Risk of end‐stage renal disease in Korean patients with type 2 diabetes according to cumulative low‐density lipoprotein cholesterol levels

ABSTRACT

Objective

Lifelong cumulative high low‐density lipoprotein cholesterol (LDL‐C) levels are associated with atherosclerosis burden. This study analyzed the association between cumulative LDL‐C levels and the risk of end‐stage renal disease (ESRD) in Korean adults with type 2 diabetes (T2D).

Research Design and Methods

The incidence of ESRD was analyzed in Korean type 2 diabetes patients aged >20 years who underwent national health examinations from 2015 to 2016. Cumulative LDL‐C levels were categorized based on the number of times the levels were ≥130 mg/dL during the examinations. The risk of ESRD across groups was analyzed using the Kaplan–Meier method and Cox proportional hazards model.

Results

Among 318,645 patients, the number of occurrences of cumulative LDL ≥130 mg/dL were 0 (154,535), 1 (63,521), 2 (42,563), 3 (32,423), and ≥4 (25,594). Using a multivariable model with adjustment for multiple confounding variables, the risk of ESRD increased with the number of high cumulative LDL‐C: hazard ratio (HR) 1.33 (95% confidence interval [CI] 1.17–1.50), 1.51 (1.29–1.76), 1.99 (1.29–1.76), and 2.05 (1.66–2.54) for 1 to ≥4 occurrences, respectively. In patients taking three oral antidiabetic medications, the HR of ESRD was 1.51 (1.23–1.84), 2.10 (1.66–2.66), 3.07 (2.34–4.03), and 3.11 (2.05–2.72) for 1 to ≥4 occurrences compared with 0, respectively. For patients with diabetes duration ≥10 years, the HR of ESRD was 1.45 (1.24–1.70), 1.93 (1.59–2.35), 2.47 (1.94–3.15), and 2.97 (2.18–4.03) for 1 to ≥4 occurrences, respectively.

Conclusions

ESRD risk increased with high cumulative LDL‐C levels. This difference was more pronounced in patients with diabetes taking multiple antidiabetic medications and in those with a longer duration of diabetes.

High cumulative LDL‐C levels increase ESRD risk in type 2 diabetes, particularly among patients using insulin, those receiving multiple antidiabetic medications, and those with longer diabetes duration. Longer exposure to elevated LDL‐C further increases risk, underscoring the need for intensive LDL‐C control.

INTRODUCTION

Owing to an increase in the aging global population, the incidence of age‐related diseases has increased ¹ . Korea is experiencing one of the fastest rates of population aging, with the prevalence of diabetes among individuals aged 65 years and older reaching 29.6%, representing a significant emerging social concern ² .

The incidence of diabetic complications has increased with the increasing number of older patients with diabetes. Complications, including cardiovascular disease, kidney problems, neuropathy, and visual impairment, are more severe in older individuals ³ . In particular, the prevalence of diabetic kidney disease (DKD) has increased significantly. DKD develops in ~ 30–40% of all individuals with diabetes and is the leading cause of end‐stage renal disease (ESRD) worldwide ⁴ . Moreover, patients with ESRD have a significantly higher mortality rate than the general population, emphasizing the need for close monitoring ⁵ .

The increased prevalence of age‐related diseases is associated with increased healthcare costs, which are likely to become a long‐term national burden. Identifying and avoiding potential risk factors is necessary to reduce the burden of age‐related diseases ⁶ . Reduction of atherosclerosis is recommended as an approach to prevent diabetic complications. This is particularly important because atherosclerosis is a chronic immune‐inflammatory, progressive, and age‐related disorder, and its frequency increases with age. As atherosclerosis progresses, various vascular complications have been reported to arise ⁷ , ⁸ . Atherosclerosis management aims to lower low‐density lipoprotein cholesterol (LDL‐C) levels, with target levels varying according to the risk groups. For high‐risk diabetes patients, guidelines recommend lowering LDL‐C to as low as 70 mg/dL ⁹ , ¹⁰ .

