Browsing by Subject "Biological ageing"

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  • Sillanpää, Elina; Heikkinen, Aino; Kankaanpää, Anna; Paavilainen, Aini; Kujala, Urho M.; Tammelin, Tuija H.; Kovanen, Vuokko; Sipilä, Sarianna; Pietiläinen, Kirsi H.; Kaprio, Jaakko; Ollikainen, Miina; Laakkonen, Eija K. (BioMed Central, 2021)
    Abstract The aim of this study was to investigate the correspondence of different biological ageing estimates (i.e. epigenetic age) in blood and muscle tissue and their associations with physical activity (PA), physical function and body composition. Two independent cohorts (N = 139 and N = 47) were included, whose age span covered adulthood (23–69 years). Whole blood and m. vastus lateralis samples were collected, and DNA methylation was analysed. Four different DNA methylation age (DNAmAge) estimates were calculated using genome-wide methylation data and publicly available online tools. A novel muscle-specific methylation age was estimated using the R-package ‘MEAT’. PA was measured with questionnaires and accelerometers. Several tests were conducted to estimate cardiorespiratory fitness and muscle strength. Body composition was estimated by dual-energy X-ray absorptiometry. DNAmAge estimates from blood and muscle were highly correlated with chronological age, but different age acceleration estimates were weakly associated with each other. The monozygotic twin within-pair similarity of ageing pace was higher in blood (r = 0.617–0.824) than in muscle (r = 0.523–0.585). Associations of age acceleration estimates with PA, physical function and body composition were weak in both tissues and mostly explained by smoking and sex. The muscle-specific epigenetic clock MEAT was developed to predict chronological age, which may explain why it did not associate with functional phenotypes. The Horvath’s clock and GrimAge were weakly associated with PA and related phenotypes, suggesting that higher PA would be linked to accelerated biological ageing in muscle. This may, however, be more reflective of the low capacity of epigenetic clock algorithms to measure functional muscle ageing than of actual age acceleration. Based on our results, the investigated epigenetic clocks have rather low value in estimating muscle ageing with respect to the physiological adaptations that typically occur due to ageing or PA. Thus, further development of methods is needed to gain insight into muscle tissue-specific ageing and the underlying biological pathways.
  • Chadeau-Hyam, Marc; Bodinier, Barbara; Vermeulen, Roel; Karimi, Maryam; Zuber, Verena; Castagné, Raphaële; Elliott, Joshua; Muller, David; Petrovic, Dusan; Whitaker, Matthew; Stringhini, Silvia; Tzoulaki, Ioanna; Kivimäki, Mika; Vineis, Paolo; Elliott, Paul; Kelly-Irving, Michelle; Delpierrel, Cyrille (2020)
    Background: Socioeconomic position as measured by education may be embodied and affect the functioning of key physiological systems. Links between social disadvantage, its biological imprint, and cause-specific mortality and morbidity have not been investigated in large populations, and yet may point towards areas for public health interventions beyond targeting individual behaviours. Methods: Using data from 366,748 UK Biobank participants with 13 biomarker measurements, we calculated a Biological Health Score (BHS, ranging from 0 to 1) capturing the level of functioning of five physiological systems. Associations between BHS and incidence of cardiovascular disease (CVD) and cancer, and mortality from all, CVD, cancer, and external causes were examined. We explored the role of education in these associations. Mendelian randomisation using genetic evidence was used to triangulate these findings. Findings: An increase in BHS of 0.1 was associated with all-cause (HR = 1.14 [1.12–1.16] and 1.09 [1.07–1.12] in men and women respectively), cancer (HR = 1.11 [1.09–1.14] and 1.07 [1.04–1.10]) and CVD (HR = 1.25 [1.20–1.31] and 1.21 [1.11–1.31]) mortality, CVD incidence (HR = 1.15 [1.13–1.16] and 1.17 [1.15–1.19]). These associations survived adjustment for education, lifestyle-behaviours, body mass index (BMI), co-morbidities and medical treatments. Mendelian randomisation further supported the link between the BHS and CVD incidence (HR = 1.31 [1.21–1.42]). The BHS contributed to CVD incidence prediction (age-adjusted C-statistic = 0.58), other than through education and health behaviours. Interpretation: The BHS captures features of the embodiment of education, health behaviours, and more proximal unknown factors which all complementarily contribute to all-cause, cancer and CVD morbidity and premature death. © 2020 The Author(s)