Protocol for a Prospective Cohort Study Investigating Premature Aging Phenotypes and Associated Outcomes in Maintenance Hemodialysis Patients: the Kumamoto Hemodialysis Cohort Study

Jun Morinaga; Sayaka Shimizu; Yoshikazu Miyasato; Eiji Matsunaga; Hirotaka Fukami; Yoshihiro Onishi; Tatsuyuki Kakuma; Teruhiko Mizumoto; Yutaka Kakizoe; Yuichiro Izumi; Masataka Adachi; Takashige Kuwabara; Yuichi Oike; Hideki Yokoi; Masashi Mukoyama; on behalf of the Kumamoto Hemodialysis Cohort Study

doi:10.37737/ace.26014

ABSTRACT

BACKGROUND

The incidence of end-stage kidney disease (ESKD) caused by chronic kidney disease is increasing worldwide. Patients with ESKD often exhibit significant premature aging and have a higher risk of premature mortality than the general population. Here, we conducted a prospective cohort study, the Kumamoto Hemodialysis Cohort Study, to explore the mechanisms of the premature aging phenotype exhibited by these patients using biological markers and highly detailed clinical information in a real-world setting.

METHODS

This multicenter prospective cohort study targeted adult outpatients receiving maintenance hemodialysis therapy in Kumamoto Prefecture, Japan. After enrollment, the patients were followed up for 10 years. Data were collected from the electronic medical records of medical facilities that participated retrospectively and prospectively from January 1, 2017. Clinical information was collected from electronic medical records, medical chart reviews, and patient-completed questionnaires. Serum and plasma samples were collected every 3 years. Thus far, 1,241 patients have been enrolled in this study, and we found the median age of the participants was 69 years, and 67.5% were male. Among the primary causes of kidney failure, diabetic nephropathy accounted for 38.6% of all cases, followed by glomerulonephritis (27.2%) and nephrosclerosis (9.9%). The median hemodialysis vintage was 5.8 years.

CONCLUSION

This study contributes to the characterization of a premature aging phenotype together with morbidity and mortality of patients with ESKD, thereby providing insights into the prevention of the onset of premature aging-related diseases in patients receiving maintenance hemodialysis therapy.

INTRODUCTION

The number of patients with end-stage kidney disease (ESKD) due to chronic kidney disease is increasing, and more than 340,000 patients will undergo maintenance hemodialysis in Japan by 2022¹⁾. Hemodialysis therapy successfully prolongs the survival rate of patients with ESKD. However, patients with ESKD often exhibit significant premature aging features, such as early onset of cardiovascular or cerebrovascular diseases, severe infectious diseases, and bone fractures, and have a higher risk of premature mortality than the general population^2–4). Its complex pathophysiology comprises multiple factors, including chronic exposure to highly concentrated circulating uremic toxins, intravital fluid volume changes, chronic hypoxia, renal anemia, electrolyte imbalance such as hyperphosphatemia, and chronic inflammation, all of which are accelerators of a premature aging phenotype in patients with ESKD^3),4). Several clinical studies in Japan have focused on the pathogenesis of early death due to premature aging in patients undergoing maintenance hemodialysis^5–7). These studies have provided crucial evidence for improving patient complications and mortality related to uremic conditions, and the treatment guidelines for hemodialysis therapy have been updated accordingly. Although most clinical studies have attempted to collect information about clinical parameters at shorter observation intervals, the observation intervals have been restricted to months or years due to limited human and financial resources. Consequently, there is a definite need for observational studies that gather highly detailed clinical information at more frequent intervals, such as hours or days, in the field of clinical epidemiology, to target patients receiving maintenance hemodialysis. Moreover, incorporating biological samples, such as serum and plasma, alongside hourly or daily clinical data, may significantly enhance the applicability and importance of the study. Biological samples enable the exploration of biomarkers that are poorly defined during study initiation but may have crucial roles in the progression of the aging phenotype of patients.

In the present report, we describe the protocol of an observational study entitled the Kumamoto Hemodialysis Cohort Study, which targets patients receiving maintenance hemodialysis treatment in Kumamoto Prefecture, Japan. We investigated the premature aging phenotype exhibited by ESKD patients using biological samples and highly detailed clinical information collected at hourly or daily observation intervals in a real-world setting. The objective of this study was to identify significant biological factors that affect the lifespan and onset of diseases related to the premature aging phenotype in patients receiving hemodialysis. This study was also designed to analyze data collected at frequent time intervals to assess various other clinical questions and exploratory biomarker studies, as needed.

