GRAPHICAL ABSTRACT

Highlights

• LE8 health behavior score inversely predicts all-cause mortality in CKM syndrome.

• A 10-point higher behavior score lowers mortality by 22% in early CKM stages (0–1).

• A linear dose-response links health behavior score and survival across CKM stages.

INTRODUCTION

Cardiovascular-kidney-metabolic (CKM) syndrome, a newly defined clinical entity integrating cardiovascular, renal, and metabolic dysfunctions [1,2], affects almost 90% of United Statement adults (stage 1 or higher) [3] and has become the major cause of death in 2021 [4,5]. The American Heart Association (AHA)’s staging system identifies escalating risks from no metabolic disturbances (stage 0) to established multiorgan injury (stage 3–4), yet current risk stratification tools inadequately address the behavioral determinants of disease progression [1]. While the Life’s Essential 8 (LE8) score—a composite metric of diet, physical activity, nicotine exposure, sleep health, body mass index (BMI), blood lipids, glucose, and blood pressure—has demonstrated cardiovascular benefits in general populations [6], its prognostic value across CKM stages remains unexplored. This gap is critical because early-stage CKM (0–2) represents a window for reversible behavioral interventions, whereas stage 3–4 marks irreversible organ damage where biological factors may dominate.

Emerging evidence suggests that health behavior patterns differentially influence CKM trajectory. For instance, poor sleep quality exacerbates insulin resistance and glomerular hyperfiltration in early-stage CKM [7], while physical inactivity accelerates endothelial dysfunction in advanced stages [8]. However, existing studies predominantly focus on isolated risk factors rather than the synergistic effects captured by LE8. Furthermore, socioeconomic disparities in LE8 adherence—particularly in low-income populations with limited access to healthy foods and preventive care—may partially explain the higher CKM mortality rates in marginalized communities [9,10]. A unified framework evaluating LE8’s stage-specific associations with mortality could inform precision prevention strategies tailored to CKM severity.

This study leverages the National Health and Nutrition Examination Survey (NHANES 2005–2018) to address three critical questions: (1) whether LE8 exhibits differential mortality associations across CKM stages 0–3; (2) how behavioral (diet, activity, sleep) versus biological (BMI, glucose) LE8 components contribute to risk modulation; and (3) if LE8 optimization attenuates excess mortality disproportionately affecting early-stage CKM populations.

METHODS

Study population

This study utilized data from the NHANES spanning 2005 to 2018, a nationally representative cross-sectional and longitudinal cohort with linked mortality records through December 31, 2019. Participants aged 20 to 79 years with complete data on LE8 components and CKM staging criteria were included (n=9,152). Exclusion criteria encompassed pregnancy, or incomplete clinical information or mortality follow-up (Fig. 1).

Assessment of LE8 score

The LE8 score (0–100 scale) was calculated as per AHA guidelines [6]: Health behaviors: diet (24-hour recall), physical activity (metabolic equivalent-minutes/week), sleep duration (hours/night), and nicotine exposure (self-reported smoking); Health factors: BMI, non-high-density lipoprotein (HDL) cholesterol, fasting glucose, and systolic blood pressure. Scores were standardized to population percentiles and categorized into poor (0–49), moderate (50–79), and ideal (80–100) cardiovascular health (CVH), detailed in Supplementary Table 1.

CKM syndrome staging

CKM stages were classified into five stages according to the AHA framework [1]: stage 0: no risk factors (normal BMI, blood glucose, lipids, blood pressure, and kidney function); stage 1: subclinical metabolic risk (overweight/obesity, prediabetes, or borderline lipid/blood pressure abnormalities); stage 2: clinical metabolic risk factors (such as type 2 diabetes mellitus, hypertension, or dyslipidemia) or moderate- to high-risk chronic kidney disease (CKD); stage 3: subclinical cardiovascular disease (CVD) or risk equivalents (10-year atherosclerotic cardiovascular disease risk ≥20% or very high-risk CKD); stage 4: clinical CVD or kidney failure.

