Abstract
Background
: Cardiovascular health (CVH), as defined by the American Heart Association's Life’s Essential 8 (LE8) metric, is associated with reduced cardiovascular disease (CVD) risk. However, its quantitative impact on acute myocardial infarction (AMI)—including risk reduction magnitude, onset delays, and population-level preventable burden—remains unclear.
Methods
: In this prospective cohort study, we analysed 122,914 UK Biobank participants aged 40–69 years who were free from CVD at baseline. CVH was evaluated using LE8 metrics, and was categorised as low (<50), moderate (50–79), or high (≥80). Associations between CVH and AMI risk/onset were assessed through multivariable Cox regression, accelerated failure time models, and restricted cubic splines. Mediation analysis evaluated the contributions of inflammatory (hs-CRP, leukocytes, platelets), metabolic (triglycerides, urate), renal function (eGFR), and mental health status (anxiety and depression).
Results
: Over 163.2-month median follow-up, 2892 AMI cases (844 STEMI, 1490 NSTEMI) occurred. Each 1-unit LE8 increase reduced AMI risk by 3 % (HR 0.970, 95 % CI: 0.967–0.973). Moderate and high CVH groups exhibited 41.2 % (HR 0.588, 95 % CI: 0.534–0.648) and 75 % (HR 0.25, 95 % CI: 0.205–0.306) risk reductions versus low CVH, with consistent trends for STEMI/NSTEMI. AMI onset was delayed by 14.5 months in the moderate CVH group and 33.6 months in the high group compared with the low group. The population attributable fraction for AMI was 58.01 % (95 % CI, 57.15 %–58.86 %) when comparing the combined moderate or high CVH group with the low CVH group. Inflammatory/metabolic biomarkers mediated 1.57–8.62 % of the CVH-AMI relationship.
Conclusion
: Higher CVH levels were associated with reduced AMI risk and delayed onset, with inflammatory and metabolic biomarkers partially mediating this relationship. In the low-CVH group, a hypothetical shift to higher CVH levels was associated with a scenario-based population attributable fraction of approximately 60 %, highlighting the potential population impact of improving cardiovascular health.
Keywords: Life’s essential 8, Cardiovascular health, Acute myocardial infarction, Prospective cohort, UK Biobank
Central Illustration.
1. Introduction
Cardiovascular disease (CVD) remains the leading cause of death worldwide, accounting for approximately 20.5 million deaths in 2021—nearly one-third of all global mortality [1]. Acute myocardial infarction (AMI), an acute and severe manifestation of ischemic heart disease, imposes substantial burdens in terms of disability, mortality, and healthcare utilization, although a significant proportion is preventable. Cardiovascular health (CVH) encompasses modifiable behaviors (diet, physical activity, nicotine exposure, and sleep) and clinical factors (adiposity, lipids, glycemia, and blood pressure). In 2022, the American Heart Association introduced Life’s Essential 8 (LE8), which integrates eight health domains into a continuous CVH score ranging from 0 to 100, increasingly used in risk stratification and prevention [2]. While higher CVH scores have been associated with reduced risk of CVD risk, specific evidence linking the LE8 framework and AMI risk remains limited [3].
Beyond the broad benefits of healthy lifestyles, clinicians and policymakers require precise, quantitative insights: the magnitude of AMI risk reduction associated with realistic CVH improvements (e.g., per one-point LE8 increase or shift from low to moderate/high CVH); the delay in AMI onset across CVH strata; and the population-level preventable AMI burden under shifts toward higher CVH (e.g., population-attributable fractions and impact numbers). The association between CVH and AMI is likely mediated by inflammatory, metabolic, and mental health pathways, but the proportions mediated by these mechanisms remain unclear.
Accordingly, we evaluated the associations of LE8 with AMI and its subtypes (ST-segment elevation myocardial infarction [STEMI] and non–ST-segment elevation myocardial infarction [NSTEMI]) across three dimensions: incidence risk, time to onset, and population-level preventable burden. We also quantify mediation by inflammatory, metabolic, and mental health indicators to provide actionable evidence for precision prevention and clinical decision-making.
2. Method
2.1. Study design and population
This prospective cohort study utilised data from the UK Biobank, a biomedical research database of over 502,000 adults aged 40–69 from England, Scotland and Wales, established between 2006 and 2010 [4]. Participant health outcomes were monitored using electronic health records, including Hospital Episode Statistics, cancer registries and death registers, with detailed study design and methods previously published [5].
For this analysis, we included 136,425 participants who were free from cardiovascular diseases at baseline, excluding those with coronary artery disease, heart failure, cardiomyopathy, heart valve disease and arrhythmias (diagnostic criteria detailed in Table S1). Participants with cancer (n = 12,576), those who were pregnant (n = 41), those lost to follow-up (n = 358) and 536 individuals who experienced AMI within the first two years of follow-up (to minimise reverse causality) were further excluded. The final cohort comprised 122,914 individuals.
Ethical approval for the UK Biobank was granted by the North West Multicenter Research Ethics Committee (REC reference: 11/NW/0382), and all participants provided written informed consent.
2.2. Assessment of CVH
CVH was evaluated using Life’s Essential 8 (LE8) metrics: (1) Dietary health, (2) BMI, (3) Physical activity, (4) Sleep duration, (5) Tobacco/nicotine exposure, (6) Blood pressure, (7) non-HDL cholesterol and (8) Blood glucose. Dietary health was measured based on the average score of five 24-hour dietary recalls following the Dietary Approaches to Stop Hypertension (DASH) guidelines, as outlined in Table S2 [6]. BMI, physical activity, sleep duration and smoking status were assessed at the assessment centre using standardised methods (Table S3). Blood pressure, HbA1c and non-HDL cholesterol levels were measured using established clinical methods as described in a previous study [7]. The overall CVH score was calculated by averaging the scores across the eight metrics, with possible scores ranging from 0 to 100 (Table S4). According to AHA guidelines, CVH was categorised as low (<50), moderate (50–79), or high (≥80).
2.3. Assessment of other variables
Baseline data were evaluated via a touchscreen questionnaire. At the assessment centre, trained staff conducted structured interviews to collect participants' chronic disease histories, referencing primary care and hospital records where available. Nurses recorded past and current medical conditions, including diagnosis types, numbers and dates. Participants confirmed the information on a touchscreen, allowing any errors identified during the interview to be corrected. Hypertension status was additionally determined based on records of antihypertensive medication. The estimated glomerular filtration rate (eGFR) was derived from the 2021 race-free CKD-EPI creatinine equation, incorporating age, sex, and serum creatinine; detailed calculation procedures are provided in Appendix 1. Depression and anxiety were ascertained from first-occurrence data (Category 1712), which maps primary care, hospital diagnoses, death registry, and self-reported conditions to three-character International Classification of Diseases (ICD-10) codes. Depression was defined as ICD-10 codes F32–F33, and anxiety as F40–F41. Any depression or anxiety diagnoses recorded before cohort entry were considered prevalent cases, indicating pre-existing disease at baseline.
