Water sources and kidney function: investigating chronic kidney disease of unknown etiology in a prospective study

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Kidney progression project

The Kidney Progression Project was initiated in 2017 in the Wilgamuwa Divisional Secretariat, a highly endemic CKDu area of 40,000 people in the lowland dry zone area of the Central Province (Supplementary Fig. 1). All protocols were reviewed and approved by review boards at the University of Connecticut in the US and National Hospital in Kandy, Sri Lanka. The detailed methodological approach including a description of behavioral and clinical and expanded environmental variables is described in Vlahos et al. (2018)13. Briefly, in 2016, the Ministry of Health conducted a screening of urine and blood in Wilgamuwa for residents 11 years and older to identify those with CKDu. Using the resulting serum creatinine values obtained during this screening effort, the KiPP team calculated CKD-EPI eGFR23, which resulted in a total of 330 people at Stage 3 and 4 of CKDu (eGFR in the range of 20-60 ml/min/1.73 m2), who did not have identifiable cause for CKD with evidence of chronic interstitial nephritis in renal biopsies or small echogenic kidney. Of these, 304 agreed to participate but ultimately 293 answered the baseline questionnaire and came for at least one serum creatinine measurement and were included in this analysis.

Baseline survey components

All participants were administered a baseline survey that focused on environmental exposure, behavioral and occupational factors, and clinical values as described in the KiPP protocol13. We probed water sources in detail. Water sources in the study area and the dry zone in general include household wells dug by hand that are 10 meters deep or shallower, tube wells dug to a depth of 20–30 m with drilling equipment, and lesser-used sources including surface water (tanks, channels and river water), rainwater collection, natural spring water, publicly supplied pipe water, and public water delivered to individual houses by truck (bowsers) and stored in large roof containers. The rise in CKDu cases led the government to invest heavily in reverse osmosis (RO) units and nanofiltration membrane technology for many dry zone villages14. These were installed at the end of 2017 and early 2018 to provide rationed, free drinking water.

Baseline water samples and analysis

The wells of each participant household were sampled once for target agrochemicals as described in Shipley et al.24. In all, 272 household wells were sampled with 31 households sharing wells.

Agrochemical analyses

Agrochemical analyses follow methods of Shipley et al., (2022)24 and EPA (2018)25. Briefly, 1 L well water samples were collected at each participant’s home and pre-filtered through a 0.45 µm nominal GFF to remove particulates. The sample was then extracted using 3 mL Chromabond C-18 SPE cartridges and a Supelco Visiprep SPE vacuum manifold. Three deuterated surrogate standards (chrysense d12, acenaphthene d10, and 1,4-dichlorobenzene d4) were loaded onto the cartridge before elution with 5 ml of acetonitrile and nitrogen reduction to 1 ml. Recoveries ranged from 70 to 101%.

An initial non-targeted analysis was run on samples in scan mode which identified over 100 compounds, including pyrolytic compounds that are likely the result of field burning practices in preparation for the new season. We supplemented these analyses with data from a local list of agrochemicals for the year 2017–2018 supplied by the Sri Lankan Ministry of Environment. Based on these data, targeted analyses were performed for 30 agrochemicals using selective ion mode.

Inorganic analyses

Phosphate in samples was measured with an Ion Chromatograph (Thermo Dionex ICS-1100). For repeated analyses of selected samples, an analytical precision better than ±5% of relative standard deviations was achieved. Total hardness was determined by EDTA titration method (APHA 2012)26.

Follow up: From December 2017 to the beginning of 2020, study participants had quarterly follow-up visits assessing behavioral changes including water consumption and serum creatinine testing. Serum creatinine was tested using an IDMS-calibrated enzymatic assay and converted to estimated glomerular filtration using the CKD-EPI equation.

GIS Analysis: Using GPS coordinates recorded by the field team for the domestic wells of each participant, individual eGFR at baseline and eGFR slopes over the study period were plotted over the ArcMap World Topographic map. For the baseline eGFR map, values were separated into five categories using Jenks Natural breaks provided by the ArcGIS software. The uppermost category was manually set to 65 mL/min/1.73 m2 and points with null or <15 mL/min/1.73 m2 are not displayed. Annual slopes were measured in mL/min/1.73 m2/year and integrated over the area covered by the eGFR points using the default settings of the Inverse Distance Weighted (Spatial Analysis) tool. Five categories were determined based on the severity of the increase or decrease. RO plants and hospitals were plotted using GPS data from the field team to convey proximity to clean water and health care across participants.

GIS analyses

Analysis was performed on the annual slopes using ArcGIS Hot Spot Analysis which uses Getis-Ord Gi*. This analysis identifies significant clusters of points which are higher or lower than expected relative to surrounding points on a two-dimensional grid. Default values for grouping neighbors were used which maximizes the probability that all points have included neighbors. Visualization was changed such that the points with particularly negative slopes (rapid progressors) were labeled in red to provide clarity as to the areas of greatest eGFR progression. Blue points indicate places where slopes are significantly higher (less negative (slow progressors) or even positive (improvers)).

Statistical analysis

Baseline characteristics are described using mean (SD) and n (proportion) as appropriate. In order to test the hypothesis that drinking water exposure was associated with eGFR decline, we used liner mixed models (LMM) accounting for age, sex and time points following Boucquemont et al. (2014)27. The major advantage of LMMs is that they do not require equally spaced time intervals between patient follow ups (consecutive measurements), nor the same number of measurements per patient. LMM therefore uses all available information in the analysis, including participants who may have fewer (even one) follow up-visits. In our study, the response variable was eGFR and each participant had up to six data points at three to four-month interval clinical follow ups. Exposure categories were current and historic (ever) water sources including well water or not, and baseline water sources including reverse osmosis water or not. Before conducting the model we used Q-Q plots to check the normality of residuals of eGFR (assumption of normality held), and checked for correlations among the predictors with the Variance Inflation Factor (values ranging 1.04–1.50) indicating no significant multicollinearity. The two types of water categories (historic or baseline) were tested in separate models, in which the time point of eGFR collection, water source, age, gender were included as fixed effects, and participants as random effects. The model was fitted in the MIXED procedure in SAS version 9.5 (SAS Institute Inc, Cary, NC, USA).

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