Category: Methodology

Assessing Functional Capacity in ME/CFS: A Patient Informed Questionnaire

Abstract:

Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) is an acquired disease with significant morbidity that affects both children and adults. Effective tools to assess functional capacity (FC) are severely lacking which has significant consequences for timely diagnosis, assessments for patient disability benefits and assessing the impact and effectiveness of interventions.

In interventional research the inability to assess FC can result in an incomplete assessment of the potential effect of the intervention. Specifically of concern is that if an intervention is effective in reducing symptom load, patients may increase their activity level to reach a pre-intervention symptom load. Thus, if FC is not accurately assessed, beneficial treatment outcomes may be missed.

To address this issue, using extensive, repeated patient feedback we have developed a new questionnaire, FUNCAP, to achieve optimal FC assessment in ME/CFS patients.

The questionnaire covers eight domains and 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.

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) to improve diagnostic and disability benefit/ insurance FC assessments and a shorter version (FUNCAP27) for interventional research and less extensive FC assessments. FUNCAP may also be useful in other conditions where fatigue and PEM is present, such as Long Covid.

Source: Sommerfelt, K.; Schei, T.; Seton, K.A.; Carding, S.R. Assessing Functional Capacity in ME/CFS: A Patient Informed Questionnaire. Preprints 2023, 2023092091 https://www.preprints.org/manuscript/202309.2091/v1 (Full text available as PDF file) Final version https://www.mdpi.com/2077-0383/13/12/3486 (Full text)

Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

Abstract:

Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power.

We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as “prior information” into the next study, thereby improving the overall assessment of the likelihood of finding specific differences between plasma metabolite levels.

Using classic statistics and Benjamini-Hochberg FDR-corrections, Study 1 detected 18 metabolic differences and Study 2 detected no differences. Using Bayesian statistics on the same data, we found a high likelihood that 97 compounds were altered in concentration in Study 2, after using the results of Study 1 as the prior distributions. These findings included lower levels of peroxisome-produced ether-lipids, higher levels of long-chain unsaturated triacylglycerides, and the presence of exposome compounds that are explained by the difference in diet and medication between healthy subjects and ME/CFS patients.

Although Study 3 reported only 92 compounds in common with the other two studies, these major differences were confirmed. We also found that prostaglandin F2alpha, a lipid mediator of physiological relevance, was reduced in ME/CFS patients across all three studies. The use of Bayesian statistics led to biological conclusions from metabolomic data that were not found through frequentist approaches. We propose that Bayesian statistics is highly useful for studies with similar research designs if similar metabolomic assays are used.

Source: Brydges C, Che X, Lipkin WI, Fiehn O. Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts. Metabolites. 2023 Aug 31;13(9):984. doi: 10.3390/metabo13090984. PMID: 37755264; PMCID: PMC10535181. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535181/ (Full text)

Developing and validating a brief screening scale for ME/CFS

Abstract:

Objective: The purpose of the current study was to develop and evaluate a brief screening instrument for ME/CFS. The current study identified 4 symptom items that identify those positive for the IOM ME/CFS case definition.

Study Design: A data set of over 2,000 patients with Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and over 350 controls were assessed for the 4-item DePaul Symptom Questionnaire-Brief (DSQ-Brief). All respondents also completed the longer 54-item DePaul Symptom Questionnaire (DSQ-1) as well as the 14-item DePaul Symptom Questionnaire-Short Form (DSQ-SF). These data sets were collected from multiple countries.

We also examined the DSQ-Brief, DSQ-1, and DSQ-SF with other chronic illness groups [Multiple Sclerosis (MS) and Post-Polio Syndrome (PPS)] and those with Long COVID. Random Forest comparisons were employed in these analyses.

Results: When contrasting ME/CFS from controls, high levels of accuracy occurred using the DSQ-1, DSQ-SF, and DSQ-Brief. High accuracy again occurred for differentiating those with ME/CFS from MS, PPS, and Long COVID using the DSQ-1 and DSQ-SF, but accuracy was less for the DSQ-Brief.

Conclusions: The DSQ-Brief had high sensitivity, meaning it could identify those with ME/CFS versus controls, whereas accuracy dropped with other chronic illnesses. However, it was possible to achieve better accuracy and identify those cases where misidentification occurred by administering the DSQ-SF or DSQ-1 following the DSQ-Brief. It is now possible to screen individuals for ME/CFS using the DSQ-Brief and in so doing, identify those who are most likely to have ME/CFS.

