Decelerated Biological Aging Mediates the Association Between Life’s Essential 8 and Mortality in Hypertension: Evidence from NHANES and the Klemera-Doubal Model

Decelerated Biological Aging Mediates the Association Between Life’s Essential 8 and Mortality in Hypertension: Evidence from NHANES and the Klemera-Doubal Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decelerated Biological Aging Mediates the Association Between Life’s Essential 8 and Mortality in Hypertension: Evidence from NHANES and the Klemera-Doubal Model Zhaoling Sun, Zhaohui Wang, Na Wei, Weimin Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6539231/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Hypertension is a major modifiable risk factor for cardiovascular diseases (CVD) and premature mortality, yet clinical outcomes vary significantly even among individuals with similar blood pressure control. Life’s Essential 8 (LE8), a comprehensive cardiovascular health (CVH) metric, may influence outcomes through biological aging pathways, but evidence in hypertensive populations remains limited. Methods: This prospective cohort study analyzed data from 9,376 hypertensive adults in the National Health and Nutrition Examination Survey (NHANES, 2007–2018). LE8 scores were derived from eight modifiable components (diet, physical activity, nicotine exposure, sleep, BMI, non-HDL cholesterol, blood glucose, and blood pressure). Biological age was estimated using the Klemera-Doubal method (KDM-BA) based on six clinical biomarkers. Mortality outcomes were ascertained via linkage to the National Death Index. Survey-weighted Cox regression and mediation analyses assessed associations and mediating effects of KDM-BA. Results: Over a median follow-up of 6.34 years, higher LE8 scores were dose-dependently associated with lower all-cause mortality (High vs. Low CVH: HR 0.22, 95% CI 0.16–0.30) and CVD mortality (HR 0.31, 95% CI 0.20–0.50). Each 10-point LE8 increase reduced all-cause mortality risk by 27% (HR 0.73, 95% CI 0.68–0.78). KDM-BA mediated 13.5% (all-cause mortality) and 32.9% (CVD mortality) of these associations (both p < 0.001). High CVH participants exhibited slower biological aging (KDM-BA acceleration: −9.97 vs. +6.87 in Low CVH, p < 0.001). Conclusions: In hypertensive adults, better CVH measured by LE8 is associated with decelerated biological aging and reduced mortality, with biological aging mediating approximately one-third of the protective effect against CVD mortality. Optimizing LE8 metrics may offer dual benefits for blood pressure control and aging modulation. Life’s Essential 8 biological aging Hypertension NHANES Klemera-Doubal method Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Hypertension, a global health challenge with high prevalence and strong association with cardiovascular diseases (CVD), remains a critical public health concern. As one of the most modifiable risk factors, hypertension contributes significantly to CVD morbidity and premature mortality[ 1 ]. Notably, even among patients with comparable blood pressure control, substantial heterogeneity exists in clinical outcomes, suggesting the involvement of additional modulating factors[ 2 ]. This variability may stem from multifactorial determinants, including genetic predispositions, environmental influences, and lifestyle choices [ 3 ] .Consequently, more comprehensive and individualized strategies are required to improve prognostic outcomes in hypertension. The American Heart Association's Life’s Essential 8 (LE8) provides a comprehensive cardiovascular health (CVH) metric encompassing eight key components across health behaviors(diet, physical activity, nicotine exposure, sleep health) and health factors(body mass index, non-high-density lipoprotein cholesterol (non-HDL-C), blood glucose, and blood pressure) [ 4 ].Accumulating evidence demonstrates that higher LE8 scores correlate with reduced all-cause and cardiovascular-specific mortality, positioning LE8 as a robust tool for health promotion and CVD prevention[ 5 ]. However, the impact of LE8 on clinical outcomes and heterogeneity among hypertensive populations, along with its underlying mechanisms, warrants further investigation. Emerging evidence suggests that the health behaviors and factors encompassed by LE8 significantly participate in the modulation of ageing processes[ 4 ]. For instance, healthy dietary patterns and moderate physical activity decelerate biological age acceleration and reduce the risk of age-related diseases [ 6 ].Similarly, metabolic factors within LE8 (e.g., optimal blood glucose and blood pressure control) are strongly associated with delayed biological ageing[ 7 ]. Higher CVH levels correlate with attenuated biological aging, suggesting that accelerated ageing may serve as a critical mediator linking cardiovascular health behaviors with clinical endpoints - potentially explaining the heterogeneity in hypertension outcomes. Advancements in biological age(BA) modeling now enable comprehensive assessment of ageing using clinical biomarkers, epigenetic markers, and functional indicators[ 8 ]. The Klemera-Doubal method (KDM-BA), which estimates BA from routine clinical biomarkers, has demonstrated superior predictive validity for mortality and physical dysfunction compared to chronological age alone[ 9 ]. In Chinese populations, KDM-BA effectively predicts mortality rates, surpassing models relying solely on chronological age[ 10 ]. Accelerated KDM-BA (KDM-BA acceleration) reflects the degree of biological ageing relative to chronological age and is closely associated with cardiovascular disease and diabetes[ 11 ]. In this study, we conducted a prospective cohort analysis using the National Health and Nutrition Examination Survey (NHANES) database to examine the association between LE8 and mortality in hypertensive populations. We further investigated the mediating role of KDM-BA in this relationship. By elucidating how modifiable CVH behaviours may reduce mortality through decelerated biological ageing, our study provides actionable insights for precision prevention strategies in high-risk hypertensive populations. Methods Study Population The National Health and Nutrition Examination Survey (NHANES), conducted by the Centers for Disease Control and Prevention (CDC), represents a series of nationally representative cross-sectional surveys that combine interview questionnaires, physical examinations, and laboratory tests to assess the health status of the US population(https://wwwn.cdc.gov/nchs/nhanes/). The survey protocol was approved by the National Center for Health Statistics Institutional Review Board, with written informed consent obtained from all participants. We analyzed data from six consecutive NHANES cycles (2007-2018) with mortality follow-up through December 31, 2019 via linkage to the National Death Index (NDI). From the initial 59,842 participants, exclusion criteria were applied sequentially: individuals aged <20 years (n=25,072), those without hypertension (defined as absence of self-reported diagnosis, no antihypertensive medication use, and average systolic/diastolic blood pressure <140/90 mmHg based on≥2 measurements; n=20,014), and participants with missing hypertension status (n=33) were excluded. Additional exclusions comprised participants with incomplete LE8 score components (n=4,173), missing mortality data (n=14), unavailable KDM biological age biomarkers (n=268), or incomplete covariate data (including education, marital status, and poverty-income ratio; n=892). The final analytic cohort consisted of 9,376 hypertensive adults with complete data on all study variables (Figure 1). LE8 Assessment The LE8 score was derived following the American Heart Association (AHA) 2022 guidelines, encompassing eight modifiable cardiovascular health components: diet, physical activity, nicotine exposure, sleep health, body mass index(BMI), non-high-density lipoprotein cholesterol (non-HDL-C), blood glucose, and blood pressure. Each component was scored on a 0-100 scale, with the total LE8 score computed as the unweighted average of all component scores (theoretical range: 0-100). The calculation algorithms for NHANES-derived LE8 scores have been previously validated[4]. Participants were categorized into three cardiovascular health (CVH) groups according to their composite scores: high CVH (LE8 ≥80), moderate CVH (LE8 50-79), and low CVH (LE8 <50). Diet quality was evaluated using the Healthy Eating Index-2015 (HEI-2015). Physical activity levels, smoking status, sleep duration, hypertension diagnosis, and medication use were obtained through standardized questionnaires administered during NHANES interviews. During physical examinations, certified technicians measured blood pressure using mercury sphygmomanometers following a standardized protocol, while height and weight were measured using calibrated stadiometers and digital scales, respectively. