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Glycans Are a Novel Biomarker of Chronological and Biological Ages
Open accessAbstract
Fine structural details of glycans attached to the conserved N-glycosylation site significantly not only affect function of individual immunoglobulin G (IgG) molecules but also mediate inflammation at the systemic level. By analyzing IgG glycosylation in 5,117 individuals from four European populations, we have revealed very complex patterns of changes in IgG glycosylation with age. Several IgG glycans (including FA2B, FA2G2, and FA2BG2) changed considerably with age and the combination of these three glycans can explain up to 58% of variance in chronological age, significantly more than other markers of biological age like telomere lengths. The remaining variance in these glycans strongly correlated with physiological parameters associated with biological age. Thus, IgG glycosylation appears to be closely linked with both chronological and biological ages. Considering the important role of IgG glycans in inflammation, and because the observed changes with age promote inflammation, changes in IgG glycosylation also seem to represent a factor contributing to aging. SIGNIFICANCE STATEMENT: Glycosylation is the key posttranslational mechanism that regulates function of immunoglobulins, with multiple systemic repercussions to the immune system. Our study of IgG glycosylation in 5,117 individuals from four European populations has revealed very extensive and complex changes in IgG glycosylation with age. The combined index composed of only three glycans explained up to 58% of variance in age, considerably more than other biomarkers of age like telomere lengths. The remaining variance in these glycans strongly correlated with physiological parameters associated with biological age; thus, IgG glycosylation appears to be closely linked with both chronological and biological ages. The ability to measure human biological aging using molecular profiling has practical applications for diverse fields such as disease prevention and treatment, or forensics.
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“Although it is not yet possible to comprehensively survey the glycome, recent mass spectrometry studies examining the concentrations of just a few well-studied glycans have been able to accurately predict chronological age and ageing phenotypes.”
From Measuring biological age using omics data (2022) · cites this paper as [129]
In the citing paper
“Krištić et al.129 observed changes in the N-glycosylation pattern of serum IgG protein, which predicted age in multiple European cohorts.”
From Measuring biological age using omics data (2022) · cites this paper as [129]
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“Moreover, IgG glycan age correlated with clinical markers of metabolic health129.”
From Measuring biological age using omics data (2022) · cites this paper as [129]
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“We could thus not assess the association of facial aging with individual lifestyle parameters, endocrine status, or molecular aging markers (Krištić et al., 2014).”
From Facial aging trajectories: A common shape pattern in male and female faces is disrupted after menopause (2019) · cites this paper as Krištić et al., 2014
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Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Inflamm‐aging: An Evolutionary Perspective on Immunosenescence
FRANCESCHI, C., BONAFÈ, M., VALENSIN, S., OLIVIERI, F., DE LUCA, M., OTTAVIANI, E., & DE BENEDICTIS, G. (2000) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2000.tb06651.x · Cited by 5,358
Algebraic decay in hierarchical graphs
