数据日期 2026-10-29 · 生成时间 2026-10-29 06:03
证据级别：摘要级。本条依据 PubMed 结构化摘要与元数据撰写，付费墙全文未获取、未逐节核验，本站未做任何数值推算补全。
研究问题
Biological age, reflecting the extent of age-related changes in the organism at the molecular and systemic levels, serves as a more informative predictor of the risk of age-associated pathology and mortality compared to chronological age.
方法
This review systematizes current omics and integrative approaches to biological age assessment, analyzing their prognostic potential and limitations.
核心结论
Three generations of epigenetic clocks (Horvath, Hannum, PhenoAge, GrimAge, DunedinPACE), transcriptomic markers of cellular senescence (p16INK4a, mTOR, S100A9), as well as proteomic and metabolomic biomarkers, including indicators of inflammation (IL-6, TNF-α, С-RP), glycan clocks (GlycanAge), clinical-biochemical panels, and oxidative stress markers, are examined.；It is shown that the integration of heterogeneous omics data (genomic, epigenomic, transcriptomic, proteomic, metabolomic) using machine learning algorithms (concatenation, ensemble methods) enables the creation of multi-omics models that outperform single-layer approaches in accuracy and allow for the assessment of aging heterogeneity across individual organs and systems (ageotype).；Key limitations hindering the clinical implementation of these methods are… ‖ 结论：Further progress in this field requires large-scale longitudinal studies, unification of methodological approaches, and validation of multi-omics models in independent cohorts to facilitate their integration into personalized preventive medicine algorithms.
局限
未能获取全文，具体设计细节、失访与统计学处理无法核验。
对本项目的价值
★★★☆☆ 方法学进展决定我们能否在有限样本下检出真实效应，属基础设施级收益。
原文题录
- 英文标题：[Omics and integrative methods for assessing biological age.].
- 期刊与标识：Adv Gerontol · PMID 42935341 · DOI 10.34922/AE.2026.39.4.015**
C 级证据分级：C 级（预印本 / 观点评论，非原始研究）
本文属观点/评论类文献，不是原始研究。其论点依赖于作者对既有证据的梳理与判断，不构成新的实验证据。本站标注 C 级即为此意——阅读时请把它当作「领域内的思考框架」，而非「研究发现」。
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