Source: Leonard A. Jason, Sage Benner, Jacob Furst & Paul Cathey (2023) Developing and validating a brief screening scale for ME/CFS, Fatigue: Biomedicine, Health & Behavior, 11:2-4, 176-187, DOI: 10.1080/21641846.2023.2252613 https://www.tandfonline.com/doi/abs/10.1080/21641846.2023.2252613

Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes

Abstract:

Background: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and fibromyalgia have overlapping neurologic symptoms particularly disabling fatigue. This has given rise to the question whether they are distinct central nervous system (CNS) entities or is one an extension of the other.

Material and methods: To investigate this, we used unbiased quantitative mass spectrometry-based proteomics to examine the most proximal fluid to the brain, cerebrospinal fluid (CSF). This was to ascertain if the proteome profile of one was the same or different from the other. We examined two separate groups of ME/CFS, one with (n = 15) and one without (n = 15) fibromyalgia.

Results: We quantified a total of 2083 proteins using immunoaffinity depletion, tandem mass tag isobaric labelling and offline two-dimensional liquid chromatography coupled to tandem mass spectrometry, including 1789 that were quantified in all the CSF samples. ANOVA analysis did not yield any proteins with an adjusted p value <.05.

Conclusion: This supports the notion that ME/CFS and fibromyalgia as currently defined are not distinct entities.

Key message: ME/CFS and fibromyalgia as currently defined are not distinct entities. Unbiased quantitative mass spectrometry-based proteomics can be used to discover cerebrospinal fluid proteins that are biomarkers for a condition such as we are studying.

Source: Schutzer SE, Liu T, Tsai CF, Petyuk VA, Schepmoes AA, Wang YT, Weitz KK, Bergquist J, Smith RD, Natelson BH. Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes. Ann Med. 2023 Dec;55(1):2208372. doi: 10.1080/07853890.2023.2208372. Epub 2023 Sep 18. PMID: 37722890. https://www.tandfonline.com/doi/full/10.1080/07853890.2023.2208372 (Full text)

Myalgic Encephalomyelitis-Chronic Fatigue Syndrome Common Data Element item content analysis

Abstract:

Introduction: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a multisystem chronic disease estimated to affect 836,000-2.5 million individuals in the United States. Persons with ME/CFS have a substantial reduction in their ability to engage in pre-illness levels of activity. Multiple symptoms include profound fatigue, post-exertional malaise, unrefreshing sleep, cognitive impairment, orthostatic intolerance, pain, and other symptoms persisting for more than 6 months. Diagnosis is challenging due to fluctuating and complex symptoms. ME/CFS Common Data Elements (CDEs) were identified in the National Institutes of Health (NIH) National Institute of Neurological Disorders and Stroke (NINDS) Common Data Element Repository. This study reviewed ME/CFS CDEs item content.

Methods: Inclusion criteria for CDEs (measures recommended for ME/CFS) analysis: 1) assesses symptoms; 2) developed for adults; 3) appropriate for patient reported outcome measure (PROM); 4) does not use visual or pictographic responses. Team members independently reviewed CDEs item content using the World Health Organization International Classification of Functioning, Disability and Health (ICF) framework to link meaningful concepts.

Results: 119 ME/CFS CDEs (measures) were reviewed and 38 met inclusion criteria, yielding 944 items linked to 1503 ICF meaningful concepts. Most concepts linked to ICF Body Functions component (b-codes; n = 1107, 73.65%) as follows: Fatiguability (n = 220, 14.64%), Energy Level (n = 166, 11.04%), Sleep Functions (n = 137, 9.12%), Emotional Functions (n = 131, 8.72%) and Pain (n = 120, 7.98%). Activities and Participation concepts (d codes) accounted for a smaller percentage of codes (n = 385, 25.62%). Most d codes were linked to the Mobility category (n = 69, 4.59%) and few items linked to Environmental Factors (e codes; n = 11, 0.73%).

Discussion: Relatively few items assess the impact of ME/CFS symptoms on Activities and Participation. Findings support development of ME/CFS-specific PROMs, including items that assess activity limitations and participation restrictions. Development of psychometrically-sound, symptom-based item banks administered as computerized adaptive tests can provide robust assessments to assist primary care providers in the diagnosis and care of patients with ME/CFS.

