Metformin Could Be Repurposed To Treat Atrial Fibrillation


A new study by researcher from Cleveland Clinic, Ohio-USA And Northwestern University-Illinois-USA has found that the common generic drug used to treat type 2 diabetes..metformin, can be used to treat atrial fibrillation.

The study findings were published in the peer reviewed journal: Cell Reports Medicine.

Pharmacological management of AF is limited by adverse effects and toxicity rates of anti-arrhythmic drugs and poor long-term efficacy.12 Here we generated an AF disease network module and utilized a network-based approach to prioritize alternative drug options for AF (Figure 1).

We showed that our mechanism-based protein-protein interactome can identify drug-AF associations. Specifically, we identified metformin as a high-confidence candidate for drug repurposing for AF using network proximity analysis of drug targets to the AF disease module and validate it using gene expression analysis of drug treatments in human cell lines and large-scale pharmacoepidemiologics analysis.

Recent advances in network medicine have enabled approaches for drug repurposing in cardiovascular diseases.25 We highlighted several improvements of our current study compared with previous network-based approaches.21,58 A strength of our study is the biorepository of LA tissue cohort of patients with AF and control individuals used to generate the AF disease module compared with traditional disease-associated gene approaches in the literature.36,58

We also validate the network proximity-based predictions using drug-gene signature-based enrichments. We posited that, if a drug significantly reverses dysregulated gene expression in human cell lines, then such a drug can potentially reverse gene neighborhood expression in AF disease modules derived from our LA tissues cohort of patients with AF. Finally, we validated a highly promising drug candidate (metformin) using large-scale EHR data.

We tested whether our transcriptomics-based disease module approach outperformed using traditional disease-associated gene-based approaches. We utilized 34 AF risk genes from the HGMD34 and identified 282 candidate drugs (Z < −1.0, p < 0.05). Using this approach, neither metformin (Z = 0.037, p = 0.419) nor phenformin (Z = −1.36, p = 0.116) passed the significance threshold for drug repurposing. The data suggest that human samples as well additional ES analyses can provide a higher-quality list of putative repurposed drug candidates for AF.

Several studies have provided strong evidence for the association of AF with metabolic syndrome diseases. Metformin is a first-line FDA-approved medication for type 2 diabetes mellitus (T2DM). The Framingham study showed a significant risk of developing AF in individuals with diabetes (OR = 1.4 for men and 1.6 for women, respectively) after multivariable adjustment.45

Several follow-up studies have strengthened this observation, showing an increased AF risk with longer disease duration or worse glycemic control.46,47 This evidence suggests that managing metabolic and inflammatory pathways in patients with AF may provide therapeutic benefits.

Metformin targets and activates AMPK, a master regulator of the metabolic stress response that senses AMP/ATP levels. The heterodimeric protein is composed of alpha (catalytic) and beta/gamma (regulatory) subunits encoded by genes such as PRKAA1, PRKAA2, and PRKAB1. PRKAA1 was present in the AF subnetwork.59 AMPK regulates glucose metabolism, fatty acid oxidation, and autophagy via mTORC1.60

AMPK also phosphorylates and inactivates acetyl-CoA carboxylase, a gene (ACACB) our network-based analysis identified as a target of metformin.61 Additionally, metformin suppresses DRP-1-mediated mitochondrial fission via an AMPK mechanism, reducing mitochondrial fragmentation in mice,62 promoting mitophagy to clear dysfunctional mitochondria,63 and reducing endoplasmic reticulum (ER) stress, reactive oxygen species production,64 and protein synthesis, which may reduce proteotoxic stress.65 Isoproterenol suppresses AMPK and can lead to cardiomyocyte apoptosis and ER stress, and metformin protects against this stress.66

Here we found that TGFB1, a significant DEG node in our AF network module, was downregulated following metformin treatment in a-iCMs. In clinical studies of patients with AF compared with patients with sinus rhythm, serum concentrations of tumor necrosis factor alpha (TNF-α) and TGFB1 were increased.67,68 TGFB1 is a well-known pro-fibrotic cytokine that promotes structural and electrical remodeling of the atria.69

Interstitial fibrosis promotes slow heterogeneous electrical conduction between myocytes, contributing to a substrate for reentrant electrical activity. TGFB1 may also affect calcium handling. Reduced L-type calcium channel currents (ICaL) and reduced expression of Cav1.2 (CACNA1C) following TGFB1 exposure have been reported in neonatal rat atrial myocytes but have not yet been reported in human atrial tissues.70 Figure S7 shows an inverse relation of mRNA for TGFB1 with CACNA1C in adult human LA tissues (n = 265, p < 0.001).