2.4. Detection of potential mediating variables
High-sensitivity C-reactive protein (hs-CRP), triglycerides (TG) and urate levels were measured on the Beckman Coulter AU5800 platform (Beckman Coulter UK Ltd). hs-CRP assays used an immuno-turbidimetric method, while triglyceride and urate levels were assessed through enzymatic methods, with total coefficients of variation ranging from 1.40 % to 1.67 %, 2.18 % to 2.37 % and 1.33 % to 1.52 %, respectively. White blood cell (WBC) and platelet counts were measured using the COULTER LH 750 automated haematology analyser (Beckman Coulter, USA, Inc.), which operates on the electrical impedance principle.
2.5. Diagnosis of AMI
The primary study endpoint was AMI, with secondary endpoints being ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI). AMI, including STEMI and NSTEMI, was identified using a validated clinical code-based algorithm developed by the UK Biobank in collaboration with the Outcome Adjudication Group, based on the International Classification of Diseases, 10th Revision (ICD-10) codes. Detailed classification criteria are provided in Tables S5 and S6. The last recorded AMI event in this cohort occurred on 29 November 2022, marking the end of follow-up.
2.6. Statistical analysis
Categorical variables were reported as frequencies and percentages, while continuous variables were presented as means with standard deviations. Differences between groups were assessed using the Chi-square test for categorical variables and t-tests or one-way ANOVA for continuous variables. AMI incidence was evaluated through cumulative incidence ( %) and incidence rates (per 1000 person-years), with differences across CVH score groups (low, moderate and high) assessed using Chi-square and Poisson regression analyses.
To estimate the association between CVH and AMI risk, Cox proportional hazard models were employed to calculate hazard ratios (HRs) and 95 % confidence intervals (CIs). The proportional hazards assumption was verified using Schoenfeld residuals, with no violations detected. CVH was analysed both as a continuous and categorical variable, with trend p-values calculated for categorical groups. Missing data for categorical variables were treated as missing indicators, while continuous variables were imputed using mean values. In the main analysis, we did not adjust for mediator variables, as our goal was to capture the overall effect of CVH on AMI risk. Three regression models were developed: Model 1 was unadjusted; Model 2 was adjusted for age, sex, and race; and Model 3 was further adjusted for additional covariates, including Townsend Deprivation Index, education level, annual household income, alcohol use, prior disease history (including diabetes mellitus, hypertension, and hypercholesterolemia), and use of antihypertensive or lipid-lowering medications. Model 3 served as the primary analytical model in this study. Multicollinearity among covariates was assessed using the variance inflation factor (VIF), excluding any covariate with a VIF ≥ 10 from the Cox analysis. In a sensitivity analysis, potential mediators were further adjusted to approximate the ‘net effect’ of CVH on AMI risk. All-cause and non-cardiovascular deaths occurring prior to AMI were treated as competing events, and Fine–Gray subdistribution hazard models were applied. Restricted cubic splines (RCS) with knots positioned at the 5th, 35th, 65th and 95th percentiles were plotted to illustrate the association trend between CVH and AMI risk.
Subgroup analyses examined the association between each 1-unit increase in CVH and AMI risk across various clinical characteristics, with interaction p-values calculated. Subgroups included sex (men/women), age (<55/≥55 years), ethnicity (White/non-White), annual household income (<£18,000, £18,000–£30,999, £31,000–£51,999, £52,000–£100,000, >£100,000, or unknown), alcohol use status (never/former/current) and history of prior disease and medication use (yes/no).
Additionally, an accelerated time-to-failure (AFT) model was constructed to assess the impact of different CVH score groups on the time to AMI onset. The AFT model assumes that covariates can accelerate or delay the time trajectory of the event, making it suitable for studies not reliant on proportional hazards assumptions [[16], [17], [18]]. In this analysis, a flexible Weibull distribution was chosen to model the time to event, reflecting the increasing risk of AMI occurrence over time [19]. The median time difference in AMI onset between groups was calculated in months, with values derived by subtracting the comparison group’s median from the low CVH group. A negative value indicated delayed AMI onset, while a positive value suggested earlier onset. Results from the AFT model were compared with those from the Cox model to evaluate robustness.
Given the role of inflammatory and metabolic markers, as well as mental health factors, in the pathogenesis of AMI and their potential modulation by CVH, causal mediation analyses were conducted to evaluate the mediating effects of these biomarkers and indicators—specifically hs-CRP, TG, urate, WBC, platelets, eGFR, anxiety, and depression—on the relationship between CVH and AMI risk (Figure S1). Potential mediators were initially screened using the Baron and Kenny four-step method [20], which systematically evaluated the direct effect of CVH on AMI, the impact of CVH on each mediator (e.g., CRP) and the impact of each mediator on AMI. Changes in the direct effect of CVH on AMI after adjusting for each mediator were analysed to confirm potential mediation effects. For rigour, the Bootstrapping method with 1000 resamples was used to estimate CIs for direct, indirect and total effects, ensuring robustness in the mediation analysis [21]. This approach allowed a more precise evaluation of the indirect effects of CVH on AMI mediated by inflammatory and metabolic biomarkers.
To assess the potential reduction in AMI burden through improvements in CVH, a multivariable-adjusted Cox proportional hazards model was applied to estimate the population attributable fraction (PAF) and the impact number of eevents prevented (IER) across different CVH strata. PAF quantifies the proportion of AMI events attributable to low CVH, reflecting the theoretical reduction in incidence if the exposure were eliminated. IER represents the absolute number of AMI cases that could be prevented by improving the health status of individuals in the low CVH group to moderate or high levels. Simulated intervention scenarios modeled transitions from low to higher CVH categories, and PAFs were calculated for each scenario. The Delta method was used to estimate the standard errors and 95 % confidence intervals for PAF. IER denotes the model-based estimate of the absolute number of preventable AMI events in the low-CVH stratum over the observed follow-up under a hypothetical shift to a higher CVH category (e.g., moderate or high). For IER estimation, the Bootstrap method was employed to resample the low CVH group and compute risk differences, providing corresponding 95 % confidence intervals. This analytical approach allows for quantification of the potential preventive effect of CVH improvement on AMI incidence.
The association between the overall CVH score and incident AMI (including STEMI and NSTEMI) was prespecified as the primary analysis of this study. Analyses evaluating CVH as a continuous or categorical exposure within Cox proportional hazards models, together with the AFT models and the estimation of population-level impact metrics (PAF and IER), constituted the primary analytical framework. Subgroup analyses, component-specific analyses in which each individual LE8 metric was evaluated separately, as well as all mediation analyses, were treated as exploratory. These analyses were undertaken to provide additional clinical context or mechanistic insight beyond the primary findings.
All statistical analyses were performed using R software (version 4.2.0) and GraphPad (version 9.0). Two-sided p-values <0.05 were considered statistically significant.
