Literature DB >> 31346934

Genetic Variability and Trajectories of DNA Methylation May Support a Role for HAMP in Patient Outcomes After Aneurysmal Subarachnoid Hemorrhage.

Lacey W Heinsberg1, Annie I Arockiaraj2, Elizabeth A Crago3, Dianxu Ren4, John R Shaffer2,5, Paula R Sherwood3, Susan M Sereika4, Daniel E Weeks2,6, Yvette P Conley7,2.   

Abstract

BACKGROUND/
OBJECTIVE: Preclinical evidence suggests that iron homeostasis is an important biological mechanism following aneurysmal subarachnoid hemorrhage (aSAH); however, this concept is underexplored in humans. This study examined the relationship between patient outcomes following aSAH and genetic variants and DNA methylation in the hepcidin gene (HAMP), a key regulator of iron homeostasis.
METHODS: In this exploratory, longitudinal observational study, participants with verified aSAH were monitored for acute outcomes including cerebral vasospasm (CV) and delayed cerebral ischemia (DCI) and evaluated post-discharge at 3 and 12 months for long-term outcomes of death and functional status using the Modified Rankin Scale (mRS; poor = 3-6) and Glasgow Outcome Scale (GOS; poor = 1-3). Participants were genotyped for two genetic variants, and DNA methylation data were collected from serial cerebrospinal fluid over 14 days post-aSAH at eight methylation sites within HAMP. Participants were grouped based on their site-specific DNA methylation trajectory, with and without correcting for cell-type heterogeneity (CTH), and the associations between genetic variants and inferred DNA methylation trajectory groups and patient outcomes were tested. To correct for multiple testing, an empirical significance threshold was computed using permutation testing.
RESULTS: Genotype data for rs10421768 and rs7251432 were available for 241 and 371 participants, respectively, and serial DNA methylation data were available for 260 participants. Acute outcome prevalence included CV in 45% and DCI in 37.1% of the overall sample. Long-term outcome prevalence at 3 and 12 months included poor GOS in 23% and 21%, poor mRS in 31.6% and 27.3%, and mortality in 15.1% and 18.2%, respectively, in the overall sample. Being homozygous for the rs7251432 variant allele was significantly associated with death at 3 months (p = 0.003) and was the only association identified that passed adjustment for multiple testing mentioned above. Suggestive associations (defined as trending toward significance, p value < 0.05, but not meeting empirical significance thresholds) were identified between the homozygous variant allele for rs7251432 and poor GOS and mRS at 3 months (both p = 0.04) and death at 12 months (p = 0.02). For methylation trajectory groups, no associations remained significant after correction for multiple testing. However, for methylation trajectory groups not adjusted for CTH, suggestive associations were identified between cg18149657 and poor GOS and mRS at 3 months (p = 0.003 and p = 0.04, respectively) and death at 3 months (p = 0.04), and between cg26283059 and DCI (p = 0.01). For methylation trajectory groups adjusted for CTH, suggestive associations were identified between cg02131995 and good mRS at 12 months (p = 0.02), and between cg26283059 and DCI (p = 0.01).
CONCLUSIONS: This exploratory pilot study offers preliminary evidence that HAMP may play a role in patient outcomes after aSAH. Replication of this study and mechanistic investigation of the role of HAMP in patient outcomes after aSAH are needed.

Entities:  

Keywords:  DNA methylation; Hepcidins; Patient outcome assessment; Polymorphism; Subarachnoid hemorrhage

Mesh:

Substances:

Year:  2020        PMID: 31346934      PMCID: PMC6981002          DOI: 10.1007/s12028-019-00787-4

Source DB:  PubMed          Journal:  Neurocrit Care        ISSN: 1541-6933            Impact factor:   3.210


  35 in total

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Review 2.  Epidemiology of aneurysmal subarachnoid hemorrhage.

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Review 3.  Outcome and cost of aneurysmal subarachnoid hemorrhage.

Authors:  André A le Roux; M Christopher Wallace
Journal:  Neurosurg Clin N Am       Date:  2010-04       Impact factor: 2.509

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Authors:  Tomas Ganz
Journal:  Blood       Date:  2011-02-23       Impact factor: 22.113

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Journal:  Neurocrit Care       Date:  2014-10       Impact factor: 3.210

6.  Hepcidin regulates cellular iron efflux by binding to ferroportin and inducing its internalization.

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7.  Neurologic disorders, in-hospital deaths, and years of potential life lost in the USA, 1988-2011.

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Journal:  J Clin Neurosci       Date:  2014-07-08       Impact factor: 1.961

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Authors:  Kenneth R Wagner; Frank R Sharp; Timothy D Ardizzone; Aigang Lu; Joseph F Clark
Journal:  J Cereb Blood Flow Metab       Date:  2003-06       Impact factor: 6.200

9.  Expression of iron-related genes in human brain and brain tumors.

Authors:  Milla M Hänninen; Joonas Haapasalo; Hannu Haapasalo; Robert E Fleming; Robert S Britton; Bruce R Bacon; Seppo Parkkila
Journal:  BMC Neurosci       Date:  2009-04-22       Impact factor: 3.288

10.  Subarachnoid hemorrhage: who dies, and why?

Authors:  Hector Lantigua; Santiago Ortega-Gutierrez; J Michael Schmidt; Kiwon Lee; Neeraj Badjatia; Sachin Agarwal; Jan Claassen; E Sander Connolly; Stephan A Mayer
Journal:  Crit Care       Date:  2015-08-31       Impact factor: 9.097

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Authors:  Lacey W Heinsberg; Daniel E Weeks; Sheila A Alexander; Ryan L Minster; Paula R Sherwood; Samuel M Poloyac; Sandra Deslouches; Elizabeth A Crago; Yvette P Conley
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2.  ANGPT1 methylation and delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage patients.

Authors:  Dongjing Liu; Annie I Arockiaraj; John R Shaffer; Samuel M Poloyac; Paula R Sherwood; Sheila A Alexander; Elizabeth A Crago; Daniel E Weeks; Yvette P Conley
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3.  Acute Brain-Derived Neurotrophic Factor DNA Methylation Trajectories in Cerebrospinal Fluid and Associations With Outcomes Following Severe Traumatic Brain Injury in Adults.

Authors:  Amery Treble-Barna; Lacey W Heinsberg; Ava M Puccio; John R Shaffer; David O Okonkwo; Sue R Beers; Daniel E Weeks; Yvette P Conley
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4.  Identify specific gene pairs for subarachnoid hemorrhage based on wavelet analysis and genetic algorithm.

Authors:  Pengcheng Zhao; Shaonian Xu; Zhenshan Huang; Pengcheng Deng; Yongming Zhang
Journal:  PLoS One       Date:  2021-06-17       Impact factor: 3.240

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