Introduction
Globally, chronic kidney disease (CKD) poses a major public health challenge. An estimated 697.5 million people were affected by CKD, and CKD-related mortality increased by 41.5% from 1990 to 2017 [1]. Furthermore, impaired kidney function is associated with increased cardiovascular disease (CVD)-related mortality, contributing to an additional 1.4 million cardiovascular deaths and accounting for 7.6% of total CVD mortality [1]. Previous literature have also suggested a close interrelationship between CKD and CVD: While patients with advanced CKD are at high risk of cardiovascular morbidity and mortality [2], growing evidence indicates that even early-stage CKD is associated with increased cardiovascular risk [3]. Approximately half of patients with CKD die from CVD before progressing to end-stage kidney disease (ESKD) [4]. These associations may be attributed to the bidirectional relationship between CKD and CVD, as well as their shared risk factors, which contribute to the onset and progression of both conditions [5,6]. Accordingly, elucidating the associations between cardiovascular risk factors and CKD could inform targeted prevention strategies for both CVD and CKD.
The American Heart Association (AHA) introduced “Life’s Simple 7” (LS7) in 2010 to outline modifiable cardiovascular risk factors, and later updated these metrics to “Life’s Essential 8” (LE8), which includes diet, physical activity, smoking status, sleep health, body mass index (BMI), fasting glucose, total cholesterol, and blood pressure [7,8]. LE8 is characterized by a quantitative, point-based scoring system, in which each component is scored from 0 to 100 [8]. This approach facilitates continuous risk modeling and cardiovascular health (CVH) monitoring [9]. Prior studies have shown that ideal CVH based on LE8 is associated not only with a reduced risk of CVD incidence and cardiovascular mortality, but also with a lower risk of other conditions, including cancer, dementia, and depression [10–12].
Despite this broad relevance, LE8 has been less extensively studied in association with CKD [13,14]. A United States study reported that higher LE8 scores was associated with lower prevalence of CKD [13], but data remain scarce in Asian populations, where CKD burden, risk factor distributions, and prognosis differ from Western populations [15,16]. Furthermore, prior studies have not distinguished decreased eGFR, albuminuria, and early-stage CKD. Notably, albuminuria without decreased eGFR may be relatively more common among Asians and independently associated with poor outcomes [3,15].
Using nationally representative data of Korean adults, we aimed to evaluate the association of LE8 score with CKD, as well as with decreased eGFR, albuminuria, and earlier CKD stages.
Methods
Data source and study participants
We used data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2019 to 2021. KNHANES is a nationwide, population-based, cross-sectional annual survey administered by the Korea Centers for Disease Control and Prevention (KCDC). Each year, approximately 10,000 individuals are selected through a complex, multi-stage, stratified probability sampling design to ensure national representativeness [17]. The details for the KNHANES are elaborated elsewhere [17].
Of the total survey participants (n = 22,559), we excluded individuals under the age of 20, pregnant women, and those with incomplete data for any of the LE8 components, urine albumin-to-creatinine rate (UACR) measurements, or covariates. The final analytic sample included 12,264 individuals aged 20 years and older (5,353 males and 6,911 females) (Supplementary Fig. 1, available online).
The KNHANES is a national de-identified database for public use. Phase VIII (2019–2021) was approved by the KCDC Institutional Review Board (2018-01-03-C-A, 2018-01-03-2C-A, and 2018-01-03-5C-A). All participants provided written informed consent.
Cardiovascular health metrics in Life’s Essential 8
LE8 consists of two primary domains: health behaviors (diet, physical activity, nicotine exposure, and sleep health) and health factors (BMI, blood lipids, blood glucose, and blood pressure). LE8 scoring criteria, based on the AHA presidential advisory [8], are summarized in Supplementary Table 1 (available online). Each LE8 component was scored on a scale of 0 to 100. Total LE8 score was calculated as the unweighted average of the 8 component scores. Participants were classified into three groups based on the total score: low CVH (0 to <50), moderate CVH (50 to <80), and high CVH (80 to 100), as recommended by the AHA presidential advisory [8].
