INTRODUCTION

End-stage renal disease (ESRD) is defined as a glomerular filtration rate (GFR) of less than 15 mL/min/1.73 m², requiring kidney transplantation or hemodialysis [1]. The estimated incidence of ESRD in the United States increased from 117,162 in 2013 [2] to 124,111 in 2015 [3]. In Korea, approximately 56,396 patients (1,113.6 patients per million population) were estimated to have ESRD at the end of 2009 [4], and 70,211 patients (1,353.3 patients per million population) were estimated to have ESRD at the end of 2012 [5]. ESRD is associated with increased comorbidities, such as hypertension, cardiovascular disease (CVD), diabetes mellitus (DM), and higher mortality [6,7]. Additionally, patients with ESRD require ongoing treatment, including dialysis or kidney transplantation, creating a substantial socioeconomic burden for individuals and society alike [6,7].

Known risk factors for ESRD include smoking, exposure to nephrotoxins, socioeconomic status, acute kidney injury, hypertension, DM, and obesity [8–11]. Several studies have examined the relationship between obesity and ESRD, showing that obesity is an independent risk factor for ESRD and chronic kidney disease (CKD) [12–15]. However, other studies have reported inverse associations between obesity and ESRD [16–19], suggesting that a lower body mass index (BMI) could increase both the incidence of ESRD and mortality among ESRD patients [19,20]. Thus, conventional obesity parameters such as BMI have yielded controversial results regarding their relationship with ESRD incidence. Furthermore, studies have examined the relationship between changes in body weight and GFR, but the results have been inconsistent due to variations in the methodologies used to measure kidney function [21,22]. Recently, weight fluctuation—beyond simple weight change—has been investigated in relation to cardiovascular outcomes, demonstrating that greater weight fluctuation increases cardiovascular events and mortality [23,24]. However, to date, no studies have directly explored the relationship between weight fluctuation and the incidence of ESRD in the Korean population. Thus, this study aimed to examine the association between weight fluctuation and ESRD incidence using health checkup data from the Korean National Health Insurance Service (NHIS).

METHODS

Ethics statement

This study was approved by the Institutional Review Board of Korea University Anam Hospital (No. ED17115), with a waiver of informed consent due to the use of deidentified data. Permission to use the health checkup data was granted by the NHIS (No. NHIS-2017-4-006).

Data

This study utilized the NHIS health checkup data from 2009 to 2015. All insured Koreans aged ≥40 years are required to undergo NHIS health checkups biennially, while employed individuals aged ≥20 years must participate annually. Approximately 97% of Koreans participate in this mandatory health checkup program. The NHIS manages the National Health Insurance Program, covering approximately 50 million Koreans. The health checkup collects demographic data, including age, sex, insurer payment coverage, area of residence, medical utilization, transaction information, deductions, and claims data. A trained examiner measures anthropometric data including height (cm), weight (kg), waist circumference (WC; cm), systolic blood pressure (SBP; mmHg), and diastolic blood pressure (DBP; mmHg). Laboratory tests performed include fasting blood glucose (mg/dL), total cholesterol (mg/dL), low-density lipoprotein cholesterol (mg/dL), high-density lipoprotein cholesterol (mg/dL), triglycerides (mg/dL), aspartate aminotransferase (IU/L), alanine aminotransferase (IU/L), and serum creatinine (mg/dL). General health behaviors, including alcohol consumption, smoking, and physical activity, were collected using self-administered questionnaires. The Korean Association of Laboratory Quality Control evaluated laboratory test quality, and the health checkups were conducted only at NHIS-certified hospitals. Further details on NHIS data and procedures are described elsewhere [2,25].

Subjects

Initially, we included 4,365,574 individuals who underwent NHIS health checkups between 2009 and 2012. We excluded subjects younger than 40 years (n=1,548,388), those with chronic liver disease (n=383,165), cancer (n=48,842), stroke (n=50,831), and missing data or preexisting ESRD (n=23,681). Ultimately, 2,310,667 subjects (1,546,749 men and 763,918 women) were enrolled and followed until the end of 2015 (mean follow-up duration, 4.38±0.34 years).

