Biomarkers of aging and lung function in the normative aging study

Results

Descriptive results

The present study included 696 elderly men with 1,070 visits during years of 1999-2013. Of the 696 subjects, 345 (50%) had two follow-up visits, and 29 (4%) completed three follow-up visits. Table 1 shows the personal characteristics of the participants across visits. The mean chronological age was 72.5, 75.2, and 76.3 in the first, second, and third visit, respectively. The cohort was well educated and made up of one third never smokers. Summary statistics of seven different BoA are presented in Table 2. The means of four age accelerations were close to zero, with GrimAgeAccel had the smallest standard deviation (4 years) (see Table 2).

Table 1. Personal characteristics of 696 (N=1,070) elderly white men from the Normative Aging Study, 1999-2013.

Variable	First visit (N=696)	Second visit (N=345)	Third visit (N=29)	All visits (N=1,070)
Chronological age (years)
Mean ± SD	72.51 ± 6.74	75.20 ± 6.39	76.34 ± 5.94	73.48 ± 6.74
Range	55-100	57-94	64-86	55-100
BMI (kg/m2), mean ± SD	28.09 ± 4.10	27.81 ± 4.13	27.96 ± 4.32	28.00 ± 4.11
Height (m), mean ± SD	1.74 ± 0.07	1.74 ± 0.07	1.74 ± 0.07	1.74 ± 0.07
Smoking status, n (%)
Never smokers	216 (31.03)	112 (32.46)	13 (44.83)	341 (31.87)
Current or former smokers	480 (68.97)	233 (67.54)	16 (55.17)	729 (68.13)
Pack-year smoked (years), mean ± SD	21.33 ± 25.33	19.07 ± 23.60	14.88 ± 20.18	20.43 ± 24.68
Years of education, mean ± SD	14.98 ± 2.97	15.17 ± 3.18	15.24 ± 3.45	15.05 ± 3.05
Corticosteroids, n (%)	51 (7.33)	30 (8.70)	1 (3.45)	82 (7.66)
Estimated cell types (%) [14]
Monocytes	10.7 (1.7-25.1)	10.1 (4.0-18.7)	9.5 (6.4-12.6)	10.5 (1.7-25.1)
B cells	1.5 (0.0-31.4)	1.2 (0.0-17.5)	0.6 (0.0-5.3)	1.4 (0.0-31.4)
CD4+ T lymphocytes	8.7 (0.0-22.6)	8.3 (0.0-24.1)	8.5 (0.9-20.1)	8.6 (0.0-24.1)
CD8+ T lymphocytes	8.7 (0.0-20.2)	8.5 (0.0-15.5)	8.0 (3.0-13.8)	8.6 (0.0-20.2)
Natural killer cells	7.3 (0.0-22.1)	7.5 (0.0-25.6)	7.8 (1.6-21.6)	7.4 (0.0-25.6)
FEV1 (mL 1st sec), mean ± SD	2500 ± 580	2540 ± 610	2680 ± 440	2520 ± 586
FEV1/FVC, mean ± SD	0.75 ± 0.08	0.74 ± 0.07	0.73 ± 0.08	0.75 ± 0.08
MMEF (L/s), mean ± SD	0.24 ± 0.11	0.23 ± 0.10	0.22 ± 0.08	0.24 ± 0.11
Abbreviations: SD = standard deviation; BMI = body mass index; FEV1 = forced expiratory volume in 1 second; FVC = forced vital capacity; MMEF = maximum mid-expiratory flow.

Table 2. Summary statistics of different biomarkers of aging in 1,070 visits among 696 elderly white men from the Normative Aging Study, 1999-2013.

Variables	mean ± SD	Percentile
		5th	50th	95th
Epigenetic BoA
GrimAgeAccel (years)	0.01 ± 4.07	-5.52	-0.63	7.82
PhenoAgeAccel (years)	-0.17 ± 5.99	-9.31	-0.56	10.09
IEAA (years)	-0.16 ± 5.08	-7.87	-0.62	8.26
EEAA (years)	-0.10 ± 5.81	-9.41	-0.24	9.61
Zhang’s DNAmRiskScore	-1.87 ± 0.44	-2.54	-1.90	-1.07
Non-epigenetic BoA
TL	1.25 ± 0.49	0.58	1.19	2.15
mtDNA-CN	1.03 ± 0.22	0.73	1.01	1.38
Abbreviations: SD = standard deviation; IEAA = intrinsic epigenetic age acceleration; EEAA = extrinsic epigenetic age acceleration; TL = Telomere length; mtDNA-CN = mitochondrial DNA copy number.