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m²). Fasting venous blood samples were collected and analyzed at certified laboratories for lipid profiles (including non-HDL-C) and glucose metabolism markers (fasting plasma glucose and hemoglobin A1c). Mortality Assessment Mortality follow-up data were obtained for all study participants through probabilistic linkage with the National Death Index (NDI), with follow-up extending through December 31, 2019. The underlying cause of death was determined using International Classification of Diseases, Tenth Revision (ICD-10) codes. In this study, we examined all-cause mortality as the primary endpoint, with CVD-specific mortality (ICD-10 codes I00-I09, I11, I13, and I20-I51) as a secondary outcome. Assessment of Biological ageing: KDM-BA and KDM-BA Acceleration Biological age was estimated using the KDM, a validated algorithm incorporating six routinely measured clinical biomarkers available in NHANES: serum albumin, creatinine, C-reactive protein (CRP), total cholesterol, lymphocyte percentage, and alkaline phosphatase. Each biomarker was first standardized by age and sex using the NHANES reference population. The KDM-BA was derived through multivariate linear regression, with biomarkers weighted according to their association with mortality risk . We calculated accelerated ageing (KDM-BA acceleration) as the residual from regressing KDM-BA on chronological age, representing the deviation from expected ageing trajectories. Positive values indicated faster-than-expected biological ageing, while negative values represented slower biological ageing relative to chronological age. Covariates Demographic and socioeconomic covariates were obtained from standardized NHANES questionnaires and examination data. The selected covariates included: Age was categorized into four groups (20-39, 40-59, 60-79, and ≥80 years) based on established life stage classifications. Sex was recorded as male or female. Race/ethnicity was classified as non-Hispanic White, non-Hispanic Black, Hispanic, and other racial/ethnic groups to reflect the major population subgroups in the United States. Marital status was dichotomized into coupled versus single or separated. Educational attainment was grouped into three categories: below high school, high school or equivalent, and above high school. The poverty-income ratio (PIR) was categorized into three levels (3.5) corresponding to the federal poverty line, near-poor, and middle-income ranges respectively. Statistical Analysis All analyses accounted for the complex survey design of NHANES through incorporation of sampling weights, stratification variables, and clustering effects in alignment with CDC analytical guidelines. Baseline characteristics were presented as weighted means with standard errors for continuous variables and weighted percentages for categorical variables. Multicollinearity assessment using variance inflation factors revealed no substantial collinearity (all values <2), as detailed in Table S1. Follow-up duration was calculated from enrolment date until the earliest occurrence of death or censoring. We employed weighted Cox proportional hazards regression models to evaluate associations between LE8 scores, biological ageing markers, and mortality outcomes, while weighted linear regression models assessed relationships between LE8 scores and biological ageing markers. We performed sequential multivariable adjustment using three models: Model 1 (unadjusted), Model 2 (adjusted for age, sex, and race/ethnicity), and Model 3 (further adjusted for education, marital status, and poverty-income ratio). This hierarchical approach allowed systematic evaluation of associations while progressively controlling for potential confounders. Nonlinear associations were examined using restricted cubic splines, and mediation analysis quantified the intermediary role of biological ageing markers in LE8-mortality relationships. This approach distinguished direct effects (LE8's independent influence) from indirect effects (operating through biological ageing), with mediation proportions calculated as indirect-to-total effect ratios. Weighted Kaplan-Meier curves with log-rank tests compared survival distributions across LE8 categories. To ensure robustness, we performed sensitivity analyses by: (1) excluding early deaths (80 years; (3) adjusting for antihypertensive medication use; and (4) repeating analyses without survey weighting. All tests were two-sided with statistical significance set at p<0.05. Analyses were conducted using R software (v4.3.2) with specialised packages for complex survey analysis, survival modelling, and mediation effects estimation. Result Baseline characteristics of study population The study included 9,376 hypertensive adult participants categorized into three groups based on CVH status: Low CVH (n=1,333, 14.22%), Moderate CVH (n=6,931, 73.92%), and High CVH (n=1,112, 11.86%). Table 1 presents the baseline characteristics of the study population. Significant differences were observed across groups for age, PIR, biological ageing markers (KDM-BA and KDM-BA acceleration), survival time, healthy behavior scores (HBS, including physical activity, smoke, sleep and diet), and healthy factor scores (HFS, including NON-HDL, blood glucose, blood pressure, BMI). The High CVH group exhibited the most favorable profiles, with lower biological age scores and higher behavior and factor scores (all p<0.05). Demographic variables such as race, education, marital status, and income (PIR) also differed significantly among groups (p3.5: 54.99%). Mortality analysis revealed the high CVH group showed lower rates of all-cause and CVD-specific mortality in the High CVH group (7.70% and 3.59%, respectively) compared to the Low CVH group (19.25% and 6.06%, p<0.05). Association between LE8 and mortality in hypertensive Adults Over a median follow-up period of 6.34 years, 1515 deaths were recorded, representing a weighted percentage of 12.76%. Additionally, there were 491 new cases of CVD, with a weighted percentage of 4.17%. As detailed in Table 2, cox regression analyses demonstrated significant inverse associations between LE8 scores and mortality risks in a dose-dependent manner. Compared to the low CVH group, participants with moderate and high CVH exhibited 53% (HR 0.47, 95% CI 0.37-0.58) and 78% (HR 0.22, 95% CI 0.16-0.30) lower risks of all-cause mortality, respectively, in the fully adjusted model that accounted for demographics and socioeconomic factors. Similarly, each 10-point increment in LE8 score was associated with a 27% reduction in all-cause mortality risk (HR 0.73, 95% CI 0.68-0.78). Comparable protective effects were observed for CVD-specific mortality, with high CVH showing a 69% risk reduction (HR 0.31, 95% CI 0.20-0.50). The robust associations persisted across progressively adjusted models (all p<0.001).The multivariate adjusted HRs for every standard deviation (SD) in LE8 in the association with all-cause and CVD-specific mortality were 0.66 (95% CI 0.60-0.72) and 0.78 (95% CI 0.70–0.86), respectively. Moreover, Figure 2 showed people with higher LE8 scores had a notably greater cumulative survival probability for both all-cause and CVD-specific mortality (p < 0.001, p = 0.009, respectively).The relationships between LE8 and mortality from all causes and CVD-specific causes were visualized using RCS curves, as shown in Figure 3.In the cohort, LE8 and HFS demonstrated a nonlinear relationship with all-cause mortality (overall p-value <0.001, nonlinearity p-value <0.01). Furthermore, inverse linear relationships were observed between: (1) LE8/HBS/HFS and cardiovascular mortality, and (2) HBS and all-cause mortality. Associations of LE8 with KDM-BA and of KDM-BA with all-cause and CVD-specific mortality The links between LE8 and KDM-BA, as determined by survey-weighted linear regression models, are displayed in Table 3. After full adjustment for all covariates, the multivariate-adjusted βs of KDM-BA and KDM-BA acceleration for every SD increase in LE8 both decreased by 3.55(95% CI 3.94~3.15). Table 3 presents the links between KDM-BA and both all-cause and CVD-specific mortality based on Cox regression models. The multivariate-adjusted hazard ratios (HRs) for each one-year increment in KDM-BA and the acceleration of KDM-BA in relation to all-cause mortality were 1.02 (95% CI: 1.01 - 1.02) and 1.02 (95% CI: 1.01 - 1.02), respectively. Similarly, for cardiovascular disease (CVD)-specific mortality, the HRs were 1.03 (95% CI: 1.02 - 1.03) and 1.03 (95% CI: 1.02 - 1.03), respectively. Mediation analyses of KDM-BA and correlations of LE8 with all-cause and CVD-specific mortality In line with Table 4 and Figure 4, causal mediation analyses were utilized to investigate whether biological age serves as a mediator in the relationship between LE8 and mortality from all causes and CVD. KDM-BA significantly mediated the relationship between LE8 and both all-cause and CVD-specific mortality, with mediation proportions of 13.5% (p < 0.001) and 32.86% (p < 0.001), respectively. KDM-BA acceleration also contributed to the mediation of the associations, with 13.76% (p < 0.001) for all-cause mortality and 33.29% (p < 0.001) for CVD mortality. Sensitivity analyses In the sensitivity analyses, the results remained robust after excluding deaths with a follow-up period of fewer than 2 years(Table S2), excluding participants over 80 years old(Table S3), additional adjusting for covariates- hypertension treatment(Table S4), and repeating the main analyses without consideration of complex sampling designs(Table S5). Discussion In this nationally representative cohort of hypertensive individuals, higher LE8 scores was significantly associated with decelerated biological ageing and demonstrated a dose-dependent reduction in all-cause mortality and cardiovascular mortality. Biological ageing measured by the KDM algorithm was identified as a mediating factor through which LE8 confers protective effects against both all-cause mortality and cardiovascular mortality.Our study demonstrates that higher LE8 scores are associated with slower biological ageing (measured by KDM-BA) and reduced mortality