Source: Slavin MD, Bailey HM, Hickey EJ, Vasudevan A, Ledingham A, Tannenbaum L, Bateman L, Kaufman DL, Peterson DL, Ruhoy IS, Systrom DM, Felsenstein D, Kazis LE. Myalgic Encephalomyelitis-Chronic Fatigue Syndrome Common Data Element item content analysis. PLoS One. 2023 Sep 12;18(9):e0291364. doi: 10.1371/journal.pone.0291364. PMID: 37698999. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0291364 (Full text)

A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires

Abstract:

Artificial intelligence or machine-learning-based models have proven useful for better understanding various diseases in all areas of health science. Myalgic Encephalomyelitis or chronic fatigue syndrome (ME/CFS) lacks objective diagnostic tests. Some validated questionnaires are used for diagnosis and assessment of disease progression.

The availability of a sufficiently large database of these questionnaires facilitates research into new models that can predict profiles that help to understand the etiology of the disease. A synthetic data generator provides the scientific community with databases that preserve the statistical properties of the original, free of legal restrictions, for use in research and education.

The initial databases came from the Vall Hebron Hospital Specialized Unit in Barcelona, Spain. 2522 patients diagnosed with ME/CFS were analyzed. Their answers to questionnaires related to the symptoms of this complex disease were used as training datasets. They have been fed for deep learning algorithms that provide models with high accuracy [0.69-0.81]. The final model requires SF-36 responses and returns responses from HAD, SCL-90R, FIS8, FIS40, and PSQI questionnaires. A highly reliable and easy-to-use synthetic data generator is offered for research and educational use in this disease, for which there is currently no approved treatment.

Source: Lacasa M, Prados F, Alegre J, Casas-Roma J. A synthetic data generation system for myalgic encephalomyelitis/chronic fatigue syndrome questionnaires. Sci Rep. 2023 Aug 31;13(1):14256. doi: 10.1038/s41598-023-40364-6. PMID: 37652910; PMCID: PMC10471690. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10471690/ (Full text)

Pediatric and Adult Patients with ME/CFS following COVID-19: A Structured Approach to Diagnosis Using the Munich Berlin Symptom Questionnaire (MBSQ)

Abstract:

Purpose A subset of patients with post-COVID-19 condition (PCC) fulfill the clinical criteria of myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS). To establish the diagnosis of ME/CFS for clinical and research purposes, comprehensive scores have to be evaluated.

Methods We developed the Munich Berlin Symptom Questionnaires (MBSQs) and supplementary scoring sheets (SSSs) to allow for a rapid evaluation of common ME/CFS case definitions. The MBSQs were applied to young patients with chronic fatigue and post-exertional malaise (PEM) who presented to the MRI Chronic Fatigue Center for Young People (MCFC). Trials were retrospectively registered (NCT05778006, NCT05638724).

Results Using the MBSQs and SSSs, we report on ten patients aged 11 to 25 years diagnosed with ME/CFS after asymptomatic SARS-CoV-2 infection or mild to moderate COVID-19. Results from their MBSQs and from well-established patient-reported outcome measures indicated severe impairments of daily activities and health-related quality of life.

Conclusions ME/CFS can follow SARS-CoV-2 infection in patients younger than 18 years, rendering structured diagnostic approaches most relevant for pediatric PCC clinics. The MBSQs and SSSs represent novel diagnostic tools that can facilitate the diagnosis of ME/CFS in children, adolescents, and adults with PCC and other post-viral syndromes.

What is known ME/CFS is a frequent debilitating illness. For diagnosis, an extensive differential diagnostic workup is required and the evaluation of clinical ME/CFS criteria. ME/CFS following COVID-19 has been reported in adults but not in pediatric patients younger than 19 years of age.

What is new We present novel questionnairs (MBSQs), as tools to assess common ME/CFS case definitions in pediatric and adult patients with post-COVID-19 condition and beyond. We report on ten patients aged 11 to 25 years diagnosed with ME/CFS following asymptomatic SARS-CoV-2 infection or mild to moderate COVID-19.

