J Stroke.  2023 May;25(2):266-271. 10.5853/jos.2022.03230.

The Impact of Genetically Proxied AMPK Activation, the Target of Metformin, on Functional Outcome Following Ischemic Stroke

Affiliations
  • 1Department of Neurology, The Third Affiliated Hospital of Soochow University, Changzhou, China
  • 2Department of Neurology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
  • 3Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig-Maximilians-University (LMU), Munich, Germany
  • 4Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
  • 5Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  • 6Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
  • 7Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
  • 8Department of Integrated Traditional Chinese and Western Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China

Abstract

Background and Purpose
We performed a two-sample Mendelian randomization (MR) analysis to evaluate the causal effect of genetically proxied AMP-activated protein kinase (AMPK) activation, which is the target of metformin, on functional outcome following ischemic stroke onset.
Methods
A total of 44 AMPK-related variants associated with HbA1c (%) were used as instruments for AMPK activation. The primary outcome was the modified Rankin Scale (mRS) score at 3 months following the onset of ischemic stroke, evaluated as a dichotomous variable (3–6 vs. 0–2) and subsequently as an ordinal variable. Summary-level data for the 3-month mRS were obtained from the Genetics of Ischemic Stroke Functional Outcome network, including 6,165 patients with ischemic stroke. The inverse-variance weighted method was used to obtain causal estimates. The alternative MR methods were used for sensitivity analysis.
Results
Genetically predicted AMPK activation was significantly associated with lower odds of poor functional outcome (mRS 3–6 vs. 0–2, odds ratio [OR]: 0.06, 95% confidence interval [CI]: 0.01–0.49, P=0.009). This association was maintained when 3-month mRS was analyzed as an ordinal variable. Similar results were observed in the sensitivity analyses, and there was no evidence of pleiotropy.
Conclusion
This MR study provided evidence that AMPK activation by metformin may exert beneficial effects on functional outcome following ischemic stroke.

Keyword

Mendelian randomization; Stroke; Metformin; Drug-repurposing

Reference

References

1. GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet Neurol. 2021; 20:795–820.
2. Aroda VR, Knowler WC, Crandall JP, Perreault L, Edelstein SL, Jeffries SL, et al. Metformin for diabetes prevention: insights gained from the diabetes prevention program/diabetes prevention program outcomes study. Diabetologia. 2017; 60:1601–1611.
Article
3. Luo S, Schooling CM, Wong ICK, Au Yeung SL. Evaluating the impact of AMPK activation, a target of metformin, on risk of cardiovascular diseases and cancer in the UK Biobank: a Mendelian randomisation study. Diabetologia. 2020; 63:2349–2358.
Article
4. Triggle CR, Ding H. Cardiovascular impact of drugs used in the treatment of diabetes. Ther Adv Chronic Dis. 2014; 5:245–268.
Article
5. Graham GG, Punt J, Arora M, Day RO, Doogue MP, Duong JK, et al. Clinical pharmacokinetics of metformin. Clin Pharmacokinet. 2011; 50:81–98.
Article
6. Ma T, Tian X, Zhang B, Li M, Wang Y, Yang C, et al. Low-dose metformin targets the lysosomal AMPK pathway through PEN2. Nature. 2022; 603:159–165.
Article
7. Jia J, Cheng J, Ni J, Zhen X. Neuropharmacological actions of metformin in stroke. Curr Neuropharmacol. 2015; 13:389–394.
Article
8. Li J, Benashski SE, Venna VR, McCullough LD. Effects of metformin in experimental stroke. Stroke. 2010; 41:2645–2652.
Article
9. Westphal LP, Widmer R, Held U, Steigmiller K, Hametner C, Ringleb P, et al. Association of prestroke metformin use, stroke severity, and thrombolysis outcome. Neurology. 2020; 95:e362–e373.
Article
10. Smith GD, Ebrahim S. ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003; 32:1–22.
Article
11. Burgess S, Thompson SG. Mendelian randomization: methods for using genetic variants in causal estimation. 1st ed. London: Chapman & Hall/CRC Press;2015.
12. Wheeler E, Leong A, Liu CT, Hivert MF, Strawbridge RJ, Podmore C, et al. Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: a transethnic genome-wide meta-analysis. PLoS Med. 2017; 14:e1002383.
13. Söderholm M, Pedersen A, Lorentzen E, Stanne TM, Bevan S, Olsson M, et al. Genome-wide association meta-analysis of functional outcome after ischemic stroke. Neurology. 2019; 92:e1271–e1283.
Article
14. Paternoster L, Tilling K, Davey Smith G. Genetic epidemiology and Mendelian randomization for informing disease therapeutics: conceptual and methodological challenges. PLoS Genet. 2017; 13:e1006944.
Article
15. Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018; 50:524–537.
16. Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, et al. The trans-ancestral genomic architecture of glycemic traits. Nat Genet. 2021; 53:840–860.
17. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013; 37:658–665.
Article
18. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016; 40:304–314.
Article
19. Zhao Q, Wang J, Hemani G, Bowden J, Small DS. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. Ann Statist. 2020; 48:1742–1769.
Article
20. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015; 44:512–525.
Article
21. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018; 50:693–698.
Article
22. Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017; 46:1734–1739.
Article
23. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-base platform supports systematic causal inference across the human phenome. Elife. 2018; 7:e34408.
Article
Full Text Links
  • JOS
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr