Evaluating carbon content in airway macrophages as a biomarker of personal exposure to fine particulate matter and its acute respiratory effects

Application of artificial intelligence in quantifying lung deposition dose of black carbon in people with exposure to ambient combustion particles

BACKGROUND

Understanding lung deposition dose of black carbon is critical to fully reconcile epidemiological evidence of combustion particles induced health effects and inform the development of air quality metrics concerning black carbon. Macrophage carbon load (MaCL) is a novel cytology method that quantifies lung deposition dose of black carbon, however it has limited feasibility in large-scale epidemiological study due to the labor-intensive manual counting.

OBJECTIVE

To assess the association between MaCL and episodic elevation of combustion particles; to develop artificial intelligence based counting algorithm for MaCL assay.

METHODS

Sputum slides were collected during episodic elevation of ambient PM2.5 (n = 49, daily PM2.5 > 10 µg/m3 for over 2 weeks due to wildfire smoke intrusion in summer and local wood burning in winter) and low PM2.5 period (n = 39, 30-day average PM2.5 < 4 µg/m3) from the Lovelace Smokers cohort.

RESULTS

Over 98% individual carbon particles in macrophages had diameter <1 µm. MaCL levels scored manually were highly responsive to episodic elevation of ambient PM2.5 and also correlated with lung injury biomarker, plasma CC16. The association with CC16 became more robust when the assessment focused on macrophages with higher carbon load. A Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP) was developed based on the Mask Region-based Convolutional Neural Network. MacLEAP algorithm yielded excellent correlations with manual counting for number and area of the particles. The algorithm produced associations with ambient PM2.5 and plasma CC16 that were nearly identical in magnitude to those obtained through manual counting.

IMPACT STATEMENT

Understanding lung black carbon deposition is crucial for comprehending health effects of combustion particles. We developed "Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP)", the first artificial intelligence algorithm for quantifying airway macrophage black carbon. Our study bolstered the algorithm with more training images and its first use in air pollution epidemiology. We revealed macrophage carbon load as a sensitive biomarker for heightened ambient combustion particles due to wildfires and residential wood burning.

CC16 as an Inflammatory Biomarker in Induced Sputum Reflects Chronic Obstructive Pulmonary Disease (COPD) Severity

Purpose

The progression of an abnormal inflammatory response plays a crucial role in the lung function decline of chronic obstructive pulmonary disease (COPD) patients. Compared to serum biomarkers, inflammatory biomarkers in induced sputum would be a more reliable reflection of inflammatory processes in the airways.

Patients and Methods

A total of 102 COPD participants were divided into a mild-to-moderate group (FEV1%pred≥ 50%, n=57) and a severe-to-very-severe group (FEV1%pred<50%, n=45). We measured a series of inflammatory biomarkers in induced sputum and analyzed their association with lung function and SGRQ in COPD patients. To evaluate the relationship between inflammatory biomarkers and the inflammatory phenotype, we also analyzed the correlation between biomarkers and airway eosinophilic phenotype.

Results

We found increased mRNA levels of MMP9, LTB4R, and A1AR and decreased levels of CC16 mRNA in induced sputum in the severe-to-very-severe group. After adjustment for age, sex and other biomarkers, CC16 mRNA expression was positively associated with FEV1%pred (r=0.516, p=0.004) and negatively correlated with SGRQ scores (r=-0.3538, p=0.043). As previously known, decreased CC16 was related to the migration and aggregation of eosinophils in airway. It was also found that CC16 had a moderate negative correlation with the eosinophilic inflammation in airway (r=-0.363, p=0.045) in our COPD patients.

Conclusion

Low CC16 mRNA expression levels in induced sputum were associated with low FEV1%pred and a high SGRQ score in COPD patients. Sputum CC16 as a potential biomarker for predicting COPD severity in clinical practice might attribute to the involvement of CC16 in airway eosinophilic inflammation.

PyCoCa:A quantifying tool of carbon content in airway macrophage for assessment the internal dose of particles

Given the lack of a comprehensive understanding of the complex metabolism and variable exposure environment, carbon particles in macrophages have become a potentially valuable biomarker to assess the exposure level of atmospheric particles, such as black carbon. However, the tedious and subjective quantification method limits the application of carbon particles as a valid biomarker. Aiming to obtain an accurate carbon particles quantification method, the deep learning and binarization algorithm were implemented to develop a quantitative tool for carbon content in airway macrophage (CCAM), named PyCoCa. Two types of macrophages, normal and foamy appearance, were applied for the development of PyCoCa. In comparison with the traditional methods, PyCoCa significantly improves the identification efficiency for over 100 times. Consistency assessment with the gold standard revealed that PyCoCa exhibits outstanding prediction ability with the Interclass Correlation Coefficient (ICC) values of over 0.80. And a proper fresh dye will enhance the performance of PyCoCa (ICC = 0.89). Subsequent sensitivity analysis confirmed an excellent performance regarding accuracy and robustness of PyCoCa under high/low exposure environments (sensitivity > 0.80). Furthermore, a successful application of our quantitative tool in cohort studies indicates that carbon particles induce macrophage foaming and the foaming decrease the carbon particles internalization in reverse. Our present study provides a robust and efficient tool to accurately quantify the carbon particles loading in macrophage for exposure assessment.

Black carbon content in airway macrophages is associated with increased severe exacerbations and worse COPD morbidity in SPIROMICS

BACKGROUND

Airway macrophages (AM), crucial for the immune response in chronic obstructive pulmonary disease (COPD), are exposed to environmental particulate matter (PM), which they retain in their cytoplasm as black carbon (BC). However, whether AM BC accurately reflects environmental PM2.5 exposure, and can serve as a biomarker of COPD outcomes, is unknown.

METHODS

We analyzed induced sputum from participants at 7 of 12 sites SPIROMICS sites for AM BC content, which we related to exposures and to lung function and respiratory outcomes. Models were adjusted for batch (first vs. second), age, race (white vs. non-white), income (<$35,000, $35,000~$74,999, ≥$75,000, decline to answer), BMI, and use of long-acting beta-agonist/long-acting muscarinic antagonists, with sensitivity analysis performed with inclusion of urinary cotinine and lung function as covariates.

RESULTS

Of 324 participants, 143 were current smokers and 201 had spirometric-confirmed COPD. Modeled indoor fine (< 2.5 μm in aerodynamic diameter) particulate matter (PM2.5) and urinary cotinine were associated with higher AM BC. Other assessed indoor and ambient pollutant exposures were not associated with higher AM BC. Higher AM BC was associated with worse lung function and odds of severe exacerbation, as well as worse functional status, respiratory symptoms and quality of life.

CONCLUSION

Indoor PM2.5 and cigarette smoke exposure may lead to increased AM BC deposition. Black carbon content in AMs is associated with worse COPD morbidity in current and former smokers, which remained after sensitivity analysis adjusting for cigarette smoke burden. Airway macrophage BC, which may alter macrophage function, could serve as a predictor of experiencing worse respiratory symptoms and impaired lung function.