Predictive modeling of chronic kidney disease integrating social factors, lifestyle, and retinal optical coherence tomography images_West China Medical Journal

Authors：

PEI Linlin ¹ , HUANG Lexi ¹ , LI Chunyang ¹ , CHEN Jing ² , YANG Huazhen ¹ , SONG Huan ^1,3 , LUO Li ⁴ , FU Ping ¹ ,  ZENG Xiaoxi ¹

1. Department of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Department of Nephrology, the Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou 563003, P. R. China;
3. Centre for Public Health Sciences, Faculty of Medicine, University of Iceland, Reykjavik 101, Iceland;
4. Business School, Sichuan University, Chengdu, Sichuan 610065, P. R. China;

Corresponding?author：

ZENG Xiaoxi, Email: zengxiaoxi@wchscu.edu.cn

Keywords：

Chronic kidney disease; prediction model; optical coherence tomography imaging; multimodal data; random survival forest

DOI：

10.7507/1002-0179.202511173

Video：

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Objective To integrate multi-dimensional and multimodal data to develop a tool for predicting the risk of chronic kidney disease (CKD). Methods Data from the UK Biobank were utilized, involving 6561 participants recruited between 2006 and 2010, with a follow-up window from April 17, 2007 to November 30, 2022. In the development cohort (n=5248), a multimodal random survival forest (RSF) model was constructed, integrating conventional risk factors [variables from the CKD Prognosis Consortium (CKD-PC) equation], social determinants of health, Life’s Essential 8 data, and retinal optical coherence tomography imaging features. Comparative models included a base model (based solely on estimated glomerular filtration rate and urine albumin-to-creatinine ratio), the CKD-PC equation, a unimodal RSF model (conventional risk factors + social determinants of health + Life’s Essential 8 data), and an extended model (the multimodal RSF model plus a polygenic risk score). The performance of these models was compared in the validation cohort (n=1313). Results After a median follow-up of 12.7 years, 3.57% (234/6561) of the participants developed CKD. In the validation cohort, the 5-year concordance index (C-index) of the multimodal RSF model was 0.73 [95% confidence interval (CI) (0.68, 0.77)], which was significantly higher than that of the base model [C-index=0.67, 95%CI (0.65, 0.71)], the CKD-PC equation [C-index=0.64, 95%CI (0.58, 0.70)], and the unimodal RSF model [C-index=0.69, 95%CI (0.64, 0.73)], and the extended model with the inclusion of the polygenic risk score did not significantly improve predictive performance [C-index=0.72, 95%CI (0.67, 0.76)]. The results of the time-dependent area under the receiver operating characteristic curve analysis were consistent with these findings. Based on the predicted CKD risk derived from the multimodal RSF model for risk stratification, the actual proportions of individuals who developed CKD in the high-, medium-, and low-risk strata were 19.7%, 4.1%, and 1.5%, respectively. In addition to established risk factors, retinal imaging information, healthcare accessibility, and financial status were among the top-ranked predictors for CKD risk. Conclusion Integrating multi-dimensional and multimodal data—including conventional risk factors, social factors, lifestyle factors, and retinal imaging—can improve the performance of CKD risk prediction and stratification.

Citation： PEI Linlin, HUANG Lexi, LI Chunyang, CHEN Jing, YANG Huazhen, SONG Huan, LUO Li, FU Ping, ZENG Xiaoxi. Predictive modeling of chronic kidney disease integrating social factors, lifestyle, and retinal optical coherence tomography images. West China Medical Journal, 2026, 41(7): 1114-1120. doi: 10.7507/1002-0179.202511173 Copy