in hypertensive individuals, with biological ageing mediating approximately 30% of this protective effect. Ageing represents one of the fundamental determinants of human health and longevity [12]. During this process, organisms undergo a series of complex biological alterations that not only impair physiological function but also exhibit strong associations with the onset and progression of age-related diseases [13]. Our study focuses on hypertensive populations, due to the well-established link between hypertension and ageing[14]. Current evidence reveals a bidirectional relationship between the two: ageing-induced vascular changes (e.g., arterial stiffening and endothelial dysfunction) contribute to hypertension pathogenesis processes[15]: ageing-induced vascular changes (e.g., arterial stiffening and endothelial dysfunction) contribute to hypertension pathogenesis[15, 16], while shared mechanisms—such as chronic low-grade inflammation and oxidative stress—create a vicious cycle of accelerated vascular ageing and blood pressure elevation[12] . Our findings demonstrate that biological ageing mediates the relationship between LE8 and mortality in hypertensive individuals, bridging cardiovascular health (CVH) metrics, ageing, and clinical outcomes. The LE8, a comprehensive CVH assessment tool developed by the American Heart Association (AHA), incorporates health behaviours and clinical factors. Robust evidence indicates that higher LE8 scores correlate with slower biological ageing and reduced mortality[17]. Sustained CVH improvement is linked to long-term cardiovascular disease (CVD) risk reduction, particularly in older populations[5]. Key LE8 components, such as healthy diet and physical activity, mitigate epigenetic age acceleration and ageing-related disease risks, while metabolic factors (e.g., optimal blood glucose and blood pressure control) are strongly associated with delayed biological ageing[6, 18]. Our results align with these observations, suggesting that enhanced CVH may decelerate biological ageing, thereby lowering mortality risk. Mechanistically, biological ageing explains the association between CVH levels and clinical outcomes in hypertension, positioning LE8 as a modifiable tool to reduce mortality in this high-risk group. Epigenetic clocks, such as GrimAge and DunedinPACE, were among the earliest tools for estimating biological age and have been validated as robust mortality predictors, particularly in individuals with elevated genetic susceptibility[19, 20]. However, their clinical utility is limited by reliance on DNA methylation data, which requires specialised epigenetic profiling[21]. In contrast, our study employed the KDM biological age (KDM-BA) model, which estimates ageing trajectories using routine clinical biomarkers. The KDM algorithm selects blood-based markers reflective of core physiological processes (e.g., metabolism, inflammation) and optimizes alignment between biological and chronological age via multivariate regression[9]. This approach quantifies the pace of ageing, making it suitable for long-term assessment, intervention guidance, and evaluation of health-promoting behaviors. KDM-BA captures deviations from normal ageing trajectories induced by environmental exposures, disease states, or genetic predispositions, rendering it particularly valuable for investigating ageing heterogeneity in high-risk populations like hypertensive individuals. Our analysis revealed a robust inverse relationship between higher CVH levels and reduced KDM-BA, with significant differences observed across CVH strata. KDM-BA consistently mediated the association between CVH and clinical outcomes, demonstrating stable effects in sensitivity analyses. Notably, KDM-BA offers clinical advantages over epigenetic ageing measures, as it relies on routinely available biomarkers, facilitating practical risk assessment and intervention monitoring in hypertension management[22]. These findings underscore KDM-BA’s utility as a feasible metric for improving risk stratification and therapeutic monitoring in hypertensive patients. We observed that KDM-BA had a stronger mediating effect on cardiovascular mortality than on all-cause mortality, likely due to the elevated baseline cardiovascular risk in hypertensive individuals. The KDM model incorporates biomarkers linked to CVD (e.g., inflammatory markers, metabolic parameters), while LE8 components targeting cardiovascular risk factors (e.g., blood pressure control, lipid management) may decelerate cardiovascular ageing through specific pathways. Both KDM-BA and KDM-BA acceleration exhibited similar mediation proportions in the LE8-mortality relationship, suggesting that linear ageing effects predominantly drive mortality risk in this population. This consistency reflects the biological coherence of KDM biomarkers (e.g., inflammatory and metabolic markers) in assessing ageing processes. The comparable performance of these metrics may stem from relatively homogeneous ageing patterns among hypertensive individuals. To ensure robustness, we conducted sensitivity analyses addressing reverse causality (e.g., excluding early mortality cases) and antihypertensive treatment adjustments. The protective association between LE8 scores and mortality remained stable, reinforcing the validity of our findings. Clinic Implications These results collectively highlight LE8's value as a comprehensive intervention tool that extends beyond blood pressure management alone, with coordinated improvements in cardiovascular health metrics and consequent deceleration of biological ageing emerging as key pathways for mortality risk reduction in hypertension. The consistent mediation effects across both ageing metrics underscore the importance of targeting biological ageing processes through cardiovascular health optimization as a viable approach to improve clinical outcomes in this high-risk population. The robust association carries significant implications for hypertension management particularly in the context of precision prevention. Our findings suggest that biological ageing mediated over 30% of LE8’s protective effect against CVD mortality, interventions targeting cardiovascular health may yield dual benefits: not only mitigating traditional risk factors but also slowing the underlying ageing processes that exacerbate vascular dysfunction. For example, integrating LE8 optimization into standard hypertension care—such as dietary counseling tailored to reduce inflammation or personalized exercise regimens to improve metabolic resilience—could address both immediate risk factors and long-term ageing trajectories. Notably, these findings align remarkably well with geroscience-guided strategies for chronic disease management. The geroscience paradigm posits that targeting fundamental ageing processes—rather than treating individual diseases or comorbidities in isolation—represents a transformative approach to population health improvement. This integrated intervention strategy not only enhances individual health outcomes but may also reduce healthcare disparities while increasing healthspan at the population level[23]. Future Research Directions From a public health perspective, LE8 and KDM biomarkers offer scalable solutions to reduce ageing-related disparities in hypertensive populations. Socioeconomic factors (e.g., income, education) were strongly associated with LE8 scores in our study, emphasizing the need for policies improving access to CVH resources in underserved communities. Community-based programmers promoting LE8 components (e.g., healthy food initiatives, sleep health campaigns) could attenuate biological ageing at a population level. The KDM model’s reliance on routine biomarkers makes it adaptable to diverse healthcare settings, including resource-limited regions where advanced epigenetic testing is impractical. Our findings support a paradigm shift from reactive hypertension management to proactive, ageing-aware prevention. By integrating LE8 optimisation into standard care (e.g., dietary counselling, personalised exercise), clinicians may simultaneously address immediate risk factors and long-term ageing trajectories. This approach holds promise for curbing the global burden of CVD and ageing-related morbidity. Study Limitations Several limitations should be considered when interpreting our findings. First, the observational nature of NHANES data precludes definitive causal inferences between LE8, biological ageing, and mortality. While we employed rigorous adjustment for confounders and mediation analysis, residual confounding from unmeasured factors (e.g., genetic predisposition, environmental exposures) may persist. Second, the KDM model, while clinically practical, may not capture all aspects of biological ageing compared to more comprehensive multi-omics approaches. Third, LE8 components were assessed at a single timepoint, limiting our ability to evaluate how changes in cardiovascular health over time influence ageing trajectories. Future studies incorporating longitudinal LE8 assessments and advanced ageing biomarkers could address these limitations while exploring personalized intervention strategies. Conclusion In this large, nationally representative study of hypertensive adults, we demonstrated that better cardiovascular health as measured by LE8 is associated with significantly lower all-cause and CVD mortality, partially mediated through decelerated biological ageing. These findings highlight the importance of comprehensive cardiovascular health optimization in