Each component was assessed as follows: (1) Dietary information was collected through a 24-hour dietary recall method. Dietary intake was evaluated with the nutrient-based Dietary Approaches to Stop Hypertension (DASH) score, which includes protein, fiber, calcium, potassium, magnesium, total fat, cholesterol, saturated fat, and sodium [18]. The cutoffs in the original DASH scoring criteria were derived from the United States population [19]. To account for differences between Western and Korean dietary patterns, we applied Korean population-specific quintiles, thereby adapting the LE8 diet score to reflect the characteristics of the Korean diet. Specifically, LE8 diet score was derived as follows: First, population-specific quintile cutoffs were calculated for each DASH nutrient using the 8th KNHANES population (Supplementary Table 2, available online). Then, DASH diet scores were assigned according to these population-specific quintiles (Supplementary Table 3, available online). Finally, each individual’s LE8 diet score was assigned based on the corresponding quintile category. (2) Self-reported data on physical activity duration and intensity were collected using the Global Physical Activity Questionnaire. Total weekly time (in minutes) spent on moderate and high-intensity activity was calculated, with each minute of high-intensity activity weighted as 2 minutes. (3) The LE8 nicotine exposure score was assigned based on smoking status: never-smoker, former smoker stratified by cessation duration, former smoker who currently uses inhaled nicotine products, or current smoker. Additionally, 20 points were subtracted for participants exposed to secondhand smoke at home. (4) The average sleep duration per night was calculated as a weighted mean of sleep hours on 5 weekdays and 2 weekend days.
(5) BMI was calculated as weight in kilograms divided by height in meters squared. The LE8 BMI score was based on the BMI categories for Asian populations: <23.0, 23.0–24.9, 25.0–29.9, 30.0–34.9, and ≥35.0 kg/m2. (6) Blood samples were obtained after a minimum of 8 hours of fasting. Non-high-density lipoprotein (non-HDL) cholesterol was calculated by subtracting HDL cholesterol from total cholesterol. For participants taking lipid-lowering medication, 20 points were subtracted from the LE8 blood lipid score. (7) The LE8 blood glucose score was assigned based on fasting blood glucose levels, hemoglobin A1c levels, and the presence of diabetes (the use of oral diabetes medications, insulin administration, or a prior diagnosis of diabetes). (8) The LE8 blood pressure score was calculated based on the average of two systolic and diastolic blood pressure measurements. For participants taking antihypertensive medication, 20 points were subtracted.
Covariates
Covariates were selected a priori, including age, sex, residential area, household income, educational attainment, and alcohol consumption [20]. Residential areas were categorized as urban or rural according to administrative classification. Household income was divided into tertiles: low (<25th percentile), moderate (25th‒74th percentile), and high (≥75th percentile). Educational attainment was classified as middle school or below, high school graduate, and college or above. Alcohol consumption was categorized by drinking frequency as none, ≤4 times/mo, or >4 times/mo.
Chronic kidney disease
CKD and its indicators were defined based on the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, incorporating both estimated glomerular filtration rate (eGFR) and UACR [21]. eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, which estimates kidney function based on serum creatinine, age, and sex [22]. A decreased eGFR was defined as a value below 60 mL/min/1.73 m2. Albuminuria was defined as a UACR of 30 mg/g or higher [21]. Participants with either decreased eGFR, albuminuria, or both were classified as having CKD.
For stratified analyses by CKD stage, decreased eGFR and albuminuria were further classified into G and A stages, respectively: eGFR as G1 (eGFR, ≥90 mL/min/1.73 m2), G2 (60–89 mL/min/1.73 m2), G3 (30–59 mL/min/1.73 m2), G4 (15–29 mL/min/1.73 m2), and G5 (<15 mL/min/1.73 m2); UACR as A1 (UACR, <30 mg/g), A2 (30–300 mg/g), and A3 (>300 mg/g).
