Impact of lifetime air pollution exposure patterns on the risk of chronic disease

Probabilistic risk assessment and scenario analysis of ambient PM2.5 in Bangkok for short-term respiratory and cardiovascular diseases

This study presents a four-year analysis of cardiopulmonary hospital admissions related to PM2.5 exposure in Bangkok to assess the short-term effects of air pollution on health. The Monte Carlo simulation-based AirQ+ model was employed to estimate hospital admissions attributable to various PM2.5 concentrations. The average PM2.5 concentration was 40 ± 17 µg/m3. The monthly median contribution of PM2.5 to total particulate matter pollution ranged from 0.40 to 0.60. Individuals were exposed to PM2.5 levels classified as "unhealthy for sensitive groups" (36-56 µg/m3) or "unhealthy for all" (57-150 µg/m3) on approximately 50% days annually). Cardiopulmonary admissions peaked during the winter, with 5,755 to 7,000 respiratory cases and approximately 7,000 cardiovascular cases, while both conditions were least prevalent in the summer (respiratory: 4,000; cardiovascular: 5,300). The PM2.5 concentrations mirrored this seasonal pattern, reaching approximately 50 µg/m3 in winter and decreasing to approximately 25 µg/m3 in summer. The AirQ+ simulation estimated that PM2.5 exposure exceeding 15 µg/m3 was associated with 3,306 (95% CI: 0 -15,841) additional respiratory cases and 1,497 (95% CI: 701-6,723) additional cardiovascular cases. Conversely, a 5 µg/m3 reduction in PM2.5 levels could lead to a 22% decrease in hospital admissions for cardiopulmonary diseases, whereas a 5 µg/m3 increase could result in a 16% increase in hospitalizations.

Leveraging Subjective Parameters and Biomarkers in Machine Learning Models: The Feasibility of lnc-IL7R for Managing Emphysema Progression

Background/Objectives: Chronic obstructive pulmonary disease (COPD) remains a leading cause of death worldwide, with emphysema progression providing valuable insights into disease development. Clinical assessment approaches, including pulmonary function tests and high-resolution computed tomography, are limited by accessibility constraints and radiation exposure. This study, therefore, proposed an alternative approach by integrating the novel biomarker long non-coding interleukin-7 receptor α-subunit gene (lnc-Il7R), along with other easily accessible clinical and biochemical metrics, into machine learning (ML) models. Methods: This cohort study collected baseline characteristics, COPD Assessment Test (CAT) scores, and biochemical details from the enrolled participants. Associations with emphysema severity, defined by a low attenuation area percentage (LAA%) threshold of 15%, were evaluated using simple and multivariate-adjusted models. The dataset was then split into training and validation (80%) and test (20%) subsets. Five ML models were employed, with the best-performing model being further analyzed for feature importance. Results: The majority of participants were elderly males. Compared to the LAA% <15% group, the LAA% ≥15% group demonstrated a significantly higher body mass index (BMI), poor pulmonary function, and lower expression levels of lnc-Il7R (all p < 0.01). Fold changes in lnc-IL7R were strongly and negatively associated with LAA% (p < 0.01). The random forest (RF) model achieved the highest accuracy and area under the receiver operating characteristic curve (AUROC) across datasets. A feature importance analysis identified lnc-IL7R fold changes as the strongest predictor for emphysema classification (LAA% ≥15%), followed by CAT scores and BMI. Conclusions: Machine learning models incorporated accessible clinical and biochemical markers, particularly the novel biomarker lnc-IL7R, achieving classification accuracy and AUROC exceeding 75% in emphysema assessments. These findings offer promising opportunities for improving emphysema classification and COPD management.