hypertension management, with biological ageing emerging as a promising therapeutic target. Future research should focus on implementing LE8-based interventions while further elucidating the mechanistic links between cardiovascular health and ageing processes. Abbreviations CVD cardiovascular diseases LE8 Life’s Essential 8 CVH cardiovascular health KDM Klemera-Doubal method BA biological ageing non-HDL-C non-high-density lipoprotein cholesterol NHANES National Health and Nutrition Examination Survey CDC Centers for Disease Control and Prevention NDI National Death Index BMI body mass index ICD-10 International Classification of Diseases Tenth Revision CRP C-reactive protein Declarations Ethics approval and consent to participate The study protocol was approved by the NHANES Institutional Review Board, and was performed in accordance with the Declaration of Helsinki, with all NHANES participants providing signed informed consent. Availability of data and materials Publicly available datasets were analyzed in this study. These data can be found here: https://www.cdc.gov/nchs/nhanes/ Competing interest The authors declare that they have no competing interests. Funding This study received no external funding. Author contributions W.M.L. and N.W. conceived and designed the study.Z.L.S. and N.W. collected and curated the data.Z.H.W. performed the data analysis and interpretation.Z.L.S., Z.H.W., N.W., and W.M.L. wrote the manuscript.All authors reviewed and approved the final manuscript. Acknowledgements We are grateful to the NHANES database for providing access to the data. References Nolde JM, Beaney T, Carnagarin R, Stergiou GS, Poulter NR, Schutte AE, Schlaich MP. Age-Related Blood Pressure Gradients Are Associated With Blood Pressure Control and Global Population Outcomes. Hypertension. 2024;81(10):2091–100. Wei J, Miao Y, Zhang J, Wu J, Shen Z, Bai J, Zhu D, Ren R, Li X, Zhen M, et al. Spatial heterogeneity of blood pressure control and its influencing factors in elderly patients with essential hypertension: A small-scale spatial analysis. Health Place. 2025;92:103428. Cho SMJ, Koyama S, Ruan Y, Lannery K, Wong M, Ajufo E, Lee H, Khera AV, Honigberg MC, Natarajan P. 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Nat Aging. 2024;4(2):261–74. Machado AV, Silva J, Colosimo EA, Needham BL, Maluf CB, Giatti L, Camelo LV, Barreto SM. Clinical biomarker-based biological age predicts deaths in Brazilian adults: the ELSA-Brasil study. Geroscience. 2024;46(6):6115–26. Forman DE, Kuchel GA, Newman JC, Kirkland JL, Volpi E, Taffet GE, Barzilai N, Pandey A, Kitzman DW, Libby P, et al. Impact of Geroscience on Therapeutic Strategies for Older Adults With Cardiovascular Disease: JACC Scientific Statement. J Am Coll Cardiol. 2023;82(7):631–47. Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6539231","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463153806,"identity":"56279243-367c-44c4-85fa-0e3582df325c","order_by":0,"name":"Zhaoling Sun","email":"","orcid":"","institution":"The First Affiliated Hospital of Shandong First Medical University \u0026 Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhaoling","middleName":"","lastName":"Sun","suffix":""},{"id":463153807,"identity":"291db1e7-3f0f-4f36-9ba6-ce3e42a2de93","order_by":1,"name":"Zhaohui Wang","email":"","orcid":"","institution":"Qingdao Public Health Clinical center","correspondingAuthor":false,"prefix":"","firstName":"Zhaohui","middleName":"","lastName":"Wang","suffix":""},{"id":463153808,"identity":"aebd0fef-10b1-431e-b28d-7dbf573c3da7","order_by":2,"name":"Na Wei","email":"","orcid":"","institution":"The First Affiliated Hospital of Shandong First Medical University \u0026 Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Wei","suffix":""},{"id":463153809,"identity":"ce48fd4f-0c3c-44b2-af59-82b815132d22","order_by":3,"name":"Weimin Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYDCCA8wNIIqHgZmB8QGDAVFaGOFamA1I0gICbBJEuYvv9sE2iY9tNjK67bzHKn8U3JFnYD98dAM+LZLnEtskZ7al8Zgd5ku7zWPwzLCBJy3tBj4tBmcY26R5tx0GauExu81gcJixQYLHjBgt/8FaCn8YHLYnVssBsBYGHoPDiQS1SJ5hbLac+S8ZpMVYGqgluY2QX/jOMB+88eGMnb3Z+TOGH3/8OWzbz374GF4tmICNNOWjYBSMglEwCrABAB/rR/JJS+i4AAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Shandong First Medical University \u0026 Shandong Provincial Qianfoshan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-04-27 08:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6539231/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6539231/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83783905,"identity":"d61630bf-7e9c-4ffe-ad4e-010776528e4e","added_by":"auto","created_at":"2025-06-02 16:17:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21147,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flowchart. Of 59,824 participants in the 2007–2018 National Health and Nutrition Examination Survey (NHANES), 9376 participants remained after fulfilling inclusion and exclusion criteria. Abbreviations: KDM-BA, Klemera-Doubal method biological age; LE8, Life’s Essential 8.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6539231/v1/0e7a551cbf55702a622ab867.png"},{"id":83784495,"identity":"9731b445-9b69-4405-a412-f19c334c0e51","added_by":"auto","created_at":"2025-06-02 16:25:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48534,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves between LE8 and all-cause(a) and CVD-specific(b) mortality. LE8, life’s Essential 8; CVH, cardiovascular health; CVD, cardiovascular disease.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6539231/v1/c536afdf2ec67e3ece779c8f.png"},{"id":83783909,"identity":"03f0efd4-cc02-43df-8150-23c013ff623c","added_by":"auto","created_at":"2025-06-02 16:17:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81896,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationships between LE8/HBS/HFS and all-cause and CVD-specific mortality. Adjusted for age, sex, race/ethnicity, education level, marital status, and PIR. The shaded part represents the 95% CI. LE8, life’s Essential 8; HFS, health factor score; HBS, health behavior score; CVD, cardiovascular disease; HR, hazard ratio.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6539231/v1/528bd44a04927c6ab0388f33.png"},{"id":83783914,"identity":"48bf99d4-f9d9-496e-88a2-c72a7b833ee3","added_by":"auto","created_at":"2025-06-02 16:17:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87312,"visible":true,"origin":"","legend":"\u003cp\u003eThe mediating proportion of KDM-BA on the associations between LE8 and all-cause and CVD mortality. Adjusted for age, sex, race/ethnicity, education level, marital status, PIR. Abbreviations: KDM, Klemera-Doubal method; BA, biological age; CVD, cardiovascular disease; SD, standard deviation; HR, hazard ratio; CI: Confidence Interval.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6539231/v1/d9fa3e3f1697ac0d13c14b88.png"},{"id":88460132,"identity":"6c803d09-4b6c-4002-831a-18b49c3198a4","added_by":"auto","created_at":"2025-08-06 16:17:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":866967,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6539231/v1/35ef8a85-7dc6-4a56-8539-f4a8e5aec434.pdf"},{"id":83784496,"identity":"f44e128d-8273-4970-ae96-1b6921d2cea3","added_by":"auto","created_at":"2025-06-02 16:25:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37024,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6539231/v1/1f81ec2ba4a1bda75f9594c7.docx"},{"id":83784859,"identity":"3aacd16a-b73d-4b4e-b74e-e0e62337c181","added_by":"auto","created_at":"2025-06-02 16:33:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":33355,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6539231/v1/88064b4ab8e0ccc07928377c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decelerated Biological Aging Mediates the Association Between Life’s Essential 8 and Mortality in Hypertension: Evidence from NHANES and the Klemera-Doubal Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension, a global health challenge with high prevalence and strong association with cardiovascular diseases (CVD), remains a critical public health concern. As one of the most modifiable risk factors, hypertension contributes significantly to CVD morbidity and premature mortality[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Notably, even among patients with comparable blood pressure control, substantial heterogeneity exists in clinical outcomes, suggesting the involvement of additional modulating factors[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This variability may stem from multifactorial determinants, including genetic predispositions, environmental influences, and lifestyle choices [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] .Consequently, more comprehensive and individualized strategies are required to improve prognostic outcomes in hypertension.\u003c/p\u003e \u003cp\u003eThe American Heart Association's \u003cem\u003eLife\u0026rsquo;s Essential 8\u003c/em\u003e(LE8) provides a comprehensive cardiovascular health (CVH) metric encompassing eight key components across health behaviors(diet, physical activity, nicotine exposure, sleep health) and health factors(body mass index, non-high-density lipoprotein cholesterol (non-HDL-C), blood glucose, and blood pressure) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].Accumulating evidence demonstrates that higher LE8 scores correlate with reduced all-cause and cardiovascular-specific mortality, positioning LE8 as a robust tool for health promotion and CVD prevention[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the impact of LE8 on clinical outcomes and heterogeneity among hypertensive populations, along with its underlying mechanisms, warrants further investigation.