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估计个人疾病风险和造血干细胞系统发育多样性的方法。

Methods for Estimating Personal Disease Risk and Phylogenetic Diversity of Hematopoietic Stem Cells.

机构信息

Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA.

Department of Biology, Temple University, Philadelphia, PA, USA.

出版信息

Mol Biol Evol. 2024 Jan 3;41(1). doi: 10.1093/molbev/msad279.

Abstract

An individual's chronological age does not always correspond to the health of different tissues in their body, especially in cases of disease. Therefore, estimating and contrasting the physiological age of tissues with an individual's chronological age may be a useful tool to diagnose disease and its progression. In this study, we present novel metrics to quantify the loss of phylogenetic diversity in hematopoietic stem cells (HSCs), which are precursors to most blood cell types and are associated with many blood-related diseases. These metrics showed an excellent correspondence with an age-related increase in blood cancer incidence, enabling a model to estimate the phylogeny-derived age (phyloAge) of HSCs present in an individual. The HSC phyloAge was generally older than the chronological age of patients suffering from myeloproliferative neoplasms (MPNs). We present a model that relates excess HSC aging with increased MPN risk. It predicted an over 200 times greater risk based on the HSC phylogenies of the youngest MPN patients analyzed. Our new metrics are designed to be robust to sampling biases and do not rely on prior knowledge of driver mutations or physiological assessments. Consequently, they complement conventional biomarker-based methods to estimate physiological age and disease risk.

摘要

个体的实际年龄并不总是与其身体不同组织的健康状况相对应,尤其是在患病的情况下。因此,估计和对比个体组织的生理年龄与实际年龄可能是诊断疾病及其进展的有用工具。在这项研究中,我们提出了新的指标来量化造血干细胞(HSCs)中系统发育多样性的丧失,HSCs 是大多数血细胞类型的前体,与许多与血液相关的疾病有关。这些指标与血液癌发病率随年龄增长的情况非常吻合,从而可以建立一种模型来估计个体中存在的 HSCs 的系统发育年龄(phyloAge)。HSC 的 phyloAge 通常比患有骨髓增生性肿瘤(MPN)的患者的实际年龄大。我们提出了一个与 HSC 过度老化与 MPN 风险增加相关的模型。根据分析的最年轻 MPN 患者的 HSC 系统发育,该模型预测风险增加了 200 多倍。我们的新指标旨在克服采样偏差的影响,并且不依赖于驱动突变或生理评估的先验知识。因此,它们补充了基于传统生物标志物的方法来估计生理年龄和疾病风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9802/10768883/c957010d2685/msad279f1.jpg

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