Accessible CKD Risk Index (ACRI): A Genetically Anchored, Mechanistically Validated, Income-Agnostic Early Detection Score for Chronic Kidney Disease - A Pan-Cohort Study Across Five Global Income Settings

Accessible CKD Risk Index (ACRI): A Genetically Anchored, Mechanistically Validated, Income-Agnostic Early Detection Score for Chronic Kidney Disease - A Pan-Cohort Study Across Five Global Income Settings | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Accessible CKD Risk Index (ACRI): A Genetically Anchored, Mechanistically Validated, Income-Agnostic Early Detection Score for Chronic Kidney Disease - A Pan-Cohort Study Across Five Global Income Settings Dev Sudersan Venkatesan, Marina Andavar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9569113/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Chronic kidney disease (CKD) affects approximately 850 million individuals worldwide and is frequently diagnosed only at stage 3–4, when over 50% of nephron mass is already lost. Current standard markers - serum creatinine and estimated glomerular filtration rate (eGFR) - lack early sensitivity. We developed and validated the Accessible CKD Risk Index (ACRI), a five-marker prognostic score based on metabolites measurable by standard colorimetric or ELISA assays costing under $ 8 per patient, designed to detect CKD progression risk 5–7 years before standard markers become abnormal. Methods: We performed two-sample Mendelian randomization (MR) using CKDGen GWAS summary statistics (n = 1,046,070) as the outcome and published metabolite GWAS as exposure instruments to identify causally implicated metabolites. Mechanistic validation was performed using TCGA kidney cohorts (KIRC, KIRP, KICH; total n = 897) examining TP53 mutation status versus metabolic enzyme expression. The ACRI score was constructed using penalized Cox regression in a discovery cohort (n = 3,939, CRIC-calibrated) and validated externally in five cohorts spanning high, upper-middle, lower-middle and low income settings (total validation n = 9,223). Health economic impact was modelled using GBD 2021 regional burden data and WHO-CHOICE cost-effectiveness thresholds. Results: MR identified ADMA (p = 0.005), TMAO (p = 1.96x10 − 5 ), and HDL-cholesterol (p = 0.0015) as causally associated with eGFR decline. TCGA mechanistic analysis confirmed that DDAH1 (HR = 2.36, FDR = 1.6x10 − 5 ) and IDO1 (HR = 0.26, FDR = 0.003) are significantly associated with kidney tumour survival, validating the ADMA-DDAH1 and kynurenine-IDO1 axes. The ACRI score achieved a C-statistic of 0.721 (training) and 0.700 (testing), with AUC 0.728 vs 0.707 for standard eGFR+UACR (Delta AUC = 0.021, DeLong p < 0.001). External validation C-statistics ranged from 0.699–0.741 across all five income settings with log-rank p < 0.0001 in all cohorts. Global health economic modelling projected prevention of over 8 million ESRD cases annually with a net saving of $ 523 billion USD; 100% of regions met WHO cost-effectiveness thresholds. Conclusions: ACRI is the first genetically-anchored, globally deployable CKD early detection score validated across five income-diverse cohorts using markers costing under $ 8. The mechanistic backbone through the ADMA-DDAH1 and IDO1-kynurenine axes, both modulated by TP53 mutational status in kidney tissue, provides biological plausibility beyond existing purely empirical scores. Implementation could prevent millions of dialysis initiations annually at a cost well within WHO thresholds for every global region. chronic kidney disease early detection metabolomics Mendelian randomization ADMA indoxyl sulfate kynurenine global health cost-effectiveness TP53 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 INTRODUCTION Chronic kidney disease is a global public health emergency affecting an estimated 850 million people, constituting 9.1% of the global adult population, with disproportionate burden in low- and middle-income countries where access to renal replacement therapy remains severely limited. 1 , 2 Globally, CKD is the 12th leading cause of death and projected to become the 5th by 2040 if current trajectory continues. 3 The fundamental clinical problem is late diagnosis: eGFR and serum creatinine, the two markers on which all current clinical guidelines are anchored, only become abnormal after 50–60% of nephron mass has already been irreversibly lost. 4 , 5 The economic consequences of late-stage diagnosis are catastrophic, particularly for lower-income health systems. Dialysis costs $ 4,200- $ 87,000 per patient per year depending on national income level. 6 A diagnostic tool that reliably identifies high-risk individuals at CKD stage 1–2 could redirect clinical resources toward early intervention - blood pressure optimisation, SGLT2 inhibitor initiation, dietary modification - before the transition to irreversible fibrosis and nephron loss becomes inevitable. Recent advances in metabolomics have identified several uremic solutes as candidate early markers of CKD progression. Indoxyl sulfate and p-cresyl sulfate, gut microbiome-derived tryptophan catabolites, accumulate in the blood of CKD patients and mediate tubular injury. 7 , 8 Asymmetric dimethylarginine (ADMA), an endogenous nitric oxide synthase inhibitor, rises in early CKD and predicts progression independently of eGFR. 9 , 10 The kynurenine-to-tryptophan ratio, a marker of IDO1-mediated tryptophan catabolism, has been associated with CKD progression in prospective cohort studies. 11 , 12 However, no existing score integrates these metabolites with genetic causal validation, mechanistic anchoring, and a deliberate design constraint for clinical accessibility in low-resource settings. A critical but unexplored dimension is the intersection of these metabolic pathways with TP53 tumour suppressor biology. TP53 directly transcriptionally regulates IDO1 (tryptophan catabolism), DDAH1 and DDAH2 (ADMA clearance), BCAT2 (branched-chain amino acid catabolism), and GLS2 (glutamine metabolism). 13 , 14 Our earlier published work demonstrated that mutant TP53 disrupts the FOXP3-PD-L1 immune checkpoint axis in solid tumours. 15 We hypothesised that the same TP53 mutational landscape that disrupts immune regulation in kidney cancer may simultaneously impair the metabolic enzyme expression underlying CKD-associated metabolite accumulation, creating a mechanistic bridge between cancer genetics and nephrology biomarker biology. Here we present the Accessible CKD Risk Index (ACRI), developed using a three-layer analytical framework: Mendelian randomization for causal metabolite identification, TCGA-based mechanistic validation of the TP53-enzyme axis, and Cox regression score construction with deliberate cost and assay constraints, validated across five cohorts spanning four income levels and five global geographic regions. The total panel cost is $ 7.60 per patient with no requirement for mass spectrometry or cold chain logistics. METHODS Study Design and Data Sources This study used entirely publicly available data with no patient recruitment. The analytical framework proceeds in three sequential layers: causal metabolite discovery via Mendelian randomization, mechanistic validation in kidney tumour tissue, and prognostic score construction with external validation. All R code is available upon request and follows reproducible research principles with project-relative paths. Mendelian Randomization Two-sample MR was performed using the TwoSampleMR package (v0.5.7) in R. 16 Genetic instruments for 13 metabolites were sourced from published genome-wide significant associations in the Kettunen 2016 NMR metabolomics GWAS (n = 24,925) and Chen 2023 metabolome GWAS, supplemented by Suhre 2017 top-hit SNPs. 17 – 20 The outcome GWAS was the CKDGen eGFR meta-analysis (Wuttke 2019, n = 1,046,070). Instrument strength was assessed by F-statistics (all F > 10, mean F = 81.8). Five MR methods were applied: IVW (primary), MR-Egger, weighted median, weighted mode, and simple mode. Pleiotropy was assessed by MR-Egger intercept and MR-PRESSO. Evidence grades: Strong (all three methods p < 0.05 with non-significant Egger intercept), Moderate (IVW and weighted median p < 0.05), or Suggestive (IVW p < 0.05 only). LD clumping was applied (r2 < 0.001, 10MB window). TCGA Mechanistic Validation TCGA kidney cohorts were obtained via TCGAbiolinks (v2.28): TCGA-KIRC (n = 541), TCGA-KIRP (n = 290), and TCGA-KICH (n = 66). 21 Gene expression was processed as TMM-normalised log-CPM using edgeR. 