The impact of air pollution on sperm DNA methylation

A number of environmental factors have been shown to impact the sperm epigenome. Air pollution is one of the largest health and environmental hazards in the world today and has been implicated in many modern diseases. Recently, air pollution has been shown to alter methylation signatures in some body tissues, indicating that air pollution may also affect the sperm epigenome. The present experiment was conducted to analyze how seasonal air pollution in the Salt Lake Valley may impact DNA methylation patterns in sperm and to establish a relationship between air pollution and sperm epigenetic health as measured by DNA methylation. Sperm DNA methylation patterns were assessed in 74 individuals, who presented at the University of Utah Andrology Clinic for semen analysis, using the Illumina Human MethylationEPIC BeadChip array. Each semen sample collected, as per the fifth edition of WHO reference values for human semen characterization, was deemed normal. Two sample groups from the Salt Lake Valley, Urban Winter (UW, n = 20), Urban Summer (US, n = 21), and two sample groups east of the Wasatch mountains, Rural Winter (RW, n = 19) and Rural Summer (RS, n = 14), were compared to assess the effect of air pollution on sperm DNA methylation patterns. Due to seasonal inversions, urban winters are characterized by increased air pollution compared to summer months. Therefore, the UW sample group was designated as treatment and the three remaining groups (US, RW, RS) were designated as control. We conducted multiple differential methylation analyses using a sliding window approach which utilized the USeq software package. A sliding window analysis of UW versus US was conducted first, followed by a confirmatory analysis comparing UW versus RW and RS. Outputs from the USeq analysis were assessed using several tools including the Stanford GREAT analysis and an analysis of methylation instability at key promoter regions in sperm. The sliding window analysis identified six differentially methylated regions (DMRs) between the UW and US groups (Wilcoxon FDR ≥ 40, corresponding p-value of ∼0.0001). Three of these six regions were confirmed with the second confirmatory analysis of UW versus RS/RW (Wilcoxon FDR ≥ 20, p-value<0.01). According to a GREAT analysis, each of the identified regions exhibited multiple gene ontology associations. Air pollution subtly alters DNA methylation in sperm, indicating that certain regions of the sperm epigenome may be susceptible to air pollution-induced modification with possible implications for reproductive and offspring health.

Association of exposure to air pollutants and risk of mortality among people living with HIV: a systematic review

BACKGROUND

People living with HIV (PLWH) are more vulnerable to infectious and non-infectious comorbidities due to chronic inflammation and immune dysfunction. Air pollution is a major global health risk, contributing to millions of deaths annually, primarily from cardiovascular and respiratory diseases. However, the link between air pollution and mortality risk in PLWH is underexplored. This systematic review assesses the association between exposure to pollutants such as particulate matter (PM), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO) and mortality risk in PLWH.

METHODS

A systematic search of PubMed, Web of Science, and Embase was conducted for studies published up to August 2024. Eligibility criteria included cohort, case-control, and cross-sectional studies assessing air pollution exposure and mortality in PLWH. Nested-Knowledge software was used for screening and data extraction. The Newcastle-Ottawa Scale was applied for quality assessment. A narrative approach and tabular summarization were used for data synthesis and presentation.

RESULTS

Nine studies, mostly from China, demonstrated a significant association between long-term exposure to PM1, PM2.5, and PM10 and increased risks of AIDS-related and all-cause mortality in PLWH. Hazard ratios for mortality increased by 2.38-5.13% per unit increase in PM concentrations, with older adults (> 60), females, and those with lower CD4 counts (< 500 cells/µL) being more vulnerable. Short-term exposure to ozone and sulfur dioxide also increased mortality risks, particularly during the warm season and in older populations. Specific pollutants like ammonium (NH4⁺) and sulfate (SO4²⁻) had the strongest links to elevated mortality.

CONCLUSION

Air pollution, especially fine particulate matter and ozone, is associated with a higher risk of mortality in PLWH. Targeted interventions to reduce pollution exposure in vulnerable subgroups are crucial. Further research is needed to confirm these findings in diverse regions and develop effective mitigation strategies.

Predicting Survival Status in COVID-19 Patients: Machine Learning Models Development with Ventilator-Related and Biochemical Parameters from Early Stages: A Pilot Study

Objective: Coronavirus disease 2019 (COVID-19) can cause intubation and ventilatory support due to respiratory failure, and extubation failure increases mortality risk. This study, therefore, aimed to explore the feasibility of using specific biochemical and ventilator parameters to predict survival status among COVID-19 patients by using machine learning. Methods: This study included COVID-19 patients from Taipei Medical University-affiliated hospitals from May 2021 to May 2022. Sequential data on specific biochemical and ventilator parameters from days 0-2, 3-5, and 6-7 were analyzed to explore differences between the surviving (successfully weaned off the ventilator) and non-surviving groups. These data were further used to establish separate survival prediction models using random forest (RF). Results: The surviving group exhibited significantly lower mean C-reactive protein (CRP) levels and mean potential of hydrogen ions levels (pH) levels on days 0-2 compared to the non-surviving group (CRP: non-surviving group: 13.16 ± 5.15 ng/mL, surviving group: 10.23 ± 5.15 ng/mL; pH: non-surviving group: 7.32 ± 0.07, survival group: 7.37 ± 0.07). Regarding the survival prediction performanace, the RF model trained solely with data from days 0-2 outperformed models trained with data from days 3-5 and 6-7. Subsequently, CRP, the partial pressure of carbon dioxide in arterial blood (PaCO2), pH, and the arterial oxygen partial pressure to fractional inspired oxygen (P/F) ratio served as primary indicators in survival prediction in the day 0-2 model. Conclusions: The present developed models confirmed that early biochemical and ventilatory parameters-specifically, CRP levels, pH, PaCO2, and P/F ratio-were key predictors of survival for COVID-19 patients. Assessed during the initial two days, these indicators effectively predicted the likelihood of successful weaning of from ventilators, emphasizing their importance in early management and improved outcomes in COVID-19-related respiratory failure.