Other clinical characteristics

In this study, we considered the following covariates in the final analysis: age, sex, race, educational level, marital status, ratio of family income to poverty, alcohol consumption status. Race/ethnicity was classified as non-Hispanic White, non-Hispanic Black, Mexican American, or other. Education level was categorized as less than high school, high school or equivalent, or college or above. Marital status was categorized into the three groups: married/living with partner, never married, and other (including widowed, divorced, or separated). Family income-to-poverty ratio was classified as <1.3, 1.3–3.5, and >3.5. Alcohol consumption status was grouped into never, low-to-moderate and heavy drinker. Participants with Health Questionnaire-9 scores ≥10 were considered to have depression [11]. Diabetes was defined as follows: (1) self-reported doctor diagnosis; (2) fasting blood glucose ≥125 mg/dL; (3) glycosylated hemoglobin ≥6.5%; or (4) taking diabetes medication (insulin or other hypoglycemic medications) [12].

Statistical analyses

In accordance with NHANES analytical guidelines, all analyses incorporated the complex survey design and sampling weights to ensure nationally representative estimates. Participant characteristics are presented as weighted means (standard errors) for continuous variables and unweighted frequencies (weighted percentages) for categorical variables. Weighted Student’s t-tests and chi-square tests were used to compare continuous and categorical variables, respectively. Cox proportional hazards regression models were employed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for associations between LE8 scores and all-cause mortality. Three hierarchical models were constructed: Crude model: no covariate adjustment; model 1: adjusted for age, sex, race/ethnicity, educational attainment, family income-to-poverty ratio, and alcohol consumption; model 2: further adjusted for estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), renin-angiotensin system (RAS) inhibitor use, and statin use.

Interaction and subgroup analyses were conducted via stratified Cox models to evaluate effect modification by age group, sex, race/ethnicity, income-to-poverty ratio, diabetes, and hypertension status. Sensitivity analyses included: (1) incorporating participants aged ≥80 years; (2) additional adjustment for depression; and (3) exclusion of deaths within the first 2 years of follow-up. Restricted cubic spline models with knots at the 10th, 50th, and 90th percentiles tested non-linear relationships between LE8 scores and mortality. Population-attributable fractions (PAFs) were computed using the R package graphPAF [13]. All analyses were performed in R version 4.4.2 (R Foundation for Statistical Computing, Vienna, Austria) using the survey package to account for sampling weights. A two-sided P<0.05 defined statistical significance.

Ethical approval

The study was performed according to the Declaration of Helsinki. The study protocols of NHANES were approved by the National Center for Health Statistics Research Ethics Review Board (ethical approval code: Protocol #2005-06, Protocol #2011-17, Protocol #2018-01).

RESULTS

Characteristics of the study population

This nationally representative analysis included 9,152 eligible participants (weighted to 141.6 million United States adults) from 36,973 NHANES 2005–2018 candidates aged 20–79 years, after excluding 27,821 individuals with incomplete or invalid CKM or LE8 data. Weighted analyses revealed a mean age of 45.08±0.29 years, with 48.24% male and 70.14% non-Hispanic White participants. The cohort exhibited an average LE8 score of 69.79, categorized as ideal (25.24%), moderate (66.43%), and poor CVH score (8.33%), with detailed metrics provided in Supplementary Table 2.

Among the study population, 3,078 participants (37.84%) were classified into early CKM stages (0–1), while 6,074 (62.16%) were in advanced stages (2–3) (Table 1). Comparative analyses demonstrated that participants with CKM stages 2–3 were significantly older (mean age difference 10.2 years), more likely to be male (51.42% vs. 43.02%), less educated (61.52% vs. 70.51% college graduates), and with lower poverty-to-income ratios. This group also exhibited elevated metabolic risk markers, including higher BMI (30.58 kg/m² vs. 26.19 kg/m²) and UACR (35.22 mg/g vs. 6.86 mg/g), along with increased use of RAS inhibitors (23.92% vs. 0.24%) and statins (18.23% vs. 4.41%). Notably, advanced CKM stages were associated with a 13.5-point reduction in LE8 scores (64.68±0.28 vs. 78.18±0.31) and a 9.5-fold higher prevalence of poor CVH score (12.6% vs. 1.32%), highlighting the substantial CVH burden in this population.

