Category: Methodology

Classification Accuracy and Description of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome in an Integrated Health Care System, 2006-2017

Abstract:

Introduction: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic illness characterized by marked functional limitations and fatigue. Electronic health records can be used to estimate incidence of ME/CFS but may have limitations.

Methods: The authors used International Classification of Diseases (ICD) diagnosis codes to identify all presumptive cases of ME/CFS among 9- to 39-year-olds from 2006 to 2017. The authors randomly selected 200 cases for medical record review to classify cases as confirmed, probable, or possible, based on which and how many current clinical criteria they met, and to further characterize their illness. The authors calculated crude annual rates of ME/CFS coding stratified by age and sex using only those ICD codes that had identified confirmed, probable, or possible ME/CFS cases in the medical record review.

Results: The authors identified 522 individuals with presumptive ME/CFS based on having ≥ 1 ICD codes for ME/CFS in their electronic medical record. Of the 200 cases selected, records were available and reviewed for 188. Thirty (15%) were confirmed or probable ME/CFS cases, 39 (19%) were possible cases, 119 (60%) were not cases, and 12 (6%) had no medical record available. Confirmed/probable cases commonly had chronic pain (80%) or anxiety/depression (70%), and only 13 (43%) had completed a sleep study. Overall, 37 per 100,000 had ICD codes that identified confirmed, probable, or possible ME/CFS. Rates increased between 2006 and 2017, with the largest absolute increase among those 30-39 years old.

Conclusions: Using ICD diagnosis codes alone inaccurately estimates ME/CFS incidence.

Source: Liles EG, Irving SA, Koppolu P, Crane B, Naleway AL, Brooks NB, Gee J, Unger ER, Henninger ML. Classification Accuracy and Description of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome in an Integrated Health Care System, 2006-2017. Perm J. 2024 Jun 19:1-12. doi: 10.7812/TPP/23.170. Epub ahead of print. PMID: 38980763. https://www.thepermanentejournal.org/doi/10.7812/TPP/23.170 (Full text)

BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome

Abstract:

Chronic diseases like ME/CFS and long COVID exhibit high heterogeneity with multifactorial etiology and progression, complicating diagnosis and treatment. To address this, we developed BioMapAI, an explainable Deep Learning framework using the richest longitudinal multi-‘omics dataset for ME/CFS to date.

This dataset includes gut metagenomics, plasma metabolome, immune profiling, blood labs, and clinical symptoms. By connecting multi-‘omics to asymptom matrix, BioMapAI identified both disease- and symptom-specific biomarkers, reconstructed symptoms, and achieved state-of-the-art precision in disease classification. We also created the first connectivity map of these ‘omics in both healthy and disease states and revealed how microbiome-immune-metabolome crosstalk shifted from healthy to ME/CFS.

Thus, we proposed several innovative mechanistic hypotheses for ME/CFS: Disrupted microbial functions – SCFA (butyrate), BCAA (amino acid), tryptophan, benzoate – lost connection with plasma lipids and bile acids, and activated inflammatory and mucosal immune cells (MAIT, γδT cells) with INFγ and GzA secretion. These abnormal dynamics are linked to key disease symptoms, including gastrointestinal issues, fatigue, and sleep problems.

Source: Xiong R, Fleming E, Caldwell R, Vernon SD, Kozhaya L, Gunter C, Bateman L, Unutmaz D, Oh J. BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome. bioRxiv [Preprint]. 2024 Jun 28:2024.06.24.600378. doi: 10.1101/2024.06.24.600378. PMID: 38979186; PMCID: PMC11230215. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11230215/ (Full text available as PDF file)

Assessing Functional Capacity in Myalgic Encephalopathy/Chronic Fatigue Syndrome: A Patient-Informed Questionnaire