The trends of lung function and methylation based BoA among 29 men who had three visits are presented in supplementary material (Supplementary Figures 1 and 2). The supplementary material also contains trends of four DNAm ages (i.e. DNAm GrimAge, DNAm PhenoAge, the Horvath’s Clock, and the Hannum’s Clock), which increased consistently across visits (Supplementary Figure 3). We also generated four DNAm ages (i.e., DNAm GrimAge, DNAm PhenoAge, the Horvath’s clock, and the Hannum’s clock) on the Age Calculator website. Summary statistics of chronological age and four epigenetic ages are presented in Supplementary Table 1. Supplementary Figure 4 shows the distribution of chronological age and four DNAm ages, among which the distribution of DNAm GrimAge was closest to the one of chronological age (Supplementary Figure 4).

We calculated pairwise correlations between chronological age and the seven BoA (Figure 1). According to Hung et al. [29], only correlation coefficient (r) 0.30 should be considered as "clinically significant" when P < 0.05. In this study, chronological age was not clinically associated with seven BoAs, while it was statistically significantly correlated with Zhang’s DNAmRiskScore (correlation coefficient ® = 0.23; P ≤ 0.001), TL (r = -0.09; P ≤ 0.01), and mtDNA-CN (r = -0.13; P ≤ 0.01). GrimAgeAccel was clinically associated with PhenoAgeAccel (r = 0.33; P ≤ 0.001) and Zhang’s DNAmRiskScore (r = 0.54; P ≤ 0.001). IEAA and EEAA were also highly correlated with each other (r = 0.45; P ≤ 0.001). TL and MtDNA-CN was not clinically associated with other BoAs. We also computed pairwise correlations between chronological age and four types of epigenetic ages. All of them were highly correlated with each other (Supplementary Figure 5). For epidemiologic purposes, non-clinically significant associations may still represent important associations.

Figure 1. Pairwise correlations between chronological age and seven biomarkers of aging. # P ≤ 0.05; ## P ≤ 0.01; ### P ≤ 0.001; * Clinically significant; Corr = correlation coefficient.

BoA and lung function

Figure 2 shows the results of the primary analyses. FEV₁, FEV₁/FVC and MMEF was significantly negatively associated with each of GrimAgeAccel and Zhang’s DNAmRiskScore. For example, each increment of one year in GrimAgeAccel was associated with a decrease in FEV₁ of 13.64 mL (95% CI: -22.16 to -5.11; FDR_B-H = 0.006), in FEV₁/FVC of 0.002 (95% CI: -0.003 to -0.001; FDR_B-H = 0.022), and in MMEF of 0.004 L/s (95% CI: -0.005 to -0.002; FDR_B-H < 0.001). In addition, a 0.2 increase in Zhang’s DNAmRiskScore was associated with a decrease in FEV₁ of 32.01 mL (95% CI: -51.26 to -12.75; FDR_B-H = 0.006), in FEV₁/FVC of 0.004 (95% CI: -0.007 to -0.001; FDR_B-H = 0.037), and in MMEF of 0.009 L/s (95% CI: -0.013 to -0.005; FDR_B-H < 0.001). IEAA was related to lower FEV₁/FVC. No association was found between lung function and non-epigenetic BoA.

Figure 2. Associations between Seven BoAs and lung function for 696 men (1,070 visits), the NAS, 1999-2013. For GrimAgeAccel, PhenoAgeAccel, TL, and mtDNA-CN, we adjusted for chronological age, BMI, height, smoking status, cigarette pack-years, years of education, corticosteroid use, estimated cell types, and batch effects. For EEAA and IEAA, we adjusted for chronological age, BMI, height, smoking status, cigarette pack-years, years of education, corticosteroid use, and batch effects. For Zhang’s DNAmRiskScore, we adjusted for chronological age, BMI, height, smoking status, cigarette pack-years, years of education, corticosteroid use, estimated cell types, batch effects, and technical covariates: Non polymorphic Red, Specificity I Red, Bisulfite Conversion I Red, Bisulfite Conversion II, Extension Red. Abbreviations: IEAA = intrinsic epigenetic age acceleration; EEAA = extrinsic epigenetic age acceleration; TL = Telomere length; mtDNA-CN = mitochondrial DNA copy number; BoA = biomarkers of aging; BMI = body mass index; FDR_B-H = Benjamin-Hochberg false discovery rate.

