Water sources and kidney function: investigation of chronic kidney disease of unknown etiology in a prospective study
Renal progression project
The Kidney Progression project was initiated in 2017 in the Divisional Secretariat of Wilgamuwa, a highly endemic CKDu area of ââ40,000 people in the dry lowland zone of Central Province (Supplementary Fig. 1). All protocols have been reviewed and approved by review boards at the University of Connecticut in the US and Kandy National Hospital in Sri Lanka. The detailed methodological approach including a description of extended behavioral and clinical and environmental variables is described in Vlahos et al. (2018)13. In short, in 2016, the Department of Health conducted urine and blood screening in Wilgamuwa for residents 11 years of age and older to identify people with CKDu. Using the resulting serum creatinine values ââobtained during this screening effort, the KiPP team calculated the CKD-EPI eGFR23, resulting in a total of 330 people in stages 3 and 4 of CKDu (eGFR between 20 and 60 ml / min / 1.73 m2), who had no identifiable cause of CKD with evidence of chronic interstitial nephritis in kidney biopsies or an echogenic small kidney. Of these, 304 agreed to participate but ultimately 293 completed the baseline questionnaire and came for at least one serum creatinine measurement and were included in this analysis.
Components of the baseline survey
All participants received a baseline survey focusing on environmental exposure, behavioral and occupational factors, and clinical values ââas described in the KiPP protocol.13. We probed the water sources in detail. Water sources in the study area and the dry area in general include hand-dug domestic wells that are 10 meters deep or less, tube wells dug to a depth of 20-30 m with equipment. borehole and lesser used sources, including surface water (reservoirs, canals and river water), rainwater harvesting, natural spring water, public piped water and public water delivered to single-family homes by truck (bowsers) and stored in large roof containers. The increase in CKDu cases has led the government to invest heavily in reverse osmosis (RO) units and nanofiltration membrane technology for many villages in the drylands.14. These were installed in late 2017 and early 2018 to provide rationed and free drinking water.
Baseline water samples and analysis
The wells of each participating household were sampled once for the target agrochemicals, as described in Shipley et al.24. A total of 272 domestic wells were sampled with 31 households sharing wells.
Agrochemical analyzes follow the methods of Shipley et al., (2022)24 and EPA (2018)25. Briefly, 1 L well water samples were taken from each participant’s home and pre-filtered through a nominal 0.45 Âµm GFF to remove particles. The sample was then extracted using 3 ml Chromabond C-18 SPE cartridges and a Supelco Visiprep SPE vacuum manifold. Three deuterated substitution standards (chrysense d12, acenaphthene d10 and 1,4-dichlorobenzene d4) were loaded onto the cartridge before eluting with 5 ml of acetonitrile and reducing nitrogen to 1 ml. Recovery rates ranged from 70 to 101%.
An initial untargeted analysis was performed on sweep mode samples which identified over 100 compounds, including pyrolytic compounds which are likely the result of field burning practices in preparation for the new season. We supplemented these analyzes with data from a local list of agrochemicals for the year 2017-2018 provided by the Sri Lanka Ministry of Environment. Based on these data, targeted analyzes were performed for 30 agrochemicals using ion selective mode.
Phosphate in the samples was measured with an ion chromatograph (Thermo Dionex ICS-1100). For repeated analyzes of selected samples, analytical precision better than Â± 5% of the relative standard deviations has been obtained. Total hardness was determined by the EDTA titration method (APHA 2012)26.
Follow-up: From December 2017 to early 2020, study participants had quarterly follow-up visits assessing behavior changes, including water consumption and serum creatinine tests. Serum creatinine was tested using an enzymatic assay calibrated by IDMS and converted to estimated glomerular filtration using the CKD-EPI equation.
GIS Analysis: Using the GPS coordinates recorded by the field team for each participant’s household wells, the individual eGFR at baseline and eGFR slopes during the study period were plotted on the ArcMap world topographic map. For the base eGFR map, the values ââwere separated into five categories using the Jenks natural clippings provided by ArcGIS software. The highest category was manually set to 65ml / min / 1.73m2 and zero points or 2 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 parameters of the Inverse Distance Weighted (Spatial Analysis) tool. Five categories were determined based on the severity of the increase or decrease. RO factories and hospitals were mapped using GPS data from the field team to indicate proximity to clean water and healthcare between participants.
The analysis was performed on annual slopes using ArcGIS Hot Spot Analysis which uses Getis-Ord Gi *. This analysis identifies large groups of points that are higher or lower than expected relative to surrounding points on a two-dimensional grid. Default values ââfor clustering neighbors were used, which maximizes the probability that all points included neighbors. The visualization has been changed so that points with particularly negative slopes (fast progressors) have been labeled in red to clarify areas of greatest eGFR progression. Blue dots indicate places where the slopes are significantly higher (less negative (slow progressors) or even positive (improvers)).
The basic characteristics are described using the mean (SD) and n (proportion) as appropriate. In order to test the hypothesis that exposure to drinking water was associated with the decrease in eGFR, we used mixed coating models (LMM) taking into account age, gender and time points. according to Boucquemont et al. (2014)27. The major advantage of LMMs is that they do not require equidistant time intervals between patient follow-ups (consecutive measurements), nor the same number of measurements per patient. LMM therefore uses all the information available in the analysis, including participants who may have fewer (or even one) follow-up visits. In our study, the response variable was eGFR and each participant had up to six data points at clinical intervals of three to four months. The exposure categories were current and historical water sources (already), including well water or not, and benchmark water sources, including reverse osmosis water or not. Before running the model, we used QQ plots to check the normality of the eGFR residuals (maintained normality assumption) and checked the correlations between the predictors with the variance inflation factor (values ââbetween 1, 04 and 1.50) indicating no significant multicollinearity. The two types of water categories (historical or reference) were tested in separate models, in which the time of eGFR collection, water source, age, sex were included in 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).