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Sci Rep
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Scientific Reports
2045-2322
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10.1038/s41598-024-72353-8
Article
Annual variation of estimated glomerular filtration rate in health check-ups associated with end-stage kidney disease
Okada Sadanori saokada@naramed-u.ac.jp

1
Nishioka Yuichi y_n@naramed-u.ac.jp

12
Kanaoka Koshiro 3
Koizumi Miyuki 1
Kamitani Fumika 1
Nakajima Hiroki 1
Kurematsu Yukako 1
Kubo Sinichiro 2
Myojin Tomoya 2
Noda Tatsuya 2
Saito Yoshihiko 4
Imamura Tomoaki 2
Takahashi Yutaka 1
1 https://ror.org/045ysha14 grid.410814.8 0000 0004 0372 782X Department of Diabetes and Endocrinology, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8522 Japan
2 https://ror.org/045ysha14 grid.410814.8 0000 0004 0372 782X Department of Public Health, Health Management, and Policy, Nara Medical University, 840 Shijo-cho, Kashihara, Nara 634-8521 Japan
3 https://ror.org/01v55qb38 grid.410796.d 0000 0004 0378 8307 Department of Medical and Information Management, National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shimmachi, Suita, Osaka Japan
4 Nara Prefecture Seiwa Medical Center, 1-14-16 Mimuro, Sango, Ikoma-gun, Nara, Japan
10 9 2024
10 9 2024
2024
14 2106525 5 2024
5 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Estimated glomerular filtration rate (eGFR) variation is associated with end-stage kidney disease (ESKD) development in patients with chronic kidney disease; whether annual variations in eGFR at health check-ups is associated with ESKD risk in the general population is unclear. We conducted a retrospective cohort study using Japanese national medical insurance claims from 2013 to 2020. Individuals who had their eGFR levels measured three times in annual health check-ups were included (N = 115,191), and the coefficient of variation of eGFR (CVeGFR) was calculated from 3-point eGFR. The end-point was ESKD as reported in the claims data. We analyzed the association between CVeGFR and ESKD incidence after adjusting for conventional ESKD risk factors. The CVeGFR median distribution was 5.7% (interquartile range: 3.5–8.5%). During a median follow-up period of 3.74 years, 164 patients progressed to ESKD. ESKD incidence was significantly higher in the highest quartile group (CVeGFR ≥ 8.5%) than in the other groups (P < 0.0001). After adjusting for risk factors, individuals with CVeGFR ≥ 8.5% had a significantly high ESKD incidence (adjusted hazard ratio: 3.01; 95% CI 2.14–4.30). High CVeGFR in annual health check-ups was associated with high ESKD incidence, independent of its other conventional risk factors, in the general population.

Keywords

Annual health check-up
Coefficient of variation of estimated glomerular filtration rate
End-stage kidney disease
Medical insurance claims data
Subject terms

Kidney diseases
Risk factors
http://dx.doi.org/10.13039/501100001691 Japan Society for the Promotion of Science 21K10451 Japan Society for the Promotion of Science,Japan22H03355 23H00507 the Health Science and Labor Research Grants21IA1006 Imamura Tomoaki issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Chronic kidney disease is a high-risk condition for end-stage kidney disease and cardiovascular disease1,2. Given the increasing prevalence of chronic kidney disease globally, preventing the onset/progression of chronic kidney disease is a social and economic issue because it affects an individual’s health and quality of life3. In Japan, approximately 13% of the adult population (13.3 million) is diagnosed with chronic kidney disease4, and estimated glomerular filtration rate (eGFR) is evaluated during annual health check-ups for adults aged ≥ 40 years to help prevent chronic kidney disease. However, effective methods for early identification of individuals with progressive chronic kidney disease who are at high risk for end-stage kidney disease have not been established.

A decreased eGFR slope predicts end-stage kidney disease, cardiovascular events, and all-cause mortality5,6. The 2012 Kidney Disease: Improving Global Outcomes guideline defines rapid eGFR decline as > 5 mL/min/1.73 m2/year7. Reduced eGFR slope is useful as a surrogate marker for end-stage kidney disease8–10; however, end-stage kidney disease events also occur in people with an increased eGFR slope, especially in short-term evaluation11–13. A meta-analysis has shown that an increased eGFR slope is associated with increased end-stage kidney disease, indicating a “U-shape” association between eGFR slope and the incidence of end-stage kidney disease13. This phenomenon implies that eGFR variability affects end-stage kidney disease development.