\u003c/p\u003e \u003cp\u003eEmerging evidence suggests that the health behaviors and factors encompassed by LE8 significantly participate in the modulation of ageing processes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For instance, healthy dietary patterns and moderate physical activity decelerate biological age acceleration and reduce the risk of age-related diseases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].Similarly, metabolic factors within LE8 (e.g., optimal blood glucose and blood pressure control) are strongly associated with delayed biological ageing[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Higher CVH levels correlate with attenuated biological aging, suggesting that accelerated ageing may serve as a critical mediator linking cardiovascular health behaviors with clinical endpoints - potentially explaining the heterogeneity in hypertension outcomes. Advancements in biological age(BA) modeling now enable comprehensive assessment of ageing using clinical biomarkers, epigenetic markers, and functional indicators[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The Klemera-Doubal method (KDM-BA), which estimates BA from routine clinical biomarkers, has demonstrated superior predictive validity for mortality and physical dysfunction compared to chronological age alone[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In Chinese populations, KDM-BA effectively predicts mortality rates, surpassing models relying solely on chronological age[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Accelerated KDM-BA (KDM-BA acceleration) reflects the degree of biological ageing relative to chronological age and is closely associated with cardiovascular disease and diabetes[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we conducted a prospective cohort analysis using the National Health and Nutrition Examination Survey (NHANES) database to examine the association between LE8 and mortality in hypertensive populations. We further investigated the mediating role of KDM-BA in this relationship. By elucidating how modifiable CVH behaviours may reduce mortality through decelerated biological ageing, our study provides actionable insights for precision prevention strategies in high-risk hypertensive populations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES), conducted by the Centers for Disease Control and Prevention (CDC), represents a series of nationally representative cross-sectional surveys that combine interview questionnaires, physical examinations, and laboratory tests to assess the health status of the US population(https://wwwn.cdc.gov/nchs/nhanes/). The survey protocol was approved by the National Center for Health Statistics Institutional Review Board, with written informed consent obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe analyzed data from six consecutive NHANES cycles (2007-2018) with mortality follow-up through December 31, 2019 via linkage to the National Death Index (NDI). From the initial 59,842 participants, exclusion criteria were applied sequentially: individuals aged \u0026lt;20 years (n=25,072), those without hypertension (defined as absence of self-reported diagnosis, no antihypertensive medication use, and average systolic/diastolic blood pressure \u0026lt;140/90 mmHg based on\u0026ge;2 measurements; n=20,014), and participants with missing hypertension status (n=33) were excluded. Additional exclusions comprised participants with incomplete LE8 score components (n=4,173), missing mortality data (n=14), unavailable KDM biological age biomarkers (n=268), or incomplete covariate data (including education, marital status, and poverty-income ratio; n=892). The final analytic cohort consisted of 9,376 hypertensive adults with complete data on all study variables (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLE8 Assessment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LE8 score was derived following the American Heart Association (AHA) 2022 guidelines, encompassing eight modifiable cardiovascular health components: diet, physical activity, nicotine exposure, sleep health, body mass index(BMI), non-high-density lipoprotein cholesterol (non-HDL-C), blood glucose, and blood pressure. Each component was scored on a 0-100 scale, with the total LE8 score computed as the unweighted average of all component scores (theoretical range: 0-100). The calculation algorithms for NHANES-derived LE8 scores have been previously validated[4]. Participants were categorized into three cardiovascular health (CVH) groups according to their composite scores: high CVH (LE8 \u0026ge;80), moderate CVH (LE8 50-79), and low CVH (LE8 \u0026lt;50). Diet quality was evaluated using the Healthy Eating Index-2015 (HEI-2015). Physical activity levels, smoking status, sleep duration, hypertension diagnosis, and medication use were obtained through standardized questionnaires administered during NHANES interviews. During physical examinations, certified technicians measured blood pressure using mercury sphygmomanometers following a standardized protocol, while height and weight were measured using calibrated stadiometers and digital scales, respectively. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m\u0026sup2;). Fasting venous blood samples were collected and analyzed at certified laboratories for lipid profiles (including non-HDL-C) and glucose metabolism markers (fasting plasma glucose and hemoglobin A1c).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMortality Assessment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMortality follow-up data were obtained for all study participants through probabilistic linkage with the National Death Index (NDI), with follow-up extending through December 31, 2019. The underlying cause of death was determined using International Classification of Diseases, Tenth Revision (ICD-10) codes. In this study, we examined all-cause mortality as the primary endpoint, with CVD-specific mortality (ICD-10 codes I00-I09, I11, I13, and I20-I51) as a secondary outcome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Biological ageing: KDM-BA and KDM-BA Acceleration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBiological age was estimated using the KDM, a validated algorithm incorporating six routinely measured clinical biomarkers available in NHANES: serum albumin, creatinine, C-reactive protein (CRP), total cholesterol, lymphocyte percentage, and alkaline phosphatase. Each biomarker was first standardized by age and sex using the NHANES reference population. The KDM-BA was derived through multivariate linear regression, with biomarkers weighted according to their association with mortality risk . We calculated accelerated ageing (KDM-BA acceleration) as the residual from regressing KDM-BA on chronological age, representing the deviation from expected ageing trajectories. Positive values indicated faster-than-expected biological ageing, while negative values represented slower biological ageing relative to chronological age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic and socioeconomic covariates were obtained from standardized NHANES questionnaires and examination data. The selected covariates included: Age was categorized into four groups (20-39, 40-59, 60-79, and \u0026ge;80 years) based on established life stage classifications. Sex was recorded as male or female. Race/ethnicity was classified as non-Hispanic White, non-Hispanic Black, Hispanic, and other racial/ethnic groups to reflect the major population subgroups in the United States. Marital status was dichotomized into coupled versus single or separated. Educational attainment was grouped into three categories: below high school, high school or equivalent, and above high school. The poverty-income ratio (PIR) was categorized into three levels (\u0026lt;1.3, 1.3-3.5, and \u0026gt;3.5) corresponding to the federal poverty line, near-poor, and middle-income ranges respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses accounted for the complex survey design of NHANES through incorporation of sampling weights, stratification variables, and clustering effects in alignment with CDC analytical guidelines. Baseline characteristics were presented as weighted means with standard errors for continuous variables and weighted percentages for categorical variables. Multicollinearity assessment using variance inflation factors revealed no substantial collinearity (all values \u0026lt;2), as detailed in Table S1.\u003c/p\u003e\n\u003cp\u003eFollow-up duration was calculated from enrolment date until the earliest occurrence of death or censoring. We employed weighted Cox proportional hazards regression models to evaluate associations between LE8 scores, biological ageing markers, and mortality outcomes, while weighted linear regression models assessed relationships between LE8 scores and biological ageing markers. We performed sequential multivariable adjustment using three models: Model 1 (unadjusted), Model 2 (adjusted for age, sex, and race/ethnicity), and Model 3 (further adjusted for education, marital status, and poverty-income ratio). This hierarchical approach allowed systematic evaluation of associations while progressively controlling for potential confounders.