Transcriptomics-based network medicine approach identifies metformin as a repurposable drug for atrial fibrillation

Jessica C Lal, Chengsheng Mao, Yadi Zhou, Shamone R Gore-Panter, Julie H Rennison, Beth S Lovano, Laurie Castel, Jiyoung Shin, A Marc Gillinov, Jonathan D Smith, John Barnard, David R Van Wagoner, Yuan Luo, Feixiong Cheng, Mina K Chung, Jessica C Lal, Chengsheng Mao, Yadi Zhou, Shamone R Gore-Panter, Julie H Rennison, Beth S Lovano, Laurie Castel, Jiyoung Shin, A Marc Gillinov, Jonathan D Smith, John Barnard, David R Van Wagoner, Yuan Luo, Feixiong Cheng, Mina K Chung

Abstract

Effective drugs for atrial fibrillation (AF) are lacking, resulting in significant morbidity and mortality. This study demonstrates that network proximity analysis of differentially expressed genes from atrial tissue to drug targets can help prioritize repurposed drugs for AF. Using enrichment analysis of drug-gene signatures and functional testing in human inducible pluripotent stem cell (iPSC)-derived atrial-like cardiomyocytes, we identify metformin as a top repurposed drug candidate for AF. Using the active compactor, a new design analysis of large-scale longitudinal electronic health record (EHR) data, we determine that metformin use is significantly associated with a reduced risk of AF (odds ratio = 0.48, 95%, confidence interval [CI] 0.36-0.64, p < 0.001) compared with standard treatments for diabetes. This study utilizes network medicine methodologies to identify repurposed drugs for AF treatment and identifies metformin as a candidate drug.

Keywords: EHR; atrial fibrillation; drug repurposing; electronic health record; human inducible pluripotent stem cells; network medicine; pharmacoepidemiology; systems biology.

Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Graphical abstract
Graphical abstract
Figure 1
Figure 1
Study overview (A) We utilized a systems pharmacology-based network medicine platform to quantify the proximity of interactions between the atrial fibrillation (AF) interactome nodes and drug targets in the PPI network. (B) GSEA of known targets are used to validate the in silico approach and nominate candidate drugs. (C and D) Using human induced pluripotent stem cell-derived atrial-like cardiomyocytes (a-iCMs; C) as well as large-scale pharmacoepidemiologic analysis (D), we are able to validate metformin as a repurposed drug for AF. Created with BioRender.com.
Figure 2
Figure 2
The atrial fibrillation interactome The network highlights the atrial fibrillation (AF) interactome that connects 245 AF enriched genes. Node size is proportional to −log10 p value, and color corresponds to log2 fold change (log2FC) in AF compared with sinus rhythm (STAR Methods). The AF disease module (defined by the largest connected component in the human interactome) shown includes 245 unique proteins (nodes) and 350 PPIs (edges). ∗p 

Figure 3

Network medicine applied to AF…

Figure 3

Network medicine applied to AF drug repurposing (A) A subnetwork is shown to…

Figure 3
Network medicine applied to AF drug repurposing (A) A subnetwork is shown to highlight the 54 candidate drugs associated with AF DEGs and their associated targets. Node size is proportional to Z score. Drugs are colored by their first-level anatomical therapeutic chemical (ATC) classification. (B–E) Four candidate drugs with gene set enrichment analysis (GSEA) ES > 0 and p 

Figure 4

Validation of the AF repurposed…

Figure 4

Validation of the AF repurposed drug candidate metformin using a-iCMs (A) A subnetwork…

Figure 4
Validation of the AF repurposed drug candidate metformin using a-iCMs (A) A subnetwork of metformin, drug targets, and PPI neighbors. The node color corresponds to the expected effect of metformin on target expression; green indicates agonist, and red indicates antagonist. (B) DE genes of a-iCMs treated with metformin (n = 3) or water (n = 3) for 30 h. Data are expressed as FC, with red designating increased expression and blue decreased expression. All experimental combinations had 3 replicates. (C) A subnetwork representing the significant differentially expressed genes (DEGs) and their PPI neighbors (gray). The node size is proportional to −log10 p value, and color corresponds to log2FC in metformin-treated a-iCMs versus the control after 1-Hz pacing stimulation. See also Figures S3, S4, and S5 and Tables S2 and S9.

Figure 5

Subnetworks of upregulated and downregulated…

Figure 5

Subnetworks of upregulated and downregulated genes after metformin treatment in a-iCMs The central…

Figure 5
Subnetworks of upregulated and downregulated genes after metformin treatment in a-iCMs The central node color corresponds to upregulated (green) or downregulated (red) genes after metformin treatment in pacing a-iCMS. Outer node colors correspond to KEGG pathway classification, and class is listed next to each pathway cluster. See also Table S9.

