Ambient air pollution associated with incident asthma, subsequent cardiovascular disease and death: A trajectory analysis of a national cohort

No previous study has examined the impact of air pollution on the cardiovascular disease (CVD) trajectory, especially among asthmatic subjects. Based on the UK Biobank cohort, we retrieved 292,227 adults free of asthma and CVD aged 37-73 years at recruitment (2006-2010). Annual mean concentrations of particulate matter (PM10 and PM2.5) and nitrogen oxides (NO2 and NOx) were assessed at each individual's addresses. We used multi-state models to estimate the associations of air pollution with the trajectory from healthy to incident asthma, subsequent CVD, and death. During a median follow-up of 11.7 years, a total of 6338 (2.2%) participants developed asthma, among which, 638 (10.1%) subsequently proceeded to CVD. We observed significant impacts of various air pollutants on the CVD dynamic transitions, with a more substantial effect of particulate matter pollutants than gaseous air pollutants. For example, the hazard ratios (95% confidence intervals) for per interquartile range increase in PM2.5 and PM10 were 1.28 (1.13, 1.44) and 1.27 (1.13, 1.43) for transitions from incident asthma to subsequent CVD. In conclusion, long-term air pollution exposure could affect the CVD trajectory. Distinguishing the effect of air pollutants on CVD transition stages has great significance for CVD health management and clinical prevention, especially among asthma patients.

Ambient air pollution associated with incidence and progression trajectory of cardiometabolic diseases: A multi-state analysis of a prospective cohort

BACKGROUND

Previous studies on the association between ambient air pollution and cardiometabolic diseases (CMDs) focused on a single disease, without considering cardiometabolic multimorbidity (CMM) and the progression trajectory of CMDs.

METHODS

Based on the UK Biobank cohort, we included 372,530 participants aged 37-73 years at baseline (2006-2010) with follow-up until September 2021. Incident CMDs cases were identified based on self-reported information and multiple health-related records in the UK Biobank. CMM was defined as the occurrence of at least two CMDs, including ischemic heart disease (IHD), stroke and type 2 diabetes (T2D). Exposure to ambient air pollutants, including particulate matter (PM) with aerodynamic diameter ≤2.5 μm (PM_2.5), ≤10 μm (PM₁₀), nitrogen dioxide (NO₂), and nitrogen oxides (NO_x) were estimated at participants' geocoded residential addresses based on the high-resolution (1 × 1 km) pollution data from 2001 to 2021 provided by UK Department for Environment, Food and Rural Affairs. Multi-state models with adjustment for potential confounders were used to examine the impact of long-term exposure to ambient air pollution on transitions from healthy to first CMD (FCMD), subsequently to CMM, and further to death.

RESULTS

During a median follow-up of 12.6 years, 40,112 participants developed at least one CMD, 3896 developed CMM, and 21,739 died. Among the four pollutants, PM_2.5 showed the strongest associations with all transitions from healthy to FCMD, to CMM, and then to death [hazard ratios (95 % confidence intervals) per interquartile range (IQR) increment: 1.62 (1.60, 1.64) and 1.68 (1.61, 1.76) for transitions from healthy to FCMD and from FCMD to CMM, and 1.62 (1.59, 1.66), 1.67 (1.61, 1.73), and 1.52 (1.38, 1.67) for death risk from healthy, FCMD, and CMM, respectively]. After dividing FCMDs into three specific CMDs, we found that ambient air pollution had differential impacts on disease-specific transitions within the same transition phase.

CONCLUSIONS

Our findings indicate that there is potential for air pollution mitigation in contributing to the prevention of the development and progression of CMDs.

A GAN based approach for inferring progression trajectories of costal cartilage calcification from cross-sectional data at image level

BACKGROUND

Costal cartilage calcification (CCC) increases with age and presents differently for men and women. In individuals, however, the cross-sectional studies that show such trends do not reveal the geometric trajectories through which calcification might accumulate across a lifetime. Generative adversarial networks have the potential to reveal such trajectories from cross-sectional data by learning population trends and synthesizing individualized images at progressive levels of calcification.

METHODS

Chest wall mid-surface CT images with normalized cartilage morphologies were produced for 379 subjects aged 6 to 90, and labeled by sex and calcification severity. A conditional GAN with added loss terms to favor one-way accumulation of CCC was trained using organized image batches. GAN performance was assessed by comparing the distributions of images between the training and synthetic groups.

RESULTS

Synthetic images generated from a common seed for a given sex and at successive calcification severity levels showed incremental and regional growth of calcification sites. CCC patterns for synthetic male and female images matched known sex-based differences, and individual CCC growth in synthetic images was consistent with previously observed population trends. These trends in the synthetic images were also quantified by structural similarity scores. Synthetic images generated from different input seeds further showed individual variance in specific regions and trajectories of CCC accumulation.

CONCLUSION

This study inferred individual progression of CCC accumulation from uncalcified to severely calcified using cross-sectional image data. This information can inform computational models of the changing chest wall biomechanics with age, and the GAN-based technique shows potential for inferring longitudinal data from population trends in other clinical areas.