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van Holstein Y, Mooijaart SP, van Oevelen M, van Deudekom FJ, Vojinovic D, Bizzarri D, van den Akker EB, Noordam R, Deelen J, van Heemst D, de Glas NA, Holterhues C, Labots G, van den Bos F, Beekman M, Slagboom PE, van Munster BC, Portielje JEA, Trompet S. The performance of metabolomics-based prediction scores for mortality in older patients with solid tumors. GeroScience 2024:10.1007/s11357-024-01261-6. [PMID: 38963649 DOI: 10.1007/s11357-024-01261-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 06/23/2024] [Indexed: 07/05/2024] Open
Abstract
Prognostic information is needed to balance benefits and risks of cancer treatment in older patients. Metabolomics-based scores were previously developed to predict 5- and 10-year mortality (MetaboHealth) and biological age (MetaboAge). This study aims to investigate the association of MetaboHealth and MetaboAge with 1-year mortality in older patients with solid tumors, and to study their predictive value for mortality in addition to established clinical predictors. This prospective cohort study included patients aged ≥ 70 years with a solid malignant tumor, who underwent blood sampling and a geriatric assessment before treatment initiation. The outcome was all-cause 1-year mortality. Of the 192 patients, the median age was 77 years. With each SD increase of MetaboHealth, patients had a 2.32 times increased risk of mortality (HR 2.32, 95% CI 1.59-3.39). With each year increase in MetaboAge, there was a 4% increased risk of mortality (HR 1.04, 1.01-1.07). MetaboHealth and MetaboAge showed an AUC of 0.66 (0.56-0.75) and 0.60 (0.51-0.68) for mortality prediction accuracy, respectively. The AUC of a predictive model containing age, primary tumor site, distant metastasis, comorbidity, and malnutrition was 0.76 (0.68-0.83). Addition of MetaboHealth increased AUC to 0.80 (0.74-0.87) (p = 0.09) and AUC did not change with MetaboAge (0.76 (0.69-0.83) (p = 0.89)). Higher MetaboHealth and MetaboAge scores were associated with 1-year mortality. The addition of MetaboHealth to established clinical predictors only marginally improved mortality prediction in this cohort with various types of tumors. MetaboHealth may potentially improve identification of older patients vulnerable for adverse events, but numbers were too small for definitive conclusions. The TENT study is retrospectively registered at the Netherlands Trial Register (NTR), trial number NL8107. Date of registration: 22-10-2019.
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Affiliation(s)
- Yara van Holstein
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands.
| | - Simon P Mooijaart
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands
- LUMC Center for Medicine for Older People, Leiden University Medical Center, Leiden, The Netherlands
| | - Mathijs van Oevelen
- Department of Internal Medicine, Section of Nephrology, Leiden University Medical Center, Leiden, The Netherlands
| | - Floor J van Deudekom
- Department of Geriatric Medicine, OLVG Hospitals Amsterdam, Amsterdam, The Netherlands
| | - Dina Vojinovic
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, University Medical Centre, Rotterdam, The Netherlands
| | - Daniele Bizzarri
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Erik B van den Akker
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands
| | - Joris Deelen
- Max Planck Institute for Biology of Ageing, Cologne, Germany
- Cologne Excellence Cluster On Cellular Stress Responses in Ageing-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands
| | - Nienke A de Glas
- Department of Medical Oncology, Leiden University Medical Center, Leiden, The Netherlands
| | - Cynthia Holterhues
- Department of Internal Medicine, Haga Hospital, The Hague, The Netherlands
| | - Geert Labots
- Department of Internal Medicine, Haga Hospital, The Hague, The Netherlands
| | - Frederiek van den Bos
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands
| | - Marian Beekman
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - P Eline Slagboom
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Barbara C van Munster
- Department of Internal Medicine, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Stella Trompet
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, PO box 9600, 2300 RC, Leiden, The Netherlands
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2
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Moqri M, Herzog C, Poganik JR, Ying K, Justice JN, Belsky DW, Higgins-Chen AT, Chen BH, Cohen AA, Fuellen G, Hägg S, Marioni RE, Widschwendter M, Fortney K, Fedichev PO, Zhavoronkov A, Barzilai N, Lasky-Su J, Kiel DP, Kennedy BK, Cummings S, Slagboom PE, Verdin E, Maier AB, Sebastiano V, Snyder MP, Gladyshev VN, Horvath S, Ferrucci L. Validation of biomarkers of aging. Nat Med 2024; 30:360-372. [PMID: 38355974 PMCID: PMC11090477 DOI: 10.1038/s41591-023-02784-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/19/2023] [Indexed: 02/16/2024]
Abstract
The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.