Garcia, A., Sonnino, G., Malek-Mansour, M.-A., & Baras, F. (1998) · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · doi:10.1023/a · Cited by 5,186
Genome-wide methylation profiles reveal quantitative views of human aging rates
Hannum, G., Guinney, J., Zhao, L., Zhang, L., Hughes, G., Sadda, S., Klotzle, B., Bibikova, M., Fan, J.-B., Gao, Y., et al. (2013) · Molecular Cell · doi:10.1016/j.molcel.2012.10.016 · Cited by 4,270
ANew Look at the Statistical Model Identification
AKAlKE, H. (2011) · Cited by 4,177
Extending Healthy Life Span—From Yeast to Humans
Fontana, L., Partridge, L., & Longo, V. D. (2010) · Science · doi:10.1126/science.1172539 · Cited by 3,023
Physical Fitness and All-Cause Mortality: A Prospective Study of Healthy Men and Women
Blair, S. N. (1989) · JAMA · doi:10.1001/jama.1989.03430170057028 · Cited by 2,658
Glycosylation and the Immune System
Rudd, P. M., Elliott, T., Cresswell, P., Wilson, I. A., & Dwek, R. A. (2001) · Science · doi:10.1126/science.291.5512.2370 · Cited by 1,588
Metabolism of Inflammation Limited by AMPK and Pseudo-Starvation
O'Neill, L. A. J., & Hardie, D. G. (2013) · Nature · doi:10.1038/nature11862 · Cited by 1,133
Research works printed but not linked: 1
- Lauc, 2013, Loci associated with N-glycosylation of human immunoglobulin G show pleiotropy with autoimmune diseases and haematological cancers, PLoS Genet, 9, 10.1371/journal.pgen.1003225 · DOI
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Russell, A. C., Šimurina, M., Garcia, M., Novokmet, M., Wang, Y., Rudan, I., Campbell, H. J., Lauc, G., Thomas, M. G., & Wang, W. (2017) · Glycobiology · doi:10.1093/glycob/cwx022 · Cited by 150
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Gray, C., Thomas, B., Upton, R., Migas, L. G., Eyers, C. E., Barran, P. E., & Flitsch, S. L. (2016) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2016.02.003 · Cited by 149
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Menni, C., Kiddle, S. J., Mangino, M., Viñuela, A., Psatha, M., Steves, C., Sattlecker, M., Buil, A., Newhouse, S., Nelson, S., et al. (2015) · The Journals of Gerontology: Series A · doi:10.1093/gerona/glu121 · Cited by 144
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Self-citationWindhager, S., Mitteroecker, P., Rupić, I., Lauc, T., Polašek, O., & Schaefer, K. (2019) · American Journal of Physical Anthropology · doi:10.1002/ajpa.23878 · Cited by 128
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Zhou, Z., Wu, Q., Yan, Z., Zheng, H., Chen, C.-J., Liu, Y., Qi, Z., Calandrelli, R., Chen, Z., Chien, S., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1908252116 · Cited by 65
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Chen, R., Wang, Y., Zhang, S., Bulloch, G., Zhang, J., Liao, H., Shang, X., Clark, M., Peng, Q., Ge, Z., et al. (2023) · MedComm – Future Medicine · doi:10.1002/mef2.50 · Cited by 63
Recent Advances in Clinical Glycoproteomics of Immunoglobulins (Igs)
Plomp, R., Bondt, A., de Haan, N., Rombouts, Y., & Wuhrer, M. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o116.058503 · Cited by 59
Aging Biomarker Consortium, Zhang, L., Guo, J., Liu, Y., Sun, S., Liu, B., Yang, Q., Tao, J., Tian, X.-L., Pu, J., et al. (2023) · Life Medicine · doi:10.1093/lifemedi/lnad033 · Cited by 60
Hou, Q., Li, C., Liu, Y., Li, W., Chen, Y., Siqinbateer, Bao, Y., saqila, W., Zhang, H., Menghe, B., et al. (2018) · Journal of Functional Foods · doi:10.1016/j.jff.2018.11.023 · Cited by 59
Quantitative characterization of biological age and frailty based on locomotor activity records
Pyrkov, T. V., Getmantsev, E., Zhurov, B., Avchaciov, K., Pyatnitskiy, M., Menshikov, L., Khodova, K., Gudkov, A. V., & Fedichev, P. O. (2018) · Aging · doi:10.18632/aging.101603 · Cited by 55
