Literature DB >> 33079603

Metabolic Age Based on the BBMRI-NL 1H-NMR Metabolomics Repository as Biomarker of Age-related Disease.

Erik B van den Akker1,2,3, Stella Trompet1,4, Jurriaan J H Barkey Wolf1, Marian Beekman1, H Eka D Suchiman1, Joris Deelen1,5, Folkert W Asselbergs6,7,8,9, Eric Boersma10, Davy Cats1, Petra M Elders11,12, J Marianne Geleijnse13, M Arfan Ikram14,15,16, Margreet Kloppenburg17,18, Haillang Mei1,19, Ingrid Meulenbelt1, Simon P Mooijaart2, Rob G H H Nelissen20, Mihai G Netea21, Brenda W J H Penninx12,22, Mariska Slofstra23, Coen D A Stehouwer24,25, Morris A Swertz23, Charlotte E Teunissen26, Gisela M Terwindt27, Leen M 't Hart1,28,12,29,30, Arn M J M van den Maagdenberg31, Pim van der Harst32, Iwan C C van der Horst33, Carla J H van der Kallen24,25, Marleen M J van Greevenbroek24,25, W Erwin van Spil34, Cisca Wijmenga23, Alexandra Zhernakova23, Aeilko H Zwinderman35, Naveed Sattar36, J Wouter Jukema37, Cornelia M van Duijn14, Dorret I Boomsma37,38, Marcel J T Reinders2,3, P Eline Slagboom1,5.   

Abstract

BACKGROUND: The blood metabolome incorporates cues from the environment and the host's genetic background, potentially offering a holistic view of an individual's health status.
METHODS: We have compiled a vast resource of proton nuclear magnetic resonance metabolomics and phenotypic data encompassing over 25 000 samples derived from 26 community and hospital-based cohorts.
RESULTS: Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individual's biological age. Exploration in independent cohorts demonstrates that being judged older by one's metabolome, as compared with one's chronological age, confers an increased risk on future cardiovascular disease, mortality, and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bbmri.nl/samples-images-data.
CONCLUSIONS: In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardiometabolic health.

Entities:  

Keywords:  aging; cardiovascular disease; data science; metabolomics

Year:  2020        PMID: 33079603     DOI: 10.1161/CIRCGEN.119.002610

Source DB:  PubMed          Journal:  Circ Genom Precis Med        ISSN: 2574-8300


  9 in total

Review 1.  Measuring biological age using omics data.

Authors:  Jarod Rutledge; Hamilton Oh; Tony Wyss-Coray
Journal:  Nat Rev Genet       Date:  2022-06-17       Impact factor: 53.242

2.  A Metabolomic Aging Clock Using Human Cerebrospinal Fluid.

Authors:  Nathan Hwangbo; Xinyu Zhang; Daniel Raftery; Haiwei Gu; Shu-Ching Hu; Thomas J Montine; Joseph F Quinn; Kathryn A Chung; Amie L Hiller; Dongfang Wang; Qiang Fei; Lisa Bettcher; Cyrus P Zabetian; Elaine Peskind; Gail Li; Daniel E L Promislow; Alexander Franks
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2022-04-01       Impact factor: 6.591

Review 3.  Aging biomarkers and the brain.

Authors:  Albert T Higgins-Chen; Kyra L Thrush; Morgan E Levine
Journal:  Semin Cell Dev Biol       Date:  2021-01-25       Impact factor: 7.499

4.  An integrative study of five biological clocks in somatic and mental health.

Authors:  Rick Jansen; Laura Km Han; Josine E Verhoeven; Karolina A Aberg; Edwin Cgj van den Oord; Yuri Milaneschi; Brenda Wjh Penninx
Journal:  Elife       Date:  2021-02-09       Impact factor: 8.140

5.  A catalogue of omics biological ageing clocks reveals substantial commonality and associations with disease risk.

Authors:  Erin Macdonald-Dunlop; Nele Taba; Lucija Klarić; Azra Frkatović; Rosie Walker; Caroline Hayward; Tõnu Esko; Chris Haley; Krista Fischer; James F Wilson; Peter K Joshi
Journal:  Aging (Albany NY)       Date:  2022-01-24       Impact factor: 5.955

6.  MiMIR: R-shiny application to infer risk factors and endpoints from Nightingale Health's 1H-NMR Metabolomics data.

Authors:  D Bizzarri; M J T Reinders; M Beekman; P E Slagboom; E B van den Akker
Journal:  Bioinformatics       Date:  2022-06-13       Impact factor: 6.931

7.  Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies.

Authors:  Anna Niehues; Daniele Bizzarri; Marcel J T Reinders; P Eline Slagboom; Alain J van Gool; Erik B van den Akker; Peter A C 't Hoen
Journal:  BMC Genomics       Date:  2022-07-31       Impact factor: 4.547

Review 8.  Integrated Multi-Omics for Novel Aging Biomarkers and Antiaging Targets.

Authors:  Lei Wu; Xinqiang Xie; Tingting Liang; Jun Ma; Lingshuang Yang; Juan Yang; Longyan Li; Yu Xi; Haixin Li; Jumei Zhang; Xuefeng Chen; Yu Ding; Qingping Wu
Journal:  Biomolecules       Date:  2021-12-28

Review 9.  Utilization of Host and Microbiome Features in Determination of Biological Aging.

Authors:  Karina Ratiner; Suhaib K Abdeen; Kim Goldenberg; Eran Elinav
Journal:  Microorganisms       Date:  2022-03-21
  9 in total

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