General health behaviors and economic variables

Smoking status was classified into nonsmoker, current smoker, or ex-smoker categories. Alcohol consumption was classified into none, mild (≤2 days per week), and heavy (≥3 days per week). Regular exercise was defined as vigorous physical activity performed for at least 20 minutes per day. Income was categorized into quartiles: Q1 (lowest), Q2, Q3, and Q4 (highest).

Definitions of GFR and ESRD

The estimated GFR (eGFR) was calculated using the CKD Epidemiology Collaboration (CKD-EPI) equation developed by Levey et al. [26], which is widely validated and recommended for clinical and research purposes. We defined CKD as an eGFR <60 mL/min/1.73 m², and ESRD was defined as eGFR <15 mL/min/1.73 m² combined with International Classification of Disease, 10th Revision (ICD-10) codes (N18-19, Z49, Z94.0, Z99.2) and special procedure codes (V codes) for hemodialysis (V001), peritoneal dialysis (V003), or kidney transplantation (V005), as assigned to CKD patients [27].

Definition of obesity

BMI was calculated as weight (kg) divided by height squared (m²). Obesity was categorized following World Health Organization recommendations for Asian populations as follows [28]: underweight (BMI <18.5 kg/m²), normal weight (BMI, 18.5 to <23 kg/m²), overweight (BMI, 23 to <25 kg/m²), obesity stage I (BMI, 25 to <30 kg/m²), and obesity stage II (BMI ≥30 kg/m²).

Definition of chronic diseases

Due to the unique characteristics of NHIS data, operational definitions from the Korean Diabetes Association were applied [29]. Type 1 DM patients were excluded. Type 2 DM was defined as a fasting plasma glucose level ≥126 mg/dL or at least one annual claim for antidiabetic medication under ICD-10 codes E11–E14. Dyslipidemia was defined as total cholesterol ≥240 mg/dL or at least one annual claim for antihyperlipidemic medication under ICD-10 code E78. Hypertension was defined as SBP/DBP ≥140/90 mmHg or at least one annual claim for antihypertensive medication under ICD-10 codes I10–I15. Heart failure was defined using ICD-10 code I50, and myocardial infarction was defined using ICD-10 codes I21–I22.

Body weight fluctuation and body weight changes

Weight fluctuation was defined as intraindividual variability during NHIS checkups between 2009 and 2012. Average real variability (ARV) was used to quantify weight fluctuation, and detailed ARV distribution is provided in Table S1. ARV was calculated as the average absolute difference between consecutive weight measurements using the following equation (n indicates the number of anthropometric measurements):

ARV=1n-1∑k=1n-1WTk+1 - WTk

This method was used for evaluating weight fluctuation since it reflects recent trends in weight changes [30].

Subjects were classified based on weight status from the initial visit (2009 or 2010) to the final visit (2012): weight-sustained group (weight change ±5%), weight-loss group (weight decrease >5%), and weight-gain group (weight increase >5%).

Statistical analysis

Subject characteristics were summarized as mean±standard deviation for continuous variables and count and percentages for categorical variables, stratified according to the five BMI categories. Hazard ratios (HRs) and 95% confidence intervals for ESRD were calculated using multivariable Cox proportional hazard models, based on BMI categories and quartiles of weight ARV. Cumulative incidence probabilities for ESRD over 7 years were obtained according to weight ARV quartiles. Additionally, HRs and 95% confidence intervals for ESRD were calculated according to BMI categories combined with weight fluctuation quartiles (ARV Q1–Q4). ARV Q1 to Q3 and nonobese (BMI, 18.5 to <23 kg/m²) groups served as reference categories. Models were adjusted for age, sex, smoking, alcohol consumption, regular exercise, income, DM, hypertension, dyslipidemia, eGFR, myocardial infarction, heart failure, proteinuria, initial BMI, and WC. Subgroup analyses were conducted using multivariable Cox proportional hazard models stratified by sex, age (≥65 or <65 years), and weight status (weight loss, weight sustained, or weight gain). Statistical significance was defined as P<0.05 (two-tailed), and analyses were performed using SAS ver. 9.3 (SAS Institute).

