Longitudinal study of genome-wide DNA methylation in individuals with and without post-acute symptoms following SARS-CoV-2 infection, 2026, Bohlin+

[SNT Gatchaman]

Longitudinal study of genome-wide DNA methylation in individuals with and without post-acute symptoms following SARS-CoV-2 infection

Spoiler: Authors

Jon Bohlin; Yunsung Lee; Ida Henriette Caspersen; Anna Hayman Robertson; Christian M Page; Håkon K Gjessing; Astanand Jugessur; Per Magnus; Siri Mjaaland; Lill Trogstad

BACKGROUND
Symptoms following SARS-CoV-2 infection, referred to as Long-COVID, have been reported since the pandemic. We investigated whether COVID-19 or Long-COVID is associated with persistent genome-wide DNA methylation (DNAm) changes in whole blood using a longitudinal design.

METHODS
DNAm was measured using the Illumina EPIC V2 platform (859,651 CpGs) in 297 adult participants (594 samples in total) from two Norwegian population-based cohorts, with samples collected pre-infection (2020) and during the pandemic (2023). Participants were classified as Long-COVID, COVID-19 (no persistent symptoms), or not infected.

RESULTS
No significant DNAm differences were observed between Long-COVID and not infected at either time point (p = 0.745(FDR)) or during the pandemic specifically (p = 0.629(FDR)). Likewise, no differences were detected between COVID-19 and not infected across both time points (p = 0.883(FDR)) or during the pandemic (p = 0.287(FDR)). Sex-stratified analyses of the X chromosome revealed no significant DNAm differences for Long-COVID or COVID-19 in males (both p = 0.999(FDR)) or females (both p = 0.999(FDR)). Epigenetic age acceleration was also evaluated using DunedinPACE (DP) and PhenoAge (PA), but no significant differences were detected for Long-COVID (p = 0.695 [DP], p = 0.528 [PA]) or COVID-19 (p = 0.624 [DP], p = 0.348 [PA]).

CONCLUSION
No persistent epigenetic age- or DNAm based differences due to Long-COVID or SARS-CoV-2 infection were detected in our cohorts.

Web | DOI | PDF | Epigenomics | Open Access

 

[Utsikt]

What kind of disease mechanisms would be expected to show up in tests like these?

 

[ScoutB]

Well, I appreciate the clear reporting of a null result.

They were using the definition of LC that we typically see -- where, from the sounds of it, loss of smell for 3 months would qualify?

The Long-COVID strata (group 1) included participants with COVID-19 according to the criteria given above who after 3–5 months post infection (June 2022) still reported one of the following symptoms: fatigue, poor memory, brain fog, dizziness, heart palpitations, shortness of breath, reduced lung function, and altered smell and/or taste.

What kind of disease mechanisms would be expected to show up in tests like these?

I am wondering this too. One thing I saw skimming it is that apparently smoking and obesity do cause significant changes to methylation. So they checked these held, to make sure their data was good.

To verify the integrity of [the methylation] data, additional models assessed known associations with smoking status and BMI. These included [methylation] patterns at established smoking-associated CpG sites (see Supplementary Methods), as well as associations with both epigenetic clocks, which were consistent with prior literature.

This was an interesting point:

No significant genome-wide differences were detected either within or between groups: Long-COVID vs. not infected (PFDR = 0.745), or COVID-19 vs. not infected (PFDR = 0.883). These null findings suggest that infection status, as confirmed by positive PCR or antigen self-test, did not significantly influence genome-wide [methylation] profiles (see Figures 1–3). Our study could also indicate that vaccination against COVID did not have any genome-wide effect on [methylation] as well as most participants across all three groups were vaccinated at the time of second sampling.

Do we know if other viruses or vaccines cause genome-wide effects on methylation in blood samples?

I appreciate that they did also restrict to people with fatigue and brain fog specifically. That might be the best they could do with the input data they were working with. Still found no association, but they also only had 19 cases:

To refine the Long-COVID phenotype, we conducted targeted analyses focusing on individuals reporting fatigue and brain fog symptoms, reducing the Long-COVID group to 19 participants for each time point (see Table 1). Despite the more specific phenotype definition, there were no significant [methylation] differences for Long-COVID with fatigue (PFDR = 0.554) or brain fog (PFDR = 0.999) when compared to the not infected group. These models included the same covariate adjustments described above (see Methods section for details).

Has anything like this study been done in ME/CFS?

 

[Yann04]

To refine the Long-COVID phenotype, we conducted targeted analyses focusing on individuals reporting fatigue and brain fog symptoms, reducing the Long-COVID group to 19 participants for each time point (see Table 1). Despite the more specific phenotype definition, there were no significant [methylation] differences for Long-COVID with fatigue (PFDR = 0.554) or brain fog (PFDR = 0.999) when compared to the not infected group. These models included the same covariate adjustments described above (see Methods section for details).

That‘s still very broad and with a subset of the sample size.

I think it‘s great the authors did this study and clearly reported a null result.

But I really don‘t think this rules out the sort of subtle changes that might exist if any changes exist. You probably need to have far stricter diagnostic criteria and subtyping and higher subgroup sample size to properly test for that.

 

[Utsikt]

I think they make a good effort of discussing the limitations of this approach.

Although we did not observe DNAm alterations associated with Long-COVID or COVID-19, this does not preclude the possibility that such effects may exist.

The Illumina EPIC V2 array interrogates only a small fraction of the estimated 28 million CpG sites in the human genome [Citation40], and while many of these are in regulatory regions, it is estimated that up to 80% of CpGs are constitutively methylated and may not have any regulatory relevance [Citation41].

Moreover, the methylome is highly correlated within individuals [Citation42], meaning that subtle or localized changes could be missed in genome-wide analyses.

Since biological samples were collected approximately one year after the last time point it is not impossible that DNAm changes can have reverted, in particular for the COVID-19 infected group.

Taken together, our findings do not support the presence of persistent, detectable DNAm alterations associated with Long-COVID or COVID-19 infection in our cohorts. Further research using larger and more diverse populations, as well as higher-resolution methylation profiling, as opposed to the array-based EPIC V2 platform employed here, may be required to detect more subtle and phenotype-specific effects.

But I’m still wonder what would have shown up on these tests, or more detailed tests for that matter. Like is this a relevant way to approach a disease?