Tag: clinical phenotypes

Heterogeneity in Measures of Illness among Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Is Not Explained by Clinical Practice: A Study in Seven U.S. Specialty Clinics

Abstract:

Background: One of the goals of the Multi-site Clinical Assessment of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (MCAM) study was to evaluate whether clinicians experienced in diagnosing and caring for patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) recognized the same clinical entity.
Methods: We enrolled participants from seven specialty clinics in the United States. We used baseline data (n = 465) on standardized questions measuring general clinical characteristics, functional impairment, post-exertional malaise, fatigue, sleep, neurocognitive/autonomic symptoms, pain, and other symptoms to evaluate whether patient characteristics differed by clinic.
Results: We found few statistically significant and no clinically significant differences between clinics in their patients’ standardized measures of ME/CFS symptoms and function. Strikingly, patients in each clinic sample and overall showed a wide distribution in all scores and measures.
Conclusions: Illness heterogeneity may be an inherent feature of ME/CFS. Presenting research data in scatter plots or histograms will help clarify the challenge. Relying on case–control study designs without subgrouping or stratification of ME/CFS illness characteristics may limit the reproducibility of research findings and could obscure underlying mechanisms.
Source: Unger ER, Lin J-MS, Chen Y, Cornelius ME, Helton B, Issa AN, Bertolli J, Klimas NG, Balbin EG, Bateman L, et al. Heterogeneity in Measures of Illness among Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Is Not Explained by Clinical Practice: A Study in Seven U.S. Specialty Clinics. Journal of Clinical Medicine. 2024; 13(5):1369. https://doi.org/10.3390/jcm13051369 https://www.mdpi.com/2077-0383/13/5/1369 (Full text)

A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms

Abstract:

Background: Accurate COVID-19 prognosis is a critical aspect of acute and long-term clinical management. We identified discrete clusters of early stage-symptoms which may delineate groups with distinct disease severity phenotypes, including risk of developing long-term symptoms and associated inflammatory profiles.

Methods: 1,273 SARS-CoV-2 positive U.S. Military Health System beneficiaries with quantitative symptom scores (FLU-PRO Plus) were included in this analysis. We employed machine-learning approaches to identify symptom clusters and compared risk of hospitalization, long-term symptoms, as well as peak CRP and IL-6 concentrations.

Results: We identified three distinct clusters of participants based on their FLU-PRO Plus symptoms: cluster 1 (“Nasal cluster”) is highly correlated with reporting runny/stuffy nose and sneezing, cluster 2 (“Sensory cluster”) is highly correlated with loss of smell or taste, and cluster 3 (“Respiratory/Systemic cluster”) is highly correlated with the respiratory (cough, trouble breathing, among others) and systemic (body aches, chills, among others) domain symptoms. Participants in the Respiratory/Systemic cluster were twice as likely as those in the Nasal cluster to have been hospitalized, and 1.5 times as likely to report that they had not returned-to-activities, which remained significant after controlling for confounding covariates (P < 0.01). Respiratory/Systemic and Sensory clusters were more likely to have symptoms at six-months post-symptom-onset (P = 0.03). We observed higher peak CRP and IL-6 in the Respiratory/Systemic cluster (P < 0.01).

Conclusions: We identified early symptom profiles potentially associated with hospitalization, return-to-activities, long-term symptoms, and inflammatory profiles. These findings may assist in patient prognosis, including prediction of long COVID risk.

Source: Epsi NJ, Powers JH, Lindholm DA, Mende K, Malloy A, Ganesan A, Huprikar N, Lalani T, Smith A, Mody RM, Jones MU, Bazan SE, Colombo RE, Colombo CJ, Ewers EC, Larson DT, Berjohn CM, Maldonado CJ, Blair PW, Chenoweth J, Saunders DL, Livezey J, Maves RC, Sanchez Edwards M, Rozman JS, Simons MP, Tribble DR, Agan BK, Burgess TH, Pollett SD; EPICC COVID-19 Cohort Study Group. A machine learning approach identifies distinct early-symptom cluster phenotypes which correlate with hospitalization, failure to return to activities, and prolonged COVID-19 symptoms. PLoS One. 2023 Feb 9;18(2):e0281272. doi: 10.1371/journal.pone.0281272. PMID: 36757946; PMCID: PMC9910657. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9910657/ (Full text)

