#1

Abstract

Objectives

To explore the association of Life’s Essential 8 (LE8) levels with leukocyte telomere length (LTL) and mitochondrial DNA copy number (mtDNA-CN).

Design

A cross-sectional study.

Setting and participants

225,692 participants aged 37−73 year from the UK Biobank cohort enrolled from 2006 to 2010.

Measurements

The LE8 score (0–100) was divided into low (<50), moderate (50–79), and high cardiovascular health (CVH) (≥80) categories, based on health behaviors and factors defined by the American Heart Association. LTL was measured by a validated quantitative polymerase chain reaction method. mtDNA-CN was reacted by standardized SNP probe intensities. The association of CVH (as both a continuous and categorical variable) with LTL and mtDNA-CN was examined using multiple linear regression.

Results

Of 225,692 participants, 5.3% had low CVH, 81.2% had moderate CVH, and 13.4% had high CVH. Participants with higher CVH were usually younger, female, better educated, of higher socioeconomic status, and with a lower prevalence of comorbidities. After adjusting for confounders, a higher LE8 score is associated with longer LTL (Beta = 0.075, P < 0.05) and increased mtDNA-CN (Beta = 0.094, P < 0.05). We also observed that this association was evident in the health behavior score (diet, physical activity, nicotine exposure, and sleep) and the health factors score (BMI, non-HDL cholesterol, blood glucose, and blood pressure), with a stronger positive association of health factors with LTL and mtDNA-CN (Beta = 0.019, P < 0.05; Beta = 0.037, P < 0.05).

Conclusions

Higher CVH is associated with longer LTL and increased mtDNA-CN.

O3

Here’s a concise, structured take on the paper you linked.

What the paper is about (one-paragraph summary)

Using cross-sectional data from 225,692 UK Biobank participants (ages 37–73), the authors examined whether better cardiovascular health—measured by the American Heart Association’s Life’s Essential 8 (LE8) score—correlates with molecular markers of biological aging: leukocyte telomere length (LTL) and mitochondrial DNA copy number (mtDNA-CN). Higher LE8 scores were associated with longer LTL (β≈0.075 SD) and higher mtDNA-CN (β≈0.094 SD) after multivariable adjustment; “health factor” components (BMI, non-HDL cholesterol, glucose, blood pressure) showed stronger links than “health behavior” components (diet, activity, nicotine exposure, sleep). Associations appeared broadly consistent across subgroups. (PubMed , ResearchGate)

What they did (methods, in brief)

  • Design & sample. Cross-sectional analysis of UK Biobank; exclusions yielded N=225,692. (PubMed )
  • Exposures. LE8 computed from four behaviors (diet, activity, nicotine exposure, sleep) and four factors (BMI, non-HDL cholesterol, glucose, BP), adapted to available UKB variables. (ResearchGate)
  • Outcomes.
    • LTL: UKB qPCR assay (z-standardized; field 22192).
    • mtDNA-CN: derived from SNP-array probe intensities using AutoMitoC (field 22431). (ResearchGate)
  • Statistics. Multiple linear regression with staged adjustment (demographics, socioeconomic factors, disease history, WBC count, CRP, ancestry PCs), restricted cubic splines, subgroup analyses, and polygenic risk scores (PRS) for LTL (20 SNPs) and mtDNA-CN (6 SNPs) to test effect modification. (ResearchGate)

Key findings (with a few numbers)

  • Overall association: Each SD increase in LE8 linked to longer LTL (β≈0.075, P<0.05) and higher mtDNA-CN (β≈0.094, P<0.05). (PubMed )
  • Subscales:Saúde factors had stronger associations than health behaviors with both LTL and mtDNA-CN. (ResearchGate)
  • Dose-response: Spline analyses showed monotonic positive trends across the LE8 spectrum. (PubMed )
  • Genetics: Associations were similar across PRS strata; no convincing interaction with genetic susceptibility. (ResearchGate)

What’s novel here?

