Chronic kidney disease of unknown origin is associated with environmental urbanisation in Belfast, UK

Abstract

Chronic kidney disease (CKD), a collective term for many causes of progressive renal failure, is increasing worldwide due to ageing, obesity and diabetes. However, these factors cannot explain the many environmental clusters of renal disease that are known to occur globally. This study uses data from the UK Renal Registry (UKRR) including CKD of uncertain aetiology (CKDu) to investigate environmental factors in Belfast, UK. Urbanisation has been reported to have an increasing impact on soils. Using an urban soil geochemistry database of elemental concentrations of potentially toxic elements (PTEs), we investigated the association of the standardised incidence rates (SIRs) of both CKD and CKD of uncertain aetiology (CKDu) with environmental factors (PTEs), controlling for social deprivation. A compositional data analysis approach was used through balances (a special class of log contrasts) to identify elemental balances associated with CKDu. A statistically significant relationship was observed between CKD with the social deprivation measures of employment, income and education (significance levels of 0.001, 0.01 and 0.001, respectively), which have been used as a proxy for socio-economic factors such as smoking. Using three alternative regression methods (linear, generalised linear and Tweedie models), the elemental balances of Cr/Ni and As/Mo were found to produce the largest correlation with CKDu. Geogenic and atmospheric pollution deposition, traffic and brake wear emissions have been cited as sources for these PTEs which have been linked to kidney damage. This research, thus, sheds light on the increasing global burden of CKD and, in particular, the environmental and anthropogenic factors that may be linked to CKDu, particularly environmental PTEs linked to urbanisation.

Keywords: Compositional data analysis; Renal disease; Social deprivation measures; Soil geochemistry; Tweedie model; Uncertain aetiology.

Fig. 1

Standardised incidence rates (SIRs) for patients starting renal replacement therapy (RRT) between 2006 and 2016, by Super Output Areas (SOAs) for the greater Belfast area. The SIRs for chronic kidney disease (CKD) were provided by the UK Renal Registry (UKRR) in age brackets: (a) all ages > 16 years; (b) 16–39 years; (c) 40–64 years; (d) > 65 years; and (e) for chronic kidney disease of uncertain aetiology (CKDu). The key for all maps. (f) Location inset map

Fig. 2

Northern Ireland Multiple Deprivation Measures (MDMs) for 2017 for all Belfast urban Super Output areas (SOAs) including information on (a) an overall MDM ranking and (b–h) seven individual domains of deprivation across the greater Belfast area. MDM data provided by Northern Ireland Statistics and Research Agency (NISRA 2017) Colour scheme conforms with NISRA

Fig. 3

(a) Sample locations for the Tellus project (Young and Donald 2013) urban soil geochemical data for the greater Belfast area, Northern Ireland. The Super Output Areas (SOAs) for Belfast—the smallest administrative areas are also shown. (b) Simplified geology of the greater Belfast urban area (provided by Geological Survey of Northern Ireland (GSNI), Mitchell 2004)

Fig. 4

Data analysis approach used in the study. Abbreviations: linear model (LM); generalised linear model (GLM); standardised incidence rates (SIRs) for chronic kidney disease (CKD); chronic kidney disease of uncertain aetiology (CKDu); log-transformed SIRs (log SIRs); multiple deprivation measures (MDMs); potentially toxic elements (PTEs). Note log-transformed SIRs were not used for the Tweedie model, as the aim was to test the impact of the zeros on the relationship with the PTEs

Fig. 5

Histograms showing distribution of Super Output Areas (SOAs) with (a) standardised incidence rates (SIRs) of chronic kidney disease (CKD) for all ages > 16 years along with (b) overall multiple deprivation measure (MDM) ranking and six individual domains of deprivation comprising (c) income, (d) health deprivation and disability, (e) employment, (f) education, skills and training, (g) access to services and (h) living environment. The ranking scale is from 1 which represents the most deprived to 890 for the least deprived

Fig. 6

Scatter plots of standardised incidence rates (SIRs) of chronic kidney disease (CKD) for all ages > 16 years with overall (a) multiple deprivation measures (MDMs) ranking and the individual domains of deprivation (b) income, (c) employment, (d) health, (e) education and (f) services. Lines show linear regression (red line) and quantile regression (blue lines) for 0.25, 0.5 and 0.75 quantiles

Fig. 7

Results of the forward selection method using the selbal algorithm (899 sample points) shown for Belfast urban area (Super Output Areas (SOAs)) standardised incidence rates (SIRs) of chronic kidney disease (CKD) for all ages > 16 years with soil PTEs and six individual domains of deprivation multiple deprivation measures (MDMs—comprising income, employment, health deprivation and disability, education, skills and training, access to services, and living environment), (a) mean squared error (MSE) as a function of the number of components included in the balance. The optimal number of components is highlighted with a vertical dashed line; (b) box plot of root-mean-squared error (RMSE); (c) the balance identified with the whole data set is the most frequently identified in the cross-validation (CV) procedure; and (d) global balance and other balances identified in CV procedure

Fig. 8

Results of the forward selection method using the selbal algorithm shown for Belfast urban area (340 sample points) standardised incidence rates (SIRs) of chronic kidney disease of uncertain aetiology (CKDu) with soil PTEs and six individual domains of deprivation multiple deprivation measures (MDMs comprising income, employment, health deprivation and disability, education, skills and training, access to services, and living environment), (a) mean squared error (MSE) as a function of the number of components included in the balance. The optimal number of components is highlighted with a vertical dashed line; (b) box plot of root-mean-squared error (RMSE); (c) the balance identified with the whole data set is the most frequently identified in the cross-validation (CV) procedure; and (d) global balance and other balances identified in CV procedure

Fig. 9

Visualisation of average potentially toxic elements (PTEs) data for SOAs shown with the road network for interpretation purposes. Transport networks reproduced from Land and Property Services data with the permission of the Controller of Her Majesty’s Stationery Office, © Crown copyright and database rights MOU203. (a) Standardised incidence rates (SIRs) of chronic kidney disease of uncertain aetiology (CKDu); (b) average As mg/kg; (c) average Mo mg/kg; (d) balance log (As/Mo); (e) simplified geology provided by Geological Survey of Northern Ireland (GSNI), Mitchell ; (f) average Cr mg/kg; (g) average Ni mg/kg; (h) balance log(Cr/Ni)