LE8 scores associated with all-cause mortality in CKM

During a median follow-up of 7.92 years (interquartile range, 4.42 to 11.33), a total of 521 deaths occurred. Restricted cubic spline models adjusted for age, sex, race, marital status, education level, poverty income ratio, alcohol consumption, eGFR, UACR, RAS inhibitor use and statin use, demonstrated a negatively linear relationship between LE8 scores and mortality risk in full cohort, CKM stage 0–1 and CKM stage 2–3 (all non-linear P>0.05) (Fig. 2A-C). Multivariable Cox proportional hazards models demonstrated an 18% mortality risk reduction per 10-point LE8 increase (HR, 0.82; 95% CI, 0.76 to 0.89) (Supplementary Table 3). Notably, in early-stage CKM (0–1), participants with poor LE8 scores exhibited a 4.81-fold elevated mortality risk (95% CI, 1.16 to 20.05) after full adjustment, whereas no significant association was observed in advanced CKM stages (2–3) (HR, 1.75; 95% CI, 0.93 to 3.27), suggesting diminished CVH protection in progressive disease states (Fig. 3, Supplementary Table 3).

Health behaviors CVH score associated with all-cause mortality in CKM

To determine the critical components of LE8 influencing CKM, we stratified the LE8 metrics into two discrete domains for independent analysis: health behaviors score (including diet, physical activity, nicotine exposure, and sleep duration) and health factors score (including BMI, non-HDL, blood glucose, and blood pressure). This stratification enables targeted evaluation of distinct pathophysiological contributions to CKM. Multivariable-adjusted restricted cubic spline analysis demonstrated a linear inverse dose-response relationship between health behaviors scores and all-cause mortality across all CKM stages (Fig. 2D-F), whereas no such linear correlation attained for health factors score (Fig. 2G-I).

The Kaplan-Meier analysis also revealed that all-cause mortality decreased with increasing LE8 health behaviors scores in CKM (P<0.0001) (Fig. 3A), CKM 0–1 (P=0.002) (Fig. 3B) and CKM 2–3 (P<0.0001) (Fig. 3C). Cox proportional hazards models demonstrated a significant inverse association between LE8 health behaviors scores and mortality risk, with each 10-point increment in health behaviors score corresponding to an 15% reduction in all-cause mortality (HR, 0.85; 95% CI, 0.80 to 0.90). Additionally, after adjustment for multiple covariates in model 2 (Fig. 4), the poor health behaviors scores group independently related with the increased risk of mortality in CKM 0–1 participants (HR, 5.68; 95% CI, 2.14 to 15.08) and in CKM 2–3 individuals (HR, 2.22; 95% CI, 1.47 to 3.37).

Notably, the adjusted PAF for the association of health behaviors scores with all-cause mortality in CKM participants with poor health behaviors (score <50) was 10.4% when compared with the ideal or moderate health behaviors (score ≥50). However, the adjusted PAF increased to 58.1% (95% CI, 22.2% to 79.2%) for all-cause mortality in CKM 0–1 and 37.9% (95% CI, 17.5% to 54.1%) in CKM 2–3, when comparing moderate or poor (score <80) with the ideal (score ≥80) health behaviors (Table 2). This gradient relationship underscores the discriminant validity of health behavior metrics in stratifying mortality risk within the CKM population, revealing substantial modifiability through behavioral interventions.

Subgroup and sensitivity analyses

Subgroup analyses by age (<60 years vs. ≥60 years), sex (male vs. female), race (non-Hispanic White, non-Hispanic Black, and Mexican American), ratio of family income to poverty (<1.3, 1.3–3.5, >3.5), diabetes mellitus (yes vs. no), and hypertension (yes vs. no) revealed similar associations between health behaviors scores and all-cause mortality in CKM (Supplementary Fig. 1). Sensitivity analyses were summarized in Table 3. After including participants aged ≥80 years old in the CKM population, the poor health behaviors score was associated with an increased risk for all-cause mortality (HR, 2.39; 95% CI, 1.81 to 3.15). After further adjustment for depression, the HRs was 1.82 (95% CI, 1.37 to 2.43) for moderate health behaviors scores (CVH 50–79) and 2.42 (95% CI, 1.68 to 3.49) for poor health behaviors scores (CVH 0–49). In addition, after excluding individuals died in first follow-up year, the association between poor health behaviors score and all-cause mortality remained in CKM (HR, 2.57; 95% CI, 1.81 to 3.64).

