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Health-Related quality of life and DNA Methylation-Based aging biomarkers among survivors of childhood cancer.
Journal of the National Cancer Institute 2024 March 6
BACKGROUND: Childhood cancer survivors are at high risk for morbidity and mortality and poor patient-reported outcomes, typically health-related-quality-of-life (HRQOL). However, associations between DNA methylation (DNAm)-based aging biomarkers and HRQOL have not been evaluated.
METHODS: DNAm was generated with Infinium EPIC BeadChip on blood-derived DNA (median[range] for age at blood draw = 34.5[18.5-66.6] years) and HRQOL was assessed with age at survey (32.3[18.4-64.5] years) from 2,206 survivors in the St Jude Lifetime Cohort. DNAm-based aging biomarkers, including epigenetic age using multiple clocks (eg, GrimAge) and others (eg, DNAmB2M beta-2-microglobulin; DNAmADM: adrenomedullin), were derived from the DNAm Age Calculator (https://dnamage.genetics.ucla.edu). HRQOL was assessed using the Medical Outcomes Study 36-Item Short-Form Health Survey to capture eight domains, and physical and mental component summaries (PCS and MCS). General linear models evaluated associations between HRQOL and epigenetic age acceleration (EAA, eg, EAA_GrimAge) or other age-adjusted DNAm-based biomarkers (eg, ageadj_DNAmB2M) after adjusting for age at blood draw, sex, cancer treatments, and DNAm-based surrogate for smoking pack-years. All P values were 2-sided.
RESULTS: Worse HRQOL was associated with greater EAA_GrimAge (PCS β[95%CI]=-0.18[-0.251,-0.11] years, P = 1.85 × 10-5; and four individual HRQOL domains), followed by ageadj_DNAmB2M (PCS: -0.08[-0.124,-0.037], P = .003; and three individual HRQOL domains), and ageadj_DNAmADM (PCS: -0.082[-0.125,-0.039], P = .002; and two HRQOL domains). EAA_Hannum (Hannum clock) was not associated with any HRQOL.
CONCLUSIONS: Overall and domain-specific measures of HRQOL are associated with DNAm measures of biological aging. Future longitudinal studies should test biological aging as a potential mechanism underlying the association between poor HRQOL and increased risk of clinically assessed adverse health outcomes.
METHODS: DNAm was generated with Infinium EPIC BeadChip on blood-derived DNA (median[range] for age at blood draw = 34.5[18.5-66.6] years) and HRQOL was assessed with age at survey (32.3[18.4-64.5] years) from 2,206 survivors in the St Jude Lifetime Cohort. DNAm-based aging biomarkers, including epigenetic age using multiple clocks (eg, GrimAge) and others (eg, DNAmB2M beta-2-microglobulin; DNAmADM: adrenomedullin), were derived from the DNAm Age Calculator (https://dnamage.genetics.ucla.edu). HRQOL was assessed using the Medical Outcomes Study 36-Item Short-Form Health Survey to capture eight domains, and physical and mental component summaries (PCS and MCS). General linear models evaluated associations between HRQOL and epigenetic age acceleration (EAA, eg, EAA_GrimAge) or other age-adjusted DNAm-based biomarkers (eg, ageadj_DNAmB2M) after adjusting for age at blood draw, sex, cancer treatments, and DNAm-based surrogate for smoking pack-years. All P values were 2-sided.
RESULTS: Worse HRQOL was associated with greater EAA_GrimAge (PCS β[95%CI]=-0.18[-0.251,-0.11] years, P = 1.85 × 10-5; and four individual HRQOL domains), followed by ageadj_DNAmB2M (PCS: -0.08[-0.124,-0.037], P = .003; and three individual HRQOL domains), and ageadj_DNAmADM (PCS: -0.082[-0.125,-0.039], P = .002; and two HRQOL domains). EAA_Hannum (Hannum clock) was not associated with any HRQOL.
CONCLUSIONS: Overall and domain-specific measures of HRQOL are associated with DNAm measures of biological aging. Future longitudinal studies should test biological aging as a potential mechanism underlying the association between poor HRQOL and increased risk of clinically assessed adverse health outcomes.
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