GLP-1s for ME/CFS and long COVID: hype, hope and hidden dangers

The promise of GLP-1s is real but so is the risk of getting it wrong

Few drug classes have generated as much excitement in modern medicine as GLP-1 receptor agonists.

Originally developed for type 2 diabetes and obesity, drugs such as semaglutide and tirzepatide are now being investigated across more than 100 diseases. Researchers are exploring their potential in cardiovascular disease, neurodegenerative disorders, inflammatory conditions, and increasingly, complex chronic illnesses such as ME/CFS and long COVID.

The scientific interest is understandable. GLP-1s influence far more than appetite and glucose control. They affect neurotransmitter signaling, neuroplasticity, endothelial function, inflammation, immune regulation, and metabolic pathways that increasingly appear central to many chronic diseases.

But perhaps the most important question surrounding GLP-1s is not whether they work, but which patients are most likely to benefit and tolerate treatment, with reduced risk of side effects or serious adverse events.

As researchers explore the potential of GLP-1 therapies in ME/CFS and long COVID, the challenge is not only finding promising treatments, but identifying the patients most likely to benefit, those least likely to respond, and those most at risk of side effects or adverse events.

For patient populations such as ME/CFS and long COVID, where even small adverse events could have serious implications on an individual's health, that becomes increasingly important challenge sits at the heart of mechanistic patient stratification.

Why ME/CFS and long COVID researchers are paying attention

Interest in GLP-1s within the ME/CFS and long COVID communities is not driven by hype alone.

PrecisionLife's published genetics studies have revealed biological mechanisms in both conditions involving metabolic dysfunction, neurological regulation, inflammation, endothelial biology, circadian rhythm, insulin signaling, and cellular stress responses [1-4]. Long COVID studies have also demonstrated significant overlap with mechanisms identified in ME/CFS [1,2].

What makes GLP-1s particularly intriguing is that many of these same pathways sit at the center of their mechanism of action.

 That overlap doesn't guarantee that GLP-1s will help people with ME/CFS or long COVID. But it does provide a compelling biological rationale for investigating GLP-1s further. More importantly, it highlights the need for studies that stratify patients according to the biological mechanisms driving their disease, rather than assuming all patients with the same diagnosis are biologically alike. 

The challenge of biological heterogeneity

One of the most important lessons emerging from genetics research is that ME/CFS and long COVID are not single biological diseases.

PrecisionLife's analyses of UK Biobank, DecodeME, Sano GOLD, and All of Us cohorts have consistently shown that these conditions are highly polygenic and biologically heterogeneous [1-4]. Rather than being driven by a single pathway, they appear to comprise multiple mechanistic subgroups involving mitochondrial dysfunction, neurological dysregulation, inflammation, cellular stress responses, metabolic dysfunction, calcium signaling, circadian biology, and immune regulation [1,3,4].

This is important because treatments act on biological mechanisms, not on diagnostic labels. A therapy that works well for a patient whose disease is driven by metabolic dysfunction may have limited benefit in another patient whose dominant biology involves immune dysregulation or neurotransmitter signaling.

Many clinical trials in complex chronic disease assume these patients belong to a single homogeneous population. Increasingly, the evidence suggests otherwise.

This is where mechanistic patient stratification becomes important. By identifying subgroups of patients who share common biological drivers, researchers can design studies that are more likely to detect meaningful treatment effects and less likely to miss them in a biologically mixed population.

The consequence is that potentially effective therapies can appear ineffective when tested in broadly defined patient groups.

What genetics is revealing about shared biology

PrecisionLife's studies have helped establish the first reproducible genetic signals across both ME/CFS and long COVID cohorts.

Our analysis of the DecodeME dataset identified more than 22,000 disease signatures and 259 candidate genes associated with ME/CFS biology, while independent validation in the NIH All of Us program demonstrated strong reproducibility of long COVID disease signatures first identified in earlier cohorts [1,2].

Perhaps most importantly, these analyses revealed substantial overlap between the genetic architecture of ME/CFS and long COVID. Many of the genes associated with long COVID risk and symptom development were also found to be associated with ME/CFS, supporting the growing evidence that the two conditions share important biological features while remaining distinct diseases [1,2]. 