METHODS

STUDY DESIGN AND PARTICIPANTS

This is a multicenter prospective cohort study of patients undergoing maintenance hemodialysis in Kumamoto Prefecture, Japan. Enrollment for this study was initiated in December 2018 and will continue until June 2026. The recruitment period of the participants was set according to the protocol of a Japanese multicenter observational study named the Alliance for Quality Assessment in Healthcare-Dialysis (AQuAH-D)⁸⁾, which was conducted in parallel with this study. Beginning at the time of study enrollment, all participants are to be followed up for 10 years. In addition, clinical information was collected retrospectively from January 1, 2017, using electronic medical records from the participating facilities that provided medical care for each participant enrolled in this study (Fig. 1). To assess which clinical or biological parameters of the patients were related to the onset of the outcomes indicated below, we set the inclusion criterion as outpatients aged 20 years or older. The exclusion criteria were as follows: (1) patients with acute pathological conditions, including sepsis, cerebrovascular diseases, or life-threatening conditions; (2) patients receiving peritoneal dialysis therapy, including combination therapy with hemodialysis; and (3) patients who refused study enrollment by informed consent or were considered incapable of expressing their own will due to impaired consciousness or cognitive function at the time of study enrollment. However, patients who met exclusion criteria were treated as if they could be enrolled again when they no longer met any of these criteria. Based on these exclusion criteria, the target population of this study at the time of enrollment is focused on patients with stable clinical conditions.

Fig. 1 Study design of the Kumamoto hemodialysis cohort study

From the time of study enrollment, the clinical information of study participants is collected prospectively and planned to be followed for up to 10 years. In addition, clinical information from January 2017 was also collected from the facility that provided medical care for each participant at the time of study enrollment. (A and B) Patients who have been medically cared for by the medical facility of study enrollment prior to January 2017, and (A) prospectively followed for up to 10 years, or (B) could not prospectively be followed for up to 10 years. (C and D) Patients who have been cared for by the medical facility later than January 2017, and (C) prospectively followed for up to 10 years, or (D) could not be prospectively followed for up to 10 years. (E and F). Patients who started to receive hemodialysis at the medical facility upon hospital transfer or induction of dialysis, and (E) prospectively followed for up to 10 years, or (F) could not be prospectively followed for up to 10 years.

COLLECTION OF CLINICAL INFORMATION

Clinical information was collected from three sources: electronic medical records used in clinical practice, medical chart reviews, and patient-completed questionnaire surveys. To collect highly dense clinical information from the electronic medical records, we use an application termed AQuAH-D⁸⁾. We have previously reported the use of this application⁸⁾. In brief, electronic medical information used in clinical practice, such as routine periodic blood exams, records of each hemodialysis session, and prescriptions, are collected semiautomatically using this application⁸⁾. Information about patient lifestyle habits, history, and clinical events, such as hospital admissions that have been recorded in the patient referral document, are transcribed to the case report form by trained clinical research coordinators or medical doctors. Trained clinical research coordinators conducted patient-completed questionnaires at enrolment.

The clinical information collected through the AQuAH-D application at each medical facility is anonymized, encrypted for personal information protection, and transmitted to the data center twice a year. After data management and queries, the dataset for analysis are distributed back to the facilities. In the current study, a wide range of significant clinical data related to outcomes is to be analyzed, but not all available information due to the study’s feasibility study, including the necessity of data cleaning. In addition, the clinical data in this study may have included missing information. To address this problem, statistical models with full-information maximum likelihood or appropriate imputation methods, including multiple imputations, would be useful. A summary of all the clinical parameters used in this prospective cohort study is presented in Table 1.

Table 1 Summary of collected clinical parameters or biological specimens in the Kumamoto hemodialysis cohort study