3. Results
Baseline characteristics for the cohort had a maximum missing rate of 0.12 % across variables (Table S7). Among the 122,914 participants (mean age 55.5 ± 7.9 years), 45.7 % were men, with an average CVH score of 67.2 ± 12.4. Within the CVH components, scores for blood pressure, blood lipids and diet were notably low, at 8.47 %, 9.05 % and 7.40 %, respectively (Figure S2). Over a median follow-up period of 163.2 months (IQR: 24.5–194.1), 2892 AMI events were recorded. Individuals who experienced AMI were more likely to be male, have chronic diseases, report lower socioeconomic status, income and educational attainment compared to those without AMI (Table 1). Among AMI cases, 844 were classified as STEMI, 1490 as NSTEMI and 140 developed NSTEMI following an initial STEMI. In 698 cases, the AMI type was unspecified. Baseline characteristics for these subgroups are detailed in Table S8.
Table 1.
Clinical baseline characteristics of non-AMI and AMI participants.
| Total (N = 122,914) |
Non-AMI (N = 120,022) |
AMI (N = 2892) |
P-value | |
|---|---|---|---|---|
| Age | 55.5 ± 7.9 | 55.4 ± 7.9 | 59.2 ± 7.0 | <0.001 |
| Men, % | 56,205 (45.7 %) | 54,133 (45.1 %) | 2072 (71.6 %) | <0.001 |
| White, % | 117,570 (95.7 %) | 114,799 (95.7 %) | 2771 (95.8 %) | <0.001 |
| Townsend Deprivation Index | −1.6 ± 2.9 | −1.6 ± 2.9 | −1.5 ± 2.9 | 0.043 |
| Education levels | <0.001 | |||
| No qualification | 10,069 (8.2 %) | 9631 (8.0 %) | 438 (15.2 %) | |
| CSE or Ordinary levels / GCSE or equivalent | 30,278 (24.6 %) | 29,553 (24.6 %) | 725 (25.1 %) | |
| Advanced levels/Advanced Subsidiary levels or equivalent | 16,253 (13.2 %) | 15,932 (13.3 %) | 321 (11.1 %) | |
| Other professional qualification | 5941 (4.8 %) | 5796 (4.8 %) | 145 (5.0 %) | |
| NVQ or HNC or equivalent | 6585 (5.4 %) | 6353 (5.3 %) | 232 (8.0 %) | |
| College or university degree | 53,778 (43.8 %) | 52,748 (44.0 %) | 1030 (35.6 %) | |
| Annual household income before tax, £ | <0.001 | |||
| <18,000 | 15,990 (13.0 %) | 15,432 (12.9 %) | 558 (19.3 %) | |
| 18,000–30,999 | 26,260 (21.4 %) | 25,567 (21.3 %) | 693 (24.0 %) | |
| 31 000–51,999 | 32,157 (26.2 %) | 31,432 (26.2 %) | 725 (25.1 %) | |
| 52,000–100,000 | 2,8371 (23.1 %) | 27,906 (23.3 %) | 465 (16.1 %) | |
| >100,000 | 8554 (7.0 %) | 8431 (7.0 %) | 123 (4.3 %) | |
| Unknown /Not prefer to answer | 11,433 (9.3 %) | 11,113 (9.3 %) | 320 (11.1 %) | |
| Alcohol status | <0.001 | |||
| Never | 3897 (3.2 %) | 3797 (3.2 %) | 100 (3.5 %) | |
| Former | 3582 (2.9 %) | 3446 (2.9 %) | 136 (4.7 %) | |
| Current | 115,365 (93.9 %) | 112,711 (94.0 %) | 2654 (91.8 %) | |
| Diabetes mellitus, % | 3127 (2.5 %) | 2894 (2.4 %) | 233 (8.1 %) | <0.001 |
| Hypertension, % | 26,909 (21.9 %) | 25,825 (21.5 %) | 1084 (37.5 %) | <0.001 |
| Hypercholesteremia, % | 11,471 (9.3 %) | 11,040 (9.2 %) | 431 (14.9 %) | <0.001 |
| Antihypertensives, % | 18,416 (14.98 %) | 17,637 (14.69 %) | 779 (26.93 %) | <0.001 |
| Lowering lipids drugs, % | 8490 (6.9 %) | 8065 (6.7 %) | 425 (14.7 %) | <0.001 |
| Potential mediating variable | ||||
| High-sensitivity C-reactive protein (mg/L) | 2.25 ± 3.90 | 2.23 ± 3.88 | 3.11 ± 4.71 | <0.001 |
| Triglycerides (mmol/L) | 1.68 ± 0.98 | 1.67 ± 0.97 | 2.06 ± 1.12 | <0.001 |
| Urate (umol/L) | 304.13 ± 78.12 | 303.31 ± 77.9 | 338.42 ± 78.16 | <0.001 |
| White blood cells (10^9 /L) | 6.72 ± 1.74 | 6.71 ± 1.74 | 7.12 ± 1.78 | <0.001 |
| Platelets (10^9 /L) | 249.88 ± 57.08 | 249.96 ± 56.9 | 246.35 ± 61.71 | <0.001 |
| AHA Life’s Essential 8 score | 67.2 ± 12.4 | 67.4 ± 12.4 | 59.9 ± 12.2 | <0.001 |
| DASH score | 39.8 ± 31.3 | 39.9 ± 31.3 | 35.2 ± 30.4 | <0.001 |
| Blood glucose score | 92.1 ± 17.8 | 92.2 ± 17.6 | 85.9 ± 23.6 | <0.001 |
| Blood pressure score | 45.6 ± 31.9 | 45.9 ± 31.9 | 30.8 ± 27.6 | <0.001 |
| Sleep health score | 90.8 ± 17.0 | 90.8 ± 17.0 | 89.2 ± 18.8 | <0.001 |
| Body mass index score | 72.3 ± 27.6 | 72.5 ± 27.5 | 63.7 ± 28.2 | <0.001 |
| Physical activity score | 84.8 ± 33.4 | 84.9 ± 33.3 | 80.8 ± 36.7 | <0.001 |
| Tobacco/nicotine exposure score | 63.6 ± 35.5 | 63.9 ± 35.4 | 54.2 ± 37.6 | <0.001 |
| Blood lipid score | 48.6 ± 29.6 | 48.8 ± 29.6 | 40.1 ± 28.7 | <0.001 |
AMI, acute myocardial infraction.
3.1. CVH and AMI risk
Kaplan–Meier analyses demonstrated a significant inverse association between cardiovascular health (CVH) and the risk of acute myocardial infarction (AMI), including both STEMI and NSTEMI (log-rank P < 0.001; Figure S3). The cumulative incidence of AMI was substantially higher in the low CVH group compared with the moderate (5.1 % vs. 2.4 %) and high CVH groups (5.1 % vs. 0.6 %) (Fig. 1). Correspondingly, AMI incidence rates were elevated in the low CVH group relative to the moderate (3.77 vs. 1.78 per 1000 person-years) and high CVH groups (3.77 vs. 0.47), with similar patterns observed for STEMI and NSTEMI.
Fig. 1.
Above: Cumulative incidence of AMI, STEMI, and NSTEMI in different CVH score groups. Below: Incidence rate of AMI, STEMI, and NSTEMI in different CVH score groups.
AMI, acute myocardial infarction. STEMI, ST-elevation myocardial infarction. NSTEMI, non-ST-elevation myocardial infarction. CVH, cardiovascular health.
