Skip to main content
SpringerLink
Log in

The association between aging-related monocyte transcriptional networks and comorbidity burden: the Multi-Ethnic Study of Atherosclerosis (MESA)

  • Original Article
  • Published:
GeroScience Aims and scope Submit manuscript

Abstract

Translating our knowledge of the biological aging from animal models to humans may give rise to novel approaches of targeting multiple aging-related diseases simultaneously and increasing health span. Here, for the first time, we use transcriptomic signatures of monocytes to identify biological aging pathways underlying multiple aging-related diseases in humans. The ordinal logistic regression was used to cross-sectionally investigate transcriptomics of the comorbidity index in 1264 community-based Multi-Ethnic Study of Atherosclerosis (MESA) adults, 47% Caucasian, 32% Hispanic, 21% African American, and 51% female, aged 55–94 years. The comorbidity index was defined as the number of prevalent aging-related diseases including cardiovascular disease, type-2 diabetes, hypertension, cancer, dementia, chronic kidney disease, chronic obstructive pulmonary disease, and hip fracture. We identified 708 gene transcripts associated with the comorbidity index (FDR < 0.05) after adjusting for age, sex, ethnicity, and study site. In a weighted gene co-expression network analysis, as postulated, aging-related declines in apoptosis/autophagy (OR = 1.21 per SD increment, p = 0.0006) and ribosome/mitochondrion (OR = 0.90 per SD increment, p = 0.05) were positively associated with the comorbidity index. After adjusting for multiple comparisons, we identified 10 comorbidity-associated modules (FDR < 0.05), including the module of apoptosis/autophagy. There were three inter-correlated modules of these 10 involved in the complement subcomponent C1q, Fc gamma receptor I, and Fc gamma receptor III of the immune system, respectively. Aging-related upregulation of these three modules was positively associated with the comorbidity index. The odds of comorbidity increased with more of these modules acting together in a dose–response fashion. In conclusion, transcriptomic analysis of human immune cells may identify biomarker panels indicative of comprehensive biological mechanisms, especially immune signaling pathways, contributing to health aging.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Tinetti ME, Fried TR, Boyd CM. Designing health care for the most common chronic condition–multimorbidity. JAMA. 2012;307(23):2493–4.

    Article  CAS  Google Scholar 

  2. Pearson-Stuttard J, Ezzati M, Gregg EW. Multimorbidity-a defining challenge for health systems. Lancet Public Health. 2019;4(12):e599–600.

    Article  Google Scholar 

  3. Harrison DE, Strong R, Allison DB, Ames BN, Astle CM, Atamna H, et al. Acarbose, 17-alpha-estradiol, and nordihydroguaiaretic acid extend mouse lifespan preferentially in males. Aging Cell. 2014;13(2):273–82.

    Article  CAS  Google Scholar 

  4. Martin-Montalvo A, Mercken EM, Mitchell SJ, Palacios HH, Mote PL, Scheibye-Knudsen M, et al. Metformin improves healthspan and lifespan in mice. Nat Commun. 2013;4:2192.

    Article  Google Scholar 

  5. Miller RA, Harrison DE, Astle CM, Baur JA, Boyd AR, de Cabo R, et al. Rapamycin, but not resveratrol or simvastatin, extends life span of genetically heterogeneous mice. J Gerontol A Biol Sci Med Sci. 2011;66(2):191–201.

    Article  Google Scholar 

  6. Satoh A, Brace CS, Rensing N, Cliften P, Wozniak DF, Herzog ED, et al. Sirt1 extends life span and delays aging in mice through the regulation of Nk2 homeobox 1 in the DMH and LH. Cell Metab. 2013;18(3):416–30.

    Article  CAS  Google Scholar 

  7. Hwangbo DS, Lee HY, Abozaid LS, Min KJ. Mechanisms of lifespan regulation by calorie restriction and intermittent fasting in model organisms. Nutrients. 2020;12(4):1194.

