Abstracts from August 2025

A Non-Opioid Drug for Fibromyalgia Soon to Hit the US Market
For the first time in 15 years, a drug has been approved for fibromyalgia in the U.S.
On August 15th, the FDA approved Tonmya for use in fibromyalgia (FM). It’s been a long wait – 15 years – since the last drug was approved for FM. I felt like pinching myself. Health Rising has been following the Tonmya (formerly TNX-102) saga for at least five years.
Tonmya seemed as dead as a duck at some points, and over that time, we’ve seen drugs come and go, but somehow, little Tonix Pharmaceuticals hung on through thick and thin and managed to produce a winner when others could not.
Fifteen years is a long dry spell for a disease afflicting approximately ten million people in the U.S. Seth Lederman, Tonix’s CEO, and the driver behind the drug, pointed out in our talk that the lack of drug approvals is not the FDA’s fault. The FDA is eager to approve new pain drugs that meet its standards, but drug companies are largely not stepping up to the plate.
That’s too bad. With the last 3 FDA-approved drugs (Lyrica-2007, Cymbalta-2008, and Savella-2009) producing “strikingly modest” effects, FM patients are surely ready to try a new approach. One large study reported that “only a minority of fibromyalgia patients continue taking medications for more than a short period of time due to either lack of efficacy, side effects, or both.” Even though long-term opioid use carries substantial risks and is not a good solution for many, Lederman noted that their research indicates that many doctors have turned to them, not the FDA-approved drugs, to treat fibromyalgia patients.
Getting a drug approved by the FDA, particularly if you’re a small drug company, is not for the faint of heart. Check out some recent examples in the FM and ME/CFS fields.
Tomnya reduces pain by helping people with fibromyalgia achieve deeper sleep.
It took 15 years, but a 4th drug called Tonmya has been approved to treat fibromyalgia in the U.S. With the three other FDA-approved drugs (Lyrica, Cymbalta, and Savella) producing “strikingly modest” results and most patients choosing not to continue with them, the FM community was past due for a new treatment option.

• Tonix Pharmaceuticals is a small drug company, and its achievement was remarkable given the travails it went through and the inability of other drug companies (one very large) to bring their FM drugs to market.
• Tonix had to endure two failed major trials – one because the dose was too small and one because of the COVID pandemic, but persisted, and with the last two trials coming in with good numbers, the drug was approved on August 15th.
• Tonmya is nothing like the past FDA-approved drugs (an anticonvulsant and two antidepressants with pain-reducing qualities). An updated, sublingual form of Flexeril (cyclobenzaprine), which is primarily used as a muscle relaxant, Tonmya is being used by Tonix as a sleep drug.
• Tomnya shoots the drug straight into the body, thereby increasing its bioavailability and efficiency, and bypassing the toxicity problems that were relegating Flexeril to short-term use.
• Tonmya is different from other sleep drugs in that it blocks four receptors associated with increased alertness. Its goal is not to reduce insomnia but to enhance deep sleep in FM.
• It’s not a miracle drug. It will not take your fibromyalgia away, and not everyone with fibromyalgia benefits. Studies in over 1000 people with FM show, though, that Tonmya consistently produces “clinically meaningful”; i.e., clearly noticeable improvements in pain, fatigue, and sleep in the fibromyalgia population at large.
• Side effects are minimal with a very small percentage of participants reporting fatigue, and others reporting temporary sensations such as tingling in the mouth when the dose is taken.
• Tonix will become available in the US in the 4th quarter of this year. Costs and insurance coverage are not yet available. Tonix is only approved in the US but the company hopes to become available in other countries over time.
• Pain specialists were happy to see a new treatment approach become available. Citing the limited and poor treatment options available, Philip Mease, M.D., Director of Rheumatology Research at the Providence Swedish Medical Center, said “Tonmya is a novel treatment approach that targets nonrestorative sleep that… can impact core symptoms, specifically pain.”