In the unadjusted Cox model (model 1), each 1-, 5-, and 10-unit increase in CVH was associated with a 4.4 % (HR 0.956, 95 % CI: 0.954–0.959), 19.9 % (HR 0.801, 95 % CI: 0.790–0.812), and 35.8 % (HR 0.642, 95 % CI: 0.624–0.660) reduction in AMI risk, respectively (Table 2). Compared with individuals in the low CVH group, those with moderate and high CVH demonstrated a 56.7 % (HR 0.433, 95 % CI: 0.395–0.474) and 88.6 % (HR 0.114, 95 % CI: 0.094–0.139) lower risk of AMI, respectively. These inverse associations persisted after adjustment for age, sex, and race (Model 2). In the fully adjusted model (Model 3), the corresponding risk reductions were 3.0 % (HR, 0.970 [95 % CI, 0.967–0.973]), 14.1 % (HR, 0.859 [95 % CI, 0.845–0.873]), and 26.2 % (HR, 0.738 [95 % CI, 0.715–0.761]) per 1-, 5-, and 10-point increase, and 41.2 % (HR, 0.588 [95 % CI, 0.534–0.648]) and 75.0 % (HR, 0.250 [95 % CI, 0.205–0.306]) for moderate and high versus low CVH, respectively. These associations remained consistent across STEMI and NSTEMI subtypes. In subset analyses, findings were directionally consistent with the primary analysis (Fig. 2). The non–HDL cholesterol component showed the strongest association; per 1-point increase: AMI, HR 0.989 (95 % CI, 0.988–0.991); STEMI, 0.985 (0.982–0.988); NSTEMI, 0.989 (0.987–0.991), followed by blood pressure. Other components showed smaller associations; diet and physical activity were modest, and sleep was borderline for STEMI and NSTEMI.
Table 2.
Unadjusted and Multivariable-adjusted Cox Regression Models for Assessing the Risk of AMI According to Different Levels of CVH score.
| Unadjusted Model HR (95 % CI) |
P-value | Multivariate Model HRa (95 % CI) |
P-value | Multivariate Model HRb (95 % CI) |
P-value | |
|---|---|---|---|---|---|---|
| AMI | ||||||
| 1-point increase | 0.956 (0.954–0.959) | < 0.001 | 0.964 (0.961–0.967) | < 0.001 | 0.970 (0.967–0.973) | < 0.001 |
| 5-point increase | 0.801 (0.790–0.812) | 0.834 (0.822–0.846) | < 0.001 | 0.859 (0.845–0.873) | < 0.001 | |
| 10-point increase | 0.642 (0.624–0.660) | 0.695 (0.675–0.716) | < 0.001 | 0.738 (0.715–0.761) | < 0.001 | |
| Low | Ref. | Ref. | Ref. | |||
| Moderate | 0.433 (0.395–0.474) | < 0.001 | 0.492 (0.449–0.540) | < 0.001 | 0.588 (0.534–0.648) | < 0.001 |
| High | 0.114 (0.094–0.139) | < 0.001 | 0.188 (0.155–0.228) | < 0.001 | 0.250 (0.205–0.306) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
| STEMI | ||||||
| 1-point increase | 0.960 (0.955–0.965) | < 0.001 | 0.969 (0.964–0.974) | < 0.001 | 0.971 (0.965–0.976) | < 0.001 |
| 5-point increase | 0.816 (0.795–0.837) | < 0.001 | 0.855 (0.832–0.879) | < 0.001 | 0.861 (0.836–0.886) | < 0.001 |
| 10-point increase | 0.665 (0.632–0.70) | < 0.001 | 0.731 (0.693–0.772) | < 0.001 | 0.741 (0.699–0.786) | < 0.001 |
| Low | Ref. | Ref. | Ref. | |||
| Moderate | 0.520 (0.435–0.621) | < 0.001 | 0.603 (0.504–0.721) | < 0.001 | 0.643 (0.534–0.775) | < 0.001 |
| High | 0.132 (0.092–0.190) | < 0.001 | 0.228 (0.158–0.329) | < 0.001 | 0.259 (0.178–0.377) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
| NSTEMI | ||||||
| 1-point increase | 0.957 (0.953–0.960) | < 0.001 | 0.964 (0.960–0.968) | < 0.001 | 0.971 (0.967–0.975) | < 0.001 |
| 5-point increase | 0.801 (0.786–0.817) | < 0.001 | 0.833 (0.816–0.850) | < 0.001 | 0.862 (0.844–0.881) | < 0.001 |
| 10-point increase | 0.642 (0.618–0.667) | < 0.001 | 0.694 (0.667–0.722) | < 0.001 | 0.743 (0.712–0.777) | < 0.001 |
| Low | Ref. | Ref. | Ref. | |||
| Moderate | 0.426 (0.375–0.483) | < 0.001 | 0.482 (0.425–0.547) | < 0.001 | 0.592 (0.518–0.677) | < 0.001 |
| High | 0.105 (0.080–0.139) | < 0.001 | 0.171 (0.129–0.226) | < 0.001 | 0.236 (0.177–0.315) | < 0.001 |
| P for trend | < 0.001 | < 0.001 | < 0.001 | |||
CVH, cardiovascular health. AMI. Acute myocardial infraction. STEMI, ST elevation myocardial infarction. NSTEMI, Non-ST elevation myocardial infarction. HR, Hazard ratios. CI, Confidence Interval.
In the unadjusted model, no variable was adjusted.
In the multivariate model, adjustments were made for age, sex, and race.
In the multivariate model, adjustments were made for age, sex, race, Townsend deprivation index, education levels, annual household income, alcohol status, history of diseases (including diabetes mellitus, hypertension, and hypercholesterolemia), antihypertensives and lowering lipids drugs.
Fig. 2.
Associations of Overall CVH and Individual Components With Risks of AMI, STEMI, and NSTEMI.Hazard ratios (HRs) and 95 % confidence intervals (CIs) were estimated per 1-point increase in overall CVH or component scores using fully adjusted Cox proportional hazards models. Adjustments were made for age, sex, Townsend deprivation index, ethnicity, education levels, annual household income, alcohol status, history of diseases (including diabetes mellitus, hypertension, and hypercholesterolemia), antihypertensives and lowering lipids drugs.
AMI, acute myocardial infarction. STEMI, ST-elevation myocardial infarction. NSTEMI, non-ST-elevation myocardial infarction. CVH, cardiovascular health.
Restricted cubic spline analysis further confirmed a significant nonlinear inverse relationship between CVH and the risks of AMI, STEMI, and NSTEMI (P for nonlinearity < 0.05; Figure S4), with progressively lower risk observed at higher CVH levels.
3.2. CVH and time to AMI onset
The AFT model revealed a significant trend of delayed AMI onset in both the moderate and high CVH groups compared to the low CVH group (P for trend < 0.001; Figure S5). Specifically, the median time to AMI occurrence was delayed by 14.45 months in the moderate CVH group and 33.63 months in the high CVH group, relative to the low CVH group. For STEMI, the median delay was 17.56 months in the moderate CVH group and 48.57 months in the high CVH group; for NSTEMI, the delay was 18.92 months and 44.55 months, respectively (Table S9).