    Article  Google Scholar 

  8. Justice JN, Ferrucci L, Newman AB, Aroda VR, Bahnson JL, Divers J, et al. A framework for selection of blood-based biomarkers for geroscience-guided clinical trials: report from the TAME Biomarkers Workgroup. Geroscience. 2018;40(5–6):419–36.

    Article  CAS  Google Scholar 

  9. Karlmark KR, Tacke F, Dunay IR. Monocytes in health and disease - minireview. Eur J Microbiol Immunol (Bp). 2012;2(2):97–102.

    Article  CAS  Google Scholar 

  10. Reynolds LM, Ding J, Taylor JR, Lohman K, Soranzo N, de la Fuente A, et al. Transcriptomic profiles of aging in purified human immune cells. BMC Genomics. 2015;16(1):333.

    Article  Google Scholar 

  11. Rubinsztein DC, Marino G, Kroemer G. Autophagy and aging. Cell. 2011;146(5):682–95.

    Article  CAS  Google Scholar 

  12. Choi AM, Ryter SW, Levine B. Autophagy in human health and disease. N Engl J Med. 2013;368(7):651–62.

    Article  CAS  Google Scholar 

  13. Haas RH. Mitochondrial dysfunction in aging and diseases of aging. Biology (Basel). 2019;8(2):48.

    CAS  Google Scholar 

  14. Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, et al. Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol. 2002;156(9):871–81.

    Article  Google Scholar 

  15. Ding J, Reynolds LM, Zeller T, Muller C, Lohman K, Nicklas BJ, et al. Alterations of a cellular cholesterol metabolism network are a molecular feature of cbesity-related type 2 diabetes and cardiovascular disease. Diabetes. 2015;64(10):3464–74.

    Article  CAS  Google Scholar 

  16. Espeland MA, Crimmins EM, Grossardt BR, Crandall JP, Gelfond JA, Harris TB, et al. Clinical trials targeting aging and age-related multimorbidity. J Gerontol A Biol Sci Med Sci. 2017;72(3):355–61.

    Google Scholar 

  17. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 2008;9:559.

    Article  Google Scholar 

  18. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4(5):3.

    Article  Google Scholar 

  19. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA. 2003;100(16):9440–5.

    Article  CAS  Google Scholar 

  20. Tower J. Programmed cell death in aging. Ageing Res Rev. 2015;23(Pt A):90–100.

    Article  CAS  Google Scholar 

  21. Leidal AM, Levine B, Debnath J. Autophagy and the cell biology of age-related disease. Nat Cell Biol. 2018;20(12):1338–48.

    Article  CAS  Google Scholar 

  22. Marino G, Niso-Santano M, Baehrecke EH, Kroemer G. Self-consumption: the interplay of autophagy and apoptosis. Nat Rev Mol Cell Biol. 2014;15(2):81–94.

    Article  CAS  Google Scholar 

  23. de Magalhaes JP, Curado J, Church GM. Meta-analysis of age-related gene expression profiles identifies common signatures of aging. Bioinformatics. 2009;25(7):875–81.

    Article  Google Scholar 

  24. Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, et al. The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015;6:8570.

    Article  CAS  Google Scholar 

  25. Ben Mkaddem S, Benhamou M, Monteiro RC. Understanding Fc receptor involvement in inflammatory diseases: from mechanisms to new therapeutic tools. Front Immunol. 2019;10:811.

    Article  Google Scholar 

  26. Son M, Diamond B, Santiago-Schwarz F. Fundamental role of C1q in autoimmunity and inflammation. Immunol Res. 2015;63(1–3):101–6.

    Article  CAS  Google Scholar 

  27. Wong KL, Yeap WH, Tai JJ, Ong SM, Dang TM, Wong SC. The three human monocyte subsets: implications for health and disease. Immunol Res. 2012;53(1–3):41–57.

    Article  CAS  Google Scholar 

  28. Aiello A, Farzaneh F, Candore G, Caruso C, Davinelli S, Gambino CM, et al. Immunosenescence and its hallmarks: how to oppose aging strategically? A review of potential options for therapeutic intervention. Front Immunol. 2019;10:2247.

