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Chronic respiratory diseases affect over 454 million people worldwide, and rank as the third leading global cause of mortality [1]. Increasing evidence suggests that ageing significantly contributes to their pathogenesis, linked to epigenetic changes over the lifespan [2, 3]. DNA methylation markers, which capture gene–environment factors, may underlie the development of respiratory diseases and can predict epigenetic ageing rates [2, 4]. Most epigenetic clocks are based on blood DNA methylation, which represents a non-invasive biospecimen that can be used as a surrogate to study lung ageing [5, 6]. Recent findings linked accelerated epigenetic ageing with poor respiratory health [2, 5–10], suggesting that investigating the influence of ageing on chronic respiratory diseases may lead to the discovery of new therapeutic strategies and help target therapies [2].
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An observational study in the large population-based NHANES survey shows accelerated epigenetic ageing in adults with wheezing and validates the association of COPD – particularly the emphysema subtype – and asthma with epigenetic ageing https://bit.ly/3SpNdHK
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Chronic respiratory diseases affect over 454 million people worldwide, and rank as the third leading global cause of mortality [1]. Increasing evidence suggests that ageing significantly contributes to their pathogenesis, linked to epigenetic changes over the lifespan [2, 3]. DNA methylation markers, which capture gene–environment factors, may underlie the development of respiratory diseases and can predict epigenetic ageing rates [2, 4]. Most epigenetic clocks are based on blood DNA methylation, which represents a non-invasive biospecimen that can be used as a surrogate to study lung ageing [5, 6]. Recent findings linked accelerated epigenetic ageing with poor respiratory health [2, 5–10], suggesting that investigating the influence of ageing on chronic respiratory diseases may lead to the discovery of new therapeutic strategies and help target therapies [2].
This study aimed to analyse public data from the National Health and Nutrition Examination Survey (NHANES) to investigate the overall association and sex-specific associations of epigenetic age acceleration in whole blood with chronic respiratory diseases among the US adult civilian non-institutionalised population.
DNA methylation was measured in whole blood from adults aged ≥50 years from the 1999–2000 and 2001–2002 NHANES cycles using the Infinium MethylationEPICv1 array (Illumina). Methylation-derived blood cell counts and epigenetic clocks were estimated, including chronological age clocks (HorvathAge, HannumAge and SkinBloodAge), lifespan and health span clocks (PhenoAge, GrimAgeMort and GrimAge2Mort), pace of ageing (DunedinPoAm), and DNA methylation telomere length (HorvathTelo) [2]. The NHANES website details sample eligibility, laboratory procedures, quality control, batch correction and epigenetic clock calculation (more details in the NHANES 1999–2002 DNA Methylation Array and Epigenetic Biomarkers section) [11]. In this population, the epigenetic biomarkers show a strong positive correlation with chronological ageing (r ranging from 0.76 to 0.87), except for DunedinPoAm (r=0.04) and HorvathTelo (r=−0.58), as expected [12]. Demographic and respiratory health data were recorded through self-reported standardised questionnaires administered by trained interviewers. We analysed the following self-reported phenotypes: wheezing (RDQ.070), wheezing attacks (RDQ.080), asthma (MCQ010), current asthma (MCQ035), asthma exacerbations (MCQ040, MCQ050), emphysema (MCQ170G), chronic bronchitis (MCQ170K) and age of COPD onset (MCQ180G, MCQ180K). Informed consent was provided by all participants, and ethics review boards approved the study protocols.
After excluding individuals top coded as aged ≥85 years and participants with sex mismatch (between reported gender and epigenetic-predicted sex), we analysed 2346 subjects. Participants had a mean±sd age of 65.1±9.3 years, and 48.8% were females; participants self-identified as non-Hispanic white (39.3%), Mexican American (29.0%), black (21.8%), other Hispanic (6.4%), or other/multi-racial (3.5%). Among them, 323 (13.8%) reported wheezing in the past year, 196 (8.4%) had COPD (147 with chronic bronchitis and 68 with emphysema) and 248 (10.6%) had asthma (94 reported current asthma and 66 experienced asthma exacerbations in the past year, based on the report of asthma attacks and emergency room visits).
We examined the association between epigenetic age and respiratory traits using survey-weighted linear regression models adjusted for age, age2, sex, race/ethnicity and survey cycles. Participants without reporting any chronic respiratory trait were included as controls. Models accounted for survey weights to correct for selection, non-response and coverage biases following the NHANES analytical guidelines [11]. Multiple comparisons were adjusted using a false discovery rate <5%. We conducted sensitivity analyses further adjusting for blood cell counts (CD8+ T cells, CD4+ T cells, natural killer cells, B cells, monocytes and neutrophils), and for potential clinical and socioeconomic status (SES) confounders, such as smoking (never, former and current), body mass index (BMI), education level, income, occupation and poverty-to-income ratio.