3.3. Impact of CVH improvement on the burden of AMI
In this study, the PAF and IER were estimated across different CVH groups, as shown in Table 3 and Fig. 3. For AMI, the low CVH group served as the reference. The PAF for the moderate CVH group was 41.12 % (95 % CI: 39.91 %–42.33 %), while for the high CVH group, it was 74.91 % (95 % CI: 74.39 %–75.42 %), and for the combined moderate or high CVH group, it was 58.01 % (95 % CI: 57.15 %–58.86 %). Corresponding IER values were 238.2 (95 % CI: 234.8–242.3), 433.5 (95 % CI: 427.3–440.9), and 335.9 (95 % CI: 330.6–341.8). Similar PAF and IER values were observed for both NSTEMI and STEMI events. In the sex-stratified analyses, the PAF and IER were estimated across different CVH groups for men and women, as shown in Table S10 and Fig. 3. For men, with the low CVH group as the reference, the PAF was 50.36 % (95 % CI: 48.59 %–52.13 %) for shifting to moderate CVH, 74.76 % (95 % CI: 73.86 %–75.66 %) for high CVH, and 62.56 % (95 % CI: 61.23 %–63.90 %) for moderate or high CVH combined. Corresponding IER values were 74.6 (95 % CI: 72.7–76.2), 110 (95 % CI: 107.4–112.6), and 92.4 (95 % CI: 89.7–94.6). For women, the PAF was 37.60 % (95 % CI: 36.39 %–38.80 %) for shifting to moderate CVH, 75.37 % (95 % CI: 74.89 %–75.85 %) for high CVH, and 58.65 % (95 % CI: 57.64 %–59.33 %) for moderate or high CVH combined. Corresponding IER values were 162 (95 % CI: 160–164.9), 323 (95 % CI: 318.5–328.1), and 243 (95 % CI: 238.3–247.2). Similar PAF and IER trends were observed for NSTEMI and STEMI across both sexes.
Table 3.
PAF and IER Estimates for AMI, NSTEMI, and STEMI by CVH Group.
| Number of Events | PAFa | Attributable casesb | |
|---|---|---|---|
| AMI | |||
| Low | 581 | Reference | Reference |
| Moderate group | 2186 | 41.12 % (95 % CI: 39.91–42.33 %) | 238.2 (95 % CI: 234.8–242.3) |
| High group | 125 | 74.91 % (95 % CI: 74.39–75.42 %) | 433.5 (95 % CI: 427.3–440.9) |
| Moderate or high group | 2311 | 58.01 % (95 %CI: 57.15–58.86 %) | 335.9 (95 % CI: 330.6–341.8) |
| NSTEMI | |||
| Low | 306 | ||
| Moderate group | 1124 | 40.78 % (95 % CI: 39.52–42.04 %) | 124.6 (95 % CI: 122.7–126.8) |
| High group | 60 | 76.37 % (95 % CI: 75.87–76.87 %) | 232.6 (95 % CI: 229–236.8) |
| Moderate or high group | 1184 | 58.57 % (95 % CI: 57.69–59.45 %) | 178.6 (95 % CI: 175.5–182) |
| STEMI | |||
| Low | 148 | Reference | Reference |
| Moderate group | 660 | 35.72 % (95 % CI: 34.48–36.96 %) | 53.1 (95 % CI: 52.5–54) |
| High group | 36 | 74.10 % (95 % CI: 73.60–74.60 %) | 110.4 (95 % CI: 108.9–112.1) |
| Moderate or high group | 696 | 54.91 % (95 % CI: 54.04–55.78 %) | 81.8 (95 % CI: 80.6–83.1) |
CVH, cardiovascular health. AMI. Acute myocardial infraction. STEMI, ST elevation myocardial infarction. NSTEMI, Non-ST elevation myocardial infarction. PAF, population attributable fraction. IER, impact number of events prevented.
In the multivariate model, adjustments were made for age, sex, race, Townsend deprivation index, education levels, annual household income, alcohol status, history of diseases (including diabetes mellitus, hypertension, and hypercholesterolemia), antihypertensives and lowering lipids drugs.
PAF: proportion of AMI cases in the low-CVH group that would be preventable under a hypothetical shift to moderate, high, or combined moderate–high CVH, as estimated from the fitted model over the observed follow-up.
IER: model-estimated absolute number of AMI events in the low-CVH group that would be prevented under the same hypothetical shift during the observed follow-up.
Fig. 3.
PAF and IER for AMI, NSTEMI, and STEMI in the Overall Population and by Sex.
PAF represents the proportion of AMI events potentially preventable under a hypothetical shift from low to higher CVH categories. IER represents the model-estimated absolute number of preventable AMI events under the same shift. Top panels show IER values; bottom panels show PAF percentages.
AMI, acute myocardial infarction. STEMI, ST-elevation myocardial infarction. NSTEMI, non-ST-elevation myocardial infarction. CVH, cardiovascular health.
3.4. Subgroup and sensitivity analyses
Subgroup analyses demonstrated that the inverse association between each 1-unit increase in CVH and AMI risk was consistent across all strata (Figure S6). Significant interactions were observed in subgroups based on gender, age, hypertension, hypercholesterolemia and the use of antihypertensive and lipid-lowering medications (P for interaction < 0.001), with similar trends for both STEMI and NSTEMI outcomes (Figures S7 and S8). Sensitivity analyses accounting for pre-AMI deaths as competing events produced stable results (Tables S11 and S12). Additionally, including mediating variables in the multivariable Cox regression model did not alter the findings (Table S13).
3.5. Causal mediation analysis
In the causal mediation analysis using the Baron and Kenny four-step method, CRP, TG, urate, WBC platelets and eGFR each exhibited significant mediation effects in the relationship between CVH and AMI (Table S14). Specifically, the indirect effects of these biomarkers accounted for 3.83 %, 8.62 %, 2.87 %, 4.02 % and 1.57 % of the total effect of a 10-unit increase in CVH on AMI, respectively (Figure S9). By contrast, eGFR showed minimal mediation (0.34 %), and no mediation was observed for anxiety or depression. These findings suggest that the effect of CVH on AMI is partially mediated through inflammatory and metabolic pathways involving these biomarkers.
4. Discussion
In this large prospective cohort of over 120,000 UK adults, higher CVH scores, assessed using the LE8, were consistently associated with a lower risk of AMI, delayed onset of events, and partial mediation through inflammatory and metabolic pathways. In addition, among participants with low CVH, a hypothetical shift to higher CVH levels corresponded to an estimated scenario-based PAF of nearly 60 % of AMI events. Collectively, these findings underscore the clinical and public health relevance of improving CVH to reduce AMI burden.