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Affiliation(s)
- Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
- Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Chiara Herzog
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
| | - Jesse R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kejun Ying
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jamie N Justice
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Daniel W Belsky
- Department of Epidemiology, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | | | - Brian H Chen
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, San Diego, CA, USA
| | - Alan A Cohen
- Department of Environmental Health Sciences, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Martin Widschwendter
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, London, UK
- Department of Women's and Children's Health, Division of Obstetrics and Gynaecology, Karolinska Institutet, Stockholm, Sweden
| | | | | | | | - Nir Barzilai
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jessica Lasky-Su
- Department of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Douglas P Kiel
- Musculoskeletal Research Center, Hinda and Arthur Marcus Institute for Aging Research and Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Brian K Kennedy
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
| | - Steven Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - P Eline Slagboom
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Eric Verdin
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Andrea B Maier
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
- Department of Human Movement Sciences, @AgeAmsterdam, Amsterdam Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Vittorio Sebastiano
- Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Bizzarri D, Reinders MJT, Beekman M, Slagboom PE, van den Akker EB. Technical Report: A Comprehensive Comparison between Different Quantification Versions of Nightingale Health's 1H-NMR Metabolomics Platform. Metabolites 2023; 13:1181. [PMID: 38132863 PMCID: PMC10745109 DOI: 10.3390/metabo13121181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/07/2023] [Accepted: 11/17/2023] [Indexed: 12/23/2023] Open
Abstract
1H-NMR metabolomics data is increasingly used to track health and disease. Nightingale Health, a major supplier of 1H-NMR metabolomics, has recently updated the quantification strategy to further align with clinical standards. Such updates, however, might influence backward replicability, particularly affecting studies with repeated measures. Using data from BBMRI-NL consortium (~28,000 samples from 28 cohorts), we compared Nightingale data, originally released in 2014 and 2016, with a re-quantified version released in 2020, of which both versions were based on the same NMR spectra. Apart from two discontinued and twenty-three new analytes, we generally observe a high concordance between quantification versions with 73 out of 222 (33%) analytes showing a mean ρ > 0.9 across all cohorts. Conversely, five analytes consistently showed lower Spearman's correlations (ρ < 0.7) between versions, namely acetoacetate, LDL-L, saturated fatty acids, S-HDL-C, and sphingomyelins. Furthermore, previously trained multi-analyte scores, such as MetaboAge or MetaboHealth, might be particularly sensitive to platform changes. Whereas MetaboHealth replicated well, the MetaboAge score had to be retrained due to use of discontinued analytes. Notably, both scores in the re-quantified data recapitulated mortality associations observed previously. Concluding, we urge caution in utilizing different platform versions to avoid mixing analytes, having different units, or simply being discontinued.