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is an acquired disease with significant morbidity that affects both children and adults. Post-exertional malaise is a cardinal symptom of ME/CFS and impacts a patient’s functional capacity (FC). The absence of effective tools to assess FC has significant consequences for timely diagnosis, clinical follow-up, assessments for patient disability benefits, and research studies. In interventional studies, the inability to assess FC can result in an incomplete assessment of the potential benefit of the intervention, leading to beneficial treatment outcomes being missed.
Methods: Using extensive, repeated patient feedback, we have developed a new questionnaire, FUNCAP, to accurately assess FC in ME/CFS patients. The questionnaire consists of eight domains divided by activity types: A. personal hygiene/basic functions, B. walking/movement, C. being upright, D. activities in the home, E. communication, F. activities outside the home, G. reactions to light and sound, and H. concentration.
Results: Through five rounds of anonymous web-based surveys and a further test–retest validation round, two versions of the questionnaire were developed: a longer version comprising 55 questions (FUNCAP55), developed for improved diagnostic and disability benefit/insurance FC assessments; and a shorter version (FUNCAP27) for clinical patient follow-up and potential use in research. Good reliability and validity and negligible floor and ceiling effects were found, with comparable findings in all aspects in both a large Norwegian (n = 1263) and a separate English-language international sample (n = 1387) demonstrating the validity and reliability of FUNCAP.
Conclusions: Our findings support the utility of FUNCAP as an effective, reliable and valid tool for assessing FC in ME/CFS patients.
Source: Sommerfelt K, Schei T, Seton KA, Carding SR. Assessing Functional Capacity in Myalgic Encephalopathy/Chronic Fatigue Syndrome: A Patient-Informed Questionnaire. Journal of Clinical Medicine. 2024; 13(12):3486. https://doi.org/10.3390/jcm13123486 https://www.mdpi.com/2077-0383/13/12/3486 (Full text)

Data and specimen-sharing tools offer new discovery opportunities for ME/CFS researchers

Summary: Within the field of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) research, two online tools: mapMECFS (Mathur and Carnes, 2021) and searchMECFS play a crucial role in advancing the understanding of ME/CFS by encouraging researchers to share and use data and biospecimens that are stored in centralized and easily accessible data portals

Overview: mapMECFS and searchMECFS are hosted by RTI International and funded by the National Institutes of Health (NIH). mapMECFS is the largest interactive portal and repository for ME/CFS data. It offers researchers from diverse disciplines a platform to share and integrate data from their ME/CFS research studies. searchMECFS is an interactive search tool that allows researchers to identify and request available biospecimens to conduct novel experiments.

Purpose and significance: mapMECFS aims to overcome the challenge of fragmented data sources in ME/CFS research by providing access to research results across many scientific disciplines and body systems. mapMECFS offers new opportunities for researchers by providing a centralized repository and tools to connect databases and enable exploration of complex study results. mapMECFS also serves as the repository for all experimental data generated from the use of biospecimens accessed through searchMECFS.

searchMECFS addresses the logistical challenge of biospecimen selection and access by assisting with the selection of biospecimens based on demographic and clinical attributes. Currently, searchMECFS houses demographic and clinical data from the Chronic Fatigue Initiative study and information about associated biospecimens stored in a central biorepository. Future plans include adding biospecimens from the Center for Disease Control and Prevention’s Multi-Site Clinical Assessment of ME/CFS study.

User registration and access: mapMECFS user registration is open to any researcher planning to use the data for research purposes. The secure registration process involves agreeing to the Data Use Agreement (DUA) and submitting a registration form that is reviewed by Program staff at NIH. Approved new users will either be added to an existing mapMECFS Organization or to a user-specific Organization created for them. Researchers approved to upload data will also be asked to complete a User Agreement.

To ensure secure data access, mapMECFS incorporates a two-tiered dataset structure. Private datasets, characterized by restricted visibility, are only accessible to users within the same Organization. Private datasets allow researchers to upload and prepare their data while they await the publication of the supporting manuscript. In contrast, public datasets are accessible to all approved users, fostering a collaborative and inclusive environment for research exploration.