Figure 6

Pharmacoepidemiologic validation of metformin in…

Figure 6

Pharmacoepidemiologic validation of metformin in reducing AF occurrence (A–D) OR and 95% confidence…

Figure 6
Pharmacoepidemiologic validation of metformin in reducing AF occurrence (A–D) OR and 95% confidence interval (CI) for metformin versus (A) combination of the four drug groups (all, dipeptidyl-peptidase 4 sulfonylurea [DPP4], thiazolidinedione [TZD], sulfonylurea, and glucagon-like peptide 1 receptor agonist [GLP1RA]) (n = 3,578), (B) DPP4 (n = 1,244), (C) sulfonylurea (n = 2,352), and (D) TZD (n = 288). For each of the four comparisons, the results for comparisons between subgroups (including female, male, Black, and White) are also shown. Patient groups were matched using PS matching with the variables age, gender, race, and comorbidities (listed in Table 1) for the overall group comparisons. For the subgroup of male and female, the matching variables excluded gender, and for the subgroup Black and White, the matching variables excluded race. Logistic regression models were used for statistical inference of the AF ORs. Subgroup analyses were performed in females (orange), males (green), Black Americans (dark green), and White Americans (blue). ∗p 
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References
    1. Michaud G.F., Stevenson W.G. Atrial fibrillation. N. Engl. J. Med. 2021;384:353–361. doi: 10.1056/NEJMcp2023658. - DOI - PubMed
    1. Piccini J.P., Hammill B.G., Sinner M.F., Jensen P.N., Hernandez A.F., Heckbert S.R., Benjamin E.J., Curtis L.H. Incidence and prevalence of atrial fibrillation and associated mortality among Medicare beneficiaries, 1993-2007. Circ. Cardiovasc. Qual. Outcomes. 2012;5:85–93. doi: 10.1161/circoutcomes.111.962688. - DOI - PMC - PubMed
    1. Wilke T., Groth A., Mueller S., Pfannkuche M., Verheyen F., Linder R., Maywald U., Bauersachs R., Breithardt G. Incidence and prevalence of atrial fibrillation: an analysis based on 8.3 million patients. Europace. 2013;15:486–493. doi: 10.1093/europace/eus333. - DOI - PubMed
    1. Benjamin E.J., Muntner P., Alonso A., Bittencourt M.S., Callaway C.W., Carson A.P., Chamberlain A.M., Chang A.R., Cheng S., Das S.R., et al. Heart disease and stroke statistics-2019 update: a report from the American heart association. Circulation. 2019;139:e56–e528. doi: 10.1161/cir.0000000000000659. - DOI - PubMed
    1. Samol A., Masin M., Gellner R., Otte B., Pavenstädt H.J., Ringelstein E.B., Reinecke H., Waltenberger J., Kirchhof P. Prevalence of unknown atrial fibrillation in patients with risk factors. Europace. 2013;15:657–662. doi: 10.1093/europace/eus366. - DOI - PubMed
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Figure 3
Figure 3
Network medicine applied to AF drug repurposing (A) A subnetwork is shown to highlight the 54 candidate drugs associated with AF DEGs and their associated targets. Node size is proportional to Z score. Drugs are colored by their first-level anatomical therapeutic chemical (ATC) classification. (B–E) Four candidate drugs with gene set enrichment analysis (GSEA) ES > 0 and p 

Figure 4

Validation of the AF repurposed…

Figure 4

Validation of the AF repurposed drug candidate metformin using a-iCMs (A) A subnetwork…

Figure 4
Validation of the AF repurposed drug candidate metformin using a-iCMs (A) A subnetwork of metformin, drug targets, and PPI neighbors. The node color corresponds to the expected effect of metformin on target expression; green indicates agonist, and red indicates antagonist. (B) DE genes of a-iCMs treated with metformin (n = 3) or water (n = 3) for 30 h. Data are expressed as FC, with red designating increased expression and blue decreased expression. All experimental combinations had 3 replicates. (C) A subnetwork representing the significant differentially expressed genes (DEGs) and their PPI neighbors (gray). The node size is proportional to −log10 p value, and color corresponds to log2FC in metformin-treated a-iCMs versus the control after 1-Hz pacing stimulation. See also Figures S3, S4, and S5 and Tables S2 and S9.

Figure 5

Subnetworks of upregulated and downregulated…

Figure 5

Subnetworks of upregulated and downregulated genes after metformin treatment in a-iCMs The central…

Figure 5
Subnetworks of upregulated and downregulated genes after metformin treatment in a-iCMs The central node color corresponds to upregulated (green) or downregulated (red) genes after metformin treatment in pacing a-iCMS. Outer node colors correspond to KEGG pathway classification, and class is listed next to each pathway cluster. See also Table S9.

