Life’s Essential 8 and All-Cause and Cardiovascular Disease Mortality Among Korean Adults

Abstract

Background: This study examined the association between Life’s Essential 8 (LE8) and the Korean population’s risk of all-cause and cardiovascular disease (CVD) mortality.

Methods and Results: This study included 21,482 adults aged ≥19 years who were enrolled in the Korea National Health and Nutrition Examination Survey. Cardiovascular health (CVH) was assessed using the LE8 score, which comprises the following 8 components: diet, physical activity, nicotine exposure, sleep health, body mass index, blood lipids, blood glucose, and blood pressure. The LE8 score ranges from 0 to 100 and was categorized as low (0–49), middle (50–79), high (≥80), with higher scores indicating better CVH. A Cox proportional hazards model was used to estimate hazard ratios, and the population attributable fraction (PAF) was used to determine individual risks associated with each CVH metric. During a median follow-up of 6.3 years, there were 709 all-cause and 122 CVD-related deaths. In the fully adjusted model, compared with low scores, middle and high LE8 scores were associated with 34% and 42% lower risks of all-cause mortality, respectively. A similar reduction in CVD mortality was observed with higher LE8 scores. Physical activity showed the highest PAF, contributing 37% for all-cause mortality and 51% for CVD mortality.

Conclusions: Higher LE8 scores were significantly associated with lower all-cause and CVD mortality. Physical activity had the greatest impact on PAF for mortality.

Cardiovascular disease (CVD) remains the leading cause of mortality globally, including in Korea, where the burden of CVD has continued to rise over past decades.^1,2 Despite advances in medical care and public health initiatives, CVD and related comorbidities contribute significantly to all-cause mortality, emphasizing the need for effective prevention strategies.³

To better address the multifaceted nature of cardiovascular health (CVH), the American Heart Association (AHA) originally introduced “Life’s Simple 7,” a set of 7 health metrics designed to promote and maintain CVH.⁴ Life’s Simple 7 was widely accepted and used in predicting cardiovascular outcomes; however, recent developments in health research and in our understanding of cardiovascular risk have prompted the AHA to expand and refine these guidelines. The result is Life’s Essential 8 (LE8), a more comprehensive and holistic metric that builds upon Life’s Simple 7 by incorporating an additional critical factor: sleep health.⁵ The updated CVH metric LE8 now includes diet, physical activity, nicotine exposure, sleep health, body mass index (BMI), blood lipids, blood glucose, and blood pressure as key components for assessing cardiovascular risk. This new construct is scored on a scale of 0 to 100 to enhance sensitivity in capturing interindividual differences in CVH.

LE8 has been established to predict cardiovascular outcomes in Western populations; however, its applicability and prognostic utility in non-Western populations, particularly in East Asia, remain underexplored.⁶ Given the distinct lifestyle characteristics of the Korean population, including a traditional diet rich in fermented foods, a lower prevalence of obesity, unique sleep and work patterns, as well as genetic predispositions and variations in healthcare access, it is essential to evaluate whether LE8 serves as a reliable predictor of mortality outcomes in this demographic.^7,8

To the best of our knowledge, no study has comprehensively evaluated the relationship between LE8 and mortality risk in the Korean population. This study aimed to fill this gap by analyzing a large cohort of Korean adults and assessing the overall predictive value of LE8 and the individual contributions of its components to mortality risk. Our findings are expected to provide critical insights that could inform tailored public health interventions and policies to improve CVH and reduce mortality in Korea.

Methods

Study Population

The Korea National Health and Nutrition Examination Survey (KNHANES) is a nationally representative survey conducted annually since 1998 by the Korea Disease Control and Prevention Agency (KDCA) to assess the health and related behaviors of the Korean population. Participants were selected using stratified, multistage probability sampling based on sex, age, and region. The survey includes a health examination, health interview, and nutrition survey, with physical assessments conducted in mobile examination centers. Further details of the KNHANES methodology are available in previous publications.^9,10 In this analysis, we included participants aged ≥19 years from the KNHANES surveys conducted between 2014 and 2018. We excluded individuals who did not consent to death data linkage, those with missing LE8 scores, and those with a history or prevalence of CVD at baseline, resulting in a final sample of 21,482 participants (Supplementary Figure 1). Written informed consent was obtained from all participants. The study protocol was approved by the Institutional Review Boards of the KDCA and Seoul National University (IRB No. E2410/004/007). For de-identified data, the requirement for informed consent was waived.