Clinical Heterogeneity in ME/CFS. A Way to Understand Long-COVID19 Fatigue

Abstract:

The aim of present paper is to identify clinical phenotypes in a cohort of patients affected of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Ninety-one patients and 22 healthy controls were studied with the following questionnaires, in addition to medical history: visual analogical scale for fatigue and pain, DePaul questionnaire (post-exertional malaise, immune, neuroendocrine), Pittsburgh sleep quality index, COMPASS-31 (dysautonomia), Montreal cognitive assessment, Toulouse-Piéron test (attention), Hospital Anxiety and Depression test and Karnofsky scale. Co-morbidities and drugs-intake were also recorded.

A hierarchical clustering with clinical results was performed. Final study group was made up of 84 patients, mean age 44.41 ± 9.37 years (66 female/18 male) and 22 controls, mean age 45 ± 13.15 years (14 female/8 male). Patients meet diagnostic criteria of Fukuda-1994 and Carruthers-2011. Clustering analysis identify five phenotypes.

Two groups without fibromyalgia were differentiated by various levels of anxiety and depression (13 and 20 patients). The other three groups present fibromyalgia plus a patient without it, but with high scores in pain scale, they were segregated by prevalence of dysautonomia (17), neuroendocrine (15), and immunological affectation (19). Regarding gender, women showed higher scores than men in cognition, pain level and depressive syndrome.

Mathematical tools are a suitable approach to objectify some elusive features in order to understand the syndrome. Clustering unveils phenotypes combining fibromyalgia with varying degrees of dysautonomia, neuroendocrine or immune features and absence of fibromyalgia with high or low levels of anxiety-depression. There is no a specific phenotype for women or men.

Source: Murga I, Aranburu L, Gargiulo PA, Gómez Esteban JC, Lafuente JV. Clinical Heterogeneity in ME/CFS. A Way to Understand Long-COVID19 Fatigue. Front Psychiatry. 2021 Oct 11;12:735784. doi: 10.3389/fpsyt.2021.735784. PMID: 34707521; PMCID: PMC8542754.  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8542754/  (Full text)

A Chronic Fatigue Syndrome (CFS) severity score based on case designation criteria

Abstract:

BACKGROUND: Chronic Fatigue Syndrome case designation criteria are scored as physicians’ subjective, nominal interpretations of patient fatigue, pain (headaches, myalgia, arthralgia, sore throat and lymph nodes), cognitive dysfunction, sleep and exertional exhaustion.

METHODS: Subjects self-reported symptoms using an anchored ordinal scale of 0 (no symptom), 1 (trivial complaints), 2 (mild), 3 (moderate), and 4 (severe). Fatigue of 3 or 4 distinguished “Fatigued” from “Not Fatigued” subjects. The sum of the 8(Sum8) ancillary criteria was tested as a proxy for fatigue. All subjects had history and physical examinations to exclude medical fatigue, and ensure categorization as healthy or CFS subjects.

RESULTS: Fatigued subjects were divided into CFS with ≥4 symptoms or Chronic Idiopathic Fatigue (CIF) with ≤3 symptoms. ROC of Sum8 for CFS and Not Fatigued subjects generated a threshold of 14 (specificity=0.934; sensitivity=0.928). CFS (n=256) and CIF (n=55) criteria were refined to include Sum8≥14 and ≤13, respectively. Not Fatigued subjects had highly skewed Sum8 responses. Healthy Controls (HC; n=269) were defined by fatigue≤2 and Sum8≤13. Those with Sum8≥14 were defined as CFS-Like With Insufficient Fatigue Syndrome (CFSLWIFS; n=20). Sum8 and Fatigue were highly correlated (R(2)=0.977; Cronbach’s alpha=0.924) indicating an intimate relationship between symptom constructs. Cluster analysis suggested 4 clades each in CFS and HC. Translational utility was inferred from the clustering of proteomics from cerebrospinal fluid.

CONCLUSIONS: Plotting Fatigue severity versus Sum8 produced an internally consistent classifying system. This is a necessary step for translating symptom profiles into fatigue phenotypes and their pathophysiological mechanisms.