_________________________________________________________________________
New Blood Test Shows Over 90% Accuracy for Lyme Disease
Edited by Patricia McKnight
August 22, 2025
A groundbreaking new blood test for Lyme disease is showing remarkable results, with over 90% accuracy across all disease stages, as presented at the 2025 Association for Diagnostics & Laboratory Medicine meeting.
Currently, diagnosing Lyme disease presents significant challenges; while some patients develop a characteristic rash, about 30% don't show this symptom, making early detection difficult.
LymeSeek is an innovative test that simultaneously detects 10 different antigens and uses deep learning algorithms to analyze responses, delivering unprecedented sensitivity and specificity.
The results are impressive: In very early stages, LymeSeek achieved 100% accuracy compared to just 37% with standard testing. Even in posttreatment cases after 6 months, it identified 97% of cases while standard testing caught less than half.
The test is now moving toward FDA clinical trials, potentially revolutionizing how we diagnose and treat Lyme disease across all stages.
This content was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. 
Medscape Medical News © 2025 WebMD, LLC
Cite this: New Blood Test Shows Over 90% Accuracy for Lyme Disease - Medscape - August 22, 2025.
_________________________________________________________________________

Artificial Intelligence Arrives in Long COVID Diagnostic and Treatment Fight

• Solarina Ho       August 26, 2025
• As healthcare systems continue to grapple with identifying and managing long COVID, artificial intelligence (AI) is showing promise as an important tool that could one day expand scientists’ understanding and even lead to new diagnostics for the condition. 
• Three recent studies demonstrated how machine learning and AI algorithms can be leveraged to process vast amounts of complex clinical notes, hospital data, and patient data. All three highlighted how AI could potentially tackle different aspects of long COVID and advance how clinicians identify, track, predict, and treat it.
• Because of the way the SARS-CoV-2 virus binds to human cells, long COVID complications can develop almost anywhere, experts said — from the brain to the heart to the gastrointestinal system — causing upwards of more than 200 symptoms. Many of these symptoms can also be caused by other diseases and conditions, making diagnosis and treatment challenging.
• “That’s hard from a medical point of view, because that’s not typical of how we think of most illnesses that we deal with,” said Fahad Razak, MD, internist at St. Michael’s Hospital and Canada Research Chair in Healthcare Data and Analytics at the University of Toronto in Toronto, Ontario, Canada.
• “Probably all of our data underestimates the real population-level burden of how many people are affected by [long COVID], and I think many people suffer in silence,” he said.
• Three Studies, Three AI Applications
• In a study published in Med, scientists at Mass General Brigham developed a type of AI tool called precision phenotyping to analyze millions of data points from the electronic health records of nearly 300,000 patients across 14 hospitals and 20 community health centers in Massachusetts. The technique identifies and tracks symptoms and conditions linked to COVID-19 to distinguish them from other illnesses.
• Scientists said the tool was nearly 3% more accurate than current diagnostic methods for long COVID. It allows for very detailed and precise analysis of information that could help with the challenging task of diagnosis, ensuring patients receive appropriate care. It is an example of AI’s ability to synthesize, curate, and sift through enormous volumes of information.
• The system only considers long COVID if the symptoms cannot be explained by anything else in the patient’s medical history. After exhausting all other possibilities, the tool flagged about 22.8% of cases as long COVID.
• “It’s been really, really challenging to define and diagnose long COVID. One of the reasons is because its symptoms are very heterogeneous, overlapping with many things,” said co-author Hossein Estiri, PhD, head of AI research at the Center for AI and Biomedical Informatics of the Learning Healthcare System at Mass General Brigham and associate professor of medicine at Harvard Medical School, Boston.
• “The more we can find innovative ways of using AI to address these complex, evolving phenotypes, the better,” he said.
• The algorithm is already public and is packaged in a software tool that can be implemented in different institutions across the US and internationally, according to Estiri, adding that the center is looking for more institutions to participate. At this stage, it is helping to advance research designed to better understand the condition through larger sample sizes and flag potential patients to enroll in future studies or clinical trials.
• “One of the most difficult things about long COVID is that it can affect almost any organ system, and in many ways, it’s a mimicker of many other illnesses that we have to deal with,” said Razak, who was also the scientific director of the Ontario COVID-19 Science Advisory Table and coauthored dozens of papers that shaped the policy, public health, and clinical response to the pandemic. “This is an interesting example where AI could do something much more efficiently than any of us, individually, clinically could do.”
