Plasma proteome mediates the associations between air pollution exposure and disease risk

Domain: neuroscience
Published: 2026-01-31
Editorial outcome: major_revision

Abstract
Air pollution has been increasingly linked to a wider range of diseases, yet the underlying biological mechanisms remain poorly understood. Here, we systematically investigate how circulating proteins mediate the health effects of multiple air pollutants and quantify individual susceptibility. Using large-scale data from the UK Biobank, we identify 30 diseases significantly associated with air pollution exposure. We uncover 1,089 pollutant-associated proteins enriched in immune pathways, among which 296 significantly mediate disease risk. Notably, the mediating pathways of air pollution vary by disease types, involving MAPK signaling in cardiovascular diseases, innate immunity in immune disorders, carbohydrate metabolism in metabolic diseases, and cell proliferation in respiratory conditions. Globally, the proteome mediates 23.69% of the association between air pollution and diseases. Based on these mediators, we develop an Air Pollution Protein Risk Score (APPRS), which shows robust associations with disease risk in the UKBB and two external validation cohorts. APPRS also improves disease prediction when integrated into baseline models. Collectively, our study highlights the central role of circulating proteins in mediating the health impacts of air pollution and introduces APPRS as a tool for personalized risk assessment and precision public health interventions.

Introduction
Air pollution, including particulate matter (PM2.5, PM10) and nitrogen oxides (NO2, NOx), is widely recognized as an environmental risk factor for multiple diseases, linked to respiratory, cardiovascular, immune, and metabolic health1–21. Epidemiological studies have extensively studied the associations between air pollutants and adverse health effects over the past decades, providing evidence of increased morbidity and mortality through various physiological pathways22–24.

However, despite significant evidence linking air pollution to numerous health outcomes25,26, the underlying molecular mechanisms that mediate the effect of air pollution on human health remain largely unexplored27.

Previous research has identified inflammation, oxidative stress, immune dysregulation, epigenetic alterations, and endocrine disruption as shared pathogenic pathways in pollution-related diseases28. To further investigate the molecular mechanisms underlying these processes, recent studies have begun integrating exposure data with proteomics profiles in narrow contexts29,30. For example, controlled exposure experiments have demonstrated dose-dependent changes in cardiovascular-related proteins29, and cohort studies in young adults have shown that ambient air pollution significantly perturbs the cardiometabolic proteome30. However, these studies focus solely on molecular responses to air pollution without linking them to clinical outcomes, leaving the mediating role of proteomic signatures in pollution-related disease development still unclear.

With the release of UKBB proteomic data, plasma proteins have been increasingly linked to various complex diseases31–33. Meanwhile, emerging evidence suggests that inflammation-related proteins may mediate the effect of air pollution on health34–37. For instance, a recent study has investigated the mediating role of proteins in the association between air pollution and depression38. However, the existing studies have been limited to a few specific proteins or a single disease outcome. A systematic investigation is needed to identify the proteomic signatures that underlie the health impacts of air pollution and their potential role in mediating individual disease susceptibility.

The effect of air pollution on populations varies, with significant heterogeneity in individual susceptibility39. Previous studies have highlighted these differences, showing that some individuals are more vulnerable to developing diseases than others40–42. While one individual has a unique proteomic profile, proteomic mediation analysis offers a promising approach to understanding the individual variability associated with environmental exposures43,44. However, despite the potential of leveraging proteomics to better understand the impact of air pollution on diseases, current research has yet to integrate large-scale cohort data and omics approaches to quantify this relationship.

In this work, we address these gaps by assessing the role of proteins in mediating the effect of air pollution on diseases. Leveraging data from UKBB, we systematically investigated the associations between air pollutants, proteomic profiles, and a wide range of diseases. Based on the findings of mediation analysis, we constructed an index named APPRS to quantify individual susceptibility to the health effects of air pollution. Together, our study not only elucidates proteomic pathways that mediate air pollution’s impact on human health but also introduces a quantification that may facilitate precision environmental health strategies and personalized disease prevention.

Results
Study design
We conducted our analysis using data from the UK Biobank (UKBB), a large-scale prospective cohort that recruited 506,319 participants between April 4, 2006, and October 1, 2010 (Fig. 1A). For each participant, long-term air pollution exposures were estimated at baseline (2005–2010) based on residential address, and disease outcomes were prospectively ascertained through linkage to health records. Incident cases were defined as diagnoses occurring after 2010, which ensured that exposure assessment preceded disease onset in all analyses. Data on four air pollutants (PM2.5, PM10, NO2, and NOx) and 36 disease outcomes were collected and processed (Supplementary Data 1, 2). First, a systematic association analysis between air pollutants and 36 diseases was carried out on 502,131 participants who had available data on air pollution and disease but lacked proteomic measurements. Then, in the following analysis, we systematically excluded individuals without proteomic data, resulting in a study population of 49,242 participants with available data on air pollutants, 1,463 circulating proteins, and 36 disease outcomes.

As illustrated in Supplementary Fig. 1, we assessed the paired associations within air pollution (PM2.5, PM10, NO2, NOx, and an aggregated air pollution index), proteins, and diseases. To gain biological insights, we performed functional enrichment analyses for the identified proteins. Next, to further elucidate the role of proteins in linking air pollution to disease risk, we performed a mediation analysis in 49,242 participants, identifying key proteins that significantly mediated the effect of air pollution on disease development. Besides, to quantify individual susceptibility to air pollution, we developed an Air Pollution Protein Risk Score (APPRS) based on identified mediating proteins and incorporated it into disease prediction models. Finally, to assess the robustness and generalizability of the APPRS, we validated it in two independent external cohorts, demonstrating its predictive performance across diverse populations.

Overview of the impact of air pollutants on diseases
While some of the diseases have been previously studied to be associated with air pollution in UKBB (Supplementary Fig. 2A, B; Supplementary Data 3), the effect of air pollution on 20 of the diseases have not been reported in the UKBB cohort. Therefore, we first systematically assessed the effect of air pollution on various diseases using Cox models. Our findings reveal that mental health disorders (schizophrenia), immune diseases (multiple sclerosis, peripheral artery disease, and systemic lupus erythematosus), cardiovascular diseases (hypertension and anemia), skin diseases, and metabolic disorders (obesity) are affected by different air pollutants (Supplementary Fig. 2C). Besides, we found that distinct diseases were primarily associated with different air pollutants. PM pollutants were predominantly linked to immune diseases, consistent with their ability to induce systemic inflammation and oxidative stress, while NO2 and NOx had greater impacts on metabolic diseases and neuron diseases. In contrast, lung diseases and cardiovascular diseases exhibited relatively balanced associations across all pollutants, suggesting potential synergistic interactions across the effects (Supplementary Fig. 3).

Since individuals are often exposed to multiple pollutants simultaneously, we proposed the concept of an air pollution index (AP index) to systematically evaluate the degree of air pollution exposure by integrating multiple pollutants. We observed a significant consistency between our AP index and the Living Environment Score proposed by the UKBB, which reflects the quality of an individual’s immediate surroundings both indoors and outdoors (Rcor = 0.524; Supplementary Fig. 4). Next, we examined the effect of the AP index on diseases and found that 29 diseases were influenced by AP index (Fig. 1B). This suggests that the AP index allows for a more comprehensive understanding of the relationship between air pollution and diseases. Among these associations, we found that air pollution had the strongest impact on pulmonary and immune diseases, while its association with cancer was relatively weaker (Fig. 1C). Besides, we observed discrepancies in statistical significance between individual pollutants and the aggregated AP index across several disease outcomes, potentially due to the integration of heterogeneous exposure signals or differences in statistical power (Supplementary Texts and Supplementary Fig. 5).

Effects of air pollutants on circulating proteins
Of all participants in UKBB, 49,242 individuals (9.8%) had valid proteomic data, which were used to investigate the correlation between air pollutants and the proteome. We performed a proteome-wide association study (PWAS) analysis for each type of air pollutant and identified a total of 852 proteins significantly influenced by air pollution (Fig. 2A and Supplementary Fig. 6; Supplementary Data 4). Additionally, 770 proteins were identified to be associated with the AP index, among which 237 were not captured by the PWAS of individual pollutants (Fig. 2B; Supplementary Data 5). Moreover, we found that while a small subset of proteins was associated with only a single air pollutant—30.66% for PM2.5, 36.99% for PM10, 33.05% for NO2, and 7.05% for NOx—the majority exhibited with multiple pollutants (Fig. 2C).

The proteins associated with air pollution were primarily enriched in functions related to immune processes and stimuli responses, including cytokine activity, cytokine receptor binding, chemotaxis, and leukocyte migration (Fig. 2D). The pathway enrichment analyses in KEGG45 and Reactome46 revealed consistent findings with the functional terms identified in GO (Fig. 2E-F; Supplementary Data 6). Notably, the enrichment observed with the AP index was more significant than that observed for individual pollutants, highlighting the broader impact of cumulative air pollution exposure. Moreover, the specific proteins associated with each individual air pollutant showed distinct enrichment patterns (Supplementary Fig. 7). For example, PM-specific proteins were specifically associated with the MAPK cascade and cell adhesion (P = 6.5e−12 and P = 1.9e−11; Supplementary Fig. 7A, B), while NO-specific proteins were primarily related to nervous system functions, such as the ensheathment of neurons and negative regulation of nervous system processes (P = 5.9e−05 and P = 5.9e−05; Supplementary Fig. 7C, D). Common proteins shared across the different pollutants were predominantly associated with immune processes and cell chemotaxis (Supplementary Fig. 7E). In general, although the proteins commonly associated across pollutants are primarily linked to immune functions, different air pollutants can specifically impact distinct biological pathways (Supplementary Fig. 7F).

Proteome mediates the association between air pollution and diseases
To investigate the role of the proteome in mediating the effects of air pollutants on disease risk, we calculated the associations between proteins and diseases using Cox models, selecting proteins that are significantly causal for each disease (Bonferroni-corrected P < 0.05; Supplementary Texts, Supplementary Figs. 8–10, and Supplementary Data 7). By intersecting these proteins with those associated with air pollutants identified in our study, we obtained 479 proteins that were simultaneously associated with both air pollution exposure and corresponding diseases. Then, we performed mediation analysis to assess whether the proteins serve as mediators between air pollutants and diseases (Fig. 3A; Supplementary Data 8). Among the 36 diseases examined, we found 23 diseases were significantly influenced by air pollution through proteomic mediation (Supplementary Fig. 11), of which 12 were influenced by AP index (Fig. 3B). For example, six proteins, including NOS3, TGFB1, IDUA, KYNU, THY1, and FGF21, mediated the association between AP index and ischaemic heart disease (IHD). 5 of the 6 proteins (NOS3, TGFB1, THY1, FGF21, and KYNU) were involved in the cellular catabolic process and regulation of transmembrane transport, which are the dysregulated processes resulting from oxidative stress triggered by air pollution (Fig. 3C).

Besides, a large overlap was observed among the mediating proteins across different air pollutants (Fig. 3D). The proportion of mediation varied on the specific air pollutants, and different diseases exhibited distinct mediation patterns influenced by air pollutants (Supplementary Data 9). While many diseases were influenced by common mediating proteins, some diseases were predominantly mediated by PM-related proteins, such as chronic kidney disease (CKD), heart failure, and arrhythmia. In contrast, other diseases, such as non-alcoholic fatty liver disease (NAFLD), were primarily mediated by NO-associated proteins (Fig. 3D).

The number of proteins acting as mediators varied across diseases, with an average of 29 proteins significantly mediating the associations between air pollutants and diseases (Fig. 3E; Supplementary Fig. 12). To assess the global mediation effect of the proteome, we computed the top 10 principal components of protein abundances and used the weighted sum of mediation proportions as a global mediation measure, following the approach described in refs. 47,48. On average, the proteome mediated 23.69% of the total effects of air pollution on diseases, with the highest mediation proportions observed in pneumonia (27.55%), IHD (36.73%), and CKD (35.33%; Fig. 3E; Supplementary Data 10).

To further investigate the specific pathways mediating the associations between air pollution and various diseases, we performed functional enrichment analyses on disease-specific mediator proteins and those shared across multiple diseases (Fig. 3F; Supplementary Fig. 13A). Our results revealed that mediators shared by two or more diseases were enriched in processes related to protein phosphorylation and immune responses (Supplementary Fig. 13B). Notably, stratified analyses revealed distinct enrichment patterns across disease categories. Cardiovascular disease-specific mediators were particularly associated with regulation of the MAPK cascade and protein phosphorylation pathways, while those unique to immune diseases were enriched in innate immune response and positive regulation of cell adhesion (Supplementary Fig. 13C, D). Metabolic disease mediators demonstrated strong involvement in carbohydrate and monosaccharide metabolic processes, and lung disease mediators were predominantly linked to cell population proliferation (Supplementary Fig. 13E, F).

Next, to identify key mediating proteins, we calculated the stacked mediation proportion across diseases for each mediator and ranked the mediators based on their contribution to the mediation proportion. The top ranked proteins exhibited the greatest mediating effects of AP index on disease outcomes (Fig. 3G). Among them, NUCB2, DPY30, and MMP12 were the most prominent mediators, with stacked proportions of 44.19%, 43.73%, and 39.33%, respectively. A comparison between healthy and unhealthy individuals revealed significant differences in the expression of these key mediating proteins at baseline, with the diseased populations showing distinct expression patterns (Supplementary Fig. 14). Functionally, NUCB2 is the precursor of nesfatin-1, a peptide involved in cardiovascular regulation and stress response; DPY30 is a core component of an epigenetic modification, histone methyltransferase complex, which is easily influenced by environmental factors; MMP12 is an enzyme belonging to the matrix metalloproteinase family, which plays a key role in inflammation and immune response. Besides, by querying the DrugBank database49, we found that 23 out of the 65 identified proteins have been previously documented as drug targets associated with different diseases (Supplementary Fig. 15; Fisher’s exact test P = 2.73e–82).

APPRS represents individual sensitivity to the health effects of air pollution
The design of our mediation analysis quantified the role of individual proteomic profiles in mediating the effects of air pollution on diseases. The mediating proteins identified in our study provided insights into how environmental exposure influences disease through proteomic alterations. Thus, to quantify individual susceptibility to the effects of air pollution, we developed an air pollution-related protein risk score (APPRS), integrating proteins identified as mediators of the effects of pollutants on diseases using a designed statistical model (detailed in Methods). A total of 65 proteins, identified as mediators in at least two diseases, were selected to construct the APPRS (Fig. 3G; Supplementary Data 11). The coefficients used for APPRS construction were derived from the mediation effects of each protein on the association between air pollution and a specific disease, thereby making the APPRS disease-specific (Supplementary Fig. 16).

The distribution of APPRS follows a normal distribution and shows a positive correlation with the AP index (Supplementary Figs. 17–19). To check heterogeneity in individual susceptibility, we conducted subgroup analyses of APPRS across key demographic strata. Significant inter-group differences were observed (Supplementary Texts and Supplementary Figs. 20–24). Then, we compared APPRS distributions between healthy individuals and those with diseases. Consistently, APPRS was significantly elevated in individuals with diseases compared to those in a healthy state (Fig. 4A). This finding supports the notion that APPRS represents individual susceptibility to the effects of air pollution, with substantial inter-individual variability; higher APPRS values were associated with poorer health conditions. In addition, we investigated the associations between APPRS and disease incidence rates. Across the 12 diseases, individuals with higher APPRS percentiles at baseline exhibited elevated observed event rates (Fig. 4B). Notably, the strongest associations between APPRS and disease risk were observed in lung diseases, such as chronic obstructive pulmonary disease (COPD) and pneumonia (P = 3.93e−49; P = 3.54e-30), while the associations were slightly weaker in metabolic diseases such as T2D and NAFLD (P = 8.91e-43; P = 3.97e-25).

To further assess the generalizability of APPRS, we validated its practicality in two independent external cohorts: the Panmonogenic cohort and the HERITAGE cohort. In the Panmonogenic cohort, we found that individuals with immune diseases exhibited significantly higher global APPRS compared to healthy individuals (Fig. 4C), reinforcing the link between air pollution susceptibility and immune dysfunction. In the HERITAGE cohort, participants were stratified into three groups based on their cardiorespiratory fitness (CRF) levels. Since CRF was closely associated with cardiovascular efficiency and vascular function50,51, both arrhythmias APPRS and peripheral artery disease (PAD) APPRS were calculated and compared across these groups (Fig. 4D-E). Notably, individuals with higher CRF levels exhibited lower arrhythmias APPRS values, consistent with previous findings that higher CRF is associated with improved cardiopulmonary function and more efficient oxygen utilization52. Likewise, individuals with higher CRF levels also aligned with lower PAD APPRS values, likely due to enhanced vascular health and endothelial function associated with better CRF53. We also found that APPRS did not show significant differences in phenotypes unrelated to air pollution, supporting its specificity in capturing pollution-associated disease risk (Supplementary Texts; Supplementary Fig. 25).

