Evidence map›Paper›PMID 30841604›Full record

ArticleMetabolites2019

Ageing Investigation Using Two-Time-Point Metabolomics Data from KORA and CARLA Studies.

Choiwai Maggie Chak, Maria Elena Lacruz, Jonathan Adam, Stefan Brandmaier, Marcela Covic, Jialing Huang, Christa Meisinger, Daniel Tiller, Cornelia Prehn, Jerzy Adamski and 5 more

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
2.7field-weighted citation impact, top 9% of its field
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

30 citing papers in PubMed, 47 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Metabolomics Profiling of Age-Associated Metabolites in Malay Population.Oxidative medicine and cellular longevity · 2023
    Article
  11. The Aging Process: A Metabolomics Perspective.Molecules (Basel, Switzerland) · 2022
    Article
  12. Review
  13. Article
  14. Article
  15. Review
  16. A Metabolomic Aging Clock Using Human Cerebrospinal Fluid.The journals of gerontology. Series A, Biological sciences and medical sciences · 2022
    Article
  17. Article
  18. Article
  19. Review
  20. 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

15 authors at 4 institutions in 2 countries.

Choiwai Maggie ChakResearch Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany. c.w.chak@utwente.nl.
Maria Elena LacruzInstitute of Medical Epidemiology, Biometry and Informatics, Martin-Luther University Halle-Wittenberg, 06108 Halle, Germany. elena.lacruz@uk-halle.de.ORCID 0000-0003-2036-3039
Jonathan AdamResearch Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany. jonathan.adam@helmholtz-muenchen.de.
Stefan BrandmaierResearch Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany. stefan.brandmaier@helmholtz-muenchen.de.
Marcela CovicResearch Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany. marcella.covic@gmail.com.
Jialing HuangResearch Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany. jialing.huang@helmholtz-muenchen.de.ORCID 0000-0001-7158-3782
Christa MeisingerIndependent Research Group Clinical Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany. christa.meisinger@helmholtz-muenchen.de.
Daniel TillerInstitute of Medical Epidemiology, Biometry and Informatics, Martin-Luther University Halle-Wittenberg, 06108 Halle, Germany. daniel.tiller@uk-halle.de.
Cornelia PrehnResearch Unit of Molecular Endocrinology and Metabolism, Helmholtz Zentrum München, 85764 Neuherberg, Germany. prehn@helmholtz-muenchen.de.ORCID 0000-0002-1274-4715
Jerzy AdamskiGerman Center for Diabetes Research (DZD), 85764 Neuherberg, Germany. adamski@helmholtz-muenchen.de.
Ursula BergerInstitute for Medical Informatics, Biometrics and Epidemiology, Ludwig-Maximilians-Universität München, 81377 München, Germany. berger@ibe.med.uni-muenchen.de.
Christian GiegerResearch Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany. christian.gieger@helmholtz-muenchen.de.
Annette PetersInstitute of Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany. peters@helmholtz-muenchen.de.
Alexander KluttigInstitute of Medical Epidemiology, Biometry and Informatics, Martin-Luther University Halle-Wittenberg, 06108 Halle, Germany. alexander.kluttig@uk-halle.de.
Rui Wang-SattlerResearch Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany. rui.wang-sattler@helmholtz-muenchen.de.ORCID 0000-0002-8794-8229
Helmholtz Zentrum München · DEMartin Luther University Halle-Wittenberg · DELudwig-Maximilians-Universität München · DENational University of Singapore · SG

Funding

Deutsche Forschungsgemeinschaft 598 'Heart failure in the elderly - cellular mechanisms and therapy'Horizon 2020 Framework Programme EIT Health 18428 DeTecT2D
6 · The paper itself

Abstract

Ageing, one of the largest risk factors for many complex diseases, is highly interconnected to metabolic processes. Investigating the changes in metabolite concentration during ageing among healthy individuals offers us unique insights to healthy ageing. We aim to identify ageing-associated metabolites that are independent from chronological age to deepen our understanding of the long-term changes in metabolites upon ageing. Sex-stratified longitudinal analyses were performed using fasting serum samples of 590 healthy KORA individuals (317 women and 273 men) who participated in both baseline (KORA S4) and seven-year follow-up (KORA F4) studies. Replication was conducted using serum samples of 386 healthy CARLA participants (195 women and 191 men) in both baseline (CARLA-0) and four-year follow-up (CARLA-1) studies. Generalized estimation equation models were performed on each metabolite to identify ageing-associated metabolites after adjusting for baseline chronological age, body mass index, physical activity, smoking status, alcohol intake and systolic blood pressure. Literature researches were conducted to understand their biochemical relevance. Out of 122 metabolites analysed, we identified and replicated five (C18, arginine, ornithine, serine and tyrosine) and four (arginine, ornithine, PC aa C36:3 and PC ae C40:5) significant metabolites in women and men respectively. Arginine decreased, while ornithine increased in both sexes. These metabolites are involved in several ageing processes: apoptosis, mitochondrial dysfunction, inflammation, lipid metabolism, autophagy and oxidative stress resistance. The study reveals several significant ageing-associated metabolite changes with two-time-point measurements on healthy individuals. Larger studies are required to confirm our findings.

Indexed as

ageingamino acidschronological agelongitudinal studytargeted metabolomics

Identifiers

PMID30841604
PMCPMC6468431
OpenAlexW2919914035

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.