Metformin: An Unexpected Ally Against Long COVID?

TOPLINE:

Metformin use in adults with type 2 diabetes (T2D) is associated with a slightly lower incidence of long COVID and death within 18src days after SARS-CoV-2 infection.

METHODOLOGY:

Previous studies have shown that metformin use before and during SARS-CoV-2 infection reduces severe COVID-19 and postacute sequelae of SARS-CoV-2 (PASC), also referred to as long COVID, in adults.
A retrospective cohort analysis was conducted to evaluate the association between metformin use before and during SARS-CoV-2 infection and the subsequent incidence of PASC.
Researchers used data from the National COVID Cohort Collaborative (N3C) and National Patient-Centered Clinical Research Network (PCORnet) electronic health record (EHR) databases to identify adults (age, ≥ 21 years) with T2D prescribed a diabetes medication within the past 12 months.
Participants were categorized into those using metformin (metformin group) and those using other noninsulin diabetes medications such as sulfonylureas, dipeptidyl peptidase-4 inhibitors, or thiazolidinediones (the comparator group); those who used glucagon-like peptide 1 receptor agonists or sodium-glucose cotransporter-2 inhibitors were excluded.
The primary outcome was the incidence of PASC or death within 18src days after SARS-CoV-2 infection, defined using International Classification of Diseases Usrc9.9 diagnosis code and/or computable phenotype defined by a predicted probability of> 75% for PASC using a machine learning model trained on patients diagnosed using Usrc9.9 (PASC computable phenotype).

TAKEAWAY:

Researchers identified 51,385 and 37,947 participants from the N3C and PCORnet datasets, respectively.
Metformin use was associated with a 21% lower risk for death or PASC using the Usrc9.9 diagnosis code (P <.srcsrc1) and a 15% lower risk using the PASC computable phenotype (P <.srcsrc1) in the N3C dataset than non-metformin use.
In the PCORnet dataset, the risk for death or PASC was 13% lower using the Usrc9.9 diagnosis code (P=.src8) with metformin use vs non-metformin use, whereas the risk did not differ significantly between the groups when using the PASC computable phenotype (P=.58).
The incidence of PASC using the Usrc9.9 diagnosis code for the metformin and comparator groups was similar between the two datasets (1.6% and 2.src% in N3C and 2.2 and 2.6% in PCORnet, respectively).
However, when using the computable phenotype, the incidence rates of PASC for the metformin and comparator groups were 4.8% and 5.2% in N3C and 25.2% and 24.2% in PCORnet, respectively.

IN PRACTICE:

“The incidence of PASC was lower when defined by [International Classification of Diseases] code compared with a computable phenotype in both databases,” the authors wrote. “This may reflect the challenges of clinical care for adults needing chronic medication management and the likelihood of those adults receiving a formal PASC diagnosis.”

SOURCE:

The study was led by Steven G. Johnson, PhD, Institute for Health Informatics, University of Minnesota in Minneapolis. It was published online in Diabetes Care.

LIMITATIONS:

The use of EHR data had several limitations, including the inability to examine a dose-dependent relationship and the lack of information on whether medications were taken before, during, or after the acute infection. The outcome definition involved the need for a medical encounter and, thus, may not capture data on all patients experiencing symptoms of PASC. The analysis focused on the prevalent use of chronic medications, limiting the assessment of initiating metformin in those diagnosed with COVID-19.

DISCLOSURES:

The study was supported by the National Institutes of Health Agreement as part of the RECOVER research program. One author reported receiving salary support from the Center for Pharmacoepidemiology and owning stock options in various pharmaceutical and biopharmaceutical companies. Another author reported receiving grant support and consulting contracts, being involved in expert witness engagement, and owning stock options in various pharmaceutical, biopharmaceutical, diabetes management, and medical device companies.

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.