Furthermore, we found NPPB (brain natriuretic peptide [BNP]) and CXCL12 to be PPI neighbors of the metformin target DPP4 (Figure 4A). BNP can suppress the activity of the renin-aldosterone-angiotensin system (RAAS).71 Overstimulating the RAAS via angiotensin II has been clinically shown to promote localized oxidative stress by activating nuclear factor κB (NF-κB) and increasing production of interleukin-6 (IL-6) and C-reactive protein (CRP).

As noted above, these inflammatory markers are elevated in patients with AF rhythm. The cytokine CXCL12, also a PPI neighbor of DPP4, is responsible for recruiting monocytes and lymphocytes. Systemic levels of macrophages and activated T lymphocytes are elevated in patients with persistent AF.72,73

These findings strengthen the powerful role of inflammation in AF disease etiology.

Still, one might wonder whether metformin’s benefit is due to its effect on metabolic gene expression, the pleiotropic benefits from long-term weight loss, or amelioration of metabolic syndrome.74, 75, 76, 77 It improves insulin resistance and inflammation/oxidative stress response mediated by free fatty acids, leptin, and other adipokines, which may target the pathophysiological link between obesity and AF.

Using large-scale EHR data, we associated a significantly reduced risk of AF onset in diabetic patients taking metformin compared with other diabetic drugs, such as sulfonylurea and TZD. However, our pharmacoepidemiologic analysis did not show reduced risk in AF with GLP1RA or DPP4. There are several possible explanations. First, other GLP1RAs are a new-generation anti-T2DM medication with fewer users in our current EHR database compared with sulfonylurea and TZD (Table 1), which may be underpowered during pharmacoepidemiologic analysis. GLP1RA or DPP4 may have strong efficacy in reducing blood glucose levels and weight loss compared with metformin.

This suggests that the metabolic effects of metformin may be associated with the utility against AF rather than beneficial outcomes of weight loss. Furthermore, we observed gender- and race-specific AF outcomes. Several reports indicate that AF is more common in males than females, but the prevalence of risk factors differs.50, 51, 52, 53

For example, diabetes has been observed as an AF risk factor for women but not men.53 We found that both male and female metformin users were significantly associated with reduced likelihood of AF, but female metformin users had higher protection. This could be explained by the differences in managing AF risk factors on the effect of AF disease. However, further work would be required to validate this. We also observed a greater reduction in AF risk among Black metformin users versus the combination comparator group. Further work is required to explain whether the reduced risk is environmental or genetic.

We also found that DPP4 is a metformin target responsible for regulating insulin secretion by antagonizing GLP1. Metformin had a more beneficial role than TZD and sulfonylureas, which are also responsible for regulating insulin secretion. This finding suggests that the multitarget effects of metformin are more effective than simply regulating cellular glucose utilization.

This analysis was performed using data primarily from patients with diabetes because metformin is less prescribed for non-diabetic patients outside of clinical investigations, although it has been used for pre-diabetes. Activation of AMPK has been shown to improve cardiac function and, in turn, protect from AF.78,79 However, addition of in vitro functional testing on a-iCMs helps to address the potential for more direct effects of metformin in cardiomyocytes beyond potential indirect effects on HF, obesity, or other potential in vivo effects.

In an observational study of 645,710 T2DM subjects over a 13-year follow-up, metformin use was associated with 20% less AF.48 However, no prospective trials have reported metformin for AF in non-diabetics. Metformin has been proposed as upstream therapy in patients scheduled for AF ablation ( NCT02931253).

An ongoing clinical trial, Upstream Targeting for the Prevention of Atrial Fibrillation: Targeting Risk Interventions and Metformin for Atrial Fibrillation (TRIM-AF), is investigating the benefit of metformin and lifestyle/risk factor modification interventions in patients with AF ( NCT03603912). Both interventions have been reported to target AMPK.

Two recent studies have utilized healthy insurance claims data to test AF disease burden with metformin treatment. Using the International Business Machines Corporation (IBM) MarketScan Medicare Supplemental Database, Ostropolets et al.80 have reported that patients on metformin monotherapy had significantly reduced risk of atrial arrhythmias compared with monotherapy with DPP4 or TZD medications.

In another study, Tseng81 used Taiwan’s National Health Insurance database and reported reduced AF-related hospitalizations in patients with newly diagnosed T2DM who took metformin versus those who did not. However, residual confounding may exist in these studies because insurance claims data are primarily collected for administrative purposes and do not contain detailed clinical information.

Our pharmacoepidemiologics analysis, relying on very large patient-level EHR data, has several advantages. First, we use large-scale, longitudinal EHR data which contain various detailed clinical information for adjusting various possible confounding factors, including heart disease, vascular disease, renal disease, and many others (Table 1). We believe that there will be lower residual confounding risk in our EHR data analysis compared with previous health insurance claims databases.80,81


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