Investigating the influence of environmental factors on the incidence of renal disease with compositional data analysis using balances

Jennifer McKinley*, Ute Mueller, Peter Atkinson, Ulrich Ofterdinger, Chloe Jackson, Siobhan Cox, Rory Doherty, Damian Fogarty, Vera Pawlowsky-Glahn, Juan José Egozcue

*Corresponding author for this work

Research output: Contribution to journalSpecial issuepeer-review

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This research uses an urban soil geochemistry database of elemental concentration to examine the potential relationship between Standardised Incidence Rates (SIRs) of Chronic Kidney Disease (CKD) of uncertain aetiology (CKDu), and cumulative low level geogenic and diffuse anthropogenic contamination of soils with PTEs. A compositional data analysis approach was applied to determine the elemental balance(s) of the geochemical data showing the greatest association with CKDu. The research concludes that both anthropogenic and geogenic factors may be contributing influences to explain high incidences of CKDu, up to 12 times greater in some Super Output Areas (SOA) than would be expected for the average population. The role of As, Cr, Cu, Pb, Sb and Mo was highlighted, which may be linked to anthropogenic sources such as historical industrial sources, atmospheric pollution deposition and brake emissions. Geogenic factors were shown to be important in areas with elevated relative concentrations of naturally occurring potentially toxic elements (PTE).
Original languageEnglish
Article number1000247
Number of pages7
JournalApplied Computing and Geosciences
Early online date31 Mar 2020
Publication statusPublished - 01 Jun 2020
EventThe 8th International Workshop on Compositional Data Analysis: CoDa Work 2019 - U. Politécnica de Cataluña , Terrassa, Barcelona, Spain
Duration: 03 Jun 201908 Jun 2019
Conference number: 8


  • Geochemistry
  • chronic kidney disease
  • uncertain aetiology

ASJC Scopus subject areas

  • Environmental Chemistry
  • Environmental Science (miscellaneous)
  • Health, Toxicology and Mutagenesis
  • Pollution
  • Applied Mathematics
  • Modelling and Simulation


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