DISCUSSION

Differential prognostic utility of LE8 across CKM stages

This study provides the first evidence of differential prognostic utility of the LE8 score across CKM stages. While the composite LE8 score failed to predict mortality in advanced CKM (stage 2–3), its behavioral subscore demonstrated a robust, linear inverse association with mortality risk regardless of disease severity. This divergence highlights that modifiable lifestyle factors retain prognostic relevance even in advanced disease, whereas biological drivers (e.g., insulin resistance, chronic inflammation) dominate outcomes in later stages [14-17]. Notably, the persistent mortality reduction linked to health behaviors in stage 2–3 (13% per 10-point increment) suggests lifestyle interventions may attenuate oxidative stress and endothelial dysfunction, partially counteracting irreversible organ damage [8,18].

Behavioral interventions in early-stage CKM

The strongest protective effect of LE8 health behaviors scores was observed in early-stage CKM (stage 0–1), with a 22% mortality reduction per 10-point behavioral improvement. This underscores the reversibility of metabolic dysfunction through lifestyle modifications such as diet optimization, smoking cessation, and sleep health promotion [19,20]. These findings align with the AHA’s emphasis on primordial prevention and suggest that early behavioral interventions could delay or prevent progression to organ injury. However, socioeconomic disparities in LE8 adherence—such as limited access to healthy foods and preventive care—likely contribute to the 2.3-fold higher mortality in marginalized populations [9,21], necessitating structural reforms to address systemic inequities.

Integrated management in advanced CKM

For stages 2–3, where biological and structural organ damage prevail [1], the linear dose-response relationship between LE8 health behaviors score and mortality supports integrating lifestyle optimization with guideline-directed therapies. Pharmacological agents like sodium glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like peptide-1 (GLP-1) agonists, which target both metabolic and hemodynamic pathways [22,23], may synergize with behavioral changes to mitigate residual risk. For example, physical activity could enhance endothelial function in heart failure [8], while smoking cessation might slow CKD progression [24,25]. This dual approach aligns with precision prevention paradigms, tailoring interventions to disease severity.

Implications for clinical practice

The stage-specific gradient in PAF reveals a striking opportunity for preventive medicine. The 58.1% mortality burden attributable to suboptimal health behaviors in early CKM stages (0–1) underscores a critical window for intervention, where behavioral adjustments may substantially decelerate disease progression. Conversely, the 37.9% modifiable risk in advanced stages (2–3) emphasizes that secondary prevention remains valuable, potentially through synergistic effects with pharmacological therapies. The absence of linear mortality associations with LE8 health factor scores may reflect the limited reversibility of established biomarkers (e.g., hypertension, dyslipidemia) compared to modifiable behaviors, reinforcing the need for primordial prevention strategies.

Our findings carry important implications for precision public health. The discriminant validity of LE8 behavioral metrics in stratifying mortality risk—evidenced by the 1.5-fold difference in poor CVH prevalence between CKM stages—supports their integration into routine risk assessments. Clinically, the 5.68-fold mortality hazard for poor health behaviors in CKM 0–1 suggests that behavioral screening should precede biochemical abnormalities in at-risk populations. This approach aligns with recent guidelines advocating behavior-centric management of cardiometabolic syndromes [20,26]. Furthermore, the differential PAF gradients between stages propose a tiered intervention framework: intensive lifestyle programs for early-stage patients versus integrated behavior-pharmacotherapy regimens for advanced cases.