Parameters	Electronic information	Chart review	Questionnaire	Frozen sample	Timing of data collection
Characteristics of patients					Start of observation*
Year and month of birth	✓
Sex	✓
Cause of kidney failure	✓	✓
Date of hemodialysis initiation	✓	✓
Height	✓	✓
Vascular access	✓	✓			Start of observation and status changes
Comorbidities	✓	✓
Living status		✓
Smoking status		✓
Employment status			✓		Study enrollment
Alcohol consumption			✓
Risk score for fails**			✓
Clinical practice-related data
Procedures (examinations, interventions, prescriptions)	✓				Every visit from start of observation
Hemodialysis prescription	✓				Every visit from start of observation
Data on hemodialysis results
Vital sign during hemodialysis	✓				Every visit from start of observation
Body weight before and after hemodialysis	✓				Every visit from start of observation
Laboratory data
Items measured in daily practice	✓				All tests from start of observation
Echocardiographic finding	✓
Body composition (Bioelectrical impedance analysis)	✓
Outcomes
Patient-reported outcomes			✓		Study enrollment and annual
General QOL: QGEN-10			✓
Disease-specific QOL: QDIS			✓
Symptom scale in KDQOL			✓
Receipt based health insurance claims	✓				Monthly during observation
Events
Death		✓			When the outcome occurs from start of observation
Hospitalization		✓
Vascular access intervention		✓
Kidney transplantation		✓
Transfer to peritoneal dialysis or home dialysis		✓
Transfer to another facility		✓
Discontinuation of hemodialysis		✓
Biological specimen
Serum				✓	Study enrollment and every 3 years
Plasma				✓	Study enrollment and every 3 years

* The start of observation was defined for each participant as January 1, 2017, or the first day of hemodialysis for the participant, whichever was later (Figure 2). ** denotes risk score for falls²³⁾. KDQOL, Kidney Disease Quality of Life instrument¹¹⁾; QDIS, QOL Disease Impact Scale¹⁰⁾; QGEN-10, Quality of Life General-10⁹⁾; QOL, quality of life.

OUTCOMES

The outcomes recorded in this study were clinical events, including death, hospitalization, and vascular access interventions. In particular, the causes of death or hospitalization, including acute myocardial infarction, angina pectoris, congestive heart failure, arrhythmia, cerebral infarction, cerebral hemorrhage, peripheral artery diseases, aortic disease, infectious diseases, hemorrhagic diseases, malignancies, uremia, and liver diseases or others, which are supposed to be associated with premature aging phenotypes of patients receiving hemodialysis, were also collected. As exploratory outcomes, patient-reported outcomes using the Quality of Life General-10⁹⁾, the QOL Disease Impact Scale¹⁰⁾, and the Kidney Disease Quality of Life instrument¹¹⁾ were measured at the time of enrollment and annually thereafter. Monthly receipt-based health insurance claims data during the observational period were also used for exploratory analyses. A summary is provided in Table 1.

SERUM AND PLASMA SAMPLING

In addition to clinical information, serum and plasma samples are collected at the time of enrollment and 3 and 6 years after enrollment. Delays in blood sampling of less than one year were allowed during the COVID-19 pandemic. Whole blood samples are collected into tubes with a coagulation accelerator for serum separation or powdered ethylenediaminetetraacetic acid for plasma separation and centrifuged at 1,700 × g for 10 min. Finally, components of serum and plasma are dispensed and stored at −80°C conditions at Kumamoto University. As for biomarkers, we plan to measure the serum levels of angiopoietin-like protein 2 as a possible marker of premature aging^12–14).

FOLLOW-UP

In principle, we started data collection at the time of study enrollment and continued to prospectively follow up the patients for 10 years. Concurrently, clinical information from January 1, 2017, was collected retrospectively from the electronic medical records of the facilities providing medical care for each participant at the time of study enrollment. The observation period will conclude upon patient death, kidney transplantation, withdrawal of consent, transfer to another medical facility, discontinuation of dialysis, or transfer home or peritoneal dialysis. For cases with truncated follow-up or the presence of competing risks for outcomes, appropriate survival models, including parametric/semiparametric models or competing survival models based on Fine and Gray’s method, would be applied in future statistical analyses.

ETHICAL REVIEW

Our study design, comprising the singular and collective use of epidemiological data and biological materials (serum and plasma), was approved in July 2018 by the Ethical Board of the Faculty of Life Sciences, Kumamoto University (Ethical Approval Number: 1529).

RESULTS

From January 2018 to May 2022, 1,241 patients from three hospitals and 10 clinics were enrolled in the Kumamoto Hemodialysis Cohort Study. During study recruitment, the number of enrolled patients decreased following the COVID-19 pandemic¹⁵⁾ due to difficulties in conducting clinical research amid the management of infectious diseases at medical facilities. Patient characteristics at the time of study enrollment are shown in Table 1. A summary comparing the clinical data of the present patient cohort with those recorded in the Japanese Society for Dialysis Therapy (JSDT) database in 2020¹⁶⁾, which reflects the national cohort undergoing dialysis therapy in Japan, is shown in Table 2. The median patient age was 69 years, and 67.5% of the patients were male. Among the various primary diseases leading to kidney failure, diabetic nephropathy accounted for 38.6% of all cases, followed by glomerulonephritis (27.2%) and nephrosclerosis (9.9%). The median duration of hemodialysis therapy was 5.8 years.