Previous studies have shown that visit-to-visit variability of eGFR is associated with an increased incidence of end-stage kidney disease and all-cause death14,15. A recent report has demonstrated that a 1-year coefficient of variation of eGFR (CVeGFR) is associated with developing end-stage kidney disease for patients with chronic kidney disease16. However, whether CVeGFR is associated with developing end-stage kidney disease in the general population at relatively low risk for end-stage kidney disease is unclear. In addition, it is necessary to assess whether annual eGFR variation is associated with developing end-stage kidney disease when identifying high-risk individuals for end-stage kidney disease through annual health check-ups.

In this study, we analyzed medical insurance claim data to determine whether CVeGFR, calculated from annual health check-ups, was associated with the incidence of end-stage kidney disease to identify individuals at high risk of end-stage kidney disease in the general population.

Methods

Design

We conducted a retrospective cohort study using national medical insurance claims and annual health check-up data from Nara Prefecture, Japan (Nara Kokuho Database). This study was performed according to the Declaration of Helsinki and approved by the Ethics Committee of Nara Medical University (1123-7). The Ethics Committee of Nara Medical University waived the requirement for informed consent, because all data were anonymized and de-identified. We used the STROBE cohort reporting guidelines17.

Database

The Nara Kokuho Database was constructed using monthly claims from all medical institutions and pharmacies along with annual health check-up data from April 2013 to March 2020. Health check-ups were conducted yearly for all participants aged ≥ 40 years. Individual tracking/aggregation was performed as previously described18. This database did not contain laboratory results other than annual health check-ups. According to the Nara Kokuho Database regulations, numbers < 10, excluding 0, and numbers < 10 indicated by calculation are not allowed.

Study population

Participants insured in the Nara Kokuho Database who had their serum creatinine levels measured three times during annual health check-ups during the study period were enrolled. All participants were advised to receive medical care if their eGFR was less than 45 mL/min/1.73 m2 on health check-ups. The date of the third health check-up was defined as the index date. Patients who initiated hemodialysis or peritoneal dialysis before the index date or had no record of insurance utilization after the index date were excluded. Comorbid diseases were defined using data from the medical insurance claims and health check-ups before the index date.

eGFR change scales

The results of blood tests and other laboratory tests in health check-ups, including serum creatinine, were measured by multiple laboratories in Japan. All methods of measuring serum creatinine were enzymatic methods. eGFR (mL/min/1.73 m2) was calculated using the three-variable (serum creatinine, age, and sex) Japanese equation for GFR19. CVeGFR (%) was calculated by dividing the SD of 3-point eGFRs by these means and multiplying by 100. eGFR slope (mL/min/1.73 m2/year) was calculated using a linear regression analysis of 3-point eGFRs16.

Outcome

The end-point of this study was the occurrence of end-stage kidney disease defined by the initiation of hemodialysis or peritoneal dialysis, as reported in the medical insurance claims data. The definition of dialysis initiation has been validated in our previous study20. We followed up with participants from the index date until the date of dialysis initiation or March 2020, whichever occurred first. If participants were not followed up until March 2020, they were censored on the date of their last use of medical insurance.

Statistical analyses

Continuous variables are expressed as means ± SDs or medians (interquartile ranges [IQRs]), and compared using Student’s t test, Wilcoxon rank sum test, or analysis of variance as appropriate, according to their distribution. Categorical variables are expressed as numbers and percentages, and compared using the chi-square test. CVeGFR was divided into quartiles and sorted from the lowest quartile into Q1, Q2, Q3, and Q4. The cumulative incidence of the end-point was estimated using the Kaplan–Meier method in each group, and differences between groups were assessed using the log-rank test. We constructed multivariable Cox proportional hazard models to estimate hazard ratios (HRs) and 95% CIs to analyze the association between CVeGFR (Q1–Q3 vs. Q4) and the end-point. We adjusted for the following factors at the index date: age (≥ 65 or < 65 years), sex, eGFR (≥ 60 or < 60 mL/min/1.73 m2), proteinuria (≥ 1 + or < 1 +), anemia, hyperuricemia, diabetes, hypertension, and history of cardiovascular diseases (basic model). These factors were used because of their importance in chronic kidney disease management and end-stage kidney disease risk7. The definitions of these factors are presented in Supplementary Table 1. To assess the effect of eGFR slope on the end-point, we adjusted for eGFR slope (≤ − 5 or > − 5 mL/min/1.73 m2/year) in addition to the basic model7. If there were missing data, we excluded the data in the multivariable analyses.