Next, to investigate whether air pollution and APPRS interact in influencing disease incidence, we analyzed the association between APPRS and disease risk under varying levels of air pollution exposure. Participants were stratified into three groups based on their air pollution exposure levels, and within each group, we compared disease incidence between individuals with high versus low APPRS. The results showed that, within the same exposure category, individuals with higher APPRS consistently exhibited a greater disease incidence. Moreover, increased air pollution exposure was independently associated with a higher disease risk (Supplementary Fig. 26). The most significant patterns were observed in lung diseases (COPD and pneumonia) and immune diseases (RA and LE), whereas the trends were less notable for CKD, NAFLD, and cardiovascular diseases (IHD and arrhythmia). Moreover, similar trends were observed across different pollutants (Supplementary Figs. 27–30), further supporting the general interaction between APPRS and air pollution.

APPRS improves prediction performance for the early onset of diseases
Building on the understanding that APPRS is positively associated with disease risk, we developed predictive models that integrate APPRS with basic features (age, sex, smoking status, alcohol consumption, race, and BMI) and clinical variables (ApoB/ApoA, albumin, creatinine, HbA1c, and glucose) to assess the early onset of diseases related to air pollution (Fig. 5A). Models using only basic or clinical information were established as baseline models. We compared the performance of models incorporating APPRS with that of baseline models. The results showed that incorporating APPRS as an additional feature consistently improved model performance, leading to average AUROC increases of 3.64% and 6.67% compared to basic and clinical models, respectively (Fig. 5B, C; Supplementary Data 12). The final model, which integrated APPRS with both basic and clinical features, achieved the highest predictive performance, with an average AUROC of 77.25% (Fig. 5D; Supplementary Fig. 31).

To assess the sensitivity of APPRS in the prediction model, we designed three comparative models. The first comparative model constructed a random protein score by randomly selecting the same number of proteins and derived this score by applying the same statistical models as APPRS. In the second comparative model, proteins were still randomly selected but restricted to those significantly associated with the corresponding diseases.

We then compared the performance of prediction models incorporating APPRS with those incorporating either type of random protein score in the baseline model. The results showed that across all diseases, models with APPRS significantly outperformed those with random protein scores, illustrating the predictive value of APPRS in disease prediction (Supplementary Fig. 32A, B). Next, we compared the predictive performance of models incorporating AP index versus APPRS as features in the baseline model. The results demonstrated that APPRS consistently improved prediction performance and outperformed the AP index across all diseases (Supplementary Fig. 32C). These comparative models highlight the superior ability of APPRS to quantify individual susceptibility to improve disease prediction.

To evaluate the contribution of APPRS to the prediction, we examined the model coefficients of all predictive features. Among all predictors, we found APPRS had the most significant impact, accounting for 23.34% of the variation in disease outcomes (Fig. 5E). Additionally, we validated the predictive utility of APPRS in an external cohort, the HERITAGE cohort. While the Panmonogenic cohort only provided average protein expression data across disease subtypes, the HERITAGE cohort offered individual-level proteomic data and CRF measurements, making this cohort suitable for validation. In the HERITAGE cohort, we compared the performance of the baseline model, which was built using only age, sex, race, and BMI, with the model that incorporated APPRS in addition to the baseline features (Fig. 5F). Results showed that adding APPRS improved 2.77% of AUROC in model performance, with APPRS contributing 75.41% to the prediction (Fig. 5G).

Methods
Study cohort and participants
We retrieved data from the UK Biobank under Application Number 77803. The UK Biobank is a large prospective cohort project (https://www.ukbiobank.ac.uk/)69. This project complies with all relevant ethical regulations. All participants provided informed consent. UKBB participants included both males and females. Sex was based on self-report and a genetic sex check by UKBB. The analytical sample consisted of 506,040 participants recruited between April 4, 2006, and October 1, 2010, and 502,131 of them had data on air pollutants (PM2.5, PM10, NO2, NOx; Supplementary Data 1) and health outcomes. Air pollution exposure estimates were calculated using Land Use Regression (LUR) models developed by the European Study of Cohorts for Air Pollution Effects (ESCAPE), which integrated traffic-related pollution variables and provided high spatial resolution (100 × 100m, i.e., 0.01 km² grid cells). Annual air pollution estimates for each year were calculated for all addresses, measured in micrograms per cubic meter (µg/m³). The levels of NOx and PM2.5 were available only for the year 2010, we used the data for 2010 to calculate baseline exposure. NO2 data were available for 2005-2007 and 2010, and PM10 data were available for 2007 and 2010. Given stable spatial patterns in UKBB air pollution17,70 and validation against UK-AIR8, we calculated the average values across the available years to represent the long-term exposure for each participant at baseline.

Disease outcomes encompass 36 diseases across various categories reported by refs. 31,64, including 8 neurological diseases, 9 immune diseases, 6 cardiovascular diseases, 3 lung diseases, 1 skin disease, 5 metabolic diseases, and 4 cancers. Detailed information on 36 diseases, including their prevalence rates and ICD-10 codes were shown in Supplementary Data 2. Disease outcomes were defined as time to first diagnosis after January 1, 2010, and recurrent events were not considered. Individuals with complete data on air pollutants and health outcomes were used for air pollution-disease association analysis.

For the investigation of the effects of air pollutants on diseases through proteome, we utilized data from 49,242 UKBB participants with available proteome profiles. This proteomics cohort was shown to closely mirror the full UKBB in key demographic, behavioral, and biochemical characteristics (Supplementary Data 14), indicating its broad representation of the wider UKBB population71. Proteomic data, generated from April 2021 to February 2022, were obtained using the Olink Explore platform, covering 1463 unique coding proteins. Data were provided as log2-scaled Normalized Protein eXpression (NPX) values following Olink’s standard preprocessing and quality control pipeline. For analysis, NPX values were further standardized using a z-score transformation to ensure a mean of 0 and a standard deviation of 1 across participants. We followed the standard QC pipeline, including data filtering, normalization, and missing value imputation71,72. Ethical approval for the UKBB was obtained from the National Health Service National Research Ethics Service, and all participants provided electronic informed consent.

Construction of AP index
To derive a joint evaluation for air pollution quality, our study constructed an air pollution (AP) index by integrating the effects of several air pollutants (NO2, NOx, PM2.5, and PM10). The self-reported overall health ratings (Field ID: 2178) used to construct the AP index were collected at baseline assessment during participant recruitment (2006–2010). We quantified the relationship between each air pollutant (independent variable) and self-reported overall health ratings (dependent variable) by a linear regression model, adjusting for baseline age, sex, body‑mass index (BMI), smoking status, alcohol assumption and race. Then, the AP index was calculated by summing the product of each pollutant’s regression coefficient (β) and its corresponding exposure level, normalized by the sum of the β coefficients. The formula for the AP index is as follows:

APindex = (Σ_{i=1}^{K} β_i × AP_i) / (Σ_{i=1}^{K} β_i) × K (1)

where K is the number of pollutants. The resulting AP index represents a joint exposure to multiple air pollutants, incorporating the effects of NO2, NOx, PM2.5, and PM10, and is intended to capture the combined influence of these pollutants on overall health ratings.

The Living Environment Scores were collected from UKBB (Filed ID: 26417), which were composed of four indicators: social and private housing in poor condition, presence of central heating in houses, air quality, and road traffic accidents. Pearson’s correlation test was performed to assess the consistency between the AP index and the Living Environment Scores. Sensitivity analysis was also performed by comparing the AP index constructed from overall health rating with those derived using handgrip strength and walking pace following the same method (Supplementary Texts and Supplementary Fig. 34).

PWAS analysis
A proteome-wide association study (PWAS) was conducted to identify proteins associated with air pollution exposure. We applied general linear regression models using the “limma” R package to assess the relationship between the expression levels of each protein and air pollutants (PM2.5, PM10, NO2, NOx, and AP index). The limma package employs an empirical Bayes approach to borrow strength across proteins when estimating residual variances, thereby reducing uncertainty in variance estimates and yielding greater statistical power than conventional single-protein t tests73. To account for potential confounding, the regression models were adjusted for baseline age, sex, BMI, smoking status, alcohol assumption, and race. Additionally, for each pollutant’s PWAS analysis, we included the other pollutants as covariates to adjust for their effects. We used false discovery rate (FDR) correction, the Benjamini-Hochberg (BH) method, to control multiple testing across proteins for each pollutant74.

Functional enrichment analysis
Enrichment analysis and functional annotation of air pollutants-associated proteins were performed using the “clusterProfiler” R package75. Biological processes, metabolic pathways, and biochemical reactions associated with these proteins were characterized by searching against the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome databases using Metascape76. Statistical significance of enriched terms was determined using a false discovery rate (FDR) correction for multiple testing. Terms with FDR < 0.05 and containing at least 10 associated proteins were considered statistically significant. Identical analytical workflows were applied to proteins linked to each pollutant, and the top 10 commonly enriched pathways across all pollutants were prioritized for visualization and downstream interpretation.

To capture pollutant-specific biological effects, proteins uniquely associated with individual pollutants were subjected to Gene Ontology (GO) enrichment analysis. Pathways with FDR < 0.01 were defined as significantly enriched, with the top 10 pathways selected for visualization. To further characterize shared mechanisms among pollutants, proteins co-occurring across pollutants were independently analyzed through identical enrichment workflows.

Cox proportional hazards regression analysis
Cox proportional hazards regression analysis was performed to analyze the associations between air pollution and disease outcomes, adjusting for potential confounders such as baseline age, sex, BMI, smoking status, alcohol assumption and race. Since the air pollution data provided by UKBB was collected in or before 2010, the time to disease onset (duration) was measured from the baseline date (January 1, 2010) to the date of confirmed diagnosis. Follow-up was censored on January 1, 2020, due to the COVID-19 pandemic. Our Cox models considered only first events, in line with standard UKBB practice31,64. Cox models were developed for every single disease, and Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated for each pollutant. Analyses were conducted using the “survival” package in R.

Mediation analysis
Mediation analysis was conducted to investigate the role of proteins in mediating the relationship between air pollution and disease outcomes. Proteins identified as being associated with both air pollution exposure and disease risk were tested for their mediating effect using the “mediation” package in R. Proteins involved in the significant indirect effects of air pollution on disease risk were considered as mediators. The calculation process can be formulated as:

M = a × AP + d × X + ϵ1
D = c0 × AP + b × M + e × X + ϵ2
D = c × AP + f × X + ϵ3

where a is the regression coefficient for air pollution in predicting protein levels, b is the effect of the mediator on disease risk, c is the total effect of air pollution on disease risk and d, e, f are the effects of covariates X which includes baseline age, sex, BMI, smoking status, alcohol assumption and race. The indirect effect c − c0 = a × b, which tests the effect of air pollution on disease risk mediated by the mediator. The mediation proportion is calculated as c0 / c, which represents the ratio of the mediation effect to the total effect. Although the function mediate() in the “mediation” package does not natively support Cox models, the Poisson-with-offset specification is equivalent to a Cox proportional hazards model for time-to-event data, allowing valid survival-time adjustment and effect decomposition77. The indirect effect reported by mediate() is the average causal mediation effect (ACME)78. We performed false discovery rate (FDR) multiple testing correction for the mediation analysis results of all pollutant-protein-disease pairs. A protein was considered a mediator if its ACME FDR < 0.05, total effect FDR < 0.05, and the mediation proportion was greater than 4% (median value).

To compute the global mediation proportion across all proteins, we performed dimensionality reduction on the proteomic data using principal component analysis (PCA) and derived uncorrelated components that best capture the variability in the proteome data. The advantage of this approach is that it transforms correlated protein variables into low-dimensional orthogonal components by performing spectral decomposition of the covariance matrix of 1,463 proteins, allowing the stepwise estimation of effects through a low-dimensional model. We selected the top 10 principal components (PCs) as mediators for global mediation, which explained 44.80% of the variance in the proteome. A PC was defined as having a significant mediation effect if the p-value for the indirect effect was < 0.05 and the direction of the indirect effect was consistent with the effect of air pollution on the disease. The global total indirect effect was calculated as the weighted sum of the mediation effects of the significant PCs, reflecting the contributions of each principal component to the overall mediation pathway between air pollution exposure and disease outcomes. The overall mediation proportion of the proteome was calculated as the ratio of the global total indirect effect to the total effect of air pollution on the disease, which represents how much of the total effect of air pollution on disease risk is mediated by the proteome.

Enrichment analysis of tissue-specific mediators
Mediators that exclusively linked AP index to a single disease type were classified as disease-specific mediators, while those mediating associations across disease types were designated as common mediators. Subsequent GO enrichment analyses were performed separately on each mediator subset using a cutoff of P < 0.01 to delineate disease-specific biological pathways.

Development of APPRS
The air pollution protein risk score (APPRS) was developed by integrating proteins identified as mediators in the mediation analysis between AP index and disease outcomes. We included 65 proteins to construct the APPRS based on their mediation performance across multiple diseases. The inclusion criteria were: (a) FDR < 0.05 for both the ACME and total effect in at least two or more AP index-disease mediation models; (b) Mediation proportion > 4%. These thresholds were designed to ensure both the statistical significance and to reflect proteins with broader mediation roles across diseases. To address potential arbitrariness in APPRS construction, we conducted sensitivity analyses using alternative construction criteria, details were provided in Supplementary Texts and Supplementary Fig. 35. Then, APPRS was calculated as a weighted sum of the protein expressions associated with air pollution, with the weights derived from their average causal mediation effect (ACME), which can be denoted as:

APPRS = Σ_{i=1}^{M} (ACME_i × Protein_i)

where ACME i is the coefficient of indirect effects for ith protein, Protein i the expression level of ith protein, M the total number of selected proteins. Almost all ACME values used to construct the APPRS were positive, whereas protein expression values included both positive and negative numbers due to normalization. For each disease, we derived a disease-specific APPRS by combining protein levels using the corresponding disease-specific ACME coefficients from the mediation models as weights.

Association between APPRS and observed disease rates
To examine the relationship between APPRS and disease incidence, we divided the study population into 100 groups based on APPRS quantiles, calculated the disease incidence for each group, and generated a figure depicting the results. The Pearson correlation coefficient was used to assess the relationship between APPRS percentiles and disease incidence rates.

External validation of APPRS
To assess the generalizability of the APPRS and its association with health outcomes, we validated it within two independent cohorts: the Panmonogenic cohort and the HERITAGE Family Study cohort. Both cohorts provided proteomic profiles and at least one relevant health phenotype. The validation analyses using the HERITAGE and Panmonogenic cohorts relied on publicly available data. The Panmonogenic cohort provides proteomics data of individuals with 22 monogenic immune-mediated diseases66. The HERITAGE Family Study cohort reports the cardiorespiratory fitness (CRF) levels of 481 sedentary adults from 99 families79. For both cohorts, 12 disease-specific APPRSs were constructed for each cohort using the same set of mediating proteins and weights as in the UKBB cohort. Missing values among the 65 mediating proteins were imputed as zero, and APPRSs were computed following the same methodological framework applied in UKBB.

In the Panmonogenic cohort, a global APPRS was calculated by averaging the 12 disease-specific scores to quantify overall susceptibility to air pollution-related protein-mediated disease risk. This global APPRS was then used to compare risk levels between healthy individuals and those with immune-related diseases (Autoimmune Diseases, AID; Primary Immunodeficiency, PID; Telomere Diseases, Telo). In the HERITAGE cohort, individuals’ maximal oxygen uptake (VO₂max)—a direct measure of cardiorespiratory fitness (CRF)—was recorded. Participants were stratified into tertiles based on VO₂max levels, representing low, intermediate, and high CRF groups. We subsequently assessed the association between APPRSs and these CRF categories.

Interaction between APPRS and air pollution on disease risk
To evaluate the joint interaction effects of APPRS with air pollution exposure on disease risk, we performed a stratified Cox proportional hazards regression analysis. Participants were categorized into tertiles based on their air pollution exposure levels (low, intermediate, high), and the APPRS was dichotomized into low and high groups using the median as the cutoff. To avoid potential collinearity, we examined the correlation between APPRS and air pollution exposures and found that their associations were too weak to cause multicollinearity (Supplementary Fig. 18). Then, a combined stratification variable was generated by jointly stratifying participants based on their surrounding exposure and APPRS levels (3 exposure levels × 2 APPRS levels). Cox proportional hazards regression analysis was performed to examine the incidence of 12 diseases, while the lowest-risk stratum (APPRS-low and exposure-low) serves as the reference. All models were adjusted for covariates, including age, sex, BMI, smoking status, and alcohol assumption.

