Kidney function decline in working-age adults with cancer diagnosis: A study utilizing claims and health checkup data

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This study aimed to evaluate differences in the rate of kidney function decline between working-age cancer survivors who received anticancer drug treatment and non-cancer survivors. Methods This retrospective cohort study used data from the Panasonic Health Insurance Organization (2017–2021). The cancer person (CP) and non-cancer person (NCP) groups were defined based on cancer diagnosis and anticancer drug treatment, respectively. Propensity score matching aligned demographics and health factors. Standardized and individualized eGFR changes from the year before diagnosis (Year F) to F + 4 were evaluated across age groups (≤ 50 and ≥ 51 years). Results Each group comprised 395 matched individuals. The CP group demonstrated significant decreases in both standardized and individualized eGFR by F + 4 ( p = 0.007 and p < 0.001, respectively), whereas the NCP group showed no significant decline. The decline was particularly significant in CP ≤ 50 years ( p < 0.001) but not in those ≥ 51 years. Linear mixed-effects models confirmed a significantly larger annual decline in individualized eGFR in the CP group, particularly among younger individuals. Conclusions Working-age cancer survivors, particularly those aged ≤ 50 years, face a significantly higher risk of impaired kidney function than non-cancer survivors. These findings highlight the need for long-term kidney monitoring and protective strategies, especially among younger cancer survivors, to mitigate CKD progression and preserve quality of life and employment outcomes. chronic kidney disease cancer survivor working age kidney function claims and health checkup data Figures Figure 1 Figure 2 Figure 3 Introduction With advances in medical technology, the number of people living with and surviving cancer (cancer survivors) is increasing annually, and these individuals are also aging [ 1 ]. Cancer survivors have a significantly higher prevalence of comorbidities such as diabetes and hypertension compared to those without a history of cancer (non-cancer survivors) [ 2 ]. Chronic kidney disease (CKD) increases the risk of mortality among cancer survivors [ 3 ]. The Korean National Health and Nutrition Examination Survey reported that the incidence of CKD is higher among cancer survivors than that among non-cancer survivors [ 4 ]. Furthermore, the presence of CKD increases the management costs for cancer survivors more than other comorbidities, such as heart and respiratory diseases [ 5 ]. Therefore, investigating the relationship between cancer survival and CKD incidence is of utmost importance. One potential cause is the administration of anticancer drugs, such as cisplatin or anti- vascular endothelial growth factor (VEGF) agents, which can induce acute kidney injury (AKI). Cisplatin-associated AKI adversely affects long-term kidney function and survival rates [ 6 ]. Anti-VEGF drugs can cause CKD by inducing thrombotic microangiopathy and focal-segmental glomerulosclerosis [ 7 ]. Furthermore, individuals with a history of cancer have been reported to have a significantly higher risk of developing postoperative AKI and long-term kidney dysfunction (requiring dialysis or a 25% decrease in eGFR) [ 8 ]. Thus, AKI induced by anticancer drug administration or surgery during cancer treatment is considered a factor leading to the development of CKD. Additionally, comorbid conditions frequently observed among cancer survivors, such as diabetes, hypertension [ 9 , 10 ], dyslipidemia [ 11 ], and proteinuria [ 12 ], and lifestyle factors, including smoking [ 13 ], have been identified as contributors to CKD progression and decline in kidney function. Although adult cancer survivors are generally regarded as a high-risk population for CKD following oncological treatment, comparative studies examining kidney function trajectories in cancer survivors and individuals without a history of cancer remain scarce. In particular, changes in kidney function in the working-age population, who are expected to have long-term survival following cancer treatment, are closely associated with quality of life and employment outcomes. Given its substantial health, economic, and social importance, investigating kidney function trajectories in this demographic is of considerable significance. We hypothesized that it would be possible to compare trends in kidney function between cancer survivors diagnosed with malignancy and non-cancer individuals who received anticancer drug treatment by utilizing claims data from the Panasonic Health Insurance Organization in conjunction with periodic health checkup records. Accordingly, this study aimed to investigate the differences in the rate of kidney function decline between cancer survivors treated with anticancer agents and those without cancer, focusing on individuals in the productive age group. This study aimed to highlight the importance of long-term management of kidney function in cancer survivors. Methods Study Population Selection This retrospective cohort study used claims and health examination data from generally insured individuals of the Panasonic Health Insurance Organization, which included 131,958 members as of April 1, 2023. Claims data from fiscal years 2017 (April 1, 2017, to March 31, 2018) to 2021 (April 1, 2021, to March 31, 2022) and health examination data from fiscal years 2016 to 2022 (April 1, 2016, to March 31, 2023) were used. From the claims data, information on the diagnosis start dates of malignant neoplasms (ICD10 classification codes C00-C97) and anticancer drugs (listed in the National Health Insurance drug price list with codes starting from "4200" to "4299") were extracted. The year before the diagnosis of a malignant neoplasm was defined as year F. From the health examination data for year F, age, sex, diabetes medication use, Hemoglobin A1c (HbA1c), fasting blood glucose, hyperlipidemia medication use, triglycerides, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, hypertension medication use, systolic blood pressure, diastolic blood pressure, and smoking history were extracted as confounding factors. Additionally, height, weight, urinary protein (qualitative), and serum creatinine values were extracted from the health examination data for each fiscal year. Body surface area (Male: Dubois formula: height 0.725 × weight 0.425 × 0.007184), BMI (weight [kg] ÷ (height [m]) 2 ), standardized eGFR (mL/min/1.73 m 2 ) (194 × serum creatinine − 1.094 × age − 0.287 , Female: Male × 0.739), and individualized eGFR (mL/min) (standardized eGFR × body surface area/1.73) were calculated. This individualization was aimed at accounting for the potential weight loss caused by the administration of anticancer drugs. From the claims data of Panasonic Health Insurance Organization members from fiscal years 2017 to 2021, patients with cancer who received anticancer drug treatment were designated as the cancer person (CP) group, while the rest were classified as the non-cancer person (NCP) group. Health examination data for the CP group were analyzed over a period spanning from year F to year F + 4. For the NCP group, the fiscal year 2018 was defined as year F, and health examination data from F to F + 4 (fiscal year 2022) were similarly analyzed. The selection criterion was the availability of all the study data for year F. The exclusion criteria were as follows: (1) absence of any health examination during the period from F + 1 to F + 4, (2) undergoing a health examination without serum creatinine data, and (3) missing data for any of the confounding factors in the year F health checkup data. Based on the CP group data, the NCP group data were extracted via propensity score matching using nine variables from the health examination data based on the year F baseline data. The nine variables were age, sex, BMI, standardized eGFR (mL/min/1.73 m 2 ), qualitative urinary protein (- to 3+), smoking history (never smoked, former smoker, current smoker), hypertension (present/absent), hyperlipidemia (present/absent), and diabetes (present/absent). Hypertension was defined as taking medications for hypertension, systolic blood pressure ≥ 140 mmHg, and/or diastolic blood pressure ≥ 90 mmHg. The criteria for hyperlipidemia were defined as the use of hyperlipidemia medication, triglyceride levels ≥ 150 mg/dL, HDL cholesterol levels < 40 mg/dL, or LDL cholesterol levels ≥ 140 mg/dL. Diabetes was defined as either the use of diabetes medication or having both HbA1c ≥ 6.5% and fasting blood glucose ≥ 126 mg/dL. The anticancer drugs used in the CP group were also investigated. Evaluation Methods For both the CP and NCP groups, changes in the median standardized eGFR (mL/min/1.73 m 2 ) and individualized eGFR (mL/min) by fiscal year from F to F + 4 were analyzed. Moreover, based on a previous report indicating that age-related changes in kidney tissue volume differ after the age of 50 years [ 14 ], and considering the importance of assessing the long-term impact on kidney function in younger individuals, changes in standardized and individualized eGFR were investigated by dividing the subjects into two age groups: ≤ 50 and ≥ 51 years. Furthermore, the rate of change in eGFR