• Data Sharing for Local Decision-Making
• At the University of Pennsylvania’s Perelman School of Medicine, Philadelphia, researchers used a machine learning technique called latent transfer learning to gain a clearer picture of the specific healthcare burdens of long COVID among pediatric patients in different hospitals. The technique, which boosts statistical precision by analyzing information across hospitals, tracked the electronic health records of 432,165 young patients from eight pediatric hospital systems.
• According to a study published in Patterns, researchers found that many patients fell into one of four subgroups: those with mental health conditions like anxiety, depression, and ADHD; those with atopic/allergic conditions, including asthma; those with noncomplex chronic conditions; and those with complex chronic conditions like multisystem disorders. The technique also identified the type of care patients required and the impact these patient groups had on hospitals.
• Long COVID is less common in children than in adults. But it involves a unique and understudied patient population group that is growing, gaining weight, and developing their mental and cognitive understanding of the world, explained co-author of the study, Yong Chen, PhD, professor of biostatistics in the Department of Biostatistics, Epidemiology, and Informatics with the Perelman School of Medicine.
• “The number one message we try to pass is the awareness and the complexity of pediatric long COVID,” said Chen. “It’s extremely complicated in terms of the subtype of long COVID for children and adolescents for the reason that they are highly entangled by their developmental age.” A COVID infection has a nontrivial impact on the digestive system, for example, so how would outcomes be measured without being confounded by a child’s natural growth? Chen explained.
• The transfer learning approach is much more precise because it adaptively incorporates data from other hospitals while accounting for differences in patient populations, hospital staffing, and equipment. Chen and his colleagues advocate for hospitals and health systems to work together and share data to facilitate more personalized care and improve responses to future public health crises.
• “You could start to triage your patients into better categories and different follow-up and management approaches,” said Razak. “The ability to identify that based on complex, many millions of data points of information would greatly enhance clinical care.”
• Predicting Long COVID
• In a much smaller and more limited study, researchers in Italy used three different machine learning approaches to predict with up to 94% accuracy which patients would eventually develop pulmonary long COVID. Clinical data collected early in the pandemic from patients with COVID-19 hospitalized across four different Italian hospitals were analyzed. The different AI methods used in the study illustrated effective strategies for predicting long COVID, the scientists said, even when the patient sample size was small.
• Researchers said these approaches could help healthcare professionals identify which patients were more susceptible to developing long COVID and provide support to mitigate the condition’s long-term impact. It would allow doctors to identify high-risk patients early, allowing a more tailored approach to care and management strategies, and help healthcare providers allocate resources more efficiently.
• Coming to a Hospital Near You?
• Electronic health records make these kinds of insights not only possible but also easy to deploy and almost immediately useful.
• “20 years ago, this information would have been largely unactionable, because most of the healthcare encounters would have been largely pen and paper, handwritten information that cannot be extracted out to use for analytics,” said Razak.
• “What these papers are harnessing is the many billions of dollars of investments that have already occurred within the Canadian and the US healthcare systems to largely digitize the system,” he said.
• Yet experts agree there are important ethical and feasibility constraints in medical AI that need to be addressed before AI can be used broadly in clinical settings.
• “There’s governance, privacy, cost, and technical barriers that I would say are all solvable, but they would take resources and will to do it,” said Razak. He noted that even at St. Michael’s Hospital, where he works — “one of the most data-advanced hospitals in Canada” — there is only one algorithm implemented in its clinical practice.
• Still, these findings are promising and intriguing from a research perspective, and these algorithms may one day help clinicians directly in decision-making on patient care.
• “Could they be implemented in the health system? I think they could be implemented pretty quickly,” said Razak. “Would clinicians know what to do with them to change the way they’re managing clinical care? No, not yet.”
• Medscape Medical News © 2025 WebMD, LLC
  Cite this: Artificial Intelligence Arrives in Long COVID Diagnostic and Treatment Fight - Medscape - August 26, 2025.