Construction of disease prediction models
LASSO prediction regression models were developed to assess the early onset of diseases associated with air pollution exposure, using the “glmnet” R package, predictors incorporated APPRS, basic features (age, sex, smoking, alcohol consumption, race, and BMI), and clinical features (ApoB/ApoA, albumin, creatinine, HbA1c, and glucose). To assess the predictive power of the Air Pollution Protein Risk Score (APPRS) for disease risk, we established three baseline models: (1) basic demographic features alone, (2) clinical features alone, and (3) a combination of basic and clinical features. Each of these baseline models was then augmented with the APPRS to evaluate its added value in predicting 12 distinct disease outcomes. The analysis was conducted using baseline data from both healthy individuals and patients. Model training and evaluation were performed using 10-fold cross-validation to ensure robustness and generalizability. Predictive performance was measured by the area under the receiver operating characteristic curve (AUROC), and improvements in classification accuracy were assessed by comparing the baseline models with their corresponding APPRS-enhanced counterparts.

Evaluation of APPRS contribution in the prediction model
To quantify the contribution of predictors, particularly the APPRS, to disease prediction, we extracted the absolute values of the final feature coefficients from the combined model incorporating basic features, clinical features, and APPRS. Before fitting the model, all features were standardized, ensuring that the coefficient magnitudes are directly comparable across variables. These coefficients, derived from the LASSO regression, reflect the relative importance of each predictor in the model, with larger absolute values indicating stronger associations with disease risk. Then we calculated the proportion of each feature’s absolute coefficient relative to the total sum of all absolute coefficients in the combined model (basic + clinical + APPRS). These proportions were then visualized using bar plots. This approach quantifies the weight of each variable in the model’s decision-making process, with higher proportions indicating stronger contributions to disease risk stratification.

Sensitivity analysis of APPRS to the prediction models
To confirm that the predictive contribution of the APPRS was neither stochastic nor confounded by the AP index, we conducted sensitivity analyses by constructing three alternative LASSO models. Firstly, we randomly selected 65 proteins from the 1463 proteins and calculated the APPRS using their corresponding ACME coefficients. Secondly, we restricted our selection to those proteins showing significant associations with the corresponding disease (P < 0.05), again selecting 65 proteins for APPRS calculation. 1000 iterations of random selection were performed, and the final value was derived as the mean value across all iterations. Thirdly, the APPRS was substituted with the AP index alone to isolate its specific contribution from APPRS. These values were trained with basic and clinical information, and models were evaluated under identical cross-validation and frameworks as the primary analysis. By comparing the performance (AUROC) in these alternative models against the APPRS-enhanced model, we rigorously assessed whether the predictive power attributed to the APPRS arose from systematic biological signals rather than random chance or redundancy with the AP index.

External validation of the APPRS contribution
To validate the generalizability of the APPRS in predicting health outcomes, we performed external validation in the HERITAGE cohort. The Panmonogenic cohort was excluded from external validation due to the unavailability of publicly accessible individual-level protein expression data. We focused on cardiorespiratory fitness (CRF) as the health outcome. Basic models were constructed by including age, sex, race, and BMI as predictors. Arrhythmias and peripheral artery disease APPRSs were further added to the basic models for comparison. Model performance was evaluated and compared using the area under the receiver operating characteristic curve (AUROC). This approach quantified the incremental predictive power conferred by the APPRS. The method for quantifying and visualizing the contribution of predictors was the same as that of the UKBB study.

Statistical analysis
All statistical analyses were conducted using R (version 4.1.3) and relevant R packages (“survival” [v3.6-4] for Cox proportional hazards regression, “limma” [v3.50.3] for PWAS analysis, “mediation” [v4.5.0] for mediation analysis, “glmnet” [v4.1-8] and “caret” [v6.0-94] for LASSO regression models, “stats” for basic statistical analyses, “prcomp” [v4.4.1] for PCA analysis, and “pROC” [v1.18.5] for AUROC calculation).

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
Datasets generated in this study are made available in the Supplementary Data files. Source data are provided with this paper. This research has been conducted using the UK Biobank Resource under Application Number 77803. Proteomics data, air pollution data, and disease outcomes are available as part of the UK Biobank. The data can be accessed through the UK Biobank Research Analysis Portal (https://www.ukbiobank.ac.uk/enable-your-research). The HERITAGE cohort proteomics dataset is publicly available via the MoTrPAC Data Hub (https://motrpac-data.org/related-studies/heritage-proteomics). Publicly accessible proteomics summary tables from the Pan-monogenic cohort are available through the Pan-Monogenic dashboard (https://panmonogenic.yale.edu/). Source data are provided with this paper.