Recently, the concept of cumulative LDL‐C has emerged, with evidence suggesting that lifelong cumulative LDL‐C is directly correlated with the atherosclerosis burden ¹¹ . Although many studies have explored the risk and correlation between myocardial infarction and cardiovascular diseases ¹² , ¹³ , ¹⁴ , research examining its relationship with ESRD remains limited. However, the efficacy of LDL‐C control in preventing DKD has not been conclusively established ¹⁵ , ¹⁶ , ¹⁷ , ¹⁸ , ¹⁹ . Therefore, ESRD risk was analyzed based on the cumulative LDL‐C levels in Korean adults with type 2 diabetes.

RESEARCH DESIGN AND METHODS

The Korean National Health Insurance Service (NHIS), a government‐operated healthcare organization, administers a medical insurance system for all Koreans and manages a database that includes individual medical records, such as diagnoses, hospital visits, conducted tests, and prescribed medications. The NHIS offers a biennial health screening program for all employees, except for those who must be screened annually. These health screening databases include personalized questionnaires on demographic characteristics and health behaviors, including information on drinking and smoking. The NHIS has made these databases accessible for various epidemiological and research purposes as processed ²⁰ . This study was approved by the institutional review board of Kangbuk Samsung Hospital (approval no. 2024–08‐021).

Population data

Participants aged 20 years or older with diabetes who underwent the National Health Screening Program in Korea between 2015 and 2016 were included in this study (n = 2,616,505). Patients were followed up until 2022 to analyze the risk of ESRD based on cumulative LDL‐C exposure. Individuals who underwent at least three health screenings during the study period were selected for assessment of LDL‐C levels.

Type 2 diabetes was identified using the following diagnostic criteria: (1) International Classification of Diseases, 10th revision, Clinical Modification (ICD‐10‐CM) codes E11‐E14 and prescribed at least one glucose‐lowering agent, including oral hypoglycemic agents or insulin, or (2) a fasting glucose level of ≥126 mg/dL ²¹ . ESRD cases are defined under the Special Medical Expense Reduction Code, which is a unique benefit provided by the Korean National Health Insurance. This system primarily offers cost reduction for the treatment of severe or rare diseases. The exclusion criteria were as follows: participants under the age of 20 years (n = 323), those with fewer than three health screenings (n = 2,274,159), preexisting ESRD (n = 445), individuals with missing data (n = 21,902), and those excluded owing to a 1‐year lag period (n = 1,799). After exclusion, 318,645 participants were included in the final analysis (Figure S1).

Categorized cumulative LDL

The main predictor in this study was the LDL‐C level. Cumulative LDL‐C levels were calculated using LDL‐C values recorded in the NHIS screening data. To examine the association between LDL‐C and ESRD risk, participants were classified into five groups according to the number of times their LDL‐C levels exceeded 130 mg/dL during the study period, with the groups ranging from those who never exceeded the threshold (0 times) to those who exceeded it up to four times: 0, 1, 2, 3, and ≥4. This approach was chosen to capture repeated exposure to clinically significant LDL‐C levels, rather than absolute lipid quantities, thereby reflecting the persistence of high‐risk lipid exposure over time.

In this study, we defined LDL‐C 130 mg/dL as the cutoff value for risk stratification. This threshold corresponds to the lower limit of the ‘borderline high’ category in the NCEP ATP III guidelines and aligns with the 2018 Korean Guidelines for the Management of Dyslipidemia (KSoLA), which recommend active clinical intervention at this level in patients with cardiovascular risk factors ²² , ²³ , ²⁴ .

Definition of outcome

Data were obtained from the NHIS database and analyzed based on the ICD‐10‐CM and special medical expense reduction codes. The study outcome was ESRD incidence. ESRD cases were identified as individuals for whom the Special Medical Expense Reduction Code (V001, V003, and V005) had been claimed at least once during the study period, which lasted from January 1, 2015, to December 31, 2022, or until the occurrence of the event. Special Medical Expense Reduction codes indicating chronic kidney failure requiring renal replacement therapy: V001 for hemodialysis initiation, V003 for peritoneal dialysis, and V005 for kidney transplant maintenance therapy. These codes are assigned in the Korean National Health Insurance claims when patients receive renal replacement therapy as part of the special medical expense reduction program for chronic kidney disease (CKD).