RESULTS

The general characteristics of subjects are summarized in Table 1. As BMI increased, weight, WC, glucose levels, SBP, DBP, total cholesterol, and triglyceride levels all significantly increased (all P<0.001). Additionally, the prevalence of DM, hypertension, dyslipidemia, and CKD increased significantly with higher BMI (all P<0.001). The eGFR decreased as BMI increased, up to a BMI of 30 kg/m², after which the eGFR slightly increased.

Table 2 shows the HRs for ESRD according to the five BMI categories and quartiles of weight ARV. After adjusting for all covariates, HRs for ESRD showed a decreasing trend with increasing BMI, exhibiting a J-shaped pattern. Regarding weight ARV quartiles, the HRs for ESRD increased progressively from the lowest quartile (Q1, reference) to the highest quartile (Q4), with HRs of 1.086, 1.132, and 1.420 for ARV Q2, Q3, and Q4, respectively. However, a statistically significant increase in HR for ESRD was observed only in the ARV Q4 group.

Fig. 1 illustrates the cumulative incidence probability of ESRD according to the quartiles of weight ARV in the overall population, as well as separately for men, women, and age groups. Compared to ARV Q1, Q2, and Q3, subjects in ARV Q4 consistently demonstrated the highest cumulative incidence probability of ESRD. This increased risk in ARV Q4 was evident in both men and women. Furthermore, ARV Q4 exhibited the highest probability of ESRD across both age groups (<65 and ≥65 years) compared to ARV Q1 to Q3.

Table 3 presents the HRs for ESRD in ARV Q4 compared to ARV Q1 to Q3, stratified by sex, age, and weight change status. Men exhibited a statistically significant increased risk of ESRD in ARV Q4 (HR, 1.372). Subjects aged <65 and ≥65 years both displayed increased ESRD risk in ARV Q4, consistent with Fig. 1 (HR of 1.337 and 1.361, respectively). Regarding weight change groups, individuals with sustained or increased weight showed significantly increased HRs for ESRD in ARV Q4 (HR of 1.222 and 1.490, respectively), whereas the weight-loss group did not show this association.

DISCUSSION

In this study, baseline BMI and body weight variability were associated with the risk of ESRD. The overall HR for ESRD decreased as BMI increased. However, weight fluctuation exhibited a J-shaped relationship with ESRD risk, with the highest weight fluctuation (ARV Q4) having a 1.42-fold increased incidence of ESRD after adjusting for confounding factors. This increased HR for ESRD in the highest weight fluctuation group (ARV Q4) was also significant in men, as well as in groups with sustained or increased weight, but not in the weight-loss group.

Several previous studies have examined the relationship between body weight or BMI and ESRD, yielding conflicting results [19,31–33]. In a study of a Chinese urban population, obesity parameters, including BMI, WC, and waist to height ratio, positively correlated with CKD risk [31]. Additionally, two US studies reported a positive association between BMI and ESRD [13,34]. Similarly, a Japanese study found that increased BMI correlated with decreased eGFR [32]. A prospective study in China also found a J-shaped association between BMI and ESRD risk [33]. Conversely, another US population study found that higher BMI was associated with a lower ESRD risk, consistent with the "obesity paradox." Furthermore, the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study found no significant association between BMI and ESRD incidence after adjusting for WC [19,35].

