Re: The association between glucose-dependent insulinotropic polypeptide and/or glucagon-like peptide-1 receptor agonist prescriptions and substance-related outcomes in patients with opioid and alcohol use disorders: A real-world data analysis

Abstract

We appreciate the study by Qeadan et al. [1] suggesting an association between glucose-dependent insulinotropic polypeptide (GIP) and/or glucagon-like peptide-1 receptor agonists (GLP-1) and lower rates of opioid overdose and alcohol intoxication among patients with opioid use disorder (OUD) and alcohol use disorder (AUD). Although promising, several methodological limitations warrant critical discussion.

A central issue lies in the definition of study entry for the comparator group, using a random date following the diagnosis of OUD or AUD. This approach introduces potential bias by failing to align the clinical trajectories between the groups. Unlike the defined index time for the GIP/GLP-1 group, which coincides with a specific intervention (initiation of therapy), the index time for the comparator group lacks a similar clinical context. Based on plotted incidence rates (Figure 1), it appears that the timing of study entry post OUD/AUD diagnosis is skewed toward acute substance use-related care, with peaks in opioid overdose or alcohol intoxication incidence only 1 month after study entry. In contrast, GIP/GLP-1 treatment was likely initiated during stable clinical periods, far from the acute OUD/AUD management phase, illustrated by flat outcome incidence rates, which align with those of the control group after the first year of follow-up. To better control this potential time-related bias and align substance use disorder trajectories [2, 3], matching the time intervals between OUD/AUD diagnosis and study entry between both groups would have been desirable.

Further, even if trajectories were aligned, choosing a non-active comparator design is subject to residual confounding from the differences in healthcare engagement, treatment-seeking behaviors and comorbidities [4]. This notion is reflected in the substantial differences in patient characteristics, shift in adjusted interval rate ratios toward the null and weakened protected effect when restricting the analysis to patients with a history of Type-2 diabetes or obesity. Although the authors adjusted for various covariates, unmeasured confounders related to disease severity, healthcare access and provider practices could still influence the results. A subgroup analysis with an active comparator group [5, 6], such as patients initiating other diabetes treatments [e.g. sodium-glucose cotransporter-2 (SGLT-2) inhibitors or sulfonylureas], would help minimize confounding by indication, because they ensure that both groups represent populations similarly engaged in healthcare and facing comparable clinical decisions.

Finally, using electronic health record data adds additional complexity because of potential gaps in data continuity [7-10]. Notably, patients in the comparator group were less likely to have insurance, which raises concerns about differential exposure, confounder and outcome measurement between the two groups. The authors conducted sensitivity analyses requiring 1 or 2 years of follow-up, which would exclude patients who died from opioid overdose and introduce potential selection bias. As shown in supplemental table 15, the effect on opioid overdose was non-significant among cohorts with 2-year follow-up. Accounting for data continuity and defining censoring events, including medication discontinuation or no data capture, in both primary and time-to-event analyses, will help improve study validity.

Adherence to transparency and reporting standards for pharmacoepidemiological studies using real-world data [11-13] and addressing the methodological limitations in future research is warranted to strengthen the validity of the findings.

Yanning Wang: Conceptualization; methodology; writing—original draft; writing—review and editing. Almut G. Winterstein: Conceptualization; methodology; writing—review and editing.

A.G.W. reported receiving grant funding from MSD, the National Institutes of Health, the United States Food and Drug Administration, Centers for Disease Control and Prevention, the Agency for Healthcare Research and Quality, The Bill and Melinda Gates Foundation and the State of Florida and consulting or speaker fees from Ipsen, Bayer AG, Arbor Pharmaceuticals LLC, Novo Nordisk, Lykos and Syneos Health outside the submitted work.