Tag: biobank

Exploring a genetic basis for the metabolic perturbations in ME/CFS using UK Biobank

Highlights:

  • ME/CFS shows distinct genetic influences on metabolic regulation.
  • Lipid and hormone-related pathways emerge as key areas of interest.
  • Many small genetic effects may collectively disrupt metabolic resilience in ME/CFS.

Summary:

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a clinically heterogeneous disease lacking approved therapies. To assess genetic susceptibility towards a specific metabolic phenotype, we performed a genome-wide association study on plasma biomarker levels (mGWAS) in ME/CFS patients (n=875) and healthy controls (HCs) (n=36,033).
We identified 112 significant SNP–biomarker associations in ME/CFS, compared with 4,114 in HCs. Two SNPs specific to ME/CFS, mapping to HSD11B1 and SCGN, were associated to phospholipids in extra-large very low-density lipoproteins (VLDL) and total fatty acids respectively. Genetic effects of VLDL associations were among the least correlated between ME/CFS and HCs. Heterogeneity tests found differential effects for several lipid traits at ADAP1, NR1H3 and CD40, which are involved in immune regulation.
ME/CFS mGWAS summary statistics were decomposed to uncover shared genetic-metabolic patterns, where enrichment analysis highlighted pathways in lipid metabolism, neurotransmitter transport, and inflammation. These findings provide a genetic and molecular rationale for patient heterogeneity and suggest a polygenic predisposition in which many small-effect variants may jointly perturb metabolic mechanisms.
Source: Katherine Huang, Muhammad Muneeb, Natalie Thomas, Elena K. Schneider-Futschik, Paul R. Gooley, David B. Ascher, Christopher W. Armstrong. Exploring a genetic basis for the metabolic perturbations in ME/CFS using UK Biobank. iScience, 2025, 114316 ISSN 2589-0042, https://doi.org/10.1016/j.isci.2025.114316. https://www.sciencedirect.com/science/article/pii/S2589004225025775 (Full text available as PDF file)

Defining a High-Quality Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cohort in UK Biobank

Abstract:

Background: Progress in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) research is being slowed by the relatively small-scale studies being performed whose results are often not replicated. Progress could be accelerated by analyses of large population-scale projects, such as UK Biobank (UKB), which provide extensive phenotype and genotype data linked to both ME/CFS cases and controls.

Methods: Here, we analysed the overlap and discordance among four UKB-defined ME/CFS cohorts, and additional questionnaire data when available.

Results: A total of 5,354 UKB individuals were linked to at least one piece of evidence of MECFS, a higher proportion (1.1%) than most prevalence estimates. Only a third (36%; n=1,922) had 2 or more pieces of evidence for MECFS, in part due to data missingness. For the same UKB participant, ME/CFS status defined by ICD-10 (International Classification of Diseases, Tenth Revision) code G93.3 (Post-viral fatigue syndrome) was most likely to be supported by another data type (72%); ME/CFS status defined by Pain Questionnaire responses is least likely to be supported (43%), in part due to data missingness.

Conclusions: We conclude that ME/CFS status in UKB, and potentially other biobanks, is best supported by multiple, and not single, lines of evidence. Finally, we raise the estimated ME/CFS prevalence in the UK to 410,000 using the most consistent evidence for ME/CFS status, and accounting for those who had no opportunity to participate in UKB due to being bed- or house-bound.

Source: Samms GL, Ponting CP. Defining a High-Quality Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cohort in UK Biobank. NIHR Open Res. 2025 Apr 28;5:39. doi: 10.3310/nihropenres.13956.1. PMID: 40443420; PMCID: PMC12120426. https://pmc.ncbi.nlm.nih.gov/articles/PMC12120426/ (Full text)

Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank

Abstract:

Background: Diagnosing complex illnesses like Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is complicated due to the diverse symptomology and presence of comorbid conditions. ME/CFS patients often present with multiple health issues, therefore, incorporating comorbidities into research can provide a more accurate understanding of the condition’s symptomatology and severity, to better reflect real-life patient experiences.

