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Neutrophil, lymphocyte count, and neutrophil to lymphocyte ratio predict multimorbidity and mortality—results from the Baltimore Longitudinal Study on Aging follow-up study

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Abstract

Immunosenescence is the age-related changes in the immune system, namely, progressively higher levels of circulating inflammatory markers, characteristics changes of circulating immune subset cells and altered immune function. The neutrophil to lymphocyte ratio (NL ratio) has been identified as a prognostic indicator for neoplastic disease progression, in predicting chronic degenerative diseases, and as a potential indirect marker of healthy aging. This study aims to examine the longitudinal association of neutrophil, lymphocyte absolute count, and their ratio with longitudinal risk for multimorbidity and mortality. The Baltimore Longitudinal Study of Aging (BLSA) is an open observational cohort study of community-dwelling volunteers that are followed every 1–4 years depending on their age. The sample considered in the study consists of 1769 participants (5090 follow-ups) with completed data for physical examination, health history assessment, and donated a blood sample. The NL ratio increased with age and was associated with a higher risk of mortality, while a lower NL ratio was inversely correlated with multimorbidity. Neutrophils increased with aging and an increase in their absolute number predicted mortality risk. However, the absolute number of lymphocytes was associated with age only in a cross-sectional analysis. In conclusion, this study supports the importance of the NL ratio and absolute neutrophil count as markers of aging health status, and as significant predictors of all-cause mortality and multimorbidity in aging individuals. It remains to be demonstrated whether interventions contrasting these trends in circulating cells may result in improved health outcomes.

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Data availability

Data from Baltimore Longitudinal Study of Aging are available through submission of research proposal through https://www.blsa.nih.gov/ (accessed on 01/02/2024).

References

  1. Nikolich-Žugich J. The twilight of immunity: emerging concepts in aging of the immune system. Nat Immunol. 2018;19:10–9. https://doi.org/10.1038/S41590-017-0006-X.

    Article  PubMed  Google Scholar 

  2. Ahadi S, Zhou W, Schüssler-Fiorenza Rose SM, Sailani MR, Contrepois K, Avina M, et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat Med. 2020;26:83–90. https://doi.org/10.1038/S41591-019-0719-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Van Avondt K, Strecker J, Tulotta C, Minnerup J, Schulz C, Soehnlein O. Neutrophils in aging and aging-related pathologies. Immunol Rev. 2023;314:357–75. https://doi.org/10.1111/imr.13153.

    Article  CAS  PubMed  Google Scholar 

  4. Lombardi G, Paganelli R, Abate M, Ireland A, Molino-Lova R, Sorbi S, et al. Leukocyte-derived ratios are associated with late-life any type dementia: a cross-sectional analysis of the Mugello study. GeroScience. 2021;43:2785–93. https://doi.org/10.1007/S11357-021-00474-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Franceschi C, Bonafè M, Valensin S, Olivieri F, De Luca M, Ottaviani E, et al. Inflamm-aging: an evolutionary perspective on immunosenescence. Ann N Y Acad Sci. 2006;908:244–54. https://doi.org/10.1111/j.1749-6632.2000.tb06651.x.

    Article  Google Scholar 

  6. Onofrj M, Thomas A, Iacono D, Luciano AL, Di Iorio A. The effects of a cholinesterase inhibitor are prominent in patients with fluctuating cognition: a part 3 study of the main mechanism of cholinesterase inhibitors in dementia. Clin Neuropharmacol. 2003;26:239–51. https://doi.org/10.1097/00002826-200309000-00008.

    Article  CAS  PubMed  Google Scholar 

  7. Ferrucci L, Corsi A, Lauretani F, Bandinelli S, Bartali B, Taub DD, et al. The origins of age-related proinflammatory state. Blood. 2005;105:2294–9. https://doi.org/10.1182/BLOOD-2004-07-2599.

    Article  CAS  PubMed  Google Scholar 

  8. Kologrivova I, Shtatolkina M, Suslova T, Ryabov V. Cells of the immune system in cardiac remodeling: Main players in resolution of inflammation and repair after myocardial infarction. Front Immunol. 2021;12:664457. https://doi.org/10.3389/fimmu.2021.664457.

