Chronic kidney disease (CKD) is a major global health burden. Global Burden of Disease (GBD) estimates show that CKD deaths, disability-adjusted life years (DALYs), prevalence, and incidence have continued to increase over recent decades, with population growth, ageing, diabetes, hypertension, and environmental exposures contributing to the changing burden [1–5]. Because CKD surveillance depends on diagnostic intensity, vital registration, and claims data, cross-country comparisons require careful interpretation and transparent reporting of age-standardized rates and uncertainty [1–5].

Surface water quality may influence population health through drinking-water source catchments, agricultural irrigation, food chains, and recreational contact. Nitrate and total nitrogen are widely monitored freshwater indicators, but their kidney-related interpretation is not straightforward because exposure source, co-contaminants, redox chemistry, diet, water treatment, and baseline health status may modify risk [6–11]. A large US National Health and Nutrition Examination Survey (NHANES) analysis reported an L-shaped relationship between urinary nitrate and CKD prevalence [9], whereas the Agricultural Health Study found that dietary nitrite from processed meats, rather than drinking-water nitrate alone, was associated with end-stage renal disease (ESRD) risk under low vitamin C intake [10]. In China, PM2.5 nitrate, measured as a particulate mass concentration in air (micrograms per cubic meter), was positively associated with CKD risk and is best interpreted here as an air-pollution confounder rather than a surface-water exposure [11]. These mixed findings support country-specific analyses rather than assuming a uniform nitrate-CKD relationship.

CKD of unknown etiology (CKDu), reported in Sri Lanka, Central America, Nigeria, and other settings, has been linked mainly to groundwater quality, heat stress, agrochemical exposure, dehydration, and socioeconomic vulnerability rather than to a single universal contaminant [12–17]. Multiple Sri Lankan studies identified combined effects of fluoride, water hardness, and metals in well water [12–15]. In Taiwan, China, river water quality showed weak but statistically significant correlations with CKD prevalence, while groundwater arsenic appeared more important [16]. Recent methodological work on ecological analysis and environmental concentration datasets emphasizes bias assessment, transparent exposure-data reliability criteria, and caution when ecological sampling coverage differs across places [18, 19].

We hypothesized that higher surface-water nitrate or total nitrogen would be associated with higher CKD burden, but we anticipated effect modification by country because water treatment, agricultural practices, monitoring networks, healthcare access, and CKD ascertainment differ substantially across settings. China, Canada, and Ireland were selected because they had publicly accessible annual surface-water monitoring data that could be harmonized with Institute for Health Metrics and Evaluation (IHME) GBD Results Tool CKD estimates for 2010–2017 and because they provided contrasting water-quality profiles and health-system contexts. This exploratory study aimed to: (1) describe CKD burden and surface-water quality trends within each country; (2) estimate country-specific associations and summarize them with random-effects meta-analysis; and (3) explore non-linear exposure-response patterns.

This exploratory ecological panel study integrates IHME GBD Results Tool CKD burden estimates with harmonized surface-water quality monitoring across China, Canada, and Ireland. The main finding is not a consistent cross-national association, but rather substantial country-specific heterogeneity: several inverse associations were observed within individual countries, whereas random-effects pooled estimates were not robust or statistically significant. Therefore, the null pooled findings should not be interpreted as evidence of no association; instead, the study is underpowered to detect pooled effects when country-specific directions and exposure ranges differed.

The inverse associations between nitrate and CKD in Ireland are counterintuitive when considered against the broader health concerns raised for drinking-water nitrate and related nitrogen species—concerns that centre mainly on N-nitroso compound formation and cancer and birth-defect outcomes rather than on CKD itself [7, 8]. Several explanations warrant consideration. First, ecological confounding is likely: Ireland’s nitrate changed over a narrow range while CKD DALYs and deaths declined, and shared time-varying factors may have driven both trends. Second, the broader nitrate literature is biologically and epidemiologically mixed. NHANES analyses reported an L-shaped relationship between urinary nitrate and CKD prevalence [9], and the NITRATE-CIN trial found that short-term inorganic nitrate reduced contrast-induced nephropathy in a specific clinical context [25]. These findings do not imply that surface-water nitrate is protective at the population level; rather, they illustrate that nitrate source, dose, co-exposures, and nitric oxide biology can complicate interpretation.

Conversely, the potential harm from nitrate and nitrogen species depends on the exposure context. The Agricultural Health Study found that drinking-water nitrate itself was not associated with ESRD, whereas nitrite from processed meats was associated with higher ESRD risk in participants with low vitamin C intake [10]. In China, PM2.5 nitrate was associated with CKD risk, with temperature-related effect modification reported in air-pollution analyses [11, 26]. These examples reinforce that source, route, co-contaminants, diet, and climate conditions may determine renal effects, a nuance that country-level ecological studies cannot disentangle.

The CKDu literature provides further context. Sri Lankan studies identified synergistic effects of fluoride, Mg-hardness, and metals in groundwater [12–15]. Prospective well-water analyses reported kidney-function decline in CKDu-endemic settings [27], and a Nigerian GIS study found CKD clusters coinciding with elevated nephrotoxic metals [28]. In Taiwan, China, surface-water associations were weak, and groundwater arsenic appeared more important [16]. These studies reinforce that water-source type and co-contaminants matter; the present analysis examines national surface-water indicators rather than individual groundwater or drinking-water exposures.

Divergent CKD trajectories likely reflect differences in demographic structure, metabolic risk, healthcare access, diagnostic coding, vital registration, and claims-data completeness. We removed causal language about improvements in hypertension or diabetes detection because the present analysis did not include health-system indicators. Global analyses indicate that population growth, ageing, diabetes, hypertension, and ascertainment differences are major contributors to CKD burden patterns [2, 3, 29, 30].

Limitations include: (1) ecological design preventing individual-level inference; (2) small panel size (n = 8 per country), limiting power and adjustment; (3) unmeasured confounders, including diet, medications, diabetes, hypertension, occupational heat exposure, socioeconomic conditions, healthcare access, and CKD diagnostic coding; (4) reliance on GBD point estimates without propagating GBD UIs; (5) different water-monitoring networks, sampling fractions, laboratory methods, and parameter definitions across countries; (6) surface-water indicators rather than measured individual drinking-water exposure; (7) potential latency mismatch between annual water quality and chronic CKD outcomes; and (8) data-availability bias, because countries with publicly available harmonized annual surface-water data are not representative of all global regions. Sensitivity analyses excluding 2010 or 2017 did not change the direction of the key Ireland nitrate-DALYs and Canada nitrogen-incidence associations, but they remained vulnerable to ecological time trends. Strengths include transparent use of public data, country-specific modelling, random-effects summary estimates, standardized beta coefficients, and explicit interpretation as hypothesis-generating evidence.