LE8 Assessment

CVH was assessed based on the LE8 criteria established by the AHA.⁵ The LE8 framework includes 4 health behaviors (diet, physical activity, nicotine exposure, and sleep health) and 4 health factors (BMI, blood lipids, blood glucose, and blood pressure). Supplementary Table 1, provides detailed algorithms for calculating the LE8 score of each metric using KNHANES data. Each of the 8 CVH metrics was scored on a scale of 0–100, and the overall LE8 score was calculated as the average of the individual scores. Participants were categorized into 3 groups based on their LE8 score: low (0–49), middle (50–79), and high (≥80) CVH.^6,11

Diet was assessed using the Healthy Eating Index specific to the Korean population, calculated from dietary recall data, 24-h dietary recall, and food frequency questionnaires, all based on KNHANES data.¹² Physical activity was evaluated using the self-reported Global Physical Activity Questionnaire, which quantifies the number of minutes per week participants engaged in moderate-to-vigorous physical activity.^13,14 Nicotine exposure was determined using a self-reported questionnaire assessing current and past smoking status. Sleep health was measured using a self-reported average daily sleep duration. Obesity was defined as BMI ≥25 kg/m², which is the recommended threshold for adults in the Asian and Pacific regions.¹⁵ Blood samples were obtained from the antecubital vein after an 8-h fast. Total cholesterol, high-density lipoprotein cholesterol (HDL-C), and fasting blood glucose levels were measured using an enzymatic method (Hitachi Automatic Analyzer 7600; Hitachi, Japan). HbA1c was measured using HPLC (HLC-723G7; Tosoh, Tokyo, Japan). Blood pressure was measured on the right arm using a mercury sphygmomanometer (Baumanometer; Baum, Copiague, NY, USA). Information on medication use was collected through a self-administered questionnaire.

Outcome Assessment

KDCA has established a database that integrates KNHANES data with cause-of-death statistics provided by Statistics Korea. This integration was accomplished by matching KNHANES data with the cause-of-death statistics using resident registration numbers for participants aged ≥19 years who consented to data linkage. The linked dataset includes deidentified information on survival status, with follow-up data available until December 31, 2021.¹⁶ Causes of death were classified according to the International Classification of Diseases, Tenth Revision (ICD-10).¹⁷ All-cause mortality was defined as death from any cause, whereas CVD mortality was specifically identified by ICD-10 codes I00–I99.

Statistical Analysis

Baseline characteristics were analyzed using Chi-squared tests for categorical variables and general linear models for continuous variables. We used log-log plots and Schoenfeld residuals to assess the proportional hazard assumption, which confirmed that the assumption was satisfied.

Cox proportional hazard models were used to evaluate the association between LE8 and all-cause and CVD mortality. To account for subjects without events during follow-up, right censoring was applied, defining survival time from study entry to event occurrence, with event-free individuals censored at the last follow-up. Two models were constructed: Model 1 was adjusted for age (years) and sex (male or female), whereas Model 2 was further adjusted for education level (≤elementary school, middle school, high school, and ≥college), household income level (low, low-middle, middle-high, and high), marital status (unmarried, widowed or divorced, and married), and heavy alcohol consumption (having ≥7 drinks at least twice a week for men and ≥5 drinks at least twice a week for women). In addition, we calculated the population attributable fraction (PAF) to estimate the proportion of mortality cases that could be attributed to suboptimal levels of LE8 in the population.¹⁸ To estimate the impact of low-to-moderate CVH scores and non-ideal levels of LE8 metrics on the burden of non-communicable diseases, the hypothetical population PAF% was derived using Levin’s formula as follows:¹⁹

  

$$PAF(\%)=Pe\frac{(HR-1)}{Pe\times (HR-1)+1}\times 100$$

where Pe is the prevalence of the exposure in the population, and HR is the hazard ratio associated with the exposure.