  9. Sounbuli K, Mironova N, Alekseeva L. Diverse neutrophil functions in cancer and promising neutrophil-based cancer therapies. Int J Mol Sci. 2022;23:15827. https://doi.org/10.3390/ijms232415827.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Geiger H, de Haan G, Florian MC. The ageing haematopoietic stem cell compartment. Nat Rev Immunol. 2013;13:376–89. https://doi.org/10.1038/nri3433.

    Article  CAS  PubMed  Google Scholar 

  11. Cao Dinh H, Njemini R, Onyema OO, Beyer I, Liberman K, De Dobbeleer L, et al. Strength endurance training but not intensive strength training reduces senescence-prone T cells in peripheral blood in community-dwelling elderly women. J Gerontol A Biol Sci Med Sci. 2019;74:1870–8. https://doi.org/10.1093/gerona/gly229.

    Article  CAS  PubMed  Google Scholar 

  12. Paganelli R, Scala E, Quinti I, Ansotegui IJ. Humoral immunity in aging. Aging (Milano). 1994;6:143–50. https://doi.org/10.1007/BF03324229.

    Article  CAS  PubMed  Google Scholar 

  13. O’Brien CE, Price ET. The blood neutrophil to lymphocyte ratio correlates with clinical status in children with cystic fibrosis: a retrospective study. PLoS One. 2013;8:e77420. https://doi.org/10.1371/JOURNAL.PONE.0077420.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Franceschi C, Capri M, Monti D, Giunta S, Olivieri F, Sevini F, et al. Inflammaging and anti-inflammaging: a systemic perspective on aging and longevity emerged from studies in humans. Mech Ageing Dev. 2007;128:92–105. https://doi.org/10.1016/j.mad.2006.11.016.

    Article  CAS  PubMed  Google Scholar 

  15. Grilz E, Posch F, Königsbrügge O, Schwarzinger I, Lang IM, Marosi C, et al. Association of platelet-to-lymphocyte ratio and neutrophil-to-lymphocyte ratio with the risk of thromboembolism and mortality in patients with cancer. Thromb Haemost. 2018;118:1875–84. https://doi.org/10.1055/s-0038-1673401.

    Article  PubMed  Google Scholar 

  16. Shah N, Parikh V, Patel N, Patel N, Badheka A, Deshmukh A, et al. Neutrophil lymphocyte ratio significantly improves the Framingham risk score in prediction of coronary heart disease mortality: insights from the National Health and Nutrition Examination Survey-III. Int J Cardiol. 2014;171:390–7. https://doi.org/10.1016/j.ijcard.2013.12.019.

    Article  PubMed  Google Scholar 

  17. Li H, Lu X, Xiong R, Wang S. High neutrophil-to-lymphocyte ratio predicts cardiovascular mortality in chronic hemodialysis patients. Mediators Inflamm. 2017;2017:9327136. https://doi.org/10.1155/2017/9327136.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Jhuang Y-H, Kao T-W, Peng T-C, Chen W-L, Li Y-W, Chang P-K, et al. Neutrophil to lymphocyte ratio as predictor for incident hypertension: a 9-year cohort study in Taiwan. Hypertens Res. 2019;42:1209–14. https://doi.org/10.1038/s41440-019-0245-3.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Fest J, Ruiter TR, Groot Koerkamp B, Rizopoulos D, Ikram MA, van Eijck CHJ, et al. The neutrophil-to-lymphocyte ratio is associated with mortality in the general population: the Rotterdam Study. Eur J Epidemiol. 2019;34:463–70. https://doi.org/10.1007/s10654-018-0472-y.