Figure 6

Pharmacoepidemiologic validation of metformin in…

Figure 6

Pharmacoepidemiologic validation of metformin in reducing AF occurrence (A–D) OR and 95% confidence…

Figure 6
Pharmacoepidemiologic validation of metformin in reducing AF occurrence (A–D) OR and 95% confidence interval (CI) for metformin versus (A) combination of the four drug groups (all, dipeptidyl-peptidase 4 sulfonylurea [DPP4], thiazolidinedione [TZD], sulfonylurea, and glucagon-like peptide 1 receptor agonist [GLP1RA]) (n = 3,578), (B) DPP4 (n = 1,244), (C) sulfonylurea (n = 2,352), and (D) TZD (n = 288). For each of the four comparisons, the results for comparisons between subgroups (including female, male, Black, and White) are also shown. Patient groups were matched using PS matching with the variables age, gender, race, and comorbidities (listed in Table 1) for the overall group comparisons. For the subgroup of male and female, the matching variables excluded gender, and for the subgroup Black and White, the matching variables excluded race. Logistic regression models were used for statistical inference of the AF ORs. Subgroup analyses were performed in females (orange), males (green), Black Americans (dark green), and White Americans (blue). ∗p 
All figures (7)
Comment in
Similar articles
Cited by
References
    1. Michaud G.F., Stevenson W.G. Atrial fibrillation. N. Engl. J. Med. 2021;384:353–361. doi: 10.1056/NEJMcp2023658. - DOI - PubMed
    1. Piccini J.P., Hammill B.G., Sinner M.F., Jensen P.N., Hernandez A.F., Heckbert S.R., Benjamin E.J., Curtis L.H. Incidence and prevalence of atrial fibrillation and associated mortality among Medicare beneficiaries, 1993-2007. Circ. Cardiovasc. Qual. Outcomes. 2012;5:85–93. doi: 10.1161/circoutcomes.111.962688. - DOI - PMC - PubMed
    1. Wilke T., Groth A., Mueller S., Pfannkuche M., Verheyen F., Linder R., Maywald U., Bauersachs R., Breithardt G. Incidence and prevalence of atrial fibrillation: an analysis based on 8.3 million patients. Europace. 2013;15:486–493. doi: 10.1093/europace/eus333. - DOI - PubMed
    1. Benjamin E.J., Muntner P., Alonso A., Bittencourt M.S., Callaway C.W., Carson A.P., Chamberlain A.M., Chang A.R., Cheng S., Das S.R., et al. Heart disease and stroke statistics-2019 update: a report from the American heart association. Circulation. 2019;139:e56–e528. doi: 10.1161/cir.0000000000000659. - DOI - PubMed
    1. Samol A., Masin M., Gellner R., Otte B., Pavenstädt H.J., Ringelstein E.B., Reinecke H., Waltenberger J., Kirchhof P. Prevalence of unknown atrial fibrillation in patients with risk factors. Europace. 2013;15:657–662. doi: 10.1093/europace/eus366. - DOI - PubMed
Show all 102 references
Publication types
[x]
Cite
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Format: AMA APA MLA NLM
Figure 4
Figure 4
Validation of the AF repurposed drug candidate metformin using a-iCMs (A) A subnetwork of metformin, drug targets, and PPI neighbors. The node color corresponds to the expected effect of metformin on target expression; green indicates agonist, and red indicates antagonist. (B) DE genes of a-iCMs treated with metformin (n = 3) or water (n = 3) for 30 h. Data are expressed as FC, with red designating increased expression and blue decreased expression. All experimental combinations had 3 replicates. (C) A subnetwork representing the significant differentially expressed genes (DEGs) and their PPI neighbors (gray). The node size is proportional to −log10 p value, and color corresponds to log2FC in metformin-treated a-iCMs versus the control after 1-Hz pacing stimulation. See also Figures S3, S4, and S5 and Tables S2 and S9.
Figure 5
Figure 5
Subnetworks of upregulated and downregulated genes after metformin treatment in a-iCMs The central node color corresponds to upregulated (green) or downregulated (red) genes after metformin treatment in pacing a-iCMS. Outer node colors correspond to KEGG pathway classification, and class is listed next to each pathway cluster. See also Table S9.
Figure 6
Figure 6
Pharmacoepidemiologic validation of metformin in reducing AF occurrence (A–D) OR and 95% confidence interval (CI) for metformin versus (A) combination of the four drug groups (all, dipeptidyl-peptidase 4 sulfonylurea [DPP4], thiazolidinedione [TZD], sulfonylurea, and glucagon-like peptide 1 receptor agonist [GLP1RA]) (n = 3,578), (B) DPP4 (n = 1,244), (C) sulfonylurea (n = 2,352), and (D) TZD (n = 288). For each of the four comparisons, the results for comparisons between subgroups (including female, male, Black, and White) are also shown. Patient groups were matched using PS matching with the variables age, gender, race, and comorbidities (listed in Table 1) for the overall group comparisons. For the subgroup of male and female, the matching variables excluded gender, and for the subgroup Black and White, the matching variables excluded race. Logistic regression models were used for statistical inference of the AF ORs. Subgroup analyses were performed in females (orange), males (green), Black Americans (dark green), and White Americans (blue). ∗p 
All figures (7)

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