Methods: We performed association studies and machine learning on 1194 ME/CFS individuals with blood plasma nuclear magnetic resonance (NMR) metabolomics profiles, and seven exclusive comorbid cohorts: hypertension (n = 13,559), depression (n = 2522), asthma (n = 6406), irritable bowel syndrome (n = 859), hay fever (n = 3025), hypothyroidism (n = 1226), migraine (n = 1551) and a non-diseased control group (n = 53,009).

Results: We present a lipoprotein perspective on ME/CFS pathophysiology, highlighting gender-specific differences and identifying overlapping associations with comorbid conditions, specifically surface lipids, and ketone bodies from 168 significant individual biomarker associations. Additionally, we searched for, trained, and optimised a machine learning algorithm, resulting in a predictive model using 19 baseline characteristics and nine NMR biomarkers which could identify ME/CFS with an AUC of 0.83 and recall of 0.70. A multi-variable score was subsequently derived from the same 28 features, which exhibited ~2.5 times greater association than the top individual biomarker.

Conclusions: This study provides an end-to-end analytical workflow that explores the potential clinical utility that association scores may have for ME/CFS and other difficult to diagnose conditions.

Source: Huang K, G C de Sá A, Thomas N, Phair RD, Gooley PR, Ascher DB, Armstrong CW. Discriminating Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and comorbid conditions using metabolomics in UK Biobank. Commun Med (Lond). 2024 Nov 26;4(1):248. doi: 10.1038/s43856-024-00669-7. PMID: 39592839; PMCID: PMC11599898.  https://pmc.ncbi.nlm.nih.gov/articles/PMC11599898/ (Full text)

Replicated blood-based biomarkers for Myalgic Encephalomyelitis not explicable by inactivity

Abstract:

Myalgic Encephalomyelitis (ME; sometimes referred to as chronic fatigue syndrome) is a relatively common and female-biased disease of unknown pathogenesis that profoundly decreases patients’ health-related quality-of-life. ME diagnosis is hindered by the absence of robustly-defined and specific biomarkers that are easily measured from available sources such as blood, and unaffected by ME patients’ low level of physical activity.

Previous studies of blood biomarkers have not yielded replicated results, perhaps due to low study sample sizes (n<100). Here, we use UK Biobank (UKB) data for up to 1,455 ME cases and 131,303 population controls to discover hundreds of molecular and cellular blood traits that differ significantly between cases and controls. Importantly, 116 of these traits are replicated, as they are significant for both female and male cohorts.

Our analysis used semi-parametric efficient estimators, an initial Super Learner fit followed by a one-step correction, three types of mediators, and natural direct and indirect estimands, to decompose the average effect of ME status on molecular and cellular traits. Strikingly, these trait differences cannot be explained by ME cases’ restricted activity.

Of 3,237 traits considered, ME status had a significant effect on only one, via the “Duration of walk” (UKB field 874) mediator. By contrast, ME status had a significant direct effect on 290 traits (9%). As expected, these effects became more significant with increased stringency of case and control definition.

Significant female and male traits were indicative of chronic inflammation, insulin resistance and liver disease. Individually, significant effects on blood traits, however, were not sufficient to cleanly distinguish cases from controls. Nevertheless, their large number, lack of sex-bias, and strong significance, despite the ‘healthy volunteer’ selection bias of UKB participants, keep alive the future ambition of a blood-based biomarker panel for accurate ME diagnosis.

Source: Sjoerd V Beentjes, Julia Kaczmarczyk, Amanda Cassar, Gemma Louise Samms, Nima S Hejazi, Ava Khamseh, Chris P Ponting. Replicated blood-based biomarkers for Myalgic Encephalomyelitis not explicable by inactivity. medRxiv 2024.08.26.24312606; doi: https://doi.org/10.1101/2024.08.26.24312606 https://www.medrxiv.org/content/10.1101/2024.08.26.24312606v1 (Full text available as PDF file)

The German Multicenter Registry for ME/CFS (MECFS-R)

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating multisystemic disease characterized by a complex, incompletely understood etiology.

Methods: To facilitate future clinical and translational research, a multicenter German ME/CFS registry (MECFS-R) was established to collect comprehensive, longitudinal, clinical, epidemiological, and laboratory data from adults, adolescents, and children in a web-based multilayer-secured database.

