Life’s Essential 8 for carotid artery stenosis prevention: stratified risk reduction by family cardiovascular history in the UK Biobank

Life’s Essential 8 for carotid artery stenosis prevention: stratified risk reduction by family cardiovascular history in the UK Biobank | 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 Life’s Essential 8 for carotid artery stenosis prevention: stratified risk reduction by family cardiovascular history in the UK Biobank Yue Wang, Yusufu Aisha, Tao Zhuang, Yeqing Xie, Jinyun Zhang, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6332879/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 To quantify cardiovascular health (CVH), the American Heart Association (AHA) recently launched an updated version of the “Life’s Simple 7” (LS7) score, now known as the “Life’s Essential 8” (LE8) score. Our study aimed to investigate the associations among the LE8 score, family history of cardiovascular diseases (including heart disease, stroke, hypertension, and diabetes in first-degree relatives), and carotid artery stenosis (CAS) via prospective data from the UK Biobank while evaluating the value of the LE8 score in reducing CAS risk across populations with distinct family history profiles. Methods After exclusion, 270,695 participants in the UK Biobank who were free of CAS at recruitment remained for analysis (55.2% women). According to the AHA definitions, CVH levels were categorized as low (0–49), moderate (50–79), or high (80–100) on the basis of the LE8 score. Cox proportional hazard models were used to estimate HRs between the LE8 score and CAS. A family risk score was developed to quantify the aggregated risk of CAS on the basis of family history of heart disease, stroke, high blood pressure, and diabetes, including data from fathers, mothers, and siblings, via univariate logistic regression. Results During a median follow-up of 13.3 years, 1158 incident cases of CAS were identified. Compared with participants with low-CVH, participants with moderate-CVH had a 36% lower risk of CAS, and those with high-CVH had a 60% lower risk of CAS incidence after adjustment for covariates. Compared to low-CVH, high-CVH showed progressively stronger CAS risk reduction: 40% lower risk in low-risk, 59% in medium-risk, and 67% in high-risk groups. The behavior and biological subscales contributed 31.9% and 27.9%, respectively, to the population attributable risk (PAR), with the total LE8 PAR reaching 57.6%. Conclusion High-CVH, as defined by the LE8 score, is significantly associated with a lower risk of CAS, and its protective effect is particularly significant in individuals whose parents or siblings have two or more conditions, including heart disease, stroke, high blood pressure, and diabetes. Life’s Essential 8 (LE8) carotid artery stenosis (CAS) UK Biobank prospective study family history of CVD Figures Figure 1 Figure 2 1. Introduction Carotid artery stenosis (CAS) is a critical contributor to ischemic stroke and cardiovascular morbidity and is predominantly attributed to atherosclerotic plaque progression, leading to luminal stenosis or occlusion of the carotid vasculature[ 1 ][ 2 ][ 3 ]. Epidemiological data indicate that CAS accounts for approximately 15–20% of ischemic stroke cases globally, with its prevalence exhibiting a marked age-dependent increase[ 4 ]. The underlying pathophysiology of CAS encompasses chronic inflammatory cascades, endothelial dysfunction, and dysregulated lipid homeostasis, while established modifiable risk factors include hypertension, dyslipidemia, diabetes mellitus, tobacco use, and obesity[ 5 ]. Although invasive interventions such as carotid endarterectomy (CEA) and stenting mitigate stroke risk in high-risk cohorts, procedural complications and sustained healthcare expenditures underscore the imperative for primordial prevention strategies[ 6 ]. Consequently, elucidating modifiable determinants and implementing multimodal preventive approaches are pivotal for attenuating the global burden of CAS. Life’s Essential 8 (LE8), introduced by the American Heart Association (AHA) in 2022, constitutes a multidimensional framework for assessing cardiovascular health (CVH), integrating eight modifiable domains: dietary quality, physical activity, nicotine exposure, sleep health, body mass index (BMI), blood lipids, glycemic status, and blood pressure[ 7 ]. This refined metric supersedes the prior "Life’s Simple 7" by incorporating sleep health metrics and adopting refined scoring algorithms, thereby enhancing its clinical relevance to contemporary population health dynamics. Compelling evidence from prospective cohort studies has demonstrated robust inverse associations between elevated LE8 scores and incident coronary artery disease, heart failure, and all-cause mortality, positioning LE8 as a predictive biomarker for cardiovascular risk stratification[ 8 ][ 9 ][ 10 ]. Emerging data further suggest that optimal LE8 adherence correlates with attenuated subclinical atherosclerosis, including reduced carotid plaque burden [ 11 ][ 12 ], reinforcing its utility in vascular health prognostication. Despite these advancements, the direct relationship between LE8 adherence and incident CAS remains underexplored, necessitating rigorous investigation through longitudinal epidemiological designs to delineate its etiological and preventive implications. This study aimed to analyze the association between the LE8 score and CAS via prospective data from the UK Biobank. 2. Materials 2.1 Study population The UK Biobank comprises data from a population-based cohort study that recruited more than 500,000 participants (37 to 73 years old). The participants attended one of the 22 assessment centers across England, Scotland, or Wales between 2006 and 2010. Their extensive information on social demography, lifestyle, health, and physical assessments was collected via questionnaires, interviews, health records, physical measures, and blood samples. In the present study, among the 502,357 individuals in the UK Biobank, 270,695 were included after excluding patients with CAS at baseline (n = 423), those with incomplete information on any LE8 metrics (n = 138,937), and participants lacking illness of family history records (n = 101,447). The complete flowchart of participant selection is shown in Supplemental Fig. 1 . 2.2 CAS outcomes Through medical record linkages in the UK Biobank, CAS outcomes were ascertained via the 10th and 9th revisions of the International Classification of Diseases. We defined the incident CAS as follows: ICD-9 , 433.10; and ICD-10 , I65.2 ( Table S1 ). Dates and causes of hospital admission were identified via record linkage to Health Episode Statistics (England and Wales) and the Scottish Morbidity Records (SMR01) (Scotland). Details of the linkage procedure can be found at http://content.digital.nhs.uk/services . 2.3 Assessment of Life's Essential 8 score We constructed an LE8 score that includes 8 components defined by the AHA. Table S4 details the measurement and calculation of health factors (body mass index [BMI], non-high-density lipoprotein cholesterol [HDL], blood glucose, and blood pressure) and health behaviors (diet, physical activity, nicotine exposure, and sleep health) for the LE8 score. Each metric within the LE8 score ranges from 0 to 100 points, and the total individual CVH score is calculated by obtaining the unweighted average of the scores across all eight components. As a result, the total CVH score also ranges from 0 to 100 points, providing a standardized assessment of an individual’s overall CVH. CVH was categorized according to the AHA’s recommendations as follows: high CVH (LE8 score: 80–100), moderate CVH (LE8 score: 50–79), and low CVH (LE8 score: 0–49). We calculated subscales for each of the LE8 metrics. 2.4 Covariates In the present study, a series of covariates were collected from touch-screen questionnaires, anthropometric measurements, biochemical indexes, and 24-hour dietary recall questionnaires. The potential confounders included age (continuous), sex (males/female), ethnicity (White/other), Townsend deprivation index (continuous), educational level (no qualification/any other qualification/degree or above), annual household income (<£18,000/≥£18,000), employment status (employed/unemployed), sedentary time (continuous), alcohol drinker status (never/previous/current), use of medication(yes/no), and sedentary time (continuous) ( Table S2 ). The Townsend deprivation index is a composite measure of deprivation based on unemployment, non-car ownership, nonhome ownership, and household overcrowding. Table S3 shows the percentages of participants with missing covariates, which were imputed via logistic regression for binary variables, polynomial regression for categorical variables, and predictive mean matching (PMM) for continuous variables. More details about the method of imputation were reported in previous studies [ 13 ][ 14 ]. 2.5 Family risk score A family risk score was developed to quantify the aggregated risk of CAS on the basis of familial predisposition. First, univariate logistic regression was conducted to assess the associations between each binary family history variable (including hypertension, diabetes, heart disease, or stroke in the father, mother, and siblings) and CAS status (0 = non-diseased, 1 = diseased). OR values were rounded to the nearest integer for weight simplification, and nonsignificant variables (p > 0.05) were excluded. Second, the final score was calculated as a weighted sum: Family risk score= \(\:\sum\:_{i=1}^{n}({\text{V}\text{a}\text{r}\text{i}\text{a}\text{b}\text{l}\text{e}}_{i}\times\:{\text{W}\text{e}\text{i}\text{g}\text{h}\text{t}}_{i})\) where \(\:{\text{v}\text{a}\text{r}\text{i}\text{a}\text{b}\text{l}\text{e}}_{i}\) includes hypertension, diabetes, heart disease, and stroke in fathers, mothers, and siblings, totaling 12 variables. Each variable was coded as 0 for no family history or 1 for positive family history, and weights reflected effect size and statistical significance. The participants were stratified into low-, medium-, and high-risk groups using tertiles to ensure balanced subgroup proportions. Figure S3 displays the distribution of family risk scores across risk categories, with scores ranging from 0–18. This approach aligns with established methods for constructing cardiovascular risk prediction models, such as the Framingham risk score and QRISK2, which integrate family history and clinical variables through regression-based weighting[ 15 ][ 16 ]. Although polygenic risk scores (PRSs) were not feasible in this study due to genetic data limitations, the weighting strategy mirrors PRS methodologies that prioritize effect sizes and statistical significance[ 17 ]. Similar frameworks have been validated in studies leveraging family history as a surrogate for genetic predisposition when the PRS is unavailable[ 18 ]. 