per year from F to year F + 4 was also investigated in each group. Statistical Analysis Propensity scores were estimated using logistic regression analysis, incorporating the nine aforementioned variables as covariates. A 1:1 matched-pair analysis was conducted based on the propensity scores, with the caliper width set at 0.2 of the standard deviation of the logit of the propensity score. For differences in personal backgrounds, the Mann–Whitney U test was applied for continuous variables, and Fisher’s exact test or chi-square test was used for categorical variables. The Kruskal–Wallis test followed by Steel's multiple comparison test was used to compare the standardized and individualized eGFR between the CP and NCP groups in years F and years F + 1 to F + 4. The annual rates of change in both standardized and individualized eGFR from baseline to year 4 were compared between the CP and NCP groups using a linear mixed effects model. The fixed effects included group, time (F to F + 4 years), and their interactions, whereas the random effects accounted for subject-specific intercepts and slopes. All statistical analyses were performed using EZR ver1.65 [ 15 ], and statistical significance was set at p -value < 0.05 in two-sided tests. Results Person Characteristics A total of 506 individuals diagnosed with cancer between April 1, 2017, and March 31, 2022, received anticancer treatment and had available eGFR data either in year F or during the follow-up period from years F+1 to F+4. Among these, 397 persons had complete data for all confounding variables in the year F health checkup data. After propensity score matching of these cases with non-cancer cases in the claims data, 395 cases were extracted from both the CP and NCP groups (Fig.1). The age distribution in the CP group (median: 52 years; range: 23–68 years) was similar to that of the NCP group (median: 52 years; range: 22–69 years). There was no significant difference in standardized eGFR between the CP group (median: 76.5 mL/min/1.73 m 2 , range: 5.5–124.2 mL/min/1.73 m 2 ) and the NCP group (median: 75.1 mL/min/1.73 m 2 , range: 31.5–127.9 mL/min/1.73 m 2 ) (Table 1). In the CP group, 78 types of anticancer drugs were used, with the most common being cyclophosphamide (88 cases, 22.3%), paclitaxel (78 cases, 19.7%), oxaliplatin (64 cases, 16.2%), docetaxel (54 cases, 13.7%), carboplatin (43 cases, 10.9%), epirubicin (39 cases, 9.9%), doxorubicin (36 cases, 9.1%), fluorouracil (33 cases, 8.4%), tamoxifen (28 cases, 7.1%), trastuzumab (28 cases, 7.1%), gemcitabine (25 cases, 6.3%), cisplatin (25 cases, 6.3%), rituximab (19 cases, 4.8%), and bevacizumab (16 cases, 4.1%). Yearly Analysis of Standardized and Individualized eGFR for the CP and NCP group The number of eGFR data points per year (F/F+1/F+2/F+3/F+4) was 395/352/270/186/114 and 395/386/360/334/308 in the CP and NCP groups, respectively. The standardized eGFR (median: 10–90% interval, mL/min/1.73 m 2 ) for all data in the CP group in year F was 76.5 (61.5–94.6) and significantly decreased in year F+4 to 72.3 (53.6–93.1) (Fig. 2A, p = 0.007). In contrast, the NCP group did not exhibit a significant decrease in year F+4 (72.4:58.8–90.9) compared to that in year F (75.1:60.9–95.0) (Fig. 2B; p = 0.068).For those aged 50 years and younger, the CP group demonstrated a significant decrease in standardized eGFR in year F+4 (72.7:55.8–93.1) compared to year F (80.5:65.6–98.9) (Fig. 2C, p = 0.005). In contrast, the NCP group did not exhibit a significant decrease in year F+4 (76.2:61.0–97.3) compared to year F (80.8:65.1–101.5) (Fig. 2D, p = 0.130). For those aged ≥ 51 years, the CP group did not exhibit a significant decrease in standardized eGFR in year F+4 (71.3:53.3–91.4) compared to year F (73.6:58.8–93.7) (Fig. 2E, p = 0.274). Furthermore, the NCP group did not exhibit a significant decrease in year F+4 (71.2:55.7–85.2) compared to that in year F (71.3:59.1–87.6) (Fig. 2F, p = 0.119). In the individualized eGFR (median: 10–90% interval, mL/min), all data in the CP group showed a significant decrease at year F+4 to 68.4 (51.1–88.5) compared to year F (74.7:58.3–95.0) (Fig. 3A, p < 0.001). In contrast, no significant decrease was observed in year F+4 (70.1:54.8–92.5) compared to year F (72.9:57.1–94.4) for all data in the NCP group (Fig. 3B, p = 0.097). For those aged 50 years and younger, the CP group demonstrated a significant decrease in year F+4 (68.5:50.0–88.5) compared to year F (75.2:62.4–98.6) (Fig. 3C, p < 0.001). In contrast, the NCP group did not exhibit a significant decrease in year F+4 (73.6:56.4–97.4) compared to that in year F (78.3:59.0–99.5) (Fig. 3D, p = 0.347). For those aged ≥ 51 years, the CP group did not exhibit a significant decrease in year F+4 (68.4:53.6–86.5) compared to year F (74.0:56.8–92.2) (Fig. 3E, p = 0.083). Additionally, the NCP group did not show a significant decrease in year F+4 (67.2:54.0–86.2) compared to that in year F (69.5:56.2–88.9) (Fig. 3F, p = 0.146). Changes in Standardized and Individualized eGFR Per Year According to the linear mixed-effects model, which analyzed all data without considering age classification, there was no significant difference in the annual rate of decline in the standardized eGFR between the CP (-0.96 mL/min/1.73 m 2 /year) and NCP groups (-0.67 mL/min/1.73 m 2 /year) ( p = 0.141). However, the CP group (-1.14 mL/min/year) exhibited a significantly greater decrease in individualized eGFR than the NCP group (-0.68 mL/min/year) ( p = 0.015). Age-specific analysis revealed that in patients aged ≤ 50 years, the CP group demonstrated a significantly faster decline in both standardized eGFR (CP group -1.33 mL/min/1.73 m 2 /year, NCP group -0.66 mL/min/1.73 m 2 /year, p = 0.018) and individualized eGFR (CP group -1.35 mL/min/year, NCP group -0.65 mL/min/year, p = 0.011). In contrast, for those aged ≥ 51 years, no difference was observed between the CP and NCP groups in both the standardized eGFR (CP group -0.61 mL/min/1.73 m 2 /year, NCP group -0.71 mL/min/1.73 m 2 /year, p = 0.715) and individualized eGFR (CP group -0.94 mL/min/year, NCP group -0.71 mL/min/year, p = 0.392). Discussion The present study demonstrated that cancer survivors exhibit a significantly greater annual decline in individualized eGFR than non-cancer survivors, particularly among individuals aged 50 years or younger. Both standardized and individualized eGFR values declined significantly in this age group, suggesting that cancer survivors, especially younger individuals, are at an increased risk of progressive kidney dysfunction following cancer treatment, even after adjusting for confounding variables. These findings align with those of previous reports, indicating that cancer survivors are susceptible to CKD and its sequelae [3,4]. Importantly, this study contributes new evidence by quantifying the annual rate of individualized eGFR decline in a large general population cohort, highlighting a vulnerable subgroup of cancer survivors who warrant targeted interventions. Several mechanisms may explain the accelerated decline in kidney function. Anticancer pharmacotherapy plays a central role. In our cohort, nearly 80% of cancer survivors received cytotoxic chemotherapy, including cyclophosphamide, paclitaxel, and agents with well-recognized nephrotoxic potential, such as cisplatin [6,16–18]. In addition to classic nephrotoxins, molecularly targeted agents (e.g., VEGF inhibitors and tyrosine kinase inhibitors) and immune checkpoint inhibitors have been increasingly implicated in glomerular and tubular injury, as well as immune-mediated renal disorders [7,19–21]. These findings are consistent with reports of long-term CKD risk after AKI episodes induced by these therapies. Second, surgical intervention and cancer-related systemic effects may have contributed to renal dysfunction. The perioperative period represents a high-risk window for AKI, driven by surgical stress, hemodynamic instability, inflammation, and potential complications such as infection or sepsis [8,27,28]. AKI is a well-established risk factor for the development of CKD. Moreover, specific cancer-related metabolic disturbances, including tumor lysis syndrome in hematological malignancies and monoclonal protein deposition in multiple myeloma, can cause or exacerbate kidney injury [25,26]. However, these factors are often underrecognized in survivorship care, underscoring the need for comprehensive peri-treatment risk assessment. Third, although aging and comorbidities such as hypertension and diabetes are well-known CKD risk factors [9,10,22–24], the magnitude of eGFR decline observed in our cancer survivor cohort remained significant after adjusting for these variables using propensity score matching. Notably, the attenuated difference in renal decline in individuals aged > 50 years may reflect age-related structural changes, such as cortical volume loss and reduced renal mass, which may mask or confound the impact of cancer treatment in older adults [14]. Our findings have several significant clinical implications. Given the growing global population of cancer survivors, the early identification of individuals at high risk of kidney function decline is critical. Our study highlights the need for longitudinal kidney monitoring as an integral component of survivorship care plans, particularly in