Association study between trajectories of kidney function related indicators and urinary stone disease incidence based on a large-scale longitudinal cohort study in China_Chinese Journal of Evidence-Based Medicine

Authors：

ZHANG Yixiao ^1,2 ,  LIN Yifei ² , XIONG Yuqin ² , WANG Le ^1,2 , FU Tingting ^1,2 , XIANG Nanyan ^1,2 , YANG Yong ² , JIANG Meilin ² , XU Xueru ² , LIAO Banghua ² ,  HUANG Jin ²

1. Department of Urology, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. Department of Urology, Lab of Health Data Science, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding?author：

LIN Yifei, Email: ylin@wchscu.edu.cn; HUANG Jin, Email: michael_huangjin@163.com

Keywords：

Health check-up; Longitudinal studies; WHALE; Urinary stone disease; Kidney function; Trajectory analysis

DOI：

10.7507/1672-2531.202601069

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To investigate the association between kidney function indicators in routine health examinations and the incidence of urinary stone disease using longitudinal repeated-measurement data. Methods This study was based on the large-scale longitudinal data in the WHALE study. Cox proportional hazards models were first applied to examine the associations between baseline kidney function indicators and incident urolithiasis. Subsequently, Poisson regression and mixed-effects models were used to analyze repeated-measurement data to further explore the relationships between kidney function indicators and urolithiasis. Latent class trajectory modeling was then employed to characterize longitudinal patterns of kidney function indicators. Based on the optimal trajectory models, logistic regression analyses and Cox regression analyses were performed to assess the associations between trajectory classes and the risk of urolithiasis. Results Among 313 280 health examination participants included in the study, 8 864 incident cases of urolithiasis were identified during follow-up. In the baseline Cox analyses, cystatin C showed the strongest association with urolithiasis (HR=1.230, 95%CI 1.083 to 1.396, FDR=0.001). In analyses incorporating repeated measurements, serum uric acid demonstrated a strong association in both Poisson regression and mixed-effects models (RR=1.123, 95%CI 1.094 to 1.152, FDR<0.001; RR=1.106, 95%CI 1.075 to 1.137, FDR<0.001). In trajectory analyses, U-shaped trajectories of blood urea (HR=0.548, 95%CI 0.438 to 0.685, FDR<0.001) and uric acid (HR=0.910, 95%CI 0.857 to 0.685, FDR=0.005) were associated with a lower risk of urolithiasis. Conclusion Both baseline levels and longitudinal changes in kidney function indicators measured during routine health examinations are significantly associated with the incidence of urolithiasis. Regular assessment and longitudinal monitoring of kidney function may contribute to the prevention and early identification of urolithiasis development and progression.

Citation： ZHANG Yixiao, LIN Yifei, XIONG Yuqin, WANG Le, FU Tingting, XIANG Nanyan, YANG Yong, JIANG Meilin, XU Xueru, LIAO Banghua, HUANG Jin. Association study between trajectories of kidney function related indicators and urinary stone disease incidence based on a large-scale longitudinal cohort study in China. Chinese Journal of Evidence-Based Medicine, 2026, 26(7): 814-820. doi: 10.7507/1672-2531.202601069 Copy