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Affiliation(s)
- Daniele Bizzarri
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Leiden Computational Biology Center, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Delft Bioinformatics Lab., Department of Intelligent Systems, TU Delft, 2628 XE Delft, The Netherlands
| | - Marcel J. T. Reinders
- Leiden Computational Biology Center, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Delft Bioinformatics Lab., Department of Intelligent Systems, TU Delft, 2628 XE Delft, The Netherlands
| | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
| | - P. Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Max Planck Institute for the Biology of Ageing, 50931 Cologne, Germany
| | - Erik B. van den Akker
- Molecular Epidemiology, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Leiden Computational Biology Center, Department of Biomedical Data Science, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
- Delft Bioinformatics Lab., Department of Intelligent Systems, TU Delft, 2628 XE Delft, The Netherlands
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4
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Kuiper LM, Polinder-Bos HA, Bizzarri D, Vojinovic D, Vallerga CL, Beekman M, Dollé MET, Ghanbari M, Voortman T, Reinders MJT, Verschuren WMM, Slagboom PE, van den Akker EB, van Meurs JBJ. Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk. J Gerontol A Biol Sci Med Sci 2023; 78:1753-1762. [PMID: 37303208 PMCID: PMC10562890 DOI: 10.1093/gerona/glad137] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Indexed: 06/13/2023] Open
Abstract
Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.
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Affiliation(s)
- Lieke M Kuiper
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- Center for Nutrition, Prevention and Health Services, Bilthoven, The Netherlands
| | | | - Daniele Bizzarri
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - Dina Vojinovic
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
| | - Martijn E T Dollé
- Center for Health Protection, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands
| | - Marcel J T Reinders
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - W M Monique Verschuren
- Center for Nutrition, Prevention and Health Services, Bilthoven, The Netherlands
- Julius Center for Health Sciences and Primary Care Utrecht, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Max Planck Institute for the Biology of Ageing, Cologne, Germany
| | - Erik B van den Akker
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Orthopaedics and Sports Medicine, Erasmus MC, Rotterdam, The Netherlands
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5
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van den Berg N, Rodríguez-Girondo M, van Dijk IK, Slagboom PE, Beekman M. Increasing number of long-lived ancestors marks a decade of healthspan extension and healthier metabolomics profiles. Nat Commun 2023; 14:4518. [PMID: 37500622 PMCID: PMC10374564 DOI: 10.1038/s41467-023-40245-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 07/10/2023] [Indexed: 07/29/2023] Open
Abstract
Globally, the lifespan of populations increases but the healthspan is lagging behind. Previous research showed that survival into extreme ages (longevity) clusters in families as illustrated by the increasing lifespan of study participants with each additional long-lived family member. Here we investigate whether the healthspan in such families follows a similar quantitative pattern using three-generational data from two databases, LLS (Netherlands), and SEDD (Sweden). We study healthspan in 2143 families containing index persons with 26 follow-up years and two ancestral generations, comprising 17,539 persons. Our results provide strong evidence that an increasing number of long-lived ancestors associates with up to a decade of healthspan extension. Further evidence indicates that members of long-lived families have a delayed onset of medication use, multimorbidity and, in mid-life, healthier metabolomic profiles than their partners. We conclude that both lifespan and healthspan are quantitatively linked to ancestral longevity, making family data invaluable to identify protective mechanisms of multimorbidity.
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Affiliation(s)
- Niels van den Berg
- Department of Biomedical Data Sciences, section of Molecular Epidemiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands.
- Centre for Economic Demography, Department of Economic History, Lund University, Scheelevägen 15B, 223 63, Lund, Sweden.
| | - Mar Rodríguez-Girondo
- Department of Biomedical Data Sciences, section of Medical Statistics, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands
| | - Ingrid K van Dijk
- Centre for Economic Demography, Department of Economic History, Lund University, Scheelevägen 15B, 223 63, Lund, Sweden
| | - P Eline Slagboom
- Department of Biomedical Data Sciences, section of Molecular Epidemiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands
- Max Planck Institute for Biology of Ageing, Joseph-Stelzmann-Str. 9b, D-50931, Cologne, Germany
| | - Marian Beekman
- Department of Biomedical Data Sciences, section of Molecular Epidemiology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, the Netherlands
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