Statistical analysis

Continuous variables between the cumulative LDL‐C groups were compared using one‐way anova, whereas categorical variables were analyzed using the Chi‐squared test. For continuous variables, the results were presented as mean ± standard deviation, whereas for categorical variables, frequencies, and percentages were reported. ESRD incidence was expressed as per 1,000 person‐years. Kaplan–Meier survival analysis and Cox proportional hazard models were used to evaluate the risk of disease occurrence across different groups during the follow‐up period. The Kaplan–Meier method was used to estimate the cumulative incidence rates of the outcomes, with log‐rank tests comparing survival distributions across different cumulative LDL‐C groups. Cox proportional hazard regression models were used to calculate the hazard ratios (HRs) and 95% confidence intervals (CI). Multivariate analyses were performed to control for potential confounding factors. Initially, we calculated the unadjusted HR (Model 1). Subsequently, Model 2 incorporated both age and sex. In Model 3, a comprehensive multivariable‐adjusted analysis was performed that included all variables from Model 2 and additionally accounted for low income, smoking status, alcohol consumption, exercise habits, hypertension, body mass index, CKD, fasting blood glucose levels, insulin usage, number of oral antidiabetic medications (OAD) prescribed, duration of diabetes, and statin use. In additional analyses, participants were categorized according to baseline LDL‐C levels, and the association between baseline LDL‐C categories and incident ESRD was evaluated using Cox proportional hazards models. All statistical analyses were performed using SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA), with statistical significance defined as P < 0.05.

RESULTS

Baseline characteristicss

In total, 318,645 participants were included in the analysis. The median follow‐up was 5.58 years. The number of cumulative LDL ≥130 mg/dL occurrences was: 0 (154,535), 1 (63,521), 2 (42,563), 3 (32,423), and ≥4 (25,594). The baseline characteristics of the study population are presented in Table 1. The mean ages were 53.87 ± 10.08, 52.88 ± 9.65, 52.04 ± 9.65, 50.97 ± 9.57, and 49.76 ± 9.29 years for individuals in the groups of 0–4 or more occurrences, respectively. The proportion of men was higher than that of women, and the majority of the participants were aged between 40 and 65 years. The prevalence of CKD was the highest in Group 0 (4.85%) and decreased across the groups (4.37, 3.54, 2.83, and 2.23%, respectively).

Table 1.