Once a dataset’s supporting manuscript has been published, users are highly encouraged to make their results publicly available to registered users.

The registration process for searchMECFS mirrors that of mapMECFS, requiring similar information. Following approval, users can query the available biospecimens and associated demographic and clinical data. Once users identify their desired samples, they follow the link to the Biospecimen Resource Access Committee Application webpage where they find instructions and links to request the biospecimens of interest.

Data types and formats: mapMECFS supports a wide array of data types including proteomics, metabolomics, methylation, gene expression, microbiome, demographic, and health and survey data. The platform also accommodates supporting phenotype (clinical and demographic data) and data dictionaries. Data must be deidentified, so that all participant information is protected.

While formatting requirements vary for each data type, detailed documentation on the website guides researchers through the submission process, ensuring data consistency and integrity. The mapMECFS support team is also available to assist researchers with their data submissions.

Search and discovery: mapMECFS offers several features designed to assist researchers in accessing relevant data. These include (1) a user-friendly data explorer tool that enables researchers to effortlessly search for datasets containing specific analytes of interest and (2) a data integration tool which allows researchers to seamlessly merge clinical data files from specific cohorts.

searchMECFS provides an interactive query tool that can be used to build and execute data queries to find available biospecimens that meet the user’s specified search criteria. Users can add multiple search criteria to further enhance their search and refine their results.

Privacy and data standards: Participant privacy, a main priority, is covered within the DUAs and User Agreements. All data uploaded on mapMECFS must be free of Personally Identifiable Information (PII). Agreements also cover responsible data use including refraining from attempting to identify participants, safeguards against unintentional disclosure, prompt reporting of any unauthorized use, and restrictions on using the data for clinical or medical purposes. It is the uploader’s responsibility to ensure that PII is not present, participant privacy is fully protected, and sharing is compliant with all other governing policies (e.g., IRB-approved protocols, embargos).

Future directions and user engagement: The development of mapMECFS is guided by user input, prioritizing site enhancements according to the feedback received from researchers actively engaging with the platform. All site users are encouraged to contact the mapMECFS support team with suggestions to improve the site. RTI plans to expand the clinical and biological data within the site’s integration tool and improve data standards and automated quality assurance pipelines. RTI is also working with the ME/CFS research community to expand the number of datasets publicly available in mapMECFS and to increase the number of biospecimens available through searchMECFS.

Funders: US National Institutes of Health (NIH): National Institute of Neurological Disorders and Stroke (NINDS); National Institute of Allergy and Infectious Diseases (NIAID); National Heart, Lung, and Blood Institute (NHLBI); National Center for Complementary and Integrative Health (NCCIH); and National Institute on Alcohol Abuse and Alcoholism (NIAAA)

Acknowledgement: This work was supported by the National Institutes of Health (NIH) under award number U24NS105535. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or RTI International.

Source: RTI International, USA

Mixed methods system for the assessment of post-exertional malaise in myalgic encephalomyelitis/chronic fatigue syndrome: an exploratory study

Abstract:

Background A central feature of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is post-exertional malaise (PEM), which is an acute worsening of symptoms after a physical, emotional and/or mental exertion. Dynamic measures of PEM have historically included scaled questionnaires, which have not been validated in ME/CFS. To enhance our understanding of PEM and how best to measure it, we conducted semistructured qualitative interviews (QIs) at the same intervals as visual analogue scale (VAS) measures after a cardiopulmonary exercise test (CPET).

Methods Ten ME/CFS and nine healthy volunteers participated in a CPET. For each volunteer, PEM symptom VAS (12 symptoms) and semistructured QIs were administered at six timepoints over 72 hours before and after a single CPET. QI data were used to plot the severity of PEM at each time point and identify the self-described most bothersome symptom for each ME/CFS volunteer. Performance of QI and VAS data was compared with each other using Spearman correlations.

