysfunction, particularly among individuals with poor CVH.

Atmospheric elemental carbon pollution and its regional health disparities in China.

Previous studies have reported that atmospheric elemental carbon (EC) may pose potentially elevated toxicity when compared to total ambient fine particulate matter (PM2.5). However, most research on EC has been conducted in the US and Europe, whereas China experiences significantly higher EC pollution levels. Investigating the health impact of EC exposure in China presents considerable challenges due to the absence of a monitoring network to document long-term EC levels. Despite extensive studies on total PM2.5 in China over the past decade and a significant decrease in its concentration, changes in EC levels and the associated mortality burden remain largely unknown. In our study, we employed a combination of satellite remote sensing, available ground observations, machine learning techniques, and atmospheric big data to predict ground EC concentrations across China for the period 2005-2018, achieving a spatial resolution of 10 km. Our findings reveal that the national average annual mean EC concentration has remained relatively stable since 2005, even as total PM2.5 levels have substantially decreased. Furthermore, we calculated the all-cause non-accidental deaths attributed to long-term EC exposure in China using baseline mortality data and pooled mortality risk from a cohort study. This analysis unveiled significant regional disparities in the mortality burden resulting from long-term EC exposure in China. These variations can be attributed to varying levels of effectiveness in EC regulations across different regions. Specifically, our study highlights that these regulations have been effective in mitigating EC-related health risks in first-tier cities. However, in regions characterized by a high concentration of coal-power plants and industrial facilities, additional efforts are necessary to control emissions. This observation underscores the importance of tailoring environmental policies and interventions to address the specific challenges posed by varying emission sources and regional contexts.

Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China.

Ambient PM2.5 pollution is recognized as a leading environmental risk factor, causing significant mortality and morbidity in China. However, the specific contributions of individual PM2.5 constituents remain unclear, primarily due to the lack of a comprehensive ground monitoring network for constituents. This issue is particularly critical for carbonaceous species such as organic carbon (OC) and elemental carbon (EC), which are known for their significant health impacts, and understanding the OC/EC ratio is crucial for identifying pollution sources. To address this, we developed a Super Learner model integrating Multi-angle Imaging SpectroRadiometer (MISR) retrievals to predict daily OC concentrations across China from 2003 to 2019 at a 10-km spatial resolution. Our model demonstrates robust predictive accuracy, as evidenced by a random cross-validation R2 of 0.84 and an RMSE of 4.9 µg/m3, at the daily level. Although MISR is a polar-orbiting instrument, its fractional aerosol data make a significant contribution to the OC exposure model. We then use the model to explore the spatiotemporal distributions of OC and further calculate the EC/OC ratio in China. We compared regional pollution discrepancies and source contributions of carbonaceous pollution over three selected regions: Beijing-Tianjin-Hebei, Fenwei Plain, and Yunnan Province. Our model observes that OC levels are elevated in Northern China due to industrial operations and central heating during the heating season, while in Yunnan, OC pollution is mainly contributed by local forest fires during fire seasons. Additionally, we found that OC pollution in China is likely influenced by climate phenomena such as the El Niño-Southern Oscillation. Considering that climate change is increasing the severity of OC concentrations with more frequent fire events, and its influence on OC formation and dispersion, we suggest emphasizing the role of climate change in future OC pollution control policies. We believe this study will contribute to future epidemiological studies on OC, aiding in refining public health guidelines and enhancing air quality management in China.