We observed that individuals reporting wheezing in the past year had accelerated epigenetic ageing compared to non-wheezing individuals (figure 1). Individuals reporting wheezing were 3.1 years higher in GrimAge2Mort (95% CI 2.1 to 4.1) and 2.7 years higher in GrimAgeMort (95% CI 1.8 to 3.6). Individuals with wheezing also exhibited shorter average telomere length of −77.6 bp (95% CI −118.0 to −37.3) and 4.2% higher (95% CI 2.2 to 6.2) pace of ageing.
FIGURE 1.
Forest plot of the significant associations between epigenetic ageing biomarkers and chronic respiratory diseases. Epigenetic clocks included biomarkers predictive of chronological age (HorvathAge, HannumAge and SkinBloodAge), lifespan and health span (PhenoAge, GrimAgeMort and GrimAge2Mort), pace of ageing (DunedinPoAm), and telomere length as a replicative history of cells (HorvathTelo). Effect estimates and 95% confidence intervals are represented on the x-axis. Effect estimates for GrimAgeMort, GrimAge2Mort, HannumAge and PhenoAge are in years, for DunedinPoAm are in unit differences of the pace of ageing (a score of 1 represents an average ageing pace), and for HorvathTelo are in kilobases. Positive estimates indicate accelerated epigenetic ageing, except for HorvathTelo, since shorter telomere lengths indicate accelerated ageing. The vertical dashed line at zero represents no association. *: false discovery rate <0.05.
Furthermore, we observed that adults with COPD had a 2.0 years greater GrimAgeMort (95% CI 1.2 to 2.8) and 2.0 years greater GrimAge2Mort (95% CI 1.2 to 2.9) than those without COPD, and an increase of 3.4% (95% CI 1.3 to 5.6) in the pace of ageing (figure 1). These associations remained robust to the adjustment for clinical and SES factors (p>0.05), except for the association in the pace of ageing, which could be driven by smoking (p=0.10). Notably, smoking is a primary risk factor for COPD, significantly affecting DNA methylation and mortality risk [13]. Among COPD patients, those with early onset (before age 40 years) suggestively showed 2.3 years higher (95% CI 0.5 to 4.1) HannumAge than those with late onset. Our findings reinforced previous evidence linking accelerated epigenetic ageing with COPD [2, 7, 14], as well as poorer outcomes such as exacerbation rate and increased mortality [6, 15], in addition to identifying early onset as a potential risk factor.
Among COPD subtypes, emphysema was associated with a mean accelerated mortality-related ageing of 4.4 (95% CI 3.1 to 5.8; GrimAge2Mort) and 4.2 years (95% CI 2.8 to 5.6; GrimAgeMort), along with a 7.3% (95% CI 4.5 to 10.1) increase in their pace of ageing and an average telomere reduction of −100.9 bp (95% CI −163.5 to −38.2) (figure 1). Notably, these associations remained robust after further adjustment for clinical and SES variables, except that telomere length reduction remained borderline significant after SES adjustment (−68.4 bp, 95% CI −113.1 to −5.7; p=0.05). Conversely, only a suggestive yet not significant accelerated epigenetic ageing was observed in individuals with chronic bronchitis (1.2 years GrimAgeMort, 95% CI 0.3 to 2.0). Our results suggest that accelerated epigenetic ageing may be COPD subtype-sensitive and pronounced in emphysema patients.
Regarding asthma, we observed a suggestive epigenetic age acceleration in participants who ever had asthma (0.7 years GrimAgeMort, 95% CI 0.1 to 1.3; and 0.8 years GrimAge2Mort, 95% CI 0.1 to 1.6) (figure 1), supporting previous results observed in children with asthma [8, 10].
Notably, although only a few associations reported in this study were sensitive to adjustments for smoking and SES, most of the reported associations of epigenetic ageing with wheezing and COPD (including disease subtype and age of onset) remained robust in sensitivity analyses, supporting no confounding effect of smoking, BMI, SES and blood cell counts (see additional table in the Zenodo repository under accession number 14549722).