    Article  CAS  PubMed  Google Scholar 

  20. Pellegrino R, Paganelli R, Di Iorio A, Bandinelli S, Moretti A, Iolascon G, et al. Temporal trends, sex differences, and age-related disease influence in neutrophil, lymphocyte count and neutrophil to lymphocyte-ratio: results from InCHIANTI follow-up study. Immun Ageing. 2023;20:46. https://doi.org/10.1186/S12979-023-00370-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Azab B, Camacho-Rivera M, Taioli E. Average values and racial differences of neutrophil lymphocyte ratio among a nationally representative sample of United States subjects. PLoS One. 2014;9:e112361. https://doi.org/10.1371/JOURNAL.PONE.0112361.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Tollerud DJ, Clark JW, Brown LM, Neuland CY, Pankiw-Trost LK, Blattner WA, et al. The influence of age, race, and gender on peripheral blood mononuclear-cell subsets in healthy nonsmokers. J Clin Immunol. 1989;9:214–22. https://doi.org/10.1007/BF00916817.

    Article  CAS  PubMed  Google Scholar 

  23. Palmblad J, Höglund P. Ethnic benign neutropenia: A phenomenon finds an explanation. Pediatr Blood Cancer. 2018;65(12):e27361. https://doi.org/10.1002/pbc.27361.

    Article  PubMed  Google Scholar 

  24. Kuo PL, Schrack JA, Shardell MD, Levine M, Moore AZ, An Y, et al. A roadmap to build a phenotypic metric of ageing: insights from the Baltimore Longitudinal Study of Aging. J Intern Med. 2020;287:373–94. https://doi.org/10.1111/joim.13024.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Simonsick EM, Maffeo CE, Rogers SK, Skinner EA, Davis D, Guralnik JM, et al. Methodology and feasibility of a home-based examination in disabled older women: the Women’s Health and Aging Study. J Gerontol Ser A Biol Sci Med Sci. 1997;52A:M264–74. https://doi.org/10.1093/gerona/52A.5.M264.

    Article  Google Scholar 

  26. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83. https://doi.org/10.1016/0021-9681(87)90171-8.

    Article  CAS  PubMed  Google Scholar 

  27. Pawelec G. Age and immunity: what is “immunosenescence”? Exp Gerontol. 2018;105:4–9. https://doi.org/10.1016/j.exger.2017.10.024.

    Article  CAS  PubMed  Google Scholar 

  28. Kovtonyuk LV, Fritsch K, Feng X, Manz MG, Takizawa H. Inflamm-aging of hematopoiesis, hematopoietic stem cells, and the bone marrow microenvironment. Front Immunol. 2016;7:502. https://doi.org/10.3389/fimmu.2016.00502.

  29. Olsson J, Wikby A, Johansson B, Löfgren S, Nilsson B-O, Ferguson FG. Age-related change in peripheral blood T-lymphocyte subpopulations and cytomegalovirus infection in the very old: the Swedish longitudinal OCTO immune study. Mech Ageing Dev. 2001;121:187–201. https://doi.org/10.1016/S0047-6374(00)00210-4.

    Article  Google Scholar 

  30. Ley K, Hoffman HM, Kubes P, Cassatella MA, Zychlinsky A, Hedrick CC, Catz SD. Neutrophils: New insights and open questions. Sci Immunol. 2018;3(30):eaat4579. https://doi.org/10.1126/sciimmunol.aat4579.

  31. Wilson D, Drew W, Jasper A, Crisford H, Nightingale P, Newby P, et al. Frailty is associated with neutrophil dysfunction which is correctable with phosphoinositol-3-kinase inhibitors. J Gerontol Ser A. 2020;75:2320–5. https://doi.org/10.1093/gerona/glaa216.

    Article  CAS  Google Scholar 

  32. Gagnon M-F, Provost S, Sun M, Ayachi S, Buscarlet M, Mollica L, et al. Interplay between hereditary and acquired factors determines the neutrophil counts in older individuals. Blood Adv. 2023;7:3232–43. https://doi.org/10.1182/bloodadvances.2022008793.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Dorshkind K, Höfer T, Montecino-Rodriguez E, Pioli PD, Rodewald HR. Do haematopoietic stem cells age? Nat Rev Immunol. 2020;20:196–202. https://doi.org/10.1038/S41577-019-0236-2.