  1. Dual aging markers at scale. Prior work tied LE8 to epigenetic age and clinical outcomes, and separately linked healthy behaviors to LTL; this study evaluates LE8 against both LTL and mtDNA-CN together in a very large sample. That dual-marker framing—and the consistency across both—adds weight to the idea that LE8 tracks molecular aging. (AHA Journals, The Lancet)
  2. Genetic context. Incorporating PRS for LTL and mtDNA-CN and showing largely non-modified associations suggests the LE8–aging link is not confined to people with favorable genetics. (Most LE8–aging signal held regardless of PRS.) (ResearchGate)
  3. Component granularity. Separately evaluating behavior vs factor subscales (and individual components) helps identify which levers may align most strongly with LTL vs mtDNA-CN. (ResearchGate)

Positioned against the literature: LE8 has been connected to younger DNA-methylation ages and lower CVD/mortality risk, and healthy behaviors have been associated with longer LTL; bringing LE8, LTL, and mtDNA-CN into one analysis with PRS is the main incremental contribution. (AHA Journals, The Lancet)

Critique

Strengths

  • Huge sample with harmonized biomarker pipelines (UKB qPCR LTL; standardized AutoMitoC mtDNA-CN), improving precision and reproducibility. (ResearchGate)
  • Thorough adjustment (socio-demographics, comorbidities, inflammation, ancestry PCs) and spline/subgroup analyses; findings robust across strata. (PubMed , ResearchGate)
  • Genetic stratification with PRS adds context on potential effect modification. (ResearchGate)

Limitations (some acknowledged by the authors)

  • Cross-sectional design precludes causal inference or directionality (health status ↔ molecular aging). Longitudinal change in LTL/mtDNA-CN isn’t assessed. (ResearchGate)
  • Measurement constraints.
    • LTL by qPCR is noisy relative to TRF/NGS approaches; batch and cell-composition effects can linger despite standardization/adjustment.
    • mtDNA-CN from arrays (AutoMitoC) is a proxy rather than a direct quantification; platform/batch artifacts and NUMTs can bias estimates. (These are typical concerns for array-based mtDNA-CN.) (ResearchGate)
  • Construct validity of LE8 in UKB. The score is adapted to UKB (e.g., non-HDL lipids; UKB-specific diet/sleep items). That pragmatism is necessary but can deviate from canonical AHA definitions, limiting comparability. (ResearchGate)
  • Residual and structural confounding. Behaviors and factors can be on the causal path (e.g., BMI, BP), so mutually adjusting components risks over-adjustment or collider issues; unmeasured lifestyle, medication, or disease severity could still confound. (The authors note residual confounding.) (ResearchGate)
  • Generalizability. UK Biobank’s volunteer cohort is healthier and less diverse than the general population; effects may differ in other ancestries or settings (not directly probed here).
  • Effect size interpretation. SD-scaled β≈0.075–0.094 are modest; clinical meaning (e.g., “years of telomere aging”) isn’t translated, unlike some prior LTL studies that benchmarked trait effects against age-equivalent change. (PubMed , The Lancet)

What would strengthen the work

  • Prospective analyses with repeated LTL/mtDNA-CN to test whether improving LE8 precedes slower molecular aging.
  • Causal inference (e.g., Mendelian randomization for LE8 components → LTL/mtDNA-CN, or vice-versa).
  • Richer measurement: direct mtDNA-CN (qPCR/NGS), cell-type deconvolution, and additional aging clocks (e.g., epigenetic) to triangulate mechanisms.
  • Multi-ancestry replication beyond UKB and harmonization to canonical AHA LE8 definitions.
  • Clinical translation: express effects in interpretable units (e.g., telomere “age-equivalents”) and explore mediation between LE8 → LTL/mtDNA-CN → hard outcomes.

Bottom line

The paper provides coherent, biologically plausible evidence—at very large scale—that better LE8 cardiovascular health tracks with “younger” molecular profiles (longer LTL and higher mtDNA-CN), and this appears robust to genetic susceptibility. The work is incremental rather than radical: its novelty lies in bringing both LTL and mtDNA-CN under the LE8 umbrella, plus the PRS context, rather than in establishing new causal mechanisms. The main caveat is cross-sectional design and proxy biomarker limitations, so claims should be framed as associations that motivate longitudinal and mechanistic follow-up. (PubMed , ResearchGate, ResearchGate)

Sources

Note: The PubMed Central link in your message points to the same article; if you’d like, I can extract specific figure panels or tables once that page is accessible.