Subgroup analyses were performed with groups according to age, sex, eGFR, proteinuria, anemia, hyperuricemia, diabetes, hypertension, and history of cardiovascular diseases. We used the same multivariable model (basic model) to assess the association between CVeGFR (Q1–Q3 vs. Q4) and the end-point in each subgroup.

Several sensitivity analyses were performed to test the robustness of the results. First, to confirm the possibility that the relationship between CVeGFR and the end-point was dependent on eGFR, we analyzed by further stratifying eGFR (≥ 60, 30–60, and < 30 mL/min/1.73 m2). Second, considering that the cut-off value of CVeGFR may have affected the results, we performed the same analyses using CVeGFR as a continuous variable. Third, considering that the cut-off value of eGFR slope may have affected the results, we analyzed the association between CVeGFR and the end-point by adjusting for eGFR slope as a continuous variable, in addition to the basic model.

Statistical analyses were performed using Microsoft SQL Server 2017 Standard (Microsoft Corp., Redmond, WA, USA) and JMP 12.2 (SAS Institute, Cary, NC, USA). P values < 0.05 were considered statistically significant in all analyses.

Results

Participant backgrounds

We extracted the data of 228,556 individuals who underwent at least one annual health check-up from the Nara Kokuho Database (N = 780,027). Of them, we enrolled 116,409 individuals who had serum creatinine levels measured three times during annual health check-ups. The median (IQR) intervals between the first and second health check-ups and index dates were 2.03 (1.95–2.34) and 1.01 (0.95–1.14) years, respectively (Fig. 1). We excluded 28 individuals who had undergone dialysis therapy before the index date and 1,190 individuals with no record of insurance utilization after the index date. Finally, 115,191 individuals were enrolled in the study (Fig. 2).Fig. 1 Periods of health check-ups and follow-up. We followed up participants from the index date (the third health check-up) until the date of dialysis initiation or March 2020, whichever occurred first. The median (IQR) intervals between the first and second health check-ups and index dates were 2.03 (1.95–2.34) and 1.08 (0.95–1.14) years, respectively. IQR, interquartile range.

Fig. 2 Study flowchart. ESKD, end-stage kidney disease.

CVeGFR had a median distribution (IQR) of 5.7 (3.5–8.5) %. The distribution of CVeGFR and correlation between CVeGFR and eGFR slope are presented in Supplementary Fig. 1. CVeGFR did not correlate with eGFR slope. Table 1 presents the baseline characteristics grouped according to the CVeGFR quartiles. The Q4 group, the highest quartile group of CVeGFR, differed from the other groups in various parameters, including lower eGFR, higher prevalence of proteinuria, anemia, diabetes, hypertension, hyperuricemia, and history of cardiovascular and cerebrovascular diseases.Table 1 Baseline characteristics in each CVeGFR quartile.