Infant’s DNA Methylation Age at Birth and Epigenetic Aging Accelerators
Javed, R., Chen, W., Lin, F., & Liang, H. (2016) · BioMed Research International · doi:10.1155/2016/4515928 · Cited by 56
Macdonald-Dunlop, E., Taba, N., Klarić, L., Frkatović-Hodžić, A., Walker, R. M., Hayward, C., Esko, T., Haley, C., Fischer, K., Wilson, J. F., et al. (2022) · Aging · doi:10.18632/aging.203847 · Cited by 60
Self-citationHinneburg, H., Korać, P., Schirmeister, F., Gašparov, S., Seeberger, P. H., Zoldoš, V., & Kolarich, D. (2017) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m116.062414 · Cited by 54
Self-citationSubedi, G. P., & Barb, A. W. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ra118.004998 · Cited by 55
Trzos, S., Link‐Lenczowski, P., & Pocheć, E. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1188838 · Cited by 50
Vadrevu, S. K., Trbojević‐Akmačić, I., Kossenkov, A. V., Colomb, F., Giron, L. B., Anzurez, A., Lynn, K., Mounzer, K., LANDAY, A. L., Kaplan, R. C., et al. (2018) · Journal of Leukocyte Biology · doi:10.1002/jlb.3hi1217-500r · Cited by 49
Self-citationMartin, T. C., Šimurina, M., Ząbczyńska, M., Kavur, M. M., Rydlewska, M., Pezer, M., Kozłowska, K., Burri, A. V., Vilaj, M. Ć., Turek-Jabrocka, R., et al. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra119.001860 · Cited by 51
Self-citationComparative Analysis and Validation of Different Steps in Glycomics Studies
Trbojević‐Akmačić, I., Ugrina, I., & Lauc, G. (2016) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2016.09.027 · Cited by 48
Self-citationMulti-omics approaches to human biological age estimation
Solovev, I. A., Shaposhnikov, M. V., & Moskalev, A. (2019) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2019.111192 · Cited by 49
Coillie, J. V., Pongrácz, T., Rahmöller, J., Chen, H., Geyer, C. E., van Vught, L. A., Buhre, J. S., Šuštić, T., van Osch, T. L. J., Steenhuis, M., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104408 · Cited by 48
Itakura, Y., Sasaki, N., Kami, D., Gojo, S., Umezawa, A., & Toyoda, M. (2016) · Cell & Bioscience · doi:10.1186/s13578-016-0079-5 · Cited by 46
Immune Memory in Aging: a Wide Perspective Covering Microbiota, Brain, Metabolism, and Epigenetics
Bulut, O. P., Kılıç, G., & Domínguez‐Andrés, J. (2021) · Clinical Reviews in Allergy & Immunology · doi:10.1007/s12016-021-08905-x · Cited by 50
Assessing the rate of aging to monitor aging itself
Xia, X., Wang, Y., Yu, Z., Chen, J., & Han, J. J. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101350 · Cited by 46
Han, J. J. (2023) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2023.09.007 · Cited by 45
Association of N-Linked Glycoprotein Acetyls and Colorectal Cancer Incidence and Mortality
Chandler, P. D., Akinkuolie, A. O., Tobias, D. K., Lawler, P. R., Li, C., Moorthy, M. V., Wang, L., Duprez, D. A., Jacobs, D. R., Glynn, R. J., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0165615 · Cited by 43
Intense Physical Exercise Induces an Anti-inflammatory Change in IgG N-Glycosylation Profile
Tijardović, M., Marijančević, D., Bok, D., Kifer, D., Lauc, G., Gornik, O., & Keser, T. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.01522 · Cited by 44
Self-citationThe role of antibody glycosylation in autoimmune and alloimmune kidney diseases
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Increased central adiposity is associated with pro-inflammatory immunoglobulin G N-glycans
Russell, A. C., Kępka, A., Trbojević‐Akmačić, I., Ugrina, I., Song, M., Hui, J., Hunter, M. L., Laws, S. M., Lauc, G., & Wang, W. (2018) · Immunobiology · doi:10.1016/j.imbio.2018.10.002 · Cited by 40
