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Kidney function may partially mediated the protective effect of urinary uromodulin on kidney stone

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Abstract

The causal links between urinary uromodulin (uUMOD) and kidney stone disease (KSD) are still not clarified in general population. We assessed their relationships combining 2-sample Mendelian randomization (MR) and multivariable (MVMR) designs among general population of European ancestry. The summary information for uUMOD indexed to creatinine levels (29,315 individuals) and KSD (395,044 individuals) were from 2 independent genome-wide association studies (GWAS). The primary causal effects of exposures on outcomes were evaluated using inverse variance-weighted (IVW) regression model. Multiple sensitivity analyses were also performed. In 2-sample MR, we found that 1-unit higher genetically predicted uUMOD levels were associated with a lower risk of KSD (OR = 0.62; 95% CI 0.55–0.71; P = 2.83E−13). In reverse, we did not find the effect of KSD on uUOMD using IVW (beta = 0.00; 95% CI − 0.06–0.05; P = 0.872) and other sensitivity analyses. In MVMR, uUMOD indexed to creatinine levels were directly associated with the risk of KSD after introducing eGFR, SBP, urinary sodium or all three factors (OR = 0.71; 95% CI 0.64–0.79; P = 1.57E−09). Furthermore, our study supported that the protective effect of uUMOD on KSD may be partially mediated by eGFR (beta = − 0.09; 95% CI − 0.13 to − 0.06; mediation proportion = 20%). Our study supported that the protective effect of genetically predicted higher uUMOD levels on KSD may be partially mediated by eGFR decline, but not via SBP or urinary sodium. uUMOD might be a treatment target in preventing KSD in general population.

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All data generated or analyzed during this study are included in this published article and its Supplementary information files.

References

  1. Schaeffer C, Devuyst O, Rampoldi L (2021) Uromodulin: roles in health and disease. Annu Rev Physiol 83:477–501

    Article  CAS  PubMed  Google Scholar 

  2. Glauser A, Hochreiter W, Jaeger P, Hess B (2000) Determinants of urinary excretion of Tamm-Horsfall protein in non-selected kidney stone formers and healthy subjects. Nephrol Dial Transplant 15:1580–1587

    Article  CAS  PubMed  Google Scholar 

  3. Lau WH, Leong WS, Ismail Z, Gam LH (2008) Qualification and application of an ELISA for the determination of Tamm Horsfall protein (THP) in human urine and its use for screening of kidney stone disease. Int J Biol Sci 4:215–222

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Chaiyarit S, Thongboonkerd V (2022) Oxidized forms of uromodulin promote calcium oxalate crystallization and growth, but not aggregation. Int J Biol Macromol 214:542–553

    Article  CAS  PubMed  Google Scholar 

  5. Noonin C, Peerapen P, Yoodee S, Kapincharanon C, Kanlaya R, Thongboonkerd V (2022) Systematic analysis of modulating activities of native human urinary Tamm−Horsfall protein on calcium oxalate crystallization, growth, aggregation, crystal-cell adhesion and invasion through extracellular matrix. Chem Biol Interact 357:109879

    Article  CAS  PubMed  Google Scholar 

  6. Ponte B, Sadler MC, Olinger E, Vollenweider P, Bochud M, Padmanabhan S et al (2021) Mendelian randomization to assess causality between uromodulin, blood pressure and chronic kidney disease. Kidney Int 100:1282–1291

    Article  CAS  PubMed  Google Scholar 

  7. Uribarri J (2020) Chronic kidney disease and kidney stones. Curr Opin Nephrol Hypertens 29:237–242

    Article  PubMed  Google Scholar 

  8. Sekula P, Del Greco MF, Pattaro C, Kottgen A (2016) Mendelian randomization as an approach to assess causality using observational data. J Am Soc Nephrol 27:3253–3265

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lin BB, Huang RH, Lin BL, Hong YK, Lin ME, He XJ (2020) Associations between nephrolithiasis and diabetes mellitus, hypertension and gallstones: a meta-analysis of cohort studies. Nephrology (Carlton) 25:691–699

    Article  PubMed  Google Scholar 

  10. Afsar B, Kiremit MC, Sag AA, Tarim K, Acar O, Esen T et al (2016) The role of sodium intake in nephrolithiasis: epidemiology, pathogenesis, and future directions. Eur J Intern Med 35:16–19

    Article  CAS  PubMed  Google Scholar 

  11. Sanderson E, Davey Smith G, Windmeijer F, Bowden J (2019) An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol 48:713–727

    Article  PubMed  Google Scholar 

  12. Sanderson E (2021) Multivariable Mendelian Randomization and Mediation. Cold Spring Harb Perspect Med 11(2):a038984. https://doi.org/10.1101/cshperspect.a038984

  13. Joseph CB, Mariniello M, Yoshifuji A, Schiano G, Lake J, Marten J et al (2022) Meta-GWAS reveals novel genetic variants associated with urinary excretion of uromodulin. J Am Soc Nephrol 33:511–529

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Howles SA, Wiberg A, Goldsworthy M, Bayliss AL, Gluck AK, Ng M et al (2019) Genetic variants of calcium and vitamin D metabolism in kidney stone disease. Nat Commun 10:5175

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wuttke M, Li Y, Li M, Sieber KB, Feitosa MF, Gorski M et al (2019) A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet 51:957–972