Baseline characteristics of the study population

	LDL ≥130 cumulative count					P‐value
	0	1	2	3	4
	154,535	63,521	42,563	32,432	25,594
Age, years	53.87 ± 10.08	52.88 ± 9.65	52.04 ± 9.65	50.97 ± 9.57	49.76 ± 9.29	<0.0001
Sex, male, n	129,933 (84.08)	50,680 (79.78)	33,373 (78.41)	25,365 (78.21)	20,374 (79.6)	<0.0001
Low income 25%, n	31,384 (20.31)	12,766 (20.1)	8,161 (19.17)	5,819 (17.94)	4,099 (16.02)	<0.0001
Smoking, n						<0.0001
Non	55,606 (35.98)	24,826 (39.08)	16,978 (39.89)	12,772 (39.38)	9,841 (38.45)
Ex	48,372 (31.3)	18,779 (29.56)	12,316 (28.94)	9,266 (28.57)	6,992 (27.32)
Current	50,557 (32.72)	19,916 (31.35)	13,269 (31.17)	10,394 (32.05)	8,761 (34.23)
Drinking, n						<0.0001
Non	58,645 (37.95)	25,609 (40.32)	17,657 (41.48)	13,266 (40.9)	9,912 (38.73)
Mild	78,525 (50.81)	31,360 (49.37)	20,770 (48.8)	16,051 (49.49)	13,190 (51.54)
Heavy	17,365 (11.24)	6,552 (10.31)	4,136 (9.72)	3,115 (9.6)	2,492 (9.74)
Regular exercise, n	42,231 (27.33)	16,671 (26.24)	10,996 (25.83)	8,246 (25.43)	6,090 (23.79)	<0.0001
Underlying comorbidities, n						<0.0001
Hypertension	80,805 (52.29)	30,748 (48.41)	19,061 (44.78)	12,731 (39.25)	8,758 (34.22)
Dyslipidemia	68,737 (44.48)	31,959 (50.31)	22,698 (53.33)	18,605 (57.37)	17,645 (68.94)
Chronic kidney disease	7,501 (4.85)	2,779 (4.37)	1,506 (3.54)	919 (2.83)	572 (2.23)
OAD ≥3 user, n	37,632 (24.35)	13,215 (20.8)	7,097 (16.67)	3,984 (12.28)	1,735 (6.78)	<0.0001
Insulin user, n	10,434 (6.75)	3,582 (5.64)	2,028 (4.76)	1,228 (3.79)	629 (2.46)	<0.0001
Statin user, n	68,160 (44.11)	29,241 (46.03)	18,406 (43.24)	11,845 (36.52)	6,323 (24.71)	<0.0001
DM duration, n						<0.0001
New onset	54,227 (35.09)	25,267 (39.78)	19,467 (45.74)	17,786 (54.84)	17,884 (69.88)
< 5 years	36,713 (23.76)	18,929 (29.8)	13,300 (31.25)	9,418 (29.04)	5,278 (20.62)
< 10 years	31,251 (20.22)	9,993 (15.73)	5,319 (12.5)	2,871 (8.85)	1,359 (5.31)
≥ 10 years	32,344 (20.93)	9,332 (14.69)	4,477 (10.52)	2,357 (7.27)	1,073 (4.19)
Height, cm	167.26 ± 7.87	166.88 ± 8.26	166.8 ± 8.4	167.03 ± 8.39	167.55 ± 8.36	<0.0001
Weight, kg	70.92 ± 12.47	71.53 ± 12.65	71.85 ± 12.79	72.58 ± 13.05	73.65 ± 13.29	<0.0001
Body mass index, kg/m2	25.26 ± 3.52	25.58 ± 3.47	25.72 ± 3.49	25.91 ± 3.56	26.12 ± 3.59	<0.0001
Waist circumference, cm	86.21 ± 8.91	86.53 ± 8.75	86.57 ± 8.78	86.87 ± 8.84	87.24 ± 8.81	<0.0001
Systolic blood pressure, mmHg	126.84 ± 13.82	127.07 ± 13.77	127.4 ± 13.91	127.73 ± 14	128.57 ± 14.37	<0.0001
Diastolic blood pressure, mmHg	78.55 ± 9.41	79 ± 9.42	79.43 ± 9.56	79.95 ± 9.63	80.83 ± 9.93	<0.0001
Low‐density lipoprotein cholesterol, mg/dL	84.84 ± 24.48	105.45 ± 32.52	118.72 ± 35.8	135.7 ± 35.22	165.61 ± 31.24	<0.0001
High‐density lipoprotein cholesterol, mg/dL	50.68 ± 14.92	51.08 ± 14.2	51.37 ± 14.54	51.42 ± 14.18	51.73 ± 14.68	<0.0001
Fasting glucose, mg/dL	144.76 ± 42.9	145.88 ± 43.62	147.23 ± 43.74	149.94 ± 44.41	155.39 ± 46.79	<0.0001
Total cholesterol, mg/dL	167.24 ± 30.01	189.17 ± 36.55	202.73 ± 39.35	220.61 ± 39.61	251.6 ± 32.49	<0.0001
Glomerular filtration rate, mL/min/1.73 m2	95.22 ± 70.66	94.74 ± 65.87	94.92 ± 64.21	95.68 ± 66.36	95.47 ± 64.06	0.2464
Triglyceride, mg/dL †	139.12 (138.7–139.54)	146.73 (146.06–147.41)	148.15 (147.34–148.95)	153.42 (152.5–154.35)	161.75 (160.72–162.79)	<0.0001

OAD, oral antidiabetic medication; DM, diabetes mellitus.

^† Geometric mean (95% CI).

Effect of cumulative LDL‐C level on ESRD risk

In the total study population, the incidence rates (per 1,000 person‐years) for cumulative LDL‐C were 0.99, 1.05, 0.87, 0.88, and 0.71 for 0 to ≥4 occurrence group, respectively. Using a multivariable model with adjustment for multiple confounding variables and 0 occurrences as a reference, the risk of ESRD increased with the number of high cumulative LDL‐C occurrences: HR: 1.33 (95% CI: 1.17–1.50), 1.51 (1.29–1.76), 1.99 (1.29–1.76), and 2.05 (1.66–2.54) for 1 to ≥4 occurrences, respectively (Table 2 and Figure 1). In supplementary analyses, baseline LDL‐C levels measured at study entry were also associated with ESRD risk in a dose–response manner. While baseline LDL‐C captures lipid status at a single time point, cumulative LDL‐C exposure reflects the clinical reality of sustained dyslipidemia burden throughout the exposure period (Table S1).

