Particulate Matter Air Pollution and the Risk of Incident CKD and Progression to ESRD

Abstract

Elevated levels of fine particulate matter ,2.5 mm in aerodynamic diameter (PM 2.5 ) are associated with increased risk of cardiovascular outcomes and death, but their association with risk of CKD and ESRD is unknown. We linked the Environmental Protection Agency and the Department of Veterans Affairs databases to build an observational cohort of 2,482,737 United States veterans, and used survival models to evaluate the association of PM 2.5 concentrations and risk of incident eGFR ,60 ml/min per 1.73 m 2 , incident CKD, eGFR decline $30%, and ESRD over a median follow-up of 8.52 years. In time-varying analyses, a 10-mg/m 3 increase in PM 2.5 concentration was associated with similarly increased risk of eGFR,60 ml/min per 1.73 m 2 , CKD, eGFR decline $30%, and ESRD. Spline analyses showed a linear relationship between PM 2.5 concentrations and risk of kidney outcomes. Exposure estimates derived from National Aeronautics and Space Administration satellite data yielded consistent results. Our findings demonstrate a significant association between exposure to PM 2.5 and risk of incident CKD, eGFR decline, and ESRD.

The activation of antioxidant and apoptosis pathways involved in damage of human proximal tubule epithelial cells by PM2.5 exposure

“…The nanoscale particles, often containing large amounts of toxic compounds such as metals and hydrocarbons, can also exert direct or indirect toxicity toward extrapulmonary organs such as the kidneys [3,4]. Recently, some epidemiological studies have reported a strong and consistent association between PM 2.5 exposure and reduced kidney function as well as an increased rate of renal function decline [5][6][7]. Studies have also shown that mid-/long-term exposure to high levels of PM 2.5 can induce kidney damage in rodents [8,9].…”

Big data in nephrology: Are we ready for the change?

“…In conclusion, problem-driven big data research in nephrology is important for promoting the interdisciplinary 10 and CKD progression [28][29][30] • To build a CKD surveillance system and collaborative network nationally or globally: surveillance systems based on existing databases; international collaborative network through sharing data and research outputs • To build real-world cohorts in a cost-effective manner: design and implementation of large cohorts and pragmatic randomized controlled trials Clinical practice, innovation and decision-making • Risk prediction and clinical decision support: risk prediction of CKD and end-stage kidney disease using machine learning and natural language processing 33,35,36 • Management of acute kidney injury: detection, classification, and automated alerting of acute kidney injury [40][41][42] • To strengthen the application and transformation of artificial intelligence and big data based CDSS: collaboration with computer and data scientists; improvement of interpretability and usability of CDSS • To stimulate the activeness of medical imaging: analysis of renal biopsy images and urine formed elements using machine learning CDSS, clinical decision support systems; CKD, chronic kidney disease; PM 2.5 , fine particulate matter of <2.5 μm in aerodynamic diameter; PM 10 , coarse particulate matter of <10 μm in aerodynamic diameter.…”

“…The association between air pollution and specific types of glomerular diseases have been evaluated in China, showing that longterm exposure to high levels of fine particulate matter of <2.5 μm in aerodynamic diameter (PM 2.5 ) could increase the risk of membranous nephropathy. 28 Bowe et al 29 reported a significant association between exposure to PM 2.5 and risk of incident CKD, CKD progression, and ESKD using data from a longitudinal national cohort of more than 2 million United States veterans. They also found that higher concentrations of coarse particulate matter of <10 μm (PM 10 ), nitrogen dioxide (NO 2 ) and carbon monoxide (CO) were associated with increased risk of adverse kidney outcomes based on the same large dataset.…”

A big data approach to examine the association of high density lipoprotein cholesterol and mortality: lessons for future investigations

“…A big data approach might provide further insight and offer significant value that should be realized through innovative research endeavors to challenge prior assumptions, and fill important and consequential knowledge gaps (11)(12)(13). We predict that the pursuit of big data research will very likely upend several long-held assumptions, and as a result will enrich our understanding of health and disease and favorably impact the human condition.…”