Evidence map›Paper›PMID 38877341›Full record

ArticleGeroScience2024

Glycan clock of ageing-analytical precision and time-dependent inter- and i-individual variability.

Borna Rapčan, Manshu Song, Azra Frkatović-Hodžić, Tea Pribić, Jakov Vuk, Anđelo Beletić, Maja Hanić, Julija Jurić, Petra Tominac, Josip Milas and 8 more

Abstract read
In one paragraph

Article in GeroScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Trial
  2. Review
  3. Review
  4. Article
  5. Article
  6. Quantitative Modeling of IgG N-Glycosylation Profiles from Population Data.International journal of molecular sciences · 2025
    Article
  7. Article
  8. Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Borna RapčanGenos Ltd, Borongajska Cesta 83H, 10000, Zagreb, Croatia. brapcan@genos.hr.ORCID 0009-0000-7265-5581
Manshu SongCentre for Precision Health, Edith Cowan University, Perth, WA, 6027, Australia.ORCID 0000-0003-1433-7192
Azra Frkatović-HodžićGenos Ltd, Borongajska Cesta 83H, 10000, Zagreb, Croatia.ORCID 0000-0002-0751-4421
Tea PribićGenos Ltd, Borongajska Cesta 83H, 10000, Zagreb, Croatia.ORCID 0000-0002-1071-8759
Jakov VukFaculty of Pharmacy and Biochemistry, University of Zagreb, Ante Kovačića 1, 10000, Zagreb, Croatia.ORCID 0009-0009-3194-5027
Anđelo BeletićGenos Ltd, Borongajska Cesta 83H, 10000, Zagreb, Croatia.ORCID 0000-0001-8975-9600
Maja HanićGenos Ltd, Borongajska Cesta 83H, 10000, Zagreb, Croatia.ORCID 0000-0001-6152-2054
Julija JurićGlycanAge Ltd, Helix, 3 Science Square, The Catalyst, Newcastle Upon Tyne, NE4 5TG, UK.ORCID 0000-0002-1336-0772
Petra TominacGenos Ltd, Borongajska Cesta 83H, 10000, Zagreb, Croatia.
Josip MilasDepartment of Public Health, Faculty of Medicine Osijek, J. J, Strossmayer University of Osijek, Huttlerova 4, 31 000, Osijek, Croatia.ORCID 0000-0001-6722-9289
Vedrana IvićDepartment of Medical Biology and Genetics Osijek, J. J. Strossmayer Faculty of Medicine, University of Osijek, Huttlerova 4, 31 000, Osijek, Croatia.ORCID 0000-0002-8185-1960
Sven VilandDepartment of Medical Biology and Genetics Osijek, J. J. Strossmayer Faculty of Medicine, University of Osijek, Huttlerova 4, 31 000, Osijek, Croatia.ORCID 0009-0007-6087-9554
Sara BonetDepartment of Medical Biology and Genetics Osijek, J. J. Strossmayer Faculty of Medicine, University of Osijek, Huttlerova 4, 31 000, Osijek, Croatia.ORCID 0009-0006-1686-5318
Branko ŠegoDepartment of Medical Biology and Genetics Osijek, J. J. Strossmayer Faculty of Medicine, University of Osijek, Huttlerova 4, 31 000, Osijek, Croatia.ORCID 0009-0005-0680-934X
Marija HefferDepartment of Medical Biology and Genetics Osijek, J. J. Strossmayer Faculty of Medicine, University of Osijek, Huttlerova 4, 31 000, Osijek, Croatia.ORCID 0000-0001-6770-7359
Wei WangCentre for Precision Health, Edith Cowan University, Perth, WA, 6027, Australia.ORCID 0000-0002-1430-1360
Michael P SnyderDepartment of Genetics, Stanford University, 450 Jane Stanford Way, Stanford, CA, 94305, USA.ORCID 0000-0003-0784-7987
Gordan LaucGenos Ltd, Borongajska Cesta 83H, 10000, Zagreb, Croatia.ORCID 0000-0003-1840-9560

Funding

Australia-China International Collaborative Grant NHMRC APP1112767-NSFC 81561128020National Natural Science Foundation of China 81573215National Natural Science Foundation of China NFSC 81773527SynHealth 101159018
6 · The paper itself

Abstract

Ageing is a complex biological process with variations among individuals, leading to the development of ageing clocks to estimate biological age. Glycans, particularly in immunoglobulin G (IgG), have emerged as potential biomarkers of ageing, with changes in glycosylation patterns correlating with chronological age.For precision analysis, three different plasma pools were analysed over 26 days in tetraplicates, 312 samples in total. In short-term variability analysis, two cohorts were analysed: AstraZeneca MFO cohort of 26 healthy individuals (median age 20) and a cohort of 70 premenopausal Chinese women (median age 22.5) cohort monitored over 3 months. Long-term variability analysis involved two adult men aged 47 and 57, monitored for 5 and 10 years, respectively. Samples were collected every 3 months and 3 weeks, respectively. IgG N-glycan analysis followed a standardized approach by isolating IgG, its subsequent denaturation and deglycosylation followed by glycan cleanup and labelling. Capillary gel electrophoresis with laser-induced fluorescence (CGE-LIF) and ultra-performance liquid chromatography analyses were employed for glycan profiling. Statistical analysis involved normalization, batch correction, and linear mixed models to assess time effects on derived glycan traits.The intermediate precision results consistently exhibited very low coefficient of variation values across all three test samples. This consistent pattern underscores the high level of precision inherent in the CGE method for analysing the glycan clock of ageing. The AstraZeneca MFO cohort did not show any statistically significant trends, whereas the menstrual cycle cohort exhibited statistically significant trends in digalactosylated (G2), agalactosylated (G0) and fucosylation (F). These trends were attributed to the effects of the menstrual cycle. Long-term stability analysis identified enduring age-related trends in both subjects, showing a positive time effect in G0 and bisected N-acetylglucosamine, as well as a negative time effect in G2 and sialylation, aligning with earlier findings. Time effects measured for monogalactosylation, and F remained substantially lower than ones observed for other traits.The study found that IgG N-glycome analysis using CGE-LIF exhibited remarkably high intermediate precision. Moreover, the study highlights the short- and long-term stability of IgG glycome composition, coupled with a notable capacity to adapt and respond to physiological changes and environmental influences such as hormonal changes, disease, and interventions. The discoveries from this study propel personalized medicine forward by deepening our understanding of how IgG glycome relates to age-related health concerns. This study underscores the reliability of glycans as a biomarker for tracking age-related changes and individual health paths.

Indexed as

AgingBiomarkersImmunoglobulin GPolysaccharidesAdultFemaleGlycosylationHumansMaleMiddle AgedTime FactorsYoung AdultBiomarkersImmunoglobulin GPolysaccharidesAgeingBiomarkersGlycansIgG N-glycosylationTime-dependent stability

Identifiers

PMID38877341
PMCPMC11494675

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.