Restricted cubic splines with 3 knots were used to explore potential non-linear relationships between continuous variables and mortality outcomes. Kaplan-Meier survival curves were generated to visualize the cumulative incidence of mortality across different levels of CVH metrics. Correlation coefficients were also calculated to examine relationships between the key variables.

Sensitivity analyses were conducted by excluding individuals who died within the first 2 years of follow-up and by including C-reactive protein levels and estimated glomerular filtration rate as additional covariates to test the robustness of our findings. All analyses were performed using STATA 17.0 (Stata Corp LP, College Station, TX, USA), and statistical significance was set at 2-sided P<0.05.

Results

Baseline Characteristics

Table 1 presents the baseline characteristics of the 21,482 participants across the 3 categories of total LE8 score (low, middle, and high). Our study identified 709 deaths due to any cause, including 122 CVD-related deaths, recorded after a median follow-up duration of 6.3 years. The participants had a mean (±SD) age of 50.2±16.3 years and 12,580 (58.6%) were women. The mean (±SD) of the total LE8 scores was 69.3±13.5. Participants with higher LE8 scores were more likely to be women, high school graduates, have mid-high income, and consume less alcohol.

Table 1.

Baseline Characteristics of Participants Based on Life’s Essential 8 Scores and Levels of Cardiovascular Health

	Overall	CVH score			P value
		Low (0–49)	Mid (50–79)	High (≥80)
No. participants	21,482	1,752 (8.2)	14,465 (67.3)	5,265 (24.5)
Age (years)	50.2±16.3	53.8±14.6	52.4±16.1	43.0±15.2	<0.001
Female sex	12,580 (58.6)	471 (26.9)	7,853 (54.3)	4,256 (80.8)	<0.001
Education
≥High school graduate	15,061 (70.1)	1,040 (59.3)	9,303 (64.3)	4,523 (85.9)	<0.001
Household income
≥Mid-highA	12,485 (58.1)	843 (48.1)	7,992 (55.3)	3.650 (69.3)	<0.001
Marital status
Married	15,168 (70.6)	1,752 (69.0)	10,401 (71.9)	3,559 (67.6)	<0.001
Heavy alcohol drinkingB	2,304 (10.7)	447 (25.5)	1,635 (11.3)	222 (4.2)	<0.001
LE8 scores
Total score	69.3±13.5	43.1±5.6	66.3±8.1	86.1±4.7	<0.001
Diet score	39.6±31.2	19.2±23.0	37.2±30.1	53.1±31.3	<0.001
Physical activity score	62.2±44.2	20.3±36.5	57.5±45.0	89.3±24.2	<0.001
Nicotine score	73.5±37.3	33.8±39.1	70.5±37.4	94.8±16.8	<0.001
Sleep health score	82.6±23.8	67.8±30.3	81.5±24.1	90.7±16.8	<0.001
BMI score	75.7±24.4	54.6±24.0	72.7±23.9	91.1±16.2	<0.001
Blood lipid score	66.4±28.7	43.6±26.8	62.1±27.7	83.2±22.8	<0.001
Blood glucose score	80.5±24.1	59.6±24.5	78.2±24.2	93.9±14.8	<0.001
Blood pressure score	73.5±30.5	46.1±29.2	69.8±30.4	92.9±17.3	<0.001

Unless indicated otherwise, values are expressed as the mean±SD or n (%). ^AHousehold income refers to households with a monthly income of at least 2 million KRW. ^BHeavy alcohol drinking was defined as having ≥7 drinks at least twice a week for men and ≥5 drinks at least twice a week for women. BMI, body mass index; CVH, cardiovascular health; LE8, Life’s Essential 8.