Most prior studies have linked CVH metrics to composite CVD endpoints, consistently demonstrating inverse associations in cohorts and meta-analyses. However, evidence specific to AMI remains limited. Our analysis addresses this gap in three principal ways [[8], [9], [10], [11]]. First, using the Life’s Essential 8 framework, we provide AMI–specific risk estimates and demonstrate a clear graded association across the CVH spectrum, with consistent findings for both STEMI and NSTEMI. Second, we extend prior evidence by showing that better CVH is associated with delayed onset of AMI, highlighting meaningful gains in event-free survival that are not captured by incidence analyses alone. Third, the mediation analyses suggest that inflammatory and metabolic biomarkers account for part of the observed association, whereas kidney function and psychological factors contribute minimally. These results support inflammation- and metabolism-related pathways as plausible mechanisms through which CVH influences infarction risk. However, causal mediation analysis requires several strong assumptions, including sequential ignorability and the absence of unmeasured mediator–outcome confounding. These assumptions cannot be fully verified in an observational cohort such as the UK Biobank. Therefore, these indirect effects should be interpreted with caution and viewed as indicative of potential biological pathways rather than definitive causal mechanisms.
Component-level analyses revealed that non-HDL cholesterol and blood pressure were the strongest contributors to AMI risk reduction, consistent with extensive evidence supporting the causal role of apolipoprotein B–containing lipoproteins[12,13] and systolic blood pressure lowering in reducing major cardiovascular events [14]. By contrast, lifestyle components such as physical activity and sleep demonstrated more modest associations, likely reflecting their indirect influence through downstream improvements in cardiometabolic status [[15], [16], [17]]. These findings reinforce the importance of lipid management and blood pressure control as central targets for AMI prevention within the broader CVH construct.
In UK adults, diet, blood lipids, and blood pressure consistently received the lowest LE8 scores, contributing to generally suboptimal CVH. The traditional British dietary pattern—characterised by high intake of fat, salt, sugar, fried foods, processed meats, and sweets—has been linked to increased risks of dyslipidaemia, hypertension, and type 2 diabetes, particularly among women [[18], [19], [20]]. Low consumption of fruits, vegetables, and dietary fibre further underscores substantial room for improvement in dietary quality [18]. In our cohort, only 16.07 % of participants achieved high LE8 scores, consistent with global estimates (23.3 % in the Chinese Prediction for ASCVD Risk cohort and 19.6 % in NHANES), highlighting the widespread difficulty of achieving optimal CVH [10,21]. Prior UK Biobank analyses have shown that a 10-point improvement in LE8 scores among individuals in the lowest quartile could reduce major cardiovascular events by up to 9.2 % [22]. Similarly, each 10-point increase in CVH in our study was associated with a 26.2 % lower risk of AMI, emphasising that even modest improvements in lifestyle behaviours may yield meaningful cardiovascular benefits.
Our findings suggest that suboptimal CVH contributes substantially to the burden of AMI, with a scenario-based PAF of approximately 58 % for a shift from low to moderate or high CVH. This magnitude aligns with global evidence indicating that >70 % of myocardial infarctions are attributable to modifiable risk factors, underscoring the potential population benefit of improving lipid profiles, blood pressure, and lifestyle behaviours [23]. Similar PAFs were observed for NSTEMI and STEMI, supporting the relevance across AMI subtypes. Sex-stratified analyses showed slightly higher PAF estimates in men than in women, reflecting their greater baseline cardiometabolic risk. Transitioning from low to moderate CVH yielded larger relative benefits in men, whereas high CVH conferred similarly high PAFs in both sexes. These patterns are broadly consistent with INTERHEART, which demonstrated that most AMI risk is driven by modifiable exposures with modest sex differences overall [24,25]. These findings support the need for sex-specific prevention strategies. While the observed PAFs highlight the substantial preventable burden of AMI, causal inference is limited by the observational nature of the study. Accordingly, the PAF estimates in our analysis should be interpreted as theoretical, model-based projections derived from observed associations rather than causal intervention effects. These values do not imply that 60 % of AMI events would be prevented in practice, but instead reflect scenario-based estimates.
Furthermore, our findings suggest that inflammatory and metabolic pathways contribute meaningfully to the association between CVH and AMI. Higher CVH levels were associated with lower concentrations of hs-CRP, WBC count and triglycerides, indicating reduced systemic inflammation and improved metabolic status. This aligns with prior evidence showing that ideal cardiovascular health favourably influences circulating CVD biomarkers and subclinical disease measures [26]. Chronic inflammation and metabolic dysregulation, which are common among individuals with low CVH, can promote lipid accumulation, impair endothelial function, and destabilise atherosclerotic plaques, thereby increasing the likelihood of acute coronary events [27,28]. These observations reinforce inflammation and metabolic abnormalities as key pathways through which CVH influences AMI risk and highlight the potential value of integrated lifestyle and inflammation-targeted interventions, particularly for individuals with poor CVH. By contrast, anxiety and depression did not mediate the relationship between CVH and AMI risk. This likely reflects their distinct aetiological pathways, which are shaped largely by upstream social and environmental determinants rather than the behavioural and cardiometabolic factors captured within the CVH construct [29]. Any mental health–related influence on AMI risk may therefore operate in parallel rather than as a direct mediator and may already be partly reflected in existing CVH components.
Subgroup analyses indicated stronger associations between CVH improvements and AMI risk reduction among women, individuals younger than 55 years, those without hypertension, and those with normal cholesterol levels. This aligns with the concept of baseline risk burden, whereby these subgroups—with lower CVD burden and less accumulated vascular damage—offer greater potential for risk mitigation through early CVH interventions, a pattern consistent with our previous observations [7]. Conversely, in men, older adults, and individuals with metabolic abnormalities (e.g., hypertension or hypercholesterolemia), the elevated preexisting CVD burden may attenuate CVH benefits [30]. Although CVH optimization still reduces AMI risk in these groups, the relative effect size is typically smaller. While residual confounding (e.g., from environmental or genetic factors) cannot be excluded, the association between cardiovascular health and AMI risk remained significant after extensive covariate adjustment, supporting its robustness, particularly in lower-risk subgroups. Collectively, these findings suggest that improvements in CVH are associated with greater relative benefits in lower-risk individuals, whereas those with established metabolic or cardiovascular disease may require multifaceted and sustained interventions.
5. Strengths and limitations
This study benefits from a large sample size and extended follow-up duration, providing substantial statistical power to assess the association between CVH and AMI risk. The use of mediation analysis incorporating inflammatory and metabolic biomarkers offers mechanistic insights, suggesting that these factors partially mediate the relationship between CVH and AMI. Additionally, the application of an AFT model enabled the evaluation of AMI onset timing and supported the findings derived from multivariable Cox regression, thereby strengthening the robustness and internal validity of the results.