	Q1: < 3.5	Q2: 3.5–5.7	Q3: 5.7–8.5	Q4: 8.5 ≤ 	P value	
(n = 28,246)	(n = 29,123)	(n = 29,139)	(n = 28,683)	
Age (years)	70.9 ± 8.9	71.1 ± 9.1	71.5 ± 9.3	72.9 ± 9.7	 < 0.0001	
Men, n (%)	11,815 (42)	12,098 (42)	12,053 (41)	10,981 (38)	 < 0.0001	
BMI (kg/m2)	22.6 ± 3.2	22.6 ± 3.2	22.6 ± 3.2	22.7 ± 3.5	 < 0.0001	
SBP (mmHg)	129 ± 16	130 ± 16	130 ± 16	130 ± 16	0.0004	
DBP (mmHg)	74 ± 10	74 ± 10	74 ± 10	73 ± 10	 < 0.0001	
eGFR (mL/min/1.73 m2)	71.9 ± 13.1	71.6 ± 13.5	71.6 ± 14.5	69.6 ± 18.7	 < 0.0001	
 ≥ 90, n (%)	2,398 (8.5)	2,424 (8.3)	2,763 (9.5)	3611 (13)	 < 0.0001	
60–90, n (%)	21,036 (74)	21,330 (73)	20,464 (70)	16,687 (58)		
45–60, n (%)	4317 (15)	4778 (16)	5108 (18)	5982 (21)		
30–45, n (%)	464 (1.6)	539 (1.9)	726 (2.5)	1876 (6.5)		
 < 30, n (%)	31 (0.11)	52 (0.18)	78 (0.27)	527 (1.8)		
HbA1c (%)	5.7 ± 0.54	5.7 ± 0.56	5.7 ± 0.58	5.7 ± 0.67	 < 0.0001	
Proteinuria, n (%)	1361 (4.8)	1640 (5.6)	1841 (6.3)	2804 (9.8)	 < 0.0001	
Anemia, n (%)	1456 (5.2)	1486 (5.2)	1733 (6.0)	2710 (9.5)	 < 0.0001	
Diabetes, n (%)	2778 (10)	3048 (10)	3362 (12)	4253 (15)	 < 0.0001	
Hypertension, n (%)	16,290 (58)	17,162 (59)	17,831 (61)	19,458 (68)	 < 0.0001	
Dyslipidemia, n (%)	19,192 (68)	20,135 (69)	20,067 (69)	20,322 (71)	 < 0.0001	
Hyperuricemia, n (%)	3875 (14)	4195 (14)	4508 (16)	5733 (20)	 < 0.0001	
CAD, n (%)	4001 (14)	4249 (15)	4478 (15)	5542 (19)	 < 0.0001	
Stroke, n (%)	3041 (11)	3328 (11)	3615 (12)	4534 (16)	 < 0.0001	
The Q1–Q4 groups indicate the quartiles of CVeGFR (Q1: CVeGFR < 3.5%, Q2: 3.5% ≤ CVeGFR < 5.7%, Q3: 5.7% ≤ CVeGFR < 8.5%, and Q4: 8.5% ≤ CVeGFR). BMI, body mass index; CAD, coronary artery disease; CVeGFR, coefficient of variation of eGFR; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; HbA1c, hemoglobin A1c; SBP, systolic blood pressure.

The numbers for missing data were: BMI, 32; SBP, 13; DBP, 17; HbA1c, 391; proteinuria, 255; anemia, 1190; diabetes, 369; hypertension; dyslipidemia, 37; and hyperuricemia, 265. The other variables had no missing data.

End-stage kidney disease incidence stratified by the CVeGFR

The median follow-up period was 3.74 (95% CI 3.73–3.75) years, and 164 individuals developed end-stage kidney disease. The backgrounds of those who developed end-stage kidney disease during the follow-up period are presented in Supplementary Table 2. The number who developed end-stage kidney disease in each quartile group derived from CVeGFR was 13, 15, 24, and 112 individuals in the Q1, Q2, Q3, and Q4 groups, respectively. The renal survival curves showed that the incidence of end-stage kidney disease was significantly higher in the Q4 group than in the other groups (log-rank test, P < 0.0001) (Fig. 3A). Moreover, the renal survival curves of the integrated Q1–Q3 groups indicated a similar result, compared with the Q4 group (log-rank test, P < 0.0001) (Fig. 3B).Fig. 3 Renal survival curves stratified by CVeGFR. The CVeGFR results were divided into quartiles and sorted from the lowest quartile into Q1, Q2, Q3, and Q4. (A) Individuals stratified by CVeGFR quartiles (Q1, Q2, Q3, and Q4). (B) Individuals stratified by integrated Q1, Q2, Q3 (Q1–3), and Q4 groups. * indicates a number < 10 that cannot be represented because of regulations. CVeGFR, coefficient of variation of eGFR.