Code availability
The custom code used for data processing and statistical analysis in this study has been deposited on GitHub at https://github.com/kaixuanli99/AirPollution-Proteome-Diseases-Mediation, and stored at https://doi.org/10.5281/zenodo.1777418080.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is interesting and complex study to systematically investigate how human disease risk is potentially associated with air pollution exposure through changes in protein expression. It has done lots of works. I have several concerns on it:\nMajor concern:\n1. I am not sure AP index is the good indicator to analyze the combined AP effects. Given that the weight of AP index is derived from self-reported overall health ratings, I wonder whether the stronger hazard ratios (HRs) observed for mental disorders than other chronic outcome (Fig 2) might be partially driven by this subjective health reporting bias embedded in the AP index, rather than reflecting solely the biological impact of air pollution exposure. I am concerned that mental health conditions such as depression and schizophrenia are more likely to influence individuals’ self-assessed health status. Thus, I am not sure whether AP index is the good indicator to calculate the HR and compare HR across diseases.\n2. Also, I am curious why result can be controversial between pollutant and ap index (Fig2). e.g., sometimes the effect of all pollutants is significant but AP index is not. Sometimes is another opposite situation.\n3. The calculation of AP index is unclear. When do they collect health ratings? In 2010? If not, the unmatched time between exposure data and health ratings may raise some bias. How could you calculate the weights for AP index when data for NO2 and PM10 were available for multiple years? And PM2.5 and NOx are only available in 2010, will it bring some problem when calculating index? Why multiply K for this index? If the coefficient of linear regression model is negative, how could you calculate them for the weight? Will they offset with each other?\n4. The calculation of APPRS is also unclear and confusing. How could you make sure the effect from different protein are not overlap and can be additive? Why only include 65 proteins? How could you get ACME? Is the from each protein model? But should multiply pollutant not protein, right?\n5. What is the range of the APPRS? Figure 4 shows that many values are negative — what does a negative APPRS value represent? Does the negativity come from the direction of the ACME coefficients or from the protein expression values? When calculating the weighted sum of protein expression levels, how do you account for the fact that some proteins may have positive ones while others have negative ones? Wouldn’t this cause offsetting effects between positive and negative contributions?\n6. I am not sure whether you will have serious multicollinearity problem when analyzing Interaction between APPRS and air pollution, because the calculation of APPRS are strongly associated with AP.\n7. Did you conduct the data normalization before PWAS analysis? How? Describe it.\n8. I would like to remind that this study is association analysis not the causation analysis, so be careful with the words or sentences you used in discussion and introduction.\n9. Fig2 you did not mention what −log₁₀(p-value) mean. Higher mean more significant but did not mean stronger effect, right? And the effect can be positive or negative? I am worried about whether Fig2 will cause misleading. Do you consider to report the effect size for all panel?\nMinor:\n“The effect of air pollution on populations varies, with significant heterogeneity in individual susceptibility. Previous studies have highlighted these differences, showing that some individuals are more vulnerable to developing diseases than others. While one individual has a unique proteomic profile, proteomic mediation analysis offers a promising approach to understanding the individual variability in response to environmental exposures”\nI am not sure why this point is emphasized in the introduction, as this study does not directly address this issue. Specifically, there is no analysis of heterogeneity in proteomic responses to air pollution exposure across subgroups such as age, sex, or other demographic factors. If individual variability is a key motivation, it would strengthen the paper to include subgroup analyses or at least discuss this limitation explicitly in the discussion section.\nFig2A I cannot see the grey horizontal line.\nFig2C. and “Moreover, we found that while a small subset of proteins was associated with only a single air pollutant— 30.66% for PM2.5, 36.99% for PM10, 33.05% for NO2, and 7.05% for NOx—the majority exhibited associations with multiple pollutants”\nI’m not sure I fully understand this point. Are you saying that certain proteins are significant in single-pollutant models but not in the AP index model? If so, could you clarify why that might be the case? Also, are there proteins showing inconsistent or even opposite associations between individual pollutants and the AP index — similar to the patterns shown in Figure 2B?\nFig3 is a bit difficult to follow. Panel F may have some overlap pathways?\nFig5, what is the metric of model performance? what do you mean contribution? Contribute to the r-square or final predicted value?\nP18 Lines -18-19 What is spatial resolution of Land Use Regression? pls mention which multiple years for NO2 and PM10", "reviewer_2": "The authors presented a study that analyzed the mediation role of circulating proteins between multiple air pollutants and 30 diseases in the UKBB cohort. The study is comprehensive in terms of using multiple data sources (e.g., air pollutants, proteins, disease outcomes, etc.) and using various analytic approaches (e.g., PWAS, survival analyses, functional enrichments, etc.).\nThe following list includes my detailed comments regarding the study design, data, and methodology.\n1. The major concern of the methodology is that in this PWAS-type analysis, there should be careful consideration of multiple comparisons, as many pairs of exposure-outcomes. The use of a significance level of 0.05 in this manuscript could lead to large Type I errors. For example, in the following study with multiple disease outcomes, they documented as “adjusting for multiple comparisons using the Bonferroni correction.”\na. Wei, Y., Wang, Y., Di, Q., Choirat, C., Wang, Y., Koutrakis, P., Zanobetti, A., Dominici, F. and Schwartz, J.D., 2019. Short term exposure to fine particulate matter and hospital admission risks and costs in the Medicare population: time stratified, case crossover study. bmj, 367.\n2. How are the individuals with and without valid proteomic data different in their population characteristics, as well as their exposure levels to air pollution?\n3. Related to Question 2, how are the results obtained from 49,242 individuals (9.8%) generalizable to the entire UKBB cohort? In the discussion section, the sentences suggested that the results utilized “data from over 500,000 participants in the UK Biobank,” which is not entirely accurate, given that the important merit of this paper is on mediation analyses.\n4. It is unclear to me how the air pollutants data were linked to the participants; are they air pollutants between April 4, 2006 and October 1, 2010, or earlier years? How many years, and whether they are multi-year averaged measures?\n5. The specific time periods/orders of air pollution exposures and disease outcomes are unclear to me. For example, is the air pollution exposure measured before the onset of these diseases? According to the Study Design section, it sounds like the entire study is cross-sectional, with only one air pollution measurement and disease outcomes for each participant. Is it possible that the disease onset is actually prior to the air pollution exposure measured, which leads to an important concern about the validity of the causal link?\n6. The same logic in Question 5 applied to the relationship between air pollutants and proteins and proteins to diseases, since “this study relies on single time point measurements of proteomic data.” Given that the study was considered a mediation analysis, the time order of air pollution -> proteins -> disease outcomes seems rather important.\n7. In the mediation analysis, it is unclear what regression techniques were used. The formulation looks like a linear regression in Line 568. However, this is different from the Cox models used in other analyses. Given that the mediation analyses also used disease outcomes, which are assumed to be time-to-event. How do the authors adjust for the use of the regression formulation?\n8. In the analyses using Cox models, are they under the assumption that each disease outcome only happened once? How about repeated measures of outcomes during 4 years?\n9. The use of “exhibited mediation effects in more than two diseases” as one of the inclusion criteria of APPRS sounds ad hoc. Is it possible that one protein is very important to one disease, yet is being excluded because it failed to meet this criterion? Also, why not three diseases?\n10. In the development of APPRS, if this procedure followed the development of polygenic risk scores (PRS), the score should be disease-specific. I found the APPRS is agnostic to multiple diseases. Any reason not to develop a disease-specific APPRS?", "reviewer_3": "This study explores the biological mechanisms underlying the association between air pollution and disease, with a particular focus on the mediating role of circulating proteins. Leveraging large-scale data from the UK Biobank, the authors identified 30 diseases significantly associated with air pollution exposure and uncovered 1,089 pollution-associated proteins, among which 296 were found to significantly mediate disease risk. These mediating proteins were enriched in immune-related pathways and exhibited disease-specific patterns, including MAPK signaling, innate immunity, carbohydrate metabolism, and respiratory cell proliferation. Based on these findings, the authors developed a novel Air Pollution Protein Risk Score (APPRS), which demonstrated strong predictive capacity for disease risk in both the UK Biobank and two external validation cohorts. Moreover, the integration of APPRS into baseline models yielded improved prediction performance. This work highlights the central role of circulating proteins in mediating the health impacts of air pollution and introduces APPRS as a promising tool for personalized risk assessment and precision public health strategies. The study is well-conceived, methodologically sound, and clearly written. However, I believe the manuscript would benefit from addressing the following concerns prior to acceptance:\n(1)Given that the temporal dynamics of exposure can vary significantly across diseases, the authors should clarify how the exposure window for air pollution was selected. Was a disease-specific sensitive window considered, or was a uniform window applied to all outcomes?\n(2) In the Introduction section, the authors are encouraged to summarize existing methodologies that integrate air pollution exposure with biological markers such as proteins. This context would help readers understand how APPRS builds upon or advances previous approaches.\n(3) While APPRS showed overall improvement in prediction, the gains appear modest for many diseases. The authors should discuss the limitations of APPRS more explicitly, especially in terms of its discriminatory power and practical applicability.\n(4) Although the statistical mediation analysis provides a screening tool to identify candidate proteins, further evidence is needed to support their biological plausibility. The authors are encouraged to elaborate on whether there is experimental or literature-based support for the mediating roles of the highlighted proteins."}, "author_response": "Point-by-point response to the review comments\nWe thank the reviewers for their very insightful comments. We have revised the\nmanuscript thoroughly.\nReviewer #1 (Remarks to the Author):\nThis is interesting and complex study to systematically investigate how human disease\nrisk is potentially associated with air pollution exposure through changes in protein\nexpression. It has done lots of works. I have several concerns on it:\nMajor concerns:\n1. I am not sure AP index is the good indicator to analyze the combined AP effects.\nGiven that the weight of AP index is derived from self-reported overall health ratings,\nI wonder whether the stronger hazard ratios (HRs) observed for mental disorders than\nother chronic outcome (Fig 2) might be partially driven by this subjective health\nreporting bias embedded in the AP index, rather than reflecting solely the biological\nimpact of air pollution exposure. I am concerned that mental health conditions such as\ndepression and schizophrenia are more likely to influence individuals’ self-assessed\nhealth status. Thus, I am not sure whether AP index is the good indicator to calculate\nthe HR and compare HR across diseases.\nResponse:\nWe thank the reviewer for raising this insightful concern regarding the potential\nbias introduced by using self-reported overall health ratings to construct the AP index,\nparticularly in relation to mental disorders. To address potential subjectivity, we\nconducted a sensitivity analysis by replacing the original health rating with two widely\naccepted objective markers of overall health and frailty—mean handgrip strength (Field\nID: 46-0.0 and 47-0.0) and usual walking pace (Field ID: 924-0.0). These measures\nhave been validated in previous studies as reliable indicators of general health and\nphysical resilience [1-3]. Using the same regression-based weighting strategy, we\nreconstructed AP indexes based on each of these alternative health indicators. The\nresulting AP indexes were highly correlated with our original index: Grip strength-\nbased AP index (Pearson's r = 0.997, P < 2e-16; Supplementary Fig. 26A), Walking\npace-based AP index (Pearson's r = 0.999, P < 2e-16; Supplementary Fig. 26B). This\nhigh concordance demonstrates that the original AP index based on self-rated health\nperforms similarly to objectively derived proxies, supporting its use as a summary\nmeasure of pollution-related health burden.\nFurthermore, we observed a significant consistency between our AP index and the\nLiving Environment Score proposed by UKBB, which reflects the quality of an\nindividual’s immediate surroundings both indoors and outdoors (Pearson's r = 0.524;\nSupplementary Fig. 1). This further supports the validity of the AP index as a\ncomprehensive indicator of environmental exposure burden.\nAdditionally, the use of self-rated health as a proxy for overall health status is well-\nestablished in population-based research [4, 5]. Regression-based approaches that\nderive composite pollution scores using health-related endpoints have been widely\napplied in air pollution epidemiology to assess cumulative exposure risks [6, 7]. Our\nmethod aligns with this mathematical framework and aims to capture the overall impact\nof multi-pollutant exposure on general health.\nTaken together, we believe that our method for constructing the AP index is both\nmethodologically supported and empirically validated. We have revised the Methods\nsection (Page 21, Lines 549-556) to clarify this rationale and added Supplementary\nTexts and Supplementary Fig. 26 to support these points.\nSupplementary Fig. 26: Correlation of AP indexes reconstructed using alternative health\nindicators. AP indexes based on (A) handgrip strength and (B) walking pace were highly\ncorrelated with the original self-rated health-based index (Pearson’s r = 0.997 and 0.999,\nrespectively; P value < 2 × 10-16).\n2. Also, I am curious why result can be controversial between pollutant and ap index\n(Fig2). e.g., sometimes the effect of all pollutants is significant but AP index is not.\nSometimes is another opposite situation.\nResponse:\nWe thank the reviewer for raising this important question. We have carefully\nexamined the observed discrepancies between the effects of individual pollutants and\nthe aggregated AP index on disease outcomes. As shown in Supplementary Fig. 2, we\nidentified six diseases (vascular dementia, multiple sclerosis, lung cancer, Parkinson’s\ndisease, inflammatory bowel disease, and endometriosis) where the associations were\nstatistically significant for either the individual pollutants or the AP index, but not both.\nDespite these inconsistencies in statistical significance, we found that the effect\ndirections between individual pollutants and the AP index were largely consistent. This\nsuggests that the aggregated index retains the overall trend in exposure-disease\nrelationships, even when the P values diverge.\nThe integrated nature of the AP index allows it to amplify modest but directionally\nconcordant signals across pollutants. In cases where each pollutant demonstrates a\nmoderate association with a disease, their weighted combination in the AP index may\nyield a stronger, statistically significant signal, even when the individual effects do not\nreach significance on their own (Supplementary Fig. 2A). This property enables the\nAP index to detect subtle, cumulative exposure effects that may otherwise be missed in\nsingle-pollutant models.\nSupplementary Fig. 2: Forest plots illustrating inconsistencies between the AP index and\nindividual pollutants in disease associations. (A) Cases where the AP index is statistically\nsignificant while none of the individual pollutants are significant. (B) Cases where individual\npollutants are statistically significant while the AP index is not.\nConversely, in scenarios where a single pollutant has a dominant and disease-\nspecific effect, the aggregation process may dilute this specific signal, especially if\nother pollutants in the index are less relevant. This is particularly relevant when\ndifferent pollutants influence distinct biological pathways, resulting in diluted statistical\npower for the composite AP index. Furthermore, from a statistical perspective, this\nreflects the classic signal-to-noise trade-off: some pollutants may exhibit greater\nvariability within the study population, making it easier to detect significant\nassociations for individual pollutants. The AP index is a weighted aggregation of\nmultiple correlated exposures, which improves robustness and generalizability, but may\nalso introduce noise. This explains why, in certain scenarios, a single pollutant can be\nsignificantly associated with disease risk, while the AP index does not reach statistical\nsignificance (Supplementary Fig. 2B).\nWe have revised the Results section (Page 7, Lines 157-160) and added\nSupplementary Texts to address the question and included Supplementary Fig. 2 in\nthe Supplementary materials.\n3. The calculation of AP index is unclear. When do they collect health ratings? In 2010?\nIf not, the unmatched time between exposure data and health ratings may raise some\nbias. How could you calculate the weights for AP index when data for NO2 and PM10\nwere available for multiple years? And PM2.5 and NOx are only available in 2010,\nwill it bring some problem when calculating index? Why multiply K for this index?\nIf the coefficient of linear regression model is negative, how could you calculate them\nfor the weight? Will they offset with each other?\nResponse:\nWe thank the reviewer for these detailed comments. We have revised the Methods\nsection (Page 19, Lines 499-509) to clarify the construction of the AP index. Below we\nprovide point-by-point clarifications:\n(Q1) “When do they collect health ratings? In 2010? If not, the unmatched time\nbetween exposure data and health ratings may raise some bias.”\nThe self-rated health score used to construct the AP index was collected at baseline\nassessment during participant recruitment (2006-2010), with the majority of responses\nrecorded around 2010. This timeframe closely aligns with the available air pollution\nexposure data.\nWe acknowledge the reviewer’s concern about potential temporal mismatches.\nWhile PM and NO data were available only for 2010, NO and PM included\n2.5 X 2 10\nmeasurements from earlier years (2005-2007). However, previous studies have\ndemonstrated that spatial patterns of air pollution in the UKBB are relatively stable over\ntime [8, 9], and the modeled exposure estimates have been validated against\nauthoritative data from the UK Air Information Resource (UK-AIR) [10]. Therefore, in\nline with established practice in environmental epidemiology, we considered these\nexposure estimates as valid proxies for baseline long-term exposure. We have now\nclarified this rationale in the Methods section.\n(Q2) “How could you calculate the weights for AP index when data for NO2 and\nPM10 were available for multiple years? And PM2.5 and NOx are only available in\n2010, will it bring some problem when calculating index?”