ON DEMAND WEBINAR

On Demand: Accelerated Metabolic Aging in Chronic Obstructive Pulmonary Disease

Dr. Bowlers has 25-years’ experience as a physician-scientist with a focus on exposures such as how smoking impacts respiratory health. His expertise with integrating metabolomics with genomics and proteomics has been leveraged to explore the relationship between chronic lung diseases and aging.

Chronic Obstructive Pulmonary Disease (COPD) is a complex and heterogeneous disease influenced by smoking, environmental exposures, aging, and genetic susceptibility. Recent metabolomics research has sought to identify biomarkers and pathways that underlie COPD pathogenesis and progression, with growing evidence suggesting metabolic dysregulation as a central feature of disease biology. Across studies, COPD was consistently associated with perturbations in amino acid metabolism (notably branched-chain and aromatic amino acids), lipid classes (sphingolipids, phosphatidylcholines, and lysophospholipids), carnitines, and the tricarboxylic acid (TCA) cycle. Metabolomic signatures in Bronchoalveolar lavage fluid (BALF) and sputum were more strongly associated with emphysema and exacerbation risk than plasma signatures, implicating localized oxidative and nitrative stress markers. Markers of accelerated aging and energy dysregulation were recurrent, particularly disruptions in amino acid-derived and lipid-derived energy pathways. Inflammatory lipid mediators (e.g., ceramides, sphingomyelins) were variably expressed across COPD severity, suggesting their potential role in early lung injury and disease exacerbations. Collectively, the data support a central role for systemic metabolic perturbations in aging and COPD pathophysiology. The overlap between aging and COPD metabolic signatures—particularly involving carnitines and sphingolipids—suggests shared pathophysiologic mechanisms and provides key insights for stratification and treatment.

In This Webinar You Will Learn:

  • How biological changes (particularly in energy-related molecules) seen in COPD patients mirrors those patterns seen in aging
  • How lifelong environmental influences shape aging
  • Understand the molecular processes that contribute to aging and how diseases accelerate these processes
  • Explore the clinical impacts of environmental exposures
  • The importance of metabolomics to truly understanding the biological impact of environmental exposures

Authors Publication List: https://www.lerner.ccf.org/genomic-medicine/bowler/#lab-publications

Program

Time
Presenter
Title/Abstract
2 min
Kari Wong, Ph.D.
Welcome and Introductions
20 min
Russell P. Bowler, MD, PhD
Presentation
5-10 min
Kari Wong, Ph.D.
Questions and Answers

Guest Speakers

K
L

Russell P. Bowler, MD, PhD

Russell Bowler, MD, PhD, is a practicing physician-scientist with over 25 years of experience as a pulmonologist and researcher. He is trained in mathematical and computational sciences, cell and developmental biology, internal medicine and pulmonary critical care. Dr. Bowler built his research program on learning about how biological and environmental factors influence someone's risk of developing lung diseases. Dr. Bowler is one of the highest-cited proteomics experts in the world and has published over 300 manuscripts. His research is supported by federally funded grants and industry contracts and has led to multiple patents. He is a leader of genetics, proteomics and multi-omics in the NIH TransOmic Precision Medicine Program. The Bowler Lab seeks to understand how smoke inhalation from cigarettes and e-cigarettes/vapes (tobacco and cannabis) causes chronic obstructive pulmonary disease (COPD), the third leading cause of death in the United States. Our lab employs a combination of computational and experimental techniques to achieve our goals.
  1. We integrate genetic, protein and metabolic data to identify biological factors that can be used as diagnostic markers and/or therapeutic targets.
  2. We work with clinical and patient cohorts to observe the impact of inhaled smoke on lung disease in humans.
  3. We use preclinical models to directly understand the impact of inhaled smoke on lung disease.
  4. We use wearable sensors and artificial intelligence/machine learning to develop algorithms for at-home detection of COPD exacerbations.
Prior to joining Cleveland Clinic, Dr. Bowler was director of the Precision Medicine Program at National Jewish Health in Colorado. Professional Highlights • 2023 Keynote, American Thoracic Society Innovation Summit • 2017 Elected, American Society of Clinical Investigation (ASCI) • 2008 Career Investigator Award, American Lung Association 2001

WATCH WEBINAR

References

1. Zgoda-Pols, J.R., et al., Metabolomics analysis reveals elevation of 3-indoxyl sulfate in plasma and brain during chemically-induced acute kidney injury in mice: investigation of nicotinic acid receptor agonists. Toxicol Appl Pharmacol, 2011. 255(1): p. 48-56.