Results: Here, we present the research protocol and first results of a pilot cohort of 174 ME/CFS patients diagnosed at two specialized tertiary fatigue centers, including 130 (74.7%) adults (mean age 38.4; SD 12.6) and 43 (25.3%) pediatric patients (mean age 15.5; SD 4.2). A viral trigger was identified in 160/174 (92.0%) cases, with SARS-CoV-2 in almost half of them. Patients exhibited severe functional and social impairment, as reflected by a median Bell Score of 30.0 (IQR 30.0 to 40.0) and a poor health-related quality of life assessed with the Short Form-36 health survey, resulting in a mean score of 40.4 (SD 20.6) for physical function and 59.1 (SD 18.8) for mental health.

Conclusions: The MECFS-R provides important clinical information on ME/CFS to research and healthcare institutions. Paired with a multicenter biobank, it facilitates research on pathogenesis, diagnostic markers, and treatment options. Trial registration: ClinicalTrials.gov NCT05778006.

Source: Hieber H, Pricoco R, Gerrer K, Heindrich C, Wiehler K, Mihatsch LL, Haegele M, Schindler D, Donath Q, Christa C, Grabbe A, Kircher A, Leone A, Mueller Y, Zietemann H, Freitag H, Sotzny F, Warlitz C, Stojanov S, Hattesohl DBR, Hausruckinger A, Mittelstrass K, Scheibenbogen C, Behrends U. The German Multicenter Registry for ME/CFS (MECFS-R). J Clin Med. 2024 May 28;13(11):3168. doi: 10.3390/jcm13113168. PMID: 38892879; PMCID: PMC11172639. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11172639/ (Full text)

Actigraphic and Genetic Characterization of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Phenotypes in the UK Biobank (P10-9.007)

Abstract:

Objective: Patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) often experience debilitating fatigue and autonomic dysregulation, yet objective measurements of these symptoms are limited. This study utilized actigraphic data from the United Kingdom Biobank (UKBB) to investigate (1) reduced activity in those with CFS, (2) decreased amplitudes of daily temperature rhythms as a potential indicator of autonomic dysregulation, and (3) the impact of specific single nucleotide polymorphisms (SNPs) associated with CFS on these actigraphic parameters.

Background: ME/CFS is a complex and poorly understood condition characterized by profound fatigue, postural orthostasis, and temperature dysregulation. Objective metrics reflecting these fatigue-related symptoms are scarce. Previous research explored small-scale actigraphic analyses, shedding light on movement and temperature patterns in CFS, but large-scale investigations remain limited. Genetic factors have also emerged as potential contributors to CFS risk, although how they affect phenotypic manifestations remains unclear.

Design/Methods: Actigraphic data from the UKBB were analyzed to compare those with CFS (n = 295) to controls (n = 63,133). Movement parameters, acceleration amplitudes, and temperature amplitudes were assessed. Additionally, the impact of specific SNPs associated with CFS on actigraphic measurements and subjective fatigue experiences was examined.

Results: In addition to profound fatigue, those with CFS exhibited significantly reduced overall movement (Cohen’s d = −0.220, p-value = 2.42 × 10–15), lower acceleration amplitudes (Cohen’s d = −0.377, p-value = 1.74 × 10−6), and decreased temperature amplitudes (Cohen’s d = −0.173, p-value = 0.002) compared to controls. Furthermore, certain SNPs associated with CFS were found to significantly influence both actigraphic measurements and subjective fatigue experiences.

Conclusions: This study provides valuable insights into the objective characterization of CFS using actigraphy, shedding light on the interaction between genetics and symptomatology in CFS. The findings offer avenues for further research into the pathophysiology of CFS and may contribute to a better understanding of fatigue-related conditions in general.

Source: Patrick Liu, David Raizen, Carsten Skarke, Thomas Brooks, and Ron Anafi. Actigraphic and Genetic Characterization of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Phenotypes in the UK Biobank (P10-9.007). Neurology, April 9, 2024 issue
102 (17_supplement_1) https://doi.org/10.1212/WNL.0000000000204829 https://www.neurology.org/doi/abs/10.1212/WNL.0000000000204829

The importance of estimating prevalence of ME/CFS in future epidemiological studies of long COVID

Abstract:

The resolution of the COVID-19 pandemic is giving rise to another public health challenge due to the explosion of long COVID (LC) cases. In many cases, LC results in persistent fatigue, post-exertional malaise (PEM), and other debilitating symptoms that resemble the clinical manifestation of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). The similarity of these two diseases suggests that future epidemiological studies of LC could take the opportunity to also estimate the prevalence of ME/CFS at a minimal cost.