2.6 Statistical analysis The baseline characteristics of the participants were summarized via descriptive statistics and standard deviations for continuous variables and frequencies and percentages for categorical variables. Initially, we conducted a comprehensive analysis of the associations between the LE8 score and the incidence of CAS via multivariable Cox proportional hazards models. Model 1 was adjusted for age, sex, and ethnicity. Model 2 included additional adjustments for the Townsend deprivation index, education level, annual household income, employment status, alcohol drinker status, use of medication, and sedentary time. Model 3 was further adjusted for family risk level. Furthermore, we used restricted cubic spline (RCS) analyses to explore the dose‒response relationship. Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were calculated to measure the associations. To investigate potential heterogeneity in the effects of the LE8 score on CAS incidence, subgroup analyses stratified by age (≤ 50 years, 51–60 years, > 60 years), sex (male/female), ethnicity (White/other), Townsend deprivation index (≥ median/<median), education level (no qualification/any other qualification/degree or above), annual household income (<£18,000/≥£18,000), employment status (employed/unemployed), alcohol drinker status (never/previous/current), and family risk level (low/medium/high) were performed. To assess the public health impact of CVH categories on the risk of CAS, we calculated the overall PARs for each transition between CVH categories via the following formula: PAR= \(\:\frac{\sum\:\text{P}\text{i}\times\:\:(\text{H}\text{R}\text{i}-\:1)}{\:1\:+\:\sum\:\text{P}\text{i}\:\times\:\:(\text{H}\text{R}\text{i}\:-\:1)}\) ×100% where P i represents the prevalence of the CVH category (low/moderate/high) in the cohort, and HR i is the hazard ratio for the association between the CVH category and CAS risk. Sensitivity analyses were conducted in three ways on the basis of the main model. First, to address reverse causation, we excluded participants who experienced CAS events within the first two years of follow-up. Second, we excluded participants with missing covariate data and subsequent model adjustments. Additionally, we conducted the main analyses excluding the family risk score by revising the inclusion/exclusion criteria. A two-sided P < 0.05 was considered statistically significant. All the statistical analyses were conducted via R 4.3.2. 3. Results 3.1 Baseline characteristics A total of 270,695 participants were included in the present study. Over a follow-up period of 3,596,976 person-years (median follow-up year: 13.3), we recorded 1,158 cases of CAS. The baseline characteristics of the participants stratified by CVH categories are presented in Table 1 . Among 270,695 participants, 13.4% (n = 36,400) were classified as high-CVH, 78.7% (n = 213,066) as moderate-CVH, and 7.8% (n = 21,229) as low-CVH. The participants with high CVH were younger, predominantly female, and had lower socioeconomic deprivation. They also had higher levels of educational attainment and household income, along with lower unemployment rates. The high-CVH group exhibited significantly better health behaviors, including a higher DASH diet, greater physical activity levels, lower rates of tobacco nicotine exposure, and better sleep health. Biometric measures also showed advantages, with lower blood pressure, lower BMI, improved lipid profiles, and better glycemic control. Notably, the use of medication and family risk scores decreased progressively from the low to the high CVH groups. Table 1 Baseline characteristics of the participants according to the categories of LE8 score Life’s Essential 8 score P values Characteristic Low-CVH (n = 21,229) Moderate-CVH(n = 213,066) High-CVH (n = 36,400) Cases, n (%) 177 (0.8) 926 (0.4) 55 (0.2) < 0.001 Age (year), mean (SD) 56.41 (7.58) 56.32 (7.99) 52.92 (8.21) < 0.001 Ethnicity (white, %) 95.5 95.4 95.6 0.109 Sex (female, %) 46.0 53.5 70.8 < 0.001 Townsend deprivation index 0.68 (3.28) 1.52 (2.94) 1.74 (2.79) < 0.001 Education (%) < 0.001 No qualification 21.7 14.0 5.8 Any other qualification 54.7 51.5 45.7 Degree or above 23.5 34.5 48.5 Income level (≥ 18,000 £, %) 45.1 54.0 65.0 < 0.001 Employment (Unemployment, %) 42.6 39.0 29.8 < 0.001 Use of medication (yes, %) 44.8 25.7 9.1 < 0.001 Sedentary time, mean (SD) 2.88 2.34 2.02 < 0.001 Alcohol drinker status (%) < 0.001 Never 3.5 3.8 4.3 Previous 5.1 3.1 3.0 Current 91.3 93.1 92.7 Body mass Index (BMI), kg/m 2 32.2 (5.53) 27.3 (4.29) 23.5 (2.59) < 0.001 Systolic BP, mmHg 145.82 (17.14) 138.46 (17.88) 122.61 (14.76) < 0.001 Diastolic BP, mmHg 88.07 (10.15) 82.72 (9.69) 74.22 (7.98) < 0.001 HbA1c, mmol/mol 39.99 (11.05) 35.67 (5.67) 33.83 (3.62) < 0.001 Blood Glucose, mmol/L 5.70 (2.09) 5.07 (1.06) 4.78 (0.66) < 0.001 Total Cholesterol, mmol/L 6.05 (1.25) 5.77 (1.12) 5.14 (0.89) < 0.001 HDL, mmol/L 1.28 (0.32) 1.45 (0.38) 1.61 (0.38) < 0.001 LDL, mmol/L 3.88 (0.94) 3.62 (0.85) 3.06 (0.65) < 0.001 Family risk score, mean (SD) 3.20 (2.64) 2.78 (2.47) 2.21 (2.19) < 0.001 Life’s Essential 8 score, mean (SD) Total LE8 Score 44.01 (4.92) 65.91 (7.76) 84.70 (3.96) < 0.001 DASH diet score 34.60 (18.17) 44.36 (20.21) 57.32 (21.27) < 0.001 Physical activity score 31.27 (35.55) 71.68 (34.82) 91.37 (18.32) < 0.001 Tobacco nicotine exposure score 31.52 (39.50) 68.46 (38.57) 91.96 (17.65) < 0.001 Sleep health score 76.83 (25.52) 89.95 (17.70) 95.78 (11.03) < 0.001 Body mass Index score 39.88 (27.42) 69.47 (26.82) 92.85 (13.87) < 0.001 Blood lipids score 31.32 (25.77) 45.74 (27.49) 73.02 (26.22) < 0.001 Blood pressure score 26.10 (21.83) 44.43 (28.46) 76.84 (25.58) < 0.001 Use of medication: Defined as the use of lipid-lowering medications, antihypertensive medications, or insulin. Sedentary time: The total time spent watching television, using a computer, and driving. 3.2 Associations between the LE8 score and CAS incidence The crude cumulative incidence of CAS demonstrated a graded relationship with the three levels of CVH categories during follow-up (log-rank test, P < 0.001), as shown in Fig. 1 . The associations between the LE8 score and CAS incidence are presented in Table 2 . In the analysis, we identified 117, 926, and 55 CAS cases over 275,481, 2,831,200, and 490,295 pearson-years in the low, moderate, and high CVH categories, respectively. In Model 1, higher CVH, as indicated by the LE8 score, was significantly associated with reduced CAS risk, with HRs of 1 (reference), 0.51 (95% CI: 0.43–0.60), and 0.26 (95% CI: 0.19–0.36) for the low, moderate, and high CVH categories, respectively. Model 2, which included additional adjustments for socioeconomic factors and behavioral variables, showed attenuated associations but maintained statistical significance, with HRs of 1 (reference), 0.63 (95% CI: 0.54–0.75), and 0.40 (95% CI: 0.29–0.54). Additional adjustment for family risk level in Model 3 revealed minimal differences, with HRs of 1 (reference), 0.64 (95% CI: 0.54–0.75), and 0.40 (95% CI: 0.30–0.55). A consistent inverse trend between CVH categories and CAS risk was observed across all the models ( P- trend < 0.0001). Notably, participants with high-CVH presented a 60% lower CAS risk than did those with low-CVH in fully adjusted analyses. Table 2 Main analysis of the association between the LE8 score and CAS LE8 score P for trend Low-CVH Moderate-CVH High-CVH Total No. of Participants 270,695 21,229 213,066 36,400 Incidence rate per person-years 1158/3,596,976 177/275,481 926/2,831,200 55/490,295 Crude model 1.00(reference) 0.50 (0.43, 0.59) 0.17 (0.13, 0.23) < 0.0001 Model1 1.00 (reference) 0.51 (0.43, 0.60) 0.26 (0.19, 0.36) < 0.0001 Model2 1.00(reference) 0.63 (0.54, 0.75) 0.40 (0.29, 0.54) < 0.0001 Model3 1.00(reference) 0.64 (0.54, 0.75) 0.40 (0.30, 0.55) < 0.0001 The crude model was unadjusted. Model 1 was adjusted for age, sex, and ethnicity. Model 2 was additionally adjusted for the Townsend deprivation index, alcohol drinker status, use of medication, sedentary time, average household income and education level. Model 3 was adjusted for age, sex, ethnicity, Townsend deprivation index, alcohol drinker status, sedentary time, average household income, education level, use of medication and family risk level. The relationship between the LE8 score and CAS incidence was further estimated through restricted cubic spline analyses, with the findings shown in Fig. 2 (A-C) . The overall LE8 score demonstrated a significant linear association with CAS risk (P for non-linearity = 0.881), with a minimal beneficial threshold of 67 points (HR = 1.00). Similarly, the behavior subscale score showed no evidence of nonlinearity ( P for non-linearity = 0.482), supporting a linear trend. In contrast, the biological subscale score exhibited a nonlinear relationship with CAS risk ( P for non-linearity = 0.044), with a minimal beneficial threshold of 70 points (HR = 1.00). When we calculated the PAR for the total LE8 score and the behavior and biological subscale scores, the overall PAR for the LE8 score was 57.63%, whereas the biological subscale accounted for 27.94% of the attributable risk, and the behavioral subscale contributed 31.93%. 3.3 Associations between the family risk score and CAS incidence The crude cumulative incidence of CAS exhibited a graded relationship with the three levels of CVH categories during follow-up (log-rank test, P < 0.001), as shown in Figure S5. The associations between the family risk score and CAS incidence are presented in Table S8. The analysis revealed a significant increasing trend in the incidence of carotid artery stenosis with increasing familial risk score ( P -trend < 0.0001). Compared with the low-family-risk group, the risk of CAS increased by 9% (HR: 1.09, 95% CI: 0.93–1.28) and 24% (HR: 1.24, 95% CI: 1.07–1.44) in the medium-family-risk group and the high-family-risk group, respectively, and this trend was still significant after adjusting for age, sex, ethnicity, socioeconomic factors, lifestyle, and the LE8 score. However, as more covariates were adjusted for, the gap between the different risk groups narrowed, suggesting that some of the risk factors may be mediated by confounding factors such as socioeconomic status and lifestyle. Nonetheless, the family risk score remained an independent predictor of CAS, and the risk increased significantly with increasing scores. 