Sensitivity analyses

Sensitivity analyses further validated the reliability of our primary findings. Results were remarkably consistent after excluding visits at which corticosteroid use was reported (N=82) (Supplementary Figure 6) or adjusting for coronary heart disease (31% of visits), stroke (8%), and diabetes (15%) (Supplementary Figure 7). When we used inverse probability weighting to account for potential selection bias, the associations were similar to those from the primary analysis (Supplementary Figure 8). Subset analyses suggested strong negative associations of lung function and BoA (e.g., GrimAgeAccel, and Zhang’s DNAmRiskScore) among smokers, whereas almost no associations between BoA and lung function among never smokers were present (Supplementary Figure 9A and 9B).

Materials and Methods

Study population

The Normative Aging Study is a closed cohort established by the U.S. Veterans Administration in 1963 [45]. It enrolled 2,280 men volunteers living in the Boston area, aged between 21-80 years, who were free of known chronic medical conditions at enrollment. The study population had undergone clinical examinations every 3 to 5 years. These examinations took place in the morning after an overnight fast. Data on demography, health status, mediation use, and blood sample were collected at each visit.

We narrowed down our analyses to visits with DNA samples, which were collected from 1999 to 2013 [46]. To reduce study heterogeneity, those who were non-white (3%) were excluded. Subjects with a diagnosis of leukemia (n=11) were also be removed, due to its potential influence on DNAm [19]. This study was approved by the Institutional Review boards of the Department of Veterans Affairs and the Harvard TH Chan School of Public Health. All participants provided their written informed consent. The flowchart of our study participants is shown in Figure 3.

Figure 3. Flowchart of study participants.

Pulmonary health measures

Spirometry was assessed in the standing position with a nose clip using a Collins Survey Spirometer with Eagle II Microprocessor (Warren E. Collins, Braintree, Massachusetts), and a protocol that adhere to American Thoracic Society standards [44]. Acceptability of spirograms was judged according to American Thoracic Society/European Respiratory Society criteria [43]. A minimum of three acceptable spirograms was obtained, of which at least two were reproducible within 5% for FVC, and FEV₁. Spirometric values were adjusted by body temperature and pressure [44].

BoA measures

Epigenetic BoA

DNA samples were extracted from the buffy coat of whole blood collected from each visit using the QIAamp DNA Blood Kit (Qiagen, CA, USA) between 1999 and 2013. To ensure a similar age distribution and avoid confounding across plates, we randomized samples based on a two-stage age stratified algorithm [47]. We measured DNAm using the Illumina Infinium HumanMethylation450 BeadChip (Infinium HD Methylation protocol, Illumina, San Diego, CA, USA), which includes ~ 485, 000 CpGs at a single nucleotide resolution. In the quality control step, we removed samples with a detection p-value < 0.01 based on an estimation of the background distribution using non-specific fluorescence [48], and corrected for dye-bias [49].

In the website of New Methylation Age Calculator (https://dnamage.genetics.ucla.edu/new), we uploaded our methylation data file and sample annotation file. We selected “Normalize Data”, and “Advanced Analysis” before submitting our data. We acquired four measures of BoA - epigenetic age accelerations (i.e. GrimAgeAccel, PhenoAgeAccel, EEAA, IEAA), and four DNAm ages (i.e. DNAm GrimAge, DNAm PhenoAge, the Hannum’s clock, the Horvath’s clock) via email, which was used to register in the website.

Zhang’s DNAm RiskScore was calculated based on 10 selected CpGs. The formula [21] is: cg01612140*(-0.38253) + cg05575921*(-0.92224) + cg06126421*(-1.70129) + cg08362785*(2.71749) + cg10321156*(-0.02073) + cg14975410*(-0.04156) + cg19572487*(-0.28069) + cg23665802*(-0.89440) + cg24704287*(-2.98637) + cg25983901*(-1.80325).

