Background: The association of the American Heart Association's updated cardiovascular health score, the Life's Essential 8 (LE8), with cardiovascular disease (CVD) and death is not described in the FHS (Framingham Heart Study). Methods and Results: We evaluated Framingham Offspring participants at examinations 2 and 6 (n=2888 and 1667; and mean age, 44 and 57 years, respectively), free of CVD with information on LE8 components. Using age‐sex–adjusted Cox models, we related LE8 and its change (examination 2 to examination 6) with CVD and death risk and compared associations with those of the Life's Simple 7 score. Mean LE8 score at examination 2 was 67 points (minimum, 26 points; maximum, 100 points). At both examinations, participants were reclassified to a different cardiovascular health status, depending on which method (LE8 versus Life's Simple 7) was used (60% of participants in ideal Life's Simple 7 status were in intermediate LE8 category). On follow‐up after examination 2 (median, 30 and 33 years for CVD and death, respectively), we observed 966 CVD events, and 1195 participants died. Participants having LE8≥68 (sample median) were at lower CVD and death risk compared with those with LE8<68 (examination 2: CVD hazard ratio [HR], 0.47 [95% CI, 0.41–0.54]; death HR, 0.55 [95% CI, 0.49–0.62]; all P <0.001). Participants maintaining low LE8 scores during life course were at highest CVD and death risk (CVD: HRs ranging from 1.8 to 2.3; P <0.001; death HR, 1.45 [95% CI, 1.13–1.85]; P =0.003 versus high‐high group). Conclusions: Further studies are warranted to elucidate whether the LE8 score is a better marker of CVD and death risk, compared with Life's Simple 7 score.

Background: Life's Essential 8 (LE8) is an enhanced metric for cardiovascular health. The interrelations among LE8, biomarkers of aging, and disease risks are unclear. Methods and Results: LE8 score was calculated for 5682 Framingham Heart Study participants. We implemented 4 DNA methylation‐based epigenetic age biomarkers, with older epigenetic age hypothesized to represent faster biological aging, and examined whether these biomarkers mediated the associations between the LE8 score and cardiovascular disease (CVD), CVD‐specific mortality, and all‐cause mortality. We found that a 1 SD increase in the LE8 score was associated with a 35% (95% CI, 27–41; P =1.8E‐15) lower risk of incident CVD, a 36% (95% CI, 24–47; P =7E‐7) lower risk of CVD‐specific mortality, and a 29% (95% CI, 22–35; P =7E‐15) lower risk of all‐cause mortality. These associations were partly mediated by epigenetic age biomarkers, particularly the GrimAge and the DunedinPACE scores. The potential mediation effects by epigenetic age biomarkers tended to be more profound in participants with higher genetic risk for older epigenetic age, compared with those with lower genetic risk. For example, in participants with higher GrimAge polygenic scores (greater than median), the mean proportion of mediation was 39%, 39%, and 78% for the association of the LE8 score with incident CVD, CVD‐specific mortality, and all‐cause mortality, respectively. No significant mediation was observed in participants with lower GrimAge polygenic score. Conclusions: DNA methylation‐based epigenetic age scores mediate the associations between the LE8 score and incident CVD, CVD‐specific mortality, and all‐cause mortality, particularly in individuals with higher genetic predisposition for older epigenetic age.

Background: The relation of cardiorespiratory fitness (CRF) to lifestyle behaviors and factors linked with cardiovascular health remains unclear. We aimed to understand how the American Heart Association's Life's Essential 8 (LE8) score (and its changes over time) relate to CRF and complementary exercise measures in community‐dwelling adults. Methods and Results: Framingham Heart Study (FHS) participants underwent maximum effort cardiopulmonary exercise testing for direct quantification of peak oxygen uptake (V̇O ₂ ). A 100‐point LE8 score was constructed as the average across 8 factors: diet, physical activity, nicotine exposure, sleep, body mass index, lipids, blood glucose, and blood pressure. We related total LE8 score, score components, and change in LE8 score over 8 years with peak V̇O ₂ (log‐transformed) and complementary CRF measures. In age‐ and sex‐adjusted linear models (N=1838, age 54±9 years, 54% women, LE8 score 76±12), a higher LE8 score was associated favorably with peak V̇O ₂ , ventilatory efficiency, resting heart rate, and blood pressure response to exercise (all P <0.0001). A clinically meaningful 5‐point higher LE8 score was associated with a 6.0% greater peak V̇O ₂ (≈1.4 mL/kg per minute at sample mean). All LE8 components were significantly associated with peak V̇O ₂ in models adjusted for age and sex, but blood lipids, diet, and sleep health were no longer statistically significant after adjustment for all LE8 components. Over an ≈8‐year interval, a 5‐unit increase in LE8 score was associated with a 3.7% higher peak V̇O ₂ ( P <0.0001). Conclusions: Higher LE8 score and improvement in LE8 over time was associated with greater CRF, highlighting the importance of the LE8 factors in maintaining CRF.