    Article  CAS  Google Scholar 

  29. Ding J, Kritchevsky SB, Newman AB, Taaffe DR, Nicklas BJ, Visser M, et al. Effects of birth cohort and age on body composition in a sample of community-based elderly. Am J Clin Nutr. 2007;85(2):405–10.

    Article  CAS  Google Scholar 

  30. Yi SJ, Kim K. New insights into the role of histone changes in aging. Int J Mol Sci. 2020;21(21):8241.

    Article  CAS  Google Scholar 

  31. Zhong H, Yang X, Kaplan LM, Molony C, Schadt EE. Integrating pathway analysis and genetics of gene expression for genome-wide association studies. Am J Hum Genet. 2010;86(4):581–91.

    Article  CAS  Google Scholar 

  32. Dimas AS, Deutsch S, Stranger BE, Montgomery SB, Borel C, Attar-Cohen H, et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science. 2009;325(5945):1246–50.

    Article  CAS  Google Scholar 

  33. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194–217.

    Article  CAS  Google Scholar 

  34. Lorusso JS, Sviderskiy OA, Labunskyy VM. Emerging omics approaches in aging research. Antioxid Redox Signal. 2018;29(10):985–1002.

    Article  CAS  Google Scholar 

  35. Frenk S, Houseley J. Gene expression hallmarks of cellular ageing. Biogerontology. 2018;19(6):547–66.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank the other MEA investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.

Funding

The MESA project is conducted and supported by the National Heart, Lung, and Blood Institute (NHLBI) in collaboration with MESA investigators. Support for MESA is provided by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, UL1TR001881, DK063491, and R01HL105756. The MESA Transcriptomics Studies were funded by NIH grants R01HL101250, R01HL119962, R01DK101921, R01HL135009, RF1AG054474, and U01AG060897. This work was partially supported by P30 AG21332.

Author information

Authors and Affiliations

  1. Department of Internal Medicine, Wake Forest School of Medicine, Medical Center Blvd., Winston-Salem, NC, 27157, USA

    Jingzhong Ding, Osvaldo Delbono & Stephen B. Kritchevsky

  2. Department of Medicine, Duke University School of Medicine, Durham, NC, 27701, USA

    Kurt Lohman & Yongmei Liu

  3. Department of Medicine, School of Medicine, University of California, San Diego, La Jolla, CA, 92093, USA

    Anthony Molina

  4. Department of Epidemiology & Prevention, Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, 27157, USA

    Alain Bertoni

  5. Departments of Medicine and Epidemiology, Columbia University, New York, NY, 10032, USA

    Steven Shea

  6. Departments of Pathology and Cardiology, Johns Hopkins University, Baltimore, MD, 21205, USA

    Wendy Post

  7. The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA

    Xiuqing Guo & Jerome I. Rotter

  8. Department of Medicine, Columbia University, New York, NY, 10032, USA

    R. Graham Barr

  9. Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA

    Ani W. Manichaikul

  10. Division of Epidemiology & Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, 55454, USA

    James S. Pankow

  11. Biocomplexity Institute, Virginia Tech, Blacksburg, VA, 24061, USA

    Ina Hoeschele

Authors
  1. Jingzhong Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kurt Lohman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anthony Molina
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Osvaldo Delbono
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alain Bertoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Steven Shea
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Wendy Post
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xiuqing Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. R. Graham Barr
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ani W. Manichaikul
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. James S. Pankow
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jerome I. Rotter
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ina Hoeschele
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Stephen B. Kritchevsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Yongmei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jingzhong Ding.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 75 KB)

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, J., Lohman, K., Molina, A. et al. The association between aging-related monocyte transcriptional networks and comorbidity burden: the Multi-Ethnic Study of Atherosclerosis (MESA). GeroScience 45, 197–207 (2023). https://doi.org/10.1007/s11357-022-00608-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11357-022-00608-1

Keywords

Search

Navigation