Research output: Contribution to journal › Article › peer-review
Gregory Livshits, Ida Malkin, Ruth C E Bowyer, Serena Verdi, Jordana Bell, Cristina Menni, Frances M K Williams, Claire J Steves
Original language | English |
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Pages (from-to) | 2565-2572 |
Journal | Pain |
Volume | 159 |
Issue number | 12 |
Early online date | 6 Aug 2018 |
DOIs | |
Accepted/In press | 25 Jul 2018 |
E-pub ahead of print | 6 Aug 2018 |
Published | Dec 2018 |
Multi-OMICS Analyses of Frailty_LIVSHITS_Firstonline6August2018_GREEN AAM
00006396_900000000_98881.pdf, 798 KB, application/pdf
Uploaded date:10 Sep 2018
Version:Accepted author manuscript
Multi-OMICS Analyses of Frailty_LIVSHITS_Firstonline6August2018_GOLD VoR (CC BY)
Multi_OMICS_Analyses_of_Frailty_LIVSHITS_Firstonline6August2018_GOLD_VoR_CC_BY_.pdf, 426 KB, application/pdf
Uploaded date:24 Jun 2019
Version:Final published version
Licence:CC BY
Common widespread pain (CWP) and frailty are prevalent conditions in older people. We have shown previously that interindividual variation in frailty and CWP is genetically determined. We also reported an association of frailty and CWP caused by shared genetic and common environmental factors. The aim of the present study was to use omic approaches to identify molecular genetic factors underlying the heritability of frailty and its genetic correlation with CWP.Frailty was quantified through the Rockwood Frailty Index (FI) as a proportion of deficits from 33 binary health deficit questions in 3626 female twins. CWP was assessed using a screening questionnaire. Omics analysis included 305 metabolites and whole-genome (>2.5x10 SNPs) and epigenome (∼1x10 MeDIP-seq regions) assessments performed on fasting blood samples. Using family-based statistical analyses, including path analysis, we examined how FI scores were related to molecular genetic factors and to CWP, taking into account known risk factors such as fat mass and smoking.FI was significantly correlated with 51 metabolites after correction for multiple testing, with 20 metabolites having P-values between 2.1x10 and 4.0x10. Three metabolites (uridine, C-glycosyl tryptophan, N-acetyl glycine) were statistically independent and thought to exert a direct effect on FI. Epiandrosterone sulphate, previously shown highly inversely associated with CWP, was found to exert an indirect influence on FI. Bioinformatics analysis of GWAS and EWAS showed FI and its covariation with CWP was through genomic regions involved in neurological pathways.
CONCLUSION: Neurological pathway involvement accounts for the associated conditions of aging CWP and FI.
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