3.4 Associations of the family risk score and LE8 score with CAS incidence The joint associations between family risk level, LE8 score, and CAS incidence are detailed in Table 3 . Stratified by family risk level (low/medium/high), participants with higher CVH consistently demonstrated reduced CAS risk across all strata. In the low-family-risk group, high CVH was associated with 40% lower CAS risk than low CVH was (Model 3 HR: 0.60; 95% CI: 0.33–1.09), although the difference was statistically significant after full adjustment. For the medium-family-risk stratum, high CVH was associated with a 59% risk reduction (Model 3 HR: 0.41; 95% CI: 0.24–0.72), whereas moderate CVH was associated with a 30% lower risk (HR: 0.70; 95% CI: 0.52–0.95). The strongest graded inverse associations emerged in the high-family-risk group: high CVH corresponded to a 67% lower CAS risk (HR: 0.33; 95% CI: 0.20–0.54), and moderate CVH corresponded to a 47% reduction (HR: 0.53; 95% CI: 0.42–0.67) in fully adjusted models. Across all the family risk strata, Model 1 revealed the most pronounced protective effects, which attenuated progressively with sequential adjustments for demographics (Model 2) and socioeconomic/behavioral factors (Model 3). Notably, the high-family-risk subgroup presented the steepest risk gradient, with high CVH attenuating CAS risk by 81% (HR: 0.19, 95% CI: 0.12–0.31) in crude analyses. Table 3 The joint associations between the family risk score, LE8 score and the risk of CAS. Family Risk Level Total Incidence rate per person, years HR (95% CI) Model 1 a Model 2 b Model 3 c Low family risk Low-CVH 6036 30/78,561 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Moderate-CVH 73,005 238/972,969 0.64 (0.43, 0.93)* 0.68 (0.47, 1.00) 0.86 (0.59, 1.27) High-CVH 15,937 18/214,924 0.22 (0.12, 0.39)*** 0.40 (0.22, 0.72)** 0.60 (0.33, 1.09) Medium family risk Low-CVH 6769 50/88,011 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Moderate-CVH 69,664 295/925,941 0.56 (0.41, 0.75)*** 0.56 (0.41, 0.76)*** 0.70 (0.52, 0.95)* High-CVH 11,764 17/158,458 0.19 (0.11, 0.32)*** 0.27 (0.15, 0.46)*** 0.41 (0.24, 0.72)** High family risk Low-CVH 8424 97/108,909 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Moderate-CVH 70,397 393/932,290 0.47 (0.38, 0.59)*** 0.44 (0.35, 0.55)*** 0.53 (0.42, 0.67)*** High-CVH 8699 20/116,912 0.19 (0.12, 0.31)*** 0.23 (0.14, 0.38)*** 0.33 (0.20, 0.54)*** HR and 95% CI represent significance at a P value < 0.05(*), P value < 0.01(**) or P value < 0.001(***). a Model 1: Cox regression models adjusted for none; b Model 2: Cox regression models adjusted for age, sex, and ethnicity; c Model 3: Cox regression models adjusted for age, sex, ethnicity, education, income level, employment, medication use, sedentary time, and alcohol drinker status. Abbreviations: CAS, carotid artery stenosis; HR, hazard ratio; CI, confidence interval. 3.5 Joint associations between family risk score, LE8 score and the risk of CAS incidence Stratified analyses revealed inverse associations between the LE8 score and CAS incidence across demographic and clinical subgroups ( Table S5 ). Age significantly modified the relationship ( P -interaction = 0.001), with stronger protective effects in younger participants (≤ 50 years: HR = 0.18 vs. >60 years: HR = 0.55). Protective associations persisted across sex, income level, and employment status (all P values < 0.001), with pronounced risk reductions in individuals with low education (HR = 0.18) or high familial risk (HR = 0.33). No significant interactions were detected for sex, ethnicity, or socioeconomic factors ( P -interaction > 0.05). Trend significance ( P < 0.05) was consistent across most subgroups, except nondrinkers, white populations and nondrinkers. 3.6 Sensitivity analyses To assess the stability of our findings, multiple sensitivity analyses were conducted, the results of which are detailed in Tables S9-11 . These included omitting events occurring within the first two follow-up years, limiting analyses to participants with complete covariate data, and excluding family risk scores (revised inclusion/exclusion criteria). Overall, the results of all the sensitivity analyses were generally consistent with the main conclusion. 4. Discussion To our knowledge, this is the first prospective study to investigate the associations of new CVH metrics, defined by LE8, with the risk of CAS. Our results are consistent with the current knowledge that the risk of CAS is inversely associated with CVH levels. Over a median follow-up of 13.3 years, participants with high CVH presented a 60% reduction in CAS risk compared with the low-CVH group, and this association remained robust after adjusting for the family risk score. These findings not only validate LE8 as a predictive tool for atherosclerotic diseases but also extend prior research on subclinical plaque burden to clinically significant vascular endpoints, providing critical evidence for precision-based cardiovascular health management[ 19 ]. Notably, the protective effect of higher LE8 scores was most pronounced in individuals aged ≤ 50 years, corroborating Wang et al.’s findings that early-life health behaviors reduce cumulative inflammatory and oxidative damage[ 20 ]. To translate this into practice, we propose integrating LE8 into community-based screening programs that target high-family-risk youth and deploying digital platforms (e.g., wearable devices) for real-time LE8 monitoring and personalized feedback. The strength of LE8 lies in its integration of behavioral and biological metrics. In this study, the behavioral and biological subscales contributed 31.9% and 27.9% of the PAR, respectively, with the total LE8 PAR reaching 57.6%, exceeding estimates for CHD (PAR = 40 ~ 50%)[ 21 ] and stroke (PAR = 37 ~ 43%)[ 22 ]. This underscores the necessity of combined lifestyle and metabolic interventions for CAS prevention. These results align with the protective effects of LE8 on other cardiovascular outcomes, such as CHD and stroke. For example, Xanthakis et al. reported a 23% reduction in HF risk per 10,point LE8 increase in the Framingham cohort, which was driven primarily by blood pressure control[ 23 ]. Conversely, Jiang et al. identified behavioral factors as the dominant contributors to the association of LE8 with all-cause mortality (PAR = 42%)[ 24 ]. Notably, our study highlights the equivalence of biological and behavioral metrics in mitigating CAS risk, emphasizing the need for dual-target strategies in atherosclerotic endpoint prevention. To address genetic susceptibility, we developed a weighted family risk score using self-reported family history. This contrasts with polygenic risk scores (PRSs), such as Khera et al.’s CHD-PRS, which identifies 20% high-risk individuals and requires costly genome-wide data [ 17 ]. In this study, we categorized family risk scores as low (0–1), moderate (2–3), or high (≥ 4) and found that individuals with higher levels of CVH had a significantly lower risk of CAS, regardless of the family risk score. In particular, high-CVH was associated with a 67% lower risk of CAS (HR = 0.33, 95% CI: 0.20–0.54) in those in the high-family-risk group. This finding emphasizes the importance of elevating CVH levels by improving lifestyle and biomarkers in populations with high family risk. Therefore, early preventive interventions targeting people with high family risk may be an effective strategy to reduce the incidence of CAS. Future public health policies should focus on this high-risk population and promote healthy lifestyles to reduce the burden of CAS. Our family risk score, which relies solely on family history of illness, offers a pragmatic tool for resource-limited settings. However, its predictive accuracy remains inferior to that of PRS and excludes rare variants. Future integration of familial and polygenic risk may enhance precision. This study also has several limitations. First, familial risk scores depend on self-reported histories, which may miss recessive cases. Second, LE8 was assessed only at baseline and does not account for temporal changes in CVH. Third, the predominantly European cohort (95.4% White) limits generalizability. Finally, unmeasured confounders may bias estimates. Future studies could develop a “LE8-PRS integration model” with genetic data and validate the causal effect of LE8 intervention on CAS through randomized controlled trials. 5. Conclusion This large perspective cohort study from the UK Biobank demonstrated that a higher LE8 score is an independent protective factor against CAS, with the strongest protective effects observed in individuals with a family history of CVD. Integrating family history evaluation with LE8 monitoring offers a practical approach to CAS prevention. Declarations Acknowledgments This study was conducted under the UK Biobank (Application No. 99628). The authors thank the investigators and participants in the UK Biobank for their contributions to this study. Funding This work was supported by the National Natural Science Foundation of China [Grant number 82173610]; the China Postdoctoral Science Foundation [Grant numbers 2024T171043, 2023MD744266 and GZC20233120]; and the Department of Science and Technology of Liaoning Province [Grant numbers 2023JH2/20200025 and 2023-MSLH-371]. Conflict of interest None declared. Authors’ contributions Wanyang Liu had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Yusufu·Aisha, Yue Wang, Wanyang Liu. Acquisition, analysis, or interpretation of data: Yusufu·Aisha, Wanyang Liu. Drafting of the manuscript: Yusufu·Aisha, Yue Wang, Wanyang Liu. Critical revision of the manuscript for important intellectual content: Yue Wang, Tao Zhuang, Yeqing Xie, Jinyun Zhang, Lin Du, Xinyu Li, Yuchen Qin, Gan Chen, Yunzhu Li, Yaru Hou, Yanling Liu, and Xiaonan Tang. Statistical analysis: Yusufu·Aisha, Tao Zhuang, Yusufu·Aisha, Yeqing Xie, Jinyun Zhang, Lin Du, Xinyu Li. Funding: Wanyang Liu, Yue Wang. Administrative, technical, or material support: Wanyang Liu. Supervision: Wanyang Liu Ethics approval All participants provided written informed consent, and ethical approval of the UK Biobank was received from the North West Multicenter Research Ethics Committee (reference number: 21/NW/0157). This study conformed to the ethical guidelines of the Declaration of Helsinki and its amendments. Data availability The UK Biobank is an open access resource. Researchers can apply to use the UK Biobank dataset by registering and applying at [http://ukbiobank.ac.uk/register-apply/]. References Paraskevas KI, Nicolaides AN, Kakkos SK. Asymptomatic Carotid Stenosis and Risk of Stroke (ACSRS) study: what have we learned from it? Ann Transl Med. 2020;8:1271–1271. Autret A, Saudeau D, Bertrand PH, Pourcelot L, Marchal C, De Boisvilliers S. STROKE RISK IN PATIENTS WITH CAROTID STENOSIS. Lancet. 