Methods for Estimating Personal Disease Risk and Phylogenetic Diversity of Hematopoietic Stem Cells.
Methods for Estimating Personal Disease Risk and Phylogenetic Diversity of Hematopoietic Stem Cells.
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DOI：
10.1093/molbev/msad279
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发表时间：
2024-01-03
影响因子：
10.7
通讯作者：
Kumar, Sudhir
中科院分区：
文献类型：
--
作者：
Craig, Jack M.;Gerhard, Glenn S.;Sharma, Sudip;Yankovskiy, Anastasia;Miura, Sayaka;Kumar, Sudhir
一个人的实际年龄并不总是与他们身体不同组织的健康状况相对应，特别是在疾病的情况下。因此，估计组织的生理年龄并将其与个体的实际年龄进行对比，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)系统发育多样性的丧失，HSCs是大多数血细胞类型的前体，与许多血液相关疾病有关。这些指标显示了与年龄相关的血癌发病率增加的很好的一致性，使得模型能够估计个体中存在的造血干细胞的系统发育起源的年龄(PhyloAge)。HSC系统年龄一般大于骨髓增生性肿瘤(MPN)患者的实际年龄。我们提出了一个模型，将过度的HSC老化与MPN风险增加联系起来。根据最年轻的MPN患者的HSC系统发育分析，它预测的风险要高出200倍以上。我们的新指标旨在对采样偏差具有健壮性，并且不依赖于驱动器突变或生理评估的先验知识。因此，它们补充了传统的基于生物标记物的方法来估计生理年龄和疾病风险。
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.
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影响因子：
64.8
作者：
Bick AG;Weinstock JS;Nandakumar SK;Fulco CP;Bao EL;Zekavat SM;Szeto MD;Liao X;Leventhal MJ;Nasser J;Chang K;Laurie C;Burugula BB;Gibson CJ;Lin AE;Taub MA;Aguet F;Ardlie K;Mitchell BD;Barnes KC;Moscati A;Fornage M;Redline S;Psaty BM;Silverman EK;Weiss ST;Palmer ND;Vasan RS;Burchard EG;Kardia SLR;He J;Kaplan RC;Smith NL;Arnett DK;Schwartz DA;Correa A;de Andrade M;Guo X;Konkle BA;Custer B;Peralta JM;Gui H;Meyers DA;McGarvey ST;Chen IY;Shoemaker MB;Peyser PA;Broome JG;Gogarten SM;Wang FF;Wong Q;Montasser ME;Daya M;Kenny EE;North KE;Launer LJ;Cade BE;Bis JC;Cho MH;Lasky-Su J;Bowden DW;Cupples LA;Mak ACY;Becker LC;Smith JA;Kelly TN;Aslibekyan S;Heckbert SR;Tiwari HK;Yang IV;Heit JA;Lubitz SA;Johnsen JM;Curran JE;Wenzel SE;Weeks DE;Rao DC;Darbar D;Moon JY;Tracy RP;Buth EJ;Rafaels N;Loos RJF;Durda P;Liu Y;Hou L;Lee J;Kachroo P;Freedman BI;Levy D;Bielak LF;Hixson JE;Floyd JS;Whitsel EA;Ellinor PT;Irvin MR;Fingerlin TE;Raffield LM;Armasu SM;Wheeler MM;Sabino EC;Blangero J;Williams LK;Levy BD;Sheu WH;Roden DM;Boerwinkle E;Manson JE;Mathias RA;Desai P;Taylor KD;Johnson AD;NHLBI Trans-Omics for Precision Medicine Consortium;Auer PL;Kooperberg C;Laurie CC;Blackwell TW;Smith AV;Zhao H;Lange E;Lange L;Rich SS;Rotter JI;Wilson JG;Scheet P;Kitzman JO;Lander ES;Engreitz JM;Ebert BL;Reiner AP;Jaiswal S;Abecasis G;Sankaran VG;Kathiresan S;Natarajan P
通讯作者：
Natarajan P
影响因子：
3.3
作者：
Groarke EM;Young NS
通讯作者：
Young NS
全基因组甲基化谱揭示了人类衰老速度的定量观点。
影响因子：
16
作者：
Hannum, Gregory;Guinney, Justin;Zhao, Ling;Zhang, Li;Hughes, Guy;Sadda, SriniVas;Klotzle, Brandy;Bibikova, Marina;Fan, Jian-Bing;Gao, Yuan;Deconde, Rob;Chen, Menzies;Rajapakse, Indika;Friend, Stephen;Ideker, Trey;Zhang, Kang
通讯作者：
Zhang, Kang
寻找癌症中的驱动突变：阐明背景突变过程的作用
影响因子：
4.3
作者：
Brown, Anna-Leigh;Li, Minghui;Panchenko, Anna R.
通讯作者：
Panchenko, Anna R.
影响因子：
12.3
作者：
Horvath S
通讯作者：
Horvath S