 

Source: Baraniuk JN, Adewuyi O, Merck SJ, Ali M, Ravindran MK, Timbol CR, Rayhan R, Zheng Y, Le U, Esteitie R, Petrie KN. A Chronic Fatigue Syndrome (CFS) severity score based on case designation criteria. Am J Transl Res. 2013;5(1):53-68. Epub 2013 Jan 21. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3560481/ (Full article)

 

Microbial infections in eight genomic subtypes of chronic fatigue syndrome/myalgic encephalomyelitis

Abstract:

BACKGROUND: The authors have previously reported genomic subtypes of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) based on expression of 88 human genes.

AIM: To attempt to reproduce these findings, determine the specificity of this signature to CFS/ME, and test for associations between CFS/ME subtype and infection.

METHODS: Expression levels of 88 human genes were determined in blood of 62 new patients with idiopathic CFS/ME (according to Fukuda criteria), six patients with Q-fever-associated CFS/ME from the Birmingham Q-fever outbreak (according to Fukuda criteria), 14 patients with endogenous depression (according to DSM-IV criteria) and 29 normal blood donors.

RESULTS: In patients with CFS/ME, differential expression was confirmed for all 88 genes. Q-CFS/ME had similar patterns of gene expression to idiopathic CFS/ME. Gene expression in patients with endogenous depression was similar to that in the normal controls, except for upregulation of five genes (APP, CREBBP, GNAS, PDCD2 and PDCD6). Clustering of combined gene data in CFS/ME patients for this and the authors’ previous study (117 CFS/ME patients) revealed genomic subtypes with distinct differences in SF36 scores, clinical phenotypes, severity and geographical distribution. Antibody testing for Epstein-Barr virus, enterovirus, Coxiella burnetii and parvovirus B19 revealed evidence of subtype-specific relationships for Epstein-Barr virus and enterovirus, the two most common infectious triggers of CFS/ME.

CONCLUSIONS: This study confirms the involvement of these genes in CFS/ME.

 

Source: Zhang L, Gough J, Christmas D, Mattey DL, Richards SC, Main J, Enlander D, Honeybourne D, Ayres JG, Nutt DJ, Kerr JR. Microbial infections in eight genomic subtypes of chronic fatigue syndrome/myalgic encephalomyelitis. J Clin Pathol. 2010 Feb;63(2):156-64. doi: 10.1136/jcp.2009.072561. Epub 2009 Dec 2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2921262/ (Full article)

 

Phenotypes of chronic fatigue syndrome in children and young people

Abstract:

OBJECTIVE: To investigate the heterogeneity of chronic fatigue syndrome (CFS/ME) in children and young people.

SETTING: Regional specialist CFS/ME service Patients Children and young people aged <19 years old.

METHODS: Exploratory factor analysis was performed on symptoms present at assessment in 333 children and young people with CFS/ME. Linear and logistic regression analysis of data from self-completed assessment forms was used to explore the associations between the retained factors and sex, age, length of illness, depression, anxiety and markers of severity (fatigue, physical function, pain and school attendance).

RESULTS: Three phenotypes were identified using factor analysis: muscoloskeletal (factor 1) had loadings on muscle and joint pain and hypersensitivity to touch, and was associated with worse fatigue (regression coefficient 0.47, 95% CI 0.25 to 0.68, p<0.001), physical function (regression coefficient -0.52, 95% CI -0.83 to -0.22, p=0.001) and pain. Factor 2 (migraine) loaded on noise and light hypersensitivity, headaches, nausea, abdominal pain and dizziness and was most strongly associated with physical function and pain. Sore throat phenotype (factor 3) had loadings on sore throat and tender lymph nodes and was not associated with fatigue or pain. There was no evidence that phenotypes were associated with age, length of illness or symptoms of depression (regression coefficient for association of depression with musculoskeletal pain -0.02, 95% CI -0.27 to 0.23, p=0.87). The migraine phenotype was associated with anxiety (0.40, 95% CI 0.06 to 0.74, p=0.02).

IMPLICATIONS: CFS/ME is heterogeneous in children with three phenotypes at presentation that are differentially associated with severity and are unlikely to be due to age or length of illness.

