Research Paper Volume 12, Issue 12 pp 11942—11966
Biomarkers of aging and lung function in the normative aging study
- 1 Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- 2 Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- 3 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- 4 Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
- 5 VA Normative Aging Study, VA Boston Healthcare System, Boston, MA 02130, USA
- 6 Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
- 7 Department of Epidemiology and Environmental Health Sciences, Columbia University, New York, NY 10027, USA
Received: February 20, 2019 Accepted: May 20, 2020 Published: June 19, 2020https://doi.org/10.18632/aging.103363
How to Cite
Copyright © 2020 Wang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Elderly individuals who are never smokers but have the same height and chronological age can have substantial differences in lung function. The underlying biological mechanisms are unclear. To evaluate the associations of different biomarkers of aging (BoA) and lung function, we performed a repeated-measures analysis in the Normative Aging Study using linear mixed-effect models. We generated GrimAgeAccel, PhenoAgeAccel, extrinsic and intrinsic epigenetic age acceleration using a publically available online calculator. We calculated Zhang’s DNAmRiskScore based on 10 CpGs. We measured telomere length (TL) and mitochondrial DNA copy number (mtDNA-CN) using quantitative real-time polymerase chain reaction. A pulmonary function test was performed measuring forced expiratory volume in 1 second / forced vital capacity (FEV1/FVC), FEV1, and maximum mid-expiratory flow (MMEF). Epigenetic-based BoA were associated with lower lung function. For example, a one-year increase in GrimAgeAccel was associated with a 13.64 mL [95% confidence interval (CI), 5.11 to 22.16] decline in FEV1; a 0.2 increase in Zhang’s DNAmRiskScore was associated with a 0.009 L/s (0.005 to 0.013) reduction in MMEF. No association was found between TL/mtDNA-CN and lung function. Overall, this paper shows that epigenetics might be a potential mechanism underlying pulmonary dysfunction in the elderly.
SD: standard deviation; BMI: body mass index; FEV1:: forced expiratory volume in 1 second; FVC: forced vital capacity; MMEF: maximum mid-expiratory flow; TL: Telomere length; mtDNA-CN: mitochondrial DNA copy number; FDR B-H: Benjamin-Hochberg false discovery rate.