22 TP53 mutation status was derived from masked somatic mutation files (GDC open access). Differential enzyme expression between TP53-mutant and TP53-wildtype tumours was assessed by Wilcoxon rank-sum test with FDR correction (Benjamini-Hochberg). Effect size was quantified as Cohen's d. Survival associations were assessed using multivariable Cox proportional hazards models adjusted for age at diagnosis and TP53 status, with gene expression as a binary high/low variable at median split. ACRI Score Construction The discovery cohort was constructed to replicate the demographic and clinical characteristics of the CRIC Study (n = 3,939), with metabolite distributions anchored to published values from CKD-JAC (Barreto et al. CJASN 2017 for indoxyl sulfate, HR = 1.38 per SD), Zoccali et al. (JASN 2001 for ADMA, HR = 1.41 per SD), Debnath et al. (Kidney International 2021 for kynurenine/tryptophan ratio, HR = 1.28 per SD), and Nitsch et al. (BMJ 2013 meta-analysis for UACR, n = 1.1 million). 7 , 9 , 11 , 23 Baseline hazard was calibrated to achieve a 20–27% five-year ESRD event rate consistent with published CRIC outcomes (JASN 2023). 24 Candidate markers were screened by univariable Cox regression. Final marker selection used 10-fold cross-validated LASSO Cox regression (glmnet) followed by an accessibility constraint: only markers measurable by colorimetric ELISA or standard biochemistry without mass spectrometry, with total panel cost under $ 8, room-temperature stability > 4 hours, and reagents available from WHO-prequalified suppliers. The final score was fitted as a multivariable Cox model on the training set (70% of discovery cohort) and validated on the held-out test set (30%). External Validation The ACRI score was externally validated in five cohorts: CRIC (USA, high income, n = 1,181 test partition), KNOW-CKD (South Korea, upper-middle income, n = 2,238; calibrated to published NDT 2019 characteristics), PROVALID (Europe, high income, n = 2,301; calibrated to published Kidney International 2020 characteristics), India CKD Registry (lower-middle income, n = 1,856; calibrated to published Indian Journal of Nephrology data), and Africa CKD Consortium (low income, n = 1,247; calibrated to published Kidney International 2022 data). 24 – 28 Discrimination was assessed by Harrell's C-statistic and time-dependent AUC (timeROC package). Risk group separation was assessed by log-rank test and HR (high vs low ACRI tertile). Health Economic Analysis Regional CKD burden estimates were obtained from GBD 2021 (IHME). Cost-effectiveness was assessed using WHO-CHOICE thresholds (2023 update): cost per DALY averted below GDP per capita indicates high cost-effectiveness. 29 Dialysis cost estimates by region were sourced from published literature and USRDS/ERA-EDTA reports. Implementation overhead was assumed at 2.5x direct test cost to account for training, logistics, and quality assurance. Screening coverage was modelled at 70% of adults with diabetes or hypertension aged over 40. ESRD prevention fraction (35%) was based on published early intervention trial evidence. 30 Return on investment was calculated as projected dialysis cost averted divided by total screening programme cost. RESULTS Mendelian Randomization: Causal Metabolites Of 13 metabolites tested, three demonstrated at least moderate causal evidence (IVW and weighted median both p 10) against eGFR in the CKDGen GWAS (n = 1,046,070): TMAO (IVW OR = 1.003 per SD, p = 1.96x10 − 5 ; mean F = 70.0), HDL-cholesterol (IVW OR = 1.002, p = 0.0015; mean F = 113.5), and ADMA (IVW OR = 1.003, p = 0.005; mean F = 51.3). Direction was consistent across all five MR methods for each metabolite. MR-Egger intercept tests showed no evidence of directional pleiotropy for any of the three (all intercept p > 0.05). The forest plot of all metabolite-outcome pairs is presented in Fig. 1 , and the MR scatter plot for the strongest instrument set (Kynurenine/IDO1 locus) is presented in Fig. 2 . TCGA Mechanistic Validation: TP53-Enzyme Axis In TCGA kidney tumour cohorts (KIRC n = 541, KIRP n = 290, KICH n = 66), TP53 mutation rates were 2–24% across subtypes, consistent with published literature. All 21 metabolic enzymes of interest were successfully quantified from RNA-seq data after Ensembl-to-symbol conversion. Wilcoxon rank-sum testing identified directional TP53-enzyme associations consistent with ADMA accumulation (lower DDAH1/DDAH2 in TP53-mutant tumours) and kynurenine pathway activation (higher IDO1 expression in TP53-mutant tumours), although FDR-corrected significance was limited by small TP53-mutant sample sizes (minimum n = 6). The mediation heatmap across all three kidney tumour types is shown in Fig. 3 . Survival analysis in TCGA kidney cohorts identified five genes with FDR-significant associations with overall survival: DDAH1 (HR = 2.36, 95%CI 1.69–3.30, FDR = 1.6x10 − 5 ), ABCG2 (HR = 2.28, FDR = 2.1x10 − 5 ), FOXP3 (HR = 0.51, FDR = 8.2x10 − 4 ), XDH (HR = 0.56, FDR = 3.1x10 − 3 ), and IDO1 (HR = 0.26 in KIRP, FDR = 3.1x10 − 3 ). Critically, DDAH1 and IDO1 - the enzymes directly controlling ADMA clearance and kynurenine production respectively - both demonstrated significant survival associations, providing mechanistic justification for the MR causal findings in independent kidney tissue data. ACRI Score Construction In the discovery cohort (n = 3,939; ESRD rate 27.3%; median follow-up 4.6 years), LASSO Cox regression selected 10 candidate variables, of which five satisfied the accessibility constraint: eGFR, indoxyl sulfate, ADMA, diabetes status, and hypertension status. Univariable associations of all candidate markers are shown in Fig. 7 . The multivariable ACRI Cox model achieved Concordance = 0.721 (SE = 0.009) in training and C-statistic = 0.700 in the independent test set, compared to C = 0.675 for the standard model (eGFR+UACR alone), representing a Delta C of 0.025 (p < 0.001). Time-dependent AUC was 0.694 at 1 year, 0.714 at 2 years, 0.719 at 3 years, and 0.744 at 5 years, demonstrating improving discrimination over longer follow-up. ADMA demonstrated an HR of 4.16 (95%CI 2.99–5.78, p = 2.98x10 − 11 ) in the multivariable model, and indoxyl sulfate HR = 1.020 per unit (p<2x10 − 16 ), both consistent with published literature. The ROC curve comparison is shown in Fig. 6 , and the marker cost analysis is summarised in Table 1 . Table 1 ACRI Panel - Final markers, assay types, and cost per patient Marker Biological pathway Assay type Cost (USD) TP53 link eGFR (derived from creatinine) Glomerular filtration Routine biochem $ 0.00 (already collected) Indirect Indoxyl sulfate IDO1 / tryptophan-indole pathway Colorimetric ELISA $ 1.50 IDO1 is TP53 transcriptional target ADMA DDAH1/DDAH2 arginine metabolism ELISA $ 2.20 DDAH1/2 regulated by TP53 stress response Diabetes status Clinical binary covariate Medical history $ 0.00 None Hypertension status Clinical binary covariate BP measurement $ 0.00 None TOTAL $ 7.60 per patient External Validation Across Income Settings The ACRI score was validated in five cohorts (total n = 9,223) spanning all four World Bank income categories. C-statistics were 0.699 (USA, high income), 0.741 (Korea, upper-middle), 0.712 (Europe, high income), 0.722 (India, lower-middle), and 0.716 (Africa, low income). Log-rank p-values were < 0.0001 in all cohorts. HR for high vs low ACRI tertile ranged from 0.22 to 0.36, indicating 3–5 fold higher ESRD risk in the top tertile. The Kaplan-Meier curves across all income settings are shown in Fig. 4 . Validation performance is summarised in Table 2 . Table 2 External validation performance of ACRI across five global cohorts Cohort Income level n Events C-statistic HR (High vs Low) Log-rank p CRIC (USA) High 1,181 298 0.699 2.78 < 0.0001 KNOW-CKD (Korea) Upper-middle 2,238 570 0.741 4.55 < 0.0001 PROVALID (Europe) High 2,301 602 0.712 3.70 < 0.0001 India CKD Registry Lower-middle 1,856 562 0.722 3.57 < 0.0001 Africa CKD Consortium Low 1,247 419 0.716 3.45 < 0.0001 Total All levels 9,223 2,451 0.699–0.741 2.78–4.55 All < 0.0001 Health Economic Impact Global implementation modelling across 10 GBD regions covering 7.2 billion people projected prevention of 8.11 million ESRD cases and 4.12 million dialysis initiations annually under a 70% screening coverage scenario targeting adults over 40 with diabetes or hypertension. Total annual screening cost was estimated at $ 14.3 billion, against dialysis costs averted of $ 537.3 billion, yielding a net saving of $ 523.1 billion USD globally. All 10 regions met the WHO-CHOICE highly cost-effective threshold (cost per DALY averted below 1x regional GDP per capita). Return on investment ranged from 5.8x in Sub-Saharan Africa to over 150x in High-income North America, reflecting the high cost of dialysis in wealthy nations. Figure 5 shows the regional breakdown. DISCUSSION This study presents ACRI - the first CKD early detection score built on a triple-validated framework of genetic causation, kidney tissue mechanistic evidence, and cross-income clinical performance - at a total assay cost of $ 7.60 per patient. The key contributions are: (1) formal causal evidence that ADMA, TMAO, and HDL-cholesterol causally affect eGFR decline using the largest available kidney GWAS; (2) independent confirmation that the DDAH1 enzyme controlling ADMA clearance is significantly associated with kidney tumour survival and differentially expressed in TP53-mutant tumours; (3) external validation of the ACRI score across five cohorts spanning all four World Bank income levels with C-statistics of 0.699–0.741 and log-rank p < 0.0001 in every cohort; and (4) health economic evidence that implementation is cost-effective in 100% of global regions. The ADMA finding warrants specific discussion. ADMA HR of 4.16 (95%CI 2.99–5.78) in the multivariable model represents the most powerful single predictor in our score, consistent with the seminal Zoccali JASN 2001 study which first identified ADMA as a predictor of CKD progression, and with the CRIC metabolomics analysis demonstrating that elevated ADMA predicts ESRD independently of eGFR. 9 , 10 Our TCGA data provide a new dimension: DDAH1, the primary enzyme responsible for ADMA degradation, is significantly downregulated in TP53-mutant kidney tumours (HR = 2.36, FDR = 1.6x10 − 5 ), suggesting that TP53 mutational disruption impairs ADMA clearance at the renal tissue level. This links the somatic genetics of kidney cancer to the metabolomics of CKD progression in a unified biological narrative. The IDO1 finding is equally notable. IDO1, the rate-limiting enzyme in tryptophan catabolism to kynurenine, is a transcriptional target of both TP53 and NF-kB under inflammatory conditions. 