1987;329:888–90. Poorthuis MHF, Hageman SHJ, Fiolet ATL, Kappelle LJ, Bots ML, Steg PG, et al. Prediction of Severe Baseline Asymptomatic Carotid Stenosis and Subsequent Risk of Stroke and Cardiovascular Disease. Stroke. 2024;55:2632–40. Dossabhoy S, Arya S. Epidemiology of atherosclerotic carotid artery disease. Semin Vasc Surg. 2021;34:3–9. Poorthuis MHF, Hageman SHJ, Fiolet ATL, Kappelle LJ, Bots ML, Steg PG, et al. Prediction of Severe Baseline Asymptomatic Carotid Stenosis and Subsequent Risk of Stroke and Cardiovascular Disease. Stroke. 2024;55:2632–40. Brott TG, Howard G, Roubin GS, Meschia JF, Mackey A, Brooks W, et al. Long-Term Results of Stenting versus Endarterectomy for Carotid-Artery Stenosis. N Engl J Med. 2016;374:1021–31. Lloyd-Jones DM, Allen NB, Anderson CAM, Black T, Brewer LC, Foraker RE, 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:e18–43. Rempakos 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:e030764. Cai A, Chen C, Wang J, Ou Y, Nie Z, Feng Y. Life’s Essential 8 and risk of incident heart failure in community population without cardiovascular disease: Results of the sub-cohort of China PEACE Million Persons Project. Prev Med. 2024;178:107797. Sebastian SA, Shah Y, Paul H, Arsene C. Life’s Essential 8 and the risk of cardiovascular disease: a systematic review and meta-analysis. Eur J Prev Cardiol. 2024;:zwae280. Zhou S-Y, Liu F-C, Chen S-F, Li J-X, Cao J, Huang K-Y, et al. Life’s essential 8 and risk of subclinical atherosclerosis progression: a prospective cohort study. J Geriatr Cardiol. 2024;21:751–9. Herraiz-Adillo Á, Ahlqvist VH, Higueras-Fresnillo S, Berglind D, Wennberg P, Lenander C et al. Life’s Essential 8 and carotid artery plaques: the Swedish cardiopulmonary bioimage study. Front Cardiovasc Med. 2023;10. van Buuren S. Flexible Imputation of Missing Data, Second Edition. 2nd edition. New York: Chapman and Hall/CRC; 2018. Morris TP, White IR, Royston P. Tuning multiple imputation by predictive mean matching and local residual draws. BMC Med Res Methodol. 2014;14:75. D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General Cardiovascular Risk Profile for Use in Primary Care. Circulation. 2008;117:743–53. Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A, et al. Predicting Cardiovascular Risk in England and Wales: Prospective Derivation and Validation of QRISK2. BMJ. 2008;336:1475–82. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50:1219–24. Kendler KS, Ohlsson H, Sundquist J, Sundquist K. Family Genetic Risk Scores and the Genetic Architecture of Major Affective and Psychotic Disorders in a Swedish National Sample. JAMA Psychiatry. 2021;78:735–43. Zhou S-Y, Liu F-C, Chen S-F, Li J-X, Cao J, Huang K-Y, et al. Life’s essential 8 and risk of subclinical atherosclerosis progression: a prospective cohort study. J Geriatr Cardiol. 2024;21:751–9. Ying Y, Lin S, Kong F, Li Y, Xu S, Liang X, et al. Ideal Cardiovascular Health Metrics and Incidence of Ischemic Stroke Among Hypertensive Patients: A Prospective Cohort Study. Front Cardiovasc Med. 2020;7:590809. Sebastian SA, Shah Y, Paul H, Arsene C. Life’s Essential 8 and the risk of cardiovascular disease: a systematic review and meta-analysis. Eur J Prev Cardiol. 2024;:zwae280. Wu S, Wu Z, Yu D, Chen S, Wang A, Wang A, et al. Life’s Essential 8 and Risk of Stroke: A Prospective Community-Based Study. Stroke. 2023;54:2369–79. Xanthakis V, Enserro DM, Murabito JM, Polak JF, Wollert KC, Januzzi JL, et al. Ideal Cardiovasc Health Circulation. 2014;130:1676–83. Ning N, Fan X, Zhang Y, Wang Y, Liu Y, Li Y, et al. Joint association of cardiovascular health and frailty with all-cause and cause-specific mortality: a prospective study. Aging. 2024;53:afae156. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterial.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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6332879","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452396050,"identity":"19a37599-9b33-44e8-86b7-49dbc5242ca6","order_by":0,"name":"Yue Wang","email":"","orcid":"","institution":"Ministry of Education (China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Wang","suffix":""},{"id":452396052,"identity":"1d73a07d-71be-4c78-ab49-49f7cfcceee2","order_by":1,"name":"Yusufu Aisha","email":"","orcid":"","institution":"Ministry of Education (China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yusufu","middleName":"","lastName":"Aisha","suffix":""},{"id":452396058,"identity":"816050d4-c0aa-4042-b7df-cfee4d2ea70b","order_by":2,"name":"Tao Zhuang","email":"","orcid":"","institution":"Ministry of Education (China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhuang","suffix":""},{"id":452396060,"identity":"ed219a51-6129-46c3-a7ac-349a049a4a72","order_by":3,"name":"Yeqing Xie","email":"","orcid":"","institution":"Ministry of Education (China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yeqing","middleName":"","lastName":"Xie","suffix":""},{"id":452396062,"identity":"f4e5d88b-6af7-495f-8d0d-dd866c2e0f01","order_by":4,"name":"Jinyun Zhang","email":"","orcid":"","institution":"Ministry of Education (China Medical 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University","correspondingAuthor":false,"prefix":"","firstName":"Yuchen","middleName":"","lastName":"Qin","suffix":""},{"id":452396071,"identity":"064366d2-6d1c-4a6f-8d0c-ca3dabfdd56c","order_by":8,"name":"Gan Chen","email":"","orcid":"","institution":"Ministry of Education (China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gan","middleName":"","lastName":"Chen","suffix":""},{"id":452396072,"identity":"400e645e-6d40-4977-bd1f-6600b65a97ac","order_by":9,"name":"Yunzhu Li","email":"","orcid":"","institution":"Ministry of Education (China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yunzhu","middleName":"","lastName":"Li","suffix":""},{"id":452396075,"identity":"f622a130-5220-4672-a37f-f34fba41a02e","order_by":10,"name":"Yaru Hou","email":"","orcid":"","institution":"Ministry of Education (China Medical 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Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYFAC5oYDDBUQpgSRWhiBWs6QqoWBsY0ULQY3EhsP886zizY4wHzwNg+DXR4xWhoO825Lzt1wgC3ZmochuZigFjOIlgNALTxm0jwMBxIbiNMyB6SF/xspWhrAtrARp8X+zMOGg3OOJefOPMxmbDnHIJmwFsn25MMf3tTY5fYdb354402FHWEtDAIJUAYziDAgqB4I+A8Qo2oUjIJRMApGNAAAFrNBqbC7pH4AAAAASUVORK5CYII=","orcid":"","institution":"Ministry of Education (China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wanyang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-03-29 08:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6332879/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6332879/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82359218,"identity":"ca7cfa6a-3cd6-40cd-b515-d8def0ec87c2","added_by":"auto","created_at":"2025-05-09 11:28:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84296,"visible":true,"origin":"","legend":"\u003cp\u003eCrude cumulative incidence of CAS according to the LE8 score\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6332879/v1/57d48d811d4d57d7a8af7cc5.png"},{"id":82356950,"identity":"a2c9cc1f-0ac1-4d0a-8511-9245c6ead0ef","added_by":"auto","created_at":"2025-05-09 11:20:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122186,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline models for the associations between total LE8 (A), the behavior subscale score (B), the biological subscale score (C) and the risk of CAS among participants.\u003c/p\u003e\n\u003cp\u003eThe 95% CIs of the adjusted HRs are shaded. The restricted cubic spline model is adjusted for age, sex, ethnicity, Townsend deprivation index, alcohol drinker status, sedentary time, average household income, education level, use of medication and family risk level.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6332879/v1/8f131104342d35537b07b3d3.png"},{"id":90241009,"identity":"bc396dce-9b3c-4e31-a950-b1a5b53a0eb8","added_by":"auto","created_at":"2025-08-30 21:31:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1226287,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6332879/v1/104fe946-81b7-415b-994b-e5ad04c0add2.pdf"},{"id":82356954,"identity":"908651d0-00f3-4ea2-8da3-1e9b1a0ffae2","added_by":"auto","created_at":"2025-05-09 11:20:15","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":601590,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6332879/v1/b370ca56c031eeedd2a06123.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Life’s Essential 8 for carotid artery stenosis prevention: stratified risk reduction by family cardiovascular history in the UK Biobank","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCarotid artery stenosis (CAS) is a critical contributor to ischemic stroke and cardiovascular morbidity and is predominantly attributed to atherosclerotic plaque progression, leading to luminal stenosis or occlusion of the carotid vasculature[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e][\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Epidemiological data indicate that CAS accounts for approximately 15\u0026ndash;20% of ischemic stroke cases globally, with its prevalence exhibiting a marked age-dependent increase[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The underlying pathophysiology of CAS encompasses chronic inflammatory cascades, endothelial dysfunction, and dysregulated lipid homeostasis, while established modifiable risk factors include hypertension, dyslipidemia, diabetes mellitus, tobacco use, and obesity[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Although invasive interventions such as carotid endarterectomy (CEA) and stenting mitigate stroke risk in high-risk cohorts, procedural complications and sustained healthcare expenditures underscore the imperative for primordial prevention strategies[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consequently, elucidating modifiable determinants and implementing multimodal preventive approaches are pivotal for attenuating the global burden of CAS.