Table 2 Clinical background of study participants

		Kumamoto hemodialysis cohort			JSDT renal data registry (2020)¹⁶⁾
No of patients		1241	—		347671
Age		1241	69	[60, 76]	—	—
		1241	67.5	(12.4)	69	(12.5)
Sex male		1241	838	67.5%	222510	66.1%
Cause of kidney failure		1241	—
	Diabetic nephropathy		479	38.6%	133103	39.5%
	Glomerulonephritis		337	27.2%	85139	25.3%
	Nephrosclerosis		123	9.9%	40831	12.1%
	Other diseases		302	24.3%	—	—
Duration of hemodialysis (years)		1241	5.8	[1.9, 11.7]	—	—
		1241	8.7	(8.9)	7.4	(7.7)
Blood access		1241	—
	Arteriovenous fistula		1053	84.8%	—	—
	Arteriovenous graft		88	7.1%	—	—
	Other		100	8.1%	—	—
Comorbidity
	Coronary artery disease	1239	288	23.2%	—	—
	Diabetes mellitus	1240	596	48.1%	—	—
	Lung disease	1240	121	9.8%	—	—
	Liver disease	1240	98	7.9%	—	—
	Malignancy	1239	214	17.3%	—	—
Type of dialysis treatment		1196
	Hemodialysis		920	76.9%	171324	49.3%
	Hemodiafiltration		276	23.1%	163825	47.1%
Single pool Kt/V		989	1.6	[1.4, 1.8]	—	—
			1.7	(0.4)
Laboratory data
	Hemoglobin (g/dl)	1172	11.0	[10.3, 11.7]	—	—
		1172	11.0	(1.2)	—	—
	Serum albumin (g/dl)	1174	3.7	[3.5, 3.9]	—	—
		1174	3.7	(0.3)	—	—
	Serum phosphorus (mg/dl)	1177	5.1	[4.2, 5.9]	—	—
		1177	5.1	(1.3)	—	—
	Corrected serum calcium (mg/dl)	1177	8.8	[8.4, 9.2]	—	—
		1177	8.8	(0.6)	—	—

Continuous variables are described by the median [inter quartile range] or the mean (standard deviation), and categorical variables are described by the number and percentage. Abbreviations: JSDT, The Japanese Society for Dialysis Therapy ; Kt/V, Single pool Kt/V values were calculated using the equation by Daugirdas²⁴⁾.

DISCUSSION

In the present report, we describe the initiation of a prospective cohort study of patients receiving maintenance hemodialysis therapy in Kumamoto Prefecture, Japan. In a strict sense, the target population of this Kumamoto Hemodialysis Cohort Study are outpatients receiving maintenance hemodialysis therapy in various medical facilities in Kumamoto Prefecture, Japan. Maintenance hemodialysis treatment for outpatients in Kumamoto Prefecture began in April 1970, and the dialysis treatment offered to this population has a history of more than 50 years in Kumamoto (based on written material published by the local patient association). Considering that the history of dialysis treatment in Japan began in 1967, the record of hemodialysis therapy in Kumamoto Prefecture is not very different from that of Japan, and hemodialysis therapy has been firmly rooted in this region. Currently, Kumamoto Prefecture has the second highest prevalence of ESKD patients receiving dialysis therapy in Japan¹⁾. Notably, a report from the United States Renal Data System suggested that Japan has the third-highest prevalence of ESKD worldwide^1),17), implying that this region is one of the most populated areas for patients receiving dialysis therapy in the world¹⁾. In addition to the clinical significance of investigating the mechanisms of premature aging in patients with ESKD, such social background may also support the feasibility of conducting this cohort study targeting patients receiving hemodialysis treatment in this region.