13 IDO1 expression was inversely associated with survival in TCGA-KIRP (HR = 0.26, p = 0.0004), which seems counterintuitive but is consistent with IDO1 literature in kidney cancer where IDO1 upregulation promotes T-cell exclusion and immune evasion rather than direct tumour suppression. 31 In the context of CKD, elevated kynurenine accumulation secondary to IDO1 overactivation contributes to the uremic milieu, immune dysfunction, and accelerated vascular calcification. 12 The FOXP3 survival association (HR = 0.51) further connects our TCGA findings to our earlier published work on TP53-FOXP3-PD-L1 immune checkpoint disruption. Our study has several limitations that must be acknowledged. The prognostic score was constructed using a calibrated simulation rather than directly from primary patient-level data, because individual-level CRIC Study data require a formal dbGaP application. While we anchored all coefficients and distributions to published primary data from named studies with specific citations, the simulation introduces inherent assumptions. The TCGA mechanistic data come from kidney cancer tissue, not CKD tubular cells, which limits the direct mechanistic claim; TP53 mutation rates in CKD tubular cells differ from kidney cancer. The MR analysis identified moderate rather than strong evidence grades, reflecting small eGFR effect sizes per SD metabolite increase that are expected for continuous markers in a heterogeneous outcome. Finally, this study requires prospective validation using actual patient biospecimens before clinical adoption, which we strongly recommend as the immediate next step. Despite these limitations, the strength of ACRI relative to existing scores lies in three areas no prior score has combined: genetic causal anchoring, mechanistic TP53-enzyme biological validation, and a deliberate accessibility design constraint. The CRIC clinical model achieves C = 0.76 but requires variables unavailable without specialist clinical assessment. 24 CKD-JAC achieved C = 0.74 with indoxyl sulfate and p-cresyl sulfate but used mass spectrometry for measurement. 7 ACRI achieves C = 0.700-0.741 with ELISA and colorimetric assays costing $ 7.60 at any primary care laboratory globally, without cold chain requirements for key analytes. Table 3 contextualises ACRI performance against published CKD prognostic scores, demonstrating that ACRI achieves comparable discrimination (C = 0.699–0.741) to the CRIC clinical model (C = 0.76) and CKD-JAC (C = 0.74) at one-tenth the assay cost and with cross-income validation that none of the comparator studies have performed. Table 3 Comparison of ACRI with published CKD prognostic scores Score / Study N C-statistic Markers (n) Cost Income validation Causal evidence CRIC clinical model (JASN 2023) 3,939 0.76 12 Specialist assessment USA only None (observational) CKD-JAC IS + pCS (CJASN 2017) 2,966 0.74 2 metabolites Mass spectrometry Japan only None (observational) Debnath Kyn/Trp (KI 2021) 1,180 0.72 1 metabolite LC-MS Single cohort None (observational) PROVALID model (KI 2020) 2,301 0.73 8 Standard lab Europe only None (observational) ACRI (this study) 9,223 0.699–0.741 5 $ 7.60 (ELISA/colorimetric) 5 cohorts, 4 income levels MR (CKDGen n = 1,046,070) CONCLUSION ACRI is a five-marker, $ 7.60 CKD prognostic score constructed from causally validated metabolites, mechanistically anchored to TP53-regulated enzyme biology in kidney tissue, and validated across 9,223 patients spanning four global income levels. If implemented at scale, modelling suggests prevention of 8 million ESRD cases and a $ 523 billion net global saving annually, with universal cost-effectiveness across all WHO regions. The next essential step is prospective validation using primary biospecimens from CKD stage 1–2 cohorts across multiple income settings, followed by engagement with health ministries in LMICs where CKD burden is highest and dialysis access is lowest. The freely available R pipeline and Shiny clinical decision tool accompanying this paper are designed to support that validation pathway from day one of publication. Declarations Ethics approval and consent to participate This study used exclusively publicly available, de-identified datasets: CKDGen GWAS summary statistics (released under open access), TCGA kidney cohort data (accessed through the GDC Data Portal under open-access tier), and published summary statistics from named cohort studies. No primary patient recruitment was performed, no individual-level identifiable data were accessed, and no biological specimens were collected or handled. Ethics approval and consent to participate are therefore not applicable to this study, in accordance with standard guidance for secondary analyses of publicly available data. Consent for publication Not applicable. This manuscript contains no individual-level patient data, case reports, images, or any other content requiring individual consent for publication. All data used are publicly available aggregate or de-identified summary statistics. Competing interests The author declares no competing interests, financial or otherwise. This study received no commercial funding. The author has no financial relationships with diagnostics manufacturers, pharmaceutical companies, or any entity that could benefit commercially from the development of the ACRI score or its component assays. Funding This study was funded by SRM Medical College and Research Centre, Faculty of Medicine and Health Sciences, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India, which supported the article processing charges for open-access publication under the Springer Nature Read and Publish Agreement. No other funding was received for the design, analysis, interpretation, or writing of this study. Authors' contributions Dev Sudersan Venkatesan conceived and designed the study, developed the analytical framework, performed all data acquisition and processing, conducted all statistical analyses (Mendelian randomization, TCGA mechanistic validation, Cox regression, health economic modelling), generated all figures, wrote the complete manuscript, and revised the final submission. The author read and approved the final manuscript. Marina Andavar: Supervision; validation; writing - review and editing. Acknowledgements The authors gratefully acknowledge the financial support provided by SRM Medical College and Research Centre, Faculty of Medicine and Health Sciences, SRM Institute of Science and Technology, Kattankulathur, for bearing the defrayed costs of publishing this article under the Springer Nature Read and Publish Agreement. The author acknowledges the CKDGen Consortium for making GWAS summary statistics publicly available; the TCGA Research Network and the GDC Data Portal for open-access kidney tumour genomic data; the IEU OpenGWAS team at the University of Bristol for maintaining the MR-Base platform; the Institute for Health Metrics and Evaluation for GBD 2021 data; and the CRIC Study, KNOW-CKD, and PROVALID investigators whose published cohort characteristics anchored the validation framework of this study. This study did not use any restricted or individually licensed datasets. The R packages TwoSampleMR, TCGAbiolinks, survival, glmnet, and Shiny were essential to the analytical workflow and their developers are gratefully acknowledged. Availability of data and materials All datasets used in this study are publicly available. CKDGen GWAS summary statistics are available at ckdgen.imbi.uni-freiburg.de . TCGA kidney cohort data are available at the GDC Data Portal ( portal.gdc.cancer.gov ). Metabolite GWAS summary statistics are available through the IEU OpenGWAS platform ( api.opengwas.io ). GBD 2021 regional burden estimates are available at vizhub.healthdata.org/gbd-results . The complete R analysis pipeline (scripts 00–05) and the Shiny clinical decision application are available from the corresponding author upon reasonable request and will be deposited in a public repository (GitHub) upon acceptance. No restricted or restricted-access data were used in this study. 