Non-epigenetic BoA

We measured leukocyte TL by quantitative real-time polymerase chain reaction [50]. Relative leukocyte TL was calculated using a method previously described [12, 13]. Briefly, it is calculated as the ratio of the telomere (T) repeat copy number to a single-copy gene (S) copy number (T:S ratio) in a given sample, and reported as relative units expressing the ratio between TL in the test DNA and TL in a DNA pool used to generate a stand curve in each PCR run. We ran all samples in triplicates, and the average of the three T measurements was divided by the average of the three S measurements to calculate the average T:S ratio.

For mitochondrial DNA copy number (mtDNA-CN), we also adapted a real-time polymerase chain reaction and quantified it using mtDNA 12S ribosomal RNA TaqMan probe, as described in detail previously [51]. We adapted a multiplex RT-PCR to measure mtDNA content [52]. The mtDNA 12 S ribosomal ribonucleic acid TaqMan (Applied Biosystems, Waltham, Massachusetts) probe (6FAM-5′ TGCCAGCCACCGCG 3′-MGB) was used to measure mtDNA-CN. The sequences of primers used for amplification in mtDNA were mtF805 (5′CCACGGGAAACAGCAGTGATT3′) and mtR927 (5′CTATTGACTTGGGTTAATCGTGTGA3′). All samples were run in triplicate. The mean of the three measurements was used for statistical analyses.

Statistical analysis

Separate linear mixed-effect models were applied to estimate the associations between each BoA and lung function, with subject-specific intercept to account for repeated estimation of lung function. For outcome Y _ij representing either FEV₁, FEV₁/FVC or MMEF for subject i on occasion j, the model is

${\text{Y}}_{ij}={\beta }_0+{\mu }_i+{\beta }_1{\text{BoA}}_{ij}+{\beta }_2{\text{X}}_{2ij}+\cdots +{\beta }_p{X}_{pij}+{\epsilon }_{ij},$(1)

where β₀ is the intercept, μ_i is the random intercept for participant i; β₁ is the association between a chosen BoA measure and lung function for that subject on that occasion; X_2ij to X_pij represent the p-1 covariates, the selection of which differed slightly depending on the measure of BoA used in the analysis (see below). Effect estimates are expressed per one year increase in GrimAgeAccel / PhenoAgeAccel / IEAA / EEAA, or 0.2 increase in Zhang DNAmRiskScore / mtDNA-CN, or 1 increase in TL.

We selected the fundamental covariates a priori: chronological age, body mass index (BMI), height, smoking status [never smokers who never smoked before versus former or current smokers)], cigarette pack-years, years of education, corticosteroid use, estimated cell types (CD4⁺ T lymphocytes, CD8⁺ T lymphocytes, natural killer cells, B cells, and monocytes) derived by the method of Houseman et al. [53], batch effects for all BoA except for IEAA which measures “pure” epigenetic aging effects that are not confounded by differences in blood cell counts, and EEAA which is accounted for cell types in the calculation steps [22].

For Zhang’s DNAmRiskScore, which was calculated from the formula (1), we additionally adjusted for important technical covariates because normalization on the methylation data might diminish biologic signals. Therefore, we included important technical covariates from the control metrics monitoring the performance of various experimental steps [54]. The potential technical covariates included time when DNA methylation measures were performed and experimental parameters including Staining Green, Staining Red, Extension Green, Extension Red, Hybridization High Medium, Hybridization Medium Low, Target Removal 1, Target Removal 2, Bisulfite Conversion I Green, Bisulfite Conversion I Red, Bisulfite Conversion II, Specificity I Green, Specificity I Red, Specificity II, Non Polymorphic Green, and Non Polymorphic Red. We used elastic-net regularized generalized linear model and selected five important technical covariates: Non polymorphic Red, Specificity I Red, Bisulfite Conversion I Red, Bisulfite Conversion II, Extension Red.

Statistical analysis were performed using R (3.5.1) with “lme4” package (linear mixed-effect model) and “glmnet” package (lasso and elastic-net regularized generalized linear model). To adjust for multiple comparisons, we applied Benjamin-Hochberg false discovery rate (FDR_B-H) and set the false positive threshold as 0.05 [55].

