Persistent Cerebral 18-FDG PET Changes in Patients With Long COVID Presenting With Fatigue and Post Exertional Malaise, 2026, Ganesh et al

[forestglip]

Persistent Cerebral 18-FDG PET Changes in Patients With Long COVID Presenting With Fatigue and Post Exertional Malaise

Spoiler: Authors

Ganesh, Ravindra; Nair, Kaavya; Grach, Stephanie L.; Croghan, Ivana T.; Yadav, Siddhant; Hurt, Ryan T.; Mueller, Michael R.

Abstract
Objective
Long COVID (LC) refers to the long-term symptoms occurring after SARS-CoV-2 infection and resolution of the initial disease prodrome. While LC is increasingly recognized, knowledge about its biomarkers is still limited.

This study examines imaging findings in 18 F-FDG PET-CT scans in 40 patients with LC from an academic LC Clinic, for potential imaging biomarkers.

Methods
This study included patients aged 18 and older with confirmed positive SARS-CoV-2 PCR test and at least 28 days post-symptom onset. 18 F-FDG PET-CT scans were performed, and brain metabolic activity was compared to a control database to generate Z-scores. Participants were grouped based on their clinical phenotype and Z-scores of different brain regions were analyzed using t-tests.

Results
The study group was mainly female (70%), with a median age of 53 years, and predominantly non-Hispanic White (90%). Most had pre-existing conditions such as gastrointestinal, cardiovascular, endocrine, and psychiatric disorders.

The findings showed significant cerebral hypometabolism in 29 patients with fatigue and post-exertional malaise (PEM), particularly in the left sensorimotor cortex (p=0.0253) and bilateral primary visual cortex (right: p=0.0096, left: p=0.0016), which persisted up to two years after infection.

Conclusions
In summary, this study identified persistent cerebral hypometabolism in LC patients, especially those with fatigue with PEM, up to two years post-infection.

These results suggest that 18 F-FDG PET-CT could be a valuable tool for diagnosing and managing LC. Further research is essential to confirm these findings and improve treatment strategies for patients with LC.

Web | DOI | PDF | Journal of Primary Care & Community Health | Open Access

 

[forestglip]

There seems to be an inconsistency regarding multiple test correction. The methods seem to suggest it was used:

The Z-scores were analyzed for normality using Shapiro-Wilk test and then using an ANOVA t-test with repeated measures and Bonferroni correction for multiple comparisons and p-values were considered significant if <0.05.

But the discussion section recommends future studies use Bonferroni correction in case of false positives in this study:

Lastly, given that we simultaneously performed multiple comparisons between brain regions, there is a recognized potential for false positives. Future studies in this arena would benefit from either comparison of a smaller number of variables defined a priori based on the existing literature or using a statistical method for multiple comparisons such as the Bonferroni correction.

Table 2 includes the p-values for each region, but uses a threshold of p<0.05. It's possible they multiplied the raw p-values by the number of comparisons and compared to 0.05, which would still be Bonferroni correction, but then I would expect at least some of the p-values to be 1. There were 25 tests, so if any raw p-values were equal to or greater than 0.04, then the adjusted value would be 1 for these. Instead, the highest is 0.6477.

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It's also unclear what the methods mean by "ANOVA t-test with repeated measures", as they don't explain how ANOVA was used or what the repeated measures were. The table with results just says the p-values were based on an "Independent samples t-test".