Limitations

Several limitations warrant consideration. First, the reliance on self-reported behavioral data in NHANES may introduce recall or social desirability bias, potentially misclassifying true exposure levels. Second, the absence of advanced diagnostic data (e.g., echocardiography, coronary angiography, or electrophysiological assessments for atrial fibrillation) likely resulted in underestimation of subclinical and clinical cardiovascular pathology, leading to potential misclassification of CKM stages 3–4. Third, the observational design inherently prohibits the establishment of causal inferences, despite our efforts to control for extensive demographic and clinical confounders and perform sensitivity analyses. Forth, and importantly, the LE8 scoring system was primarily developed and validated based on American populations from the AHA’s recommendations. Therefore, the external validity and generalizability of our findings to other ethnic, cultural, and geographic contexts (e.g., Asian, European, African, and Latin American cohorts) remain uncertain. Consequently, our results necessitate cautious interpretation and highlight an imperative for future research to validate and potentially adapt the LE8 metrics in multinational and multi-ethnic settings to ensure its global applicability. Finally, the bidirectional interplay between health behaviors and health factors was not explored through mediation analysis due to methodological constraints. Additionally, the cross-sectional nature of baseline data limits causal inference, warranting further validation in longitudinal studies with repeated measurements.

In conclusion, the LE8 health behaviors score is a scalable tool for stratifying mortality risk across CKM stages, with particular relevance for early disease. Clinicians should prioritize behavioral counseling in stage 0–1 and integrate lifestyle modification with advanced therapies in stage 2–3 to mitigate synergistic organ injury.

SUPPLEMENTARY MATERIALS

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conception or design: J.Z., L.Y., X.X.

Acquisition, analysis, or interpretation of data: all authors.

Drafting the work or revising: J.Z., L.Y.

Final approval of the manuscript: all authors.

FUNDING

This research was supported by the Natural Science Foundation of Sichuan Province (No. 2023NSFSC1529).

ACKNOWLEDGMENTS

None

DATA AVAILABILITY

Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm.

Fig. 1.

The flow chart of individual inclusion and exclusion in this study. NHANES, National Health and Nutrition Examination Survey; NDI, National Death Index; CKM, cardiovascular-kidney-metabolic; LE8, Life’s Essential 8; CVH, cardiovascular health.

Fig. 2.

Association of different cardiovascular health (CVH) scores with all-cause mortality in cardiovascular-kidney-metabolic (CKM) using restricted cubic spline models. (A-C) Life’s Essential 8 (LE8) score. (D-F) Health behaviors score. (G-I) Health factors score. Hazard ratios (solid lines in restricted cubic spline) and 95% confidence intervals (shaded areas) were estimated after adjusting for age, sex, race, marital status, educational level, poverty income ratio, alcohol consumption, estimated glomerular filtration rate, urine albumin-to-creatinine ratio, renin-angiotensin system inhibitor use and statin use.

Fig. 3.

Association between all-cause mortality and health behaviors score (HBS) in patients with cardiovascular-kidney-metabolic (CKM). (A) Kaplan-Meier curves of HBS group in all participants. (B) Kaplan-Meier curves of HBS group in CKM 0–1. (C) Kaplan-Meier curves of HBS group in CKM 2–3.

Fig. 4.

(A) Association between all-cause mortality and LE8 health behaviors cardiovascular health (CVH) score in patients with CKM. (B) Association between all-cause mortality and Life’s Essential 8 (LE8) score in patients with cardiovascular-kidney-metabolic (CKM). The multivariable Cox regression model was adjusted for age, sex, race, marital status, educational level, poverty income ratio, alcohol consumption, estimated glomerular filtration rate, urine albumin-to-creatinine ratio, renin-angiotensin system inhibitor and statin use. HR, hazard ratio; CI, confidence interval.

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About this article

Zhang J, Yin L, Liu Y, Xiao X, Shuai P. Association between the Life’s Essential 8 Health Behaviors Score and Mortality Risk in US Adults with Cardiovascular-Kidney-Metabolic Syndrome Stage 0–3. Diabetes Metab J. 2025 Dec 12. doi: 10.4093/dmj.2025.0366. Epub ahead of print.

Received: Apr 26, 2025; Accepted: Jun 16, 2025

DOI: https://doi.org/10.4093/dmj.2025.0366.

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