With this opportunity in mind, we revisited the most consensual case definitions of ME/CFS for research purposes. We then compared the symptoms assessed at the participants’ enrollment in the UK ME/CFS Biobank with those documented in three systematic reviews encompassing hundreds of LC epidemiological studies. We found that published epidemiological studies of LC did not consistently assess or report the prevalence of PEM, which is a compulsory symptom for ME/CFS diagnosis. However, these studies assessed many neuro-cognitive, immunologic, and autonomic symptoms.

In this scenario, we recommend that the estimation of ME/CFS prevalence in the context of LC epidemiology is easily achievable by deploying tested and validated diagnosis tools used in ME/CFS. The knowledge of ME/CFS prevalence within the LC population is of cardinal importance to optimal allocation of resources and better design of healthcare interventions to manage and treat patients with this devastating disease.

Source: Anna D. Grabowska, Francisco Westermeier, Luís Nacul, Eliana Lacerda, Nuno Sepúlveda. The importance of estimating prevalence of ME/CFS in future epidemiological studies of long COVID. DOI:10.13140/RG.2.2.20997.52967 https://www.researchgate.net/publication/373043778_The_importance_of_estimating_prevalence_of_MECFS_in_future_epidemiological_studies_of_long_COVID (Full text)

Genetic Risk Factors for Severe and Fatigue Dominant Long COVID and Commonalities with ME/CFS Identified by Combinatorial Analysis

Abstract:

Background Long COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as ME/CFS that present with similar symptoms.

Methods We used a combinatorial analysis approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms. We compared two subpopulations of long COVID patients from Sano Genetics’ Long COVID GOLD study cohort, focusing on patients with severe or fatigue dominant phenotypes. We evaluated the genetic signatures previously identified in an ME/CFS population against this long COVID population to understand similarities with other fatigue disorders that may be triggered by a prior viral infection. Finally, we also compared the output of this long COVID analysis against known genetic associations in other chronic diseases, including a range of metabolic and neurological disorders, to understand the overlap of pathophysiological mechanisms.

Results Combinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis. Of these, 9 genes have prior associations with acute COVID-19, and 14 were differentially expressed in a transcriptomic analysis of long COVID patients. A pathway enrichment analysis revealed that the biological pathways most significantly associated with the 73 long COVID genes were mainly aligned with neurological and cardiometabolic diseases.

Expanded genotype analysis suggests that specific SNX9 genotypes are a significant contributor to the risk of or protection against severe long COVID infection, but that the gene-disease relationship is context dependent and mediated by interactions with KLF15 and RYR3.

Comparison of the genes uniquely associated with the Severe and Fatigue Dominant long COVID patients revealed significant differences between the pathways enriched in each subgroup. The genes unique to Severe long COVID patients were associated with immune pathways such as myeloid differentiation and macrophage foam cells. Genes unique to the Fatigue Dominant subgroup were enriched in metabolic pathways such as MAPK/JNK signaling. We also identified overlap in the genes associated with Fatigue Dominant long COVID and ME/CFS, including several involved in circadian rhythm regulation and insulin regulation. Overall, 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patient from UK Biobank.

Among the 73 genes associated with long COVID, 42 are potentially tractable for novel drug discovery approaches, with 13 of these already targeted by drugs in clinical development pipelines. From this analysis for example, we identified TLR4 antagonists as repurposing candidates with potential to protect against long term cognitive impairment pathology caused by SARS-CoV-2. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS.

Conclusion This study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies. These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID.