In the univariable analysis, the Q4 group had a significantly higher incidence of end-stage kidney disease than the Q1–Q3 groups (HR: 6.83; 95% CI 4.95–9.56). In the multivariable analysis, the Q4 group had a significantly higher incidence of end-stage kidney disease than the Q1–Q3 groups in the basic model (adjusted HR: 3.01; 95% CI 2.14–4.30). Additionally, even when eGFR slope ≤ − 5 mL/min/1.73 m2/year was added to the basic model, the Q4 group exhibited a significantly higher incidence of end-stage kidney disease than the Q1–Q3 groups (adjusted HR: 3.11; 95% CI 2.17–4.50).

Subgroup analyses

In the subgroup analyses, individuals in the CVeGFR ≥ 8.5% group had a significantly higher incidence of end-stage kidney disease, except for those aged < 65 years, with eGFR ≥ 60 mL/min/1.73 m2 and no hypertension (Fig. 4). Significant interactions between CVeGFR and end-stage kidney disease incidence were observed in the subgroups stratified by eGFR, proteinuria, hypertension, and hyperuricemia, indicating that the CVeGFR had different effects on end-stage kidney disease in individuals with these risk factors. When the analysis was limited to individuals with eGFR < 60 mL/min/1.73 m2, proteinuria, and comorbid hypertension and hyperuricemia (N = 1,326), the incidence of end-stage kidney disease was significantly higher in individuals with CVeGFR ≥ 8.5% than in those with CVeGFR < 8.5% (adjusted HR: 4.45; 95% CI 2.52–8.48; Fig. 5).Fig. 4 Subgroup analyses. CAD, coronary artery disease; eGFR, estimated glomerular filtration rate. * indicates a number < 10 or < 10 known by calculation, which cannot be represented because of regulations.

Fig. 5 Renal survival curves stratified by CVeGFR in the high-risk subgroup. When the analysis was limited to individuals with eGFR < 60 mL/min/1.73 m2, proteinuria, and comorbid hypertension and hyperuricemia (N = 1,326), the incidence of ESKD was significantly higher in individuals with CVeGFR ≥ 8.5%. * indicates a number < 10 that cannot be represented because of regulations. CVeGFR, coefficient of variation of estimated glomerular filtration rate; ESKD, end-stage kidney disease.

Sensitivity analyses

There was no association between CVeGFR and the incidence of end-stage kidney disease in individuals with eGFR ≥ 60 mL/min/1.73 m2 (adjusted HR: 1.20, 95% CI 0.46–2.75); however, CVeGFR was associated with end-stage kidney disease incidence in those with eGFR < 60 mL/min/1.73 m2 (adjusted HR: 3.74, 95% CI 2.51–5.73). A low eGFR is a crucial predictor of end-stage kidney disease, and we further stratified individuals by eGFR as the first sensitivity analysis. The occurrence of end-stage kidney disease was 73 cases per 664 individuals with eGFR of < 30 mL/min/1.73 m2 and 61 cases per 23,362 individuals with eGFR of 30–60 mL/min/1.73 m2. The adjusted HRs (95% CI) of CVeGFR ≥ 8.5% for end-stage kidney disease incidence were 2.79 (1.43–6.11) and 2.66 (1.58–4.60) in individuals with eGFRs of < 30 and 30–60 mL/min/1.73 m2, respectively (the basic model). These results did not change after adjusting for eGFR slope ≤ -5 mL/min/1.73 m2/year: the adjusted HRs (95% CI) were 3.31 (1.67–7.32) and 2.62 (1.49–4.65) in individuals with eGFRs of < 30 and 30–60 mL/min/1.73 m2, respectively.

In the second sensitivity analysis, CVeGFR was used as a continuous variable. The adjusted HR of CVeGFR for end-stage kidney disease incidence was similar to that of the original analysis (adjusted HR [for each 10% increment in CVeGFR]: 1.89; 95% CI 1.71–2.09), suggesting that the risk of end-stage kidney disease increases with increased CVeGFR.

In the third sensitivity analysis, we analyzed the association between CVeGFR ≥ 8.5% and end-stage kidney disease incidence by adjusting eGFR slope as a continuous variable. The adjusted HR (95% CI) of CVeGFR ≥ 8.5% was 2.92 (2.04–4.22). The cut-off value for eGFR slope did not affect the results.

