Biological mechanisms of aging predict age-related disease co-occurrence in patients.
Biological mechanisms of aging predict age-related disease co-occurrence in patients.
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Genetic, environmental, and pharmacological interventions into the aging process can confer resistance to multiple age‐related diseases in laboratory animals, including rhesus monkeys. These findings imply that individual mechanisms of aging might contribute to the co‐occurrence of age‐related diseases in humans and could be targeted to prevent these conditions simultaneously. To address this question, we text mined 917,645 literature abstracts followed by manual curation and found strong, non‐random associations between age‐related diseases and aging mechanisms in humans, confirmed by gene set enrichment analysis of GWAS data. Integration of these associations with clinical data from 3.01 million patients showed that age‐related diseases associated with each of five aging mechanisms were more likely than chance to be present together in patients. Genetic evidence revealed that innate and adaptive immunity, the intrinsic apoptotic signaling pathway and activity of the ERK1/2 pathway were associated with multiple aging mechanisms and diverse age‐related diseases. Mechanisms of aging hence contribute both together and individually to age‐related disease co‐occurrence in humans and could potentially be targeted accordingly to prevent multimorbidity. Using text‐mining, gene set enrichment and network analysis, we found that five aging hallmarks (deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion and altered intercellular communication) were associated with co‐occurrence of age‐related diseases. Innate and adaptive immunity, Ras‐ERK and intrinsic apoptotic signalling pathways were enriched for all nine aging hallmarks, indicating that multiple diseases may be prevented by targeting these pathways.
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DOI:
10.1093/bioinformatics/btv301
发表时间:
2015-09-15
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
作者:
Gutiérrez-Sacristán A;Grosdidier S;Valverde O;Torrens M;Bravo À;Piñero J;Sanz F;Furlong LI
通讯作者:
Furlong LI
影响因子:
7.7
作者:
Austad SN;Hoffman JM
通讯作者:
Hoffman JM
DOI:
10.1093/bioinformatics/btp163
发表时间:
2009-06-01
期刊:
Bioinformatics (Oxford, England)
影响因子:
--
作者:
Cock PJ;Antao T;Chang JT;Chapman BA;Cox CJ;Dalke A;Friedberg I;Hamelryck T;Kauff F;Wilczynski B;de Hoon MJ
通讯作者:
de Hoon MJ
影响因子:
14.9
作者:
NCBI Resource Coordinators
通讯作者:
NCBI Resource Coordinators
DOI:
10.1016/s0735-1097(00)00611-2
发表时间:
2000-05-01
影响因子:
24
作者:
Goette, A;Staack, T;Lendeckel, U
通讯作者:
Lendeckel, U