Exploring the association between knee osteoarthritis outpatient visits and Asian dust storms: a time-series analysis

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases in Taiwan, posing a significant public health challenge. In recent years, outdoor air pollution has become an increasingly critical global health issue. Asian Dust Storms (ADS) are known to exacerbate various health conditions due to elevated levels of particulate matter and other pollutants. However, the relationship between ADS and knee OA remains insufficiently explored. This study investigates the association between ADS occurrences and knee OA outpatient visits from January 2006 to December 2012, aiming to understand the potential health impacts of dust storms on OA patients. Using data from the National Health Insurance Research Database (NHIRD), the Taiwan Environmental Protection Agency (TEPA), and the Taiwan Central Weather Bureau, we conducted a time-series analysis employing the autoregressive moving average with exogenous variables (ARMAX) model. This approach accounted for daily outpatient visits related to knee OA, ADS events, and various environmental and meteorological factors. The results revealed a significant increase in knee OA outpatient visits on days immediately following ADS events, with peaks observed one to two days after the event. This increase was most pronounced among females, individuals aged 61 and above, and residents in the western regions. The study demonstrates an association between ADS and increased knee OA outpatient visits, highlighting the need for public health strategies to mitigate the health impacts of dust storms.

Association between combined exposure to ambient air pollutants, genetic risk, and incident gout risk: A prospective cohort study in the UK Biobank

BACKGROUND

Limited research has been conducted on the association between long-term exposure to air pollutants and the incidence of gout.

OBJECTIVES

This study aims to assess the individual and combined effects of prolonged exposure to five air pollutants (NO2, NOx, PM10, PMcoarse and PM2.52) on the incidence of gout among 458,884 initially gout-free participants enrolled in the UK Biobank.

METHODS

Employing a land use regression model, we utilized an estimation method to ascertain the annual concentrations of the five air pollutants. Subsequently, we devised a weighted air pollution score to facilitate a comprehensive evaluation of exposure. The Cox proportional hazards model was utilized to investigate the association between ambient air pollution and gout risk. Interaction and stratification analyses were conducted to evaluate age, sex, BMI, and genetic predisposition as potential effect modifiers in the air pollution-gout relationship. Furthermore, mediation analyses were conducted to explore the potential involvement of biomarkers in mediating the association between air pollution and gout.

RESULTS

Over a median follow-up time of 12.0 years, 7,927 cases of gout were diagnosed. Significant associations were observed between the risk of gout and a per IQR increase in NO2 (HR3: 1.05, 95 % CI4: 1.02-1.08, p = 0.003), NOx (HR: 1.04, 95 % CI: 1.01-1.06, p = 0.003), and PM2.5 (HR: 1.03, 95 % CI: 1.00-1.06, p = 0.030). Per IQR increase in the air pollution score was associated with an elevated risk of gout (p = 0.005). Stratified analysis revealed a significant correlation between the air pollution score and gout risk in participants ≥60 years (HR: 1.05, 95 % CI: 1.02-1.09, p = 0.005), but not in those <60 years (p = 0.793), indicating a significant interaction effect with age (p-interaction=0.009). Mediation analyses identified five serum biomarkers (SUA:15.87 %, VITD: 5.04 %, LDLD: 3.34 %, GGT: 1.90 %, AST: 1.56 %5) with potential mediation effects on this association.

CONCLUSIONS

Long-term exposure to air pollutants, particularly among the elderly population, is associated with an increased risk of gout. The underlying mechanisms of these associations may involve the participation of five serum biomarkers.