Association Between LE8 and the Risk of All-Cause and CVD Mortality

Table 2 presents all-cause and CVD mortality among participants based on the 3 categories of total LE8 score. The higher the LE8 scores, the lower the risk of all-cause and CVD mortality. According to the fully adjusted model, participants with mid LE8 scores had a 34% lower risk of all-cause mortality than those with low LE8 scores, whereas participants with high LE8 scores had a 42% lower risk. In addition, for every 1-SD increase in the LE8 score, the hazard ratio (HR) was 0.82 (95% confidence interval [CI] 0.76–0.91). A similar trend of reduction was observed in CVD mortality. Using a fully adjusted model and the group with a low total LE8 score as the reference, the groups with middle and high total LE8 scores had HRs of 0.55 (95% CI 0.35–0.85) and 0.30 (95% CI 0.12–0.74), respectively. For every 1-SD increase in the LE8 score, the HR decreased to 0.68 (95% CI 0.56–0.84). Sensitivity analyses were performed by excluding individuals who died within 2 years and by adjusting for high-sensitivity C-reactive protein and estimated glomerular filtration rate values. The results demonstrated similar HRs to those observed in the main analysis (Supplementary Tables 2,3).

Table 2.

Association Between Life’s Essential 8 and the Risk of All-Cause and Cardiovascular Disease Mortality

	No. participants	No. deaths	Hazard ratio (95% CI)
			Model 1	Model 2
All-cause mortality
Total CVH scoreA
Low CVH	1,752	123	1 (Ref.)	1 (Ref.)
Mid CVH	14,465	526	0.58 (0.48–0.71)	0.66 (0.54–0.81)
High CVH	5,265	60	0.48 (0.33–0.61)	0.58 (0.42–0.79)
Per 1-SD increment in LE8B			0.77 (0.71–0.83)	0.82 (0.76–0.91)
P for trend			<0.001	<0.001
CVD mortality
Low CVH	1,752	26	1 (Ref.)	1 (Ref.)
Mid CVH	14,465	90	0.45 (0.29–0.70)	0.55 (0.35–0.85)
High CVH	5,265	6	0.21 (0.08–0.51)	0.30 (0.12–0.74)
Per 1-SD increment in LE8			0.61 (0.50–0.74)	0.68 (0.56–0.84)
P for trend			<0.001	0.002

^ACardiovascular health (CVH) scores are measured on a scale of 0–100 and categorized as low CVH (0–49), middle CVH (50–79), and high CVH (80–100). ^BA 1-SD variation in the Life’s Essential 8 (LE8) score corresponds to a difference of 13.5 points. Model 1 was adjusted for age and sex. Model 2 was adjusted for age, sex, education level, house income level, marital status, and heavy alcohol drinking. CI, confidence interval; CVD, cardiovascular disease.

PAF of LE8 Factors for All-Cause and CVD Mortality

Of the 8 distinct metrics making up the LE8 score, physical activity had the highest PAF of 37.7% (95% CI 30.0–45.2) for all-cause mortality and 51.1% (95% CI 30.9–67.4) for CVD mortality (Figure 1). The next highest PAF for both overall and CVD mortality was diet (15.7% [95% CI 8.2–22.9] and 38.3% [95% CI 20.7–53.3], respectively). The LE8 factors of blood pressure, sleep health, and diet made a substantially greater contribution to CVD mortality than all-cause mortality.

Figure 1.

Population attributable fraction (PAF) factors for (Left) all-cause and (Right) cardiovascular disease (CVD) mortality of Life’s Essential 8 (LE8). Histograms show data as the mean±SD. CVD, Cardiovascular disease; PAF, population attributable fraction; CI, Confidence interval.

Dose-Response Association Between LE8 Score and All-Cause and CVD Mortality

The Cox proportional hazards models with restricted cubic splines revealed a non-linear relationship between LE8 scores and the risk of all-cause mortality (P for non-linearity <0.001) and CVD-specific mortality (P for non-linearity=0.001; Figure 2). As LE8 scores increased, the mortality risk decreased significantly. However, the reduction did not occur uniformly across all score ranges, and the association had notable non-linearity. This suggests that higher LE8 scores are generally associated with a lower mortality risk; however, the rate of risk reduction varies at different levels of LE8 score.

Figure 2.

Dose-response association between the Life’s Essential 8 (LE8) score and (Left) all-cause and (Right) cardiovascular disease (CVD) mortality. Adjusted for age, sex, education level, household income level, marital status, and heavy alcohol drinking. CI, Confidence interval.