younger individuals who may face decades of increased CKD risk. Furthermore, multidisciplinary strategies, including nephrology consultations during cancer treatment planning, judicious use of nephrotoxic therapies, and proactive management of perioperative risks, could mitigate long-term renal sequelae. Importantly, our findings identify key knowledge gaps in the literature. The inability to disaggregate the effects of cancer type, treatment regimens, or cumulative drug exposure limits our understanding of specific risk pathways. Future prospective studies incorporating granular clinical data, including cancer staging, detailed pharmacological profiles, surgical variables, and biomarkers of kidney injury, are needed to clarify the causal mechanisms and guide personalized prevention strategies. This study had some limitations. First, detailed clinical data, such as cancer type, stage, treatment duration, dosage of anticancer agents, and surgical details, were unavailable, limiting the ability to pinpoint the precise etiologies of kidney decline. Second, reliance on health checkup data may have introduced follow-up bias, as annual participation was not uniform. Third, we were unable to assess sarcopenia or body composition changes, which may have influenced the individualized eGFR estimates. Finally, residual confounding by unmeasured factors, such as nutritional status or socioeconomic conditions, cannot be excluded. In summary, our findings indicate that cancer treatment is associated with an accelerated annual decline in individualized eGFR, particularly among younger cancer survivors. These results reinforce the importance of long-term kidney monitoring and integrated survivorship care and highlight the need for prospective studies to delineate the specific contributions of anticancer therapies and surgical interventions to kidney function decline in childhood cancer survivors. Statements and Declarations The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors declare no competing financial or non-financial interests that could influence the results or discussion reported in this paper. Author Contributions All authors contributed to the conception and design of the study. HW obtained claims and health checkup data as pseudonymized processed information from Panasonic Health Insurance Organization. HW also organized the data to the minimum necessary for sharing with university co-researchers, providing information only in the form of representative values and dispersions without sharing personally identifiable information. Data analysis and evaluation were performed by HW, MT, RI, YM, HO, YH and MF. Specifically, HO, YH, and MF contributed to the study planning, data analysis, and evaluation by leveraging their clinical research experience using big data. HW wrote the first draft of the manuscript, and all authors commented on the previous versions of the manuscript. All authors have read and approved the final version of this manuscript. Data Availability Statement The datasets generated and/or analyzed in the current study are available from the corresponding author upon reasonable request. Ethics approval This study was conducted in accordance with the Declaration of Helsinki and the “Ethical Guidelines for Life Science and Medical Research Involving Human Subjects” issued by the Ministry of Health, Labor and Welfare of Japan. The study protocol was reviewed and approved by the ethics committees of the following institutions. Panasonic Health Insurance Organization Health Management Center (Approval No. 2024-003) Kyoto Pharmaceutical University (Approval No. E-00047) Kyoto Prefectural University of Medicine (Approval No. ERB-C-3162) Consent to participate This was a retrospective cohort study that used pseudonymized data. Given the nature of this study, obtaining direct consent was deemed impractical. Informed consent was obtained through an opt-out approach approved by the ethics committees of all participating institutions (Approval Nos.: 2024-003, E-00047, and ERB-C-3162). Consent to publish The authors affirm that this manuscript does not contain any data that would require specific consent for publication. References Matsuoka YJ, Okubo R, Shimizu Y, Tsuji K, Narisawa T, Sasaki J, et al. (2020) Developing the structure of Japan's cancer survivorship guidelines using an expert panel and modified Delphi method. J Cancer Surviv 14:273-283. https://doi.org/10.1007/s11764-019-00840-3 Keats MR, Cui Y, DeClercq V, Grandy SA, Sweeney E, Dummer TJB (2021) Burden of multimorbidity and polypharmacy among cancer survivors: a population-based nested case-control study. Support Care Cancer 29: 713-23. https://doi.org/10.1007/s00520-020-05529-3 Na SY, Sung JY, Chang JH, Kim S, Lee HH, Park YH, et al. (2011) Chronic kidney disease in cancer persons: an independent predictor of cancer-specific mortality. Am J Nephrol 33: 121-30. https://doi.org/10.1159/000323740 Shin HY, Linton JA, Shim JY, Kang HT. (2015) Cancer survivors aged 40 years or elder are associated with high risk of chronic kidney disease: the 2010-2012 Korean National Health and Nutrition Examination Survey. Asian Pac J Cancer Prev 16:1355-60. https://doi.org/10.7314/apjcp.2015.16.4.1355 Cortaredona S, Ventelou B (2017) The extra cost of comorbidity: multiple illnesses and the economic burden of non-communicable diseases. BMC Med 15:216. https://doi.org/10.1186/s12916-017-0978-2 Bhat ZY, Cadnapaphornchai P, Ginsburg K, Sivagnanam M, Chopra S, Treadway CK, et al. (2015) Understanding the Risk Factors and Long-Term Consequences of Cisplatin-Associated Acute Kidney Injury: An Observational Cohort Study. Plos One. 10:e0142225. https://doi.org/10.1371/journal.pone.0142225 Izzedine H, Escudier B, LhommeC, Pautier P, Rouvier P, Gueutin V, et al. (2014) Kidney diseases associated with anti-vascular endothelial growth factor (VEGF): an 8-year observational study at a single center. Medicine (Baltimore). 93:333-9. https://doi.org/10.1097/MD.0000000000000207 Gameiro J, Neves JB, Rodrigues N, Bekerman C, Melo MJ, Pereira M, et al. (2016) Acute kidney injury, long-term renal function and mortality in patients undergoing major abdominal surgery: a cohort analysis. Clin Kidney J. 9:192-200. https://doi.org/10.1093/ckj/sfv144 GBD Chronic Kidney Disease Collaboration (2020) Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 395:709-733. https://doi.org/10.1016/S0140-6736(20)30045-3 Chen TK, Knicely DH, Grams ME (2019) Chronic Kidney Disease Diagnosis and Management: A Review. JAMA. 322:1294-304. https://doi.org/10.1001/jama.2019.14745 Kosugi T, Eriguchi M, Yoshida H, Tasaki H, Fukata F, Nishimoto M, et al. (2021) Association between chronic kidney disease and new-onset dyslipidemia: The Japan Specific Health Checkups (J-SHC) study. Atherosclerosis. 332:24-32. https://doi.org/10.1016/j.atherosclerosis.2021.08.004 Remuzzi G, Ruggenenti P, Benigni A (1997) Understanding the nature of renal disease progression. Kidney Int. 51:2-15. https://doi.org/10.1038/ki.1997.2 Alebiosu CO (2003) An update on 'progression promoters' in renal diseases. J Natl Med Assoc. 95:30-42. https://doi.org/10.1016/s0027-9684(15)30874-x Wang X, Vrtiska TJ, Avula RT, Walters LR, Chakkera HA, Kremers WK, et al. (2014) Age, kidney function, and risk factors associate differently with cortical and medullary volumes of the kidney. Kidney Int. 85:677-85. https://doi.org/10.1038/ki.2013.359 Kanda Y (2013) Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transplant. 48:452-58. https://doi.org/10.1038/bmt.2012.244 Gork I, Xiong F, Kitchlu A (2024) Cancer drugs and acute kidney injury: new therapies and new challenges. Curr Opin Nephrol Hypertens. 33:474-85. https://doi.org/10.1097/MNH.0000000000001001 Ries F, Klastersky J (1986) Nephrotoxicity induced by cancer chemotherapy with special emphasis on cisplatin toxicity. Am J Kidney Dis. 8:368-79. https://doi.org/10.1016/s0272-6386(86)80112-3 Abbas A, Mirza MM, Ganti AK, Tendulkar K (2015) Renal Toxicities of Targeted Therapies. Target Oncol. 10:487-99. https://doi.org/10.1007/s11523-015-0368-7 Estrada CC, Maldonado A, Mallipattu SK (2019) Therapeutic Inhibition of VEGF Signaling and Associated Nephrotoxicities. J Am Soc Nephrol. 30:187-200. https://doi.org/10.1681/ASN.2018080853 Xiong Y, Wang Q, Liu Y, Wei J, Chen X (2022) Renal adverse reactions of tyrosine kinase inhibitors in the treatment of tumours: A Bayesian network meta-analysis. Front Pharmacol. 13:1023660. https://doi.org/10.3389/fphar.2022.1023660 Herrmann SM, Perazella MA (2020) Immune Checkpoint Inhibitors and Immune-Related Adverse Renal Events. Kidney Int Rep. 5:1139-48. https://doi.org/10.1016/j.ekir.2020.04.018 Lindeman RD, Tobin J, Shock NW (1985) Longitudinal studies on the rate of decline in renal function with age. J Am Geriatr Soc. 33:278-85. https://doi.org/10.1111/j.1532-5415.1985.tb07117.x Johnson TM 2nd, Sands JM, Ouslander JG (2002) A prospective evaluation of the glomerular filtration rate in older adults with frequent nighttime urination. J Urol. 167:146-50. https://doi.org/10.1097/00005392-200201000-00027 Cohen E, Nardi Y, Krause I, Goldberg E, Milo G, Garty M, et al. (2014) A longitudinal assessment of the natural rate of decline in renal function with age. J Nephrol. 