Self-citationLi, X., Wang, H., Russell, A. C., Cao, W., Wang, X., Ge, S., Zheng, Y., Guo, Z., Hou, H., Song, M., et al. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0075 · Cited by 41
Self-citationLiu, S., & Liu, X. (2021) · Advances in clinical chemistry · doi:10.1016/bs.acc.2021.02.001 · Cited by 39
High-throughput analysis of immunoglobulin G glycosylation
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Adua, E., Russell, A. C., Roberts, P., Wang, Y., Song, M., & Wang, W. (2017) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2017.0035 · Cited by 39
Immunoglobulin G Subclass-Specific Glycosylation Changes in Primary Epithelial Ovarian Cancer
Wieczorek, M., Braicu, E. I., Oliveira‐Ferrer, L., Sehouli, J., & Blanchard, V. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00654 · Cited by 39
Genetic regulation of post-translational modification of two distinct proteins
Landini, A., Trbojević‐Akmačić, I., Navarro, P., Tsepilov, Y. A., Sharapov, S., Vučković, F., Polašek, O., Hayward, C., Pribić, T., Vilaj, M. Ć., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29189-5 · Cited by 42
Self-citation
Metadata
- Authors
- Krištić, J., Vučković, F., Menni, C., Klarić, L., Keser, T., Beceheli, I., Pučić-Baković, M., Novokmet, M., Mangino, M., Thaqi, K., Rudan, P., Novokmet, N., Šarac, J., Missoni, S., Kolčić, I., Polašek, O., Rudan, I., Campbell, H., Hayward, C., Aulchenko, Y., Valdes, A., Wilson, J. F., Gornik, O., Primorac, D., Zoldoš, V., Spector, T., & Lauc, G.
- Affiliations
- King's College London (GB)
- University of Zagreb (HR)
- Institute for Anthropological Research (HR)
- University of Split (HR)
- University of Edinburgh (GB)
- Institute of Genetics and Cancer (GB)
- Institute of Cytology and Genetics (RU)
- University of Osijek (HR)
- Corresponding author
- Krištić, Jasminka
- Vučković, Frano
- ORCID
- Krištić, Jasminka
- Vučković, Frano
- Menni, Cristina
- Klarić, Lucija
- Keser, Toma
- Novokmet, Mislav
- Mangino, Massimo
- Novokmet, Natalija
- Šarac, Jelena
- Missoni, Saša
- Kolčić, Ivana
- Polašek, Ozren
- Rudan, Igor
- Hayward, Caroline
- Aulchenko, Yurii
- Valdes, Ana
- Wilson, James F.
- Gornik, Olga
- Primorac, Dragan
- Zoldoš, Vlatka
- Spector, Tim
- Lauc, Gordan
- Venue
- The Journals of Gerontology: Series A
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-12-10
- Volume
- 69
- Issue
- 7
- Pages
- 779-789
- ISSN
- 1079-5006 (ISSN-L)
- 1758-535X
- PMC
- PMC4049143
- OpenAlex
- W2136722035
- Cited by
- 400
- References
- 52
- Citations by year
- 2026: 33 · 2025: 41 · 2024: 50 · 2023: 41 · 2022: 40 · 2021: 37 · 2020: 44 · 2019: 25 · 2018: 28 · 2017: 18 · 2016: 25 · 2015: 13 · 2014: 2 · 2013: 1
- FWCI
- 6.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- The Journals of Gerontology Series A (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion, other-oa)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc)
- Funders
- Wellcome Trust
- National Institute for Health and Care Research
- European Commission (305479)
- Russian Foundation for Basic Research
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Glycosylation and Glycoproteins Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Advanced Glycation End Products research
- Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
- Keywords
- IgG glycosylation, glycans, biological age, chronological age, aging biomarkers, N-glycosylation, telomere length, inflammation, post-translational modification, IgG
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Aging, Female, Glycosylation, United Kingdom, Humans, Immunoglobulin G, Longevity, Male, Middle Aged, Models, Biological, Polysaccharides, Protein Processing, Post-Translational, Scotland, Cohort Studies, Biomarkers, Croatia, Inflammation Mediators, Young Adult
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