\nFor pollutants measured at multiple time points, we used the average values across\nall available years (NO : 2005-2007, 2010; PM : 2007, 2010) to obtain a more stable\n2 10\nand representative estimate of baseline exposure. This approach is consistent with prior\nstudies [6, 7, 10, 11] and helps to reduce short-term fluctuations in exposure data. We\nhave revised the Methods section to clarify this strategy.\n(Q3) “Why multiply K for this index?”\nThe AP index was constructed as the weighted sum of standardized pollutant levels,\n𝐴𝑃 𝑖𝑛𝑑𝑒𝑥 =\n∑𝐾\n𝑖=1\n(𝛽𝑖×𝐴𝑃𝑖)\n×𝐾 =\n∑𝐾\n𝑖=1\n(𝛽𝑖×𝐴𝑃𝑖)\n∑𝐾\n𝑖=1\n𝛽𝑖 (∑𝐾\n𝑖=1\n𝛽𝑖)/𝐾\nwhere weights (β) were obtained by separately regressing each pollutant against overall\nhealth rating, adjusting for age, sex, BMI, smoking status, and drinking frequency. We\nthen multiplied the weighted sum by K = 4, corresponding to the number of pollutants\nincluded. Multiplying by K standardizes the index, allowing its mean and variance to\nremain interpretable and comparable across studies and pollutants. This scaling step is\na conventional practice in constructing pollution or risk indexes, and has been adopted\nin prior air pollution score studies [6, 7, 10, 11].\n(Q4) “If the coefficient of linear regression model is negative, how could you\ncalculate them for the weight? Will they offset with each other?”\nIn our case, all four pollutants had positive coefficients (β₁ = 0.0279, β₂ = 0.0016,\nβ₃ = 0.0028, and β₄ = 0.0075, corresponding to PM₂.₅, NOₓ, NO₂, and PM₁₀,\nrespectively), indicating a consistent direction of effect with worsening health.\nTherefore, offsetting was not an issue in this dataset. Theoretically, if negative\ncoefficients arose, they would indicate opposite health effects for a pollutant, and\nfurther sensitivity analyses would be warranted.\nWe have incorporated all these clarifications in the revised Methods section (Page\n19, Lines 499-509) and thank the reviewer again for helping improve the clarity of our\nwork.\n4. The calculation of APPRS is also unclear and confusing. How could you make sure\nthe effect from different protein are not overlap and can be additive? Why only include\n65 proteins? How could you get ACME? Is the from each protein model? But should\nmultiply pollutant not protein, right?\nResponse:\nWe thank the reviewer for these insightful questions regarding the construction\nand interpretation of the APPRS. Below, we provide detailed point-by-point\nclarifications:\n(Q1) “How could you make sure the effects from different proteins are not overlap\nand can be additive?”\nThe APPRS is constructed under the assumption that proteins contribute additively\nand independently to the risk of air pollution-related diseases. This is a common\napproach in composite risk score construction (e.g., polygenic risk scores, PRS) [12],\nespecially when the underlying biological mechanisms are complex and potentially\nnon-linear.\nWhile protein pathways may overlap biologically, our goal was not to model\nmechanistic dependencies, but rather to aggregate statistically estimated mediation\neffects (ACMEs) of individual proteins that represent their independent contributions\nto disease risk via air pollution exposure. Furthermore, the proteins included in APPRS\nwere selected based on stringent significance and consistency criteria (ACME FDR <\n0.05 and mediation proportion > 4%), which helps minimize redundancy and enhance\nrobustness in the final score.\n(Q2) “Why only include 65 proteins?”\nWe included 65 proteins to construct the APPRS based on their mediation\nperformance across multiple diseases. The inclusion criteria were: a) FDR < 0.05 for\nboth the ACME and total effect in at least two or more AP index-disease mediation\nmodels; b) Mediation proportion > 4%. These thresholds were designed to ensure both\nthe statistical significance and biological relevance of the mediators. The final list of 65\nproteins thus represents a robust subset with reproducible mediation signals across\ndiseases.\n(Q3) “How could you get ACME?”\nThe calculation of ACME (Average Causal Mediation Effect) for each pollutant-\nprotein-disease triplet strictly follows the causal mediation analysis framework\nproposed by [13], implemented in the “mediation” R package. Each ACME was derived\nfrom the following steps:\n An Exposure–Mediator model estimating the effect of air pollution exposure\non protein expression:\nProtein = α0 + α1⋅Pollution + ε1\n An Exposure–Mediator–Outcome model assessing the effects of both\npollution and protein mediator on disease outcome:\nDisease = β0 + β1⋅Pollution + β2⋅Protein + ε2\n The ACME for each protein represents the indirect effect of air pollution on\ndisease risk mediated through that specific protein, calculated as:\nACME = α1⋅β2\nTo account for potential sampling variability and ensure the robustness of the\nmediation effect estimates, we performed bootstrap resampling (1,000 iterations) to\nestimate 95% confidence intervals for the ACME values.\n(Q4) “Is the from each protein model? But should multiply pollutant not protein,\nright?”\nYes, each ACME is estimated from its respective protein-specific mediation model,\nrepresenting the protein’s individual contribution to the mediation of air pollution's\neffects on disease.\nIn constructing the APPRS, we multiply each protein’s normalized expression\nlevel by its corresponding normalized ACME, and sum across all 65 proteins:\n∑𝑀(𝐴𝐶𝑀𝐸 ×𝑃𝑟𝑜𝑡𝑒𝑖𝑛 )\n𝑖 𝑖 𝑖\nAPPRS = .\n∑𝑀𝐴𝐶𝑀𝐸\n𝑖 𝑖\nThe rationale is similar to how risk alleles are weighted by effect sizes in genetic\nrisk scores [14], where each genetic variant is weighted by its estimated effect size and\napplied to individual-level genotypes. In our case, the APPRS quantifies the protein-\nmediated susceptibility to air pollution effects, rather than directly measuring pollutant\nexposure. Hence, multiplication involves protein levels, not pollutant concentrations.\nThis is conceptually aligned with our aim to capture intermediate molecular phenotypes\nthat reflect how air pollution impacts disease risk through biological mediators.\n5. What is the range of the APPRS? Figure 4 shows that many values are negative —\nwhat does a negative APPRS value represent? Does the negativity come from the\ndirection of the ACME coefficients or from the protein expression values? When\ncalculating the weighted sum of protein expression levels, how do you account for the\nfact that some proteins may have positive ones while others have negative ones?\nWouldn’t this cause offsetting effects between positive and negative contributions?\nResponse:\nWe appreciate the reviewer’s thoughtful questions regarding the interpretation of\nAPPRS values. We have revised the Methods section (Page 25, Lines 659-661) and\ncited Supplementary Fig. 12 and Supplementary Table 11 to clarify these aspects.\nPlease find our detailed responses below:\n(Q1) “What is the range of the APPRS?”\nThe APPRS is a continuous variable with both positive and negative values. In our\nstudy population, the APPRS ranged approximately from –1.13 to +2.36, with a mean\nnear zero (Supplementary Fig. 12).\nSupplementary Fig. 12: Distribution of APPRS scores, defined according to combinations of\nmediating proteins linked to various diseases.\n(Q2) “Figure 4 shows that many values are negative — what does a negative\nAPPRS value represent?”\nThe APPRS is a continuous, relative risk score that quantifies an individual’s\nprotein-mediated susceptibility to air pollution-associated diseases. A negative APPRS\nvalue indicates lower-than-average molecular susceptibility relative to the study\npopulation, and a positive value suggests higher susceptibility. The score is not an\nabsolute risk measure, but rather reflects cumulative weighted protein signatures\nrelevant to pollution-related disease burden.\n(Q3) “Does the negativity come from the direction of the ACME coefficients or\nfrom the protein expression values?”\nThe negative APPRS values arise primarily from individual variation in protein\nexpression levels, rather than from the direction of the ACME coefficients. All proteins\nincluded in the APPRS construction had positive mediation effects (Supplementary\nTable 11) and were directionally consistent in mediating increased disease risk\nassociated with air pollution exposure. Therefore, the sign of the APPRS is determined\nby the individual's relative expression of these proteins after standardization (z-scoring),\nnot by conflicting directions in mediation weights.\n(Q4) “When calculating the weighted sum of protein expression levels, how do\nyou account for the fact that some proteins may have positive ones while others have\nnegative ones? Wouldn’t this cause offsetting effects between positive and negative\ncontributions?”\nThe positive and negative values in protein expression arise from the z-score\nnormalization of protein levels across the population. Negative values indicate below-\naverage expression, and positive values indicate above-average expression. In the\nweighted sum used to compute APPRS, each protein’s standardized expression level is\nmultiplied by its corresponding normalized mediation weight (ACME), and then\nsummed across proteins. While it is true that some protein expression values are\nnegative, this does not introduce bias or invalidity. Rather, this reflects relative\nexpression differences across individuals.\nBecause all weights (ACMEs) are positive and directionally consistent, a higher\nAPPRS value reflects a greater cumulative burden of high-expression mediating\nproteins, while a lower (or negative) APPRS indicates lower expression of these risk-\nmediating proteins. The potential for offsetting is inherent to any standardized additive\nscore, but in our case, it retains interpretability: a higher APPRS corresponds to a higher\npredicted pollution-mediated disease susceptibility, and a lower APPRS reflects lower\nsusceptibility. We have clarified this point in the revised Methods section (Page 25,\nLines 659-661).\n6. I am not sure whether you will have serious multicollinearity problem when\nanalyzing Interaction between APPRS and air pollution, because the calculation of\nAPPRS are strongly associated with AP.\nResponse:\nWe thank the reviewer for this insightful question regarding potential\nmulticollinearity between APPRS and air pollution exposure when modeling their\ninteraction effects on disease risk. We would like to clarify that while APPRS is derived\nfrom proteins that mediate the effects of AP on disease, the APPRS itself captures\nindividual-level molecular susceptibility to air pollution, whereas AP exposure\nrepresents external environmental levels. Therefore, although conceptually related, the\ntwo variables reflect different biological layers—external exposure vs. internal\nresponse.\nTo further assess the concern, we calculated Pearson correlation coefficients\nbetween APPRS and each air pollutant. Results showed that the correlations between\nAPPRS and air pollutants were weak, ranging from 0.03 to 0.07, indicating minimal\nlinear dependency between the two variables (Supplementary Fig. 13). Such low\ncorrelations are well below conventional thresholds for multicollinearity concerns in\nepidemiological regression analyses. Biologically, this weak correlation is expected—\nAPPRS was constructed to represent protein-level susceptibility based on mediating\nproteins that respond differently among individuals, even under the same pollution\nexposure. Therefore, the variance in APPRS across individuals is not fully explained\nby AP levels, but also reflects individual biological heterogeneity.\nTaken together, we believe that multicollinearity between APPRS and air pollution\nexposure is unlikely to pose a serious issue in our interaction analysis. We have added\nrelevant clarifications to the Methods section (Page 26, Lines 697-699) and provided\nthe correlation matrix in Supplementary Fig. 13.\nSupplementary Fig. 13: Heatmap showing Pearson correlation coefficients between 12\ndisease-specific APPRS values, four individual air pollutants, and the AP index. Correlations\nwere calculated across all participants to assess the relationships among these variables.\n7. Did you conduct the data normalization before PWAS analysis? How? Describe it.\nResponse:\nYes. The UKBB proteomics data (Olink platform) were provided as Normalized\nProtein eXpression (NPX) values on a log scale, with sample selection, processing,\n2\nand quality control procedures documented by the UKB Pharma Proteomics Project\nconsortium. These upstream procedures included standard Olink normalization steps\nsuch as extension control, inter-plate control, and intensity normalization to minimize\ntechnical variation. For our analysis, we further standardized each protein’s NPX values\nacross participants by applying a z-score transformation (subtracting the mean and\ndividing by the standard deviation). This step ensured that each protein had a mean of\n0 and a standard deviation of 1, facilitating comparability across proteins and satisfying\nthe normality assumption required for the subsequent PWAS analysis. In the revised\nmanuscript, we have added a description of the data normalization procedure to the\nStudy cohort and participants section of the Methods (Page 20, Lines 524-527).\n8. I would like to remind that this study is association analysis not the causation analysis,\nso be careful with the words or sentences you used in discussion and introduction.\nResponse:\nWe thank the reviewer for this important suggestion. We have carefully rephrased\nseveral sentences in the Introduction and Discussion to avoid implying causation. For\nexample,\n In the first paragraph of Introduction, the original sentence: “…is widely\nrecognized as an environmental risk factor for multiple diseases, affecting\nrespiratory…” has been revised to: “…is widely recognized as an environmental\nrisk factor for multiple diseases, linked to respiratory…”\n In the fourth paragraph of Introduction, the original sentence:“…to understanding\nthe individual variability in response to environmental exposures…” has been\nrevised to:“…to understanding the individual variability associated with\nenvironmental exposures…”\n In the first paragraph of Discussion, the original sentence: “…we explored the role\nof proteins in mediating the effects of air pollution on diseases…” has been revised\nto: “…we examined the potential mediating role of proteins in the associations\nbetween air pollution and diseases…”\n In the fourth paragraph of Discussion, the original sentence: “…revealing both the\nshared and unique mechanisms through which pollutants affect different organ\nsystems.” has been revised to: “…suggesting both shared and unique pathways\nthat may be relevant to how pollutants are associated with different organ systems.”\n9. Fig2 you did not mention what −log₁₀(p-value) mean. Higher mean more significant\nbut did not mean stronger effect, right? And the effect can be positive or negative? I am\nworried about whether Fig2 will cause misleading. Do you consider to report the effect\nsize for all panel?\nResponse:\nWe thank the reviewer for this helpful comment. In the revised manuscript, we\nhave revised the legend of Figure 2 to explicitly clarify the meaning of the −log₁₀(p-\nvalue) in the panels. Specifically, for the Manhattan plots in Figure 2A and the volcano\nplot in Figure 2B, the −log₁₀(p-value) of y-axis represents the statistical significance of\nthe protein-pollutant association derived from PWAS, where a higher −log₁₀(p-value)\nindicates greater statistical significance but does not imply a larger effect size; the effect\nsize can be either positive or negative. To address the reviewer’s concern about potential\nmisleading, we have reported supplementary volcano plots for each pollutant\n(Supplementary Fig. 3), which display both the effect size (x-axis) and the statistical\nsignificance (y-axis) of the associations. This allows readers to interpret direction and\nmagnitude alongside significance.\nSupplementary Fig. 3: Volcano plots of PWAS results for individual air pollutants. Volcano\nplots showing associations between plasma proteins and (A) PM , (B) PM , (C) NO , and (D)\n2.5 10 2\nNO . The x-axis represents the effect size, while the y-axis represents −log₁₀(p-value),\nX\nindicating the statistical significance of each association. Red dots indicate significantly\nupregulated proteins, and blue dots indicate significantly downregulated proteins, both defined\nat FDR < 0.05 (Benjamini-Hochberg correction).\nMinor:\n1. “The effect of air pollution on populations varies, with significant heterogeneity in\nindividual susceptibility. Previous studies have highlighted these differences, showing\nthat some individuals are more vulnerable to developing diseases than others. While\none individual has a unique proteomic profile, proteomic mediation analysis offers a\npromising approach to understanding the individual variability in response to\nenvironmental exposures”\nI am not sure why this point is emphasized in the introduction, as this study does not\ndirectly address this issue. Specifically, there is no analysis of heterogeneity in\nproteomic responses to air pollution exposure across subgroups such as age, sex, or\nother demographic factors. If individual variability is a key motivation, it would\nstrengthen the paper to include subgroup analyses or at least discuss this limitation\nexplicitly in the discussion section.\nResponse:\nWe thank the reviewer for this insightful suggestion. The APPRS in our study is\nan individualized measure derived from mediation-effect proteins, designed to capture\ndifferences in susceptibility to the health effects of air pollution across individuals. As\nthe reviewer correctly noted, we had not explicitly presented subgroup analyses in the\noriginal manuscript. In the revision, we have addressed this by adding subgroup\nanalyses of APPRS heterogeneity in the Results section (Page 11, Lines 270-273),\nSupplementary Texts and Supplementary Figs. 15-19. Details of the analysis are as\nfollows:\nWe performed systematic subgroup analyses to check heterogeneity in APPRS\nacross six key demographic and socioeconomic dimensions, including age, sex, BMI,\neducation level, and household income (Supplementary Figs. 15-19). These analyses\nrevealed statistically significant differences in APPRS distributions across subgroups.\nSpecifically, APPRS increased significantly with age (Supplementary Fig. 15),\nconsistent with cumulative exposure burden theories [15]. Males exhibited 0.028 higher\nmedian APPRS than females (Supplementary Fig. 16), accordant with reported sex-\ndimorphic immune responses to environmental exposures [16]. Individuals with higher\nBMI tended to have higher APPRS (Supplementary Fig. 17), suggesting possible\ninteractions between metabolic health and pollution susceptibility. Furthermore,\nparticipants with lower income or education had 0.076-0.162 higher APPRS\n(Supplementary Figs. 18-19), aligning with environmental injustice paradigms [17].\nThese findings indicate that APPRS captures systematic variation in susceptibility\nacross population subgroups, supporting its value as an indicator of individual risk. We\nhave incorporated these results into the revised Results section with the sentence: “To\ncheck heterogeneity in individual susceptibility, we conducted subgroup analyses of\nAPPRS across key demographic strata. Significant inter-group differences were\nobserved across subgroups (Supplementary Texts; Supplementary Figs. 15-19).”\nDetailed subgroup plots and statistical outputs are provided in Supplementary Texts\nand Supplementary Figs. 15-19.\nSupplementary Fig. 15: Comparison of APPRS stratified by age. Violin and box plots show\nthe distribution of each APPRS between younger (<58 years) and older (≥58 years) groups,\ndefined by the median age. Group differences were assessed using the Wilcoxon rank-sum test.\nSignificant associations between APPRS and age groups are marked with asterisks: * P<0.05,\n** P<0.01, *** P <0.001.\nSupplementary Fig. 16: Comparison of APPRS stratified by sex. Violin and box plots show\nthe distribution of each APPRS between males and females. Group differences were assessed\nusing the Wilcoxon rank-sum test. Significant associations between APPRS and sex groups are\nmarked with asterisks: * P < 0.05, ** P < 0.01, *** P < 0.001.\nSupplementary Fig. 17: Comparison of APPRS stratified by BMI. Violin and box plots\nshow the distribution of each APPRS across three BMI groups: Normal (<25), Overweight (25–\n30, OW), and Obese (>30, OB). Group differences were assessed using one-way ANOVA.\nSignificant associations between APPRS and BMI groups are marked with asterisks: * P < 0.05,\n** P < 0.01, *** P < 0.001.\nSupplementary Fig. 18: Comparison of APPRS stratified by household income. Violin and\nbox plots show the distribution of each APPRS across three income groups: Low (<£18,000),\nMedium (£18,001–£51,999), and High (>£52,000). Group differences were assessed using one-\nway ANOVA. Significant associations between APPRS and income groups are marked with\nasterisks: * P < 0.05, ** P < 0.01, *** P < 0.001.