An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging.
An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging.
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DOI：
10.1038/s43587-021-00082-y
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发表时间：
2021-07
期刊：
影响因子：
--
通讯作者：
Furman D
中科院分区：
文献类型：
--
作者：
Sayed N;Huang Y;Nguyen K;Krejciova-Rajaniemi Z;Grawe AP;Gao T;Tibshirani R;Hastie T;Alpert A;Cui L;Kuznetsova T;Rosenberg-Hasson Y;Ostan R;Monti D;Lehallier B;Shen-Orr SS;Maecker HT;Dekker CL;Wyss-Coray T;Franceschi C;Jojic V;Haddad F;Montoya JG;Wu JC;Davis MM;Furman D
虽然许多老年病都与免疫系统有关，但缺乏能够识别风险最高的个人的免疫指标。从1,001名年龄在8-96岁的人的血液免疫组中，我们开发了一种基于全身性年龄相关炎症模式的深度学习方法。由此产生的炎症衰老时钟(IAge)与多发病、免疫衰老、虚弱和心血管衰老密切相关，也与百岁老人的非凡长寿有关。IAge的最强贡献者是趋化因子CXCL9，它与心脏老化、不利的心脏重塑和血管功能低下有关。此外，人类和小鼠衰老的内皮细胞表现出功能丧失、细胞衰老和动脉僵硬的标志性表型，所有这些都可以通过沉默CXCL9而逆转。总之，我们确定了CXCL9在年龄相关慢性炎症中的关键作用，并得出了一种可用于早期检测与年龄相关的临床表型的多发病指标。
While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8–96 years, we developed a deep-learning method based on patterns of systemic age-related inflammation. The resulting inflammatory clock of aging (iAge) tracked with multimorbidity, immunosenescence, frailty and cardiovascular aging, and is also associated with exceptional longevity in centenarians. The strongest contributor to iAge was the chemokine CXCL9, which was involved in cardiac aging, adverse cardiac remodeling and poor vascular function. Furthermore, aging endothelial cells in human and mice show loss of function, cellular senescence and hallmark phenotypes of arterial stiffness, all of which are reversed by silencing CXCL9. In conclusion, we identify a key role of CXCL9 in age-related chronic inflammation and derive a metric for multimorbidity that can be utilized for the early detection of age-related clinical phenotypes.
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影响因子：
3.9
作者：
Boisen, Louise;Drasbek, Kim Ryun;Kristensen, Peter
通讯作者：
Kristensen, Peter
影响因子：
64.5
作者：
Brodin P;Jojic V;Gao T;Bhattacharya S;Angel CJ;Furman D;Shen-Orr S;Dekker CL;Swan GE;Butte AJ;Maecker HT;Davis MM
通讯作者：
Davis MM
影响因子：
3.7
作者：
Dai H;Leeder JS;Cui Y
通讯作者：
Cui Y
影响因子：
1.2
作者：
Bertsch, Thomas;Triebel, Jakob;Heppner, Hans Juergen
通讯作者：
Heppner, Hans Juergen
影响因子：
32.8
作者：
Bengio, Yoshua
通讯作者：
Bengio, Yoshua