Table 2.

ESRD incidence rates on cumulative LDL count

LDL ≥130 cumulative count	N	Event	Duration (per year)	IR per 1,000	Model 1 HR (95% CI)	Model 2 HR (95% CI)	Model 3 HR (95% CI)
0	154,535	853	859,104.95	0.99	1 (ref.)	1 (ref.)	1 (ref.)
1	63,521	373	354,327.71	1.05	1.06 (0.938–1.197)	1.139 (1.008–1.287)	1.325 (1.172–1.497)
2	42,563	208	238,081.01	0.87	0.879 (0.756–1.023)	0.985 (0.846–1.147)	1.506 (1.292–1.756)
3	32,432	160	181,791.50	0.88	0.886 (0.748–1.049)	1.035 (0.874–1.227)	1.989 (1.673–2.366)
4	25,594	102	143,570.48	0.71	0.716 (0.583–0.879)	0.871 (0.708–1.071)	2.05 (1.656–2.537)

Model 1: non‐adjusted, Model 2: adjusted for age and sex; Model 3: age, sex, income, smoking, drinking, exercise, hypertension, dyslipidemia, BMI, GFR, glucose, insulin, oral antidiabetic medication number, diabetes duration, and statin use; CI, confidence interval; HR, hazard ratio; IR, incidence rate.

Figure 1.

Effect of cumulative LDL‐C level on ESRD risk. aHR, adjusted hazard ratio; ESRD, end‐stage renal disease; LDL‐C, low‐density lipoprotein cholesterol.

Effect of cumulative LDL‐C level on cofounding factors of diabetes mellitus

The results of the subgroup analyses according to the participants' age, sex, diabetes duration, OAD count, insulin use, statin use, and comorbidities, including CKD, are presented in Tables S2–S4. In the OAD subgroup analysis, a progressive increase in the ESRD risk was observed with increasing cumulative LDL levels and the P‐value was <0.001. For patients using fewer than three oral medications, the HR (95% CI) compared with the reference group (0 occurrences) was 1.22 (1.04–1.42), 1.21 (0.99–1.48), 1.54 (1.24–1.93), and 1.73 (1.35–2.01), respectively. Similarly, in patients using three or more oral medications, the HR was 1.51 (1.23–1.84), 2.10 (1.66–2.66), 3.07 (2.34–4.03), and 3.11 (2.05–4.73), respectively, with both subgroups demonstrating a statistically significant increasing trend in ESRD risk with increasing cumulative LDL counts. However, the risk was more pronounced in individuals with more intensive diabetes medication use (Figure 2a).

Figure 2.

Effect of cumulative LDL‐C level on diabetes mellitus cofounding factors. (a) OAD count. (b) Insulin use. (c) Diabetes mellitus duration. aHR, adjusted hazard ratio (aHR); DM, diabetes mellitus; LDL‐C, low‐density lipoprotein cholesterol; OAD, oral antidiabetic medications.

In the analysis comparing patients using or not using insulin, an increased risk of ESRD was observed (P‐value <0.001), with higher cumulative LDL counts in both groups. For patients not using insulin, the HR (95% CI) was 1.25 (1.08–1.46), 1.33 (1.10–1.61), 1.65 (1.33–2.04), and 1.77 (1.47–2.29) for increasing cumulative LDL counts. Notably, patients using insulin demonstrated a more pronounced increase in ESRD risk, with an HR (95% CI) of 1.46 (1.19–1.79), 1.92 (1.48–2.49), 2.96 (2.21–3.96), and 2.78 (1.91–4.03), indicating a sharper risk progression with cumulative LDL exposure in individuals using insulin (Figure 2b).