2. Bryant, J.A., et al., The impact of an oral purified microbiome therapeutic on the gastrointestinal microbiome. Nat Med, 2026. 32(1): p. 186-196

3. McGovern, B .H., et al., SER-109, an Investigational Microbiome Drugto Reduce Recurrence After Clostridioides difficile Infection: Lessons Learned From a Phase 2 Trial. Clin Infect Dis, 2021. 72(12): p. 2132-2140.

4. Feuerstadt, P., et al., SER-109, an Oral Microbiome Therapy for Recurrent Clostridioides difficile Infection. N Engl J Med, 2022. 386(3): p. 220-229.

5. Hu, Z., et al., Targeted metabolomics reveals novel diagnostic biomarkers for colorectal cancer. Mol Oncol, 2025. 19(6): p. 1737-1750.

6. Butler, F.M., et al., Vegetarian Dietary Patterns and Diet-Related Metabolites Are Associated With Kidney Function in the Adventist Health Study-2 Cohort. J Ren Nutr, 2025.

7. Stanford, J., et al., Metabolomic Profiling and Diet Quality Scoring in a Randomized Crossover Trial of Healthy and Typical Dietary Patterns. Mol Nutr Food Res, 2025 . 69(23): p. e70271.

8. O’Connor, L.E., et al., Metabolomic Profiling of an Ultraprocessed Dietary Pattern in a Domiciled Randomized Controlled Crossover Feeding Trial. J Nutr, 2023. 153(8): p. 2181-2192.

9. Fritsch, D.A., et al., Microbiome function underpins the efficacy of a fiber-supplemented dietary intervention in dogs with chronic large bowel diarrhea. BMC Vet Res, 2022. 18(1): p. 245.

10. Leal, L.N., et al., Preweaning nutrient supply improves lactation productivity and reduces the risk of culling in Holstein cows. J Dairy Sci, 2025. 108(6): p. 5875-5888.

11. Ahsin, M., et al., Soil and pasture health underlie improved beef nutrient density determined by untargeted metabolomics in Southern US grass finished beef systems. NPJ Sci Food, 2025. 9(1): p. 151.

12. Yin, W., et al., Plasma lipid profiling across species for the identification of optimal animal models of human dyslipidemia. J Lipid Res, 2012. 53(1): p. 51-65.

13. Porter, F .D., et al., Cholesterol oxidation products are sensitive and specific blood-based biomarkers for Niemann-Pick C1 disease. Sci Transl Med, 2010. 2(56): p. 56ra81.

14. Needham, B .D., et al., Plasma and Fecal Metabolite Profiles in Autism Spectrum Disorder. Biol Psychiatry, 2021. 89(5): p. 451-462

15. Li, C., et al., Estradiol and mTORC2 cooperate to enhance prostaglandin biosynthesis and tumorigenesis in TSC2-deficient LAM cells. J Exp Med, 2014. 211(1): p. 15-28.

16. Green, P.G., et al., Metabolic flexibility and reverse remodelling of the failing human heart. Eur Heart J, 2025. 46(25): p. 2422-2433.

17. Maekawa, H., et al., SGLT2 inhibition protects kidney function by SAM-dependent epigenetic repression of inflammatory genes under metabolic stress. J Clin Invest, 2025. 135(19).

18. Wu, D., et al., Integrated screens reveal that guanine nucleotide depletion, which is irreversible via targeting IMPDH2, inhibits pancreatic cancer and potentiates KRAS inhibition. Gut, 2026.

19. Schwerdtfeger, L.A., et al., Gut microbiota and metabolites are linked to disease progression in multiple sclerosis. Cell Rep Med, 2025. 6(4): p. 102055.

20. Wu, H., et al., Microbiome-metabolome dynamics associated with impaired glucose control and responses to lifestyle changes. Nat Med, 2025. 31(7): p. 2222-2231.

21. Jacobs, J.P., et al., Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement. Microbiome, 2021. 9(1): p. 236.

22. Pietzner, M., et al., Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med, 2021. 27(3): p. 471-479.

23. Faquih, T.O., et al., Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. J Gerontol A Biol Sci Med Sci, 2025. 80(3).

24. Scherer, N., et al., Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits. Nat Genet, 2025. 57(1): p. 193-205.

25. Holmes, Z.C., et al., Untargeted metabolomic analysis of human milk from healthy mothers reveals drivers of metabolite variability. Sci Rep, 2024. 14(1): p. 20827.

26. Titz, B., et al., Implications of Ocular Confounding Factors for Aqueous Humor Proteomic and Metabolomic Analyses in Retinal Diseases. Transl Vis Sci Technol, 2024. 13(6): p. 17.

27. Bloom, S.M., et al., Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation. Nat Microbiol, 2022. 7(3): p. 434-450.

28. Leimer, E.M., et al., Lipid profile of human synovial fluid following intra-articular ankle fracture. J Orthop Res, 2017. 35(3): p. 657-666.