ArticleAging2021
Aging-related markers in rat urine revealed by dynamic metabolic profiling using machine learning.
Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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.
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Who cites it
9 citing papers in PubMed.
- Integrating Bidirectional Mendelian Randomization with Multi-Omics Reveals Causal Serum Metabolites and Novel Metabolic Drivers of Multiple Myeloma.International journal of molecular sciences · 2026Article
- A comprehensive review of artificial intelligence as a catalyst in aging research: insights, gaps and future perspectives.Frontiers in aging · 2026Review
- Integrating transcriptome and metabolome reveals sex-dependent meat quality regulation in Qiandongnan Xiaoxiang chickens.BMC genomics · 2025Article
- LC-MS-based conventional metabolomics combined with machine learning models to identify metabolic markers for the diagnosis of type I diabetes.Frontiers in endocrinology · 2025Article
- Nonlinear DNA methylation trajectories in aging male mice.Nature communications · 2024Article
- Causal associations of genetically predicted gut microbiota and blood metabolites with inflammatory states and risk of infections: a Mendelian randomization analysis.Frontiers in microbiology · 2024Article
- A framework of biomarkers for vascular aging: a consensus statement by the Aging Biomarker Consortium.Life medicine · 2023Article
- Serum Metabolomic Profiling in Aging Mice Using Liquid Chromatography-Mass Spectrometry.Biomolecules · 2022Article
- Urinary Biomarkers of Oxidative Stress in Aging: Implications for Prediction of Accelerated Biological Age in Prospective Cohort Studies.Oxidative medicine and cellular longevity · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The process of aging and metabolism is intimately intertwined; thus, developing biomarkers related to metabolism is critical for delaying aging. However, few studies have identified reliable markers that reflect aging trajectories based on machine learning. We generated metabolomic profiles from rat urine using ultra-performance liquid chromatography/mass spectrometry. This was dynamically collected at four stages of the rat's age (20, 50, 75, and 100 weeks) for both the training and test groups. Partial least squares-discriminant analysis score plots revealed a perfect separation trajectory in one direction with increasing age in the training and test groups. We further screened 25 aging-related biomarkers through the combination of four algorithms (VIP, time-series, LASSO, and SVM-RFE) in the training group. They were validated in the test group with an area under the curve of 1. Finally, six metabolites, known or novel aging-related markers, were identified, including epinephrine, glutarylcarnitine, L-kynurenine, taurine, 3-hydroxydodecanedioic acid, and N-acetylcitrulline. We also found that, except for N-acetylcitrulline (
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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.