_________________________________________________________________________

International Journal of Molecular Sciences
Precision Medicine Study of Post-Exertional Malaise Epigenetic Changes in Myalgic Encephalomyelitis/Chronic Fatigue Patients During Exercise
Sayan Sharma 1,† , Lynette D. Hodges 2,† , Katie Peppercorn 3 , Jemma Davis 3, Christina D. Edgar 3,Euan J. Rodger 1 , Aniruddha Chatterjee 1,* and Warren P. Tate 1,*
1 Department of Pathology and Molecular Medicine, Dunedin School of Medicine, University of Otago, Dunedin 9016, New Zealand; [email protected] (S.S.); [email protected] (E.J.R.)
2 School of Sport, Exercise and Nutrition, College of Health, Massey University, Palmerston North 4410, New Zealand; [email protected]
3 Department of Biochemistry, School of Biomedical Sciences, University of Otago, Dunedin 9016, New Zealand; [email protected] (K.P.); [email protected] (J.D.); [email protected] (C.D.E.)
* Correspondence: [email protected] (A.C.); [email protected] (W.P.T.)
† These authors contributed equally to this work.
Abstract
Post-exertional malaise (PEM) is a defining symptom of Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS), yet its molecular underpinnings remain elusive. This study investigated the temporal–longitudinal DNA methylation changes associated with PEM using a structured two-day maximum repeated effort cardiopulmonary exercise testing (CPET) protocol involving pre- and two post-exercise blood samplings from five ME/CFS patients. Cardiopulmonary measurements revealed complex heterogeneous profiles among the patients compared to typical healthy controls, and VO2 peak indicated all patients had poor normative fitness. The switch to anaerobic metabolism occurred at a lower workload in some patients on Day Two of the test. Reduced Representation Bisulphite Sequencing followed by analysis with Differential Methylation Analysis Packageversion 2 (DMAP2) identified differentially methylated fragments (DMFs) present in the DNA genomes of all five ME/CFS patients through the exercise test compared with ‘before exercise’. With further filtering for >10% methylation differences, there were early DMFs (0–24 h after first exercise test) and late DMFs between (24–48 h after the second exercise test), as well as DMFs that changed gradually (between 0 and 48 h). Of these, 98% were ME/CFS-specific, compared with the two healthy controls accompanying the longitudinal study. Principal component analysis illustrated the three distinct clusters at the 0 h, 24 h, and 48 h timepoints, but with heterogeneity among the patients within the clusters, highlighting dynamic methylation responses to exertion in individual patients. There were 24 ME/CFS-specific DMFs at gene promoter fragments that
revealed distinct patterns of temporal methylation across the timepoints. Functional enrichment of ME-specific DMFs revealed pathways involved in endothelial function, morphogenesis, inflammation, and immune regulation. These findings uncovered temporally dynamic epigenetic changes in stress/immune functions in ME/CFS during PEM and suggest molecular signatures with potential for diagnosis and of mechanistic significance.
Keywords: ME/CFS; CPET; epigenetics; DNA methylation; post-exertional malaise
Int.

_________________________________________________________________________

Nature  npj metabolic health and disease  Open access  Published: 03 September 2025
Heightened innate immunity may trigger chronic inflammation, fatigue and post-exertional malaise in ME/CFS
Xiaoyu Che, Amit Ranjan, Cheng Guo, Keming Zhang, Rochelle Goldsmith, Susan Levine, Kegan J. Moneghetti, Yali Zhai, Liner Ge, Nischay Mishra, Mady Hornig, Lucinda Bateman, Nancy G. Klimas, Jose G. Montoya, Daniel L. Peterson, Sabra L. Klein, Oliver Fiehn, Anthony L. Komaroff & W. Ian Lipkin 
npj Metabolic Health and Disease volume 3, Article number: 34 (2025) Cite this article
Abstract
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by unexplained fatigue, post-exertional malaise (PEM), and cognitive dysfunction. ME/CFS patients often report a prodrome consistent with infection. We present a multi-omics analysis based on plasma metabolomic and proteomic profiling, and immune responses to microbial stimulation, before and after exercise. We report evidence of an exaggerated innate immune response after exposure to microbial antigens; impaired energy production involving the citric acid cycle, beta-oxidation of fatty acids, and urea cycle energy production from amino acids; systemic inflammation linked to lipid abnormalities; disrupted extracellular matrix homeostasis with release of endogenous ligands that promote inflammation; reduced cell-cell adhesion and associated gut dysbiosis; complement activation; redox imbalance reflected by disturbances in copper-dependent antioxidant pathways; and dysregulation of tryptophan-serotonin-kynurenine pathways. Many abnormalities were worse following exercise and correlated with the intensity of symptoms. Our findings may inform development of targeted therapeutic interventions for ME/CFS and PEM.