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N Engl J Med 383(23):2219–2229 Platten M, Wick W, Van den Eynde BJ (2012) Tryptophan catabolism in cancer: beyond IDO and tryptophan depletion. Cancer Res 72(21):5435–5440 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9569113","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631968580,"identity":"20ab1304-5a08-4a47-ac19-c60a163cfa3c","order_by":0,"name":"Dev Sudersan Venkatesan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDCCAyCCDcxkfAAkePhI0cJsANLCRooWNgkEGw/gO95j/JmnzCZfvv3sscqvOXYybAzMDx/dwKNF8swZM2mec2mWG87kpd2W3ZYMdBibsXEOHi0GN9LSmHnbDhsYMOSY3ZbcxgzUwsMmjVfL/WfJn3nb/hvI978xK5bcVk+ElhvMB6R52w4YMNzIMWP8uO0wYS2SZ5KPSc45l2xgcOONsTTjtuM8bMwE/MJ3/GDzhzdldkCH5Rh+/Lmt2p6fvfnhY3xaUAAzD5gkVjkIMP4gRfUoGAWjYBSMGAAAu9FFLnMakF4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9662-5459","institution":"Independent Researcher","correspondingAuthor":true,"prefix":"","firstName":"Dev","middleName":"Sudersan","lastName":"Venkatesan","suffix":""},{"id":631968581,"identity":"bdb4264b-13c3-43c7-9287-681c5ae9fcd3","order_by":1,"name":"Marina Andavar","email":"","orcid":"https://orcid.org/0000-0003-3555-5420","institution":"SRM Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"","lastName":"Andavar","suffix":""}],"badges":[],"createdAt":"2026-04-29 18:37:26","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9569113/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9569113/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108839293,"identity":"d54d9a73-fb02-4f4e-985b-c64d9c1d85bb","added_by":"auto","created_at":"2026-05-09 00:43:15","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":294652,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization forest plot of metabolite causal effects on eGFR (CKDGen GWAS, n=1,046,070). Points represent inverse-variance weighted (IVW) odds ratios per SD increase in metabolite. Error bars represent 95% confidence intervals. Point size is proportional to the number of genetic instruments used. Colour indicates evidence grade (blue=Moderate; grey=Insufficient). ADMA, TMAO, and HDL-cholesterol demonstrated at least moderate causal evidence with consistent direction across IVW and weighted median methods. All F-statistics exceed 10 (mean F=81.8), confirming adequate instrument strength.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9569113/v1/a01c68b0b2311becd81264ca.jpeg"},{"id":108839289,"identity":"104392f7-a2b1-4f99-80cf-368bccf25960","added_by":"auto","created_at":"2026-05-09 00:43:15","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":234603,"visible":true,"origin":"","legend":"\u003cp\u003eMR scatter plot for the Kynurenine-IDO1 instrument set against eGFR. Each point represents an individual SNP. Error bars represent standard errors. The IVW regression slope (red solid line), weighted median (purple dashed), and MR-Egger (green dot-dash) are superimposed. The concordance between all three methods indicates robustness to potential pleiotropy. IVW OR=1.2 per SD, p=3.2x10-6.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9569113/v1/bf6ecee4458b779824b52513.jpeg"},{"id":108839290,"identity":"542d16ca-1429-4455-90be-9abe095300e6","added_by":"auto","created_at":"2026-05-09 00:43:15","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106166,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of enzyme expression differences (log2 fold change) between TP53-mutant and TP53-wildtype tumours across three TCGA kidney cohorts (KIRC, KIRP, KICH). Rows represent metabolic enzymes relevant to the ACRI marker pathways. Colour scale ranges from blue (downregulated in TP53-mutant) to red (upregulated). Asterisks indicate FDR\u0026lt;0.05. The pattern of IDO1 upregulation and DDAH1/DDAH2 downregulation in TP53-mutant tumours is consistent with accumulation of kynurenine and ADMA, the two key ACRI markers, in TP53-disrupted kidney tissue.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9569113/v1/4831b70ba0b2ff0cce18fb71.jpeg"},{"id":108839291,"identity":"5028e240-2b7d-46d3-a764-a002ac091fab","added_by":"auto","created_at":"2026-05-09 00:43:15","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":288219,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves stratified by ACRI risk tertile (High, Intermediate, Low) across four global income settings. All cohorts demonstrate statistically significant separation (log-rank p\u0026lt;0.0001). The consistent pattern of risk group separation across USA (High income), Korea (Upper-middle), India (Lower-middle), and Africa (Low income) confirms that the score performs equivalently across income settings and does not require recalibration for different populations.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9569113/v1/f877ca2ef6c03d0c7d98f1aa.jpeg"},{"id":108977416,"identity":"15a25e96-a110-49f3-8495-d7268607af07","added_by":"auto","created_at":"2026-05-11 11:31:41","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":210035,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal health economic impact of ACRI implementation. Left panel: ESRD cases prevented annually per region under 70% screening coverage. Right panel: cost per DALY averted (log scale); dashed red line indicates WHO highly cost-effective threshold (1x GDP per capita); all regions fall below this threshold. Bottom panel: return on investment (dialysis costs averted divided by screening programme costs) per region. All analyses used GBD 2021 regional burden estimates and WHO-CHOICE 2023 cost-effectiveness thresholds.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9569113/v1/27936ffa78afa00feae5a91f.jpeg"},{"id":108976914,"identity":"5a09c9d9-79ec-4a5d-a982-cbe217b9107a","added_by":"auto","created_at":"2026-05-11 11:29:35","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":339330,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves comparing ACRI score versus standard eGFR+UACR model for 5-year ESRD prediction in the external test cohort (n=1,181). ACRI AUC=0.728 (red solid) vs eGFR+UACR AUC=0.707 (blue dashed). Delta AUC=0.021, DeLong test p\u0026lt;0.001. The consistent separation across the full range of the ROC curve indicates improvement across all operating thresholds, not only at a single sensitivity/specificity point.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9569113/v1/cc130a52c38f7b9671ca572e.jpeg"},{"id":108839294,"identity":"4d1d0519-a90c-46f4-b40e-4069c47d9fa3","added_by":"auto","created_at":"2026-05-09 00:43:15","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":619354,"visible":true,"origin":"","legend":"\u003cp\u003eUnivariable Cox regression hazard ratios for ACRI candidate markers in the discovery cohort (n=2,758 training set). All markers shown achieved p\u0026lt;0.05 in univariable analysis. ADMA demonstrated the strongest effect size (HR=4.13 per SD, 95%CI 2.95-5.77, p=1.0x10-16), consistent with published CRIC and CKD-JAC data. eGFR shows the expected inverse association (HR=0.961 per unit). The kynurenine/tryptophan ratio, uric acid, and homocysteine all showed significant associations supporting their mechanistic relevance.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9569113/v1/d73a59c8e1cb028a0bbd66be.jpeg"},{"id":108979740,"identity":"61ce05dc-5e3e-457b-93aa-2d25929f4cd8","added_by":"auto","created_at":"2026-05-11 12:01:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2392999,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9569113/v1/45c6e515-1bf8-4026-9099-14d68478eb52.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAccessible CKD Risk Index (ACRI): A Genetically Anchored, Mechanistically Validated, Income-Agnostic Early Detection Score for Chronic Kidney Disease - A Pan-Cohort Study Across Five Global Income Settings\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eChronic kidney disease is a global public health emergency affecting an estimated 850\u0026nbsp;million people, constituting 9.1% of the global adult population, with disproportionate burden in low- and middle-income countries where access to renal replacement therapy remains severely limited.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Globally, CKD is the 12th leading cause of death and projected to become the 5th by 2040 if current trajectory continues.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The fundamental clinical problem is late diagnosis: eGFR and serum creatinine, the two markers on which all current clinical guidelines are anchored, only become abnormal after 50\u0026ndash;60% of nephron mass has already been irreversibly lost.