The association between PM 2.5 components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models

PM is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develop an analytical methodology combining traditional and novel machine learning models to evaluate PM 's combined effects on blood pressure (BP) and identify the most toxic components. We measured late-pregnancy BP of 1138 women from the Heshan cohort while simultaneously analyzing 31 PM components. We utilized multiple linear regression modeling to establish the relationship between PM components and late-pregnancy BP and applied Random Forest (RF) and generalized Weighted Quantile Sum (gWQS) regression to identify the most toxic components contributing to elevated BP and to quantitatively evaluate the cumulative effect of the PM component mixtures. The results revealed that 16 PM components, such as EC, OC, Ti, Fe, Mn, Cu, Cd, Mg, K, Pb, Se, Na , K , Cl , NO , and F , contributed to elevated systolic blood pressure (SBP), while 26 components, including two carbon components (EC, OC), fourteen metallics (Ti, Fe, Mn, Cr, Mo, Co, Cu, Zn, Cd, Na, Mg, Al, K, Pb), one metalloid (Se), and nine water-soluble ions (Na , K , Mg , Ca , NH , Cl , NO , SO , F ), contributed to elevated diastolic blood pressure (DBP). Mn and Cr were the most toxic components for elevated SBP and DBP, respectively, as analyzed by RF and gWQS models and verified against each other. Exposure to PM component mixtures increased SBP by 1.04 mmHg (95% CI: 0.33-1.76) and DBP by 1.13 mmHg (95% CI: 0.47-1.78). Our study highlights the effectiveness of combining traditional and novel models as an analytical strategy to quantify the health effects of PM constituent mixtures.