Association between life’s essential 8 and biological ageing among US adults

Abstract

Background Biological ageing is tightly linked to cardiovascular disease (CVD). We aimed to investigate the relationship between Life’s Essential 8 (LE8), a currently updated measure of cardiovascular health (CVH), and biological ageing. Methods This cross-sectional study selected adults ≥ 20 years of age from the 2005–2010 National Health and Nutrition Examination Survey. LE8 scores (range 0–100) were obtained from measurements based on American Heart Association definitions, divided into health behavior and health factor scores. Biological ageing was assessed by different methods including phenotypic age, phenotypic age acceleration (PhenoAgeAccel), biological age and biological age acceleration (BioAgeAccel). Correlations were analyzed by weighted linear regression and restricted cubic spline models. Results Of the 11,729 participants included, the mean age was 47.41 ± 0.36 years and 5983 (51.01%) were female. The mean phenotypic and biological ages were 42.96 ± 0.41 and 46.75 ± 0.39 years, respectively, and the mean LE8 score was 67.71 ± 0.35. After adjusting for potential confounders, higher LE8 scores were associated with lower phenotypic age, biological age, PhenoAgeAccel, and BioAgeAccel, with nonlinear dose–response relationships. Negative associations were also found between health behavior and health factor scores and biological ageing, and were stronger for health factors. In health factor-specific analyses, the β negativity was greater for blood glucose and blood pressure. The inverse correlations of LE8 scores with phenotypic age and biological age in the stratified analyses remained solid across strata. Conclusions LE8 and its subscale scores were strongly negatively related to biological ageing. Encouraging optimal CVH levels may be advantageous in preventing and slowing down ageing.

Relationship between Life’s Essential 8 and metabolic syndrome among older Americans (NHANES, 2007–2010): navigating biological aging and inflammation

“…In particular, they observed a substantial decrease in phenotypic age of 1.14 years for every 10-point rise in the LE8 score. These results were strongly corroborated in another investigation, indicating that the LE8, health behavior and health factor scores were inversely proportional to the phenotypic age, biological age, with evidences of stronger correlations between health factors and MetS risk, and the correlation was stronger for health factors, which is similar to our findings ( 42 ). Improving health factors and healthy behaviors, such as, good BMI, lipids, blood sugar and BP can significantly enhance the LE8 scores, and assist in slowing down the aging process.…”

Associations of combined phenotypic ageing and genetic risk with incidence of chronic respiratory diseases in the UK Biobank: a prospective cohort study

“…According to data from the UK Biobank, the composite indicator, phenotypic age, demonstrated superior predictive effectiveness for chronic lung diseases compared to any of the nine clinical parameters (data not shown). In addition, although results from population-based cohort studies have demonstrated that the derived PhenoAgeAccel is associated with the mortality of cerebrovascular disease, cancer and diabetes [ 36 – 40 ]; however, few efforts have been made to investigate the associations of PhenoAgeAccel with chronic respiratory diseases.…”

Associations between prenatal loss of control eating and cardiovascular health during pregnancy.

“…Binary logistic regression models were chosen over ordinal logistic regression models for easy interpretation and because, as shown in Table 1, distributions across score categories were nonnormal in this sample, and some categories had insufficient numbers of participants. Previous studies have used cutoffs of 0–49 ( low ), 50–79 ( moderate ), and 80–100 ( high ) to reduce the number of CVH metric score categories (Sun et al, 2023; Zhang et al, 2023), but issues with nonnormal distributions and insufficient numbers of participants per score category would remain if these were used in the current study. Thus, we used a cutoff of 50 (i.e., the midpoint) to create “low” and “moderate–high” CVH metric score categories, with CVH metric scores below 50 points categorized as “low” and those of 50 points or above as “moderate–high” (see Table 1).…”