27:635-41. https://doi.org/10.1007/s40620-014-0077-9 Barbar T, Sathick IJ (2021) Tumor Lysis Syndrome. Adv Chronic Kidney Dis. 28:438-46. https://doi.org/10.1053/j.ackd.2021.09.007 Hutchison CA, Batuman V, Behrens J, Bridoux F, Sirac C, Dispenzieri A, et al. (2011) The pathogenesis and diagnosis of acute kidney injury in multiple myeloma. Nat Rev Nephrol. 8:43-51. https://doi.org/10.1038/nrneph.2011.168 Hobson CE, Yavas S, Segal MS, Schold JD, Tribble CG, Laskowski D, et al. (2009) Acute kidney injury is associated with increased long-term mortality after cardiothoracic surgery. Circulation. 119: 2444-53. https://doi.org/10.1161/CIRCULATIONAHA.108.800011 Bagshaw SM, Uchino S, Bellomo R, Morimatsu H, Morgera S, Schetz M, et al. (2007) Septic acute kidney injury in critically ill patients: clinical characteristics and outcomes. Clin J Am Soc Nephrol. 2:431-39. https://doi.org/10.2215/CJN.03681106 Table Table 1. Person’s characteristics for CP and NCP groups after propensity score matching Variables CP N = 395 NCP N = 395 p value Median age [range] — years old 52 [23, 68] 52 [22, 69] 0.97 a) Male sex — no. (%) 207 (52.4) 203 (51.4) 0.83 b) Median BMI [range] — kg/m 2 22.7 [15.0, 39.1] 22.8 [16.0, 41.5] 0.94 a) Median eGFR [range] — mL/min/1.73m 2 76.5 [5.5, 124.2] 75.1 [31.5, 127.9] 0.14 a) Urinary protein — no. (%) - 374 (94.7) 373 (94.4) 0.33 c) +/- 8 (2.0) 15 (3.8) 1+ 8 (2.0) 5 (1.3) 2+ 4 (1.0) 2 (0.5) 3+ 1 (0.3) 0 (0.0) Hypertension — no. (%) 96 (24.3) 101 (25.6) 0.74 b) Hyperlipidemia — no. (%) 185 (46.8) 189 (47.8) 0.83 b) Diabetes — no. (%) 28 (7.1) 30 (7.6) 0.89 b) Smoker — no. (%) Never 222 (56.2) 232 (58.7) 0.30 c) Former 83 (21.0) 66 (16.7) Current 90 (22.8) 97 (24.6) Hypertension: medication present or systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg Hyperlipidemia: medication present or TG ≥ 150 mg/dL or HDL < 40 mg/dL OR LDL ≥ 140 mg/dL Diabetes: medication present or HbA1c ≥ 6.5 mg/dL and fasting blood glucose ≥ 126 mg/dL a) Mann-Whitney U test, b) Fisher's exact test, c) chi-square test Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2026 Read the published version in Supportive Care in Cancer → Version 1 posted Editorial decision: Revision requested 22 Feb, 2026 Reviews received at journal 22 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 14 Jan, 2026 Reviewers invited by journal 10 Oct, 2025 Editor assigned by journal 10 Oct, 2025 Submission checks completed at journal 04 Sep, 2025 First submitted to journal 03 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7527080","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532403029,"identity":"bf2bcb0f-f6f4-4fe8-8d15-ce813d70b938","order_by":0,"name":"Hiroyuki Watanabe","email":"data:image/png;base64,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","orcid":"","institution":"Matsushita Memorial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Watanabe","suffix":""},{"id":532403030,"identity":"a0727cf9-32e6-4f42-ba86-3d827d488951","order_by":1,"name":"Masayuki Tsujimoto","email":"","orcid":"","institution":"Kyoto Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Masayuki","middleName":"","lastName":"Tsujimoto","suffix":""},{"id":532403031,"identity":"621d5a03-8fdb-404e-9690-9aed916d0bf3","order_by":2,"name":"Hiroshi Okada","email":"","orcid":"","institution":"Kyoto Prefectural University of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Okada","suffix":""},{"id":532403032,"identity":"c387c314-e432-4f74-a756-d56cbea28157","order_by":3,"name":"Yoshitaka Hashimoto","email":"","orcid":"","institution":"Matsushita Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yoshitaka","middleName":"","lastName":"Hashimoto","suffix":""},{"id":532403033,"identity":"1bf83ee5-f654-4935-b873-3da1bce54dab","order_by":4,"name":"Ryo Inose","email":"","orcid":"","institution":"Kyoto Pharmaceutical 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12:16:16","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88136,"visible":true,"origin":"","legend":"","description":"","filename":"07533d95759245cfa16edc277280bf7a1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7527080/v1/bcd745d8c1ae97a68a817730.xml"},{"id":94329639,"identity":"5dba24bc-f7d6-42e2-9a6a-fbed89f5dd8b","added_by":"auto","created_at":"2025-10-27 12:16:17","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":95220,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7527080/v1/8852a441dc0e035a6a82df70.html"},{"id":94329532,"identity":"c4151d7c-f3a4-43fa-9c35-5209405e92ff","added_by":"auto","created_at":"2025-10-27 12:16:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132835,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy cohort selection and data availability for propensity score matched analysis of renal function in individuals with cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFY: Fiscal Year\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7527080/v1/22fa08c9e0e800cbb4db436a.png"},{"id":94329521,"identity":"d0e84e59-4c68-4952-9273-0db15191d33f","added_by":"auto","created_at":"2025-10-27 12:16:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChange over time in standardized eGFR in the CP and NCP groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF: year prior to cancer diagnosis and treatment initiation (2018 for the NCP group)\u003c/p\u003e\n\u003cp\u003e**\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.01, *** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001 (Steel multiple comparison test vs. year F)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7527080/v1/9f03aefed328d0b5cf21baf7.png"},{"id":94329519,"identity":"6fe03c93-44fe-4e0c-88a9-4d6e80cf2597","added_by":"auto","created_at":"2025-10-27 12:16:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges over time in individualized eGFR in CP and NCP groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF: year prior to cancer diagnosis and treatment initiation (2018 for the NCP group)\u003c/p\u003e\n\u003cp\u003e*** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001 (Steel multiple comparison test vs. F year).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7527080/v1/0da96cc689c62394f3286bc5.png"},{"id":105755562,"identity":"48118541-8350-46bc-87f4-2c03c36032a1","added_by":"auto","created_at":"2026-03-30 16:28:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1034745,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7527080/v1/6a82819f-aa38-4601-b6ca-a5fef7f96aa6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Kidney function decline in working-age adults with cancer diagnosis: A study utilizing claims and health checkup data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith advances in medical technology, the number of people living with and surviving cancer (cancer survivors) is increasing annually, and these individuals are also aging [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Cancer survivors have a significantly higher prevalence of comorbidities such as diabetes and hypertension compared to those without a history of cancer (non-cancer survivors) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Chronic kidney disease (CKD) increases the risk of mortality among cancer survivors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The Korean National Health and Nutrition Examination Survey reported that the incidence of CKD is higher among cancer survivors than that among non-cancer survivors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, the presence of CKD increases the management costs for cancer survivors more than other comorbidities, such as heart and respiratory diseases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, investigating the relationship between cancer survival and CKD incidence is of utmost importance.\u003c/p\u003e\u003cp\u003eOne potential cause is the administration of anticancer drugs, such as cisplatin or anti- vascular endothelial growth factor (VEGF) agents, which can induce acute kidney injury (AKI). Cisplatin-associated AKI adversely affects long-term kidney function and survival rates [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Anti-VEGF drugs can cause CKD by inducing thrombotic microangiopathy and focal-segmental glomerulosclerosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Furthermore, individuals with a history of cancer have been reported to have a significantly higher risk of developing postoperative AKI and long-term kidney dysfunction (requiring dialysis or a 25% decrease in eGFR) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thus, AKI induced by anticancer drug administration or surgery during cancer treatment is considered a factor leading to the development of CKD.\u003c/p\u003e\u003cp\u003eAdditionally, comorbid conditions frequently observed among cancer survivors, such as diabetes, hypertension [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], dyslipidemia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and proteinuria [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and lifestyle factors, including smoking [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], have been identified as contributors to CKD progression and decline in kidney function. Although adult cancer survivors are generally regarded as a high-risk population for CKD following oncological treatment, comparative studies examining kidney function trajectories in cancer survivors and individuals without a history of cancer remain scarce. In particular, changes in kidney function in the working-age population, who are expected to have long-term survival following cancer treatment, are closely associated with quality of life and employment outcomes. Given its substantial health, economic, and social importance, investigating kidney function trajectories in this demographic is of considerable significance.