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2.	Borumandnia N, Fattahi P, Talebi A, et al. Longitudinal trend of urolithiasis incidence rates among world countries during past decades. Bmc Urol, 2023, 23(1): 166.
3.	Antonelli JA, Maalouf NM, Pearle MS, et al. Use of the national health and nutrition examination survey to calculate the impact of obesity and diabetes on cost and prevalence of urolithiasis in 2030. Eur Urol, 2014, 66(4): 724-729.
4.	Tan SS, Yuan DB, Su H, et al. Prevalence of urolithiasis in China: a systematic review and meta-analysis. Bju Int, 2024, 133(1): 34-43.
5.	Rule AD, Krambeck AE, Lieske JC. Chronic kidney disease in kidney stone formers. Clin J Am Soc Nephrol, 2011, 6(8): 2069-2075.
6.	Alexander RT, Hemmelgarn BR, Wiebe N, et al. Kidney stones and kidney function loss: a cohort study. Bmj-british Med J, 2012, 345: e5287.
7.	Sigurjonsdottir VK, Runolfsdottir HL, Indridason OS, et al. Impact of nephrolithiasis on kidney function. Bmc Nephrol, 2015, 16: 149.
8.	Ngo TC, Assimos DG. Uric acid nephrolithiasis: recent progress and future directions. Rev Urol, 2007, 9(1): 17-27.
9.	Subrata HN, Warli SM. Urinary biomarker as a predictor of urolithiasis in children: a systematic review and meta-analysis. Arch Ital Urol Androl, 2025, 97(3): 14195.
10.	劉卓偉. 泌尿系結石患者急性泌尿系感染與腎功能不全、腎積水的關聯性及臨床意義探討. 中國研究型醫院, 2019, (2): 48-53.
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12.	Herle M, Micali N, Abdulkadir M, et al. Identifying typical trajectories in longitudinal data: modelling strategies and interpretations. Eur J Epidemiol, 2020, 35(3): 205-222.
13.	Wang L, Lin YF, Fu TT, et al. Blood cell biomarker trajectories and depression risk in a sex-stratified 10-year longitudinal cohort analysis. Nat Ment Health, 2025, 3(11): 1438-1451.
14.	Van der burgh AC, Sedaghat S, Ikram MA, et al. Trajectories of kidney function and risk of mortality. Int J Epidemiol, 2023, 52(6): 1959-1967.
15.	Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol, 2010, 6: 109-138.
16.	Cao BF, Liu K, Chen HW, et al. Impact of baseline and trajectory of the cardiometabolic indices on incident microvascular complications in patients with type 2 diabetes. Atherosclerosis, 2025, 407: 120407.
17.	Wang L, Fu TT, Yang Y, et al. Longitudinal trajectories of adiposity indicators and cancer risk over 14 years: evidence from repeated health check-ups of 10 times or more - China, 2010-2023. China Cdc Weekly, 2025, 7(35): 1138-1143.
18.	Lin YF, Yang Y, Li ZY, et al. Cohort profile: the West-China hospital alliance longitudinal epidemiology wellness (WHALE) study. Eur J Epidemiol, 2025, 40(9): 1143-1159.
19.	Lin YF, Yang Y, Xiang NY, et al. Characterization and trajectories of hematological parameters prior to severe COVID-19 based on a large-scale prospective health checkup cohort in western China: a longitudinal study of 13-year follow-up. Bmc Med, 2024, 22(1): 105.
20.	Wang MY, Li SY, Zheng T, et al. Big data health care platform with multisource heterogeneous data integration and massive high-dimensional data governance for large hospitals: design, development, and application. Jmir Med Inf, 2022, 10(4): 196-210.
21.	四川大學華西醫院官網. 四川大學華西醫院實驗醫學科(檢驗科). 2026.
22.	Inker LA, Eneanya ND, Coresh J, et al. New creatinine- and cystatin C-based equations to estimate GFR without race. N Engl J Med, 2021, 385(19): 1737-1749.
23.	Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis, 2002, 40(2): 221-226.
24.	Haley WE, Enders FT, Vaughan LE, et al. Kidney function after the first kidney stone event. Mayo Clin Proc, 2016, 91(12): 1744-1752.
25.	Simmons KE, Nair HR, Phadke M, et al. Risk factors for common kidney stones are correlated with kidney function independent of stone composition. Am J Nephrol, 2023, 54(7-8): 329-336.
26.	Khan SR, Pearle MS, Robertson WG, et al. Kidney stones (vol 2, 16008, 2016). Nat Rev Dis Primers, 2016, 2(1): 16008.
27.	Shen Y, Zhu Z, Bi X, et al. Association between insulin resistance indices and kidney stones: results from the 2015-2018 National Health and Nutrition Examination Survey. Front Nutr, 2024, 11: 1444049.
28.	Huang ZG, Wang XX, Ding X, et al. Association of age of metabolic syndrome onset with cardiovascular diseases: the Kailuan study. Front Endocrinol, 2022, 13: 857985.
29.	Liu XJ, Zhou JJ, Liu CH, et al. Analysis of risk factors for kidney stones in patients with hyperuricemia: a cross-sectional study based on NHANES. Transl Androl Urol, 2025, 14(10): 3194-3202.