Nonetheless, several limitations merit consideration. First, as an observational study, causal inference is precluded, and residual confounding from unmeasured variables remains possible despite extensive covariate adjustment. Accordingly, all associations should be interpreted cautiously, and the mediation results are presented as suggestive pathways rather than definitive causal mechanisms; likewise, PAF and IER estimates represent scenario-based preventable burdens rather than causal effect measures. Second, several LE8 lifestyle components, including diet, physical activity, sleep duration, and smoking, were derived from self-reported questionnaires and are therefore subject to recall bias, social desirability bias, and other random reporting inaccuracies. In a prospective cohort setting, these biases are expected to be largely nondifferential with respect to future AMI events and consequently tend to bias hazard ratios toward the null rather than inflate the observed protective associations. As a result, the inverse relationship between higher CVH and AMI risk is likely conservative and may underestimate the true magnitude of the effect. Although a very small proportion of participants may still provide implausible lifestyle values because of misunderstanding or difficulty recalling typical behaviors, such limitations are inherent to large population-based studies that rely on questionnaire data. The LE8 cardiovascular health framework converts all components into fixed 0 to 100 scores and derives the composite CVH value as the average of eight equally weighted metrics. This structure reduces the influence of any single misreported component and further minimizes its potential impact on the overall CVH and AMI risk association. Third, although the mediation analysis identified inflammatory and metabolic markers as partial mediators, other relevant pathways—such as psychosocial stress or genetic predisposition—were not assessed, which may limit the comprehensiveness of mechanistic interpretations. Finally, given the number of exploratory analyses performed, we acknowledge the possibility of chance findings due to multiple comparisons. However, the direction and magnitude of associations were highly consistent across the primary analyses and all subgroup, component-specific, and sensitivity analyses, suggesting that any inflation of false-positive results is unlikely to materially affect the overall conclusions. Moreover, because the majority of participants were of European ancestry, the generalisability of these findings to other ethnic populations warrants further investigation in more diverse cohorts
6. Conclusion
Higher CVH levels were significantly associated with a reduced risk of AMI, with inflammatory and metabolic biomarkers partially mediating this relationship. Higher CVH scores were also associated with delayed AMI onset. In the low-CVH group, a hypothetical shift to higher CVH levels was associated with a scenario-based population attributable fraction of approximately 60 % for AMI events. These findings highlight the potential of improving CVH as a population-level strategy to reduce cardiovascular burden.
Funding
This work was supported by the Research Project of Zhejiang Chinese Medical University(2023JKZKTS11). The funder has no role in the overall study process, including design, collection of data, statistical analysis, and interpretation of results.
Availability of data and materials
Data can be accessed from a public and open repository. This study was conducted using the UK Biobank Resource, Application Number: 107,335. Interested researchers can apply for access to the UK Biobank data at www.ukbiobank.ac.uk.
Ethics approval and consent to participate
The UK Biobank was established with ethical clearance from the North West Multi-Centre Research Ethics Committee (REC reference: 11/NW/0382). Written informed consent has been provided by all participants.
Consent for publication
Not applicable.
Financial disclosure
None.
CRediT authorship contribution statement
Wenke Cheng: Writing – review & editing, Writing – original draft, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Wenbo Tang: Writing – review & editing, Writing – original draft, Project administration, Investigation, Conceptualization. Zhongyan Du: Writing – review & editing, Writing – original draft, Supervision, Project administration, Funding acquisition, Data curation. Bi Tang: Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition, Data curation, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We extend our deepest gratitude to the study participants and the members of the UK Biobank cohort. The establishment of the UK Biobank was made possible through the efforts of the Wellcome Trust, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.
Footnotes
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ajpc.2025.101393.
Contributor Information
Zhongyan Du, Email: duzhongyan@zcmu.edu.cn.
Bi Tang, Email: bitang2000@163.com.
Appendix. Supplementary materials
References
- 1.Lindstrom M., DeCleene N., Dorsey H., Fuster V., Johnson C.O., LeGrand K.E., Mensah G.A., Razo C., Stark B., Varieur Turco J., Roth G.A. Global burden of cardiovascular diseases and risks collaboration, 1990-2021. J Am Coll Cardiol. 2022;80:2372–2425. doi: 10.1016/j.jacc.2022.11.001. [DOI] [PubMed] [Google Scholar]
- 2.Lloyd-Jones D.M., Allen N.B., Anderson C.A.M., Black T., Brewer L.C., Foraker R.E., Grandner M.A., Lavretsky H., Perak A.M., Sharma G., Rosamond W. 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–e43. doi: 10.1161/CIR.0000000000001078. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Guo L., Zhang S. Association between ideal cardiovascular health metrics and risk of cardiovascular events or mortality: a meta-analysis of prospective studies. Clin Cardiol. 2017;40:1339–1346. doi: 10.1002/clc.22836. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Han H., Cao Y., Feng C., Zheng Y., Dhana K., Zhu S., Shang C., Yuan C., Zong G. Association of a healthy lifestyle with all-cause and cause-specific mortality among individuals with type 2 diabetes: a prospective study in UK Biobank. Diabetes Care. 2022;45:319–329. doi: 10.2337/dc21-1512. [DOI] [PubMed] [Google Scholar]
- 5.Sudlow C., Gallacher J., Allen N., Beral V., Burton P., Danesh J., Downey P., Elliott P., Green J., Landray M., Liu B., Matthews P., Ong G., Pell J., Silman A., Young A., Sprosen T., Peakman T., Collins R. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12 doi: 10.1371/journal.pmed.1001779. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Appel L.J., Moore T.J., Obarzanek E., Vollmer W.M., Svetkey L.P., Sacks F.M., Bray G.A., Vogt T.M., Cutler J.A., Windhauser M.M., Lin P.H., Karanja N. N Engl J Med. 1997;336:1117–1124. doi: 10.1056/NEJM199704173361601. [DOI] [PubMed] [Google Scholar]
- 7.Cheng W., Geng S., Li Y., Chen R., Du Z. Sex differences in 'Life's essential 8′ cardiovascular health and type 2 diabetes mellitus risk across menopause stages. J Clin Endocrinol Metab. 2024 doi: 10.1210/clinem/dgae557. [DOI] [PubMed] [Google Scholar]
- 8.Zhang J., Chen G., Habudele Z., Wang X., Cai M., Li H., Gao Y., Lip G.Y.H., Lin H. Relation of Life's Essential 8 to the genetic predisposition for cardiovascular outcomes and all-cause mortality: results from a national prospective cohort. Eur J Prev Cardiol. 2023;30:1676–1685. doi: 10.1093/eurjpc/zwad179. [DOI] [PubMed] [Google Scholar]