\u003c/p\u003e\u003cp\u003eWe hypothesized that it would be possible to compare trends in kidney function between cancer survivors diagnosed with malignancy and non-cancer individuals who received anticancer drug treatment by utilizing claims data from the Panasonic Health Insurance Organization in conjunction with periodic health checkup records. Accordingly, this study aimed to investigate the differences in the rate of kidney function decline between cancer survivors treated with anticancer agents and those without cancer, focusing on individuals in the productive age group. This study aimed to highlight the importance of long-term management of kidney function in cancer survivors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population Selection\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study used claims and health examination data from generally insured individuals of the Panasonic Health Insurance Organization, which included 131,958 members as of April 1, 2023.\u003c/p\u003e\u003cp\u003eClaims data from fiscal years 2017 (April 1, 2017, to March 31, 2018) to 2021 (April 1, 2021, to March 31, 2022) and health examination data from fiscal years 2016 to 2022 (April 1, 2016, to March 31, 2023) were used. From the claims data, information on the diagnosis start dates of malignant neoplasms (ICD10 classification codes C00-C97) and anticancer drugs (listed in the National Health Insurance drug price list with codes starting from \"4200\" to \"4299\") were extracted. The year before the diagnosis of a malignant neoplasm was defined as year F. From the health examination data for year F, age, sex, diabetes medication use, Hemoglobin A1c (HbA1c), fasting blood glucose, hyperlipidemia medication use, triglycerides, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, hypertension medication use, systolic blood pressure, diastolic blood pressure, and smoking history were extracted as confounding factors. Additionally, height, weight, urinary protein (qualitative), and serum creatinine values were extracted from the health examination data for each fiscal year. Body surface area (Male: Dubois formula: height\u003csup\u003e0.725\u003c/sup\u003e \u0026times; weight\u003csup\u003e0.425\u003c/sup\u003e \u0026times; 0.007184), BMI (weight [kg] \u0026divide; (height [m])\u003csup\u003e2\u003c/sup\u003e), standardized eGFR (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) (194 \u0026times; serum creatinine\u003csup\u003e\u0026minus;\u0026thinsp;1.094\u003c/sup\u003e \u0026times; age\u003csup\u003e\u0026minus;\u0026thinsp;0.287\u003c/sup\u003e, Female: Male \u0026times; 0.739), and individualized eGFR (mL/min) (standardized eGFR \u0026times; body surface area/1.73) were calculated. This individualization was aimed at accounting for the potential weight loss caused by the administration of anticancer drugs.\u003c/p\u003e\u003cp\u003eFrom the claims data of Panasonic Health Insurance Organization members from fiscal years 2017 to 2021, patients with cancer who received anticancer drug treatment were designated as the cancer person (CP) group, while the rest were classified as the non-cancer person (NCP) group. Health examination data for the CP group were analyzed over a period spanning from year F to year F\u0026thinsp;+\u0026thinsp;4. For the NCP group, the fiscal year 2018 was defined as year F, and health examination data from F to F\u0026thinsp;+\u0026thinsp;4 (fiscal year 2022) were similarly analyzed. The selection criterion was the availability of all the study data for year F. The exclusion criteria were as follows: (1) absence of any health examination during the period from F\u0026thinsp;+\u0026thinsp;1 to F\u0026thinsp;+\u0026thinsp;4, (2) undergoing a health examination without serum creatinine data, and (3) missing data for any of the confounding factors in the year F health checkup data. Based on the CP group data, the NCP group data were extracted via propensity score matching using nine variables from the health examination data based on the year F baseline data. The nine variables were age, sex, BMI, standardized eGFR (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e), qualitative urinary protein (- to 3+), smoking history (never smoked, former smoker, current smoker), hypertension (present/absent), hyperlipidemia (present/absent), and diabetes (present/absent). Hypertension was defined as taking medications for hypertension, systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg. The criteria for hyperlipidemia were defined as the use of hyperlipidemia medication, triglyceride levels\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL, HDL cholesterol levels\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL, or LDL cholesterol levels\u0026thinsp;\u0026ge;\u0026thinsp;140 mg/dL. Diabetes was defined as either the use of diabetes medication or having both HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5% and fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL. The anticancer drugs used in the CP group were also investigated.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEvaluation Methods\u003c/h3\u003e\n\u003cp\u003eFor both the CP and NCP groups, changes in the median standardized eGFR (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) and individualized eGFR (mL/min) by fiscal year from F to F\u0026thinsp;+\u0026thinsp;4 were analyzed. Moreover, based on a previous report indicating that age-related changes in kidney tissue volume differ after the age of 50 years [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and considering the importance of assessing the long-term impact on kidney function in younger individuals, changes in standardized and individualized eGFR were investigated by dividing the subjects into two age groups: \u0026le; 50 and \u0026ge;\u0026thinsp;51 years. Furthermore, the rate of change in eGFR per year from F to year F\u0026thinsp;+\u0026thinsp;4 was also investigated in each group.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003ePropensity scores were estimated using logistic regression analysis, incorporating the nine aforementioned variables as covariates. A 1:1 matched-pair analysis was conducted based on the propensity scores, with the caliper width set at 0.2 of the standard deviation of the logit of the propensity score. For differences in personal backgrounds, the Mann\u0026ndash;Whitney U test was applied for continuous variables, and Fisher\u0026rsquo;s exact test or chi-square test was used for categorical variables. The Kruskal\u0026ndash;Wallis test followed by Steel's multiple comparison test was used to compare the standardized and individualized eGFR between the CP and NCP groups in years F and years F\u0026thinsp;+\u0026thinsp;1 to F\u0026thinsp;+\u0026thinsp;4. The annual rates of change in both standardized and individualized eGFR from baseline to year 4 were compared between the CP and NCP groups using a linear mixed effects model. The fixed effects included group, time (F to F\u0026thinsp;+\u0026thinsp;4 years), and their interactions, whereas the random effects accounted for subject-specific intercepts and slopes. All statistical analyses were performed using EZR ver1.65 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and statistical significance was set at \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in two-sided tests.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePerson Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 506 individuals diagnosed with cancer between April 1, 2017, and March 31, 2022, received anticancer treatment and had available eGFR data either in year F or during the follow-up period from years F+1 to F+4. Among these, 397 persons had complete data for all confounding variables in the year F health checkup data. After propensity score matching of these cases with non-cancer cases in the claims data, 395 cases were extracted from both the CP and NCP groups (Fig.1). The age distribution in the CP group (median: 52 years; range: 23\u0026ndash;68 years) was similar to that of the NCP group (median: 52 years; range: 22\u0026ndash;69 years). There was no significant difference in standardized eGFR between the CP group (median: 76.5 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, range: 5.5\u0026ndash;124.2 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) and the NCP group (median: 75.1 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, range: 31.5\u0026ndash;127.9 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) (Table 1). In the CP group, 78 types of anticancer drugs were used, with the most common being cyclophosphamide (88 cases, 22.3%), paclitaxel (78 cases, 19.7%), oxaliplatin (64 cases, 16.2%), docetaxel (54 cases, 13.7%), carboplatin (43 cases, 10.9%), epirubicin (39 cases, 9.9%), doxorubicin (36 cases, 9.1%), fluorouracil (33 cases, 8.4%), tamoxifen (28 cases, 7.1%), trastuzumab (28 cases, 7.1%), gemcitabine (25 cases, 6.3%), cisplatin (25 cases, 6.3%), rituximab (19 cases, 4.8%), and bevacizumab (16 cases, 4.1%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYearly Analysis of Standardized and Individualized eGFR for the CP and NCP group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe number of eGFR data points per year (F/F+1/F+2/F+3/F+4) was 395/352/270/186/114 and 395/386/360/334/308 in the CP and NCP groups, respectively.