I N TRODUC TIONAdults living in the island of Puerto Rico experience a high burden of cardiometabolic diseases. Puerto Ricans have high prevalence of cardiovascular risk factors, including obesity, type 2 diabetes, hypertension and hyperlipidaemia. 1,2 In 2018, approximately 32.9% of Puerto Ricans were classified with obesity, and another 36.9% were overweight, according to the Behavioral Risk Factor and Surveillance System (BRFSS). 3 Central obesity, which is fat accumulated around

Background: Exposure to individual metals (and metalloids; hereafter 'metals') is associated with adverse cardiometabolic outcomes. Specifying analytic models to assess relationships among metal mixtures and cardiometabolic outcomes requires evidence-based models of the (assumed) causal structures; however, such models have not been previously published. Methods: We conducted a systematic literature review to develop an evidence-based directed acyclic graph (DAG) identifying relationships among metals, cardiometabolic health indicators, and potential confounders. To evaluate the consistency of the DAG with data from 1797 participants in the San Luis Valley Diabetes Study (SLVDS; mean age=54 years, 53% women, 48% Hispanic), we tested conditional independence statements suggested by the DAG and by 100 DAGs with the same structure but randomly permuted nodes using linear (continuous outcomes), logistic (dichotomous outcomes), or Bayesian kernel machine regression (BKMR; statements with metal coexposures) models. Based on minimally sufficient adjustment sets identified by the DAG, we specified BKMR models assessing associations between urinary metal mixtures and cardiometabolic outcomes in the SLVDS population. Results: Twenty-nine articles met the inclusion criteria for the systematic review. From these articles, we developed an evidence-based DAG with 382 testable conditional independence statements (71% supported by SLVDS data). Only 3% of the DAGs with randomly permuted nodes indicated more agreement with the data than our evidence-based DAG. Applying the evidence-based DAG in a pilot analysis, we did not observe evidence for an association among metal mixtures and cardiometabolic outcomes. Conclusions: We developed, tested, and applied an evidence-based approach to analyze associations between metal mixtures and cardiometabolic health.

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The association between manganese (Mn) and metabolic syndrome (MetS) is unclear, and no prior study has studied this association longitudinally. The aim of this study was to assess longitudinal associations of Mn exposure with MetS and metabolic outcomes. We used data from the San Luis Valley Diabetes Study (SLVDS), a prospective cohort from rural Colorado with data collected from 1984–1998 (n = 1478). Urinary Mn was measured at baseline (range = 0.20–42.5 µg/L). We assessed the shape of the cross-sectional association between Mn and MetS accounting for effect modification by other metals at baseline using Bayesian kernel machine regression. We assessed longitudinal associations between baseline quartiles of Mn and incident MetS using Fine and Gray competing risks regression models (competing risk = mortality) and between quartiles of Mn and metabolic outcomes using linear mixed effects models. We did not observe evidence that quartiles of Mn were associated with incident MetS (p-value for trend = 0.52). Quartiles of Mn were significantly associated with lower fasting glucose (p-value for trend < 0.01). Lead was found to be a possible effect modifier of the association between Mn and incident MetS. Mn was associated with lower fasting glucose in this rural population. Our results support a possible beneficial effect of Mn on diabetic markers.