Natural language processing of 22,467 Reddit migraine posts identified constipation, depression, and vomiting as erenumab-associated side effects, while fremanezumab showed no clear adverse event signals.
22,467 posts analyzedfrom Reddit migraine forum over 10 years, revealing different side effect profiles for erenumab vs fremanezumab
What the researchers found
NLP analysis identified "constipation," "depression," "vomiting," and "muscle" as recurring erenumab-associated terms on Reddit, while fremanezumab showed no definite adverse event signals — suggesting different real-world tolerability profiles despite targeting the same pathway.
Why it matters
Clinical trial participants are highly selected and may not represent the diverse patient populations actually taking CGRP drugs. Social media captures experiences from a broader range of patients, potentially identifying side effects that trials miss — information valuable for both patients and prescribers.
The numbers in context
Analysis covered social media posts about CGRP-targeting medications, identifying adverse events not fully captured in clinical trials.
How the study worked
Computational linguistics analysis of 22,467 posts from Reddit r/Migraine (2010-2020). Compared word frequencies between medication-specific posts and control posts. Validated approach using known adverse events of propranolol and topiramate before applying to erenumab and fremanezumab.
Who was studied
Social media posts from users of anti-CGRP monoclonal antibodies for migraine prevention
What this study cannot tell us
Social media data is self-reported and unverified. Negativity bias means adverse experiences are more likely to be posted than positive ones. Small sample for fremanezumab (73 posts) limits conclusions. Keyword associations do not prove causation. Reddit demographics may not represent all migraine patients.
How to read the evidence
Moderate evidence: validated NLP methodology applied to a large social media dataset, but inherent limitations of self-reported, unverified online data reduce reliability.
When this study was published
Published in 2024 using Reddit data from 2010-2020. Covers the early real-world experience period for CGRP antibodies.
The bigger picture
Social media pharmacovigilance is an emerging field that could transform how drug safety is monitored. With millions of people sharing health experiences online, AI-powered text analysis can detect safety signals faster and from more diverse populations than traditional adverse event reporting. This study demonstrates the approach works for CGRP migraine drugs and could be applied to other medication classes.
Questions still open
- Are the depression signals for erenumab confirmed in larger pharmacovigilance databases or controlled studies?
- Why does erenumab (receptor blocker) show more adverse event signals than fremanezumab (ligand blocker)?
- Could social media NLP analysis become a standard component of post-marketing drug safety surveillance?
Common questions
Does erenumab cause depression?
Is fremanezumab better tolerated than erenumab?
Read the original research
Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene-Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media.
JMIR formative research, 8, e58176
Citation
Zhang, Pengfei; Kamitaki, Brad K; Do, Thien Phu. (2024). Crowdsourcing Adverse Events Associated With Monoclonal Antibodies Targeting Calcitonin Gene-Related Peptide Signaling for Migraine Prevention: Natural Language Processing Analysis of Social Media.. JMIR formative research, 8, e58176. https://doi.org/10.2196/58176