Statistical analysis
Participant characteristics are presented as weighted percentages or means with 95% confidence intervals (CIs). Sampling weights provided by the KNHANES were applied to account for the complex survey design. We conducted multiple logistic regression analyses to examine the association between LE8 scores and CKD, using both categorical LE8 score (low, moderate, and high CVH) and the continuous score per 10-point higher CVH. CKD was assessed as an overall outcome and separately by its individual indicators: decreased eGFR and albuminuria. Model 1 was unadjusted; Model 2 was adjusted for age and sex; and Model 3 was further adjusted for residential area, household income, educational attainment, and alcohol consumption. In addition, the restricted cubic spline model was utilized to evaluate the nonlinear associations between LE8 scores and CKD.
To assess whether these associations persist for early-stage CKD, we sequentially excluded more advanced stages and progressively restricted the outcome to early-stage decreased eGFR or albuminuria (Supplementary Fig. 2, available online). Furthermore, to capture the earliest detectable kidney damage, we evaluated albuminuria among participants with preserved eGFR (G1–G2). All models used the same covariates and were estimated within each restricted sample.
Subgroup analyses were conducted after stratifying participants by sex, age, residential area, household income, educational attainment, and alcohol consumption. To assess statistical interaction, we added multiplicative interaction terms between the continuous LE8 score and each subgroup variable in the logistic regression models. Also, various sensitivity analyses were performed to assess the robustness of our findings. First, the associations of CKD with health behavior score and health factor score were examined separately. Second, we analyzed the associations between CKD and each component of the LE8 metric. Third, participants were categorized into quintiles based on the distribution of their CVH scores, and analyzed accordingly.
All analyses were performed using SAS version 9.4 (SAS Institute Inc.) and R version 4.0.3 (R Foundation for Statistical Computing).
Results
Participants characteristics
Table 1 presents the characteristics of the 12,264 participants. Of these, 13.3% (n = 1,632) were classified as having low CVH, 75.4% (n = 9,252) as moderate CVH, and 11.3% (n = 1,380) as high CVH. Across all CVH groups, health factor scores were lower than health behavior scores: Among participants with low, moderate, and high CVH, the mean health behavior scores were 35.4, 57.4, and 79.3, respectively, while the corresponding health factor scores were 50.1, 72.1, and 91.0. Participants in the high CVH group were younger, more likely to be female, to reside in urban areas, to have higher household income and educational attainment, and to consume alcohol less frequently.
In addition, the weighted prevalence of CKD decreased with an increasing CVH level: 16.9% in the low CVH group, 7.7% in the moderate group, and 2.6% in the high group. The prevalence of decreased eGFR (G3–G5) was 4.2%, 2.4%, and 0.2%, and that of albuminuria (A2–A3) was 15.0%, 6.0%, and 2.4%, respectively (Table 1, Fig. 1).
Primary analyses
Table 2 presents the associations of the LE8 score—analyzed categorically or per 10-point increment—with CKD and its indicators. Across all models, higher LE8 scores were significantly associated with lower odds of adverse kidney outcomes. In Model 3, compared to those in the low CVH group, the odds ratio (OR) for CKD was 0.39 (95% CI, 0.33–0.46) in the moderate CVH group and 0.22 (95% CI, 0.15–0.33) in the high CVH group. Similar inverse associations were observed for decreased eGFR (OR, 0.54; 95% CI, 0.40–0.72 for moderate CVH and OR, 0.12; 95% CI, 0.04–0.34 for high CVH, vs. low CVH) and albuminuria (OR, 0.36; 95% CI, 0.30–0.43 and OR, 0.22; 95% CI, 0.14–0.32, respectively). When analyzed as a continuous variable, each 10-point higher LE8 score was associated with 33% lower odds of CKD (OR, 0.67; 95% CI, 0.63–0.71), 31% for decreased eGFR (OR, 0.69; 95% CI, 0.63–0.76), and 35% for albuminuria (OR, 0.65; 95% CI, 0.61–0.70). Fig. 2 illustrates nonlinear associations between continuous LE8 scores and CKD, decreased eGFR, and albuminuria, using restricted cubic spline modeling.