 These findings provide the biological foundation for mechanistic patient stratification. Understanding which pathways are driving disease in different patient subgroups is far more informative than diagnosis alone when selecting therapies, designing clinical trials, or identifying new drug targets. 

The opportunity and the risk of GLP-1 studies

The biological overlap between GLP-1 pathways and mechanisms implicated in ME/CFS and long COVID creates a genuine opportunity, but it also creates the risk of oversimplification.

GLP-1s are powerful medicines. They can influence the gut, brain, metabolism, vascular biology, and immune system simultaneously. These drugs intersect with many of the same biological systems implicated in ME/CFS and long COVID, including glucose homeostasis, neurotransmitter signaling, neuroplasticity, endothelial function, inflammation, and calcium signaling.

This convergence is encouraging, but it does not mean all patients should be expected to respond equally.

Indeed, one of the hidden dangers of current enthusiasm is the assumption that a single treatment approach will suit every patient diagnosed with ME/CFS or long COVID.

The goal should not be to determine whether GLP-1s work for everyone, but to identify which patient subgroups are most likely to benefit, which are most likely to tolerate treatment, and which may be at increased risk of side effects or serious adverse events. That challenge sits at the heart of mechanistic patient stratification.

How mechanistic patient stratification could transform trial design 

Through our partnership, Ovation.io, we've been able to access and analyze their longitudinal pharmacogenomics dataset of approximately 25,000 GLP-1-treated patients with whole-genome sequencing, extensive clinical histories, treatment outcomes, side-effect data, and adverse event information.

From this analysis we have been able to identify the first predictive biomarkers associated with treatment response, side-effect susceptibility, and adverse event risk.

Taken together, these insights point toward a fundamentally different model of clinical trial design.

Rather than recruiting patients solely because they carry a diagnosis, future studies should stratify patients according to disease mechanism, predicted treatment response, side-effect liability, and adverse event risk. Predictive mechanistic biomarkers provide the means to make that stratification possible. 

For pharmaceutical developers, this could mean smaller, faster, and likely more successful clinical studies. For patients, it could mean fewer failed treatment attempts and more personalized therapeutic decisions with less risk  of adverse events.

The missing piece: disease resilience biology

There is another dimension to precision medicine that is often overlooked.

Most genetic studies focus on disease risk. PrecisionLife also studies disease resilience, in which we ask a different question: why do some people remain healthy despite carrying substantial disease risk factors? Using combinatorial analysis, our researchers have identified actively protective biology associated with reduced disease prevalence in long COVID and ME/CFS, despite the presence of risk-associated biology [5].

As well as creating potential opportunities to enable preventative medicine, this has important implications for clinical trials of new treatments too.

The strongest treatment responses may occur not simply in patients with a particular disease mechanism, but in patients who possess that mechanism and lack compensatory resilience biology that could mask treatment effects.

In other words, mechanistic patient stratification can enable researchers to consider not only disease risk and treatment response, but also biological resilience. The patients most likely to demonstrate a treatment effect may be those who possess the relevant disease mechanism but lack compensatory protective pathways.

Could GLP-1s offer a new model for drug repurposing?

Drug repurposing remains one of the fastest routes to new therapies for ME/CFS, long COVID, fibromyalgia, neurodegenerative disease, and other complex chronic disorders. Yet many studies continue to evaluate therapies in biologically diverse populations as though they are uniform.

Future success will likely depend on combining mechanistic disease understanding, predictive response biomarkers, safety biomarkers, and disease resilience biology into a single framework for patient stratification and therapeutic development.

GLP-1s may ultimately become a powerful treatment option for many patients, but they are also demonstrating that the future of medicine lies not in finding a single therapy for every patient, but in matching the right therapy to the right patient at the right time.

The future lies in matching the right therapy to the right patient at the right time - moving beyond one-size-fits-all medicine.

Mechanistic patient stratification is how we get there.

Learn more about PrecisionLife's GLP-1 and ME/CFS research