1. Huang H, Li MM, Fan HR, et al. Temporal trend of urolithiasis incidence in China: an age-period-cohort analysis. Int J Gen Med, 2021, 14: 2533-2539.
2. Borumandnia N, Fattahi P, Talebi A, et al. Longitudinal trend of urolithiasis incidence rates among world countries during past decades. Bmc Urol, 2023, 23(1): 166.
3. Antonelli JA, Maalouf NM, Pearle MS, et al. Use of the national health and nutrition examination survey to calculate the impact of obesity and diabetes on cost and prevalence of urolithiasis in 2030. Eur Urol, 2014, 66(4): 724-729.
4. Tan SS, Yuan DB, Su H, et al. Prevalence of urolithiasis in China: a systematic review and meta-analysis. Bju Int, 2024, 133(1): 34-43.
5. Rule AD, Krambeck AE, Lieske JC. Chronic kidney disease in kidney stone formers. Clin J Am Soc Nephrol, 2011, 6(8): 2069-2075.
6. Alexander RT, Hemmelgarn BR, Wiebe N, et al. Kidney stones and kidney function loss: a cohort study. Bmj-british Med J, 2012, 345: e5287.
7. Sigurjonsdottir VK, Runolfsdottir HL, Indridason OS, et al. Impact of nephrolithiasis on kidney function. Bmc Nephrol, 2015, 16: 149.
8. Ngo TC, Assimos DG. Uric acid nephrolithiasis: recent progress and future directions. Rev Urol, 2007, 9(1): 17-27.
9. Subrata HN, Warli SM. Urinary biomarker as a predictor of urolithiasis in children: a systematic review and meta-analysis. Arch Ital Urol Androl, 2025, 97(3): 14195.
10. 劉卓偉. 泌尿系結石患者急性泌尿系感染與腎功能不全、腎積水的關聯性及臨床意義探討. 中國研究型醫院, 2019, (2): 48-53.
11. 彭明, 顧向明, 杜國有, 等. 泌尿系結石患者血清胱抑素C的含量變化分析. 檢驗醫學與臨床, 2011, 8(2): 172-173.
12. Herle M, Micali N, Abdulkadir M, et al. Identifying typical trajectories in longitudinal data: modelling strategies and interpretations. Eur J Epidemiol, 2020, 35(3): 205-222.
13. Wang L, Lin YF, Fu TT, et al. Blood cell biomarker trajectories and depression risk in a sex-stratified 10-year longitudinal cohort analysis. Nat Ment Health, 2025, 3(11): 1438-1451.
14. Van der burgh AC, Sedaghat S, Ikram MA, et al. Trajectories of kidney function and risk of mortality. Int J Epidemiol, 2023, 52(6): 1959-1967.
15. Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol, 2010, 6: 109-138.
16. Cao BF, Liu K, Chen HW, et al. Impact of baseline and trajectory of the cardiometabolic indices on incident microvascular complications in patients with type 2 diabetes. Atherosclerosis, 2025, 407: 120407.
17. Wang L, Fu TT, Yang Y, et al. Longitudinal trajectories of adiposity indicators and cancer risk over 14 years: evidence from repeated health check-ups of 10 times or more - China, 2010-2023. China Cdc Weekly, 2025, 7(35): 1138-1143.
18. Lin YF, Yang Y, Li ZY, et al. Cohort profile: the West-China hospital alliance longitudinal epidemiology wellness (WHALE) study. Eur J Epidemiol, 2025, 40(9): 1143-1159.
19. Lin YF, Yang Y, Xiang NY, et al. Characterization and trajectories of hematological parameters prior to severe COVID-19 based on a large-scale prospective health checkup cohort in western China: a longitudinal study of 13-year follow-up. Bmc Med, 2024, 22(1): 105.
20. Wang MY, Li SY, Zheng T, et al. Big data health care platform with multisource heterogeneous data integration and massive high-dimensional data governance for large hospitals: design, development, and application. Jmir Med Inf, 2022, 10(4): 196-210.
21. 四川大學華西醫院官網. 四川大學華西醫院實驗醫學科(檢驗科). 2026.
22. Inker LA, Eneanya ND, Coresh J, et al. New creatinine- and cystatin C-based equations to estimate GFR without race. N Engl J Med, 2021, 385(19): 1737-1749.
23. Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis, 2002, 40(2): 221-226.
24. Haley WE, Enders FT, Vaughan LE, et al. Kidney function after the first kidney stone event. Mayo Clin Proc, 2016, 91(12): 1744-1752.
25. Simmons KE, Nair HR, Phadke M, et al. Risk factors for common kidney stones are correlated with kidney function independent of stone composition. Am J Nephrol, 2023, 54(7-8): 329-336.
26. Khan SR, Pearle MS, Robertson WG, et al. Kidney stones (vol 2, 16008, 2016). Nat Rev Dis Primers, 2016, 2(1): 16008.
27. Shen Y, Zhu Z, Bi X, et al. Association between insulin resistance indices and kidney stones: results from the 2015-2018 National Health and Nutrition Examination Survey. Front Nutr, 2024, 11: 1444049.
28. Huang ZG, Wang XX, Ding X, et al. Association of age of metabolic syndrome onset with cardiovascular diseases: the Kailuan study. Front Endocrinol, 2022, 13: 857985.
29. Liu XJ, Zhou JJ, Liu CH, et al. Analysis of risk factors for kidney stones in patients with hyperuricemia: a cross-sectional study based on NHANES. Transl Androl Urol, 2025, 14(10): 3194-3202.

Chinese Journal of Evidence-Based Medicine

Association study between trajectories of kidney function related indicators and urinary stone disease incidence based on a large-scale longitudinal cohort study in China

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