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNonlinear associations were examined using restricted cubic splines, and mediation analysis quantified the intermediary role of biological ageing markers in LE8-mortality relationships. This approach distinguished direct effects (LE8\u0026apos;s independent influence) from indirect effects (operating through biological ageing), with mediation proportions calculated as indirect-to-total effect ratios. Weighted Kaplan-Meier curves with log-rank tests compared survival distributions across LE8 categories.\u003c/p\u003e\n\u003cp\u003eTo ensure robustness, we performed sensitivity analyses by: (1) excluding early deaths (\u0026lt;2 years follow-up); (2) removing participants aged \u0026gt;80 years; (3) adjusting for antihypertensive medication use; and (4) repeating analyses without survey weighting. All tests were two-sided with statistical significance set at p\u0026lt;0.05. Analyses were conducted using R software (v4.3.2) with specialised packages for complex survey analysis, survival modelling, and mediation effects estimation.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 9,376 hypertensive adult participants categorized into three groups based on CVH status: Low CVH (n=1,333, 14.22%), Moderate CVH (n=6,931, 73.92%), and High CVH (n=1,112, 11.86%). Table 1 presents the baseline characteristics of the study population. Significant differences were observed across groups for age, PIR, biological ageing markers (KDM-BA and KDM-BA acceleration), survival time, healthy behavior scores (HBS, including physical activity, smoke, sleep and diet), and healthy factor scores (HFS, including NON-HDL, blood glucose, blood pressure, BMI). The High CVH group exhibited the most favorable profiles, with lower biological age scores and higher behavior and factor scores (all p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eDemographic variables such as race, education, marital status, and income (PIR) also differed significantly among groups (p\u0026lt;0.05). Notably, the High CVH group had a higher proportion of Non-Hispanic White individuals (77.95%), advanced education (76.83%), and higher income (PIR \u0026gt;3.5: 54.99%). Mortality analysis revealed the high CVH group showed lower rates of all-cause and CVD-specific mortality in the High CVH group (7.70% and 3.59%, respectively) compared to the Low CVH group (19.25% and 6.06%, p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between LE8 and mortality in hypertensive Adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOver a median follow-up period of 6.34 years, 1515 deaths were recorded, representing a weighted percentage of 12.76%. Additionally, there were 491 new cases of CVD, with a weighted percentage of 4.17%. As detailed in Table 2, cox regression analyses demonstrated significant inverse associations between LE8 scores and mortality risks in a dose-dependent manner. Compared to the low CVH group, participants with moderate and high CVH exhibited 53% (HR 0.47, 95% CI 0.37-0.58) and 78% (HR 0.22, 95% CI 0.16-0.30) lower risks of all-cause mortality, respectively, in the fully adjusted model that accounted for demographics and socioeconomic factors. Similarly, each 10-point increment in LE8 score was associated with a 27% reduction in all-cause mortality risk (HR 0.73, 95% CI 0.68-0.78). Comparable protective effects were observed for CVD-specific mortality, with high CVH showing a 69% risk reduction (HR 0.31, 95% CI 0.20-0.50). The robust associations persisted across progressively adjusted models (all p\u0026lt;0.001).The multivariate adjusted HRs for every standard deviation (SD) in LE8 in the association with all-cause and CVD-specific mortality were 0.66 (95% CI 0.60-0.72) and 0.78 (95% CI 0.70\u0026ndash;0.86), respectively.\u003c/p\u003e\n\u003cp\u003eMoreover, Figure 2 showed people with higher LE8 scores had a notably greater cumulative survival probability for both all-cause and CVD-specific mortality (p \u0026lt; 0.001, p = 0.009, respectively).The relationships between LE8 and mortality from all causes and CVD-specific causes were visualized using RCS curves, as shown in Figure 3.In the cohort, LE8 and HFS demonstrated a nonlinear relationship with all-cause mortality (overall p-value \u0026lt;0.001, nonlinearity p-value \u0026lt;0.01). Furthermore, inverse linear relationships were observed between: (1) LE8/HBS/HFS and cardiovascular mortality, and (2) HBS and all-cause mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations of LE8 with KDM-BA and of KDM-BA with all-cause and CVD-specific mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe links between LE8 and KDM-BA, as determined by survey-weighted linear regression models, are displayed in Table 3. After full adjustment for all covariates, the multivariate-adjusted \u0026beta;s of KDM-BA and KDM-BA acceleration for every SD increase in LE8 both decreased by 3.55(95% CI 3.94~3.15).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 presents the links between KDM-BA and both all-cause and CVD-specific mortality based on Cox regression models. The multivariate-adjusted hazard ratios (HRs) for each one-year increment in KDM-BA and the acceleration of KDM-BA in relation to all-cause mortality were 1.02 (95% CI: 1.01 - 1.02) and 1.02 (95% CI: 1.01 - 1.02), respectively. Similarly, for cardiovascular disease (CVD)-specific mortality, the HRs were 1.03 (95% CI: 1.02 - 1.03) and 1.03 (95% CI: 1.02 - 1.03), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMediation analyses of KDM-BA and correlations of LE8 with all-cause and CVD-specific mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn line with Table 4 and Figure 4, causal mediation analyses were utilized to investigate whether biological age serves as a mediator in the relationship between LE8 and mortality from all causes and CVD. KDM-BA significantly mediated the relationship between LE8 and both all-cause and CVD-specific mortality, with mediation proportions of 13.5% (p \u0026lt; 0.001) and 32.86% (p \u0026lt; 0.001), respectively. KDM-BA acceleration also contributed to the mediation of the associations, with 13.76% (p \u0026lt; 0.001) for all-cause mortality and 33.29% (p \u0026lt; 0.001) for CVD mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the sensitivity analyses, the results remained robust after excluding deaths with a follow-up period of fewer than 2 years(Table S2), excluding participants over 80 years old(Table S3), additional adjusting for covariates- hypertension treatment(Table S4), and repeating the main analyses without consideration of complex sampling designs(Table S5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationally representative cohort of hypertensive individuals, higher LE8 scores was significantly associated with decelerated biological ageing and demonstrated a dose-dependent reduction in all-cause mortality and cardiovascular mortality. Biological ageing measured by the KDM algorithm was identified as a mediating factor through which LE8 confers protective effects against both all-cause mortality and cardiovascular mortality.Our study demonstrates that higher LE8 scores are associated with slower biological ageing (measured by KDM-BA) and reduced mortality in hypertensive individuals, with biological ageing mediating approximately 30% of this protective effect.\u003c/p\u003e\n\u003cp\u003eAgeing represents one of the fundamental determinants of human health and longevity [12]. During this process, organisms undergo a series of complex biological alterations that not only impair physiological function but also exhibit strong associations with the onset and progression of age-related diseases [13]. Our study focuses on hypertensive populations, due to the well-established\u0026nbsp;link between hypertension and ageing[14]. Current evidence reveals a bidirectional relationship between the two: ageing-induced vascular changes (e.g., arterial stiffening and endothelial dysfunction) contribute to hypertension pathogenesis processes[15]:\u0026nbsp;ageing-induced vascular changes (e.g., arterial stiffening and endothelial dysfunction) contribute to hypertension pathogenesis[15, 16], while shared mechanisms\u0026mdash;such as chronic low-grade inflammation and oxidative stress\u0026mdash;create a vicious cycle of accelerated vascular ageing and blood pressure elevation[12]\u0026nbsp;.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our findings demonstrate that biological ageing mediates the relationship between LE8 and mortality in hypertensive individuals, bridging cardiovascular health (CVH) metrics, ageing, and clinical outcomes. The LE8, a comprehensive CVH assessment tool developed by the American Heart Association (AHA), incorporates health behaviours and clinical factors. Robust evidence indicates that higher LE8 scores correlate with slower biological ageing and reduced mortality[17]. Sustained CVH improvement is linked to long-term cardiovascular disease (CVD) risk reduction, particularly in older populations[5]. Key LE8 components, such as healthy diet and physical activity, mitigate epigenetic age acceleration and ageing-related disease risks, while metabolic factors (e.g., optimal blood glucose and blood pressure control) are strongly associated with delayed biological ageing[6, 18].\u0026nbsp;\u0026nbsp;Our results align with these observations, suggesting that enhanced CVH may decelerate biological ageing, thereby lowering mortality risk. Mechanistically, biological ageing explains the association between CVH levels and clinical outcomes in hypertension, positioning LE8 as a modifiable tool to reduce mortality in this high-risk group.