Subgroup analyses were conducted according to the DM duration (P‐value <0.001). In patients with newly diagnosed diabetes, a J‐curve relationship was observed, with a HR (95% CI) of 0.94 (0.65–1.36), 0.86 (0.56–1.33), 1.28 (0.86–1.90), and 1.38 (0.93–2.04) for increasing cumulative LDL counts. In contrast, for patients with a DM duration ≥10 years, a clear and progressive increase in the ESRD risk was observed with higher cumulative LDL counts, with a HR (95% CI) of 1.45 (1.24–1.70), 1.93 (1.59–2.35), 2.47 (1.94–3.15), and 2.97 (2.18–4.03), respectively, demonstrating a strong positive correlation between cumulative LDL exposure and ESRD risk in patients with long‐standing diabetes (Figure 2c). In subgroup analyses stratified by statin use, cumulative LDL‐C exposure showed a consistent dose–response association with ESRD risk in both statin users and non‐users, with no significant interaction between cumulative LDL‐C exposure and statin therapy (P for interaction = 0.103) (Table S3).

DISCUSSION

In this nationwide cohort study of 318,645 patients with type 2 diabetes with a median follow‐up of 5.58 years, the risk of ESRD was found to increase with higher cumulative LDL‐C levels. This difference was more pronounced in patients with diabetes who were taking multiple antidiabetic medications, insulin users, and in those with a longer duration of diabetes.

Previous studies on the relationship between cumulative LDL‐C levels and ESRD risk in patients with type 2 diabetes are limited, and their findings have been inconsistent. Most previous studies have relied on single‐point LDL‐C measurements, and meta‐analyses of RCTs and observational studies have shown that lowering LDL‐C levels may attenuate the loss of kidney function ²⁴ , ²⁵ , ²⁶ . In contrast, some meta‐analyses have reported that lowering LDL has a minimal impact on CKD progression ¹⁸ , ¹⁹ . In this context, our study extends prior work by incorporating longitudinal exposure to LDL‐C rather than relying solely on a single baseline measurement. Notably, our findings demonstrated that cumulative LDL‐C levels correlated with an increased ESRD incidence.

A recent study by Lee et al. ²⁴ on the KNOW‐CKD cohort provided complementary evidence to our findings. In their study of 1,886 patients with CKD, they observed a significant association between LDL‐C levels and adverse kidney outcomes. Similar to our findings, Lee et al. found that higher LDL‐C levels were independently associated with an increased risk of kidney complications. In their multivariable‐adjusted Cox model, patients with LDL‐C levels ≥130 mg/dL had a HR of 2.21 (95% CI: 1.27–3.85) compared with those with LDL‐C < 70 mg/dL. Consistent with these findings, baseline LDL‐C was also associated with ESRD risk in our cohort. However, baseline LDL‐C represents lipid status at a single time point and may be influenced by ongoing lipid‐lowering therapy or recent lifestyle changes. Although baseline LDL‐C was associated with ESRD risk, a single measurement may not adequately represent long‐term lipid burden. In contrast, cumulative LDL‐C captures repeated exposure to clinically significant LDL‐C elevations over time and therefore reflects the persistence of dyslipidemia ²⁷ . Given that ESRD is a consequence of chronic and cumulative renal injury, repeated exposure to high LDL‐C may be particularly relevant to disease progression.

The mechanisms underlying these associations likely involve complex interactions between lipids and renal damage. Although cholesterol is essential for cell membrane formation and hormone synthesis, its excessive accumulation can lead to adverse effects ²⁸ . Numerous studies have shown that LDL‐C is a primary risk factor for atherosclerosis, and higher levels of LDL‐C are strongly linked to an increased risk of developing atherosclerosis, potentially leading to serious organ‐related complications ⁸ , ²⁹ , ³⁰ , ³¹ . In DKD, lipid accumulation in the kidney itself contributes to renal damage ³² , leading to oxidative stress, inflammation, and structural damage, such as glomerulosclerosis and tubulointerstitial damage, as demonstrated in experimental studies ³³ , ³⁴ , ³⁵ .

Hypertriglyceridemia exacerbates proteinuria and induces lipotoxicity, worsening renal dysfunction ³⁶ , ³⁷ . Reduced high‐density lipoprotein cholesterol (HDL‐C) levels, which impair anti‐inflammatory and anti‐apoptotic properties, are also associated with increased proteinuria and renal impairment ³⁸ , ³⁹ . While the relationship between LDL‐C or total cholesterol and renal dysfunction remains controversial, our findings underscore the importance of long‐term LDL‐C exposure in renal disease progression, especially in high‐risk populations, such as patients with type 2 diabetes.

