In a longitudinal agricultural community cohort sampling of 65 adult farmworkers and 52 adult nonfarmworkers, we investigated agricultural pesticide exposure-associated changes in the oral buccal microbiota. We found a seasonally persistent association between the detected blood concentration of the insecticide azinphos-methyl and the taxonomic composition of the buccal swab oral microbiome. Blood and buccal samples were collected concurrently from individual subjects in two seasons, spring/summer 2005 and winter 2006. Mass spectrometry quantified blood concentrations of the organophosphate insecticide azinphosmethyl. Buccal oral microbiome samples were 16S rRNA gene DNA sequenced, assigned to the bacterial taxonomy, and analyzed after "centered-log-ratio" transformation to handle the compositional nature of the proportional abundances of bacteria per sample. Nonparametric analysis of the transformed microbiome data for individuals with and without azinphos-methyl blood detection showed significant perturbations in seven common bacterial taxa (Ͼ0.5% of sample mean read depth), including significant reductions in members of the common oral bacterial genus Streptococcus. Diversity in centered-log-ratio composition between individuals' microbiomes was also investigated using principal-component analysis (PCA) to reveal two primary PCA clusters of microbiome types. The spring/summer "exposed" microbiome cluster with significantly less bacterial diversity was enriched for farmworkers and contained 27 of the 30 individuals who also had azinphos-methyl agricultural pesticide exposure detected in the blood. IMPORTANCEIn this study, we show in human subjects that organophosphate pesticide exposure is associated with large-scale significant alterations of the oral buccal microbiota composition, with extinctions of whole taxa suggested in some individuals. The persistence of this association from the spring/summer to the winter also suggests that long-lasting effects on the commensal microbiota have occurred. The important health-related outcomes of these agricultural community individuals' pesticide-associated microbiome perturbations are not understood at this time. Future investigations should index medical and dental records for common and chronic diseases that may be interactively caused by this association between pesticide exposure and microbiome alteration.KEYWORDS farmworkers, azinphos-methyl, oral, microbiome, bacteria, buccal mucosa, 16S rRNA, sequencing
1550 Background: Real world data (RWD) is increasingly used to inform research, patient care, and population health in oncology; however, using RWD at scale requires accurate methods to identify clinically-relevant attributes. Metastatic status is a highly relevant clinical attribute in cancer patients but it is not routinely captured in structured formats and its determination conventionally requires review and interpretation by certified tumor registrars (CTRs). Clinical diagnoses, treatments, imaging procedures and other clinical variables documented in electronic health records (EHRs) can be used to differentiate metastatic from non-metastatic patients. This study describes an effective machine learning approach in utilizing prevalent and standardized data elements from EHRs across multiple health systems. Methods: 28,043 lung cancer and breast cancer patients from two large health systems within the Syapse Learning Health Network with data sources from CTR abstraction and EHRs were analyzed. Patients were labeled for reference metastatic status by CTRs and split into training (n = 22,434) and testing (n = 5,609) cohorts, with proportionate distribution of cancer type and metastatic status between cohorts. A regularized gradient boosting algorithm, XGBoost, was trained using over 750 variables from the patient records collected at the time of or after the initial cancer diagnosis. Results: Integration of ICD-10-CM codes with antineoplastic treatment history and radiologic imaging procedure orders achieved metastatic status prediction with increases to precision and recall in lung cancer (21% and 32% respectively) and breast cancer (39% and 9% respectively), when compared to the use of only ICD-10-CM diagnosis codes for secondary malignant neoplasms (Table). The addition of treatment and procedure data from different cancer types improved the model classification within individual cancer types. Conclusions: One of the biggest challenges in using RWD for precision oncology is identification of clinically-relevant phenotypes at scale. Here we demonstrate a scalable evidence-based method utilizing structured data for imputing metastatic status with high predictive power from two separate health systems. With further validation, this approach may be generalized to other cancer types, applied to temporal slices of data to identify changes in metastatic status, as well as provide a high-confidence designation of metastatic status for other use cases such as staging.[Table: see text]
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