\u003c/p\u003e\n\u003cp\u003eThe standardized eGFR (median: 10\u0026ndash;90% interval, mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e) for all data in the CP group in year F was 76.5 (61.5\u0026ndash;94.6) and significantly decreased in year F+4 to 72.3 (53.6\u0026ndash;93.1) (Fig. 2A, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.007). In contrast, the NCP group did not exhibit a significant decrease in year F+4 (72.4:58.8\u0026ndash;90.9) compared to that in year F (75.1:60.9\u0026ndash;95.0) (Fig. 2B; \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.068).For those aged 50 years and younger, the CP group demonstrated a significant decrease in standardized eGFR in year F+4 (72.7:55.8\u0026ndash;93.1) compared to year F (80.5:65.6\u0026ndash;98.9) (Fig. 2C, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.005). In contrast, the NCP group did not exhibit a significant decrease in year F+4 (76.2:61.0\u0026ndash;97.3) compared to year F (80.8:65.1\u0026ndash;101.5) (Fig. 2D, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.130). For those aged \u0026ge; 51 years, the CP group did not exhibit a significant decrease in standardized eGFR in year F+4 (71.3:53.3\u0026ndash;91.4) compared to year F (73.6:58.8\u0026ndash;93.7) (Fig. 2E, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.274). Furthermore, the NCP group did not exhibit a significant decrease in year F+4 (71.2:55.7\u0026ndash;85.2) compared to that in year F (71.3:59.1\u0026ndash;87.6) (Fig. 2F, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.119).\u003c/p\u003e\n\u003cp\u003eIn the individualized eGFR (median: 10\u0026ndash;90% interval, mL/min), all data in the CP group showed a significant decrease at year F+4 to 68.4 (51.1\u0026ndash;88.5) compared to year F (74.7:58.3\u0026ndash;95.0) (Fig. 3A, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). In contrast, no significant decrease was observed in year F+4 (70.1:54.8\u0026ndash;92.5) compared to year F (72.9:57.1\u0026ndash;94.4) for all data in the NCP group (Fig. 3B, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.097). For those aged 50 years and younger, the CP group demonstrated a significant decrease in year F+4 (68.5:50.0\u0026ndash;88.5) compared to year F (75.2:62.4\u0026ndash;98.6) (Fig. 3C, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001). In contrast, the NCP group did not exhibit a significant decrease in year F+4 (73.6:56.4\u0026ndash;97.4) compared to that in year F (78.3:59.0\u0026ndash;99.5) (Fig. 3D, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.347). For those aged \u0026ge; 51 years, the CP group did not exhibit a significant decrease in year F+4 (68.4:53.6\u0026ndash;86.5) compared to year F (74.0:56.8\u0026ndash;92.2) (Fig. 3E, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.083). Additionally, the NCP group did not show a significant decrease in year F+4 (67.2:54.0\u0026ndash;86.2) compared to that in year F (69.5:56.2\u0026ndash;88.9) (Fig. 3F, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.146).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChanges in Standardized and Individualized eGFR Per Year\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the linear mixed-effects model, which analyzed all data without considering age classification, there was no significant difference in the annual rate of decline in the standardized eGFR between the CP (-0.96 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e/year) and NCP groups (-0.67 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e/year) (\u003cem\u003ep\u003c/em\u003e = 0.141). However, the CP group (-1.14 mL/min/year) exhibited a significantly greater decrease in individualized eGFR than the NCP group (-0.68 mL/min/year) (\u003cem\u003ep\u003c/em\u003e = 0.015).\u003c/p\u003e\n\u003cp\u003eAge-specific analysis revealed that in patients aged \u0026le; 50 years, the CP group demonstrated a significantly faster decline in both standardized eGFR (CP group -1.33 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e/year, NCP group -0.66 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e/year, \u003cem\u003ep\u003c/em\u003e = 0.018) and individualized eGFR (CP group -1.35 mL/min/year, NCP group -0.65 mL/min/year, \u003cem\u003ep\u003c/em\u003e = 0.011). In contrast, for those aged \u0026ge; 51 years, no difference was observed between the CP and NCP groups in both the standardized eGFR (CP group -0.61 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e/year, NCP group -0.71 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e/year, \u003cem\u003ep\u003c/em\u003e = 0.715) and individualized eGFR (CP group -0.94 mL/min/year, NCP group -0.71 mL/min/year, \u003cem\u003ep\u003c/em\u003e = 0.392).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrated that cancer survivors exhibit a significantly greater annual decline in individualized eGFR than non-cancer survivors, particularly among individuals aged 50 years or younger. Both standardized and individualized eGFR values declined significantly in this age group, suggesting that cancer survivors, especially younger individuals, are at an increased risk of progressive kidney dysfunction following cancer treatment, even after adjusting for confounding variables. These findings align with those of previous reports, indicating that cancer survivors are susceptible to CKD and its sequelae [3,4]. Importantly, this study contributes new evidence by quantifying the annual rate of individualized eGFR decline in a large general population cohort, highlighting a vulnerable subgroup of cancer survivors who warrant targeted interventions.\u003c/p\u003e\n\u003cp\u003eSeveral mechanisms may explain the accelerated decline in kidney function. Anticancer pharmacotherapy plays a central role. In our cohort, nearly 80% of cancer survivors received cytotoxic chemotherapy, including cyclophosphamide, paclitaxel, and agents with well-recognized nephrotoxic potential, such as cisplatin [6,16\u0026ndash;18]. In addition to classic nephrotoxins, molecularly targeted agents (e.g., VEGF inhibitors and tyrosine kinase inhibitors) and immune checkpoint inhibitors have been increasingly implicated in glomerular and tubular injury, as well as immune-mediated renal disorders [7,19\u0026ndash;21]. These findings are consistent with reports of long-term CKD risk after AKI episodes induced by these therapies.\u003c/p\u003e\n\u003cp\u003eSecond, surgical intervention and cancer-related systemic effects may have contributed to renal dysfunction. The perioperative period represents a high-risk window for AKI, driven by surgical stress, hemodynamic instability, inflammation, and potential complications such as infection or sepsis [8,27,28]. AKI is a well-established risk factor for the development of CKD. Moreover, specific cancer-related metabolic disturbances, including tumor lysis syndrome in hematological malignancies and monoclonal protein deposition in multiple myeloma, can cause or exacerbate kidney injury [25,26]. However, these factors are often underrecognized in survivorship care, underscoring the need for comprehensive peri-treatment risk assessment.\u003c/p\u003e\n\u003cp\u003eThird, although aging and comorbidities such as hypertension and diabetes are well-known CKD risk factors [9,10,22\u0026ndash;24], the magnitude of eGFR decline observed in our cancer survivor cohort remained significant after adjusting for these variables using propensity score matching. Notably, the attenuated difference in renal decline in individuals aged \u0026gt; 50 years may reflect age-related structural changes, such as cortical volume loss and reduced renal mass, which may mask or confound the impact of cancer treatment in older adults [14].\u003c/p\u003e\n\u003cp\u003eOur findings have several significant clinical implications. Given the growing global population of cancer survivors, the early identification of individuals at high risk of kidney function decline is critical. Our study highlights the need for longitudinal kidney monitoring as an integral component of survivorship care plans, particularly in younger individuals who may face decades of increased CKD risk. Furthermore, multidisciplinary strategies, including nephrology consultations during cancer treatment planning, judicious use of nephrotoxic therapies, and proactive management of perioperative risks, could mitigate long-term renal sequelae.\u003c/p\u003e\n\u003cp\u003eImportantly, our findings identify key knowledge gaps in the literature. The inability to disaggregate the effects of cancer type, treatment regimens, or cumulative drug exposure limits our understanding of specific risk pathways. Future prospective studies incorporating granular clinical data, including cancer staging, detailed pharmacological profiles, surgical variables, and biomarkers of kidney injury, are needed to clarify the causal mechanisms and guide personalized prevention strategies.