\u003c/p\u003e\n\u003cp\u003eEpigenetic clocks, such as GrimAge and DunedinPACE, were among the earliest tools for estimating biological age and have been validated as robust mortality predictors, particularly in individuals with elevated genetic susceptibility[19, 20]. However, their clinical utility is limited by reliance on DNA methylation data, which requires specialised epigenetic profiling[21]. In contrast, our study employed the KDM biological age (KDM-BA) model, which estimates ageing trajectories using routine clinical biomarkers. The KDM algorithm selects blood-based markers reflective of core physiological processes (e.g., metabolism, inflammation) and optimizes alignment between biological and chronological age via multivariate regression[9]. This approach quantifies the pace of ageing, making it suitable for long-term assessment, intervention guidance, and evaluation of health-promoting behaviors. KDM-BA captures deviations from normal ageing trajectories induced by environmental exposures, disease states, or genetic predispositions, rendering it particularly valuable for investigating ageing heterogeneity in high-risk populations like hypertensive individuals.\u003c/p\u003e\n\u003cp\u003eOur analysis revealed a robust inverse relationship between higher CVH levels and reduced KDM-BA, with significant differences observed across CVH strata. KDM-BA consistently mediated the association between CVH and clinical outcomes, demonstrating stable effects in sensitivity analyses. Notably, KDM-BA offers clinical advantages over epigenetic ageing measures, as it relies on routinely available biomarkers, facilitating practical risk assessment and intervention monitoring in hypertension management[22]. These findings underscore KDM-BA\u0026rsquo;s utility as a feasible metric for improving risk stratification and therapeutic monitoring in hypertensive patients.\u003c/p\u003e\n\u003cp\u003eWe observed that KDM-BA had a stronger mediating effect on cardiovascular mortality than on all-cause mortality, likely due to the elevated baseline cardiovascular risk in hypertensive individuals. The KDM model incorporates biomarkers linked to CVD (e.g., inflammatory markers, metabolic parameters), while LE8 components targeting cardiovascular risk factors (e.g., blood pressure control, lipid management) may decelerate cardiovascular ageing through specific pathways.\u003c/p\u003e\n\u003cp\u003eBoth KDM-BA and KDM-BA acceleration exhibited similar mediation proportions in the LE8-mortality relationship, suggesting that linear ageing effects predominantly drive mortality risk in this population. This consistency reflects the biological coherence of KDM biomarkers (e.g., inflammatory and metabolic markers) in assessing ageing processes. The comparable performance of these metrics may stem from relatively homogeneous ageing patterns among hypertensive individuals. To ensure robustness, we conducted sensitivity analyses addressing reverse causality (e.g., excluding early mortality cases) and antihypertensive treatment adjustments. The protective association between LE8 scores and mortality remained stable, reinforcing the validity of our findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinic Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese results collectively highlight LE8\u0026apos;s value as a comprehensive intervention tool that extends beyond blood pressure management alone, with coordinated improvements in cardiovascular health metrics and consequent deceleration of biological ageing emerging as key pathways for mortality risk reduction in hypertension. The consistent mediation effects across both ageing metrics underscore the importance of targeting biological ageing processes through cardiovascular health optimization as a viable approach to improve clinical outcomes in this high-risk population. The robust association carries significant implications for hypertension management particularly in the context of precision prevention. Our findings suggest that biological ageing mediated over 30% of LE8\u0026rsquo;s protective effect against CVD mortality, interventions targeting cardiovascular health may yield dual benefits: not only mitigating traditional risk factors but also slowing the underlying ageing processes that exacerbate vascular dysfunction. For example, integrating LE8 optimization into standard hypertension care\u0026mdash;such as dietary counseling tailored to reduce inflammation or personalized exercise regimens to improve metabolic resilience\u0026mdash;could address both immediate risk factors and long-term ageing trajectories. Notably, these findings align remarkably well with geroscience-guided strategies for chronic disease management. The geroscience paradigm posits that targeting fundamental ageing processes\u0026mdash;rather than treating individual diseases or comorbidities in isolation\u0026mdash;represents a transformative approach to population health improvement. This integrated intervention strategy not only enhances individual health outcomes but may also reduce healthcare disparities while increasing healthspan at the population level[23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Research Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom a public health perspective, LE8 and KDM biomarkers offer scalable solutions to reduce ageing-related disparities in hypertensive populations. Socioeconomic factors (e.g., income, education) were strongly associated with LE8 scores in our study, emphasizing the need for policies improving access to CVH resources in underserved communities. Community-based programmers promoting LE8 components (e.g., healthy food initiatives, sleep health campaigns) could attenuate biological ageing at a population level. The KDM model\u0026rsquo;s reliance on routine biomarkers makes it adaptable to diverse healthcare settings, including resource-limited regions where advanced epigenetic testing is impractical.\u0026nbsp;Our findings support a paradigm shift from reactive hypertension management to proactive, ageing-aware prevention. By integrating LE8 optimisation into standard care (e.g., dietary counselling, personalised exercise), clinicians may simultaneously address immediate risk factors and long-term ageing trajectories. This approach holds promise for curbing the global burden of CVD and ageing-related morbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be considered when interpreting our findings. First, the observational nature of NHANES data precludes definitive causal inferences between LE8, biological ageing, and mortality. While we employed rigorous adjustment for confounders and mediation analysis, residual confounding from unmeasured factors (e.g., genetic predisposition, environmental exposures) may persist. Second, the KDM model, while clinically practical, may not capture all aspects of biological ageing compared to more comprehensive multi-omics approaches. Third, LE8 components were assessed at a single timepoint, limiting our ability to evaluate how changes in cardiovascular health over time influence ageing trajectories. Future studies incorporating longitudinal LE8 assessments and advanced ageing biomarkers could address these limitations while exploring personalized intervention strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this large, nationally representative study of hypertensive adults, we demonstrated that better cardiovascular health as measured by LE8 is associated with significantly lower all-cause and CVD mortality, partially mediated through decelerated biological ageing. These findings highlight the importance of comprehensive cardiovascular health optimization in hypertension management, with biological ageing emerging as a promising therapeutic target. Future research should focus on implementing LE8-based interventions while further elucidating the mechanistic links between cardiovascular health and ageing processes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"1064\" style=\"margin-right: calc(11%); width: 89%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003ecardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003eLE8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003eLife\u0026rsquo;s Essential 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003eCVH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003ecardiovascular health\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003eKDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003eKlemera-Doubal method\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003eBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003ebiological ageing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003enon-HDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003enon-high-density lipoprotein cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003eCDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003eNDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003eNational Death Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003ebody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003eICD-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003eInternational Classification of Diseases Tenth Revision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4582%;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.3706%;\"\u003e\n \u003cp\u003eC-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the NHANES Institutional Review Board, and was performed in accordance with the Declaration of Helsinki, with all NHANES participants providing signed informed consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. These data can be found here:\u0026nbsp;\u003cu\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.M.L. and \u0026nbsp;N.W. conceived and designed the study.Z.L.S. and N.W. collected and curated the data.Z.H.W. performed the data analysis and interpretation.Z.L.S., Z.H.W., N.W., and W.M.L. wrote the manuscript.All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the NHANES database for providing access to the data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNolde JM, Beaney T, Carnagarin R, Stergiou GS, Poulter NR, Schutte AE, Schlaich MP. Age-Related Blood Pressure Gradients Are Associated With Blood Pressure Control and Global Population Outcomes. Hypertension. 