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2.	Chen J, Li C, Bu CLN, et al. Global burden of non-communicable diseases attributable to kidney dysfunction with projection into 2040[J]. Chin Med J (Engl), 2025, 138(11): 1334-1344.
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6.	Tangri N, Ferguson TW, Bamforth RJ, et al. Machine learning for prediction of chronic kidney disease progression: validation of the Klinrisk model in the CANVAS Program and CREDENCE trial[J]. Diabetes Obes Metab, 2024, 26(8): 3371-3380.
7.	Nelson RG, Grams ME, Ballew SH, et al. Development of risk prediction equations for incident chronic kidney disease[J]. JAMA, 2019, 322(21): 2104-2114.
8.	Debal DA, Sitote TM. Chronic kidney disease prediction using machine learning techniques[J]. J Big Data, 2022, 9(1): 109.
9.	Li C, Chen Y, Zhao W, et al. Genetic impact on the association of sleep patterns and chronic kidney disease: a prospective cohort study of 157,175 UK Biobank participants[J]. J Psychosom Res, 2023, 169: 12023.
10.	Ren Y, Cai Z, Guo C, et al. Associations between life’s essential 8 and chronic kidney disease[J]. J Am Heart Assoc, 2023, 12(24): e030564.
11.	Xie C, Wu Q, Xie C, et al. Social determinants of health and chronic kidney disease in United States adults: a cross-sectional study from National Health and Nutrition Examination Survey 2003-2018[J]. Prev Med Rep, 2025, 55: 103132.
12.	Wong CW, Wong TY, Cheng CY, et al. Kidney and eye diseases: common risk factors, etiological mechanisms, and pathways[J]. Kidney Int, 2014, 85(6): 1290-1302.
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14.	Farrah T, Pugh D, Chapman FA, et al. Choroidal and retinal thinning in chronic kidney disease independently associate with eGFR decline and are modifiable with treatment[J]. Nat Commun, 2023, 14(1): 7720.
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16.	Joo YS, Rim TH, Koh HB, et al. Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors[J]. NPJ Digit Med, 2023, 6(4): 1026-1033.
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19.	Li C, Chen J, Chen Y, et al. The association between patterns of exposure to adverse life events and the risk of chronic kidney disease: a prospective cohort study of 140,997 individuals[J]. Transl Psychiatry, 2024, 14(1): 424.
20.	Hu W, Zhao CH, Wang JN, et al. Association of Life’s Essential 8 with incidence of heart failure modified by depressive symptoms: a prospective cohort study from UK Biobank[J]. BMC Med, 2025, 23(1): 175.
21.	Zhong J, Zhang Y, Zhu K, et al. Associations of social determinants of health with life expectancy and future health risks among individuals with type 2 diabetes: two nationwide cohort studies in the UK and USA[J]. Lancet Healthy Longev, 2024, 5(8): e542-e551.
22.	Troyanskaya O, Cantor M, Sherlock G, et al. Missing value estimation methods for DNA microarrays[J]. Bioinformatics, 2001, 17(6): 520-525.
23.	Zacharias HU, Altenbuchinger M, Schultheiss UT, et al. A predictive model for progression of CKD to kidney failure based on routine laboratory tests[J]. Am J Kidney Dis, 2022, 79(2): 217-230.
24.	He K, Chen X, Xie S, et al. Masked autoencoders are scalable vision learners[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans, LA, USA, 2022: 15979-15988.
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26.	Choi SW, Mak TS, O’Reilly PF. Tutorial: a guide to performing polygenic risk score analyses[J]. Nat Protoc, 2020, 15(9): 2759-2772.
27.	Wuttke M, Li Y, Li M, et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals[J]. Nat Genet, 2019, 51(6): 957-972.
28.	Taylor JMG. Random survival forests[J]. J Thorac Oncol, 2011, 6(12): 1974-1975.
29.	Falkner S, Klein A, Hutter F. BOHB: robust and efficient hyperparameter optimization at scale[C]//Proceedings of the 35th International Conference on Machine Learning. Stockholm, Sweden, PMLR 80, 2018: 1437-1446.
30.	Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors[J]. Stat Med, 1996, 15(4): 361-387.
31.	Uno H, Cai T, Tian L, et al. Evaluating prediction rules fort-year survivors with censored regression models[J]. J Am Stat Assoc, 2007, 102(478): 527-537.
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33.	DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach[J]. Biometrics, 1988, 44(3): 837-845.
34.	Breiman L. Random forests[J]. Mach Learn, 2001, 45(1): 5-32.
35.	Kelly JT, Su G, Zhang L, et al. Modifiable lifestyle factors for primary prevention of CKD: a systematic review and meta-analysis[J]. J Am Soc Nephrol, 2021, 32(1): 239-253.
36.	Tang L, Yang L, Chen W, et al. Sex-specific association between coffee consumption and incident chronic kidney disease: a population-based analysis of 359, 906 participants from the UK Biobank[J]. Chin Med J (Engl), 2022, 135(12): 1414-1424.
37.	Naderian M, Norland K, Schaid DJ, et al. Development and evaluation of a comprehensive prediction model for incident coronary heart disease using genetic, social, and lifestyle-psychological factors: a prospective analysis of the UK Biobank[J]. Ann Intern Med, 2025, 178(1): 1-10.
38.	Segar MW, Hall JL, Jhund PS, et al. Machine learning-based models incorporating social determinants of health vs traditional models for predicting in-hospital mortality in patients with heart failure[J]. JAMA Cardiol, 2022, 7(8): 844-854.
39.	Xie JS, Donaldson L, Margolin E. The use of optical coherence tomography in neurology: a review[J]. Brain, 2022, 145(12): 4160-4177.
40.	K?ttgen A, Cornec-Le Gall E, Halbritter J, et al. Genetics in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference[J]. Kidney Int, 2022, 101(6): 1126-1141.
41.	Ishwaran H, Kogalur UB, Blackstone EH, et al. Random survival forests[J]. Ann Appl Stat, 2008, 2(3): 841-860.
42.	US Preventive Services Task Force, Mangione CM, Barry MJ, et al. Statin use for the primary prevention of cardiovascular disease in adults: US Preventive Services Task Force Recommendation Statement[J]. JAMA, 2022, 328(8): 746-753.
43.	Madero M, Levin A, Ahmed SB, et al. Evaluation and management of chronic kidney disease: synopsis of the Kidney Disease: Improving Global Outcomes 2024 clinical practice guideline[J]. Ann Intern Med, 2025, 178(5): 705-713.
44.	Zhou Y, Chia MA, Wagner SK, et al. A foundation model for generalizable disease detection from retinal images[J]. Nature, 2023, 622(7981): 156-163.
45.	Zhang J, Lin S, Cheng T, et al. RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis[J]. NPJ Digit Med, 2024, 7(1): 108.