Source: Krystyna Taylor, Matthew Pearson, Sayoni Das, Jason Sardell, Karolina Chocian, Steve Gardners. Genetic Risk Factors for Severe and Fatigue Dominant Long COVID and Commonalities with ME/CFS Identified by Combinatorial Analysis. medRxiv 2023.07.13.23292611; doi: https://doi.org/10.1101/2023.07.13.23292611 https://www.medrxiv.org/content/10.1101/2023.07.13.23292611v1.full-text (Full text)

Genetic risk factors for ME/CFS identified using combinatorial analysis

Abstract:

Background:Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease that lacks known pathogenesis, distinctive diagnostic criteria, and effective treatment options. Understanding the genetic (and other) risk factors associated with the disease would begin to help to alleviate some of these issues for patients.

Methods: We applied both GWAS and the PrecisionLife combinatorial analytics platform to analyze ME/CFS cohorts from UK Biobank, including the Pain Questionnaire cohort, in a case-control design with 1,000 cycles of fully random permutation. Results from this study were supported by a series of replication and cohort comparison experiments, including use of disjoint Verbal Interview CFS, post-viral fatigue syndrome and fibromyalgia cohorts also derived from UK Biobank, and results compared for overlap and reproducibility.

Results: Combinatorial analysis revealed 199 SNPs mapping to 14 genes, that were significantly associated with 91% of the cases in the ME/CFS population. These SNPs were found to stratify by shared cases into 15 clusters (communities) made up of 84 high-order combinations of between 3-5 SNPs. p-values for these communities range from 2.3 × 10−10 to 1.6 × 10−72. Many of the genes identified are linked to the key cellular mechanisms hypothesized to underpin ME/CFS, including vulnerabilities to stress and/or infection, mitochondrial dysfunction, sleep disturbance and autoimmune development. We identified 3 of the critical SNPs replicated in the post-viral fatigue syndrome cohort and 2 SNPs replicated in the fibromyalgia cohort. We also noted similarities with genes associated with multiple sclerosis and long COVID, which share some symptoms and potentially a viral infection trigger with ME/CFS.

Conclusions: This study provides the first detailed genetic insights into the pathophysiological mechanisms underpinning ME/CFS and offers new approaches for better diagnosis and treatment of patients

Source: Sayoni Das, Krystyna Taylor, James Kozubek, Jason Sardell, Steve Gardner. Genetic Risk Factors for ME/CFS Identified using Combinatorial Analysis. medRxiv 2022.09.09.22279773; doi: https://doi.org/10.1101/2022.09.09.22279773  https://www.medrxiv.org/content/10.1101/2022.09.09.22279773v2.full-text (Full text)

No replication of previously reported association with genetic variants in the T cell receptor alpha (TRA) locus for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disease with a variety of symptoms such as post-exertional malaise, fatigue, and pain, but where aetiology and pathogenesis are unknown. An increasing number of studies have implicated the involvement of the immune system in ME/CFS. Furthermore, a hereditary component is suggested by the reported increased risk for disease in relatives, and genetic association studies are being performed to identify potential risk variants.

We recently reported an association with the immunologically important human leucocyte antigen (HLA) genes HLA-C and HLA-DQB1 in ME/CFS. Furthermore, a genome-wide genetic association study in 42 ME/CFS patients reported significant association signals with two variants in the T cell receptor alpha (TRA) locus (P value <5 × 10-8). As the T cell receptors interact with the HLA molecules, we aimed to replicate the previously reported findings in the TRA locus using a large Norwegian ME/CFS cohort (409 cases and 810 controls) and data from the UK biobank (2105 cases and 4786 controls).

We investigated numerous SNPs in the TRA locus, including the two previously ME/CFS-associated variants, rs11157573 and rs17255510. No associations were observed in the Norwegian cohort, and there was no significant association with the two previously reported SNPs in any of the cohorts. However, other SNPs showed signs of association (P value <0.05) in the UK Biobank cohort and meta-analyses of Norwegian and UK biobank cohorts, but none survived correction for multiple testing. Hence, our research did not identify any reliable associations with variants in the TRA locus.

Source: Ueland M, Hajdarevic R, Mella O, Strand EB, Sosa DD, Saugstad OD, Fluge Ø, Lie BA, Viken MK. No replication of previously reported association with genetic variants in the T cell receptor alpha (TRA) locus for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Transl Psychiatry. 2022 Jul 11;12(1):277. doi: 10.1038/s41398-022-02046-1. PMID: 35821115. https://www.nature.com/articles/s41398-022-02046-1 (Full text)