    Article  CAS  PubMed  Google Scholar 

  34. Franceschi C, Salvioli S, Garagnani P, de Eguileor M, Monti D, Capri M. Immunobiography and the heterogeneity of immune responses in the elderly: A focus on inflammaging and trained immunity. Front Immunol. 2017;8:982. https://doi.org/10.3389/fimmu.2017.00982.

  35. Snoeck HW. Aging of the hematopoietic system. Curr Opin Hematol. 2013;20:355–61. https://doi.org/10.1097/MOH.0B013E3283623C77.

    Article  CAS  PubMed  Google Scholar 

  36. Klein SL, Flanagan KL. Sex differences in immune responses. Nat Rev Immunol. 2016;16:626–38. https://doi.org/10.1038/nri.2016.90.

    Article  CAS  PubMed  Google Scholar 

  37. Ortona E, Pierdominici M, Rider V. Editorial: sex hormones and gender differences in immune responses. Front Immunol. 2019;10:1076. https://doi.org/10.3389/FIMMU.2019.01076.

    Article  PubMed  PubMed Central  Google Scholar 

  38. McFall-Ngai M. Care for the community. Nature. 2007;445:153–153. https://doi.org/10.1038/445153a.

    Article  CAS  PubMed  Google Scholar 

  39. Zuk M. The sicker sex. PLoS Pathog. 2009;5:e1000267. https://doi.org/10.1371/journal.ppat.1000267.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jilma B, Eichler HG, Breiteneder H, Wolzt M, Aringer M, Graninger W, et al. Effects of 17 beta-estradiol on circulating adhesion molecules. J Clin Endocrinol Metab. 1994;79:1619–24. https://doi.org/10.1210/jcem.79.6.7527406.

    Article  CAS  PubMed  Google Scholar 

  41. Pergola C, Dodt G, Rossi A, Neunhoeffer E, Lawrenz B, Northoff H, et al. ERK-mediated regulation of leukotriene biosynthesis by androgens: a molecular basis for gender differences in inflammation and asthma. Proc Natl Acad Sci. 2008;105:19881–6. https://doi.org/10.1073/pnas.0809120105.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Chen Y, Zhang Y, Zhao G, Chen C, Yang P, Ye S, et al. Difference in leukocyte composition between women before and after menopausal age, and distinct sexual dimorphism. PLoS One. 2016;11:e0162953. https://doi.org/10.1371/JOURNAL.PONE.0162953.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Byrne CA, Gomez SL, Kim S, Oddo VM, Koh TJ, Fantuzzi G. Disparities in inflammation between non-Hispanic black and white individuals with lung cancer in the Greater Chicago Metropolitan area. Front Immunol. 2022;13:1008674. https://doi.org/10.3389/fimmu.2022.1008674.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Rundle AG, Sadasivan SM, Chitale DA, Gupta NS, Williamson SR, Kryvenko ON, et al. Racial differences in the systemic inflammatory response to prostate cancer. PLoS ONE. 2021;16:e0252951. https://doi.org/10.1371/journal.pone.0252951.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Asperges E, Albi G, Zuccaro V, Sambo M, Pieri TC, Calia M, et al. Dynamic NLR and PLR in predicting COVID-19 severity: a retrospective cohort study. Infect Dis Ther. 2023;12:1625–40. https://doi.org/10.1007/s40121-023-00813-1.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Zawiah M, Hayat Khan A, Abu Farha R, Usman A, Bitar AN. Neutrophil-lymphocyte ratio, monocyte-lymphocyte ratio, and platelet-lymphocyte ratio in stroke-associated pneumonia: a systematic review and meta-analysis. Curr Med Res Opin. 2023;39:475–82. https://doi.org/10.1080/03007995.2023.2174327.

    Article  CAS  PubMed  Google Scholar 

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Funding

This work was supported in part by the National Institutes of Health (NIH) and the Intramural Research Program (Grant ID: Z01 AG000015-49).