1. Francis A, Harhay MN, Ong ACM, et al. Chronic kidney disease and the global public health agenda: an international consensus[J]. Nat Rev Nephrol, 2024, 20(7): 473-485.
2. Chen J, Li C, Bu CLN, et al. Global burden of non-communicable diseases attributable to kidney dysfunction with projection into 2040[J]. Chin Med J (Engl), 2025, 138(11): 1334-1344.
3. GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017[J]. Lancet, 2020, 395(10225): 709-733.
4. Wang L, Xu X, Zhang M, et al. Prevalence of chronic kidney disease in China: results from the Sixth China Chronic Disease and Risk Factor Surveillance[J]. JAMA Intern Med, 2023, 183(4): 298-310.
5. Luo W, Gong L, Chen X, et al. Lifestyle and chronic kidney disease: a machine learning modeling study[J]. Front Nutr, 2022, 9: 918576.
6. Tangri N, Ferguson TW, Bamforth RJ, et al. Machine learning for prediction of chronic kidney disease progression: validation of the Klinrisk model in the CANVAS Program and CREDENCE trial[J]. Diabetes Obes Metab, 2024, 26(8): 3371-3380.
7. Nelson RG, Grams ME, Ballew SH, et al. Development of risk prediction equations for incident chronic kidney disease[J]. JAMA, 2019, 322(21): 2104-2114.
8. Debal DA, Sitote TM. Chronic kidney disease prediction using machine learning techniques[J]. J Big Data, 2022, 9(1): 109.
9. Li C, Chen Y, Zhao W, et al. Genetic impact on the association of sleep patterns and chronic kidney disease: a prospective cohort study of 157,175 UK Biobank participants[J]. J Psychosom Res, 2023, 169: 12023.
10. Ren Y, Cai Z, Guo C, et al. Associations between life’s essential 8 and chronic kidney disease[J]. J Am Heart Assoc, 2023, 12(24): e030564.
11. Xie C, Wu Q, Xie C, et al. Social determinants of health and chronic kidney disease in United States adults: a cross-sectional study from National Health and Nutrition Examination Survey 2003-2018[J]. Prev Med Rep, 2025, 55: 103132.
12. Wong CW, Wong TY, Cheng CY, et al. Kidney and eye diseases: common risk factors, etiological mechanisms, and pathways[J]. Kidney Int, 2014, 85(6): 1290-1302.
13. Farrah TE, Dhillon B, Keane PA, et al. The eye, the kidney, and cardiovascular disease: old concepts, better tools, and new horizons[J]. Kidney Int, 2020, 98(2): 323-342.
14. Farrah T, Pugh D, Chapman FA, et al. Choroidal and retinal thinning in chronic kidney disease independently associate with eGFR decline and are modifiable with treatment[J]. Nat Commun, 2023, 14(1): 7720.
15. Zhang K, Liu X, Xu J, et al. Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images[J]. Nat Biomed Eng, 2021, 5(6): 533-545.
16. Joo YS, Rim TH, Koh HB, et al. Non-invasive chronic kidney disease risk stratification tool derived from retina-based deep learning and clinical factors[J]. NPJ Digit Med, 2023, 6(4): 1026-1033.
17. Günthner R, Hanssen H, Hauser C, et al. Impaired retinal vessel dilation predicts mortality in end-stage renal disease[J]. Circ Res, 2019, 124(12): 1796-1807.
18. Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age[J]. PLoS Med, 2015, 12(3): e1001779.
19. Li C, Chen J, Chen Y, et al. The association between patterns of exposure to adverse life events and the risk of chronic kidney disease: a prospective cohort study of 140,997 individuals[J]. Transl Psychiatry, 2024, 14(1): 424.
20. Hu W, Zhao CH, Wang JN, et al. Association of Life’s Essential 8 with incidence of heart failure modified by depressive symptoms: a prospective cohort study from UK Biobank[J]. BMC Med, 2025, 23(1): 175.
21. Zhong J, Zhang Y, Zhu K, et al. Associations of social determinants of health with life expectancy and future health risks among individuals with type 2 diabetes: two nationwide cohort studies in the UK and USA[J]. Lancet Healthy Longev, 2024, 5(8): e542-e551.
22. Troyanskaya O, Cantor M, Sherlock G, et al. Missing value estimation methods for DNA microarrays[J]. Bioinformatics, 2001, 17(6): 520-525.
