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Record W4405049622 · doi:10.1182/blood-2024-205639

The Role of JAK2 Allele Burden on Muscle Strength and Physical Functioning in Patients with Myeloproliferative Neoplasm: A Systematic Review and Prospective Cohort Analysis

2024· review· en· W4405049622 on OpenAlexaff
Adam El-Kadi, Awatif Alsadoon, Yousef Abumustafa, Christopher Hillis, Darryl P. Leong

Bibliographic record

VenueBlood · 2024
Typereview
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsJuravinski Cancer CentreMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMyeloproliferative neoplasmMedicineCohortProspective cohort studyInternal medicineCohort studyHematologic NeoplasmsAlleleOncologyPhysical therapyMyelofibrosisGerontologyGeneticsCancerBiologyBone marrowGene

Abstract

fetched live from OpenAlex

Introduction Fatigue is an important symptom for patients with a myeloproliferative neoplasm (MPN). JAK2 mutations, the most common clonal abnormality in MPNs, have been implicated in inflammation, constitutional symptoms and thrombotic risk in this population. In this project, we sought to describe the relationship between the JAK2 variant allele burden and markers of muscle strength, to evaluate its role as a potential determinant of the MPN phenotype. Methods This study is composed of two parts: a systematic review and a cohort analysis. We undertook a systematic review of seven English language full text human studies describing the relationship between the JAK2 mutation and physical functioning in patients/models with an MPN. We also performed a prospective cross-sectional study of consecutive patients with an MPN. In these patients, we evaluated fatigue on a scale of 0-10, muscle strength using a handgrip dynamometer, and the JAK2 mutant allele fraction. Results Our search identified seven studies of potential relevance. Three studies investigated the impact of ruxolitinib, a JAK1 and JAK2 inhibitor, on MPN patients. Through utilizing the EORTC QLQ-C30 questionnaire, patients on ruxolitinib had greater improvements in fatigue compared to placebo (-10.2; 95% CI, 15.8 to 4.5). Likewise, Mesa et al. demonstrated that a greater proportion of patients on ruxolitinib achieved an improvement of ≥50% in the individual MPN-SAF symptom scale from baseline. Despite the data these studies provide, they fail to establish a relationship between muscle weakness and the JAK2 mutation. None of the other studies identified addressed the research question regarding JAK2 allele burden, variant allele fraction, and their effects on muscle strength and physical functioning. We prospectively enrolled 109 participants (57 polycythemia vera, 38 essential thrombocythemia, 14 primary myelofibrosis). We found an inverse relationship mutant JAK2 allele fraction and fatigue. Respective allele fractions below versus above the median fatigue score of 3 were 0.37±0.31 and 0.20±0.25 (p=0.003). This pattern was consistent when the analysis was limited to those with polycythemia vera, with respective allele fractions of 0.49±0.31 and 0.29±0.26 (p=0.01) and when limited to those not taking a JAK2 inhibitor, with respective allele fractions of 0.37±0.30 and 0.22±0.25 (p=0.02). There was a trend towards an inverse relationship between handgrip strength and fatigue score, with Pearson correlation coefficient 0.19 (p=0.06). However, there was no relationship between handgrip strength and allele fraction, with Pearson correlation coefficient 0.02 (p=0.86). Discussion Our counterintuitive findings of less fatigue among MPN patients with higher JAK2 variant allele fractions and no association between allele fraction and muscle strength highlights the need to better understand the mechanisms underlying symptoms in this population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.011
Bibliometrics0.0090.011
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.256
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
Has abstractyes

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