Longitudinal changes in blood-borne geroscience biomarkers: results from a population-based study

生物年龄 生物标志物 炎症 胱抑素C 衰老 神经退行性变 脂联素 人口 医学 内科学 生物途径 生物 生物信息学 内分泌学 糖尿病 疾病 老年学 遗传学 基因表达 胰岛素抵抗 肾功能 基因 环境卫生
作者
Anna Picca,Ngoc Viet Nguyen,Riccardo Calvani,Matilda Dale,Claudia Fredolini,Emanuele Marzetti,Amaia Calderón‐Larrañaga,Davide Liborio Vetrano
出处
期刊:GeroScience [Springer International Publishing]
标识
DOI:10.1007/s11357-025-01666-x
摘要

Abstract Multi-marker approaches are well suited for untangling the intrinsic complexity of aging and related conditions. Herein, we quantified (1) baseline concentrations of a panel of geroscience biomarkers pertaining to four biological domains (i.e., metabolism, inflammation, vascular/organ dysfunction and cellular senescence, and neurodegeneration) in individuals aged ≥60 years; (2) investigated linear and non-linear changes in biomarker levels over a 6-year period according to age and sex; and (3) described the relationships among geroscience biomarkers at baseline and follow-up. We found that repeated measures of age-dependent changes of 47 blood-borne biomarkers over 6 years had differential associations depending on the biological domains. The most relevant biomolecules in the associations between age and repeated assessments were (1) adiponectin, C-peptide, renin (metabolism), (2) CXCL10, IL-1α, IL-1β, IL-6, IL-10, IL-12p70, MPO (inflammation), (3) cystatin C, MMP7, MMP12, VCAM-1 (vascular/organ dysfunction and cellular senescence), and (4) S100B and Tau protein (neurodegeneration). Among these molecules, a negative association with increasing age was found for IL-1α, IL-1β, IL-12p70, S100B, and Tau protein. Non-linear relationships were also identified with age for IGFBP-1, leptin, β2M, TNFRSF1B, fibrinogen, GDF-15, N-cadherin, and BDNF. Our results indicate that inflammatory and metabolic biomolecules are strongly associated with aging over 6 years of follow-up. Whether the biological pathways reflected by these biomarkers contribute to the aging process or are associated with negative health-related events needs to be explored through comprehensive multi-omics longitudinal analysis in larger cohorts.
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