_________________________________________________________________________


 J.Psychopharmacol  .2025 Sep 16:2698811251368371.  doi: 10.1177/02698811251368371. Online ahead of print.
Solriamfetol improves daily fatigue symptoms in adults with myalgic encephalomyelitis/chronic fatigue syndrome after 8 weeks of treatment
Joel L Young 1 2 3, Richard N Powell 2, Anna Powell 2, Lisa L M Welling 4, Lauren Granata 2, Jaime Saal 1 2
Affiliations Expand
PMID: 40958377   DOI: 10.1177/02698811251368371
Abstract
Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a long-term illness with no treatment options that address the disease directly. Solriamfetol is a selective dual norepinephrine-dopamine reuptake inhibitor that promotes wakefulness in obstructive sleep apnea and narcolepsy.
Aims: This study evaluated the efficacy and safety of solriamfetol for fatigue symptoms in adults with ME/CFS over 8 weeks of treatment.
Methods: This was a phase 4, double-blind, randomized, placebo-controlled trial of solriamfetol in adults with ME/CFS. Eligible participants (N = 38) were randomly assigned to receive 75 mg (titrated to 150 mg as needed) solriamfetol or placebo. Participants completed a battery of assessments at weekly visits. The primary outcome was Fatigue Symptom Inventory (FSI) scores, and the secondary outcome measure was Behavioral Rating Inventory of Executive Function for Adults (BRIEF-A), at Weeks 6 and 8. T-tests assessed the differences in mean change from baseline between solriamfetol and placebo. Adverse events were monitored throughout the study.
Results: At Week 8 (p = 0.039), but not Week 6 (p = 0.270), solriamfetol improved FSI severity compared to placebo. On the BRIEF-A global executive composite, solriamfetol improved more than placebo at Week 8 (p = 0.012), driven by improved metacognition index (p = 0.004), but not behavioral regulation index (p = 0.574). Solriamfetol was well tolerated, with most common AEs being sleep loss and headaches.
Conclusions: Solriamfetol demonstrated good safety and efficacy in improving fatigue and executive functioning in patients with ME/CFS. As a dual norepinephrine-dopamine reuptake inhibitor and wakefulness-promoting factors, solriamfetol has the potential to improve fatigue symptoms of ME/CFS.
Clinical trial number: NCT04622293.
Keywords: Chronic fatigue syndrome; myalgic encephalomyelitis; solriamfetol; wakefulness.
PubMed Disclaimer