Recent studies have investigated new predictive parameters for chronic diseases, including ESRD, to reduce comorbidities and related mortality. Several studies examined weight change or fluctuation in relation to health outcomes rather than BMI or weight at a single point. The Framingham population study showed associations between body weight fluctuation and increased all-cause mortality, coronary heart disease mortality, and morbidity [36]. Another study demonstrated that body weight fluctuation correlated with higher risks of coronary and cardiovascular events among patients with preexisting coronary heart disease [23]. In relation to kidney disease, weight gain increased the risk of CKD even among healthy Korean men with normal BMI [37].

The mechanisms underlying the associations between BMI, body weight variability, and ESRD incidence remain unclear, but several hypotheses can be considered. From a nutritional perspective, although patients with baseline chronic liver disease or ESRD were excluded and heart failure was adjusted, low BMI may indicate malnutrition due to reduced food intake or impaired protein-energy metabolism linked to unstable health conditions [38,39]. Another explanation is that low body weight does not necessarily indicate good health in Asian populations; Asians tend to have higher body fat percentages at a given BMI compared to Western populations [40,41], making them more susceptible to type 2 DM and hypertension even at lower BMIs [40]. Regarding weight variability and ESRD risk, direct mechanisms remain uncertain, but several studies indicate adverse effects on cardiovascular health, new-onset diabetes, and all-cause mortality [23,24,36], suggesting weight fluctuation negatively impacts general health. Therefore, since cardiovascular diseases and diabetes are known risk factors for ESRD, greater weight variability could increase ESRD risk. The observed sex difference in ESRD incidence with weight fluctuation might be attributed to differences in fat distribution, as men typically accumulate more central fat associated with metabolic complications compared to women’s peripheral fat distribution [42]. Further studies are required to elucidate the mechanism underlying the relationship between weight fluctuation and incidence of ESRD.

Strengths and limitations

This study has several strengths. First, it is the first to examine the relationship between weight fluctuation and ESRD incidence in a large Korean population. Second, the large sample size of 2,310,667 adults provides robust statistical power. Third, both BMI and weight fluctuation were evaluated regarding ESRD risk. Fourth, the study relied on objectively measured anthropometric data rather than self-reported values.

However, several limitations must be acknowledged. First, we could not determine if weight fluctuation was intentional or unintentional. Second, medication history, including diuretic use potentially affecting weight changes, was not evaluated. Third, nutritional status, which might have influenced results, was not directly assessed due to data constraints. Fourth, subjects with certain medical conditions potentially affecting outcomes (e.g., polycystic kidney disease, inflammatory bowel disease, intestinal or bariatric surgery) were not specifically considered. Lastly, the study included only Koreans, limiting the generalizability of findings to other ethnic groups.

Conclusions

High weight fluctuation (ARV Q4) was associated with increased ESRD incidence in the general Korean population, notably among men and those with sustained or increased weight. Clinicians should monitor patients’ weight fluctuations as potential indicators of ESRD risk, particularly among patients exhibiting intense weight fluctuation. Further studies are warranted to elucidate the underlying mechanisms and to confirm these associations across different ethnicities.

ARTICLE INFORMATION

Author contributions

Conceptualization: KES, BH, KH, YHK; Data curation: KES, BH, GBL, KH, YHK; Formal analysis: KH; Investigation: KH; Methodology: KES, KH, YHK; Validation: GBL, JY; Writing–original draft: KES, BH, YHK; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Conflicts of interest

The authors have no conflicts of interest to declare.

Funding

The authors received no financial support for this study.

Acknowledgements

The authors thank all the participants of the study and the Korean National Health Insurance Corporation for performing these health checkups. The authors also thank Seon Mee Kim, Kyung-Hwan Cho, Do-Hoon Kim, Yong-Gyu Park, and Ga Eun Nam for their help in writing.

Supplementary materials

Fig. 1.

Incidence probability for end-stage renal disease by quartile (Q1–Q4) of weight average real variability (ARV) in subgroups. (A) Overall population. (B) Men. (C) Women. (D) Age <65 years. (E) Age ≥ 65 years.

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