Sensitivity Analysis

Supplementary Figure 2 shows correlations between individual LE8 metrics. Participants with high LE8 scores exhibited a significantly lower cumulative incidence of all-cause and CVD mortality (log-rank test, P<0.001; Supplementary Figure 3).

Discussion

This study explored the relationship between LE8 and the risk of all-cause and CVD mortality among Korean adults. Our findings demonstrated that higher LE8 scores, which indicate better CVH, are significantly associated with reduced risks of all-cause and CVD mortality. These results align with those of previous research conducted in Western populations, reinforcing the universal applicability of LE8 as a comprehensive metric for CVH assessment.^20,21

To the best of our knowledge, the present study is the first to analyze mortality risk based on the new CVH metric and investigate the PAF of individual components in a Korean population. Previous studies have primarily focused on the relationship between CVD and Life’s Simple 7, the earlier metric developed by the AHA in 2010, with only a few studies addressing these associations.^22,23 Given that lifestyle factors vary across countries and ethnicities, more comprehensive data and an updated, refined metric like LE8 were needed to assess all-cause and CVD mortality risk. This study is particularly important because it used a dietary assessment index validated for reliability and validity within the context of Korean nutritional habits.¹² In addition, the inclusion of the newly added sleep health component further enhances the cultural relevance and reliability of our findings, ensuring that the results are more applicable to the Korean population. South Korea is one of the most sleep-deprived countries globally, and this has been linked to chronic conditions such as hypertension and diabetes, as well as increased mortality rates.^24–26 Given these effects, incorporating sleep into the analysis of LE8 is crucial for understanding its broader implications on public health. Previous studies examining the association between LE8 and mortality risk have shown results consistent with our findings, demonstrating an inverse relationship between CVH scores and mortality risk.^11,21 For example, a study conducted among Americans reported a 40% reduction in all-cause mortality risk (adjusted HR 0.60; 95% CI 0.48–0.75) and a 54% reduction in cardiovascular mortality risk (adjusted HR 0.46; 95% CI 0.31–0.68) among participants with high CVH compared with those with low CVH.²¹ Similarly, a study conducted in a Chinese population found that individuals in the low CVH group had significantly higher risks of CVD and all-cause mortality compared to those in the high CVH group,¹¹ with adjusted HRs of 7.34 (95% CI, 3.19–16.89) and 2.54 (95% CI, 1.27–5.06) for baseline CVH. Similar elevated risks were observed when using time-updated and time-varying CVH measures. These findings reinforce the critical importance of maintaining high CVH scores to reduce mortality risk across different populations. As the prevalence of metabolic syndrome, which is closely linked to CVH indicators, continues to rise in South Korea, effective management is becoming crucial.²⁷ Prevention and improvement of CVH metrics can be achieved through positive changes in LE8 health behaviors.^6,28 A healthy diet, regular physical activity, adequate sleep, and smoking cessation contribute to CVH by reducing inflammation, lowering blood pressure, and enhancing lipid profiles while also improving vascular function, reducing oxidative stress, and minimizing the risk of atherosclerosis.^24,29,30

In the present study, the PAF for BMI was among the lowest, reflecting the unique characteristics of the Korean population. Research indicates that Korea has one of the lowest obesity rates globally, with a small proportion of individuals having a BMI of ≥35 kg/m².³¹ Consequently, the relationship between BMI and mortality in our study exhibited the “BMI paradox,” whereby higher BMI was associated with lower mortality, likely reflecting ethnic-specific factors.^32,33

Of the 8 metrics in LE8, physical activity had the greatest impact on PAF for all-cause and CVD mortality. This finding is consistent with that of another study that identified physical activity as a key contributor to reducing mortality risk.³⁴ In contrast, BMI and blood lipid levels were not significantly associated with mortality risk in other studies, which is consistent with our results. Physical activity is a well-established factor in reducing cardiovascular events and mortality because it enhances metabolic health, lowers inflammation, and improves cardiovascular function.^35–37 These benefits reinforce the critical role of physical activity as a cornerstone of preventive health strategies. Another finding of the present study was that the lipid profile score, defined by non-HDL-C levels, was not significantly associated with all-cause or CVD mortality. Previous studies have reported a U-shaped relationship between non-HDL-C and HDL-C and all-cause and cardiovascular mortality.^38,39 Markedly low cholesterol levels may indicate poor health, inflammation, or malnutrition, whereas elevated HDL-C levels could paradoxically accelerate cellular aging and impair endothelial progenitor cells, leading to mixed outcomes.^40–43 The PAF analysis conclusively showed that behavioral factors, such as physical activity, a healthy diet, and good sleep health, play a significant role in mortality risk, underscoring the importance of adopting healthier behaviors to reduce mortality.