\u003c/p\u003e \u003cp\u003eLife\u0026rsquo;s Essential 8 (LE8), introduced by the American Heart Association (AHA) in 2022, constitutes a multidimensional framework for assessing cardiovascular health (CVH), integrating eight modifiable domains: dietary quality, physical activity, nicotine exposure, sleep health, body mass index (BMI), blood lipids, glycemic status, and blood pressure[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This refined metric supersedes the prior \"Life\u0026rsquo;s Simple 7\" by incorporating sleep health metrics and adopting refined scoring algorithms, thereby enhancing its clinical relevance to contemporary population health dynamics. Compelling evidence from prospective cohort studies has demonstrated robust inverse associations between elevated LE8 scores and incident coronary artery disease, heart failure, and all-cause mortality, positioning LE8 as a predictive biomarker for cardiovascular risk stratification[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Emerging data further suggest that optimal LE8 adherence correlates with attenuated subclinical atherosclerosis, including reduced carotid plaque burden [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], reinforcing its utility in vascular health prognostication. Despite these advancements, the direct relationship between LE8 adherence and incident CAS remains underexplored, necessitating rigorous investigation through longitudinal epidemiological designs to delineate its etiological and preventive implications. This study aimed to analyze the association between the LE8 score and CAS via prospective data from the UK Biobank.\u003c/p\u003e"},{"header":"2. Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThe UK Biobank comprises data from a population-based cohort study that recruited more than 500,000 participants (37 to 73 years old). The participants attended one of the 22 assessment centers across England, Scotland, or Wales between 2006 and 2010. Their extensive information on social demography, lifestyle, health, and physical assessments was collected via questionnaires, interviews, health records, physical measures, and blood samples.\u003c/p\u003e \u003cp\u003eIn the present study, among the 502,357 individuals in the UK Biobank, 270,695 were included after excluding patients with CAS at baseline (n\u0026thinsp;=\u0026thinsp;423), those with incomplete information on any LE8 metrics (n\u0026thinsp;=\u0026thinsp;138,937), and participants lacking illness of family history records (n\u0026thinsp;=\u0026thinsp;101,447). The complete flowchart of participant selection is shown in \u003cb\u003eSupplemental Fig.\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 CAS outcomes\u003c/h2\u003e \u003cp\u003eThrough medical record linkages in the UK Biobank, CAS outcomes were ascertained via the 10th and 9th revisions of the International Classification of Diseases. We defined the incident CAS as follows: \u003cem\u003eICD-9\u003c/em\u003e, 433.10; and \u003cem\u003eICD-10\u003c/em\u003e, I65.2 (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Dates and causes of hospital admission were identified via record linkage to Health Episode Statistics (England and Wales) and the Scottish Morbidity Records (SMR01) (Scotland). Details of the linkage procedure can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://content.digital.nhs.uk/services\u003c/span\u003e\u003cspan address=\"http://content.digital.nhs.uk/services\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of Life's Essential 8 score\u003c/h2\u003e \u003cp\u003eWe constructed an LE8 score that includes 8 components defined by the AHA. \u003cb\u003eTable S4\u003c/b\u003e details the measurement and calculation of health factors (body mass index [BMI], non-high-density lipoprotein cholesterol [HDL], blood glucose, and blood pressure) and health behaviors (diet, physical activity, nicotine exposure, and sleep health) for the LE8 score. Each metric within the LE8 score ranges from 0 to 100 points, and the total individual CVH score is calculated by obtaining the unweighted average of the scores across all eight components. As a result, the total CVH score also ranges from 0 to 100 points, providing a standardized assessment of an individual\u0026rsquo;s overall CVH. CVH was categorized according to the AHA\u0026rsquo;s recommendations as follows: high CVH (LE8 score: 80\u0026ndash;100), moderate CVH (LE8 score: 50\u0026ndash;79), and low CVH (LE8 score: 0\u0026ndash;49). We calculated subscales for each of the LE8 metrics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eIn the present study, a series of covariates were collected from touch-screen questionnaires, anthropometric measurements, biochemical indexes, and 24-hour dietary recall questionnaires. The potential confounders included age (continuous), sex (males/female), ethnicity (White/other), Townsend deprivation index (continuous), educational level (no qualification/any other qualification/degree or above), annual household income (\u0026lt;\u0026pound;18,000/\u0026ge;\u0026pound;18,000), employment status (employed/unemployed), sedentary time (continuous), alcohol drinker status (never/previous/current), use of medication(yes/no), and sedentary time (continuous) (\u003cb\u003eTable S2\u003c/b\u003e). The Townsend deprivation index is a composite measure of deprivation based on unemployment, non-car ownership, nonhome ownership, and household overcrowding. \u003cb\u003eTable S3\u003c/b\u003e shows the percentages of participants with missing covariates, which were imputed via logistic regression for binary variables, polynomial regression for categorical variables, and predictive mean matching (PMM) for continuous variables. More details about the method of imputation were reported in previous studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Family risk score\u003c/h2\u003e \u003cp\u003eA family risk score was developed to quantify the aggregated risk of CAS on the basis of familial predisposition. First, univariate logistic regression was conducted to assess the associations between each binary family history variable (including hypertension, diabetes, heart disease, or stroke in the father, mother, and siblings) and CAS status (0\u0026thinsp;=\u0026thinsp;non-diseased, 1\u0026thinsp;=\u0026thinsp;diseased). OR values were rounded to the nearest integer for weight simplification, and nonsignificant variables (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were excluded. Second, the final score was calculated as a weighted sum:\u003c/p\u003e \u003cp\u003eFamily risk score=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{n}({\\text{V}\\text{a}\\text{r}\\text{i}\\text{a}\\text{b}\\text{l}\\text{e}}_{i}\\times\\:{\\text{W}\\text{e}\\text{i}\\text{g}\\text{h}\\text{t}}_{i})\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{v}\\text{a}\\text{r}\\text{i}\\text{a}\\text{b}\\text{l}\\text{e}}_{i}\\)\u003c/span\u003e\u003c/span\u003e includes hypertension, diabetes, heart disease, and stroke in fathers, mothers, and siblings, totaling 12 variables. Each variable was coded as 0 for no family history or 1 for positive family history, and weights reflected effect size and statistical significance. The participants were stratified into low-, medium-, and high-risk groups using tertiles to ensure balanced subgroup proportions. \u003cb\u003eFigure S3\u003c/b\u003e displays the distribution of family risk scores across risk categories, with scores ranging from 0\u0026ndash;18. This approach aligns with established methods for constructing cardiovascular risk prediction models, such as the Framingham risk score and QRISK2, which integrate family history and clinical variables through regression-based weighting[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although polygenic risk scores (PRSs) were not feasible in this study due to genetic data limitations, the weighting strategy mirrors PRS methodologies that prioritize effect sizes and statistical significance[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similar frameworks have been validated in studies leveraging family history as a surrogate for genetic predisposition when the PRS is unavailable[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the participants were summarized via descriptive statistics and standard deviations for continuous variables and frequencies and percentages for categorical variables. Initially, we conducted a comprehensive analysis of the associations between the LE8 score and the incidence of CAS via multivariable Cox proportional hazards models. Model 1 was adjusted for age, sex, and ethnicity. Model 2 included additional adjustments for the Townsend deprivation index, education level, annual household income, employment status, alcohol drinker status, use of medication, and sedentary time. Model 3 was further adjusted for family risk level. Furthermore, we used restricted cubic spline (RCS) analyses to explore the dose‒response relationship. Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were calculated to measure the associations.\u003c/p\u003e \u003cp\u003eTo investigate potential heterogeneity in the effects of the LE8 score on CAS incidence, subgroup analyses stratified by age (\u0026le;\u0026thinsp;50 years, 51\u0026ndash;60 years, \u0026gt;\u0026thinsp;60 years), sex (male/female), ethnicity (White/other), Townsend deprivation index (\u0026ge;\u0026thinsp;median/\u0026lt;median), education level (no qualification/any other qualification/degree or above), annual household income (\u0026lt;\u0026pound;18,000/\u0026ge;\u0026pound;18,000), employment status (employed/unemployed), alcohol drinker status (never/previous/current), and family risk level (low/medium/high) were performed.\u003c/p\u003e \u003cp\u003eTo assess the public health impact of CVH categories on the risk of CAS, we calculated the overall PARs for each transition between CVH categories via the following formula:\u003c/p\u003e \u003cp\u003ePAR=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\sum\\:\\text{P}\\text{i}\\times\\:\\:(\\text{H}\\text{R}\\text{i}-\\:1)}{\\:1\\:+\\:\\sum\\:\\text{P}\\text{i}\\:\\times\\:\\:(\\text{H}\\text{R}\\text{i}\\:-\\:1)}\\)\u003c/span\u003e\u003c/span\u003e\u0026times;100%\u003c/p\u003e \u003cp\u003ewhere P\u003csub\u003ei\u003c/sub\u003e represents the prevalence of the CVH category (low/moderate/high) in the cohort, and HR\u003csub\u003ei\u003c/sub\u003e is the hazard ratio for the association between the CVH category and CAS risk.\u003c/p\u003e \u003cp\u003eSensitivity analyses were conducted in three ways on the basis of the main model. First, to address reverse causation, we excluded participants who experienced CAS events within the first two years of follow-up. Second, we excluded participants with missing covariate data and subsequent model adjustments. Additionally, we conducted the main analyses excluding the family risk score by revising the inclusion/exclusion criteria. A two-sided \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All the statistical analyses were conducted via R 4.3.