Finally, sex-stratified analyses showed similar trends in epigenetic age acceleration in males and females with chronic respiratory disease. However, we observed a significant association in males and a non-significant association in females for accelerated pace of ageing associated with COPD (males: 5.4%, 95% CI 1.9 to 8.9; p=0.007; versus females: 2.6%, 95% CI −0.08 to 5.2; p=0.07) and the telomere length reduction associated with wheezing (males: −133.6 bp, 95% CI −188.8 to −78.3; p=1.1×10−4; versus females: −40.2 bp, 95% CI −84.5 to 4.0; p=0.09). Notably, epigenetic ageing has been associated with drug response in asthma in a sex-specific manner [9]. These differences could be related to biological, social and environmental factors, and future research is needed to disentangle the underlying sex-specific ageing mechanisms in respiratory diseases.
In conclusion, our study offers insights into accelerated epigenetic ageing among adults with chronic respiratory disorders such as wheezing, COPD (especially those with emphysema and early-onset disease) and asthma, analysing a large representative sample of the US adult population with high-quality data. We employed rigorous statistical methods to address survey-design biases, confounders, multiple comparisons and sex-specific effects. However, several limitations must be acknowledged. The NHANES cross-sectional design restricts our ability to determine the direction of causation. Although NHANES is a valuable source of health data from the USA, reliance on self-reported data may bias phenotype definition.
Our results support the development of potentially predictive ageing biomarkers for multiple chronic respiratory phenotypes, utilising minimally invasive blood samples for diagnostic procedures that accurately reflect airway ageing [2, 5]. Examining further traits (e.g. lung function and inflammatory biomarkers) and evaluating paediatric populations could improve our understanding of epigenetic clocks as potential biomarkers for respiratory diseases across the lifespan. Moreover, broadening the scope to epigenetic clocks based on other key epigenetic processes in allergies (e.g. histone modifications) might enhance our understanding of ageing in chronic respiratory diseases [16, 17].
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Acknowledgements
The authors thank all the participants in the NHANES 1999–2000 and 2001–2002 cycles, as well as all researchers and people involved in the study design, data collection, data curation, sample processing, methylation data generation and open data publication.
Footnotes
Ethics statement: The NHANES study has been approved by the National Center for Health Statistics (NCHS) Ethics Review Board (ERB) (NCHS ERB protocol number: Protocol #98-12) and informed consent was provided by all participants.
Author contributions: J. Perez-Garcia and A. Cardenas were involved in the conceptualisation and design of the study; J. Perez-Garcia, D. Khodasevich and A. Cardenas in data curation, formal analyses, and/or data interpretation; S. De Matteis and M.B. Rice helped in the interpretation of results and manuscript writing; B.L. Needham and D.H. Rehkopf generated the data, supervised initial biomarker calculations and secured funding; J. Perez-Garcia led the visualisation and writing of the original draft; and A. Cardenas was responsible for funding acquisition and project supervision. All authors were involved in the critical revision of the manuscript and have read and agreed to the published version of the manuscript. The authors agree to be accountable for all aspects of the work, ensuring its scientific accuracy and integrity.
Conflict of interest: J. Perez-Garcia reports grants from the Spanish Ministry of Universities outside of this work. S. De Matteis reports leadership roles as chair of ERS Assembly 6.2, secretary of the EPICOH ICOH committee, and chair of the UK OEES committee. M.B. Rice reports grants from NIH, honoraria for research lectures, and leadership roles as chair of the ATS EOPH Assembly and chair of the ATS EOPH Program Committee. B.L. Needham reports grants from NIH (R01MD011721). A. Cardenas reports support for the present study from Stanford University faculty fund support and National Institutes of Health, and research grants R01ES031259 and P42ES004705 from the National Institute of Environmental Health Sciences (NHANES). The remaining authors have no potential conflicts of interest to disclose.
Support statement: This work was supported by grants from the National Institute of Environmental Health Sciences (NIEHS) (R01ES031259 and P42ES004705) and the National Institute on Minority Health and Health Disparities (NIMHD) (R01MD011721) of the US National Institutes of Health (NIH). The study funders had no role in study design, data collection, analysis, interpretation, or report writing. Funding information for this article has been deposited with the Crossref Funder Registry.
Data availability
Demographic data, health records and DNA methylation epigenetic clocks estimated in the analysed participants are publicly available on the NHANES website. The full summary statistic report of all association analyses supporting the main conclusions of this study, including main analyses, sensitivity analyses and sex-stratified analyses, as well as the code for data analysis, are openly available in the Zenodo repository (https://doi.org/10.5281/zenodo.14549722).
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Data Availability Statement
Demographic data, health records and DNA methylation epigenetic clocks estimated in the analysed participants are publicly available on the NHANES website. The full summary statistic report of all association analyses supporting the main conclusions of this study, including main analyses, sensitivity analyses and sex-stratified analyses, as well as the code for data analysis, are openly available in the Zenodo repository (https://doi.org/10.5281/zenodo.14549722).