23. Zacharias HU, Altenbuchinger M, Schultheiss UT, et al. A predictive model for progression of CKD to kidney failure based on routine laboratory tests[J]. Am J Kidney Dis, 2022, 79(2): 217-230.
24. He K, Chen X, Xie S, et al. Masked autoencoders are scalable vision learners[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans, LA, USA, 2022: 15979-15988.
25. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale[PP/OL]. arXiv (2021-06-03)[2025-11-01]. doi: 10.48550/arXiv.2010.11929.
26. Choi SW, Mak TS, O’Reilly PF. Tutorial: a guide to performing polygenic risk score analyses[J]. Nat Protoc, 2020, 15(9): 2759-2772.
27. Wuttke M, Li Y, Li M, et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals[J]. Nat Genet, 2019, 51(6): 957-972.
28. Taylor JMG. Random survival forests[J]. J Thorac Oncol, 2011, 6(12): 1974-1975.
29. Falkner S, Klein A, Hutter F. BOHB: robust and efficient hyperparameter optimization at scale[C]//Proceedings of the 35th International Conference on Machine Learning. Stockholm, Sweden, PMLR 80, 2018: 1437-1446.
30. Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors[J]. Stat Med, 1996, 15(4): 361-387.
31. Uno H, Cai T, Tian L, et al. Evaluating prediction rules fort-year survivors with censored regression models[J]. J Am Stat Assoc, 2007, 102(478): 527-537.
32. Lambert J, Chevret S. Summary measure of discrimination in survival models based on cumulative/dynamic time-dependent ROC curves[J]. Stat Methods Med Res, 2016, 25(5): 2088-2102.
33. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach[J]. Biometrics, 1988, 44(3): 837-845.
34. Breiman L. Random forests[J]. Mach Learn, 2001, 45(1): 5-32.
35. Kelly JT, Su G, Zhang L, et al. Modifiable lifestyle factors for primary prevention of CKD: a systematic review and meta-analysis[J]. J Am Soc Nephrol, 2021, 32(1): 239-253.
36. Tang L, Yang L, Chen W, et al. Sex-specific association between coffee consumption and incident chronic kidney disease: a population-based analysis of 359, 906 participants from the UK Biobank[J]. Chin Med J (Engl), 2022, 135(12): 1414-1424.
37. Naderian M, Norland K, Schaid DJ, et al. Development and evaluation of a comprehensive prediction model for incident coronary heart disease using genetic, social, and lifestyle-psychological factors: a prospective analysis of the UK Biobank[J]. Ann Intern Med, 2025, 178(1): 1-10.
38. Segar MW, Hall JL, Jhund PS, et al. Machine learning-based models incorporating social determinants of health vs traditional models for predicting in-hospital mortality in patients with heart failure[J]. JAMA Cardiol, 2022, 7(8): 844-854.
39. Xie JS, Donaldson L, Margolin E. The use of optical coherence tomography in neurology: a review[J]. Brain, 2022, 145(12): 4160-4177.
40. K?ttgen A, Cornec-Le Gall E, Halbritter J, et al. Genetics in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference[J]. Kidney Int, 2022, 101(6): 1126-1141.
41. Ishwaran H, Kogalur UB, Blackstone EH, et al. Random survival forests[J]. Ann Appl Stat, 2008, 2(3): 841-860.
42. US Preventive Services Task Force, Mangione CM, Barry MJ, et al. Statin use for the primary prevention of cardiovascular disease in adults: US Preventive Services Task Force Recommendation Statement[J]. JAMA, 2022, 328(8): 746-753.
43. Madero M, Levin A, Ahmed SB, et al. Evaluation and management of chronic kidney disease: synopsis of the Kidney Disease: Improving Global Outcomes 2024 clinical practice guideline[J]. Ann Intern Med, 2025, 178(5): 705-713.
44. Zhou Y, Chia MA, Wagner SK, et al. A foundation model for generalizable disease detection from retinal images[J]. Nature, 2023, 622(7981): 156-163.
45. Zhang J, Lin S, Cheng T, et al. RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis[J]. NPJ Digit Med, 2024, 7(1): 108.

West China Medical Journal

Predictive modeling of chronic kidney disease integrating social factors, lifestyle, and retinal optical coherence tomography images

Abstract Full text Figures/Tables Video References Cited by

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