Results Each ME/CFS volunteer had a unique PEM experience, with differences noted in the onset, severity, trajectory over time and most bothersome symptom. No healthy volunteers experienced PEM. QI and VAS fatigue data corresponded well an hour prior to exercise (pre-CPET, r=0.7) but poorly at peak PEM (r=0.28) and with the change from pre-CPET to peak (r=0.20). When the most bothersome symptom identified from QIs was used, these correlations improved (r=0.0.77, 0.42. and 0.54, respectively) and reduced the observed VAS scale ceiling effects.

Conclusion In this exploratory study, QIs were able to capture changes in PEM severity and symptom quality over time, even when VAS scales failed to do so. Measurement of PEM can be improved by using a quantitative–qualitative mixed model approach.

Source: Stussman B, Calco B, Norato G, et al. Mixed methods system for the assessment of post-exertional malaise in myalgic encephalomyelitis/chronic fatigue syndrome: an exploratory study. BMJ Neurology Open 2024;6:e000529. doi: 10.1136/bmjno-2023-000529  https://neurologyopen.bmj.com/content/6/1/e000529 (Full text)

How Patient Input Helped Create Culturally Sensitive Multinational Instruments Assessing Post Viral Symptoms

Our study involves collaboration/participation in order to develop culturally sensitive multinational tools for assessing post viral symptoms.

We discuss the creation of questionnaires using patient participation, and the translation of these questionnaires using international collaboration.

Patient engagement in collaboration on the creation and use of these types of instruments is of particular importance for patients who historically have not been true partners in collaborative efforts to understand diseases.

This has occurred for those with the post-viral illness called Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), which has generated considerable resentment and estrangement among the patient community.

Our article reviews:

1) why participation of diverse groups/patients is important in the development of instruments to measure key symptoms of ME/CFS,

2) why the ME/CFS group of patients needs to be included specifically (as an example),

and

3) why structured health questionnaires are important/useful.

Our article also has an overall aim of demonstrating collaborative efforts with patients and others on creating multinational scales.

We provide examples of participatory processes used in developing and translating patient-driven instruments so that they can be used in non-English speaking countries.

Our article illustrates how patients and international researchers can be involved in efforts to develop and translate international assessment instruments to validly capture domains of unexplained illness like ME/CFS.

With the onset of another post-viral illness, Long COVID, there is a world-wide need to create valid and culturally sensitive assessment instruments to measure critical symptoms, many of which are similar to ME/CFS.

Source: Leonard A. Jason and Joseph A. Dorri. How Patient Input Helped Create Culturally Sensitive Multinational Instruments Assessing Post Viral Symptoms. The Australian Community Psychologist., Volume 32 No 1  https://psychology.org.au/getmedia/c300a432-c7fd-4f97-9322-ce16429067e4/ac-vol-32(1)-2023-final-draft.pdf#page=63 (Full text)

KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome

Abstract:

The microbes present in the human gastrointestinal tract are regularly linked to human health and disease outcomes. Thanks to technological and methodological advances in recent years, metagenomic sequencing data, and computational methods designed to analyze metagenomic data, have contributed to improved understanding of the link between the human gut microbiome and disease. However, while numerous methods have been recently developed to extract quantitative and qualitative results from host-associated microbiome data, improved computational tools are still needed to track microbiome dynamics with short-read sequencing data.

Previously we have proposed KOMB as a de novo tool for identifying copy number variations in metagenomes for characterizing microbial genome dynamics in response to perturbations. In this work, we present KombOver (KO), which includes four key contributions with respect to our previous work: (i) it scales to large microbiome study cohorts, (ii) it includes both k-core and K-truss based analysis, (iii) we provide the foundation of a theoretical understanding of the relation between various graph-based metagenome representations, and (iv) we provide an improved user experience with easier-to-run code and more descriptive outputs/results.