Author information

Author notes

  1. Raffaello Pellegrino and Roberto Paganelli contributed equally to this work.

Authors and Affiliations

  1. Department of Scientific Research, Off-Campus Semmelweis University, Campus Ludes, 6912, Lugano–Pazzallo, Switzerland

    Raffaello Pellegrino

  2. Santa Chiara Institute, 73100, Lecce, Italy

    Raffaello Pellegrino

  3. Saint Camillus International, University of Health and Medical Sciences, Rome, Italy

    Roberto Paganelli

  4. Department of Innovative Technologies in Medicine & Dentistry, University “G. d’Annunzio”, 66100, Chieti-Pescara, Italy

    Angelo Di Iorio

  5. Geriatric Unit, Azienda Toscana Centro, Florence, Italy

    Stefania Bandinelli

  6. Department of Medical and Surgical Specialties and Dentistry, University of Campania “Luigi Vanvitelli”, 80138, Naples, Italy

    Antimo Moretti & Giovanni Iolascon

  7. Department of Internal Medicine, ASL Teramo, Teramo, Italy

    Eleonora Sparvieri

  8. Department of Public Health, University of Naples Federico II, 80131, Naples, Italy

    Domiziano Tarantino

  9. Longitudinal Studies Section, Translational Gerontology Branch, National Institute On Aging, National Institutes of Health, Baltimore, MD, 21224, USA

    Toshiko Tanaka & Luigi Ferrucci

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  1. Raffaello Pellegrino
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  2. Roberto Paganelli
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  4. Stefania Bandinelli
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  6. Giovanni Iolascon
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  10. Luigi Ferrucci
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Contributions

Raffaello Pellegrino: conceptualization, interpretation of data, drafted the work. Roberto Paganelli: conceptualization, interpretation of data, drafted and revised the work. Angelo Di Iorio: conceptualization, acquisition, analysis, drafted the work. Stefania Bandinelli: design of the work, interpretation of data, revised the work. Antimo Moretti: analysis, interpretation of data, drafted the work. Giovanni Iolascon: analysis, interpretation of data, drafted the work. Eleonora Sparvieri: acquisition, analysis, interpretation of data, drafted the work. Domiziano Tarantino: acquisition, analysis, interpretation of data, drafted the work. Toshiko Tanaka: acquisition, analysis, interpretation of data, revised the text. Luigi Ferrucci: design, acquisition, analysis, interpretation of data, revised the text. All authors have read and agreed to the present version of the manuscript.

Corresponding author

Correspondence to Angelo Di Iorio.

Ethics declarations

Ethics approval

The BLSA study protocol was approved by the Internal Review Board (IRB) of the National Institutes of Health and all participants provided written informed consent (Protocol number 03-AG-0325). The study was conducted in accordance with the Declaration of Helsinki.

Consent to participate

Written informed consent was obtained from the subjects to participate at the Study at each time (baseline visit and follow-ups).

Competing interests

The authors (Raffaello Pellegrino; Roberto Paganelli; Angelo Di Iorio; Stefania Bandinelli; Antimo Moretti; Giovanni Iolascon; Eleonora Sparvieri; Domiziano Tarantino; Toshiko Tanaka; Luigi Ferrucci) declare no conflict of interest.

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11357_2023_1034_MOESM1_ESM.docx

γ00 = intercept of the average trajectory; γ01= intercept of the trajectory for age; γ02= intercept of the trajectory for sex; γ03= intercept of the trajectory for multimorbidity; γ04= intercept of the trajectory for death; γ05= intercept of the trajectory for body mass index; γ061= intercept of the trajectory for black race; γ062= intercept of the trajectory for other races; γ10 = slope of the average trajectory; δ2ε = within person variance components; δ20 = in initial status variance components; δ21 = in rate of change variance components; δ01 = covariance estimate; ρ = Intraclass correlation coefficinet; R2 y,y1pseudo-R2; AIC = Akaike information criterion. *** p<0.001; ** p = 0.01; * p = 0.05 (DOCX 51.9 KB)

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Pellegrino, R., Paganelli, R., Di Iorio, A. et al. Neutrophil, lymphocyte count, and neutrophil to lymphocyte ratio predict multimorbidity and mortality—results from the Baltimore Longitudinal Study on Aging follow-up study. GeroScience 46, 3047–3059 (2024). https://doi.org/10.1007/s11357-023-01034-7

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