\nSupplementary Fig. 19: Comparison of APPRS stratified by education. Violin and box\nplots show the distribution of each APPRS between two education groups: Level 1-3 and Level\n4-7+. Group differences were assessed using the Wilcoxon rank-sum test. Significant\nassociations between APPRS and education groups are marked with asterisks: * P < 0.05, ** P\n< 0.01, *** P < 0.001.\n2. Fig2A I cannot see the grey horizontal line.\nResponse:\nWe thank the reviewer for pointing this out. We have adjusted the figure to enhance\nthe visibility of the gray horizontal line in Figure 2A.\nFigure 2A: Manhattan plots of PWAS results for PM , PM , NO , and NO . Colored dots\n2.5 10 2 x\nrepresent proteins significantly associated with the corresponding air pollutant. The −log₁₀(p)\nin y-axis represents the statistical significance of the association between proteins and the\npollutants. The gray horizontal line represents the significance threshold of P < 0.05, while the\ncolored horizontal lines indicate the threshold of FDR < 0.05.\n3. Fig2C. and “Moreover, we found that while a small subset of proteins was associated\nwith only a single air pollutant—30.66% for PM2.5, 36.99% for PM10, 33.05% for\nNO2, and 7.05% for NOx—the majority exhibited associations with multiple pollutants”\nI’m not sure I fully understand this point. Are you saying that certain proteins are\nsignificant in single-pollutant models but not in the AP index model? If so, could you\nclarify why that might be the case? Also, are there proteins showing inconsistent or\neven opposite associations between individual pollutants and the AP index — similar\nto the patterns shown in Figure 2B?\nResponse:\nWe thank the reviewer for these thoughtful comments. Below we provide\nresponses to each point:\n(1) Clarification of Figure 2C:\nWe would like to clarify that Figure 2C summarizes the number of proteins\nsignificantly associated with individual air pollutants (PM2.5, PM10, NO2, NOx) based\non the single-pollutant PWAS models, but does not involve the AP index. The bars\nindicate the number of proteins uniquely associated with one pollutant (darker portion)\nversus those associated with two or more pollutants (lighter portion). The percentages\ncited in the manuscript refer to the proportion of significant proteins for each pollutant\nthat were exclusive to that pollutant. For example, 30.66% of the proteins significantly\nassociated with PM2.5 were not found to be significantly associated with any other\npollutants. In conclusion, the analysis in Figure 2C is based solely on single-pollutant\nmodels and is independent of the AP index. We have revised the legend of Figure 2\nto clarify this point and avoid misunderstanding.\n(2) Directionality differences between individual pollutants and AP index:\nRegarding the question, “Are there proteins showing inconsistent or even opposite\nassociations between individual pollutants and the AP index — similar to the patterns\nshown in Figure 2B?”, we believe the reviewer may have been referring to Figure 2C,\nwhich shows the overlap and uniqueness of proteins associated with individual\npollutants, whereas Figure 2B presents a volcano plot for the AP index model alone and\ndoes not provide comparative information across pollutants.\nTo address this point, we conducted a comparative analysis of protein association\ndirections between each individual pollutant and the AP index. As shown in the newly\nadded Supplementary Fig. 25, while the majority of overlapping proteins exhibited\nconsistent directions, a subset showed opposite directions: 13.1% for PM₁₀, 15.8% for\nNOₓ, 28.6% for NO₂, and 64.5% for PM₂.₅. This discrepancy can be explained by the\ndifferent nature of composite versus single-pollutant exposures. As illustrated in Figure\n2C, individual pollutants often show distinct protein association profiles. The AP index\naggregates multiple pollutants using weighted regression coefficients based on their\ncontributions to self-rated health, which may result in a shift in the net direction of\nassociation for certain proteins. For example, a protein strongly positively associated\nwith PM₂.₅ may exhibit a negative association with NO₂. If NO₂ has a larger weight in\nthe AP index, the protein’s net association with the AP index may be reversed. This\nphenomenon reflects the complex and sometimes competing biological responses to\nmultiple environmental exposures and highlights the importance of examining both\nindividual and composite pollutant effects. We have added this explanation to the\nDiscussion section (Page 16, Lines 402-406) and included Supplementary Fig. 25 for\nclarity.\nSupplementary Fig. 25: Comparison of effect directions for proteins significantly\nassociated with both single pollutants and the AP index. For each pollutant, darker bars\nindicate proteins with consistent association directions, while lighter bars indicate proteins with\nopposite directions relative to the AP index.\n4. Fig3 is a bit difficult to follow. Panel F may have some overlap pathways?\nResponse:\nWe thank the reviewer for this comment and have revised the legend of Figure 3.\nFigure 3 integrates the full mediation analysis workflow, including the assessment of\nmediation effects across diseases, the comparison of mediation proportion within\ndifferent pollutants, and the functional enrichment of disease-specific mediators. To\nimprove clarity, we have expanded the figure legend to clearly describe the analysis\nshown in each panel. The revised legend now concisely describes the content of each\npanel: Panel A outlines the mediation analysis framework; Panel B presents the\nmediation results of AP index; Panel C illustrates an example mediation pathway;\nPanel D shows mediator classification by pollutant type and disease; Panel E visualizes\nthe number of mediators and global mediation proportions; Panel F summarizes functional\nenrichment of disease-specific mediators; and Panel G ranks mediators by their\ncumulative mediation proportions across diseases.\nRegarding Panel F, some pathways may appear to overlap because functional\nenrichment analyses often identify biologically related pathways that share underlying\ngene sets, particularly for processes such as immune regulation, MAPK signaling, and\ncarbohydrate metabolism. This partial overlap is an inherent feature of enrichment\nresults. For better interpretation, we have clarified this point in the revised legend of\nFigure 3 and listed the top enriched pathways along with the detailed gene sets in\nSupplementary Table 9. We believe these additions will make Panel F easier to follow.\n5. Fig5, what is the metric of model performance? What do you mean contribution?\nContribute to the r-square or final predicted value?\nResponse:\nWe thank the reviewer for pointing out the need for clarification regarding Figure\n5. The predictive performance of our models was evaluated using the Area Under the\nReceiver Operating Characteristic Curve (AUROC), which quantifies the model’s\nability to distinguish between cases and controls across all possible classification\nthresholds.\nRegarding the term “contribution”, it refers to the relative influence of each feature\n(e.g., basic information, clinical features, and APPRS) on the final predicted value in\nthe full model, estimated using the LASSO regression model. Before fitting the model,\nall features were standardized, ensuring that the coefficient magnitudes are directly\ncomparable across variables. The contribution of each feature was then calculated as\nthe proportion of the absolute value of its coefficient over the total sum of absolute\ncoefficients across all features. This provides an interpretable metric of how much each\nfeature contributes to the model’s prediction. Specifically, the contribution of APPRS\nreflects the extent to which protein-level susceptibility explains the outcome, relative\nto traditional risk factors.\nWe have clarified these points in the revised Methods (Page 27, Lines 725-727)\nand updated the legend of Figure 5 accordingly.\n6. P18 Lines -18-19 What is spatial resolution of Land Use Regression? pls mention\nwhich multiple years for NO2 and PM10.\nResponse:\nWe thank the reviewer for this helpful suggestion. The land use regression (LUR)\nmodels developed by the European Study of Cohorts for Air Pollution Effects\n(ESCAPE) used a high spatial resolution of 100 × 100 meters (i.e., 0.01 km² grid cells)\nfor estimating ambient air pollution exposures. This resolution was consistently applied\nacross pollutants including NO , PM , PM , and NO , and across all participating\n2 10 2.5 X\nEuropean cohorts, including UKBB [18].\nRegarding the availability of multi-year air pollution data: NO exposures were\n2\navailable for 2005-2007 and 2010; PM exposures were available for 2007 and 2010;\n2.5 X\ntemporal coverage are consistent with the UK Biobank air pollution exposure\ndocumentation and ESCAPE protocols [19].\nWe have added these clarifications in the Methods section (Page 19, Lines 499-\n509).\nReviewer #2 (Remarks to the Author):\nThe authors presented a study that analyzed the mediation role of circulating proteins\nbetween multiple air pollutants and 30 diseases in the UKBB cohort. The study is\ncomprehensive in terms of using multiple data sources (e.g., air pollutants, proteins,\ndisease outcomes, etc.) and using various analytic approaches (e.g., PWAS, survival\nanalyses, functional enrichments, etc.). The following list includes my detailed\ncomments regarding the study design, data, and methodology.\n1. The major concern of the methodology is that in this PWAS-type analysis, there\nshould be careful consideration of multiple comparisons, as many pairs of exposure-\noutcomes. The use of a significance level of 0.05 in this manuscript could lead to large\nType I errors. For example, in the following study with multiple disease outcomes, they\ndocumented as “adjusting for multiple comparisons using the Bonferroni correction.”\na. Wei, Y., Wang, Y., Di, Q., Choirat, C., Wang, Y., Koutrakis, P., Zanobetti, A.,\nDominici, F. and Schwartz, J.D., 2019. Short term exposure to fine particulate matter\nand hospital admission risks and costs in the Medicare population: time stratified, case\ncrossover study. bmj, 367.\nResponse:\nWe thank the reviewer for raising this important point about the potential\ninflation of Type I error due to multiple comparisons. We would like to clarify that our\nmain results of PWAS-type analysis were based on false discovery rate (FDR) correction,\nspecifically using the Benjamini-Hochberg (BH) method [20] to control multiple\ntesting across proteins for each pollutant.\nWe appreciate the reference provided [21] , and agree that adjusting for multiple\ncomparisons is essential when assessing associations across many outcomes or\nexposures. While Wei et al. applied Bonferroni in a different setting, our study focuses\non multiple exposures with shared molecular measurements (i.e. proteins), for which\nFDR is more commonly applied and statistically justified. We have now cited this\nreference in the revised Methods to acknowledge the methodological contrast and\nsupport the rationale for our choice.\nFurthermore, we have revised the main text to more explicitly state that the main\nresults rely on FDR < 0.05, and have clarified our multiple testing correction approach\nin the Methods section (Page 21, Lines 568-569).\n2. How are the individuals with and without valid proteomic data different in their\npopulation characteristics, as well as their exposure levels to air pollution?\nResponse:\nWe thank the reviewer for raising this important issue. To assess potential\ndifferences, we compared demographic characteristics (age, sex, BMI, ethnicity,\nsmoking, drinking, education and household income) and air pollution exposure levels\nbetween participants with and without proteomic data. As shown in Supplementary\nTable 14, the mean values for most characteristics are highly similar between the two\ngroups, with difference of the mean values generally less than 0.1. This finding is\nconsistent with previous studies showing that the proteomics subgroup closely matches\nthe full UK Biobank population in age, sex, BMI, ethnicity, health behaviors, and\nbiochemical traits, with only minor differences observed [22]. Thus, from a\nrepresentativeness standpoint, the main proteomics cohort is largely comparable to the\nfull UK Biobank. However, we acknowledge that the consortium-selected subset (6,376\nout of 54,239 participants) may introduce some bias due to non-random selection. This\nlimitation has been noted in previous literature [23], and we have explicitly addressed\nit as a potential source of bias in the revised Discussion.\nOverall, the proteomics subcohort used in our analysis remains highly\nrepresentative of the full UKBB. We have clarified these points in the revised Methods\n(Page 20, Lines 519-522) and Discussion (Page 18, Lines 476-477) sections, and\nsummarized the detailed comparisons in Supplementary Table 14.\nSupplementary Table 14: Baseline characteristics of participants with and without\nproteomic data, including age, sex, BMI, smoking status, alcohol consumption,\nethnicity, education, household income, and air pollution exposure levels.\nMean/Ratio of Mean/Ratio of individuals\nVariable Group Difference\nall individuals with proteomic data\nSex Female 0.544 0.554 0.01\nSex Male 0.456 0.446 0.01\nSmoking\nNever 0.545 0.552 0.007\nStatus\nSmoking\nPrevious 0.345 0.341 0.004\nStatus\nSmoking\nCurrent 0.102 0.103 0.001\nStatus\nAlcohol\nDaily 0.203 0.201 0.002\nConsumption\nAlcohol 2-3 times a\n0.230 0.227 0.003\nConsumption week\nAlcohol 1-2 times a\n0.258 0.260 0.002\nConsumption week\nAlcohol 1-3 times a\n0.111 0.110 0.001\nConsumption month\nAlcohol Special\n0.116 0.117 0.001\nConsumption occasions\nAlcohol\nNever 0.081 0.084 0.003\nConsumption\nEthnic White 0.942 0.934 0.008\nEthnic Mixed 0.006 0.007 0.001\nEthnic Asian 0.020 0.018 0.002\nEthnic Black 0.016 0.023 0.007\nEthnic Chinese 0.003 0.003 0\nEthnic Other 0.009 0.012 0.003\nEducation\nLevel 1-3 0.410 0.403 0.007\nLevel\nEducation\nLevel 4-7 0.590 0.597 0.007\nLevel\nHousehold\nUnknown 0.154 0.153 0.001\nIncome\nHousehold\n<18,000 0.193 0.197 0.004\nIncome\nHousehold 18,000-\n0.215 0.219 0.004\nIncome 30,999\nHousehold 31,000-\n0.220 0.218 0.002\nIncome 51,999\nHousehold 52,000-\n0.172 0.168 0.004\nIncome 100,000\nHousehold\n>100,000 0.046 0.045 0.001\nIncome\nAge / 56.53 56.54 0.01\nBMI / 27.43 27.37 0.06\nPM2.5 (2010) / 9.99 10.01 0.02\nPM10\n/ 19.31 19.25 0.06\n(average)\nNO2 (average) / 29.07 29.32 0.25\nNOx (2010) / 44.11 44.39 0.28\n3. Related to Question 2, how are the results obtained from 49,242 individuals (9.8%)\ngeneralizable to the entire UKBB cohort? In the discussion section, the sentences\nsuggested that the results utilized “data from over 500,000 participants in the UK\nBiobank,” which is not entirely accurate, given that the important merit of this paper is\non mediation analyses.\nResponse:\nWe appreciate the reviewer’s comment and agree that clarification is needed. In\nour study, we utilized the full UKBB cohort of over 500,000 participants to identify\nrobust and unbiased associations between air pollution and disease endpoints,\nleveraging the statistical power and generalizability provided by the full cohort.\nLeveraging the full cohort allowed us to obtain more accurate and less biased estimates\nof the exposure-disease associations, given the large sample size, extensive follow-up,\nand broad population coverage. This analysis established the foundational associations\nbetween specific air pollutants and 30 disease outcomes.\nSubsequently, the mediation analyses involving proteomic data were conducted\nwithin a well-characterized subset of 49,242 participants who had plasma proteomics\navailable. As detailed in our response to Comment 2, this subset is broadly comparable\nto the full cohort in terms of demographic and exposure characteristics, supporting the\ninternal validity of the mediation findings. However, we do not claim to generalize the\nmediation results to the entire cohort, but rather to provide mechanistic insights within\na well-characterized and representative sample. We have revised the corresponding\nsentence in the Discussion section (Page 14, Lines 361-362) to avoid any potential\nmisinterpretation.\n4. It is unclear to me how the air pollutants data were linked to the participants; are they\nair pollutants between April 4, 2006 and October 1, 2010, or earlier years? How many\nyears, and whether they are multi-year averaged measures?\nResponse:\nWe thank the reviewer for this helpful request for clarification. In the UKBB,\nindividual-level exposure estimates were assigned by linking each participant’s\nbaseline residential address to European-wide land use regression (LUR) models\ndeveloped within the ESCAPE (European Study of Cohorts for Air Pollution Effects)\nproject (http://www.escapeproject.eu/). These LUR models were constructed using\nroutine air quality monitoring data together with geographic predictor variables such as\ntraffic intensity, road networks, land use, and population density [18] . They provide\nhigh-resolution spatial surfaces of annual average concentrations for several pollutants,\nincluding PM , PM , PM absorbance, NO₂, and NOx.\n2.5 10 2.5\nWith respect to temporal coverage, NO exposures were available for 2005-2007\n2\nand 2010; PM exposures were available for 2007 and 2010; and PM2.5 and NOx\n10\nexposures were available for 2010 only. Each estimate corresponds to the annual\naverage concentration at the participant’s residential location for that specific year. In\nour study, we used multi-year averages when multiple years of data were available (e.g.,\nNO 2005-2007 and 2010; PM 2007 and 2010), and single-year estimates (2010) for\n2 10\nPM and NO .\n2.5 X\nWe have revised the Methods section (Page 19, Lines 499-509) to clearly describe\nthe details of exposure estimates.\n5. The specific time periods/orders of air pollution exposures and disease outcomes are\nunclear to me. For example, is the air pollution exposure measured before the onset of\nthese diseases? According to the Study Design section, it sounds like the entire study is\ncross-sectional, with only one air pollution measurement and disease outcomes for each\nparticipant. Is it possible that the disease onset is actually prior to the air pollution\nexposure measured, which leads to an important concern about the validity of the causal\nlink?\nResponse:\nWe thank the reviewer for highlighting this important point. We clarify that our\nstudy is based on a prospective cohort design, rather than a traditional cross-sectional\nanalysis. Exposure estimates were derived at baseline (2005-2010), while disease\nendpoints diagnosed after 2010 were included as incident cases. Participants who had\na diagnosis of the disease of interest prior to 2010 were excluded from that disease-\nspecific analysis. By design, this approach ensures that air pollution exposure was\nmeasured prior to disease onset.\nWe agree with the reviewer that our study uses a single baseline exposure\nmeasurement combined with prospective follow-up for incident outcomes. To avoid\nconfusion, we have revised the Study Design section to emphasize that this is a\nprospective cohort design, not a purely cross-sectional analysis. Revised text in the\nResults section (Page 5, Lines 107-111) now reads:\n“For each participant, long-term air pollution exposures were estimated at\nbaseline (2005-2010) based on residential address, and disease outcomes were\nprospectively ascertained through linkage to health records. Incident cases were\ndefined as diagnoses occurring after 2010, which ensured that exposure assessment\npreceded disease onset in all analyses.”\nWe believe this clarification addresses the reviewer’s concern regarding temporal\nvalidity and confirms that exposure preceded disease onset in our study design.\n6. The same logic in Question 5 applied to the relationship between air pollutants and\nproteins and proteins to diseases, since “this study relies on single time point\nmeasurements of proteomic data.” Given that the study was considered a mediation\nanalysis, the time order of air pollution -> proteins -> disease outcomes seems rather\nimportant.\nResponse:\nWe thank the reviewer for this insightful comment. We fully agree that the\ntemporal ordering of exposure, mediators, and outcomes is critical in mediation\nanalysis. In our study, the timeline was as follows: air pollution exposures were\nestimated at baseline (2005-2010); proteomic measurements were obtained from\nplasma samples collected at the same baseline visits (2005-2010); and disease outcomes\nwere defined as incident cases diagnosed after 2010. This design ensures that both\nexposure and mediator were assessed prior to disease onset.