Literature DB >> 26652958

Measuring Biological Age via Metabonomics: The Metabolic Age Score.

Johannes Hertel1,2, Nele Friedrich3,4, Katharina Wittfeld2, Maik Pietzner3, Kathrin Budde3, Sandra Van der Auwera1,2, Tobias Lohmann5, Alexander Teumer6, Henry Völzke4,6,7, Matthias Nauck3,4, Hans Jörgen Grabe1,2.   

Abstract

Chronological age is one of the most important risk factors for adverse clinical outcome. Still, two individuals at the same chronological age could have different biological aging states, leading to different individual risk profiles. Capturing this individual variance could constitute an even more powerful predictor enhancing prediction in age-related morbidity. Applying a nonlinear regression technique, we constructed a metabonomic measurement for biological age, the metabolic age score, based on urine data measured via (1)H NMR spectroscopy. We validated the score in two large independent population-based samples by revealing its significant associations with chronological age and age-related clinical phenotypes as well as its independent predictive value for survival over approximately 13 years of follow-up. Furthermore, the metabolic age score was prognostic for weight loss in a sample of individuals who underwent bariatric surgery. We conclude that the metabolic age score is an informative measurement of biological age with possible applications in personalized medicine.

Entities:  

Keywords:  Metabonomics; NMR; SHIP; aging; mortality; multimorbidity; nonlinear regression techniques

Mesh:

Year:  2015        PMID: 26652958     DOI: 10.1021/acs.jproteome.5b00561

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  31 in total

1.  Shift work, DNA methylation and epigenetic age.

Authors:  Alexandra J White; Jacob K Kresovich; Zongli Xu; Dale P Sandler; Jack A Taylor
Journal:  Int J Epidemiol       Date:  2019-10-01       Impact factor: 7.196

Review 2.  Accelerating research on biological aging and mental health: Current challenges and future directions.

Authors:  Laura K M Han; Josine E Verhoeven; Audrey R Tyrka; Brenda W J H Penninx; Owen M Wolkowitz; Kristoffer N T Månsson; Daniel Lindqvist; Marco P Boks; Dóra Révész; Synthia H Mellon; Martin Picard
Journal:  Psychoneuroendocrinology       Date:  2019-04-05       Impact factor: 4.905

Review 3.  Role of circulating factors in cardiac aging.

Authors:  Antonio Cannatà; Gabriella Marcon; Giovanni Cimmino; Luca Camparini; Giulio Ciucci; Gianfranco Sinagra; Francesco S Loffredo
Journal:  J Thorac Dis       Date:  2017-03       Impact factor: 2.895

4.  Methylation-Based Biological Age and Breast Cancer Risk.

Authors:  Jacob K Kresovich; Zongli Xu; Katie M O'Brien; Clarice R Weinberg; Dale P Sandler; Jack A Taylor
Journal:  J Natl Cancer Inst       Date:  2019-10-01       Impact factor: 13.506

Review 5.  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

6.  An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging.

Authors:  Nazish Sayed; Yingxiang Huang; Khiem Nguyen; Zuzana Krejciova-Rajaniemi; Anissa P Grawe; Tianxiang Gao; Robert Tibshirani; Trevor Hastie; Ayelet Alpert; Lu Cui; Tatiana Kuznetsova; Yael Rosenberg-Hasson; Rita Ostan; Daniela Monti; Benoit Lehallier; Shai S Shen-Orr; Holden T Maecker; Cornelia L Dekker; Tony Wyss-Coray; Claudio Franceschi; Vladimir Jojic; François Haddad; José G Montoya; Joseph C Wu; Mark M Davis; David Furman
Journal:  Nat Aging       Date:  2021-07-12

7.  Associations between sleep apnea and advanced brain aging in a large-scale population study.

Authors:  Antoine Weihs; Stefan Frenzel; Katharina Wittfeld; Anne Obst; Beate Stubbe; Mohamad Habes; András Szentkirályi; Klaus Berger; Ingo Fietze; Thomas Penzel; Norbert Hosten; Ralf Ewert; Henry Völzke; Helena U Zacharias; Hans J Grabe
Journal:  Sleep       Date:  2021-03-12       Impact factor: 5.849

8.  Sex differences in biological aging with a focus on human studies.

Authors:  Sara Hägg; Juulia Jylhävä
Journal:  Elife       Date:  2021-05-13       Impact factor: 8.140

9.  Modeling transcriptomic age using knowledge-primed artificial neural networks.

Authors:  Nicholas Holzscheck; Cassandra Falckenhayn; Jörn Söhle; Boris Kristof; Ralf Siegner; André Werner; Janka Schössow; Clemens Jürgens; Henry Völzke; Horst Wenck; Marc Winnefeld; Elke Grönniger; Lars Kaderali
Journal:  NPJ Aging Mech Dis       Date:  2021-06-01

Review 10.  Determination of Biological Age: Geriatric Assessment vs Biological Biomarkers.

Authors:  Lucas W M Diebel; Kenneth Rockwood
Journal:  Curr Oncol Rep       Date:  2021-07-16       Impact factor: 5.075

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