2024;81(10):2091\u0026ndash;100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei J, Miao Y, Zhang J, Wu J, Shen Z, Bai J, Zhu D, Ren R, Li X, Zhen M, et al. Spatial heterogeneity of blood pressure control and its influencing factors in elderly patients with essential hypertension: A small-scale spatial analysis. Health Place. 2025;92:103428.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho SMJ, Koyama S, Ruan Y, Lannery K, Wong M, Ajufo E, Lee H, Khera AV, Honigberg MC, Natarajan P. Measured Blood Pressure, Genetically Predicted Blood Pressure, and Cardiovascular Disease Risk in the UK Biobank. JAMA Cardiol. 2022;7(11):1129\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLloyd-Jones DM, Allen NB, Anderson CAM, Black T, Brewer LC, Foraker RE, Grandner MA, Lavretsky H, Perak AM, Sharma G, et al. Life's Essential 8: Updating and Enhancing the American Heart Association's Construct of Cardiovascular Health: A Presidential Advisory From the American Heart Association. Circulation. 2022;146(5):e18\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNing H, Perak AM, Siddique J, Wilkins JT, Lloyd-Jones DM, Allen NB. Association Between Life's Essential 8 Cardiovascular Health Metrics With Cardiovascular Events in the Cardiovascular Disease Lifetime Risk Pooling Project. Circ Cardiovasc Qual Outcomes. 2024;17(5):e010568.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, Tang H, Zhang X, Huang J, Luo N, Guo Q, Wang X. Adherence to Life's Essential 8 is associated with delayed biological aging: a population-based cross-sectional study. Rev Esp Cardiol (Engl Ed). 2025;78(1):37\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu D, Qu C, Huang P, Geng X, Zhang J, Shen Y, Rao Z, Zhao J. Better Life's Essential 8 contributes to slowing the biological aging process: a cross-sectional study based on NHANES 2007\u0026ndash;2010 data. Front Public Health. 2024;12:1295477.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng D, Zhang S, Huang Y, Mao K, Han JJ. Application of AI in biological age prediction. Curr Opin Struct Biol. 2024;85:102777.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Zhang Y, Yu C, Guo Y, Sun D, Pang Y, Pei P, Yang L, Millwood IY, Walters RG, et al. Modeling biological age using blood biomarkers and physical measurements in Chinese adults. EBioMedicine. 2023;89:104458.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang K, Gao J, Liu Y, Liu Z, Li Y, Chen S, Sun L, Wu S, Gao X. Biological age construction for prediction of mortality in the Chinese population. \u003cem\u003eGeroscience\u003c/em\u003e 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan LY, Jin L. Association between triglyceride glucose index and biological aging in U.S. adults: National Health and Nutrition Examination Survey. Cardiovasc Diabetol. 2025;24(1):100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng Y, Lin H, Tan H, Liu X. Heterogeneity of aging and mortality risk among individuals with hypertension: Insights from phenotypic age and phenotypic age acceleration. J Nutr Health Aging. 2024;28(5):100203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen H, Guo Z, Liu Y, Song C. Stem Cell-derived Exosomal MicroRNA as Therapy for Vascular Age-related Diseases. Aging Dis. 2022;13(3):852\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Guo Y, Liang H, Wang J, Qi L. Genome-wide association analysis of hypertension and epigenetic aging reveals shared genetic architecture and identifies novel risk loci. Sci Rep. 2024;14(1):17792.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao L, Zan G, Liu C, Xu X, Li L, Chen X, Zhang Z, Yang X. Associations Between Blood Pressure and Accelerated DNA Methylation Aging. J Am Heart Assoc. 2022;11(3):e022257.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng J, Wu H, Xie C, He Y, Mou R, Zhang H, Yang Y, Xu Q. Single-Cell Mapping of Large and Small Arteries During Hypertensive Aging. J Gerontol Biol Sci Med Sci 2024, 79(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRempakos A, Prescott B, Mitchell GF, Vasan RS, Xanthakis V. Association of Life's Essential 8 With Cardiovascular Disease and Mortality: The Framingham Heart Study. J Am Heart Assoc. 2023;12(23):e030764.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarbonneau M, Li Y, Prescott B, Liu C, Huan T, Joehanes R, Murabito JM, Heard-Costa NL, Xanthakis V, Levy D, et al. Epigenetic Age Mediates the Association of Life's Essential 8 With Cardiovascular Disease and Mortality. J Am Heart Assoc. 2024;13(11):e032743.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelsky DW, Caspi A, Corcoran DL, Sugden K, Poulton R, Arseneault L, Baccarelli A, Chamarti K, Gao X, Hannon E et al. DunedinPACE, a DNA methylation biomarker of the pace of aging. Elife 2022, 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, Hou L, Baccarelli AA, Li Y, Stewart JD, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging. 2019;11(2):303\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGriffin PT, Kane AE, Trapp A, Li J, Arnold M, Poganik JR, Conway RJ, McNamara MS, Meer MV, Hoffman N, et al. TIME-seq reduces time and cost of DNA methylation measurement for epigenetic clock construction. Nat Aging. 2024;4(2):261\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMachado AV, Silva J, Colosimo EA, Needham BL, Maluf CB, Giatti L, Camelo LV, Barreto SM. Clinical biomarker-based biological age predicts deaths in Brazilian adults: the ELSA-Brasil study. Geroscience. 2024;46(6):6115\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForman DE, Kuchel GA, Newman JC, Kirkland JL, Volpi E, Taffet GE, Barzilai N, Pandey A, Kitzman DW, Libby P, et al. Impact of Geroscience on Therapeutic Strategies for Older Adults With Cardiovascular Disease: JACC Scientific Statement. J Am Coll Cardiol. 2023;82(7):631\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Life’s Essential 8, biological aging, Hypertension, NHANES, Klemera-Doubal method","lastPublishedDoi":"10.21203/rs.3.rs-6539231/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6539231/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHypertension is a major modifiable risk factor for cardiovascular diseases (CVD) and premature mortality, yet clinical outcomes vary significantly even among individuals with similar blood pressure control. \u003cem\u003eLife’s Essential 8 \u003c/em\u003e(LE8), a comprehensive cardiovascular health (CVH) metric, may influence outcomes through biological aging pathways, but evidence in hypertensive populations remains limited.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis prospective cohort study analyzed data from 9,376 hypertensive adults in the National Health and Nutrition Examination Survey (NHANES, 2007–2018). LE8 scores were derived from eight modifiable components (diet, physical activity, nicotine exposure, sleep, BMI, non-HDL cholesterol, blood glucose, and blood pressure). Biological age was estimated using the Klemera-Doubal method (KDM-BA) based on six clinical biomarkers. Mortality outcomes were ascertained via linkage to the National Death Index. Survey-weighted Cox regression and mediation analyses assessed associations and mediating effects of KDM-BA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOver a median follow-up of 6.34 years, higher LE8 scores were dose-dependently associated with lower all-cause mortality (High vs. Low CVH: HR 0.22, 95% CI 0.16–0.30) and CVD mortality (HR 0.31, 95% CI 0.20–0.50). Each 10-point LE8 increase reduced all-cause mortality risk by 27% (HR 0.73, 95% CI 0.68–0.78). KDM-BA mediated 13.5% (all-cause mortality) and 32.9% (CVD mortality) of these associations (both p \u0026lt; 0.001). High CVH participants exhibited slower biological aging (KDM-BA acceleration: −9.97 vs. +6.87 in Low CVH, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eIn hypertensive adults, better CVH measured by LE8 is associated with decelerated biological aging and reduced mortality, with biological aging mediating approximately one-third of the protective effect against CVD mortality. Optimizing LE8 metrics may offer dual benefits for blood pressure control and aging modulation.\u003c/p\u003e","manuscriptTitle":"Decelerated Biological Aging Mediates the Association Between Life’s Essential 8 and Mortality in Hypertension: Evidence from NHANES and the Klemera-Doubal Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 16:17:01","doi":"10.21203/rs.3.rs-6539231/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f32f141a-f026-4cad-b36e-ba144024519e","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-06T16:08:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 16:17:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6539231","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6539231","identity":"rs-6539231","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}