Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites.
| Title: | Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. |
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| Authors: | Faquih, Tariq O, Vlieg, Astrid van Hylckama, Surendran, Praveen, Butterworth, Adam S, Li-Gao, Ruifang, Mutsert, Renée de, Rosendaal, Frits R, Noordam, Raymond, Heemst, Diana van, Dijk, Ko Willems van, Mook-Kanamori, Dennis O |
| Source: | Journals of Gerontology Series A: Biological Sciences & Medical Sciences; Mar2025, Vol. 80 Issue 3, p1-10, 10p |
| Subject Terms: | AGE, DISEASE risk factors, METABOLOMICS, XENOBIOTICS, PREDICTION models |
| Abstract: | Chronological age is a major risk factor for numerous diseases. However, chronological age does not capture the complex biological aging process. The difference between chronological age and biologically driven aging could be more informative in reflecting health status. Here, we set out to develop a metabolomic age prediction model by applying ridge regression and bootstrapping with 826 metabolites (678 endogenous and 148 xenobiotics) measured by an untargeted platform in relatively healthy blood donors aged 18–75 years from the INTERVAL study (N = 11 977; 50.2% men). After bootstrapping internal validation, the metabolomic age prediction models demonstrated high performance with an adjusted R 2 of 0.83 using all metabolites and 0.82 using only endogenous metabolites. The former was significantly associated with obesity and cardiovascular disease in the Netherlands Epidemiology of Obesity study (N = 599; 47.0% men; age range = 45–65) due to the contribution of medication-derived metabolites—namely salicylate and ibuprofen—and environmental exposures such as cotinine. Additional metabolomic age prediction models using all metabolites were developed for men and women separately. The models had high performance (R ² = 0.85 and 0.86) but shared a moderate correlation of 0.72. Furthermore, we observed 163 sex-dimorphic metabolites, including threonine, glycine, cholesterol, and androgenic and progesterone-related metabolites. Our strongest predictors across all models were novel and included hydroxyasparagine (Model Endo + Xeno β = 4.74), vanillylmandelate (β = 4.07), and 5,6-dihydrouridine (β = −4.2). Our study presents a robust metabolomic age model that reveals distinct sex-based age-related metabolic patterns and illustrates the value of including xenobiotic to enhance metabolomic prediction accuracy. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Faquih%2C+Tariq+O%22">Faquih, Tariq O</searchLink><br /><searchLink fieldCode="AR" term="%22Vlieg%2C+Astrid+van+Hylckama%22">Vlieg, Astrid van Hylckama</searchLink><br /><searchLink fieldCode="AR" term="%22Surendran%2C+Praveen%22">Surendran, Praveen</searchLink><br /><searchLink fieldCode="AR" term="%22Butterworth%2C+Adam+S%22">Butterworth, Adam S</searchLink><br /><searchLink fieldCode="AR" term="%22Li-Gao%2C+Ruifang%22">Li-Gao, Ruifang</searchLink><br /><searchLink fieldCode="AR" term="%22Mutsert%2C+Renée+de%22">Mutsert, Renée de</searchLink><br /><searchLink fieldCode="AR" term="%22Rosendaal%2C+Frits+R%22">Rosendaal, Frits R</searchLink><br /><searchLink fieldCode="AR" term="%22Noordam%2C+Raymond%22">Noordam, Raymond</searchLink><br /><searchLink fieldCode="AR" term="%22Heemst%2C+Diana+van%22">Heemst, Diana van</searchLink><br /><searchLink fieldCode="AR" term="%22Dijk%2C+Ko+Willems+van%22">Dijk, Ko Willems van</searchLink><br /><searchLink fieldCode="AR" term="%22Mook-Kanamori%2C+Dennis+O%22">Mook-Kanamori, Dennis O</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journals of Gerontology Series A: Biological Sciences & Medical Sciences; Mar2025, Vol. 80 Issue 3, p1-10, 10p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22AGE%22">AGE</searchLink><br /><searchLink fieldCode="DE" term="%22DISEASE+risk+factors%22">DISEASE risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22METABOLOMICS%22">METABOLOMICS</searchLink><br /><searchLink fieldCode="DE" term="%22XENOBIOTICS%22">XENOBIOTICS</searchLink><br /><searchLink fieldCode="DE" term="%22PREDICTION+models%22">PREDICTION models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Chronological age is a major risk factor for numerous diseases. However, chronological age does not capture the complex biological aging process. The difference between chronological age and biologically driven aging could be more informative in reflecting health status. Here, we set out to develop a metabolomic age prediction model by applying ridge regression and bootstrapping with 826 metabolites (678 endogenous and 148 xenobiotics) measured by an untargeted platform in relatively healthy blood donors aged 18–75 years from the INTERVAL study (N = 11 977; 50.2% men). After bootstrapping internal validation, the metabolomic age prediction models demonstrated high performance with an adjusted R <superscript>2</superscript> of 0.83 using all metabolites and 0.82 using only endogenous metabolites. The former was significantly associated with obesity and cardiovascular disease in the Netherlands Epidemiology of Obesity study (N = 599; 47.0% men; age range = 45–65) due to the contribution of medication-derived metabolites—namely salicylate and ibuprofen—and environmental exposures such as cotinine. Additional metabolomic age prediction models using all metabolites were developed for men and women separately. The models had high performance (R ² = 0.85 and 0.86) but shared a moderate correlation of 0.72. Furthermore, we observed 163 sex-dimorphic metabolites, including threonine, glycine, cholesterol, and androgenic and progesterone-related metabolites. Our strongest predictors across all models were novel and included hydroxyasparagine (Model Endo + Xeno β = 4.74), vanillylmandelate (β = 4.07), and 5,6-dihydrouridine (β = −4.2). Our study presents a robust metabolomic age model that reveals distinct sex-based age-related metabolic patterns and illustrates the value of including xenobiotic to enhance metabolomic prediction accuracy. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journals of Gerontology Series A: Biological Sciences & Medical Sciences is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/gerona/glae280 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: AGE Type: general – SubjectFull: DISEASE risk factors Type: general – SubjectFull: METABOLOMICS Type: general – SubjectFull: XENOBIOTICS Type: general – SubjectFull: PREDICTION models Type: general Titles: – TitleFull: Robust Metabolomic Age Prediction Based on a Wide Selection of Metabolites. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Faquih, Tariq O – PersonEntity: Name: NameFull: Vlieg, Astrid van Hylckama – PersonEntity: Name: NameFull: Surendran, Praveen – PersonEntity: Name: NameFull: Butterworth, Adam S – PersonEntity: Name: NameFull: Li-Gao, Ruifang – PersonEntity: Name: NameFull: Mutsert, Renée de – PersonEntity: Name: NameFull: Rosendaal, Frits R – PersonEntity: Name: NameFull: Noordam, Raymond – PersonEntity: Name: NameFull: Heemst, Diana van – PersonEntity: Name: NameFull: Dijk, Ko Willems van – PersonEntity: Name: NameFull: Mook-Kanamori, Dennis O IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 10795006 Numbering: – Type: volume Value: 80 – Type: issue Value: 3 Titles: – TitleFull: Journals of Gerontology Series A: Biological Sciences & Medical Sciences Type: main |
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