Source: Laura C. Peo, Katharina Wiehler, Johannes Paulick, Katrin Gerrer, Ariane Leone, Anja Viereck, Matthias Haegele, Silvia Stojanov, Cordula Warlitz, Silvia Augustin, Martin Alberer, Daniel B. R. Hattesohl, Laura Froehlich, Carmen Scheibenbogen, Lorenz Mihatsch, Rafael Pricoco, Uta Behrends. Pediatric and Adult Patients with ME/CFS following COVID-19: A Structured Approach to Diagnosis Using the Munich Berlin Symptom Questionnaire (MBSQ). medRxiv 2023.08.23.23293081; doi: https://doi.org/10.1101/2023.08.23.23293081 https://www.medrxiv.org/content/10.1101/2023.08.23.23293081v1.full-text (Full text)

Assessing health state utilities for people with myalgic encephalomyelitis/chronic fatigue syndrome in Australia using the EQ-5D-5L, AQoL-8D and EQ-5D-5L-psychosocial instruments

Abstract:

Purpose: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic condition with a constellation of symptoms presenting as severe and profound fatigue of ≥ 6 months not relieved by rest. ME/CFS affects health-related quality of life (HRQoL), which can be measured using multi-attribute health state utility (HSU) instruments. The aims of this study were to quantify HSUs for people living with ME/CFS, and to identify an instrument that is preferentially sensitive for ME/CFS.

Methods: Cross-sectional national survey of people with ME/CFS using the AQoL-8D and EQ-5D-5L. Additional questions from the AQoL-8D were used as ‘bolt-ons’ to the EQ-5D-5L (i.e., EQ-5D-5L-Psychosocial). Disability and fatigue severity were assessed using the De Paul Symptom Questionnaire-Short Form (DSQ-SF). HSUs were generated using Australian tariffs. Mean HSUs were stratified for sociodemographic and clinical factors. Bland-Altman plots were used to compare the three HSU instruments.

Results: For the 198 participants, mean HSUs (95% confidence intervals) were EQ-5D-5L: 0.46 (0.42-0.50); AQoL-8D: 0.43 (0.41-0.45); EQ-5D-5L-Psychosocial: 0.44 (0.42-0.46). HSUs were substantially lower than population norms: EQ-5D-5L: 0.89; AQoL-8D: 0.77. As disability and fatigue severity increased, HSUs decreased in all three instruments. Bland-Altman plots revealed interchangeability between the AQoL-8D and EQ-5D-5LPsychosocial. Floor and ceiling effects of 13.5% and 2.5% respectively were observed for the EQ-5D-5L instrument only.

Conclusions: ME/CFS has a profound impact on HRQoL. The AQoL-8D and EQ-5D-5L-Psychosocial can be used interchangeably: the latter represents a reduced participant burden.

Source: Orji NC, Cox IA, Jason LA, Chen G, Zhao T, Rogerson MJ, Kelly RM, Wills K, Hensher M, Palmer AJ, de Graaff B, Campbell JA. Assessing health state utilities for people with myalgic encephalomyelitis/chronic fatigue syndrome in Australia using the EQ-5D-5L, AQoL-8D and EQ-5D-5L-psychosocial instruments. Qual Life Res. 2023 Aug 10. doi: 10.1007/s11136-023-03498-8. Epub ahead of print. PMID: 37561337. https://link.springer.com/article/10.1007/s11136-023-03498-8 (Full text)

A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

Background: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex and debilitating disease with a significant global prevalence of over 65 million individuals. It affects various systems, including the immune, neurological, gastrointestinal, and circulatory systems. Studies have shown abnormalities in immune cell types, increased inflammatory cytokines, and brain abnormalities. Further research is needed to identify consistent biomarkers and develop targeted therapies. A multidisciplinary approach is essential for diagnosing, treating, and managing this complex disease.

The current study aims at employing explainable artificial intelligence (XAI) and machine learning (ML) techniques to identify discriminative metabolites for ME/CFS.

Material and Methods: The present study used a metabolomics dataset of CFS patients and healthy controls, including 26 healthy controls and 26 ME/CFS patients aged 22-72. The dataset encapsulated 768 metabolites, classified into nine metabolic super-pathways: amino acids, carbohydrates, cofactors, vitamins, energy, lipids, nucleotides, peptides, and xenobiotics.