\u003c/p\u003e\n\u003cp\u003eThis study had some limitations. First, detailed clinical data, such as cancer type, stage, treatment duration, dosage of anticancer agents, and surgical details, were unavailable, limiting the ability to pinpoint the precise etiologies of kidney decline. Second, reliance on health checkup data may have introduced follow-up bias, as annual participation was not uniform. Third, we were unable to assess sarcopenia or body composition changes, which may have influenced the individualized eGFR estimates. Finally, residual confounding by unmeasured factors, such as nutritional status or socioeconomic conditions, cannot be excluded.\u003c/p\u003e\n\u003cp\u003eIn summary, our findings indicate that cancer treatment is associated with an accelerated annual decline in individualized eGFR, particularly among younger cancer survivors. These results reinforce the importance of long-term kidney monitoring and integrated survivorship care and highlight the need for prospective studies to delineate the specific contributions of anticancer therapies and surgical interventions to kidney function decline in childhood cancer survivors.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e The authors declare no competing financial or non-financial interests that could influence the results or discussion reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e All authors contributed to the conception and design of the study. HW obtained claims and health checkup data as pseudonymized processed information from Panasonic Health Insurance Organization. HW also organized the data to the minimum necessary for sharing with university co-researchers, providing information only in the form of representative values and dispersions without sharing personally identifiable information. Data analysis and evaluation were performed by HW, MT, RI, YM, HO, YH and MF. Specifically, HO, YH, and MF contributed to the study planning, data analysis, and evaluation by leveraging their clinical research experience using big data. HW wrote the first draft of the manuscript, and all authors commented on the previous versions of the manuscript. All authors have read and approved the final version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e Statement The datasets generated and/or analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e This study was conducted in accordance with the Declaration of Helsinki and the “Ethical Guidelines for Life Science and Medical Research Involving Human Subjects” issued by the Ministry of Health, Labor and Welfare of Japan. The study protocol was reviewed and approved by the ethics committees of the following institutions.\u003c/p\u003e\n\u003cp\u003ePanasonic Health Insurance Organization Health Management Center (Approval No. 2024-003)\u003c/p\u003e\n\u003cp\u003eKyoto Pharmaceutical University (Approval No. E-00047)\u003c/p\u003e\n\u003cp\u003eKyoto Prefectural University of Medicine (Approval No. ERB-C-3162)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e This was a retrospective cohort study that used pseudonymized data. Given the nature of this study, obtaining direct consent was deemed impractical. Informed consent was obtained through an opt-out approach approved by the ethics committees of all participating institutions (Approval Nos.: 2024-003, E-00047, and ERB-C-3162).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e The authors affirm that this manuscript does not contain any data that would require specific consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMatsuoka YJ, Okubo R, Shimizu Y, Tsuji K, Narisawa T, Sasaki J, et al. (2020) Developing the structure of Japan\u0026apos;s cancer survivorship guidelines using an expert panel and modified Delphi method. J Cancer Surviv 14:273-283. https://doi.org/10.1007/s11764-019-00840-3\u003c/li\u003e\n \u003cli\u003eKeats MR, Cui Y, DeClercq V, Grandy SA, Sweeney E, Dummer TJB (2021) Burden of multimorbidity and polypharmacy among cancer survivors: a population-based nested case-control study. Support Care Cancer 29: 713-23. https://doi.org/10.1007/s00520-020-05529-3\u003c/li\u003e\n \u003cli\u003eNa SY, Sung JY, Chang JH, Kim S, Lee HH, Park YH, et al. (2011) Chronic kidney disease in cancer persons: an independent predictor of cancer-specific mortality. Am J Nephrol 33: 121-30. https://doi.org/10.1159/000323740\u003c/li\u003e\n \u003cli\u003eShin HY, Linton JA, Shim JY, Kang HT. (2015) Cancer survivors aged 40 years or elder are associated with high risk of chronic kidney disease: the 2010-2012 Korean National Health and Nutrition Examination Survey. Asian Pac J Cancer Prev 16:1355-60. https://doi.org/10.7314/apjcp.2015.16.4.1355\u003c/li\u003e\n \u003cli\u003eCortaredona S, Ventelou B (2017) The extra cost of comorbidity: multiple illnesses and the economic burden of non-communicable diseases. BMC Med 15:216. https://doi.org/10.1186/s12916-017-0978-2\u003c/li\u003e\n \u003cli\u003eBhat ZY, Cadnapaphornchai P, Ginsburg K, Sivagnanam M, Chopra S, Treadway CK, et al. (2015) Understanding the Risk Factors and Long-Term Consequences of Cisplatin-Associated Acute Kidney Injury: An Observational Cohort Study. Plos One. 10:e0142225. https://doi.org/10.1371/journal.pone.0142225\u003c/li\u003e\n \u003cli\u003eIzzedine H, Escudier B, LhommeC, Pautier P, Rouvier P, Gueutin V, et al. \u0026nbsp;(2014) Kidney diseases associated with anti-vascular endothelial growth factor (VEGF): an 8-year observational study at a single center. Medicine (Baltimore). 93:333-9. https://doi.org/10.1097/MD.0000000000000207\u003c/li\u003e\n \u003cli\u003eGameiro J, Neves JB, Rodrigues N, Bekerman C, Melo MJ, Pereira M, et al. (2016) Acute kidney injury, long-term renal function and mortality in patients undergoing major abdominal surgery: a cohort analysis. Clin Kidney J. 9:192-200. https://doi.org/10.1093/ckj/sfv144\u003c/li\u003e\n \u003cli\u003eGBD Chronic Kidney Disease Collaboration (2020) Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 395:709-733. https://doi.org/10.1016/S0140-6736(20)30045-3\u003c/li\u003e\n \u003cli\u003eChen TK, Knicely DH, Grams ME (2019) Chronic Kidney Disease Diagnosis and Management: A Review. JAMA. 322:1294-304. https://doi.org/10.1001/jama.2019.14745\u003c/li\u003e\n \u003cli\u003eKosugi T, Eriguchi M, Yoshida H, Tasaki H, Fukata F, Nishimoto M, et al. (2021) Association between chronic kidney disease and new-onset dyslipidemia: The Japan Specific Health Checkups (J-SHC) study. Atherosclerosis. 332:24-32. https://doi.org/10.1016/j.atherosclerosis.2021.08.004\u003c/li\u003e\n \u003cli\u003eRemuzzi G, Ruggenenti P, Benigni A (1997) Understanding the nature of renal disease progression. Kidney Int. 51:2-15. https://doi.org/10.1038/ki.1997.2\u003c/li\u003e\n \u003cli\u003eAlebiosu CO (2003) An update on \u0026apos;progression promoters\u0026apos; in renal diseases. J Natl Med Assoc. 95:30-42. https://doi.org/10.1016/s0027-9684(15)30874-x\u003c/li\u003e\n \u003cli\u003eWang X, Vrtiska TJ, Avula RT, Walters LR, Chakkera HA, Kremers WK, et al. (2014) Age, kidney function, and risk factors associate differently with cortical and medullary volumes of the kidney. Kidney Int. 85:677-85. https://doi.org/10.1038/ki.2013.359\u003c/li\u003e\n \u003cli\u003eKanda Y (2013) Investigation of the freely available easy-to-use software \u0026apos;EZR\u0026apos; for medical statistics. Bone Marrow Transplant. 48:452-58. https://doi.org/10.1038/bmt.2012.244\u003c/li\u003e\n \u003cli\u003eGork I, Xiong F, Kitchlu A (2024) Cancer drugs and acute kidney injury: new therapies and new challenges. Curr Opin Nephrol Hypertens. 33:474-85. https://doi.org/10.1097/MNH.0000000000001001\u003c/li\u003e\n \u003cli\u003eRies F, Klastersky J (1986) Nephrotoxicity induced by cancer chemotherapy with special emphasis on cisplatin toxicity. Am J Kidney Dis. 8:368-79. https://doi.org/10.1016/s0272-6386(86)80112-3\u003c/li\u003e\n \u003cli\u003eAbbas A, Mirza MM, Ganti AK, Tendulkar K (2015) Renal Toxicities of Targeted Therapies. Target Oncol. 10:487-99. https://doi.org/10.1007/s11523-015-0368-7\u003c/li\u003e\n \u003cli\u003eEstrada CC, Maldonado A, Mallipattu SK (2019) Therapeutic Inhibition of VEGF Signaling and Associated Nephrotoxicities. J Am Soc Nephrol. 30:187-200. https://doi.org/10.1681/ASN.2018080853\u003c/li\u003e\n \u003cli\u003eXiong Y, Wang Q, Liu Y, Wei J, Chen X (2022) Renal adverse reactions of tyrosine kinase inhibitors in the treatment of tumours: A Bayesian network meta-analysis. Front Pharmacol. 13:1023660. https://doi.org/10.3389/fphar.2022.1023660\u003c/li\u003e\n \u003cli\u003eHerrmann SM, Perazella MA (2020) Immune Checkpoint Inhibitors and Immune-Related Adverse Renal Events. Kidney Int Rep. 5:1139-48. https://doi.org/10.1016/j.ekir.2020.04.018\u003c/li\u003e\n \u003cli\u003eLindeman RD, Tobin J, Shock NW (1985) Longitudinal studies on the rate of decline in renal function with age. J Am Geriatr Soc. 33:278-85. https://doi.org/10.1111/j.1532-5415.1985.tb07117.x\u003c/li\u003e\n \u003cli\u003eJohnson TM 2nd, Sands JM, Ouslander JG (2002) A prospective evaluation of the glomerular filtration rate in older adults with frequent nighttime urination. J Urol. 167:146-50. https://doi.org/10.1097/00005392-200201000-00027\u003c/li\u003e\n \u003cli\u003eCohen E, Nardi Y, Krause I, Goldberg E, Milo G, Garty M, et al. (2014) A longitudinal assessment of the natural rate of decline in renal function with age. J Nephrol. 