\nPrevious studies in the UKBB and other European cohorts have shown that spatial\ncontrasts in air pollution are relatively stable over time [24], supporting the use of\nbaseline exposures as proxies for long-term patterns during follow-up [10]. Moreover,\nbaseline proteomic profiles in UKBB have been demonstrated to robustly predict future\ndisease risk [23, 25], suggesting that a single measurement can capture biologically\nmeaningful states associated with chronic exposures. However, we acknowledge that\nproteomic data were available only at a single time point, limiting our ability to account\nfor longitudinal changes in protein levels. We have explicitly noted this limitation in\nthe Discussion (Page 18, Lines 464-469) and emphasized that future studies\nincorporating repeated proteomic profiling and updated exposure assessments would\nprovide stronger causal inference.\n7. In the mediation analysis, it is unclear what regression techniques were used. The\nformulation looks like a linear regression in Line 568. However, this is different from\nthe Cox models used in other analyses. Given that the mediation analyses also used\ndisease outcomes, which are assumed to be time-to-event. How do the authors adjust\nfor the use of the regression formulation?\nResponse:\nWe thank the reviewer for this important comment and have added further\nclarification in the Methods section (Page 23, Lines 613-617). In our mediation\nanalyses, we used the “mediation” R package to estimate indirect and direct effects of\nair pollution on disease outcomes through circulating proteins. This approach requires\nspecifying two regression models corresponding to equations (1) and (2) in the\nmanuscript:\n Equation (1): the association between air pollution and each protein mediator was\nmodeled using linear regression [lm()], with appropriate covariate adjustment.\n Equation (2): the association between protein mediators and disease outcomes,\nconditional on air pollution, was modeled using a Poisson regression [glm(family\n= “poisson”, offset = log(onset time))] with covariate adjustment. This\nspecification has been shown to be equivalent to a Cox proportional hazards model\nfor time-to-event data [26].\nThus, although the mediate() function does not directly support Cox models, the\nPoisson regression with offset formulation allowed us to correctly account for survival\ntime and obtain estimates equivalent to Cox regression.\nWe have revised the manuscript to explicitly describe these model specifications\nin the Methods section (Page 23, Lines 613-617) and have corrected the text around\nLine 568 to avoid confusion.\n8. In the analyses using Cox models, are they under the assumption that each disease\noutcome only happened once? How about repeated measures of outcomes during 4\nyears?\nResponse:\nWe thank the reviewer for this important clarification. Yes, in our Cox regression\nanalyses, we modeled time to first diagnosis for each disease outcome. In UKBB,\ndisease outcomes were ascertained through hospital admission and mortality records,\nand the date of the first recorded diagnosis was used as the event time.\nWe did not incorporate repeated events (e.g., recurrent hospitalizations or re-\ndiagnoses) into our Cox models. This is consistent with standard practice in UKBB\nanalyses [23, 25], and reflects the fact that the first diagnosis typically represents the\nclinically most relevant onset of disease.\nWe have clarified this point in the Methods section (Page 19, Lines 514-515;\nPage 22, Lines 595-596).\n9. The use of “exhibited mediation effects in more than two diseases” as one of the\ninclusion criteria of APPRS sounds ad hoc. Is it possible that one protein is very\nimportant to one disease, yet is being excluded because it failed to meet this criterion?\nAlso, why not three diseases?\nResponse:\nWe appreciate the reviewer’s thoughtful comments regarding the inclusion\ncriterion for APPRS. The choice to include proteins “exhibiting mediation effects in\nmore than two diseases” was intended to prioritize proteins that show consistent\nmediation across multiple disease outcomes, thus increasing the robustness and\ngeneralizability of the APPRS.\nWe acknowledge that this approach may exclude proteins that are highly relevant\nto a specific disease but do not mediate multiple outcomes. However, our intention was\nto develop a population-level risk score (APPRS) reflecting proteins with broader\nmediation roles across diseases, rather than capturing all disease-specific mediators.\nIncorporating disease-specific proteins would significantly increase the number of\nmarkers and compromise the interpretability and translational utility of the score. Our\nfocus was to identify a compact yet informative set of proteins that capture shared\nbiological pathways linking air pollution to multiple diseases.\nTo further address the potential arbitrariness of this threshold, we compared three\nthresholds—proteins mediating ≥2, ≥3, or ≥4 diseases (yielding 69, 39, and 21 proteins,\nrespectively)—and constructed corresponding APPRSs. As shown in Supplementary\nFig. 27, we assessed the correlation between each APPRS and the prevalence of 12 air\npollution-associated diseases. The APPRS based on ≥2-disease proteins achieved the\nstrongest or comparable correlations in most diseases (e.g., COPD, T2D, CKD),\nindicating better coverage of shared air pollution-related disease mechanisms. In\ncontrast, stricter thresholds (≥3 or ≥4 diseases) excluded potentially informative\nproteins and led to reduced performance in most outcomes, despite marginal gains in a\nfew (e.g., Anemia). The choice of the two-disease threshold reflects a balance between\nselecting proteins with broad mediation roles and maintaining a manageable and\ninterpretable risk score.\nWe have clarified this threshold and its rationale in the revised Methods section\n(Page 24, Lines 652-654) and Supplementary Texts. We thank the reviewer for raising\nthis important point and allowing us to further clarify our approach.\nSupplementary Fig. 27: Comparison of correlations between APPRS and disease\nprevalence under different protein inclusion thresholds. The protein inclusion thresholds\nwere defined based on the minimum number of diseases in which a protein exhibited significant\nmediation effects: ≥2 diseases (69 proteins), ≥3 diseases (39 proteins), and ≥4 diseases (21\nproteins). The Pearson correlation was calculated between the APPRS and the prevalence of 12\nair pollution-associated diseases.\n10. In the development of APPRS, if this procedure followed the development of\npolygenic risk scores (PRS), the score should be disease-specific. I found the APPRS\nis agnostic to multiple diseases. Any reason not to develop a disease-specific APPRS?\nResponse:\nWe thank the reviewer for this important question. We agree that, analogous to\npolygenic risk scores (PRS), risk scores are typically developed in a disease-specific\nmanner. We would like to clarify that our APPRS is indeed disease-specific in both its\nconstruction and application. The APPRS for each disease is calculated based on the\nweighted sum of the expression levels of the selected mediators, where the weights\ncorrespond to the estimated average causal mediation effects (ACME) for a particular\ndisease (Supplementary Table 9).\nTo illustrate this, we have now included a heatmap (newly added Supplementary\nFig. 11) showing the APPRS values for all 49,262 individuals across 12 diseases. The\nfigure clearly demonstrates that APPRS values for the same individual differ\nsubstantially across diseases, reflecting the heterogeneity in protein mediators and\ndisease risk profiles. Thus, the APPRS is inherently disease-specific and not agnostic\nacross diseases.\nWe have clarified the disease-specific definition, calculation, and application of\nAPPRS in the revised Methods (Page 25, Lines 661-664) and Results (Page 11, Lines\n265-268) sections, and have included Supplementary Fig. 11 to further illustrate this\npoint.\nSupplementary Fig. 11: Heatmap of APPRS across 12 diseases in 49,262 individuals. The\nx-axis represents individuals, and the y-axis shows APPRS values for each disease. APPRS\nvalues are color coded from blue (negative), to white (zero), to red (positive).\nReviewer #3 (Remarks to the Author):\nThis study explores the biological mechanisms underlying the association between air\npollution and disease, with a particular focus on the mediating role of circulating\nproteins. Leveraging large-scale data from the UK Biobank, the authors identified 30\ndiseases significantly associated with air pollution exposure and uncovered 1,089\npollution-associated proteins, among which 296 were found to significantly mediate\ndisease risk. These mediating proteins were enriched in immune-related pathways and\nexhibited disease-specific patterns, including MAPK signaling, innate immunity,\ncarbohydrate metabolism, and respiratory cell proliferation. Based on these findings,\nthe authors developed a novel Air Pollution Protein Risk Score (APPRS), which\ndemonstrated strong predictive capacity for disease risk in both the UK Biobank and\ntwo external validation cohorts. Moreover, the integration of APPRS into baseline\nmodels yielded improved prediction performance. This work highlights the central role\nof circulating proteins in mediating the health impacts of air pollution and introduces\nAPPRS as a promising tool for personalized risk assessment and precision public health\nstrategies. The study is well-conceived, methodologically sound, and clearly written.\nHowever, I believe the manuscript would benefit from addressing the following\nconcerns prior to acceptance:\n1. Given that the temporal dynamics of exposure can vary significantly across diseases,\nthe authors should clarify how the exposure window for air pollution was selected. Was\na disease-specific sensitive window considered, or was a uniform window applied to\nall outcomes?\nResponse:\nWe thank the reviewer for raising this question. In our study, we applied a uniform\nexposure window for all disease outcomes. Specifically, air pollution exposures were\nestimated using annual average concentrations at each participant’s baseline residential\naddress, covering the years 2005-2010. Where multi-year data were available (e.g., for\nNO₂ and PM₁₀), we used the average across those years; for pollutants with only single-\nyear data (e.g., PM₂.₅ and NOx), we used the 2010 values. This approach reflects long-\nterm exposure status at baseline for all participants.\nWe acknowledge that different diseases may have varying sensitive windows to\nenvironmental exposures, and that a uniform exposure window may not capture\ndisease-specific susceptibility periods. However, due to limitations in the available data\nand to maintain consistency across analyses, we used this standardized approach. This\nis also the standard practice in other epidemiological studies of air pollution and disease\nin the UKBB [6, 10, 27-29], as short-term exposure data are not available in this cohort.\nWe have now clarified this methodological choice in the revised Methods section\n(Page 19, Lines 499-509), and have noted this limitation in the Discussion (Page 18,\nLines 464-469). We agree that future studies with longitudinal exposure data and\ndisease-specific modeling may provide further insight into critical exposure windows\nfor different diseases.\n2. In the Introduction section, the authors are encouraged to summarize existing\nmethodologies that integrate air pollution exposure with biological markers such as\nproteins. This context would help readers understand how APPRS builds upon or\nadvances previous approaches.\nResponse:\nWe thank the reviewer for this valuable suggestion. In the revised manuscript, we\nhave added a summary of existing studies that integrate air pollution exposure with\nbiological markers, particularly proteomic data, in the Introduction section (Page 4,\nLines 68-76), detailed as follows:\n“To further investigate the molecular mechanisms underlying these processes,\nrecent studies have begun integrating exposure data with proteomics profiles in narrow\ncontexts [30, 31]. For example, controlled exposure experiments have demonstrated\ndose-dependent changes in cardiovascular-related proteins [30], and cohort studies in\nyoung adults have shown that ambient air pollution significantly perturbs the\ncardiometabolic proteome [31]. However, these studies focus solely on molecular\nresponses to air pollution without linking them to clinical outcomes, leaving the\nmediating role of proteomic signatures in pollution-related disease development still\nunclear”.\nThis new paragraph provides context for how our study builds upon and extends\nexisting research by offering a systematic, multi-disease framework for individual risk\nprediction based on mediation-effect proteins. We believe this addition helps to better\ncontextualize the novelty and significance of our work.\n3. While APPRS showed overall improvement in prediction, the gains appear modest\nfor many diseases. The authors should discuss the limitations of APPRS more explicitly,\nespecially in terms of its discriminatory power and practical applicability.\nResponse:\nWe appreciate the reviewer’s insightful comment. We acknowledge that while\nAPPRS demonstrates an overall improvement in disease prediction, the performance\ngains appear modest for certain diseases. Below, we discuss the potential limitations of\nAPPRS, as well as the primary aim of this part of the analysis.\nFirst, the prediction performance can be limited by the degree to which air\npollution contributes to the development of each disease. Not all diseases are equally\naffected by air pollution exposures, and the mediating role of proteins may vary in\nstrength and direction, potentially reducing APPRS’s ability to distinguish between\nindividuals with and without certain conditions. Second, the baseline models already\ninclude informative demographic and clinical variables (e.g., age, sex, BMI,\nbiochemical markers) that explain much of the variance in chronic diseases. As a result,\nthe additional predictive value of APPRS may appear modest, especially for conditions\nwhere pollution-related pathways play a secondary role. Finally, we emphasize that the\ngoal of this analysis was not to build a competitive disease prediction model, but to\nassess whether APPRS captures pollution-related disease risk beyond traditional factors.\nOur findings suggest that for diseases with strong pollution links (e.g., cardiovascular\nand respiratory conditions), APPRS provides significant added value, highlighting its\npotential for identifying individuals with potentially higher pollution-related\nvulnerability.\nIn the revised manuscript, we have expanded the Discussion section (Page 16,\nLines 428-430; Page 17, Lines 431-435) to explicitly address the limitations of APPRS,\nparticularly regarding its modest improvement in disease prediction performance across\ncertain outcomes.\n4. Although the statistical mediation analysis provides a screening tool to identify\ncandidate proteins, further evidence is needed to support their biological plausibility.\nThe authors are encouraged to elaborate on whether there is experimental or literature-\nbased support for the mediating roles of the highlighted proteins.\nResponse:\nWe thank the reviewer for this important comment and agree that further evidence\nis needed to support the biological relevance of the identified mediating proteins. To\naddress this, we conducted an extensive PubMed search for experimental or\nobservational studies providing evidence for: (1) associations between the mediating\nproteins and air pollutants, (2) their involvement in pollution-related molecular\npathways, and (3) their relevance to disease outcomes. Literature-based support was\nidentified for 19 of the 65 key mediating proteins.\nFor example, experimental studies have shown that MMP12 expression is\nsignificantly upregulated following exposure to cigarette smoke and PM₂.₅ [32].\nMechanistically, PM-induced oxidative stress and inflammation lead to macrophage\nactivation and subsequent overexpression of MMP12, which contributes to elastin\ndegradation and airway remodeling. Clinically, elevated MMP12 levels have been\nimplicated in the pathogenesis of COPD, where it promotes emphysema progression\nand airflow limitation [33]. Another example is the pro-inflammatory cytokine IL6,\nwhich is significantly increased after PM and NO₂ exposure in both human and\n2.5\nanimal studies [34]. Chronically elevated IL6 levels has been reported to be involved\nin the pathogenesis of T2D and RA by promoting Th17 differentiation and systemic\ninflammation [35].\nTo ensure transparency and facilitate interpretation, we have compiled a new\nsupplementary table (Supplementary Table 13) summarizing the evidence for these\n19 proteins, including their documented links to air pollution, pollution-related\nbiological pathways, and associated diseases, with relevant references. Overall, most\nproteins participate in well-established pollution-related pathogenic processes such as\ninflammation, oxidative stress, immune dysregulation, and endothelial dysfunction.\nSeveral have been reported to be upregulated after PM₂.₅ or NO₂ exposure, triggering\ndownstream effects including cytokine activation, inflammatory cell recruitment,\nantioxidant defense, and tissue remodeling. A summary of these findings has also been\nincorporated into the revised Discussion section (Page 16, Lines 418-422).\nSupplementary Table 13: Literature-based evidence supporting the biological\nrelevance of key mediating proteins in the APPRS analysis.\nLiterature\nLiterature\nSupport\nProtein Mechanistic Association with Air Support Associated\nfor the\nName Pathway Pollution for the AP Diseases\nDisease\nAssociation\nAssociation\nCigarette smoke\nECM remodeling, upregulation mediates ECM\nPMID: PMID:\nMMP12 inflammatory degradation in COPD\n34912233 39794761\ninfiltration emphysema/asthma/lung\ncancer\nEpigenetic\nFolate receptor mitigates PMID: PMID:\nFOLR2 protective Anemia\nNO₂ reproductive toxicity 18992323 35545363\nmechanisms\nInflammatory cell\nCCR2 ligand mediates PM₂.₅\nchemotaxis, PMID: PMID:\nCCL7 monocyte recruitment to T2D\nmetabolic 24149114 40684027\nadipose tissue\ndysregulation\nTNFRSF1 Immune Epigenetic B-cell activation PMID: PMID:\nRA\n3B dysregulation disruption 34498177 24814046\nEndoplasmic\nNO₂ disrupts ER\nreticulum stress, PMID: HF, PMID:\nTXNDC5 oxidoreductase via oxidative\nprotein 35905627 Arrhythmia 40261983\nstress\nmisfolding\nImpaired Protein nitration reduces\nPMID: PMID:\nNOS3 vasodilation, NO, causing endothelial PAD\n40022828 40667131\natherosclerosis dysfunction\nInnate immune STING-mediated\nPMID: PMID:\nLGALS1 activation, macrophage recruitment in COPD\n36989925 40715322\noxidative stress pulmonary inflammation\nIron metabolism PM₂.₅ increases expression, PMID: PMID:\nTFRC Anemia\ndysregulation inducing ferroptosis 39232294 39370488\nPM₂.₅ promotes hepatic lipid PMID: PMID:\nFABP1 Hepatic steatosis Pneumonia\naccumulation 24149114 39377914\nSmad pathway\nPM₂.₅ upregulation drives\nactivation, PMID: PMID:\nTGFB1 pulmonary CKD\nfibroblast 39575691 40534408\nfibrosis/immunosuppression\nproliferation\nAntioxidant Scavenges PM₂.₅-induced PMID: PMID:\nSOD2 COPD, IHD\ndefense ROS 40505896 40082252\nPM₂.₅ activates NLRP3\nPro-inflammatory PMID: PMID:\nIL18 inflammasome in systemic Pneumonia\ncytokine release 40674839 40352606\ninflammation\nEndothelial PM₂.₅ impairs nitric oxide PMID: PMID:\nDDAH1 IHD\ndysfunction metabolism 36242005 27914500\nEpigenetic\nAir pollution alters T-cell\nregulation, PMID: PMID:\nCD4 phenotype via DNA RA\nautoimmune 34498177 40777011\nmethylation\nresponse\nLipid transport PM₂.₅ elevates hepatic PMID: NAFLD, PMID:\nFABP5\ndysregulation lipid/insulin resistance 24149114 T2D 40748031\nNLRP3\nPM₂.₅/NO₂ elevate systemic\ninflammasome PMID: PMID:\nIL6 inflammation via JAK- RA\nactivation, Th17 37020004 40769441\nSTAT\ndifferentiation\nAutoimmune Epigenetic APC dysfunction PMID: PMID:\nCD40 RA, SLE\nresponse promotes autoantibodies 34498177 40721144\nTraffic pollution recruits Th2 PMID: PMID:\nCCL17 Th2 immune bias RA\ncells in allergic inflammation 31456427 40702243\nPM₂.₅ activates JNK\nOxidative stress pathway in PMID: PMID:\nMAPK9 T2D\nsignaling inflammation/insulin 39752784 40696679\nresistance\nReferences\n1. Celis-Morales, C.A., et al., Associations of grip strength with cardiovascular,\nrespiratory, and cancer outcomes and all cause mortality: prospective cohort study of\nhalf a million UK Biobank participants. BMJ, 2018. 361: p. k1651.\n2. Hanlon, P., et al., Frailty and pre-frailty in middle-aged and older adults and its\nassociation with multimorbidity and mortality: a prospective analysis of 493 737 UK\nBiobank participants. Lancet Public Health, 2018. 3(7): p. e323-e332.