{"doi":"10.1002/ajh.70132","title":"Environmental Pollution Triggers Inflammation In Vivo and is Associated With Higher Risk <scp>MDS</scp> in an Urban Cohort","abstract":"Outdoor air pollution (OAP) exposure is the second leading risk factor for noncommunicable diseases worldwide, causing an estimated 4.5 million premature deaths in 2019 [1]. OAP consists of pollutants like black carbon (BC), particulate matter 2.5 (PM2.5), nitrogen oxides (NOx), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO) from human and natural sources. Lower-income communities, like the Bronx, NY, experience disproportionately high OAP from dense concentrations of industrial activity and transportation infrastructure. OAP exposure has been linked to cardiovascular disease, respiratory illnesses, and cancers [2]. Occupational benzene exposure is associated with myelodysplastic syndromes (MDS)—a group of bone marrow disorders with clonal hematopoiesis (CH) and genetic abnormalities causing cytopenias [3, 4]. Potential links between OAP and MDS remain poorly defined [3, 5]. While smoking is associated with CH, the effects of OAP's role are understudied. OAP's carcinogenicity is supported by evidence displaying exposure-driven genetic mutations, but existing research lacks longitudinal data in specific populations, like those in historically redlined areas like the Bronx, NY who suffer worse outcomes [6]. We addressed these gaps by correlating OAP levels with outcomes of a Bronx-based MDS cohort and developed a murine BC exposure to better understand the systemic inflammation and its role in CH. A retrospective cohort of MDS patients in the Montefiore Health System (Bronx, NY) between 2000 and 2017 (n = 138) was developed using the Montefiore Electronic Data Warehouse (EDW) (Table S1). MDS patient addresses (n = 138) were used to estimate long-term OAP exposure (2000–2017) using land use regression (LUR) models as previously performed by Wysota et al., assessing exposure to BC, NH4, NO3, PM2.5, SO4, and organic matter (OM) [7]. Pearson's Correlation statistics assessed associations between clinical parameters and both smoking status/OAP exposure. Logistic regressions were performed to model mutation status and OAP exposure. Cox proportional hazard models assessed the relationship between mortality and pollution. UKB data were accessed under application 69 235. The whole-exome cohort comprised 454 787 individuals (aged 40–70) recruited from 2006 to 2010. Outcomes were tracked from hospitalization health records and death/cancer registries. Follow-up data were censored at November 30, 2021. Baseline variables included age, sex, and the first 10 ancestry principal components (PCs). Outcome diagnoses were assessed by ICD-9/10 codes in linked hospital data. The primary outcome of interest was incident MDS (ICD10 codes D46, C497) after enrollment. Participants with a history of myeloid malignancy before enrollment were excluded, as defined by various ICD-9 codes (Table S2). Air pollution estimates for PM2.5 for 2010 were generated using a LUR model developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE), supported by the EU 7th Framework Programme. The LUR model was constructed using ESCAPE monitoring data between January 26, 2010, and January 18, 2011. Resulting OAP estimates are representative of the year 2010. Statistical analyses were performed using R software. Binomial logistic regression models estimated associations between PM2.5 and CH, using PM2.5 estimates, age, sex, and 10 ancestry PCs as independent variables. Time-to-MDS diagnosis in the UKB cohort was modeled using Cox regression with PM2.5, age, sex, the first 10 ancestry PCs, and CH status as covariates. Collinearity of smoking status with PM2.5 estimates precluded inclusion of both variables in the regression models. To delineate the effect of PM2.5, models were run separately on the never-smoking population of UKB, based on the variable “smoking status” data-field 20116. A Tisch TE-1000 PUF machine measured pollutants at the level of 2.5 μM. Chemical assessment was performed by Airzone One. Readings were taken in duplicate acr","journal":"American Journal of Hematology","year":2025,"id":535355,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9629,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":882243,"name":"Matthew P. Davidsohn","orcid":"0009-0000-1480-9423","position":1,"is_corresponding":false},{"id":1297909,"name":"Christopher Maximilian Arends","orcid":"0000-0003-4417-4750","position":2,"is_corresponding":false},{"id":652056,"name":"Divij Verma","orcid":"0000-0002-2214-9450","position":3,"is_corresponding":false},{"id":320298,"name":"Srabani Sahu","orcid":"0009-0002-3667-3592","position":4,"is_corresponding":false},{"id":586705,"name":"Kith Pradhan","orcid":"0000-0001-7089-1652","position":5,"is_corresponding":false},{"id":1418954,"name":"S. Sharareh Dehghani","orcid":"0000-0002-8887-0224","position":6,"is_corresponding":false},{"id":1418955,"name":"Hamsa Murli","orcid":"0000-0001-7139-6688","position":7,"is_corresponding":false},{"id":865051,"name":"Sakshi Jasra","orcid":"0000-0002-7348-9922","position":8,"is_corresponding":false},{"id":1418956,"name":"Ritesh K. Aggarwal","orcid":"0000-0002-5693-1758","position":9,"is_corresponding":false},{"id":790967,"name":"Hui Zhang","orcid":"0000-0002-5167-2232","position":10,"is_corresponding":false},{"id":972041,"name":"Michael Wysota","orcid":"0009-0009-0816-6922","position":11,"is_corresponding":false},{"id":566634,"name":"Dean Hosgood","orcid":null,"position":12,"is_corresponding":false},{"id":581674,"name":"Amit Verma","orcid":"0000-0002-5408-1673","position":13,"is_corresponding":false},{"id":621763,"name":"Siddhartha Jaiswal","orcid":"0000-0002-9597-0477","position":14,"is_corresponding":false},{"id":292610,"name":"Yiyu Zou","orcid":"0009-0009-7237-2173","position":15,"is_corresponding":false},{"id":71397,"name":"Aditi Shastri","orcid":"0000-0002-5366-8288","position":16,"is_corresponding":false},{"id":1418953,"name":"Megha Verma","orcid":"0000-0001-7767-612X","position":0,"is_corresponding":true}],"reference_count":8,"raw_metadata":null,"created_at":"2026-07-19T02:51:56.297114Z","pmid":"41215735","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}