 

Source: May M, Emond A, Crawley E. Phenotypes of chronic fatigue syndrome in children and young people. Arch Dis Child. 2010 Apr;95(4):245-9. doi: 10.1136/adc.2009.158162. Epub 2009 Oct 19. https://www.ncbi.nlm.nih.gov/pubmed/19843509

 

Gene expression subtypes in patients with chronic fatigue syndrome/myalgic encephalomyelitis

Abstract:

Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a multisystem disease, the pathogenesis of which remains undetermined. We set out to determine the precise abnormalities of gene expression in the blood of patients with CFS/ME. We analyzed gene expression in peripheral blood from 25 patients with CFS/ME diagnosed according to the Centers for Disease Control and Prevention diagnostic criteria and 50 healthy blood donors, using a microarray with a cutoff fold difference of expression of >or=2.5. Genes showing differential expression were further analyzed in 55 patients with CFS/ME and 75 healthy blood donors, using quantitative polymerase chain reaction.

Differential expression was confirmed for 88 genes; 85 were upregulated, and 3 were downregulated. Highly represented functions were hematological disease and function, immunological disease and function, cancer, cell death, immune response, and infection. Clustering of quantitative polymerase chain reaction data from patients with CFS/ME revealed 7 subtypes with distinct differences in Medical Outcomes Survey Short Form-36 scores, clinical phenotypes, and severity.

 

Source: Kerr JR, Petty R, Burke B, Gough J, Fear D, Sinclair LI, Mattey DL, Richards SC, Montgomery J, Baldwin DA, Kellam P, Harrison TJ, Griffin GE, Main J,Enlander D, Nutt DJ, Holgate ST. Gene expression subtypes in patients with chronic fatigue syndrome/myalgic encephalomyelitis. J Infect Dis. 2008 Apr 15;197(8):1171-84. doi: 10.1086/533453.  http://jid.oxfordjournals.org/content/197/8/1171.long (Full article)

 

Seven genomic subtypes of chronic fatigue syndrome/myalgic encephalomyelitis: a detailed analysis of gene networks and clinical phenotypes

Abstract:

AIM: Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a multisystem disease, the pathogenesis of which remains undetermined. The authors have recently reported a study of gene expression that identified differential expression of 88 human genes in patients with CFS/ME. Clustering of quantitative PCR (qPCR) data from patients with CFS/ME revealed seven distinct subtypes with distinct differences in Medical Outcomes Survey Short Form-36 scores, clinical phenotypes and severity.

METHODS: In this study, for each CFS/ME subtype, those genes whose expression differed significantly from that of normal blood donors were identified, and then gene interactions, disease associations and molecular and cellular functions of those gene sets were determined. Genomic analysis was then related to clinical data for each CFS/ME subtype.

RESULTS: Genomic analysis revealed some common (neurological, haematological, cancer) and some distinct (metabolic, endocrine, cardiovascular, immunological, inflammatory) disease associations among the subtypes. Subtypes 1, 2 and 7 were the most severe, and subtype 3 was the mildest. Clinical features of each subtype were as follows: subtype 1 (cognitive, musculoskeletal, sleep, anxiety/depression); subtype 2 (musculoskeletal, pain, anxiety/depression); subtype 3 (mild); subtype 4 (cognitive); subtype 5 (musculoskeletal, gastrointestinal); subtype 6 (postexertional); subtype 7 (pain, infectious, musculoskeletal, sleep, neurological, gastrointestinal, neurocognitive, anxiety/depression).

CONCLUSION: It was particularly interesting that in the seven genomically derived subtypes there were distinct clinical syndromes, and that those which were most severe were also those with anxiety/depression, as would be expected in a disease with a biological basis.

 

Source: Kerr JR, Burke B, Petty R, Gough J, Fear D, Mattey DL, Axford JS, Dalgleish AG, Nutt DJ. Seven genomic subtypes of chronic fatigue syndrome/myalgic encephalomyelitis: a detailed analysis of gene networks and clinical phenotypes. J Clin Pathol. 2008 Jun;61(6):730-9. Epub 2007 Dec 5. https://www.ncbi.nlm.nih.gov/pubmed/18057078