Sensitivity analyses

We repeated the models in several different ways to conduct sensitivity analyses. First, we excluded visits at which corticosteroid use was reported (N=82); second, we adjusted models for chronic diseases (coronary heart diseases, stroke, and diabetes) when their DNA samples were collected; third, to adjust for the possibility that healthier men are more likely to return for subsequent exams, we applied inverse probability weighting [12] using logistic regression to calculate the probability of having a subsequent visit given chronological age, education, BMI, blood pressure, smoking status, cigarette pack years, alcohol consumption, C-reactive protein, asthma, chronic bronchitis, and emphysema at previous visit. Finally, we divided the subjects into two groups based on their smoking status, and tested the BoA-lung function associations separately. One groups were subjects who never smoked before (never smokers), and the other one were subjects who were former or current smokers.

Abbreviations

SD: standard deviation; BMI: body mass index; FEV_1:: 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.

Author Contributions

C.W. takes full responsibility for the content of the manuscript, including data and analysis. C.W., A.B., J.S. conceived of the idea; P.S.V., A.B., J.S., D.S. contributed data; C.W., A.J., J.H., L.H., Y.Z. interpreted data; C.W., J.S drafted the manuscript; J.H., L.H., Y.Z., B. C., D.S., A.B., J.S revised the manuscript for important intellectual content; A.B., J.S. obtained funding.

Acknowledgments

We thank all those who participated in the Normative Aging Study, including the subjects as well as the technicians for lung function testing, blood sampling, methylation measures, sample storage, data management, and all those who supervised the study.

Conflicts of Interest

No financial interests or potential conflicts of interest.

Funding

This work was supported by the National Institute of Environmental Health Sciences (grants ES000002, R01ES015172, 5R01ES015172-09, 5R01ES027747-02, P30ES009089, R01ES021733, R01ES025225). The VA Normative Aging Study is supported by the Cooperative Studies Program/Epidemiology Research and Information Center of the U.S. Department of Veterans Affairs and is a component of the Massachusetts Veterans Epidemiology Research and Information Center in Boston, MA.

