Predictive Factors for Changes in Glomerular Filtration Rate in Pre-Dialysis Chronic Kidney Disease Patients
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Abstract
BACKGROUND: Identifying predictive factors for estimated glomerular filtration rate (eGFR) decline is essential for enabling healthcare professionals to implement targeted strategies for delaying renal deterioration and postponing dialysis initiation in chronic kidney disease (CKD) patients.
OBJECTIVE: To examine predictive factors for one-year eGFR change in pre-dialysis CKD patients.
METHODS: A descriptive predictive correlational study using multiple regression analysis was conducted among 103 pre-dialysis CKD patients at the CKD Clinic, Samutprakan Hospital. Data were collected using a personal and medical history record form, a CKD-specific health literacy scale, a health behavior motivation scale, and a self-management behavior questionnaire. Descriptive statistics, Pearson's product-moment correlation, and multiple regression analysis were employed.
RESULTS: Urine protein and total cholesterol were the only statistically significant predictors of one-year eGFR change. The regression model was statistically significant (F=6.480, p<0.001), with urine protein (𝛽=0.584, p=0.005) and total cholesterol (𝛽=0.273, p=0.008) collectively explaining 29.3% of the variance in eGFR change (R²=0.293; adjusted R²=0.248), with higher levels of both being associated with greater eGFR decline.
CONCLUSION: These findings suggest that proteinuria and total cholesterol may be clinically relevant targets for monitoring in pre-dialysis CKD patients. Clinicians may consider integrating closer surveillance of these parameters into routine care, pending confirmation by longitudinal studies.
Thaiclinicaltrials.org number, TCTR20260601007
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References
Cha'on U, Tippayawat P, Sae-Ung N, Pinlaor P, Sirithanaphol W, Theeranut A, et al. High prevalence of chronic kidney disease and its related risk factors in rural areas of northeast Thailand. Sci Rep [Internet]. 2022 [cited 2026 Feb 7];12(1):18188. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC9616930/pdf/41598_2022_Article_22538.pdf
Vijitsoonthornkul K. Epidemiology and review of chronic kidney disease prevention measures [Internet]. 2022 [cited 2026 Feb 17]. Available from: https://ddc.moph.go.th/uploads/publish/1308820220905025852.pdf
Anumas S, Rattanapanop P, Pattharanitima P. Predictors of rapid eGFR decline in early to moderate chronic kidney disease (stages G1-G4): insights from a real-world Thai cohort incorporating KDIGO 2024 guidelines. Ren Fail [Internet]. 2025 [cited 2026 Mar 6];47(1):2593732. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC12667306/pdf/IRNF_47_2593732.pdf
Li L, Astor BC, Lewis J, Hu B, Appel LJ, Lipkowitz MS, et al. Longitudinal progression trajectory of GFR among patients with CKD. Am J Kidney Dis 2012;59:504-12.
Tsao HM, Lai TS, Chou YH, Lin SL, Chen YM. Predialysis trajectories of estimated GFR and concurrent trends of chronic kidney disease-relevant biomarkers. Ther Adv Chronic Dis [Internet]. 2023 [cited 2026 Feb 9];14:20406223231177291. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10265358/pdf/10.1177_20406223231177291.pdf
Inaguma D, Kitagawa A, Yanagiya R, Koseki A, Iwamori T, Kudo M, et al. Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: a machine learning-based prediction model by using a big database. PLoS One [Internet]. 2020 [cited 2026 Feb 10];15(9):e0239262. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7497987/pdf/pone.0239262.pdf
Gurgel do Amaral MS, Reijneveld SA, Geboers B, Navis GJ, Winter AF. Low health literacy is associated with the onset of CKD during the life course. J Am Soc Nephrol 2021;32:1436-43.
Elisabeth Stømer U, Klopstad Wahl A, Gunnar Gøransson L, Hjorthaug Urstad K. Health literacy in kidney disease: associations with quality of life and adherence. J Ren Care 2020;46:85-94.
Wu R, Feng S, Quan H, Zhang Y, Fu R, Li H. Effect of self-determination theory on knowledge, treatment adherence, and self-management of patients with maintenance hemodialysis. Contrast Media Mol Imaging [Internet]. 2022 [cited 2026 Feb 11];2022:1416404. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC9329035/pdf/CMMI2022-1416404.pdf
Peng S, He J, Huang J, Lun L, Zeng J, Zeng S, et al. Self-management interventions for chronic kidney disease: a systematic review and meta-analysis. BMC Nephrol [Internet]. 2019 [cited 2026 Feb 12];20(1):142. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC6486699/pdf/12882_2019_Article_1309.pdf
Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale (NJ): Lawrence Erlbaum Associates; 1988.
Wei CJ, Shih CL, Hsu YJ, Chen YC, Yeh JZ, Shih JH, et al. Development and application of a chronic kidney disease-specific health literacy, knowledge and disease awareness assessment tool for patients with chronic kidney disease in Taiwan. BMJ Open [Internet]. 2021 [cited 2026 Feb 13];11(10):e052597. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8506855/pdf/bmjopen-2021-052597.pdf
Poraj-Weder M, Pasternak A, Szulawski M. The development and validation of the health behavior motivation scale. Front Psychol [Internet]. 2021 [cited 2026 Feb 7];12:706495. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8446656/pdf/fpsyg-12-706495.pdf
He X, Wang Y, Feng C, Luo L, Khaliq U, Rehman FU, et al. Preferring self-management behavior of patients with chronic kidney disease. Front Public Health [Internet]. 2022 [cited 2026 Feb 14];10:973488. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC9755185/pdf/fpubh-10-973488.pdf
Adelakun G, Boesing M, Mbata MK, Pasha Z, Lüthi-Corridori G, Jaun F, et al. Proteinuria assessment and therapeutic implementation in chronic kidney disease patients-a clinical audit on KDIGO ("kidney disease: improving global outcomes") guidelines. J Clin Med [Internet]. 2024 [cited 2026 Mar 9];13(17):5335. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11395944/pdf/jcm-13-05335.pdf
Levin A, Ahmed SB, Carrero JJ, Foster B, Francis A, Hall RK, et al. Executive summary of the KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease: known knowns and known unknowns. Kidney Int 2024;105:684-701.
Lyu K, Liu S, Liu Y, You J, Wang X, Jiang M, et al. The effect of blood lipid profiles on chronic kidney disease in a prospective cohort: based on a regression discontinuity design. Biomed Environ Sci 2024;37:1158-72.
Pan X. Cholesterol metabolism in chronic kidney disease: physiology, pathologic mechanisms, and treatment. Adv Exp Med Biol 2022;1372:119-43.