Cadmium exposure and cardiovascular disease risk: A systematic review and dose-response meta-analysis

Exposure to toxic metals is a global public health threat. Among other adverse effects, exposure to the heavy metal cadmium has been associated with greater risk of cardiovascular disease (CVD). Nonetheless, the shape of the association between cadmium exposure and CVD risk is not clear. This systematic review summarizes data on the association between cadmium exposure and risk of CVD using a dose-response approach. We carried out a literature search in PubMed, Web of Science, and Embase from inception to December 30, 2023. Inclusion criteria were: studies on adult populations, assessment of cadmium exposure, risk of overall CVD and main CVD subgroups as endpoints, and observational study design (cohort, cross-sectional, or case-control). We retrieved 26 eligible studies published during 2005-2023, measuring cadmium exposure mainly in urine and whole blood. In a dose-response meta-analysis using the one-stage method within a random-effects model, we observed a positive association between cadmium exposure and risk of overall CVD. When using whole blood cadmium as a biomarker, the association with overall CVD risk was linear, yielding a risk ratio (RR) of 2.58 (95 % confidence interval-CI 1.78-3.74) at 1 μg/L. When using urinary cadmium as a biomarker, the association was linear until 0.5 μg/g creatinine (RR = 2.79, 95 % CI 1.26-6.16), after which risk plateaued. We found similar patterns of association of cadmium exposure with overall CVD mortality and risks of heart failure, coronary heart disease, and overall stroke, whereas for ischemic stroke there was a positive association with mortality only. Overall, our results suggest that cadmium exposure, whether measured in urine or whole blood, is associated with increased CVD risk, further highlighting the importance of reducing environmental pollution from this heavy metal.

Association between long-term exposure to low level air pollutants and incident end-stage kidney disease in the UK Biobank: A prospective cohort

BACKGROUND

Previous studies suggest that air pollution can increase the risk of incident chronic kidney disease (CKD). However, the association between end-stage kidney disease (ESKD) and co-exposure to relatively low-level air pollutants remains unclear.

METHODS

A prospective cohort was designed based on UK Biobank. From 1 January 2010 to 12 November 2021, 453,347 participants were followed up over a median of 11.87 years. Principal component analysis was used to identify major patterns of five air pollutants, including PM2.5, PM2.5-10, PM10, NO2, and NOx. Sub-distribution hazards models were used to estimate the associations between air pollution, individually or jointly, and incident ESKD, CKD, and all-cause death, respectively.

RESULTS

Principal component analysis identified two principal components, namely RC1 (PM2.5, NO2, and NOx) and RC2 (PM2.5-10 and PM10). An elevated risk of incident ESKD was associated with an interquartile range (IQR) increase in PM2.5 (hazard ratio: 1.11, 95% confidence interval: 1.02-1.22), NO2 (1.16, 1.04-1.30), NOx (1.08, 1.00-1.17), and RC1 (1.12, 1.02-1.23). An elevated risk of incident CKD was associated with an IQR increase in PM2.5 (1.05, 1.03-1.07), NO2 (1.04, 1.02-1.06), NOx (1.03, 1.02-1.05), and RC1 (1.04, 1.02-1.06). An increased risk of all-cause mortality was associated with an IQR increase in PM2.5 (1.02, 1.00-1.04). Restricted cubic spline analyses indicated a monotonic elevating association of PM2.5, NO2, NOx, and RC1 with ESKD incidence.

CONCLUSIONS

Long-term exposure to PM2.5, NO2, NOx, and their complex was associated with elevated ESKD incidence, even at relatively lower levels of air pollution.

Association between living environmental quality and risk of arthritis in middle-aged and older adults: a national study in China

Background

The association between combined environmental factors and the risk of arthritis is still scarcely studied. The present study performed cross-sectional and cohort studies to explore the association between risk score of living environment quality and the risk of arthritis in middle-aged and older adults in China.

Methods

The study was based on China Health and Retirement Longitudinal Study (CHARLS), and it recruited 17,218 participants in the cross-sectional study and 11,242 participants in the seven-year follow-up study. The living environment quality was measured by household fuel types, household water sources, room temperature, residence types, and ambient concentration of PM2.5. Logistic regression and Cox proportional hazard regression models were utilized to examine the association between the living environment quality and the risk of arthritis. Competing risk models and stratified analyses were applied to further verify our results.

Results

Compared with individuals in the suitable environment group, people who lived in moderate (OR:1.28, 95%CI: 1.14-1.43) and unfavorable environments (OR:1.49, 95%CI:1.31-1.70) showed higher risks of arthritis when considering the multiple living environmental factors (P for trend <0.001) in the cross-sectional analysis. In the follow-up study, similar results (P for trend = 0.021), moderate environment group (HR:1.26, 95%CI:1.01-1.56) and unfavorable environment group (HR: 1.36, 95%CI: 1.07-1.74), were founded.

Conclusion

Inferior living environment might promote the development of arthritis. It is necessary for the public, especially old people, to improve the living environment, which may be the key to the primary prevention of arthritis.