Discussion

We found that a high CVeGFR was associated with a high incidence of end-stage kidney disease, independent of other conventional end-stage kidney disease risk factors, including eGFR slope, in a general population who had annual health check-ups. These findings determined that high variability in renal function was a risk factor for end-stage kidney disease and that annual variation of eGFR was a useful marker for future decline in renal function. Subgroup analyses indicated that the high CVeGFR was more strongly associated with end-stage kidney disease risk in the high-risk individuals, which included all these factors: eGFR < 60 mL/min/1.73 m2, proteinuria, hypertension, and hyperuricemia. These results imply that the high CVeGFR is useful for identifying individuals at a high risk of end-stage kidney disease in both general and high-risk populations.

A decreased eGFR slope is a risk factor for end-stage kidney disease; however, previous reports suggested that individuals with an increased eGFR slope are also at risk, especially in short-term evaluation13. The underlying mechanisms of these results have not been fully elucidated; however, individuals with increased eGFR slope and elevated risk cannot be identified only by measuring eGFR slope. In contrast, CVeGFR reflects various changes in renal function, such as fluctuations in eGFR. CVeGFR did not corelate with eGFR slope, which was consistent with a previous report16. Even if, when those who developed end-stage kidney disease were analyzed, there was no correlation between CVeGFR and eGFR slope in linear regression model (r2 = 0.03, Supplementary Fig. 1D). This result suggests that CVeGFR and eGFR slope are independent risk factors for end-stage kidney disease incidence. These findings are also supported by the independence of CVeGFR and eGFR slope in the multivariable analyses. Recently, a chronic kidney disease cohort study reported that CVeGFR is associated with the incidence of end-stage kidney disease in patients with chronic kidney disease16. The evaluation of CVeGFR in our study may have been more informative in the context of annual check-ups in the general population because it is more difficult to detect high-risk individuals among the general population than those with chronic kidney disease. We demonstrated that a 2-year CVeGFR (3-time examination of eGFR) was associated with end-stage kidney disease risk independently of eGFR and eGFR slope, indicating that the additional use of CVeGFR increased the accuracy of predicting end-stage kidney disease risk; this may help detect high-risk individuals from the general population in a short-term evaluation.

When CVeGFR was divided into quartiles, participant backgrounds exhibited significant differences between the Q1–Q3 and Q4 groups; those in the Q4 group had low eGFRs and a high prevalence of end-stage kidney disease risk factors. The renal survival curve of the Q4 group differed from those of the Q1–Q3 groups and the risk of end-stage kidney disease was significantly higher in the Q4 group than in the other groups. The sensitivity analysis with CVeGFR as a continuous variable revealed a similar result, indicating that individuals with a higher CVeGFR were at higher risk of end-stage kidney disease. However, determining whether end-stage kidney disease prediction by high CVeGFR depends on low eGFR was warranted, because low eGFR is a strong predictor of end-stage kidney disease13. In our study, we performed multivariable analyses adjusting for eGFR and sensitivity analyses stratified by low eGFR. The impact of CVeGFR on the development of end-stage kidney disease was similar in individuals with eGFR < 30 and 30–60 mL/min/1.73 m2.

An epidemiological survey conducted in Japan reported that diabetic nephropathy (40%) and nephrosclerosis (18%) were the most common diseases requiring dialysis21. However, in our study, the profile of individuals who developed end-stage kidney disease showed that hypertension was the most common complication (90%), whereas diabetes was present in only 32% of patients (Supplementary Table 2). In addition, the subgroup analyses determined that the adjusted HR of CVeGFR for end-stage kidney disease decreased in individuals with diabetes (adjusted HR, 1.93) compared with those without diabetes (adjusted HR, 3.66), whereas it increased in individuals with hypertension (adjusted HR, 3.47) compared with those without hypertension (adjusted HR, 1.03). These results suggest that end-stage kidney disease predicted by CVeGFR depends on complicated renal diseases: CVeGFR for predicting end-stage kidney disease may be more effective in hypertensive nephrosclerosis than that in diabetic nephropathy. A high CVeGFR (large fluctuations in eGFR) may be associated with a decreased renal functional reserve. Individuals with low renal functional reserve easily exhibit decreased eGFR because of acute kidney injury22. For example, patients with hypertension exhibit decreased renal functional reserve23, suggesting that CVeGFR for predicting end-stage kidney disease was more effective in patients with hypertension in our study. Diabetic nephropathy may be associated with high CVeGFR, especially in the hyperfiltration stage24, resulting in a lower predictive value in patients with diabetes.