We further stratified decreased eGFR and albuminuria according to the G and A staging system and conducted a series of analyses, progressively excluding participants with advanced CKD. The association between CVH and CKD remained consistent when defining the outcome as G3–G5 and was similar even when using G3 alone. Comparable findings were observed for albuminuria (Table 3). Notably, among individuals with preserved eGFR (G1–G2), the graded inverse associations persisted across all albuminuria stages, indicating that LE8 scores are associated even with the early-stage CKD (Table 4).
Subgroup and sensitivity analyses
Subgroup analyses by sex, age, residential area, household income, educational attainment, and alcohol consumption revealed consistent inverse associations between LE8 score and CKD across all subgroups. Stronger associations were observed in low- and middle-income groups compared with the high-income group (p for interaction = 0.006), whereas no significant interactions were observed in the other subgroups (Supplementary Table 4, available online).
Sensitivity analyses were conducted to assess the consistency of our findings. First, the associations of CKD with the health behavior score and health factor score were examined separately. A 10-point increase in the LE8 score was associated with lower odds of CKD in both domains, with stronger associations observed for the health factor score than for the health behavior score (Supplementary Tables 5, 6 and Supplementary Figs. 3, 4; available online). Second, the association between each LE8 component and CKD was also examined. Each ideal CVH metric was significantly associated with lower odds of CKD, except for diet and blood lipids (Supplementary Table 7, available online). Third, the LE8 score was divided into quintiles based on its distribution (Supplementary Table 8, available online). Consistent with the primary analysis, this sensitivity analysis demonstrated lower odds of CKD among higher CVH groups, with an even more pronounced dose-response association. For example, compared to CVH quintile 1 (Q1), the ORs for CKD were 0.54 (0.45–0.65) for Q2, 0.38 (0.31–0.47) for Q3, 0.34 (0.27–0.44) for Q4, and 0.28 (0.21–0.36) for Q5 (Supplementary Table 9, available online).
Discussion
This nationwide study investigated the association between LE8 scores and CKD, with a particular focus on individual CKD indicators and stages. Overall, 13.3% of participants had low CVH, 75.4% had moderate CVH, and 11.3% had high CVH. The corresponding prevalence of CKD was 16.9%, 7.7%, and 2.6%, respectively. Compared to low CVH, moderate and high CVH were progressively associated with lower odds of CKD. Similar inverse associations were observed for decreased eGFR and albuminuria. Notably, when stratified by CKD stage, higher CVH remained associated with lower odds of decreased eGFR and albuminuria, even at early stages. Multiple sensitivity analyses confirmed the robustness of these findings.
Although research examining the association between CVH, as defined by the LE8 metrics, and CKD is limited, our findings align with a prior United States study reporting that higher LE8 scores were associated with lower odds of CKD (adjusted OR per 10-point higher CVH, 0.79; 95% CI, 0.76–0.83) [13]. While the prior cross-sectional study analyzed pooled data from 2007 to 2018, our study utilized data from a shorter and more recent period (2019 to 2021), potentially providing a more temporally consistent sample and capturing current lifestyle and healthcare trends. Despite its categorical nature, the LS7 framework was also associated with a stepwise reduction in the risk of CKD, ESRD, and all-cause mortality as the number of ideal CVH components increased [23,24]. Although most studies have been conducted in Western populations, one Korean study demonstrated that sustained ideal CVH was protective against both CKD and CVD later in life; however, it was limited to middle-aged adults [25].