27:635-41. https://doi.org/10.1007/s40620-014-0077-9\u003c/li\u003e\n \u003cli\u003eBarbar T, Sathick IJ (2021) Tumor Lysis Syndrome. Adv Chronic Kidney Dis. 28:438-46. https://doi.org/10.1053/j.ackd.2021.09.007\u003c/li\u003e\n \u003cli\u003eHutchison CA, Batuman V, Behrens J, Bridoux F, Sirac C, Dispenzieri A, et al. (2011) The pathogenesis and diagnosis of acute kidney injury in multiple myeloma. Nat Rev Nephrol. 8:43-51. https://doi.org/10.1038/nrneph.2011.168\u003c/li\u003e\n \u003cli\u003eHobson CE, Yavas S, Segal MS, Schold JD, Tribble CG, Laskowski D, et al. (2009) Acute kidney injury is associated with increased long-term mortality after cardiothoracic surgery. Circulation. 119: 2444-53. https://doi.org/10.1161/CIRCULATIONAHA.108.800011\u003c/li\u003e\n \u003cli\u003eBagshaw SM, Uchino S, Bellomo R, Morimatsu H, Morgera S, Schetz M, et al. (2007) Septic acute kidney injury in critically ill patients: clinical characteristics and outcomes. Clin J Am Soc Nephrol. 2:431-39. https://doi.org/10.2215/CJN.03681106\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Person\u0026rsquo;s characteristics for CP and NCP groups after propensity score matching\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 395\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNCP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 395\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003eMedian age [range] \u0026mdash; years old\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;52 [23, 68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;52 [22, 69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.97\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003eMale sex \u0026mdash; no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e207 (52.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e203 (51.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.83\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003eMedian BMI [range] \u0026mdash; kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e22.7 [15.0, 39.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e22.8 [16.0, 41.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.94\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003eMedian eGFR [range] \u0026mdash; mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e76.5 [5.5, 124.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e75.1 [31.5, 127.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.14\u003csup\u003ea)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 251px;\"\u003e\n \u003cp\u003eUrinary protein \u0026mdash; no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e374 (94.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e373 (94.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.33\u003csup\u003ec)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e+/-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e8 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e15 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e8 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e5 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e4 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e2 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003eHypertension \u0026mdash; no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;96 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;101 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.74\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003eHyperlipidemia \u0026mdash; no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;185 (46.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e189 (47.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.83\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 251px;\"\u003e\n \u003cp\u003eDiabetes \u0026mdash; no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026nbsp;28 (7.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e30 (7.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.89\u003csup\u003eb)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 251px;\"\u003e\n \u003cp\u003eSmoker \u0026mdash; no. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e222 (56.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e232 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e0.30\u003csup\u003ec)\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e83 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e66 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e90 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e97 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHypertension: medication present or systolic blood pressure \u0026ge; 140 mmHg or diastolic blood pressure \u0026ge; 90 mmHg\u003c/p\u003e\n\u003cp\u003eHyperlipidemia: medication present or TG \u0026ge; 150 mg/dL or HDL \u0026lt; 40 mg/dL OR LDL \u0026ge; 140 mg/dL\u003c/p\u003e\n\u003cp\u003eDiabetes: medication present or HbA1c \u0026ge; 6.5 mg/dL and fasting blood glucose \u0026ge; 126 mg/dL\u003c/p\u003e\n\u003cp\u003ea) Mann-Whitney U test, b) Fisher\u0026apos;s exact test, c) chi-square test\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"chronic kidney disease, cancer survivor, working age, kidney function, claims and health checkup data","lastPublishedDoi":"10.21203/rs.3.rs-7527080/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7527080/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eCancer survivors have an elevated risk of chronic kidney disease (CKD); however, long-term alterations in kidney function among working-age individuals remain underexplored. This study aimed to evaluate differences in the rate of kidney function decline between working-age cancer survivors who received anticancer drug treatment and non-cancer survivors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study used data from the Panasonic Health Insurance Organization (2017\u0026ndash;2021). The cancer person (CP) and non-cancer person (NCP) groups were defined based on cancer diagnosis and anticancer drug treatment, respectively. Propensity score matching aligned demographics and health factors. Standardized and individualized eGFR changes from the year before diagnosis (Year F) to F\u0026thinsp;+\u0026thinsp;4 were evaluated across age groups (\u0026le;\u0026thinsp;50 and \u0026ge;\u0026thinsp;51 years).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eEach group comprised 395 matched individuals. The CP group demonstrated significant decreases in both standardized and individualized eGFR by F\u0026thinsp;+\u0026thinsp;4 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively), whereas the NCP group showed no significant decline. The decline was particularly significant in CP\u0026thinsp;\u0026le;\u0026thinsp;50 years (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not in those\u0026thinsp;\u0026ge;\u0026thinsp;51 years. Linear mixed-effects models confirmed a significantly larger annual decline in individualized eGFR in the CP group, particularly among younger individuals.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eWorking-age cancer survivors, particularly those aged\u0026thinsp;\u0026le;\u0026thinsp;50 years, face a significantly higher risk of impaired kidney function than non-cancer survivors. These findings highlight the need for long-term kidney monitoring and protective strategies, especially among younger cancer survivors, to mitigate CKD progression and preserve quality of life and employment outcomes.\u003c/p\u003e","manuscriptTitle":"Kidney function decline in working-age adults with cancer diagnosis: A study utilizing claims and health checkup data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-24 11:42:05","doi":"10.21203/rs.3.rs-7527080/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-22T22:58:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-22T21:55:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T14:01:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"218102367184469073282059169789729907443","date":"2026-02-02T22:12:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99236060755331346433628209126251810954","date":"2026-01-14T09:37:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-10T15:13:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-10T15:08:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T01:27:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Supportive Care in Cancer","date":"2025-09-03T12:26:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3e2a5735-0457-490a-919a-3096c5fd9fe6","owner":[],"postedDate":"October 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:23:49+00:00","versionOfRecord":{"articleIdentity":"rs-7527080","link":"https://doi.org/10.1007/s00520-026-10615-z","journal":{"identity":"supportive-care-in-cancer","isVorOnly":false,"title":"Supportive Care in Cancer"},"publishedOn":"2026-03-27 16:13:00","publishedOnDateReadable":"March 27th, 2026"},"versionCreatedAt":"2025-10-24 11:42:05","video":"","vorDoi":"10.1007/s00520-026-10615-z","vorDoiUrl":"https://doi.org/10.1007/s00520-026-10615-z","workflowStages":[]},"version":"v1","identity":"rs-7527080","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7527080","identity":"rs-7527080","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}