References

• 1. Ortman JM, Velkoff VA, Hogan H. An Aging Nation: The Older Population in the United States Washington D.C.: U.S. Department of Commerce May 2014. P25–1140.
• 2. Jaul E, Barron J. Age-related diseases and clinical and public health implications for the 85 years old and over population. Front Public Health. 2017; 5:335. https://doi.org/10.3389/fpubh.2017.00335 [PubMed]
• 3. Lowsky DJ, Olshansky SJ, Bhattacharya J, Goldman DP. Heterogeneity in healthy aging. J Gerontol A Biol Sci Med Sci. 2014; 69:640–49. https://doi.org/10.1093/gerona/glt162 [PubMed]
• 4. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y, Whitsel EA, Wilson JG, Reiner AP, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018; 10:573–91. https://doi.org/10.18632/aging.101414 [PubMed]
• 5. Anstey KJ, Windsor TD, Jorm AF, Christensen H, Rodgers B. Association of pulmonary function with cognitive performance in early, middle and late adulthood. Gerontology. 2004; 50:230–34. https://doi.org/10.1159/000078352 [PubMed]
• 6. Singh-Manoux A, Dugravot A, Kauffmann F, Elbaz A, Ankri J, Nabi H, Kivimaki M, Sabia S. Association of lung function with physical, mental and cognitive function in early old age. Age (Dordr). 2011; 33:385–92. https://doi.org/10.1007/s11357-010-9189-x [PubMed]
• 7. Liao D, Higgins M, Bryan NR, Eigenbrodt ML, Chambless LE, Lamar V, Burke GL, Heiss G. Lower pulmonary function and cerebral subclinical abnormalities detected by MRI: the atherosclerosis risk in communities study. Chest. 1999; 116:150–56. https://doi.org/10.1378/chest.116.1.150 [PubMed]
• 8. Drummond MB, Hansel NN, Connett JE, Scanlon PD, Tashkin DP, Wise RA. Spirometric predictors of lung function decline and mortality in early chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2012; 185:1301–06. https://doi.org/10.1164/rccm.201202-0223OC [PubMed]
• 9. Lee HM, Le H, Lee BT, Lopez VA, Wong ND. Forced vital capacity paired with framingham risk score for prediction of all-cause mortality. Eur Respir J. 2010; 36:1002–06. https://doi.org/10.1183/09031936.00042410 [PubMed]
• 10. Mannino DM, Buist AS, Petty TL, Enright PL, Redd SC. Lung function and mortality in the united states: data from the first national health and nutrition examination survey follow up study. Thorax. 2003; 58:388–93. https://doi.org/10.1136/thorax.58.5.388 [PubMed]
• 11. Neas LM, Schwartz J. Pulmonary function levels as predictors of mortality in a national sample of US adults. Am J Epidemiol. 1998; 147:1011–18. https://doi.org/10.1093/oxfordjournals.aje.a009394 [PubMed]
• 12. McCracken J, Baccarelli A, Hoxha M, Dioni L, Melly S, Coull B, Suh H, Vokonas P, Schwartz J. Annual ambient black carbon associated with shorter telomeres in elderly men: veterans affairs normative aging study. Environ Health Perspect. 2010; 118:1564–70. https://doi.org/10.1289/ehp.0901831 [PubMed]
• 13. Colicino E, Wilson A, Frisardi MC, Prada D, Power MC, Hoxha M, Dioni L, Spiro A, Vokonas PS, Weisskopf MG, Schwartz JD, Baccarelli AA. Telomere length, long-term black carbon exposure, and cognitive function in a cohort of older men: the VA normative aging study. Environ Health Perspect. 2017; 125:76–81. https://doi.org/10.1289/EHP241 [PubMed]
• 14. Blasco MA. Telomeres and human disease: ageing, cancer and beyond. Nat Rev Genet. 2005; 6:611–22. https://doi.org/10.1038/nrg1656 [PubMed]
• 15. Morten KJ, Ashley N, Wijburg F, Hadzic N, Parr J, Jayawant S, Adams S, Bindoff L, Bakker HD, Mieli-Vergani G, Zeviani M, Poulton J. Liver mtDNA content increases during development: a comparison of methods and the importance of age- and tissue-specific controls for the diagnosis of mtDNA depletion. Mitochondrion. 2007; 7:386–95. https://doi.org/10.1016/j.mito.2007.09.001 [PubMed]
• 16. Alvarez-Mora MI, Podlesniy P, Gelpi E, Hukema R, Madrigal I, Pagonabarraga J, Trullas R, Mila M, Rodriguez-Revenga L. Fragile x-associated tremor/ataxia syndrome: regional decrease of mitochondrial DNA copy number relates to clinical manifestations. Genes Brain Behav. 2019; 18:e12565. https://doi.org/10.1111/gbb.12565 [PubMed]
• 17. Oberdoerffer P, Sinclair DA. The role of nuclear architecture in genomic instability and ageing. Nat Rev Mol Cell Biol. 2007; 8:692–702. https://doi.org/10.1038/nrm2238 [PubMed]