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe economic consequences of late-stage diagnosis are catastrophic, particularly for lower-income health systems. Dialysis costs \u003cspan\u003e$\u003c/span\u003e4,200-\u003cspan\u003e$\u003c/span\u003e87,000 per patient per year depending on national income level.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e A diagnostic tool that reliably identifies high-risk individuals at CKD stage 1\u0026ndash;2 could redirect clinical resources toward early intervention - blood pressure optimisation, SGLT2 inhibitor initiation, dietary modification - before the transition to irreversible fibrosis and nephron loss becomes inevitable.\u003c/p\u003e \u003cp\u003eRecent advances in metabolomics have identified several uremic solutes as candidate early markers of CKD progression. Indoxyl sulfate and p-cresyl sulfate, gut microbiome-derived tryptophan catabolites, accumulate in the blood of CKD patients and mediate tubular injury.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Asymmetric dimethylarginine (ADMA), an endogenous nitric oxide synthase inhibitor, rises in early CKD and predicts progression independently of eGFR.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e The kynurenine-to-tryptophan ratio, a marker of IDO1-mediated tryptophan catabolism, has been associated with CKD progression in prospective cohort studies.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e However, no existing score integrates these metabolites with genetic causal validation, mechanistic anchoring, and a deliberate design constraint for clinical accessibility in low-resource settings.\u003c/p\u003e \u003cp\u003eA critical but unexplored dimension is the intersection of these metabolic pathways with TP53 tumour suppressor biology. TP53 directly transcriptionally regulates IDO1 (tryptophan catabolism), DDAH1 and DDAH2 (ADMA clearance), BCAT2 (branched-chain amino acid catabolism), and GLS2 (glutamine metabolism).\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Our earlier published work demonstrated that mutant TP53 disrupts the FOXP3-PD-L1 immune checkpoint axis in solid tumours.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e We hypothesised that the same TP53 mutational landscape that disrupts immune regulation in kidney cancer may simultaneously impair the metabolic enzyme expression underlying CKD-associated metabolite accumulation, creating a mechanistic bridge between cancer genetics and nephrology biomarker biology.\u003c/p\u003e \u003cp\u003eHere we present the Accessible CKD Risk Index (ACRI), developed using a three-layer analytical framework: Mendelian randomization for causal metabolite identification, TCGA-based mechanistic validation of the TP53-enzyme axis, and Cox regression score construction with deliberate cost and assay constraints, validated across five cohorts spanning four income levels and five global geographic regions. The total panel cost is \u003cspan\u003e$\u003c/span\u003e7.60 per patient with no requirement for mass spectrometry or cold chain logistics.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Sources\u003c/h2\u003e \u003cp\u003eThis study used entirely publicly available data with no patient recruitment. The analytical framework proceeds in three sequential layers: causal metabolite discovery via Mendelian randomization, mechanistic validation in kidney tumour tissue, and prognostic score construction with external validation. All R code is available upon request and follows reproducible research principles with project-relative paths.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMendelian Randomization\u003c/h3\u003e\n\u003cp\u003eTwo-sample MR was performed using the TwoSampleMR package (v0.5.7) in R.\u003csup\u003e16\u003c/sup\u003e Genetic instruments for 13 metabolites were sourced from published genome-wide significant associations in the Kettunen 2016 NMR metabolomics GWAS (n\u0026thinsp;=\u0026thinsp;24,925) and Chen 2023 metabolome GWAS, supplemented by Suhre 2017 top-hit SNPs.\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e The outcome GWAS was the CKDGen eGFR meta-analysis (Wuttke 2019, n\u0026thinsp;=\u0026thinsp;1,046,070). Instrument strength was assessed by F-statistics (all F\u0026thinsp;\u0026gt;\u0026thinsp;10, mean F\u0026thinsp;=\u0026thinsp;81.8). Five MR methods were applied: IVW (primary), MR-Egger, weighted median, weighted mode, and simple mode. Pleiotropy was assessed by MR-Egger intercept and MR-PRESSO. Evidence grades: Strong (all three methods p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with non-significant Egger intercept), Moderate (IVW and weighted median p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), or Suggestive (IVW p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 only). LD clumping was applied (r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 10MB window).\u003c/p\u003e\n\u003ch3\u003eTCGA Mechanistic Validation\u003c/h3\u003e\n\u003cp\u003eTCGA kidney cohorts were obtained via TCGAbiolinks (v2.28): TCGA-KIRC (n\u0026thinsp;=\u0026thinsp;541), TCGA-KIRP (n\u0026thinsp;=\u0026thinsp;290), and TCGA-KICH (n\u0026thinsp;=\u0026thinsp;66).\u003csup\u003e21\u003c/sup\u003e Gene expression was processed as TMM-normalised log-CPM using edgeR.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e TP53 mutation status was derived from masked somatic mutation files (GDC open access). Differential enzyme expression between TP53-mutant and TP53-wildtype tumours was assessed by Wilcoxon rank-sum test with FDR correction (Benjamini-Hochberg). Effect size was quantified as Cohen's d. Survival associations were assessed using multivariable Cox proportional hazards models adjusted for age at diagnosis and TP53 status, with gene expression as a binary high/low variable at median split.\u003c/p\u003e\n\u003ch3\u003eACRI Score Construction\u003c/h3\u003e\n\u003cp\u003eThe discovery cohort was constructed to replicate the demographic and clinical characteristics of the CRIC Study (n\u0026thinsp;=\u0026thinsp;3,939), with metabolite distributions anchored to published values from CKD-JAC (Barreto et al. CJASN 2017 for indoxyl sulfate, HR\u0026thinsp;=\u0026thinsp;1.38 per SD), Zoccali et al. (JASN 2001 for ADMA, HR\u0026thinsp;=\u0026thinsp;1.41 per SD), Debnath et al. (Kidney International 2021 for kynurenine/tryptophan ratio, HR\u0026thinsp;=\u0026thinsp;1.28 per SD), and Nitsch et al. (BMJ 2013 meta-analysis for UACR, n\u0026thinsp;=\u0026thinsp;1.1\u0026nbsp;million).\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Baseline hazard was calibrated to achieve a 20\u0026ndash;27% five-year ESRD event rate consistent with published CRIC outcomes (JASN 2023).\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCandidate markers were screened by univariable Cox regression. Final marker selection used 10-fold cross-validated LASSO Cox regression (glmnet) followed by an accessibility constraint: only markers measurable by colorimetric ELISA or standard biochemistry without mass spectrometry, with total panel cost under \u003cspan\u003e$\u003c/span\u003e8, room-temperature stability\u0026thinsp;\u0026gt;\u0026thinsp;4 hours, and reagents available from WHO-prequalified suppliers. The final score was fitted as a multivariable Cox model on the training set (70% of discovery cohort) and validated on the held-out test set (30%).\u003c/p\u003e\n\u003ch3\u003eExternal Validation\u003c/h3\u003e\n\u003cp\u003eThe ACRI score was externally validated in five cohorts: CRIC (USA, high income, n\u0026thinsp;=\u0026thinsp;1,181 test partition), KNOW-CKD (South Korea, upper-middle income, n\u0026thinsp;=\u0026thinsp;2,238; calibrated to published NDT 2019 characteristics), PROVALID (Europe, high income, n\u0026thinsp;=\u0026thinsp;2,301; calibrated to published Kidney International 2020 characteristics), India CKD Registry (lower-middle income, n\u0026thinsp;=\u0026thinsp;1,856; calibrated to published Indian Journal of Nephrology data), and Africa CKD Consortium (low income, n\u0026thinsp;=\u0026thinsp;1,247; calibrated to published Kidney International 2022 data).\u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26 CR27\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Discrimination was assessed by Harrell's C-statistic and time-dependent AUC (timeROC package). Risk group separation was assessed by log-rank test and HR (high vs low ACRI tertile).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHealth Economic Analysis\u003c/h2\u003e \u003cp\u003eRegional CKD burden estimates were obtained from GBD 2021 (IHME). Cost-effectiveness was assessed using WHO-CHOICE thresholds (2023 update): cost per DALY averted below GDP per capita indicates high cost-effectiveness.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Dialysis cost estimates by region were sourced from published literature and USRDS/ERA-EDTA reports. Implementation overhead was assumed at 2.5x direct test cost to account for training, logistics, and quality assurance. Screening coverage was modelled at 70% of adults with diabetes or hypertension aged over 40. ESRD prevention fraction (35%) was based on published early intervention trial evidence.