A direct comparison of LS7 and LE8 using Harrell’s C-statistic demonstrated that LE8 had a higher C-index (0.72) than LS7 (0.67),⁴⁴ indicating improved predictive accuracy for all-cause and cardiovascular mortality. This suggests that LE8 serves as a more refined CVH assessment tool, incorporating sleep health as a key role in its enhanced predictive performance. Sleep is increasingly recognized as a critical factor in CVH, and our findings further support its inclusion in comprehensive risk assessment models.⁴⁵ While the association between LS7 and mortality has been well established in Western populations, evidence from Asian cohorts remains limited. Our study provides novel insights by demonstrating the superior predictive capability of LE8, reinforcing its applicability in non-Western populations.^46,47 These results highlight the advantages of LE8 over LS7 as a more holistic and precise measure of CVH, suggesting that integrating sleep health into cardiovascular risk assessments can improve risk stratification and guide more effective public health interventions.

To the best of our knowledge, this study is the first to examine the association between CVH metrics and all-cause and CVD mortality risk using nationally representative data from a Korean population. In addition, the study used PAF analysis to assess individual risks for each metric and confirmed dose-response associations, further strengthening the findings. However, this study has some limitations that should be considered. First, behaviors such as sleep, physical activity, diet, and smoking were assessed through self-report, which may introduce recall bias and affect data accuracy. Second, we measured exposure at a single time point, which limited our ability to account for changes in these behaviors over the follow-up period. Third, despite our efforts to control for various confounders, the possibility of potential and residual confounding factors cannot be ruled out. Fourth, the exclusion of 6,940 participants due to missing data on key variables could have introduced selection bias, which may affect the validity and generalizability of our findings. This limitation should be considered when interpreting the results. Fifth, the study population consists exclusively of Koreans, which limits the generalizability of our findings to other racial or ethnic groups. However, most previous studies have focused on Western populations; our study is novel in that we analyzed the relationship between LE8 and mortality risk in a Korean population, where such studies have been lacking. Finally, we could not distinguish between fatal and non-fatal CVD events. To address this limitation, we conducted a sensitivity analysis by excluding participants who died within the first 2 years of follow-up, which partially mitigates this concern.

Conclusions

In conclusion, this study revealed that higher LE8 scores are significantly associated with reduced risks of all-cause and CVD mortality. Physical activity had the greatest influence on lowering mortality risk, followed by unhealthy diet and poor sleep health, which were also significant contributors to increased CVD mortality. These findings suggest that improving CVH and reducing mortality risks can be achieved through positive changes in health behaviors. Furthermore, these insights can inform public health policies aimed at promoting healthier lifestyles and preventing CVD at the population level.

Acknowledgments

The authors express their gratitude to the staff and participants of the Korea National Health and Nutrition Examination Survey for their significant role in providing the data for this study.

Sources of Funding

This study did not receive any specific funding.

Disclosures

The authors have no conflicts of interest to declare.

Author Contributions

Y.H. made substantial contributions to the conceptualization and design of the work. Y.H., S.L., and S.K. made substantial contributions to the investigation and methodology. Y.H. and Y.C. were responsible for data analysis and interpretation. Y.H., S.L., and S.K. drafted the paper. Y.S.K. provided supervision and project administration. All authors participated in the review, revision, and approval of the final paper.

IRB Information

This study was approved by the Ethics Committee of Seoul National University (Approval no. E2410/004/007).

Supplementary Files

Please find supplementary file(s);

https://doi.org/10.1253/circj.CJ-24-0922

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