_________________________________________________________________________

SMPDL3B a novel biomarker and therapeutic target in myalgic encephalomyelitis
Gepubliceerd op 3 september 2025
Rostami-Afshari, Elremaly, Franco, Elbakry, Akoume, Boufaied, Moezzi, Leveau, Rompré, Godbout, Mella, Fluge & Moreau
Abstract
Background
Sphingomyelin phosphodiesterase acid-like 3B (SMPDL3B) is emerging as a potential biomarker and therapeutic target in myalgic encephalomyelitis (ME), a complex multisystem disorder characterized by immune dysfunction, metabolic disturbances, and persistent fatigue. This study investigates the role of SMPDL3B in ME pathophysiology and explores its clinical relevance.
Methods
A case–control study was conducted in two independent cohorts: a Canadian cohort (249 ME patients, 63 controls) and a Norwegian replication cohort (141 ME patients). Plasma and membrane-bound SMPDL3B levels were quantified using ELISA and flow cytometry. Gene expression of SMPDL3B and PLCXD1, encoding phosphatidylinositol-specific phospholipase C (PI-PLC), was analyzed by qPCR. The effects of dipeptidyl peptidase-4 (DPP-4) inhibitors—vildagliptin, saxagliptin, and linagliptin—on modulation of membrane-bound and soluble SMPDL3B were assessed in vitro by qPCR, flow cytometry and ELISA.
Results
ME patients exhibited significantly elevated plasma SMPDL3B levels, which correlated with symptom severity. Flow cytometry revealed a reduction in membrane-bound SMPDL3B in monocytes, accompanied by increased PLCXD1 expression and elevated plasma levels of PI-PLC and SMPDL3B. These findings suggest that immune dysregulation in ME may be linked to enhanced cleavage of membrane-bound SMPDL3B by PI-PLC. Sex-specific differences were observed, with female ME patients displaying higher plasma SMPDL3B levels, an effect influenced by estrogen. In vitro, estradiol upregulated SMPDL3B expression, indicating hormonal regulation. Vildagliptin and saxagliptin were tested for their potential to inhibit PI-PLC activity independently of their role as DPP-4 inhibitors, and restored membrane-bound SMPDL3B while reduced its soluble form.
Conclusions
SMPDL3B emerges as a key biomarker for ME severity and immune dysregulation, with its activity influenced by hormonal and PI-PLC regulation. The ability of vildagliptin and saxagliptin to preserve membrane-bound SMPDL3B and reduce its soluble form via PI-PLC inhibition suggests a novel therapeutic strategy. These findings warrant clinical trials to evaluate their potential in mitigating immune dysfunction and symptom burden in ME.
Source: Journal of Translational Medicine, open access

_________________________________________________________________________

Meta-analysis of natural killer cell cytotoxicity in myalgic encephalomyelitis/chronic fatigue syndrome
Gepubliceerd op 15 september 2025
Baraniuk, Eaton-Fitch, Marshall-Gradisnik
Reduced natural killer (NK) cell cytotoxicity is the most consistent immune finding in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).
Meta-analysis of the published literature determined the effect size of the decrement in ME/CFS.
Databases were screened for papers comparing NK cell cytotoxicity in ME/CFS and healthy controls. A total of 28 papers and 55 effector:target cell ratio (E:T) data points were collected.
Cytotoxicity in ME/CFS was significantly reduced to about half of healthy control levels, with an overall Hedges’ g of 0.96 (0.75–1.18).
Heterogeneity was high but was explained by the range of E:T ratios, different methods, and potential outliers.
Frozen and shipped cells do not retain sufficient cytotoxicity. Whole blood 51Cr assays have the largest effect size, but extrapolating without showing the raw data reduced the information that can be gained. Purified NK cells with E:T of 25:1 and detection by fluorescent cytometry using Annexin V for early and late apoptosis was a reasonable non-radioactive alternative. Hedges’ g and thresholds for ME/CFS and HC % cytotoxicity at various E:T values and different cell sources and methods provide guidelines to diagnose ME/CFS in future studies.
Fresh specimens or new methods will be necessary for NK cell cytotoxicity to become a routine clinical laboratory test for diagnosis. Technical problems related to the assay methods are a limitation that may be overcome by innovative engineering.
Future studies should report NK cell cytotoxicity with subjective common data elements to understand behavioral correlations and investigate interactions with dysfunction of metabolomics, mitochondria, and brain cell function using magnetic resonance imaging (153) in order to gain a better understanding of integrated disease pathophysiology and symptom generation.
NK cells represent a model system to understand molecular mechanisms of disease in ME/CFS and for testing potential drugs in vitro for efficacy before human clinical trials. The effect sizes calculated here may allow improved design for future studies of deficient NK cell cytotoxicity in ME/CFS.
The outcomes confirm reproducible NK cell dysfunction in ME/CFS and will guide studies using the NK cell model system for pathomechanistic investigations.
NK cells from ME/CFS subjects have significantly lower cytotoxicity than control subjects. The reduction in K562 cell killing by fresh NK cells remains one of the most promising potential biomarkers for ME/CFS.
Source: Frontiers, open access
_________________________________________________________________________