BACKGROUND: The association of overall cardiovascular health (CVH) with changes in DNA methylation (DNAm) has not been well characterized. METHODS: We calculated the American Heart Association’s Life’s Essential 8 score to reflect CVH in 5 cohorts with diverse backgrounds (mean age 54 years, 55% women, and enrollment year ranging from 1989 to 2012). Epigenome-wide association studies (EWAS) for Life’s Essential 8 score were conducted, followed by bioinformatic analyses. DNAm loci significantly associated with Life’s Essential 8 score were used to calculate a CVH DNAm score. We examined the association of the CVH DNAm score with incident cardiovascular disease (CVD), cardiovascular disease–specific mortality, and all-cause mortality. RESULTS: We identified 609 cytosine-phosphate-guanines (CpGs) associated with Life’s Essential 8 score at false discovery rate<0.05 in the discovery analysis and at Bonferroni-corrected P <0.05 in the multicohort replication stage. Most had low to moderate heterogeneity (414 CpGs [68.0%] with heterogeneity <0.2) in replication analysis. Pathway enrichment analyses and a phenome-wide association study search associated these CpGs with inflammatory or autoimmune phenotypes. We observed enrichment for phenotypes in the Epigenome-Wide Association Study Catalog, with 29-fold enrichment for stroke ( P =2.4e−15) and 21-fold for ischemic heart disease ( P =7.4e−38). Two-sample Mendelian randomization (MR) analysis showed significant association between 141 CpGs and ten phenotypes (261 CpG-phenotype pairs) at false discovery rate<0.05. For example, hypomethylation at cg20544516 ( MIR33B [microRNA 33b] and SREBF1 [sterol regulatory element–binding transcription factor 1]) is associated with a lower risk of stroke ( P =8.1e−6). In multivariable prospective analyses, the CVH DNAm score was consistently associated with clinical outcomes across participating cohorts. Per SD increase in the CVH DNAm score, the decrease in risk of incident cardiovascular disease, cardiovascular disease mortality, and all-cause mortality ranged from 19% to 32%, 28% to 40%, and 27% to 45%, respectively. CONCLUSIONS: We identified new DNAm signatures for CVH across diverse cohorts. Our analyses indicate that multiple biological pathways may be relevant to the observed association between CVH and clinical outcomes.

BACKGROUND: Arterial stiffening may contribute to the pathogenesis of metabolic dysfunction–associated steatotic liver disease. We aimed to assess relations of vascular hemodynamic measures with measures of hepatic steatosis and fibrosis in the community. METHODS: Our sample was drawn from the Framingham Offspring, New Offspring Spouse, Third Generation, Omni-1, and Omni-2 cohorts (N=3875; mean age, 56 years; 54% women). We used vibration-controlled transient elastography to assess controlled attenuation parameter and liver stiffness measurements as measures of liver steatosis and liver fibrosis, respectively. We assessed noninvasive vascular hemodynamics using arterial tonometry. We assessed cross-sectional relations of vascular hemodynamic measures with continuous and dichotomous measures of hepatic steatosis and fibrosis using multivariable linear and logistic regression. RESULTS: In multivariable models adjusting for cardiometabolic risk factors, higher carotid-femoral pulse wave velocity (estimated β per SD, 0.05 [95% CI, 0.01–0.09]; P =0.003), but not forward pressure wave amplitude and central pulse pressure, was associated with more liver steatosis (higher controlled attenuation parameter). Additionally, higher carotid-femoral pulse wave velocity (β=0.11 [95% CI, 0.07–0.15]; P <0.001), forward pressure wave amplitude (β=0.05 [95% CI, 0.01–0.09]; P =0.01), and central pulse pressure (β=0.05 [95% CI, 0.01–0.09]; P =0.01) were associated with more hepatic fibrosis (higher liver stiffness measurement). Associations were more prominent among men and among participants with obesity, diabetes, and metabolic syndrome (interaction P values, <0.001–0.04). Higher carotid-femoral pulse wave velocity, but not forward pressure wave amplitude and central pulse pressure, was associated with higher odds of hepatic steatosis (odds ratio, 1.16 [95% CI, 1.02–1.31]; P =0.02) and fibrosis (odds ratio, 1.40 [95% CI, 1.19–1.64]; P <0.001). CONCLUSIONS: Elevated aortic stiffness and pressure pulsatility may contribute to hepatic steatosis and fibrosis.

BACKGROUND: Arterial pressure waveform shape conveys information regarding interactions between the left ventricle and aorta that could provide an estimate of biological heart age and cardiovascular disease (CVD) risk. METHODS: Artificial intelligence heart age (AI-HA) was estimated by averaging results from 2 convolutional neural networks trained to predict mitral annulus tissue Doppler e’ and s’ peak velocities using an uncalibrated arterial tonometry or photoplethysmography waveform as input. Models were developed using FHS (Framingham Heart Study) participant pressure waveforms and echocardiographic measurements (N=6916 participants, 38 174 waveforms, 56% women, mean age 61±12). We validated AI-HA using Cox modeling in an FHS holdout set of baseline radial waveforms (N=7018, 54% women, age 50±16 years) and in UK Biobank participants (N=67 986, 53% women, age 57±8 years). RESULTS: In FHS (up to 10 years of follow-up, 148 heart failure [HF] and 331 CVD events), using models that adjusted for PREVENT (AHA Predicting Risk of CVD Events) risk factors, AI-HA was associated with incident HF (hazard ratio, 2.09 [CI, 1.64–2.68]; continuous net reclassification, 0.22 [CI, 0.13–0.30]) and CVD (hazard ratio, 1.52 [CI, 1.28–1.81]; continuous net reclassification, 0.13 [CI, 0.07–0.20]). In UK Biobank (up to 10 years of follow-up, 1408 HF and 2709 CVD events), AI-HA was associated with incident HF (hazard ratio, 1.23 [CI, 1.13–1.33]; continuous net reclassification, 0.09 [CI, 0.06–0.12]) and CVD (hazard ratio, 1.22 [CI, 1.15–1.30]; continuous net reclassification, 0.08 [CI, 0.06–0.09]). CONCLUSIONS: AI-HA is a novel and accessible measure of left ventricle function and HF risk in community-based samples.