\n3. Yates, T., et al., Association of walking pace and handgrip strength with all-cause,\ncardiovascular, and cancer mortality: a UK Biobank observational study. Eur Heart J,\n2017. 38(43): p. 3232-3240.\n4. Argentieri, M.A., et al., Proteomic aging clock predicts mortality and risk of common\nage-related diseases in diverse populations. Nat Med, 2024. 30(9): p. 2450-2460.\n5. Shen, X., et al., A phenome-wide association and Mendelian Randomisation study of\npolygenic risk for depression in UK Biobank. Nat Commun, 2020. 11(1): p. 2301.\n6. Xiong, Y., et al., Joint exposure to multiple air pollutants, genetic risk and incident\npsoriasis: a large-scale prospective cohort study. Br J Dermatol, 2025. 192(3): p. 420-\n429.\n7. Zhang, J., et al., Association of Combined Exposure to Ambient Air Pollutants,\nGenetic Risk, and Incident Rheumatoid Arthritis: A Prospective Cohort Study in the UK\nBiobank. Environ Health Perspect, 2023. 131(3): p. 37008.\n8. Chen, L., et al., Risk/benefit tradeoff of habitual physical activity and air pollution on\nchronic pulmonary obstructive disease: findings from a large prospective cohort study.\nBMC Med, 2022. 20(1): p. 70.\n9. Huang, Y., et al., Air Pollution, Genetic Factors, and the Risk of Lung Cancer: A\nProspective Study in the UK Biobank. Am J Respir Crit Care Med, 2021. 204(7): p.\n817-825.\n10. Wang, M., et al., Joint exposure to various ambient air pollutants and incident heart\nfailure: a prospective analysis in UK Biobank. Eur Heart J, 2021. 42(16): p. 1582-1591.\n11. Li, X., et al., Obesity and the relation between joint exposure to ambient air pollutants\nand incident type 2 diabetes: A cohort study in UK Biobank. PLoS Med, 2021. 18(8):\np. e1003767.\n12. International Schizophrenia, C., et al., Common polygenic variation contributes to risk\nof schizophrenia and bipolar disorder. Nature, 2009. 460(7256): p. 748-52.\n13. Imai, K., L. Keele, and D. Tingley, A general approach to causal mediation analysis.\nPsychol Methods, 2010. 15(4): p. 309-34.\n14. Torkamani, A., N.E. Wineinger, and E.J. Topol, The personal and clinical utility of\npolygenic risk scores. Nat Rev Genet, 2018. 19(9): p. 581-590.\n15. Pu, F., et al., Heterogeneous associations of multiplexed environmental factors and\nmultidimensional aging metrics. Nat Commun, 2024. 15(1): p. 4921.\n16. Taneja, V., Chapter Fourteen - Sexual dimorphism, aging and immunity, in Vitamins\nand Hormones, G. Litwack, Editor. 2021, Academic Press. p. 367-399.\n17. Kivimäki, M., et al., Social disadvantage accelerates aging. Nat Med, 2025. 31(5):\np. 1635-1643.\n18. Eeftens, M., et al., Development of Land Use Regression models for PM(2.5), PM(2.5)\nabsorbance, PM(10) and PM(coarse) in 20 European study areas; results of the\nESCAPE project. Environ Sci Technol, 2012. 46(20): p. 11195-205.\n19. Furlong, M.A., et al., Association of Air Pollution and Physical Activity With Brain\nVolumes. Neurology, 2022. 98(4): p. e416-e426.\n20. Benjamini, Y. and Y. Hochberg, Controlling the False Discovery Rate: A Practical and\nPowerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series\nB (Methodological), 2018. 57(1): p. 289-300.\n21. Wei, Y., et al., Short term exposure to fine particulate matter and hospital admission\nrisks and costs in the Medicare population: time stratified, case crossover study. BMJ,\n2019. 367: p. l6258.\n22. Sun, B.B., et al., Plasma proteomic associations with genetics and health in the UK\nBiobank. Nature, 2023. 622(7982): p. 329-338.\n23. Gadd, D.A., et al., Blood protein assessment of leading incident diseases and mortality\nin the UK Biobank. Nat Aging, 2024. 4(7): p. 939-948.\n24. Furlong, M.A. and Y.C. Klimentidis, Associations of air pollution with obesity and\nbody fat percentage, and modification by polygenic risk score for BMI in the UK\nBiobank. Environ Res, 2020. 185: p. 109364.\n25. You, J., et al., Plasma proteomic profiles predict individual future health risk. Nat\nCommun, 2023. 14(1): p. 7817.\n26. Austin, P.C., A Tutorial on Multilevel Survival Analysis: Methods, Models and\nApplications. International Statistical Review, 2017. 85(2): p. 185-203.\n27. Gu, X., et al., Association of air pollution and genetic risks with incidence of elderly-\nonset atopic dermatitis: A prospective cohort study. Ecotoxicol Environ Saf, 2023. 253:\np. 114683.\n28. Cheng, H., et al., Ambient Air Pollutants and Traffic Factors Were Associated with\nBlood and Urine Biomarkers and Asthma Risk. Environ Sci Technol, 2022. 56(11):\np. 7298-7307.\n29. Luo, P., et al., Air Pollution and Allergic Rhinitis: Findings from a Prospective Cohort\nStudy. Environ Sci Technol, 2023. 57(42): p. 15835-15845.\n30. Mookherjee, N., et al., Defining the effects of traffic-related air pollution on the human\nplasma proteome using an aptamer proteomic array: A dose-dependent increase in\natherosclerosis-related proteins. Environ Res, 2022. 209: p. 112803.\n31. Liao, J., et al., Ambient air pollution and serum cardiometabolic proteome in young\nadults with obesity history. ISEE Conference Abstracts. 2023(1).\n32. Noel, A., et al., Mmp12 Is Upregulated by in utero Second-Hand Smoke Exposures and\nIs a Key Factor Contributing to Aggravated Lung Responses in Adult Emphysema,\nAsthma, and Lung Cancer Mouse Models. Front Physiol, 2021. 12: p. 704401.\n33. Eriksson Strom, J., et al., Airway MMP-12 and DNA methylation in COPD: an\nintegrative approach. Respir Res, 2025. 26(1): p. 10.\n34. Hill, W., et al., Lung adenocarcinoma promotion by air pollutants. Nature, 2023.\n616(7955): p. 159-167.\n35. Yang, Z., et al., Danggui Buxue Decoction inhibits pulmonary fibrosis by alleviating\nTGF-beta1-induced proliferation and apoptosis in EBL cells via PI3K/AKT/FOXO1\npathway. J Ethnopharmacol, 2025: p. 120384."}, {"round": 2, "reviewer_comments": {"reviewer_1": "In general, I appreciate the extensive revisions and additional analyses provided. Most of my concerns have been carefully addressed. However, I believe that some issues remain unresolved and would benefit from further clarification or additional analysis\n1. I felt like the concern that mental disorders may be biased self-rated health more than physical diseases remain unsolved. I remained doubtful to the vulnerability of mental disorders found by this study.\n2. The explanation for discrepancies between individual pollutants and the AP index and the addition of Supplementary Fig. 2 are helpful. The explanation for discrepancies between individual pollutants and the AP index is reasonable, and the addition of Supplementary Fig. 2 is helpful. However, I would encourage the authors to further discuss this point in main text, to help redear to know which results we can trust. From machesim or previous studies to support your findings or statements This would clarify whether inconsistencies are driven by specific pollutants dominating the index or by random biases from study.\n3. For the subgroup analyses of APPRS heterogeneity, did the authors apply correction for multiple testing?\n(Remarks on code availability)", "reviewer_2": "Overall, I believe the authors provided a comprehensive response to the previous round of reviewer comments. The finding is significant to the field of environmental health.\nOne thing that is worth noting is that the observed discrepancies between the effects of individual pollutants and the aggregated AP index showed up on multiple neurodegenerative disease outcomes, i.e., vascular dementia, multiple sclerosis, and Parkinson’s disease. I am intrigued by vascular dementia, where each air pollutant has a significantly positive correlation, while the AP index results are non-significant. I understand that \"in scenarios where a single pollutant has a dominant and disease-specific effect, the aggregation process may dilute this specific signal, especially if other pollutants in the index are less relevant.\" However, is it possible that all single pollutants presented positive effects, while the AP index presented a non-significant, nearly-negative effect? Is this mathematically possible?\n(Remarks on code availability)\nAuthors could consider adding an appropriate open source license to the code repository.", "reviewer_3": "Thanks for authors' clear responses to my comments.\nI still have an additional comments for authors to improve the manuscript. Although it is very difficult to distinguish the exposure window for various diseases, authors still can give the possible suggestion for future study, no only providing the limitation statement.\n(Remarks on code availability)"}, "author_response": "Point-by-point response to the review comments\nWe thank the reviewers for their very insightful comments. We have revised the\nmanuscript thoroughly.\nReviewer #1 (Remarks to the Author):\nIn general, I appreciate the extensive revisions and additional analyses provided. Most\nof my concerns have been carefully addressed. However, I believe that some issues\nremain unresolved and would benefit from further clarification or additional analysis.\n1. I felt like the concern that mental disorders may be biased self-rated health more than\nphysical diseases remain unsolved. I remained doubtful to the vulnerability of mental\ndisorders found by this study.\nResponse:\nWe thank the reviewer for this insightful comment. We agree that self-rated health\nmay be influenced by mental health conditions such as depression or anxiety,\npotentially introducing bias into the AP index derived from subjective health ratings.\nTo address this concern, we compared the air pollution and disease associations using\nalternative AP indexes constructed from two objective health indicators (grip strength\nand walking pace, as described in the Methods section, Page 21, Lines 556-563) that\nare independent of psychological self-perception. Notably, the resulting AP indexes\nwere highly correlated with our original index (Supplementary Fig. 26), suggesting\nthat the observed vulnerability is unlikely to be driven by subjective bias in self-rated\nhealth. Nonetheless, we acknowledge that residual reporting bias cannot be completely\nexcluded and have noted this as a limitation in the revised Discussion (Page 18, Lines\n479-482), detailed as:\n“In addition, although we verified the robustness of results using AP indexes\nderived from objective health indicators, potential bias from self-rated health cannot\nbe entirely ruled out.”\n2. The explanation for discrepancies between individual pollutants and the AP index is\nreasonable, and the addition of Supplementary Fig. 2 is helpful. However, I would\nencourage the authors to further discuss this point in main text, to help reader to know\nwhich results we can trust. From mechanism or previous studies to support your\nfindings or statements. This would clarify whether inconsistencies are driven by\nspecific pollutants dominating the index or by random biases from study.\nResponse:\nWe thank the reviewer for this constructive suggestion. In the revised manuscript,\nwe have discussed and clarified the interpretation of the observed discrepancies\nbetween single-pollutant and AP index associations. Specifically, we emphasize that\nsuch inconsistencies likely arise from disease-specific dominant pollutants rather than\nrandom study bias. For example, among the six diseases showing divergent patterns\n(vascular dementia, multiple sclerosis, lung cancer, Parkinson’s disease, inflammatory\nbowel disease, and endometriosis), previous studies have demonstrated that certain\npollutants exert stronger and more disease-specific effects [1-5]. Vascular and\nneurodegenerative diseases have been primarily linked to oxidative stress and\nendothelial dysfunction predominantly triggered by NO₂ exposure (PMID: 34747983;\n26928013; 38964584), whereas PM₂.₅ contributes more strongly to inflammatory and\ntumorigenic pathways relevant to lung cancer and multiple sclerosis (PMID: 36975376;\n34134109). Therefore, when aggregating across pollutants in the AP index, the\ndominant pollutant signal may be diluted if other pollutants are less biologically\nrelevant for that disease, resulting in reduced or null associations.\nConversely, the integrated nature of the AP index can amplify subtle but\ndirectionally consistent effects across multiple pollutants, which has been reported in\nstudies showing that combined exposure metrics better capture small additive or\nsynergistic effects of multiple pollutants on cardiometabolic and neurological outcomes\n[6, 7]. Therefore, while individual pollutant analyses reveal disease-specific\ncontributions, the AP index provides a broader, system-level view of total pollution\nburden. Both results should thus be interpreted as complementary rather than\ncontradictory.\nIn the revised Discussion (Page 16, Lines 402-409), we emphasize that results\nfrom single-pollutant models highlight disease-specific effects of pollutants, whereas\nthe AP index captures cumulative and system-level pollution burden. Discrepancies\nbetween the two are thus expected and reflect differences in biological specificity\nversus combined exposure effects.\n3. For the subgroup analyses of APPRS heterogeneity, did the authors apply correction\nfor multiple testing?\nResponse:\nYes, multiple testing correction was applied in the subgroup analyses of APPRS\nheterogeneity. Specifically, FDR correction using the Benjamini-Hochberg procedure\nwas implemented across all subgroup comparisons to control for the expected\nproportion of false positives. We have added this clarification in the section entitled\n“Subgroup heterogeneity of APPRS across demographic and socioeconomic factors”\nin the Supplementary Texts (Page 4, Lines 20-22).\nReviewer #2 (Remarks to the Author):\nOverall, I believe the authors provided a comprehensive response to the previous round\nof reviewer comments. The finding is significant to the field of environmental health.\nOne thing that is worth noting is that the observed discrepancies between the effects of\nindividual pollutants and the aggregated AP index showed up on multiple\nneurodegenerative disease outcomes, i.e., vascular dementia, multiple sclerosis, and\nParkinson’s disease. I am intrigued by vascular dementia, where each air pollutant has\na significantly positive correlation, while the AP index results are non-significant. I\nunderstand that \"in scenarios where a single pollutant has a dominant and disease-\nspecific effect, the aggregation process may dilute this specific signal, especially if\nother pollutants in the index are less relevant.\" However, is it possible that all single\npollutants presented positive effects, while the AP index presented a non-significant,\nnearly-negative effect? Is this mathematically possible?\nResponse:\nWe thank the reviewer for this insightful question. Indeed, it is mathematically\npossible that all individual pollutants show positive associations with a disease, while\nthe aggregated AP index yields a non-significant or even slightly negative association.\nThis phenomenon can arise from two main factors: the mathematical properties of\ncorrelated variables under weighted aggregation and the statistical misalignment\nbetween index weights and disease-specific effects.\nThe AP index is defined as a weighted linear combination of multiple pollutants:\n𝑛\n𝐴𝑃 𝑖𝑛𝑑𝑒𝑥 = ∑𝜔 𝑋 ,\n𝑖 𝑖\n𝑖=1\nwhere 𝑋 denotes pollutant concentration and 𝜔 the weight derived from\n𝑖 𝑖\nassociations with self-rated health. The estimated association between the index and a\ndisease outcome Y can be expressed as:\n𝐶𝑜𝑣(𝐴𝑃, 𝑌) ∑ 𝜔 𝐶𝑜𝑣(𝑋 , 𝑌)+∑ 𝜔 𝜔 𝐶𝑜𝑣(𝑋 , 𝑋 )\n𝛽̂ = = 𝑖 𝑖 𝑖 𝑖≠𝑗 𝑖 𝑗 𝑖 𝑗\n𝐴𝑃 𝑉𝑎𝑟(𝐴𝑃) 𝑉𝑎𝑟(𝐴𝑃)\nEven if all 𝐶𝑜𝑣(𝑋 , 𝑌) > 0, when pollutants are highly correlated (𝐶𝑜𝑣(𝑋 , 𝑋 ) > 0)\n𝑖 𝑖 𝑗\nand the weight vector 𝜔 differs from the true effect vector of disease-specific β’s, the\ncross-terms can offset the marginal effects. This can mathematically yield 𝛽̂ ≈ 0 or\n𝐴𝑃\neven 𝛽̂ < 0. In other words, multicollinearity and mismatched weighting can cause\n𝐴𝑃\nthe direction of the aggregated association to reverse despite all single pollutants being\npositively associated.\nBesides, The AP index weights (𝜔 ) were derived from self-rated overall health\n𝑖\nrather than disease-specific outcomes. Pollutants most strongly influencing general\nhealth (e.g., PM₂.₅) may not be the main causal drivers of specific diseases such as\nvascular dementia, which has been more strongly linked to NO₂-related neurovascular\nmechanisms. Consequently, when these non-specific pollutants receive large positive\nweights, they may dilute or even offset the true disease-relevant signals in the weighted\ncomposite, leading to attenuated or reversed overall associations.\nThus, both the mathematical interaction among correlated pollutants and the\nstatistical misalignment of weights can explain how the AP index may yield a near-zero\nor negative coefficient even when all individual pollutants have positive effects. We\nhave clarified this interpretation in the revised Discussion (Page 16, Lines 402-407),\ndetailed as:\n“Notably, while most proteins showed consistent association directions between\nindividual pollutants and the AP index, a subset exhibited opposite patterns\n(Supplementary Fig. 25). This likely reflects that the AP index, constructed as a\nweighted composite of multiple correlated pollutants, may display reversed\ndirectionality, as its weights were based on self-rated overall health rather than\ndisease-specific associations.”\nReviewer #2 (Remarks on code availability):\nAuthors could consider adding an appropriate open source license to the code repository.\nResponse:\nWe thank the reviewer for this helpful suggestion. We have now added an MIT\nopen-source license to our GitHub repository to clarify reuse permissions. The updated\nrepository is available at https://github.com/kaixuanli99/AirPollution-Proteome-\nDiseases-Mediation.\nReviewer #3 (Remarks to the Author):\nThanks for authors' clear responses to my comments.\nI still have an additional comments for authors to improve the manuscript. Although it\nis very difficult to distinguish the exposure window for various diseases, authors still\ncan give the possible suggestion for future study, no only providing the limitation\nstatement.\nResponse:\nWe appreciate the reviewer’s valuable suggestion. We have revised the Discussion\nto include potential directions for future research. Future studies could address this issue\nby leveraging longitudinal exposure modeling with finer temporal resolution (e.g.,\nmonthly or seasonal averages), and by integrating disease-specific onset timing or\nprodromal biomarkers to better capture relevant exposure windows. Moreover, causal\ninference frameworks may help to delineate short- versus long-term effects of air\npollution on disease initiation and progression. Such approaches would provide a more\nrefined understanding of temporal dynamics and improve causal interpretation. These\nrevisions have been added to the Discussion section (Page 18, Lines 469-474),\ndetailed as:\n“Future studies incorporating longitudinal exposure data with finer temporal\ngranularity, early biomarkers of disease onset, and causal modeling approaches will\nbe essential to identify disease-specific exposure windows. Longitudinal proteomic data\nand disease-specific sensitive windows would enable a more detailed examination of\ntemporal changes and adaptive responses in protein levels, providing insight into both\nacute and chronic impacts of air pollution.”\nReferences\n1. Yan, W., et al., NO2 inhalation promotes Alzheimer's disease-like progression:\ncyclooxygenase-2-derived prostaglandin E2 modulation and monoacylglycerol lipase\ninhibition-targeted medication. Sci Rep, 2016. 6: p. 22429.\n2. Peters, S., et al., Air pollution exposure and mortality from neurodegenerative diseases\nin the Netherlands: A population-based cohort study. Environ Res, 2024. 259: p.\n119552.\n3. Farias, H.R., et al., Hypercholesterolemia, oxidative stress, and low-grade\ninflammation: a potentially dangerous scenario to blood-brain barrier. Metab Brain\nDis, 2025. 40(5): p. 205.\n4. Wang, T.H., et al., PM2.5 promotes lung cancer progression through activation of the\nAhR-TMPRSS2-IL18 pathway. EMBO Mol Med, 2023. 15(6): p. e17014.\n5. Lotfi, F., et al., Association of Exposure to Particulate Matters and Multiple Sclerosis:\nA Systematic Review and Meta-Analysis. Neuroimmunomodulation, 2022. 29(1): p. 21-\n27.\n6. Fu, H., et al., Association between long-term exposure to air pollutant mixture and the\nrisk of Alzheimer’s disease among older people: a prospective cohort study in China.\nBMC Public Health, 2025. 25(1): p. 3562.\n7. Yang, T., et al., Long-term Exposure to Multiple Ambient Air Pollutants and\nAssociation With Incident Depression and Anxiety. JAMA Psychiatry, 2023. 80(4):\np. 305-313."}, {"round": 3, "reviewer_comments": {"reviewer_1": "No further cocnern. Good job.\n(Remarks on code availability)", "reviewer_2": "The authors have addressed my questions.\n(Remarks on code availability)"}, "author_response": ""}]

Decision letter