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H et al (2018) Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet 50:1412–1425

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Pazoki R, Evangelou E, Mosen-Ansorena D, Pinto RC, Karaman I, Blakeley P et al (2019) GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits. Nat Commun 10:3653

    Article  PubMed  PubMed Central  Google Scholar 

  18. Elsworth B, Lyon M, Alexander T, Liu Y, Matthews P, Hallett J et al (2020) The MRC IEU OpenGWAS data infrastructure. Biorxiv. https://doi.org/10.1101/2020.08.10.244293

    Article  Google Scholar 

  19. Verbanck M, Chen CY, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50:693–698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37:658–665

    Article  PubMed  PubMed Central  Google Scholar 

  21. Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40:304–314

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hartwig FP, Davey Smith G, Bowden J (2017) Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol 46:1985–1998

    Article  PubMed  PubMed Central  Google Scholar 

  23. Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44:512–525

    Article  PubMed  PubMed Central  Google Scholar 

  24. Bowden J, Del Greco MF, Minelli C, Zhao Q, Lawlor DA, Sheehan NA et al (2019) Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int J Epidemiol 48:728–742

    Article  PubMed  Google Scholar 

  25. Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J (2017) A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med 36:1783–1802

    Article  PubMed  PubMed Central  Google Scholar 

  26. Verbanck M, Chen C-Y, Neale B et al (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50(5):693–698

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Burgess S, Thompson DJ, Rees JMB, Day FR, Perry JR, Ong KK (2017) Dissecting causal pathways using Mendelian randomization with summarized genetic data: application to age at menarche and risk of breast cancer. Genetics 207:481–487

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Carter AR, Sanderson E, Hammerton G, Richmond RC, Davey Smith G, Heron J et al (2021) Mendelian randomisation for mediation analysis: current methods and challenges for implementation. Eur J Epidemiol 36:465–478

    Article  PubMed  PubMed Central  Google Scholar 

  29. Davies NM, Holmes MV, Davey SG (2018) Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ 362:k601

    Article  PubMed  PubMed Central  Google Scholar 

  30. Burgess S, Thompson SG, Collaboration CCG (2011) Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 40:755–764

    Article  PubMed  Google Scholar 

  31. Gorski M, van der Most PJ, Teumer A, Chu AY, Li M, Mijatovic V et al (2017) 1000 genomes-based meta-analysis identifies 10 novel loci for kidney function. Sci Rep 7:45040

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yu Z, Coresh J, Qi G, Grams M, Boerwinkle E, Snieder H et al (2020) A bidirectional Mendelian randomization study supports causal effects of kidney function on blood pressure. Kidney Int 98:708–716

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Mo L, Huang HY, Zhu XH, Shapiro E, Hasty DL, Wu XR (2004) Tamm−Horsfall protein is a critical renal defense factor protecting against calcium oxalate crystal formation. Kidney Int 66:1159–1166

    Article  CAS  PubMed  Google Scholar 

  34. Liu J, Tio MC, Verma A, Schmidt IM, Ilori TO, Knauf F et al (2022) Determinants and Outcomes associated with urinary calcium excretion in chronic kidney disease. J Clin Endocrinol Metab 107:e281–e292

    Article  PubMed  Google Scholar 

  35. Liu Y, Mo L, Goldfarb DS, Evan AP, Liang F, Khan SR et al (2010) Progressive renal papillary calcification and ureteral stone formation in mice deficient for Tamm−Horsfall protein. Am J Physiol Renal Physiol 299:F469–F478

    Article  PubMed  PubMed Central  Google Scholar 

  36. Wolf MT, Wu XR, Huang CL (2013) Uromodulin upregulates TRPV5 by impairing caveolin-mediated endocytosis. Kidney Int 84:130–137

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mutig K, Kahl T, Saritas T, Godes M, Persson P, Bates J et al (2011) Activation of the bumetanide-sensitive Na+, K+,2Cl- cotransporter (NKCC2) is facilitated by Tamm−Horsfall protein in a chloride-sensitive manner. J Biol Chem 286:30200–30210

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Torffvit O, Melander O, Hulten UL (2004) Urinary excretion rate of Tamm−Horsfall protein is related to salt intake in humans. Nephron Physiol 97:p31–p36

    Article  CAS  PubMed  Google Scholar 

  39. Liu Y, Goldfarb DS, El-Achkar TM, Lieske JC, Wu XR (2018) Tamm−Horsfall protein/uromodulin deficiency elicits tubular compensatory responses leading to hypertension and hyperuricemia. Am J Physiol Renal Physiol 314:F1062–F1076

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Funding

This study was supported by the Foundation of Science & Technology Department of Sichuan Province [2021YFS0116, 2022YFS0304] and the Post-Doctor Research Project, West China Hospital, Sichuan University, Grant/Award Number: 2020HXBH016.

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Contributions

Conceptualization and original draft preparation: ZJ and CY. Data collection: ZX. Data analysis: HL. Resources and supervision: XJ and KW. All authors read and agreed to the published version of the manuscript.

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Correspondence to Xi Jin or Kunjie Wang.

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Jian, Z., Yuan, C., Xiong, Z. et al. Kidney function may partially mediated the protective effect of urinary uromodulin on kidney stone. Urolithiasis 51, 65 (2023). https://doi.org/10.1007/s00240-023-01441-7

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