Patient-reported treatment outcomes in ME/CFS and long COVID
Gepubliceerd op 10 september 2025
Eckey, Peng Li, Morrison, Bergquist, Davis, Wenzhong Xiao
Abstract
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID are persistent multisystem illnesses affecting many patients. With no known effective FDA-approved treatments for either condition, patient-reported outcomes of treatments may prove helpful in identifying management strategies that can improve patient care and generate new avenues for research.
Here, we present the results of an ME/CFS and long COVID treatment survey with responses from 3,925 patients. We assess the experiences of these patients with more than 150 treatments in conjunction with their demographics, symptoms, and comorbidities.
Treatments with the greatest perceived benefits are identified. Patients with each condition who participated in the study shared similar symptom profiles, including all the core symptoms of ME/CFS, e.g., 89.7% of ME/CFS and 79.4% of long COVID reported postexertional malaise (PEM).
Furthermore, treatment responses between these two patient groups were significantly correlated (R2 = 0.68). Patient subgroups, characterized by distinct symptom profiles and comorbidities, exhibited increased responses to specific treatments, e.g., a POTS-dominant cluster benefiting from autonomic modulators and a cognitive-dysfunction cluster from CNS stimulants.
This study underscores the symptomatic and therapeutic similarities between ME/CFS and long COVID and highlights the commonalities and nuanced complexities of infection-associated chronic diseases and related conditions.
While this study does not provide recommendations for specific therapies, in the absence of approved treatments, insights from patient-reported experiences provide urgently needed real-world evidence for developing targeted patient care therapies and future clinical trials.
Source: PNAS, open access

_________________________________________________________________________


Steroid dynamics in myalgic encephalomyelitis / chronic fatigue syndrome: a case-control study using ultra performance supercritical fluid chromatography tandem mass spectrometry
Gepubliceerd op 24 september 2025
Thomas, Ubhayasekera, Armstrong, Huang & Bergquist
Abstract
Background
Myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) is a multisystem disorder characterised by unrelenting fatigue, post-exertional malaise, and dysfunction across immune, nervous, metabolism, and endocrine systems. Given the broad role of steroid hormones in regulating these systems, this study investigated differences in the steroid metabolome and network dynamics between ME/CFS patients and matched controls.
Methods
Blood plasma steroid levels were quantified using Ultra-Performance Supercritical Fluid Chromatography- Tandem Mass Spectrometry (UPSFC-MS/MS) in ME/CFS patients (n = 24) and age and gender matched controls (n = 24). Group comparisons of absolute steroid concentrations were performed using Mann-Whitney U tests. Partial Spearman correlation networks were evaluated to examine direct associations between steroids within each group, and centrality metrics were used to evaluate structural differences. Steroid-steroid ratios were analysed to reflect biochemical relationships. Multivariate analysis with Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) was also conducted.
Results
No significant group differences in absolute steroid concentrations were observed following FDR correction. However, network analysis revealed a marked reduction in direct steroid-steroid relationships in ME/CFS, with controls exhibiting 52 significant partial correlations, while the ME/CFS group retained only one (cortisol – corticosterone). Centrality analysis further revealed a shift in network structure, with cortisone emerging as highly central in ME/CFS (degree = 7, betweenness = 16.7), despite being peripheral in controls, and progesterone showing reduced integration in ME/CFS (degree = 3 vs. 12, eigenvector = 0.40 vs. 0.93). Steroid-steroid ratio analysis revealed a higher cortisol-to-pregnanolone ratio and a lower pregnanolone-to-progesterone ratio in ME/CFS, although these findings did not remain significant after FDR correction. OPLS-DA indicated a modest relationship between steroid levels and group classification (R²Y = 22.8%), but negative Q² values suggested poor predictive power.
Conclusions
Despite no significant differences in absolute steroid levels, network analysis revealed profound disruptions in steroid-steroid relationships in ME/CFS compared to controls, suggesting disrupted steroid homeostasis. Collectively the results suggest dysregulation of HPA axis function and progestogen pathways, as demonstrated by altered partial correlations, centrality profiles, and steroid ratios. These findings illustrate the importance of hormone network dynamics in ME/CFS pathophysiology and underscores the need for more research into steroid metabolism.
Source: Journal of Translational Medicine, open access