Random forest-based feature selection and Bayesian Approach based-hyperparameter optimization were implemented on the target data. Four different ML algorithms [Gaussian Naive Bayes (GNB), Gradient Boosting Classifier (GBC), Logistic regression (LR) and Random Forest Classifier (RFC)] were used to classify individuals as ME/CFS patients and healthy individuals. XAI approaches were applied to clinically explain the prediction decisions of the optimum model. Performance evaluation was performed using the indices of accuracy, precision, recall, F1 score, Brier score, and AUC.

Results: The metabolomics of C-glycosyltryptophan, oleoylcholine, cortisone, and 3-hydroxydecanoate were determined to be crucial for ME/CFS diagnosis.

The RFC learning model outperformed GNB, GBC, and LR in ME/CFS prediction using the 1000 iteration bootstrapping method, achieving 98% accuracy, precision, recall, F1 score, 0.01 Brier score, and 99% AUC.

Conclusion: RFC model proposed in this study correctly classified and evaluated ME/CFS patients through the selected biomarker candidate metabolites. The methodology combining ML and XAI can provide a clear interpretation of risk estimation for ME/CFS, helping physicians intuitively understand the impact of key metabolomics features in the model.

Source: Yagin, F.H., Alkhateeb, A., Raza, A., Samee, N.A., Mahmoud, N.F., Colak, C., & Yagin, B. (2023). A Proposed Explainable Artificial Intelligence-Based Machine Learning Model for Discriminative Metabolites for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Preprints. https://doi.org/10.20944/preprints202307.1585.v1 https://www.preprints.org/manuscript/202307.1585/v1 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10706650/ (Full text of completed study)

System and methods to determine ME/CFS & Long Covid disease severity using wearable sensor & survey data

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease with high probability of misdiagnosis and significant unmet medical needs that affects as many as 2.5 million people in the U.S. and causes enormous burden for patients, their caregivers, the healthcare system and society. Between 84 to 91 percent of ME/CFS patients are not yet diagnosed [6, 19], and at least one-quarter of ME/CFS patients are house- or bedbound at some point in their lives [12, 13]. The impact of ME/CFS to the U.S. economy, is about $17 to $24 billion in medical bills and lost income from lost household and labor force productivity per year [7, 13].

Current widely used diagnosis methods of ME/CFS and other diseases with similar clinical symptoms like Long COVID [6, 21] are highly dependent on patients’ self reporting [4, 5] and standardized survey, which are not optimal for medical diagnosis. In a joint study with The Bateman Horne Center (BHC)1, we designed and developed a system prototype that was able to stably collect terabytes of inertial measurement unit (IMU) time-series data, and analyzed multiple candidate parameters derived from them that could be used as reliable biomarkers for ME/CFS and other diseases with similar clinical symptoms.

Utilizing our system prototype, MetaProcessor, we conducted grouped t-tests on data collected from the EndoPAT study group (55 recruited, 51 participated, 30 ME/CFS, 15 Long COVID, 6 healthy control) to evaluate the predictive power of Upright Position Time (UpTime), Hours of Upright Activity (HUA), and Steps/Day. Through statistical analysis, we were able to assert the following for ME/CFS versus healthy control:

1. UpTime yielded a low p-value of 0.00004, indicating a significant difference between the groups and demonstrating its potential as a reliable measure for differentiating ME/CFS from healthy control populations.

2. HUA had a p-value of less than 0.00004, suggesting it could also serve as a useful measure for distinguishing ME/CFS from healthy control groups.

3. Steps/Day, x-axis and y-axis, had p-values of 0.01059 and 0.08665, respectively, indicating that step count may be relevant for differentiating ME/CFS individuals from healthy controls, but step count alone may not be sufficient to reliably distinguish between these groups.

In a linear regression analysis, we found a moderately positive correlation between UpTime and HUA with r 2 = 0.68. Overall, we can confidently conclude that UpTime is a superior overall predictor due to its objective nature and the lowest p-values observed across all groups.

Source: System and methods to determine ME/CFS & Long Covid disease severity using wearable sensor & survey data. Sun, Y. Thesis, Bachelor of Science, The University of Utah. https://ccs.neu.edu/~ysun/publications/system-and-methods-to-determine-mecfs-and-longcovid-disease-severity-using-wearable-sensor-and-survey-data.pdf (Full text)