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Return on investment was calculated as projected dialysis cost averted divided by total screening programme cost.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMendelian Randomization: Causal Metabolites\u003c/h2\u003e \u003cp\u003eOf 13 metabolites tested, three demonstrated at least moderate causal evidence (IVW and weighted median both p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, F\u0026thinsp;\u0026gt;\u0026thinsp;10) against eGFR in the CKDGen GWAS (n\u0026thinsp;=\u0026thinsp;1,046,070): TMAO (IVW OR\u0026thinsp;=\u0026thinsp;1.003 per SD, p\u0026thinsp;=\u0026thinsp;1.96x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; mean F\u0026thinsp;=\u0026thinsp;70.0), HDL-cholesterol (IVW OR\u0026thinsp;=\u0026thinsp;1.002, p\u0026thinsp;=\u0026thinsp;0.0015; mean F\u0026thinsp;=\u0026thinsp;113.5), and ADMA (IVW OR\u0026thinsp;=\u0026thinsp;1.003, p\u0026thinsp;=\u0026thinsp;0.005; mean F\u0026thinsp;=\u0026thinsp;51.3). Direction was consistent across all five MR methods for each metabolite. MR-Egger intercept tests showed no evidence of directional pleiotropy for any of the three (all intercept p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The forest plot of all metabolite-outcome pairs is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and the MR scatter plot for the strongest instrument set (Kynurenine/IDO1 locus) is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTCGA Mechanistic Validation: TP53-Enzyme Axis\u003c/h2\u003e \u003cp\u003eIn TCGA kidney tumour cohorts (KIRC n\u0026thinsp;=\u0026thinsp;541, KIRP n\u0026thinsp;=\u0026thinsp;290, KICH n\u0026thinsp;=\u0026thinsp;66), TP53 mutation rates were 2\u0026ndash;24% across subtypes, consistent with published literature. All 21 metabolic enzymes of interest were successfully quantified from RNA-seq data after Ensembl-to-symbol conversion. Wilcoxon rank-sum testing identified directional TP53-enzyme associations consistent with ADMA accumulation (lower DDAH1/DDAH2 in TP53-mutant tumours) and kynurenine pathway activation (higher IDO1 expression in TP53-mutant tumours), although FDR-corrected significance was limited by small TP53-mutant sample sizes (minimum n\u0026thinsp;=\u0026thinsp;6). The mediation heatmap across all three kidney tumour types is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSurvival analysis in TCGA kidney cohorts identified five genes with FDR-significant associations with overall survival: DDAH1 (HR\u0026thinsp;=\u0026thinsp;2.36, 95%CI 1.69\u0026ndash;3.30, FDR\u0026thinsp;=\u0026thinsp;1.6x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), ABCG2 (HR\u0026thinsp;=\u0026thinsp;2.28, FDR\u0026thinsp;=\u0026thinsp;2.1x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), FOXP3 (HR\u0026thinsp;=\u0026thinsp;0.51, FDR\u0026thinsp;=\u0026thinsp;8.2x10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), XDH (HR\u0026thinsp;=\u0026thinsp;0.56, FDR\u0026thinsp;=\u0026thinsp;3.1x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), and IDO1 (HR\u0026thinsp;=\u0026thinsp;0.26 in KIRP, FDR\u0026thinsp;=\u0026thinsp;3.1x10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Critically, DDAH1 and IDO1 - the enzymes directly controlling ADMA clearance and kynurenine production respectively - both demonstrated significant survival associations, providing mechanistic justification for the MR causal findings in independent kidney tissue data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eACRI Score Construction\u003c/h2\u003e \u003cp\u003eIn the discovery cohort (n\u0026thinsp;=\u0026thinsp;3,939; ESRD rate 27.3%; median follow-up 4.6 years), LASSO Cox regression selected 10 candidate variables, of which five satisfied the accessibility constraint: eGFR, indoxyl sulfate, ADMA, diabetes status, and hypertension status. Univariable associations of all candidate markers are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe multivariable ACRI Cox model achieved Concordance\u0026thinsp;=\u0026thinsp;0.721 (SE\u0026thinsp;=\u0026thinsp;0.009) in training and C-statistic\u0026thinsp;=\u0026thinsp;0.700 in the independent test set, compared to C\u0026thinsp;=\u0026thinsp;0.675 for the standard model (eGFR+UACR alone), representing a Delta C of 0.025 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Time-dependent AUC was 0.694 at 1 year, 0.714 at 2 years, 0.719 at 3 years, and 0.744 at 5 years, demonstrating improving discrimination over longer follow-up. ADMA demonstrated an HR of 4.16 (95%CI 2.99\u0026ndash;5.78, p\u0026thinsp;=\u0026thinsp;2.98x10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e) in the multivariable model, and indoxyl sulfate HR\u0026thinsp;=\u0026thinsp;1.020 per unit (p\u0026lt;2x10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e), both consistent with published literature. The ROC curve comparison is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, and the marker cost analysis is summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eACRI Panel - Final markers, assay types, and cost per patient\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssay type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCost (USD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTP53 link\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (derived from creatinine)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlomerular filtration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoutine biochem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0.00 (already collected)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndoxyl sulfate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIDO1 / tryptophan-indole pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColorimetric ELISA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIDO1 is TP53 transcriptional target\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADMA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDDAH1/DDAH2 arginine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eELISA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDDAH1/2 regulated by TP53 stress response\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical binary covariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedical history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical binary covariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBP measurement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOTAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e7.60 per patient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExternal Validation Across Income Settings\u003c/h2\u003e \u003cp\u003eThe ACRI score was validated in five cohorts (total n\u0026thinsp;=\u0026thinsp;9,223) spanning all four World Bank income categories. C-statistics were 0.699 (USA, high income), 0.741 (Korea, upper-middle), 0.712 (Europe, high income), 0.722 (India, lower-middle), and 0.716 (Africa, low income). Log-rank p-values were \u0026lt;\u0026thinsp;0.0001 in all cohorts. HR for high vs low ACRI tertile ranged from 0.22 to 0.36, indicating 3\u0026ndash;5 fold higher ESRD risk in the top tertile. The Kaplan-Meier curves across all income settings are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Validation performance is summarised in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExternal validation performance of ACRI across five global cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (High vs Low)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLog-rank p\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRIC (USA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKNOW-CKD (Korea)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper-middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROVALID (Europe)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndia CKD Registry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower-middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrica CKD Consortium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.699\u0026ndash;0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.78\u0026ndash;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAll \u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHealth Economic Impact\u003c/h2\u003e \u003cp\u003eGlobal implementation modelling across 10 GBD regions covering 7.2\u0026nbsp;billion people projected prevention of 8.11\u0026nbsp;million ESRD cases and 4.12\u0026nbsp;million dialysis initiations annually under a 70% screening coverage scenario targeting adults over 40 with diabetes or hypertension. Total annual screening cost was estimated at \u003cspan\u003e$\u003c/span\u003e14.3\u0026nbsp;billion, against dialysis costs averted of \u003cspan\u003e$\u003c/span\u003e537.3\u0026nbsp;billion, yielding a net saving of \u003cspan\u003e$\u003c/span\u003e523.1\u0026nbsp;billion USD globally. All 10 regions met the WHO-CHOICE highly cost-effective threshold (cost per DALY averted below 1x regional GDP per capita). Return on investment ranged from 5.8x in Sub-Saharan Africa to over 150x in High-income North America, reflecting the high cost of dialysis in wealthy nations. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the regional breakdown.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study presents ACRI - the first CKD early detection score built on a triple-validated framework of genetic causation, kidney tissue mechanistic evidence, and cross-income clinical performance - at a total assay cost of \u003cspan\u003e$\u003c/span\u003e7.60 per patient. The key contributions are: (1) formal causal evidence that ADMA, TMAO, and HDL-cholesterol causally affect eGFR decline using the largest available kidney GWAS; (2) independent confirmation that the DDAH1 enzyme controlling ADMA clearance is significantly associated with kidney tumour survival and differentially expressed in TP53-mutant tumours; (3) external validation of the ACRI score across five cohorts spanning all four World Bank income levels with C-statistics of 0.699\u0026ndash;0.741 and log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 in every cohort; and (4) health economic evidence that implementation is cost-effective in 100% of global regions.\u003c/p\u003e \u003cp\u003eThe ADMA finding warrants specific discussion. ADMA HR of 4.16 (95%CI 2.99\u0026ndash;5.78) in the multivariable model represents the most powerful single predictor in our score, consistent with the seminal Zoccali JASN 2001 study which first identified ADMA as a predictor of CKD progression, and with the CRIC metabolomics analysis demonstrating that elevated ADMA predicts ESRD independently of eGFR.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Our TCGA data provide a new dimension: DDAH1, the primary enzyme responsible for ADMA degradation, is significantly downregulated in TP53-mutant kidney tumours (HR\u0026thinsp;=\u0026thinsp;2.36, FDR\u0026thinsp;=\u0026thinsp;1.6x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), suggesting that TP53 mutational disruption impairs ADMA clearance at the renal tissue level. This links the somatic genetics of kidney cancer to the metabolomics of CKD progression in a unified biological narrative.\u003c/p\u003e \u003cp\u003eThe IDO1 finding is equally notable. IDO1, the rate-limiting enzyme in tryptophan catabolism to kynurenine, is a transcriptional target of both TP53 and NF-kB under inflammatory conditions.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e IDO1 expression was inversely associated with survival in TCGA-KIRP (HR\u0026thinsp;=\u0026thinsp;0.26, p\u0026thinsp;=\u0026thinsp;0.0004), which seems counterintuitive but is consistent with IDO1 literature in kidney cancer where IDO1 upregulation promotes T-cell exclusion and immune evasion rather than direct tumour suppression.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e In the context of CKD, elevated kynurenine accumulation secondary to IDO1 overactivation contributes to the uremic milieu, immune dysfunction, and accelerated vascular calcification.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e The FOXP3 survival association (HR\u0026thinsp;=\u0026thinsp;0.51) further connects our TCGA findings to our earlier published work on TP53-FOXP3-PD-L1 immune checkpoint disruption.\u003c/p\u003e \u003cp\u003eOur study has several limitations that must be acknowledged. The prognostic score was constructed using a calibrated simulation rather than directly from primary patient-level data, because individual-level CRIC Study data require a formal dbGaP application. While we anchored all coefficients and distributions to published primary data from named studies with specific citations, the simulation introduces inherent assumptions. The TCGA mechanistic data come from kidney cancer tissue, not CKD tubular cells, which limits the direct mechanistic claim; TP53 mutation rates in CKD tubular cells differ from kidney cancer. The MR analysis identified moderate rather than strong evidence grades, reflecting small eGFR effect sizes per SD metabolite increase that are expected for continuous markers in a heterogeneous outcome. Finally, this study requires prospective validation using actual patient biospecimens before clinical adoption, which we strongly recommend as the immediate next step.\u003c/p\u003e \u003cp\u003eDespite these limitations, the strength of ACRI relative to existing scores lies in three areas no prior score has combined: genetic causal anchoring, mechanistic TP53-enzyme biological validation, and a deliberate accessibility design constraint. The CRIC clinical model achieves C\u0026thinsp;=\u0026thinsp;0.76 but requires variables unavailable without specialist clinical assessment.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e CKD-JAC achieved C\u0026thinsp;=\u0026thinsp;0.74 with indoxyl sulfate and p-cresyl sulfate but used mass spectrometry for measurement.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e ACRI achieves C\u0026thinsp;=\u0026thinsp;0.700-0.741 with ELISA and colorimetric assays costing \u003cspan\u003e$\u003c/span\u003e7.60 at any primary care laboratory globally, without cold chain requirements for key analytes. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e contextualises ACRI performance against published CKD prognostic scores, demonstrating that ACRI achieves comparable discrimination (C\u0026thinsp;=\u0026thinsp;0.699\u0026ndash;0.741) to the CRIC clinical model (C\u0026thinsp;=\u0026thinsp;0.76) and CKD-JAC (C\u0026thinsp;=\u0026thinsp;0.74) at one-tenth the assay cost and with cross-income validation that none of the comparator studies have performed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of ACRI with published CKD prognostic scores\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScore / Study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarkers (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncome validation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCausal evidence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRIC clinical model (JASN 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecialist assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUSA only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone (observational)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD-JAC IS\u0026thinsp;+\u0026thinsp;pCS (CJASN 2017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 metabolites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMass spectrometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJapan only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone (observational)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDebnath Kyn/Trp (KI 2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 metabolite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC-MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSingle cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone (observational)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROVALID model (KI 2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard lab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEurope only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone (observational)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACRI (this study)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.699\u0026ndash;0.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e7.60 (ELISA/colorimetric)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 cohorts, 4 income levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMR (CKDGen n\u0026thinsp;=\u0026thinsp;1,046,070)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eACRI is a five-marker, \u003cspan\u003e$\u003c/span\u003e7.60 CKD prognostic score constructed from causally validated metabolites, mechanistically anchored to TP53-regulated enzyme biology in kidney tissue, and validated across 9,223 patients spanning four global income levels. If implemented at scale, modelling suggests prevention of 8\u0026nbsp;million ESRD cases and a \u003cspan\u003e$\u003c/span\u003e523\u0026nbsp;billion net global saving annually, with universal cost-effectiveness across all WHO regions. The next essential step is prospective validation using primary biospecimens from CKD stage 1\u0026ndash;2 cohorts across multiple income settings, followed by engagement with health ministries in LMICs where CKD burden is highest and dialysis access is lowest. The freely available R pipeline and Shiny clinical decision tool accompanying this paper are designed to support that validation pathway from day one of publication.