Subgroup analyses revealed that the association between cumulative LDL‐C levels and ESRD risk was more pronounced in patients with poorly controlled diabetes characterized by a higher number of OAD, longer diabetes duration, or insulin use. Insulin resistance and hyperglycemia contribute to an atherogenic lipid profile, which is characterized by elevated triglyceride levels, reduced HDL‐C levels, and the formation of small, dense LDL particles ⁴⁰ . Chronic hyperglycemia exacerbates these effects through oxidative stress and endothelial damage, accelerating atherosclerosis and kidney injury ⁴¹ .

Experimental studies have further illustrated the detrimental interactions between lipids and glucose that impair renal function. For instance, a high‐fat diet exacerbates albuminuria and glomerular lesions in diabetic mice, highlighting the synergistic harm of lipids and glucose ⁴² . Additionally, altered LDL expression in podocytes under high‐glucose conditions weakens the glomerular filtration barrier and promotes proteinuria and DKD progression ⁴³ . This finding suggests that uncontrolled diabetes increases the risk of ESRD associated with cumulative LDL‐C exposure.

Cumulative LDL‐C exposure, reflecting the duration and extent of LDL‐C elevation, offers a more comprehensive understanding of lipid‐related risks ¹¹ . Previous studies have reported associations between cumulative LDL‐C and the atherosclerosis burden or coronary heart disease, emphasizing the value of early and sustained lipid control ¹³ . Consistently, in the present study, analysis revealed a progressive increase in ESRD risk with cumulative LDL‐C levels exceeding 130 mg/dL, with HRs ranging from 1.33 to 2.05 in fully adjusted models. These findings highlight the importance of early diagnosis and proactive management to reduce lifetime risks.

The strengths of this study include its large sample size (318, 645 participants), which is much larger than that of previous studies ¹⁸ , ¹⁹ and its focus on the link between LDL‐C and ESRD, a topic that has not been extensively investigated. The use of a multivariable model helped reduce bias, and its emphasis on high‐risk type 2 diabetes patients with confounding factors adds to its importance. However, this study has several limitations. As this was a retrospective study, direct cause‐and‐effect relationships could not be confirmed, and potential confounding factors could not be completely controlled. In addition, lipid measurements from laboratory centers may introduce variability. Furthermore, ESRD was identified using claims codes (V001, V003, V005) for renal replacement therapy, which may underestimate disease burden by missing advanced CKD cases that did not progress to dialysis or transplantation. Nevertheless, these reimbursement codes are considered highly specific for dialysis and transplantation. Data on albuminuria or proteinuria were unavailable; therefore, baseline CKD was defined solely by eGFR. This may have resulted in residual confounding, given the known relationship between proteinuria, dyslipidemia, and ESRD progression. Further studies are required to address these limitations.

CONCLUSIONS

This study revealed a significant association between cumulative LDL‐C levels and ESRD risk in Korean type 2 diabetes patients. The risk of ESRD increased with high cumulative LDL‐C levels, and this difference was more pronounced in patients with diabetes who used multiple OAD, were insulin users, and had a longer duration of diabetes.

FUNDING

No potential funding or assistance was reported for this article.

DISCLOSURE

The authors declare no conflict of interest.

Approval of the research protocol: This study was approved by the institutional review board of Kangbuk Samsung Hospital (approval no. 2024–08‐021).

Informed consent: Written informed consent was obtained from all participants included in this study.

Registry and the registration no. of the study/trial: N/A.

Animal studies: N/A.

AUTHOR CONTRIBUTIONS

D.Y.L researched data, contributed to the discussion, and wrote the first draft of the manuscript. K.D.H. wrote the first draft of the manuscript, contributed to the discussion, statistical analysis and reviewed and edited the manuscript. J.H.K, H.N.J, S.J.M, H.M.K, and S.E.P contributed to the discussion and reviewed. E.J.R and W.Y.L were involved in the project conception, discussion, and revising of the manuscript. All authors approved the final version of the manuscript. E.J.R is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Supporting information

Figure S1. Flow chart of study participant selection.

Table S1. Effect of baseline LDL‐C level on ESRD risk.

Table S2. Effect of cumulative LDL‐C level on age and sex.

Table S3. Effect of cumulative LDL‐C level on comorbidity.

Table S4. Effect of cumulative LDL‐C level on DM cofounding factor.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.