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 270,695 participants were included in the present study. Over a follow-up period of 3,596,976 person-years (median follow-up year: 13.3), we recorded 1,158 cases of CAS. The baseline characteristics of the participants stratified by CVH categories are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among 270,695 participants, 13.4% (n\u0026thinsp;=\u0026thinsp;36,400) were classified as high-CVH, 78.7% (n\u0026thinsp;=\u0026thinsp;213,066) as moderate-CVH, and 7.8% (n\u0026thinsp;=\u0026thinsp;21,229) as low-CVH. The participants with high CVH were younger, predominantly female, and had lower socioeconomic deprivation. They also had higher levels of educational attainment and household income, along with lower unemployment rates. The high-CVH group exhibited significantly better health behaviors, including a higher DASH diet, greater physical activity levels, lower rates of tobacco nicotine exposure, and better sleep health. Biometric measures also showed advantages, with lower blood pressure, lower BMI, improved lipid profiles, and better glycemic control. Notably, the use of medication and family risk scores decreased progressively from the low to the high CVH groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the participants according to the categories of LE8 score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLife\u0026rsquo;s Essential 8 score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-CVH\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;21,229)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate-CVH(n\u0026thinsp;=\u0026thinsp;213,066)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh-CVH\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;36,400)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e926 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.41 (7.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.32 (7.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.92 (8.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity (white, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (female, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTownsend deprivation index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68 (3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.52 (2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.74 (2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny other qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome level (\u0026ge;\u0026thinsp;18,000 \u0026pound;, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment (Unemployment, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of medication (yes, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSedentary time, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol drinker status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass Index (BMI), kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.2 (5.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.3 (4.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.5 (2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145.82 (17.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e138.46 (17.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122.61 (14.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.07 (10.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.72 (9.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74.22 (7.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c, mmol/mol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.99 (11.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.67 (5.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.83 (3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood Glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.70 (2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.07 (1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.78 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.05 (1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.77 (1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.14 (0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.28 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.45 (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.61 (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.88 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.62 (0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.06 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily risk score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.20 (2.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.78 (2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.21 (2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLife\u0026rsquo;s Essential 8 score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal LE8 Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.01 (4.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.91 (7.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.70 (3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDASH diet score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.60 (18.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.36 (20.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.32 (21.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.27 (35.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.68 (34.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.37 (18.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTobacco nicotine exposure score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.52 (39.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.46 (38.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.96 (17.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep health score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76.83 (25.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.95 (17.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95.78 (11.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass Index score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.88 (27.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.47 (26.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.85 (13.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood lipids score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.32 (25.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.74 (27.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.02 (26.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood pressure score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.10 (21.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.43 (28.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.84 (25.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eUse of medication: Defined as the use of lipid-lowering medications, antihypertensive medications, or insulin.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSedentary time: The total time spent watching television, using a computer, and driving.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Associations between the LE8 score and CAS incidence\u003c/h2\u003e \u003cp\u003eThe crude cumulative incidence of CAS demonstrated a graded relationship with the three levels of CVH categories during follow-up (log-rank test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The associations between the LE8 score and CAS incidence are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the analysis, we identified 117, 926, and 55 CAS cases over 275,481, 2,831,200, and 490,295 pearson-years in the low, moderate, and high CVH categories, respectively. In Model 1, higher CVH, as indicated by the LE8 score, was significantly associated with reduced CAS risk, with HRs of 1 (reference), 0.51 (95% CI: 0.43\u0026ndash;0.60), and 0.26 (95% CI: 0.19\u0026ndash;0.36) for the low, moderate, and high CVH categories, respectively. Model 2, which included additional adjustments for socioeconomic factors and behavioral variables, showed attenuated associations but maintained statistical significance, with HRs of 1 (reference), 0.63 (95% CI: 0.54\u0026ndash;0.75), and 0.40 (95% CI: 0.29\u0026ndash;0.54). Additional adjustment for family risk level in Model 3 revealed minimal differences, with HRs of 1 (reference), 0.64 (95% CI: 0.54\u0026ndash;0.75), and 0.40 (95% CI: 0.30\u0026ndash;0.55). A consistent inverse trend between CVH categories and CAS risk was observed across all the models (\u003cem\u003eP-\u003c/em\u003etrend\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Notably, participants with high-CVH presented a 60% lower CAS risk than did those with low-CVH in fully adjusted analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain analysis of the association between the LE8 score and CAS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eLE8 score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c7\" namest=\"c6\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for\u003c/p\u003e \u003cp\u003etrend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-CVH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate-CVH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-CVH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal No. of Participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270,695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213,066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence rate per person-years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1158/3,596,976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177/275,481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e926/2,831,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55/490,295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.43, 0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.17 (0.13, 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51 (0.43, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26 (0.19, 0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63 (0.54, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (0.29, 0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64 (0.54, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (0.30, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe crude model was unadjusted.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1 was adjusted for age, sex, and ethnicity.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 2 was additionally adjusted for the Townsend deprivation index, alcohol drinker status, use of medication, sedentary time, average household income and education level.