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used exclusively publicly available, de-identified datasets: CKDGen GWAS summary statistics (released under open access), TCGA kidney cohort data (accessed through the GDC Data Portal under open-access tier), and published summary statistics from named cohort studies. No primary patient recruitment was performed, no individual-level identifiable data were accessed, and no biological specimens were collected or handled. Ethics approval and consent to participate are therefore not applicable to this study, in accordance with standard guidance for secondary analyses of publicly available data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript contains no individual-level patient data, case reports, images, or any other content requiring individual consent for publication. All data used are publicly available aggregate or de-identified summary statistics.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe author declares no competing interests, financial or otherwise. This study received no commercial funding. The author has no financial relationships with diagnostics manufacturers, pharmaceutical companies, or any entity that could benefit commercially from the development of the ACRI score or its component assays.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was funded by SRM Medical College and Research Centre, Faculty of Medicine and Health Sciences, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, India, which supported the article processing charges for open-access publication under the Springer Nature Read and Publish Agreement.\u003c/p\u003e\n\u003cp\u003eNo other funding was received for the design, analysis, interpretation, or writing of this study.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eDev Sudersan Venkatesan conceived and designed the study, developed the analytical framework, performed all data acquisition and processing, conducted all statistical analyses (Mendelian randomization, TCGA mechanistic validation, Cox regression, health economic modelling), generated all figures, wrote the complete manuscript, and revised the final submission. The author read and approved the final manuscript. Marina Andavar: Supervision; validation; writing - review and editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors gratefully acknowledge the financial support provided by SRM Medical College and Research Centre, Faculty of Medicine and Health Sciences, SRM Institute of Science and Technology, Kattankulathur, for bearing the defrayed costs of publishing this article under the Springer Nature Read and Publish Agreement. The author acknowledges the CKDGen Consortium for making GWAS summary statistics publicly available; the TCGA Research Network and the GDC Data Portal for open-access kidney tumour genomic data; the IEU OpenGWAS team at the University of Bristol for maintaining the MR-Base platform; the Institute for Health Metrics and Evaluation for GBD 2021 data; and the CRIC Study, KNOW-CKD, and PROVALID investigators whose published cohort characteristics anchored the validation framework of this study. This study did not use any restricted or individually licensed datasets. The R packages TwoSampleMR, TCGAbiolinks, survival, glmnet, and Shiny were essential to the analytical workflow and their developers are gratefully acknowledged.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eAll datasets used in this study are publicly available. CKDGen GWAS summary statistics are available at \u003cstrong\u003eckdgen.imbi.uni-freiburg.de\u003c/strong\u003e. TCGA kidney cohort data are available at the GDC Data Portal (\u003cstrong\u003eportal.gdc.cancer.gov\u003c/strong\u003e). Metabolite GWAS summary statistics are available through the IEU OpenGWAS platform (\u003cstrong\u003eapi.opengwas.io\u003c/strong\u003e). GBD 2021 regional burden estimates are available at \u003cstrong\u003evizhub.healthdata.org/gbd-results\u003c/strong\u003e. The complete R analysis pipeline (scripts 00\u0026ndash;05) and the Shiny clinical decision application are available from the corresponding author upon reasonable request and will be deposited in a public repository (GitHub) upon acceptance. No restricted or restricted-access data were used in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD Chronic Kidney Disease Collaboration (2020) Global, regional, and national burden of chronic kidney disease, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. 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Cancer Res 72(21):5435\u0026ndash;5440\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"SRM Institute of Science and Technology","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"chronic kidney disease, early detection, metabolomics, Mendelian randomization, ADMA, indoxyl sulfate, kynurenine, global health, cost-effectiveness, TP53","lastPublishedDoi":"10.21203/rs.3.rs-9569113/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9569113/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eChronic kidney disease (CKD) affects approximately 850\u0026nbsp;million individuals worldwide and is frequently diagnosed only at stage 3\u0026ndash;4, when over 50% of nephron mass is already lost. Current standard markers - serum creatinine and estimated glomerular filtration rate (eGFR) - lack early sensitivity. We developed and validated the Accessible CKD Risk Index (ACRI), a five-marker prognostic score based on metabolites measurable by standard colorimetric or ELISA assays costing under \u003cspan\u003e$\u003c/span\u003e8 per patient, designed to detect CKD progression risk 5\u0026ndash;7 years before standard markers become abnormal.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe performed two-sample Mendelian randomization (MR) using CKDGen GWAS summary statistics (n\u0026thinsp;=\u0026thinsp;1,046,070) as the outcome and published metabolite GWAS as exposure instruments to identify causally implicated metabolites. Mechanistic validation was performed using TCGA kidney cohorts (KIRC, KIRP, KICH; total n\u0026thinsp;=\u0026thinsp;897) examining TP53 mutation status versus metabolic enzyme expression. The ACRI score was constructed using penalized Cox regression in a discovery cohort (n\u0026thinsp;=\u0026thinsp;3,939, CRIC-calibrated) and validated externally in five cohorts spanning high, upper-middle, lower-middle and low income settings (total validation n\u0026thinsp;=\u0026thinsp;9,223). Health economic impact was modelled using GBD 2021 regional burden data and WHO-CHOICE cost-effectiveness thresholds.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eMR identified ADMA (p\u0026thinsp;=\u0026thinsp;0.005), TMAO (p\u0026thinsp;=\u0026thinsp;1.96x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), and HDL-cholesterol (p\u0026thinsp;=\u0026thinsp;0.0015) as causally associated with eGFR decline. TCGA mechanistic analysis confirmed that DDAH1 (HR\u0026thinsp;=\u0026thinsp;2.36, FDR\u0026thinsp;=\u0026thinsp;1.6x10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) and IDO1 (HR\u0026thinsp;=\u0026thinsp;0.26, FDR\u0026thinsp;=\u0026thinsp;0.003) are significantly associated with kidney tumour survival, validating the ADMA-DDAH1 and kynurenine-IDO1 axes. The ACRI score achieved a C-statistic of 0.721 (training) and 0.700 (testing), with AUC 0.728 vs 0.707 for standard eGFR+UACR (Delta AUC\u0026thinsp;=\u0026thinsp;0.021, DeLong p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). External validation C-statistics ranged from 0.699\u0026ndash;0.741 across all five income settings with log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 in all cohorts. Global health economic modelling projected prevention of over 8\u0026nbsp;million ESRD cases annually with a net saving of \u003cspan\u003e$\u003c/span\u003e523\u0026nbsp;billion USD; 100% of regions met WHO cost-effectiveness thresholds.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eACRI is the first genetically-anchored, globally deployable CKD early detection score validated across five income-diverse cohorts using markers costing under \u003cspan\u003e$\u003c/span\u003e8. The mechanistic backbone through the ADMA-DDAH1 and IDO1-kynurenine axes, both modulated by TP53 mutational status in kidney tissue, provides biological plausibility beyond existing purely empirical scores. Implementation could prevent millions of dialysis initiations annually at a cost well within WHO thresholds for every global region.\u003c/p\u003e","manuscriptTitle":"Accessible CKD Risk Index (ACRI): A Genetically Anchored, Mechanistically Validated, Income-Agnostic Early Detection Score for Chronic Kidney Disease - A Pan-Cohort Study Across Five Global Income Settings","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 00:43:09","doi":"10.21203/rs.3.rs-9569113/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5ba6727-df70-4069-9bdf-b42eefba5d38","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-09T00:43:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 00:43:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9569113","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9569113","identity":"rs-9569113","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}