• 18. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, Deconde R, Chen M, Rajapakse I, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013; 49:359–67. https://doi.org/10.1016/j.molcel.2012.10.016 [PubMed]
• 19. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013; 14:R115. https://doi.org/10.1186/gb-2013-14-10-r115 [PubMed]
• 20. Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, Hou L, Baccarelli AA, Li Y, Stewart JD, Whitsel EA, Assimes TL, Ferrucci L, Horvath S. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY). 2019; 11:303–27. https://doi.org/10.18632/aging.101684 [PubMed]
• 21. Zhang Y, Wilson R, Heiss J, Breitling LP, Saum KU, Schöttker B, Holleczek B, Waldenberger M, Peters A, Brenner H. DNA methylation signatures in peripheral blood strongly predict all-cause mortality. Nat Commun. 2017; 8:14617. https://doi.org/10.1038/ncomms14617 [PubMed]
• 22. Horvath S, Ritz BR. Increased epigenetic age and granulocyte counts in the blood of parkinson’s disease patients. Aging (Albany NY). 2015; 7:1130–42. https://doi.org/10.18632/aging.100859 [PubMed]
• 23. Elina Sillanpää, Sarianna Sipilä, Timo Törmäkangas, Jaakko Kaprio, Taina Rantanen. Genetic and environmental effects on telomere length and lung function: a twin study. The journals of gerontology. Series A., Biological sciences and medical sciences. 2017; 72:1561–1568. https://doi.org/10.1093/gerona/glw178
• 24. Minh Thien Nguyen, Richard Saffery, David Burgner, Kate Lycett, Regan Vryer, Anneke Grobler, Terence Dwyer, Sarath Ranganathan, Melissa Wake. Telomere length and lung function in a population-based cohort of children and mid-life adults. Pediatric pulmonology. 2019; 54:2044–2052. https://doi.org/10.1002/ppul.24489 [PubMed]
• 25. Liu SF, Kuo HC, Tseng CW, Huang HT, Chen YC, Tseng CC, Lin MC. Leukocyte mitochondrial DNA copy number is associated with chronic obstructive pulmonary disease. PLoS One. 2015; 10:e0138716. https://doi.org/10.1371/journal.pone.0138716 [PubMed]
• 26. Lepeule J, Baccarelli A, Motta V, Cantone L, Litonjua AA, Sparrow D, Vokonas PS, Schwartz J. Gene promoter methylation is associated with lung function in the elderly: the normative aging study. Epigenetics. 2012; 7:261–69. https://doi.org/10.4161/epi.7.3.19216 [PubMed]
• 27. Bolund AC, Starnawska A, Miller MR, Schlünssen V, Backer V, Børglum AD, Christensen K, Tan Q, Christiansen L, Sigsgaard T. Lung function discordance in monozygotic twins and associated differences in blood DNA methylation. Clin Epigenetics. 2017; 9:132. https://doi.org/10.1186/s13148-017-0427-2 [PubMed]
• 28. Carmona JJ, Barfield RT, Panni T, Nwanaji-Enwerem JC, Just AC, Hutchinson JN, Colicino E, Karrasch S, Wahl S, Kunze S, Jafari N, Zheng Y, Hou L, et al. Metastable DNA methylation sites associated with longitudinal lung function decline and aging in humans: an epigenome-wide study in the NAS and KORA cohorts. Epigenetics. 2018; 13:1039–55. https://doi.org/10.1080/15592294.2018.1529849 [PubMed]
• 29. Hung M, Bounsanga J, Voss MW. Interpretation of correlations in clinical research. Postgrad Med. 2017; 129:902–06. https://doi.org/10.1080/00325481.2017.1383820 [PubMed]
• 30. Lung Capacity and Aging. American Lung Association. https://www.lung.org/lung-health-and-diseases/how-lungs-work/lung-capacity-and-aging.html. Updated March 11, 2020. Accessed April 28, 2020.
• 31. García-Río F, Pino JM, Dorgham A, Alonso A, Villamor J. Spirometric reference equations for european females and males aged 65-85 yrs. Eur Respir J. 2004; 24:397–405. https://doi.org/10.1183/09031936.04.00088403 [PubMed]
• 32. Greider CW. Telomere length regulation. Annu Rev Biochem. 1996; 65:337–65. https://doi.org/10.1146/annurev.bi.65.070196.002005 [PubMed]
• 33. Sanders JL, Newman AB. Telomere length in epidemiology: a biomarker of aging, age-related disease, both, or neither? Epidemiol Rev. 2013; 35:112–31. https://doi.org/10.1093/epirev/mxs008 [PubMed]
• 34. Quach A, Levine ME, Tanaka T, Lu AT, Chen BH, Ferrucci L, Ritz B, Bandinelli S, Neuhouser ML, Beasley JM, Snetselaar L, Wallace RB, Tsao PS, et al. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging (Albany NY). 2017; 9:419–46. https://doi.org/10.18632/aging.101168 [PubMed]
• 35. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018; 19:371–84. https://doi.org/10.1038/s41576-018-0004-3 [PubMed]
• 36. Levine ME, Hosgood HD, Chen B, Absher D, Assimes T, Horvath S. DNA methylation age of blood predicts future onset of lung cancer in the women’s health initiative. Aging (Albany NY). 2015; 7:690–700. https://doi.org/10.18632/aging.100809 [PubMed]