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 3 was adjusted for age, sex, ethnicity, Townsend deprivation index, alcohol drinker status, sedentary time, average household income, education level, use of medication and family risk level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe relationship between the LE8 score and CAS incidence was further estimated through restricted cubic spline analyses, with the findings shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003e(A-C)\u003c/b\u003e. The overall LE8 score demonstrated a significant linear association with CAS risk \u003cem\u003e(P\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.881), with a minimal beneficial threshold of 67 points (HR\u0026thinsp;=\u0026thinsp;1.00). Similarly, the behavior subscale score showed no evidence of nonlinearity (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.482), supporting a linear trend. In contrast, the biological subscale score exhibited a nonlinear relationship with CAS risk (\u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.044), with a minimal beneficial threshold of 70 points (HR\u0026thinsp;=\u0026thinsp;1.00). When we calculated the PAR for the total LE8 score and the behavior and biological subscale scores, the overall PAR for the LE8 score was 57.63%, whereas the biological subscale accounted for 27.94% of the attributable risk, and the behavioral subscale contributed 31.93%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Associations between the family risk score and CAS incidence\u003c/h2\u003e \u003cp\u003eThe crude cumulative incidence of CAS exhibited a graded relationship with the three levels of CVH categories during follow-up (log-rank test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as shown in \u003cb\u003eFigure S5.\u003c/b\u003e The associations between the family risk score and CAS incidence are presented in \u003cb\u003eTable S8.\u003c/b\u003e The analysis revealed a significant increasing trend in the incidence of carotid artery stenosis with increasing familial risk score (\u003cem\u003eP\u003c/em\u003e-trend\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Compared with the low-family-risk group, the risk of CAS increased by 9% (HR: 1.09, 95% CI: 0.93\u0026ndash;1.28) and 24% (HR: 1.24, 95% CI: 1.07\u0026ndash;1.44) in the medium-family-risk group and the high-family-risk group, respectively, and this trend was still significant after adjusting for age, sex, ethnicity, socioeconomic factors, lifestyle, and the LE8 score. However, as more covariates were adjusted for, the gap between the different risk groups narrowed, suggesting that some of the risk factors may be mediated by confounding factors such as socioeconomic status and lifestyle. Nonetheless, the family risk score remained an independent predictor of CAS, and the risk increased significantly with increasing scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Associations of the family risk score and LE8 score with CAS incidence\u003c/h2\u003e \u003cp\u003eThe joint associations between family risk level, LE8 score, and CAS incidence are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Stratified by family risk level (low/medium/high), participants with higher CVH consistently demonstrated reduced CAS risk across all strata. In the low-family-risk group, high CVH was associated with 40% lower CAS risk than low CVH was (Model 3 HR: 0.60; 95% CI: 0.33\u0026ndash;1.09), although the difference was statistically significant after full adjustment. For the medium-family-risk stratum, high CVH was associated with a 59% risk reduction (Model 3 HR: 0.41; 95% CI: 0.24\u0026ndash;0.72), whereas moderate CVH was associated with a 30% lower risk (HR: 0.70; 95% CI: 0.52\u0026ndash;0.95). The strongest graded inverse associations emerged in the high-family-risk group: high CVH corresponded to a 67% lower CAS risk (HR: 0.33; 95% CI: 0.20\u0026ndash;0.54), and moderate CVH corresponded to a 47% reduction (HR: 0.53; 95% CI: 0.42\u0026ndash;0.67) in fully adjusted models. Across all the family risk strata, Model 1 revealed the most pronounced protective effects, which attenuated progressively with sequential adjustments for demographics (Model 2) and socioeconomic/behavioral factors (Model 3). Notably, the high-family-risk subgroup presented the steepest risk gradient, with high CVH attenuating CAS risk by 81% (HR: 0.19, 95% CI: 0.12\u0026ndash;0.31) in crude analyses.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe joint associations between the family risk score, LE8 score and the risk of CAS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily Risk Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIncidence rate per person, years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow family risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-CVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30/78,561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate-CVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238/972,969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64 (0.43, 0.93)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68 (0.47, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86 (0.59, 1.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-CVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18/214,924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22 (0.12, 0.39)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (0.22, 0.72)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.60 (0.33, 1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium family risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-CVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50/88,011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate-CVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69,664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295/925,941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56 (0.41, 0.75)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56 (0.41, 0.76)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70 (0.52, 0.95)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-CVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17/158,458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19 (0.11, 0.32)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27 (0.15, 0.46)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41 (0.24, 0.72)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh family risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-CVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97/108,909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate-CVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70,397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e393/932,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47 (0.38, 0.59)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44 (0.35, 0.55)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53 (0.42, 0.67)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-CVH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/116,912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19 (0.12, 0.31)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23 (0.14, 0.38)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33 (0.20, 0.54)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eHR and 95% CI represent significance at a \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05(*), \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.01(**) or \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.001(***).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003eModel 1: Cox regression models adjusted for none;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003eModel 2: Cox regression models adjusted for age, sex, and ethnicity;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ec\u003c/sup\u003eModel 3: Cox regression models adjusted for age, sex, ethnicity, education, income level, employment, medication use, sedentary time, and alcohol drinker status.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: CAS, carotid artery stenosis; HR, hazard ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Joint associations between family risk score, LE8 score and the risk of CAS incidence\u003c/h2\u003e \u003cp\u003eStratified analyses revealed inverse associations between the LE8 score and CAS incidence across demographic and clinical subgroups (\u003cb\u003eTable S5\u003c/b\u003e). Age significantly modified the relationship (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.001), with stronger protective effects in younger participants (\u0026le;\u0026thinsp;50 years: HR\u0026thinsp;=\u0026thinsp;0.18 vs. \u0026gt;60 years: HR\u0026thinsp;=\u0026thinsp;0.55). Protective associations persisted across sex, income level, and employment status (all \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with pronounced risk reductions in individuals with low education (HR\u0026thinsp;=\u0026thinsp;0.18) or high familial risk (HR\u0026thinsp;=\u0026thinsp;0.33). No significant interactions were detected for sex, ethnicity, or socioeconomic factors (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Trend significance (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) was consistent across most subgroups, except nondrinkers, white populations and nondrinkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Sensitivity analyses\u003c/h2\u003e \u003cp\u003eTo assess the stability of our findings, multiple sensitivity analyses were conducted, the results of which are detailed in \u003cb\u003eTables S9-11\u003c/b\u003e. These included omitting events occurring within the first two follow-up years, limiting analyses to participants with complete covariate data, and excluding family risk scores (revised inclusion/exclusion criteria). Overall, the results of all the sensitivity analyses were generally consistent with the main conclusion.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this is the first prospective study to investigate the associations of new CVH metrics, defined by LE8, with the risk of CAS. Our results are consistent with the current knowledge that the risk of CAS is inversely associated with CVH levels. Over a median follow-up of 13.3 years, participants with high CVH presented a 60% reduction in CAS risk compared with the low-CVH group, and this association remained robust after adjusting for the family risk score. These findings not only validate LE8 as a predictive tool for atherosclerotic diseases but also extend prior research on subclinical plaque burden to clinically significant vascular endpoints, providing critical evidence for precision-based cardiovascular health management[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, the protective effect of higher LE8 scores was most pronounced in individuals aged\u0026thinsp;\u0026le;\u0026thinsp;50 years, corroborating Wang et al.