To highlight the aforementioned benefits, we applied KO to nearly 1000 human microbiome samples, requiring less than 10 minutes and 10 GB RAM per sample to process these data. Furthermore, we highlight how graph-based approaches such as k-core and K-truss can be informative for pinpointing microbial community dynamics within a myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) cohort. KO is open source and available for download/use at: https://github.com/treangenlab/komb.

Source: Sapoval N, Tanevski M, Treangen TJ. KombOver: Efficient k-core and K-truss based characterization of perturbations within the human gut microbiome. Pac Symp Biocomput. 2024;29:506-520. PMID: 38160303; PMCID: PMC10764071. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10764071/ (Full text)

Bioimpedance spectroscopy characterization of Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS) peripheral blood mononuclear cells

Abstract:

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disabling and chronic disease, importantly related to the current COVID-19 pandemic. Currently, there are no specific laboratory tests to directly diagnose ME/CFS. In this work, the use of impedance spectroscopy is studied as a potential technique for the diagnosis of ME/CFS. A specific device for the electrical characterization of peripheral blood mononuclear cells was designed and implemented.

Impedance spectroscopy measurements in the range from 1 Hz to 500 MHz were carried out after the osmotic stress of the samples with sodium chloride solution at 1M concentration. The evolution in time after the osmotic stress at two specific frequencies (1.36 kHz and 154 kHz) was analyzed.

The device showed its sensitivity to the presence of cells and the evolution of the osmotic processes. Higher values of impedance (around 15% for both the real and imaginary part) were measured at 1.36 kHz in ME/CFS patients compared to control samples. No significant difference was found between patient samples and control samples at 154 kHz. Results help to further understand the diagnosis of ME/CFS patients and the relation of their blood samples with bioimpedance measurements.

Source: Sara Martinez Rodriguez, Alberto Olmo Fernandez, Daniel Martin Fernandez, Isabel Martin-Garrido. Bioimpedance spectroscopy characterization of Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS) peripheral blood mononuclear cells. Biomedical Letters, Volume 9, Issue 2: 121-128. http://thesciencepublishers.com/biomed_lett/v9i2abstract6.html (Full text available as PDF file)

Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics

Abstract:

Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30–40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms.
Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable. The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM.
Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm−1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate.
An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm−1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity. This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.
Source: Hackshaw KV, Yao S, Bao H, de Lamo Castellvi S, Aziz R, Nuguri SM, Yu L, Osuna-Diaz MM, Brode WM, Sebastian KR, et al. Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics. Biomedicines. 2023; 11(10):2704. https://doi.org/10.3390/biomedicines11102704 https://www.mdpi.com/2227-9059/11/10/2704 (Full text)

Predicting Myalgic Encephalomyelitis/Chronic Fatigue Syndrome from Early Symptoms of COVID-19 Infection

Abstract:

It is still unclear why certain individuals after viral infections continue to have severe symptoms. We investigated if predicting myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) development after contracting COVID-19 is possible by analyzing symptoms from the first two weeks of COVID-19 infection.
Using participant responses to the 54-item DePaul Symptom Questionnaire, we built predictive models based on a random forest algorithm using the participants’ symptoms from the initial weeks of COVID-19 infection to predict if the participants would go on to meet the criteria for ME/CFS approximately 6 months later.
Early symptoms, particularly those assessing post-exertional malaise, did predict the development of ME/CFS, reaching an accuracy of 94.6%. We then investigated a minimal set of eight symptom features that could accurately predict ME/CFS. The feature reduced models reached an accuracy of 93.5%. Our findings indicated that several IOM diagnostic criteria for ME/CFS occurring during the initial weeks after COVID-19 infection predicted Long COVID and the diagnosis of ME/CFS after 6 months.
Source: Hua C, Schwabe J, Jason LA, Furst J, Raicu D. Predicting Myalgic Encephalomyelitis/Chronic Fatigue Syndrome from Early Symptoms of COVID-19 Infection. Psych. 2023; 5(4):1101-1108. https://doi.org/10.3390/psych5040073 https://www.mdpi.com/2624-8611/5/4/73