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Crash-course: Studying PEM/flares in ME/CFS, PTLDS, Long COVID

Study AIM

The study will compare biological markers during mild symptoms and flare/PEM events in individuals with ME/CFS, alongside PTLDS and Long COVID patients, using finger-prick blood samples and continuous data from wearable sensors to understand symptom variability.

INVESTIGATORS
  • Linda Lan, PhD
  • Yue Wu, PhD
  • Jessi Li
  • Longsha, Liu
  • Jaime Seltzer
  • Michael Snyder, PhD
  • Christopher Armstrong, PhD
Updates and Potential
  • Some analysis of early data has identified hemodynamic data differences in all patients on an average day vs a crash/flare day.
  • Paper being written up at Stanford to summarise omics findings on ME/CFS and Long COVID.
STUDY HYPOTHESIS AND DESCRIPTION

In our study, instead of just comparing people with ME/CFS to those who are healthy or have other diseases, we’re taking a different approach. We’re looking at the same ME/CFS patients at different times: when their symptoms are mild and when they have a flare-up, which is a sudden worsening of symptoms often called post-exertional malaise (PEM). This method helps us see how symptoms change over time in the same person.

Additionally, we’re comparing these patterns with those in people who have Post-Treatment Lyme Disease Syndrome (PTLDS) and Long COVID, to understand if flare-ups are similar across these conditions.

Participants in the study will provide small blood samples using a finger-prick method at various times. We’ll need samples from seven normal days and seven days during a flare-up, collected over about three months. During the study, participants will also use wearable devices like FitBit and apps to track their symptoms, providing us with continuous data.

We will analyse the blood samples for hundreds of biological markers to try to find patterns or triggers related to the flare-ups. This detailed tracking and comparison may help us better understand and address the fluctuations in symptoms seen in ME/CFS and related conditions.

OBJECTIVES
  1. Identify biological patterns that correspond to ME/CFS,  PTLDS and Long COVID symptoms.
  2. Characterise biological differences that occur between baseline and flare/PEM.
  3. Analyse the continuous heart rate data provided by FitBit to look at differences between metabolism and hemodynamic data.
  4. Cluster patients based on similar biology-symptom  dynamics.
 

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