\u0026rsquo;s findings that early-life health behaviors reduce cumulative inflammatory and oxidative damage[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To translate this into practice, we propose integrating LE8 into community-based screening programs that target high-family-risk youth and deploying digital platforms (e.g., wearable devices) for real-time LE8 monitoring and personalized feedback.\u003c/p\u003e \u003cp\u003eThe strength of LE8 lies in its integration of behavioral and biological metrics. In this study, the behavioral and biological subscales contributed 31.9% and 27.9% of the PAR, respectively, with the total LE8 PAR reaching 57.6%, exceeding estimates for CHD (PAR\u0026thinsp;=\u0026thinsp;40\u0026thinsp;~\u0026thinsp;50%)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and stroke (PAR\u0026thinsp;=\u0026thinsp;37\u0026thinsp;~\u0026thinsp;43%)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This underscores the necessity of combined lifestyle and metabolic interventions for CAS prevention. These results align with the protective effects of LE8 on other cardiovascular outcomes, such as CHD and stroke. For example, Xanthakis et al. reported a 23% reduction in HF risk per 10,point LE8 increase in the Framingham cohort, which was driven primarily by blood pressure control[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Conversely, Jiang et al. identified behavioral factors as the dominant contributors to the association of LE8 with all-cause mortality (PAR\u0026thinsp;=\u0026thinsp;42%)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Notably, our study highlights the equivalence of biological and behavioral metrics in mitigating CAS risk, emphasizing the need for dual-target strategies in atherosclerotic endpoint prevention.\u003c/p\u003e \u003cp\u003eTo address genetic susceptibility, we developed a weighted family risk score using self-reported family history. This contrasts with polygenic risk scores (PRSs), such as Khera et al.\u0026rsquo;s CHD-PRS, which identifies 20% high-risk individuals and requires costly genome-wide data [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this study, we categorized family risk scores as low (0\u0026ndash;1), moderate (2\u0026ndash;3), or high (\u0026ge;\u0026thinsp;4) and found that individuals with higher levels of CVH had a significantly lower risk of CAS, regardless of the family risk score. In particular, high-CVH was associated with a 67% lower risk of CAS (HR\u0026thinsp;=\u0026thinsp;0.33, 95% CI: 0.20\u0026ndash;0.54) in those in the high-family-risk group. This finding emphasizes the importance of elevating CVH levels by improving lifestyle and biomarkers in populations with high family risk. Therefore, early preventive interventions targeting people with high family risk may be an effective strategy to reduce the incidence of CAS. Future public health policies should focus on this high-risk population and promote healthy lifestyles to reduce the burden of CAS. Our family risk score, which relies solely on family history of illness, offers a pragmatic tool for resource-limited settings. However, its predictive accuracy remains inferior to that of PRS and excludes rare variants. Future integration of familial and polygenic risk may enhance precision.\u003c/p\u003e \u003cp\u003eThis study also has several limitations. First, familial risk scores depend on self-reported histories, which may miss recessive cases. Second, LE8 was assessed only at baseline and does not account for temporal changes in CVH. Third, the predominantly European cohort (95.4% White) limits generalizability. Finally, unmeasured confounders may bias estimates. Future studies could develop a \u0026ldquo;LE8-PRS integration model\u0026rdquo; with genetic data and validate the causal effect of LE8 intervention on CAS through randomized controlled trials.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis large perspective cohort study from the UK Biobank demonstrated that a higher LE8 score is an independent protective factor against CAS, with the strongest protective effects observed in individuals with a family history of CVD. Integrating family history evaluation with LE8 monitoring offers a practical approach to CAS prevention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted under the UK Biobank (Application No. 99628). The authors thank the investigators and participants in the UK Biobank for their contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [Grant number 82173610]; the China Postdoctoral Science Foundation [Grant numbers 2024T171043, 2023MD744266 and GZC20233120];\u0026nbsp;and the Department of Science and Technology of Liaoning Province [Grant numbers 2023JH2/20200025 and 2023-MSLH-371].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWanyang Liu had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Yusufu\u0026middot;Aisha, Yue Wang, Wanyang Liu. Acquisition, analysis, or interpretation of data: Yusufu\u0026middot;Aisha, Wanyang Liu. Drafting of the manuscript: Yusufu\u0026middot;Aisha, Yue Wang, Wanyang Liu. Critical revision of the manuscript for important intellectual content: Yue Wang, Tao Zhuang, Yeqing Xie, Jinyun Zhang, Lin Du, Xinyu Li, Yuchen Qin, Gan Chen, Yunzhu Li, Yaru Hou, Yanling Liu, and Xiaonan Tang. Statistical analysis: Yusufu\u0026middot;Aisha, Tao Zhuang, Yusufu\u0026middot;Aisha, Yeqing Xie, Jinyun Zhang, Lin Du, Xinyu Li. Funding: Wanyang Liu, Yue Wang. Administrative, technical, or material support: Wanyang Liu. Supervision: Wanyang Liu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent, and ethical approval of the UK Biobank was received from the North West Multicenter Research Ethics Committee (reference number: 21/NW/0157). This study conformed to the ethical guidelines of the Declaration of Helsinki and its amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe UK Biobank is an open access resource. Researchers can apply to use the UK Biobank dataset by registering and applying at [http://ukbiobank.ac.uk/register-apply/].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eParaskevas KI, Nicolaides AN, Kakkos SK. Asymptomatic Carotid Stenosis and Risk of Stroke (ACSRS) study: what have we learned from it? Ann Transl Med. 2020;8:1271\u0026ndash;1271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAutret A, Saudeau D, Bertrand PH, Pourcelot L, Marchal C, De Boisvilliers S. STROKE RISK IN PATIENTS WITH CAROTID STENOSIS. 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J Geriatr Cardiol. 2024;21:751\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYing Y, Lin S, Kong F, Li Y, Xu S, Liang X, et al. Ideal Cardiovascular Health Metrics and Incidence of Ischemic Stroke Among Hypertensive Patients: A Prospective Cohort Study. Front Cardiovasc Med. 2020;7:590809.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSebastian SA, Shah Y, Paul H, Arsene C. Life\u0026rsquo;s Essential 8 and the risk of cardiovascular disease: a systematic review and meta-analysis. Eur J Prev Cardiol. 2024;:zwae280.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu S, Wu Z, Yu D, Chen S, Wang A, Wang A, et al. Life\u0026rsquo;s Essential 8 and Risk of Stroke: A Prospective Community-Based Study. Stroke. 2023;54:2369\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXanthakis V, Enserro DM, Murabito JM, Polak JF, Wollert KC, Januzzi JL, et al. 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Aging. 2024;53:afae156.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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 (LE8), carotid artery stenosis (CAS), UK Biobank, prospective study, family history of CVD","lastPublishedDoi":"10.21203/rs.3.rs-6332879/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6332879/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo quantify cardiovascular health (CVH), the American Heart Association (AHA) recently launched an updated version of the “Life’s Simple 7” (LS7) score, now known as the “Life’s Essential 8” (LE8) score. Our study aimed to investigate the associations among the LE8 score, family history of cardiovascular diseases (including heart disease, stroke, hypertension, and diabetes in first-degree relatives), and carotid artery stenosis (CAS) via prospective data from the UK Biobank while evaluating the value of the LE8 score in reducing CAS risk across populations with distinct family history profiles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter exclusion, 270,695 participants in the UK Biobank who were free of CAS at recruitment remained for analysis (55.2% women). According to the AHA definitions, CVH levels were categorized as low (0–49), moderate (50–79), or high (80–100) on the basis of the LE8 score. Cox proportional hazard models were used to estimate HRs between the LE8 score and CAS. A family risk score was developed to quantify the aggregated risk of CAS on the basis of family history of heart disease, stroke, high blood pressure, and diabetes, including data from fathers, mothers, and siblings, via univariate logistic regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring a median follow-up of 13.3 years, 1158 incident cases of CAS were identified. Compared with participants with low-CVH, participants with moderate-CVH had a 36% lower risk of CAS, and those with high-CVH had a 60% lower risk of CAS incidence after adjustment for covariates. Compared to low-CVH, high-CVH showed progressively stronger CAS risk reduction: 40% lower risk in low-risk, 59% in medium-risk, and 67% in high-risk groups. The behavior and biological subscales contributed 31.9% and 27.9%, respectively, to the population attributable risk (PAR), with the total LE8 PAR reaching 57.6%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-CVH, as defined by the LE8 score, is significantly associated with a lower risk of CAS, and its protective effect is particularly significant in individuals whose parents or siblings have two or more conditions, including heart disease, stroke, high blood pressure, and diabetes.\u003c/p\u003e","manuscriptTitle":"Life’s Essential 8 for carotid artery stenosis prevention: stratified risk reduction by family cardiovascular history in the UK Biobank","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 11:20:11","doi":"10.21203/rs.3.rs-6332879/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":"f067ad1e-2665-4c6b-b116-b27006bbe73c","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-30T21:23:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 11:20:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6332879","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6332879","identity":"rs-6332879","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}