• 37. Fernández-Vizarra E, Enríquez JA, Pérez-Martos A, Montoya J, Fernández-Silva P. Tissue-specific differences in mitochondrial activity and biogenesis. Mitochondrion. 2011; 11:207–13. https://doi.org/10.1016/j.mito.2010.09.011 [PubMed]
• 38. Marioni RE, Shah S, McRae AF, Ritchie SJ, Muniz-Terrera G, Harris SE, Gibson J, Redmond P, Cox SR, Pattie A, Corley J, Taylor A, Murphy L, et al. The epigenetic clock is correlated with physical and cognitive fitness in the lothian birth cohort 1936. Int J Epidemiol. 2015; 44:1388–96. https://doi.org/10.1093/ije/dyu277 [PubMed]
• 39. Levine ME, Lu AT, Bennett DA, Horvath S. Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and alzheimer’s disease related cognitive functioning. Aging (Albany NY). 2015; 7:1198–211. https://doi.org/10.18632/aging.100864 [PubMed]
• 40. Perna L, Zhang Y, Mons U, Holleczek B, Saum KU, Brenner H. Epigenetic age acceleration predicts cancer, cardiovascular, and all-cause mortality in a german case cohort. Clin Epigenetics. 2016; 8:64. https://doi.org/10.1186/s13148-016-0228-z [PubMed]
• 41. Kodal JB, Kobylecki CJ, Vedel-Krogh S, Nordestgaard BG, Bojesen SE. AHRR hypomethylation, lung function, lung function decline and respiratory symptoms. Eur Respir J. 2018; 51:1701512. https://doi.org/10.1183/13993003.01512-2017 [PubMed]
• 42. Bojesen SE, Timpson N, Relton C, Davey Smith G, Nordestgaard BG. AHRR (cg05575921) hypomethy-lation marks smoking behaviour, morbidity and mortality. Thorax. 2017; 72:646–53. https://doi.org/10.1136/thoraxjnl-2016-208789 [PubMed]
• 43. Miller MR, Hankinson J, Brusasco V, Burgos F, Casaburi R, Coates A, Crapo R, Enright P, van der Grinten CP, Gustafsson P, Jensen R, Johnson DC, MacIntyre N, et al, and ATS/ERS Task Force. Standardisation of spirometry. Eur Respir J. 2005; 26:319–38. https://doi.org/10.1183/09031936.05.00034805 [PubMed]
• 44. Standardization of spirometry—1987 update. Statement of the american thoracic society. Am Rev Respir Dis. 1987; 136:1285–98. https://doi.org/10.1164/ajrccm/136.5.1285 [PubMed]
• 45. Bell B, Rose CL, Damon A. The veterans administration longitudinal study of healthy aging. Gerontologist. 1966; 6:179–84. https://doi.org/10.1093/geront/6.4.179 [PubMed]
• 46. Wang C, Koutrakis P, Gao X, Baccarelli A, Schwartz J. Associations of annual ambient PM 2.5 components with DNAm PhenoAge acceleration in elderly men: the normative aging study. Environ Pollut. 2020; 258:113690. https://doi.org/10.1016/j.envpol.2019.113690 [PubMed]
• 47. Dai L, Mehta A, Mordukhovich I, Just AC, Shen J, Hou L, Koutrakis P, Sparrow D, Vokonas PS, Baccarelli AA, Schwartz JD. Differential DNA methylation and PM 2.5 species in a 450K epigenome-wide association study. Epigenetics. 2017; 12:139–48. https://doi.org/10.1080/15592294.2016.1271853 [PubMed]
• 48. Heiss JA, Just AC. Improved filtering of DNA methylation microarray data by detection P values and its impact on downstream analyses. Clin Epigenetics. 2019; 11:15. https://doi.org/10.1186/s13148-019-0615-3 [PubMed]
• 49. Xu Z, Langie SA, De Boever P, Taylor JA, Niu L. RELIC: a novel dye-bias correction method for illumina methylation BeadChip. BMC Genomics. 2017; 18:4. https://doi.org/10.1186/s12864-016-3426-3 [PubMed]
• 50. Cawthon RM. Telomere measurement by quantitative PCR. Nucleic Acids Res. 2002; 30:e47. https://doi.org/10.1093/nar/30.10.e47 [PubMed]
• 51. Zhong J, Cayir A, Trevisi L, Sanchez-Guerra M, Lin X, Peng C, Bind MA, Prada D, Laue H, Brennan KJ, Dereix A, Sparrow D, Vokonas P, et al. Traffic-related air pollution, blood pressure, and adaptive response of mitochondrial abundance. Circulation. 2016; 133:378–87. https://doi.org/10.1161/CIRCULATIONAHA.115.018802 [PubMed]
• 52. Andreu AL, Martinez R, Marti R, García-Arumí E. Quantification of mitochondrial DNA copy number: pre-analytical factors. Mitochondrion. 2009; 9:242–46. https://doi.org/10.1016/j.mito.2009.02.006 [PubMed]
• 53. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012; 13:86. https://doi.org/10.1186/1471-2105-13-86 [PubMed]
• 54. BeadArray Controls Reporter. San Diego, CA: Illumina; October 2015. 1000000004009 v00.
• 55. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological). 1995; 57:289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x