Although there are a large number of hemodialysis patients in Kumamoto Prefecture, there are no major differences in the background profiles of patients, including age, sex, cause of kidney failure, and hemodialysis history, between patients in the Kumamoto Hemodialysis Cohort Study and those in the JSDT renal data registry in 2020¹⁶⁾, suggesting the generalizability of the findings of our patient cohort to the population of patients receiving hemodialysis treatment in Japan. Furthermore, the clinical data of our patient cohort (Table 2) revealed that circulating levels of hemoglobin, phosphate, and calcium were well controlled according to the JSDT^18),19), suggesting that patients in the Kumamoto cohort were treated according to the standard hemodialysis protocol in their medical facilities. Based on these findings, the Kumamoto Hemodialysis Cohort Study may be preferred as a more representative study sample to explore the risk factors affecting the health conditions of patients receiving standardized hemodialysis therapy in Japan. The dataset from this study offers opportunities to investigate the effects of various clinical backgrounds, such as diabetes-related complications and hyperphosphatemia, on the outcomes indicated above^1),20),21). In addition, this study may facilitate the exploratory assessment of relationships between clinical parameters and quality of life maintenance or medical costs, as well as circulating biomarkers and aging-associated phenotypes or outcomes^12),13),22). To explore the significant biological factors in serum or plasma samples for predicting patient mortality or the onset of premature aging features, including cardiovascular diseases, the evaluation of research hypotheses based on molecular biology or molecular selection studies of circulating factors in the preclinical phase may be implemented.

This study has some limitations. First, the sample size of this cohort was determined based on study feasibility and is currently limited. However, we successfully collected serum, plasma, and dense electronic medical information, as well as accurate information based on medical information sheets about clinical events such as the onset of cardiovascular diseases, hospitalization, or death. In cases of exploratory biomarker study, statistical power based on the Cox regression models was estimated to be kept ≥0.8 if the absolute values of coefficient β at baseline was ≥0.24 [Number of subject: 1200, Event rate of death: 0.15, alpha <0.05 (two-sided), R-squared of the biomarker of interest with other parameter’s: ≤0.20, β for 1SD increases in continuous variables., (PASS 14. software (Kaysville, Utah, USA)]. Second, the results of this study may not be generalizable to patients receiving hemodialysis treatment in the medical facilities of large cities, such as capital cities around the world, because Kumamoto Prefecture is a medium-sized city in terms of population density. The impact of the COVID-19 pandemic on the target population or medical facilities may have affected the generalizability of the study. Third, the percentage of patients receiving hemodialysis was higher than that in the JSDT database, while the percentage of patients receiving hemodiafiltration was lower than that in the JSDT database. These differences should be considered when interpreting our findings. Fourth, we acknowledge the presence of survivor bias, as the study design inherently excludes individuals who died shortly after dialysis initiation. This exclusion may have led to an underestimation of early risks and outcomes in the dialysis population. Further investigations with study designs that address these limitations are needed.

In conclusion, we initiated a prospective cohort study of outpatients receiving maintenance hemodialysis therapy in Kumamoto Prefecture, Japan. We believe that this study contributes to the identification of efficient strategies for controlling premature aging in this cohort.

CONFLICT OF INTEREST

The authors declare no competing interests relevant to the contents of this article.

FUNDING

This study was supported by grants from the Takeda Science Foundation 2020 to Jun Morinaga and the Japanese Association of Dialysis Physicians to Jun Morinaga (Research Grant Number: 2019-10).

ACKNOWLEDGMENTS

The authors thank N. Nakagawa, K. Saito, M. Horikiri, and N. Hirano for technical assistance. The authors are grateful to all participants for their participation in this study. We also thank all the medical facilities for their participation, including the Department of Nephrology, Kumamoto University Hospital, Akebono Clinic, Akebono Daini Clinic, Jinseikai Clinic Hikarinomori, Jinseikai Clinic Kurokami, Jinseikai Clinic Nagamine, Jinseikai Clinic Ozu, Uki Clinic, Jinseikai Clinic Shinyashiki, Kumamoto Urological Hospital, Shimada Hospital, Tamana Dai-ichi Clinic, Yamaga Chuo Hospital, and Yayamachi Fukushima Clinic. The authors also thank Dr. Katusya Uemura for providing historical information on dialysis therapy in Kumamoto Prefecture, Japan. The authors gratefully acknowledge the Institute for Health Outcomes and Process Evaluation Research (iHope International), Kyoto, Japan for administering the Alliance for Quality Assessment in Healthcare-Dialysis (AQuAH-D) questionnaire^16),23),24).

ABBREVIATIONS

ESKD: end-stage kidney disease

JSDT: Japanese Society for Dialysis Therapy

INFORMED CONSENT

Written informed consent was obtained from all the participants.

AUTHORS’ CONTRIBUTIONS

J. M., S. S., and Y. Onishi conceived the study. J. M., S. S., and T. K. designed the study. Y.M., E.M., and H.F. contributed to data acquisition. J.M. and Y. Onishi analyzed the data. J. M., E. M., H. F., T. M., Y. K., Y. I., M. A., T. K., Y. Oike, H. Y., and M. M. interpreted the data. J. M., S. S., and M. M. drafted the manuscript.

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