Previous studies have reported associations between individual components of LE8 and CKD. For instance, physical activity, regardless of age or type, was associated with a reduced risk of CKD [26]. In contrast, smoking increased the risk of incident CKD, with former (hazard ratio [HR], 1.13; 95% CI, 0.95–1.35) and current smokers (HR, 1.26; 95% CI, 1.07–1.48) showing a higher risk compared to never smokers [27]. Sleep health, a component newly added to the LE8 framework, has also emerged as a critical factor for CKD. Both short and long nighttime sleep durations were associated with an increased risk of CKD, presenting a U-shaped association [28,29]. In addition, poor sleep quality was also associated with higher CKD risk [28]. These findings underscore the important role of optimal sleep duration and quality in reducing CKD risk. Meanwhile, adherence to the DASH diet has been reported to provide protective effects not only against hypertension and CVD but also against CKD [30,31]. However, in our analysis, the LE8 diet score showed only a marginal association with decreased eGFR, and no significant association with albuminuria. It may reflect that dietary patterns contribute more to chronic and progressive decline in kidney function rather than to glomerular endothelial injury. The weaker or unclear associations between dietary patterns and albuminuria existed in other studies [32,33]. Moreover, obesity has been identified as an independent risk factor for CKD, even among individuals with an otherwise healthy metabolic profile [34]. Our findings on blood glucose and blood pressure are consistent with previous research, indicating that maintaining optimal levels of each metric is significantly associated with a lower risk of CKD [35,36]. In contrast, the LE8 blood lipids score based on non-HDL cholesterol was not significantly associated with CKD in our study. Although some studies reported associations between HDL cholesterol and CKD, evidence on LDL cholesterol, total cholesterol, and triglycerides has been inconsistent [37,38].
While both health behavior scores and health factor scores were associated with CKD, the magnitude of association was greater with health factor scores than with health behavior scores. Consistent with our findings, the Atherosclerosis Risk in Communities (ARIC) study using the LS7 reported that ideal levels of fasting glucose, BP, and BMI were more strongly associated with lower CKD risk, whereas health behaviors demonstrated concordant but relatively weaker associations [23]. This is likely because health factors are more downstream and proximal to outcomes than health behaviors, and they may partially mediate the association between health behaviors and outcomes. In addition, greater measurement errors in health behaviors than in health factors may further dilute the observed associations with outcomes. Prior studies have shown that health behaviors can improve clinical parameters over time [39] and independently predict CKD risk even after adjustment for biometric factors such as BMI, blood pressure, glucose, and cholesterol [40]. Therefore, health behaviors should be emphasized alongside efforts to maintain optimal health factor scores.
Our study has several strengths. First, we used a nationally representative dataset, the KNHANES, which enhances the generalizability of our findings. Second, KNHANES includes data on all LE8 components as well as eGFR and albuminuria, allowing for both individual and comprehensive assessments of the association between CVH and CKD. In particular, we examined the association of LE8 with each CKD indicator and stage, as defined by the KDIGO guidelines, suggesting that favorable CVH may play a crucial role even in subclinical stages of kidney dysfunction. Third, most prior studies examining the relationship between LE8 and CKD have been conducted in the United States or United Kingdom populations, highlighting the value of our study in providing evidence from an East Asian population. Last, multiple sensitivity analyses consistently reaffirmed the dose-response associations observed in the primary analyses. Several limitations should also be considered. First, the cross-sectional design of the study is prone to reverse causality and limits causal interpretation of the findings. Nevertheless, the associations remained consistent even when the outcome was restricted to early-stage CKD, where reverse causation from early CKD to poor CVH is less likely. Further investigation, including longitudinal and interventional studies, is warranted to establish temporality and causality between CVH and kidney outcomes. Second, health behavior information in the KNHANES was self-reported, which may have introduced bias. In particular, dietary intake was assessed using 24-hour dietary recalls, which may not adequately capture long-term patterns. Third, according to KDIGO guidelines, CKD is defined as abnormalities of kidney structure or function that persist for at least 3 months. However, our definition was based on a single measurement.
Higher LE8 scores were inversely associated with decreased eGFR, albuminuria, and overall CKD. These associations remained consistent across all CKD stages, including early-stage CKD. Our findings suggest the potential benefits of adhering to the LE8 framework in CKD prevention.
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