- 9.Li X., Ma H., Wang X., Feng H., Qi L. Life's essential 8, genetic susceptibility, and incident cardiovascular disease: a prospective study. Arterioscler Thromb Vasc Biol. 2023;43:1324–1333. doi: 10.1161/ATVBAHA.123.319290. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Jin C., Li J., Liu F., Li X., Hui Y., Chen S., Li F., Wang G., Liang F., Lu X., Wu S., Gu D. Life's essential 8 and 10-year and lifetime risk of atherosclerotic cardiovascular disease in China. Am J Prev Med. 2023;64:927–935. doi: 10.1016/j.amepre.2023.01.009. [DOI] [PubMed] [Google Scholar]
- 11.Sebastian S.A., 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 doi: 10.1093/eurjpc/zwae280. [DOI] [PubMed] [Google Scholar]
- 12.Ference B.A., Ginsberg H.N., Graham I., Ray K.K., Packard C.J., Bruckert E., Hegele R.A., Krauss R.M., Raal F.J., Schunkert H., Watts G.F., Borén J., Fazio S., Horton J.D., Masana L., Nicholls S.J., Nordestgaard B.G., van de Sluis B., Taskinen M.-R., Tokgözoglu L., Landmesser U., Laufs U., Wiklund O., Stock J.K., Chapman M.J., Catapano A.L. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2017;38:2459–2472. doi: 10.1093/eurheartj/ehx144. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Borén J., Chapman M.J., Krauss R.M., Packard C.J., Bentzon J.F., Binder C.J., Daemen M.J., Demer L.L., Hegele R.A., Nicholls S.J., Nordestgaard B.G., Watts G.F., Bruckert E., Fazio S., Ference B.A., Graham I., Horton J.D., Landmesser U., Laufs U., Masana L., Pasterkamp G., Raal F.J., Ray K.K., Schunkert H., Taskinen M.-R., van de Sluis B., Wiklund O., Tokgozoglu L., Catapano A.L., Ginsberg H.N. Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2020;41:2313–2330. doi: 10.1093/eurheartj/ehz962. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Pharmacological blood pressure lowering for primary and secondary prevention of cardiovascular disease across different levels of blood pressure: an individual participant-level data meta-analysis. Lancet. 2021;397:1625–1636. doi: 10.1016/S0140-6736(21)00590-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Mora S., Cook N., Buring J.E., Ridker P.M., Lee I.-M. Physical activity and reduced risk of cardiovascular events: potential mediating mechanisms. Circulation. 2007;116:2110–2118. doi: 10.1161/CIRCULATIONAHA.107.729939. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Cappuccio F.P., Cooper D., D'Elia L., Strazzullo P., Miller M.A. Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies. Eur Heart J. 2011;32:1484–1492. doi: 10.1093/eurheartj/ehr007. [DOI] [PubMed] [Google Scholar]
- 17.Yin J., Jin X., Shan Z., Li S., Huang H., Li P., Peng X., Peng Z., Yu K., Bao W., Yang W., Chen X., Liu L. Relationship of sleep duration with all-cause mortality and cardiovascular events: a systematic review and dose-response meta-analysis of prospective cohort studies. J Am Heart Assoc. 2017;6 doi: 10.1161/JAHA.117.005947. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Gibson S., Ashwell M. Dietary patterns among British adults: compatibility with dietary guidelines for salt/sodium, fat, saturated fat and sugars. Public Health Nutr. 2011;14:1323–1336. doi: 10.1017/S1368980011000875. [DOI] [PubMed] [Google Scholar]
- 19.Rauber F., Louzada Maria Laura da Costa, Martinez Steele E., Rezende Leandro F M de, Millett C., Monteiro C.A., Levy R.B. Ultra-processed foods and excessive free sugar intake in the UK: a nationally representative cross-sectional study. BMJ Open. 2019;9 doi: 10.1136/bmjopen-2018-027546. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Pastorino S., Richards M., Pierce M., Ambrosini G.L. A high-fat, high-glycaemic index, low-fibre dietary pattern is prospectively associated with type 2 diabetes in a British birth cohort. Br J Nutr. 2016;115:1632–1642. doi: 10.1017/S0007114516000672. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Lloyd-Jones D.M., Ning H., Labarthe D., Brewer L., Sharma G., Rosamond W., Foraker R.E., Black T., Grandner M.A., Allen N.B., Anderson C., Lavretsky H., Perak A.M. Status of cardiovascular health in US adults and children using the American Heart Association's new "life's essential 8" metrics: prevalence estimates from the National Health and Nutrition Examination Survey (NHANES), 2013 through 2018. Circulation. 2022;146:822–835. doi: 10.1161/CIRCULATIONAHA.122.060911. [DOI] [PubMed] [Google Scholar]
- 22.Petermann-Rocha F., Deo S., Celis-Morales C., Ho F.K., Bahuguna P., McAllister D., Sattar N., Pell J.P. An opportunity for prevention: associations between the life's essential 8 score and cardiovascular incidence using prospective data from UK Biobank. Curr Probl Cardiol. 2023;48 doi: 10.1016/j.cpcardiol.2022.101540. [DOI] [PubMed] [Google Scholar]
- 23.Yusuf S., Joseph P., Rangarajan S., Islam S., Mente A., Hystad P., Brauer M., Kutty V.R., Gupta R., Wielgosz A., AlHabib K.F., Dans A., Lopez-Jaramillo P., Avezum A., Lanas F., Oguz A., Kruger I.M., Diaz R., Yusoff K., Mony P., Chifamba J., Yeates K., Kelishadi R., Yusufali A., Khatib R., Rahman O., Zatonska K., Iqbal R., Wei L., Bo H., Rosengren A., Kaur M., Mohan V., Lear S.A., Teo K.K., Leong D., O'Donnell M., McKee M., Dagenais G. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet. 2020;395:795–808. doi: 10.1016/S0140-6736(19)32008-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Anand S.S., Islam S., Rosengren A., Franzosi M.G., Steyn K., Yusufali A.H., Keltai M., Diaz R., Rangarajan S., Yusuf S. Risk factors for myocardial infarction in women and men: insights from the INTERHEART study. Eur Heart J. 2008;29:932–940. doi: 10.1093/eurheartj/ehn018. [DOI] [PubMed] [Google Scholar]
- 25.Lu Y., Li S.-X., Liu Y., Rodriguez F., Watson K.E., Dreyer R.P., Khera R., Murugiah K., D'Onofrio G., Spatz E.S., Nasir K., Masoudi F.A., Krumholz H.M. Sex-specific risk factors associated with first acute myocardial infarction in young adults. JAMA Netw Open. 2022;5 doi: 10.1001/jamanetworkopen.2022.9953. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Xanthakis V., Enserro D.M., Murabito J.M., Polak J.F., Wollert K.C., Januzzi J.L., Wang T.J., Tofler G., Vasan R.S. Ideal cardiovascular health: associations with biomarkers and subclinical disease and impact on incidence of cardiovascular disease in the Framingham Offspring Study. Circulation. 2014;130:1676–1683. doi: 10.1161/CIRCULATIONAHA.114.009273. [DOI] [PubMed] [Google Scholar]
- 27.Lubrano V., Balzan S. Consolidated and emerging inflammatory markers in coronary artery disease. World J Exp Med. 2015;5:21–32. doi: 10.5493/wjem.v5.i1.21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Mozos I., Malainer C., Horbańczuk J., Gug C., Stoian D., Luca C.T., Atanasov A.G. Inflammatory markers for arterial stiffness in cardiovascular diseases. Front Immunol. 2017;8:1058. doi: 10.3389/fimmu.2017.01058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Nakada S., Celis-Morales C., Pell J.P., Ho F.K. Shared and distinct pathways from anxiety disorder and depression to cardiovascular disease: a UK Biobank prospective cohort study. Eur J Prev Cardiol. 2025 doi: 10.1093/eurjpc/zwaf110. [DOI] [PubMed] [Google Scholar]
- 30.Wahabi H., Esmaeil S., Zeidan R., Jamal A., Fayed A.A. Age and gender-specific pattern of cardiovascular disease risk factors in Saudi Arabia: a subgroup analysis from the heart health promotion study. Healthcare (Basel) 2023;11 doi: 10.3390/healthcare11121737. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data can be accessed from a public and open repository. This study was conducted using the UK Biobank Resource, Application Number: 107,335. Interested researchers can apply for access to the UK Biobank data at www.ukbiobank.ac.uk.