Our study had several limitations. First, although it had a large sample from the general population, there were relatively few incidences of end-stage kidney disease. We could not evaluate alternative end-points, such as eGFR < 15 mL/min/1.73 m2, because the claims database only included laboratory results for health check-ups. Second, several drugs have been reported to affect renal function. For example, angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers are used for renal protection1. We adjusted the effect of CVeGFR by comorbid hypertension; however, each class of anti-hypertensive drugs could not be considered in the analyses. Similarly, sodium-glucose cotransporter 2 inhibitors have been reported as renal-protective medications25; however, we could not analyze these drug prescriptions. Third, individuals with an eGFR of < 45 mL/min/1.73 m2 on health check-ups were advised to receive medical care in Japan, but it was not possible to evaluate whether individuals received appropriate medical care, such as nephrologists, after health check-ups, due to the nature of the medical insurance claim database. We could not evaluate the prognosis of individuals who did not receive medical care after health check-ups, because this study included only those who used medical insurance after health check-ups. However, there was only a small number (1%) of individuals who did not use medical insurance. Finally, our study only assessed a Japanese population; the results might differ in other countries and cultures.

Our study demonstrated that a high CVeGFR was an independent risk factor for the incidence of end-stage kidney disease in both the general and high-risk populations. CVeGFR is easy to calculate and suitable for identifying high-risk individuals from health check-up data, which can only be measured once a year. Early prediction of end-stage kidney disease was possible by including CVeGFR with conventional risk factors, which indicated that it can be useful as a basis for early intervention in high-risk populations. Further studies and validations using other databases are required to identify the appropriate basis for interventions to prevent end-stage kidney disease.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72353-8.

Acknowledgements

We thank Editage (http://www.editage.com) for English language editing. This study was supported by the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI) (21K10451, 22H03355, 23H00507, and 21K10474) and by the Health Science and Labor Research Grants (21IA1006).

Author contributions

SO designed the study, analyzed the data, and wrote and edited the manuscript. YN performed data curation, analyzed the data, and reviewed/edited the manuscript. KK contributed to data curation. MK, FK, HN, and YK contributed to discussion and reviewed the manuscript. SK, TM, and TN contributed to developing methodology. YS contributed to the discussion and reviewed the manuscript. TI contributed to developing the methodology and reviewed/edited the manuscript. YT contributed to the discussion and reviewed/edited the manuscript.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to the Nara Kokuho Database regulation, but are available from the corresponding author on reasonable request.

Competing interests

SO has received research grants from Japanese Red Cross Society and speaker fees from Taisho, Mitsubishi Tanabe, Sumitomo, Eli Lilly, Boehringer Ingelheim, Daiichi Sankyo, Novartis, Novo Nordisk, Mochida, Kyowa Kirin, Terumo, and Ono. YN has received consultant fees from Novo Nordisk and speaker fees from Daiichi Sankyo and Sanofi. FK has received speaker fees from Sumitomo, Sanofi, and Kyowa Kirin. HN has received speaker fees from Sumitomo, Novo Nordisk, Kowa, and Sanofi. TM has received consultant fees from Health Insurance Claims Review & Reimbursement services. YS has received research grants from Otsuka, Boehringer Ingelheim, and Novartis and speakers’ bureau/honorarium from Otsuka, Boehringer Ingelheim, and Novartis. YT has received consultant fees from Novo Nordisk, Otsuka, and Recordati and speaker fees from Novo Nordisk, Sumitomo, Eli Lilly, Ono, Novartis, Boehringer Ingelheim, AstraZeneca, and Kyowa Kirin. The other authors declare no conflicts of interest.

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These authors contributed equally: Sadanori Okada and Yuichi Nishioka.
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