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Evaluation of Dyspnea With Exercise in Hypermobile Ehlers Danlos Syndrome and Generalized Hypermobility Spectrum Disorder

2025· article· en· W4410273914 on OpenAlexaff
Dmitry Rozenberg, A. Salman Al-Timimi, Megha Ibrahim Masthan, N. Al Kaabi, Encarna Camacho Pérez, Sahar Nourouzpour, L. McGillis, Ewan C. Goligher, W. Darlene Reid, Chung‐Wai Chow, C.M. Ryan, Dinesh Kumbhare, Ella Huszti, Wanda Truong, A. Belluzzo, Seyed Hesam Hashemi, K. Champagne, Satish R. Raj, Susanna Mak, Daniel Santa Mina, Hance Clarke, Nimish Mittal

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsMount Sinai HospitalToronto Rehabilitation InstituteUniversity Health NetworkToronto General HospitalUniversity of TorontoUniversity of Calgary
Fundersnot available
KeywordsHypermobility (travel)Ehlers–Danlos syndromeMedicineJoint hypermobilitySpectrum disorderPhysical therapyPhysical medicine and rehabilitationCardiologyDermatologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background/Rationale: Hypermobile Ehlers-Danlos Syndromes (hEDS, most common subtype) and Generalized Hypermobility Spectrum Disorder (G-HSD) are hereditary connective tissue disorders with multisystemic involvement. Individuals with hEDS/G-HSD commonly report exertional dyspnea, which has not been well characterized. Ventilatory and cardiac limitations during exercise may contribute to respiratory limitations. The study aimed to characterize respiratory symptoms and the cardiorespiratory response with graded exercise in people with hEDS/G-HSD. Methods: Baseline cross-sectional assessment of cardiopulmonary exercise testing (CPET) was undertaken in hEDS/G-HSD participants and age and sex matched controls without any cardiopulmonary conditions, as part of an ongoing trial (NCT 04972565). Participants had baseline characterization of respiratory symptoms (MRC dyspnea, 18-Qualitative Dyspnea Descriptors), Short Form-36, and self-reported physical activity assessed with the Godin Leisure-Time Exercise Questionnaire (GLTEQ). Participants underwent spirometry, lung volumes, and respiratory muscle strength measures and symptom-limited CPET on a cycle ergometer using a ramp protocol. Tidal flow volume curves and measures of cardiac autonomic function such as chronic response index ([peak heart rate (HR) – resting HR[asterisk]100]/([220-age]-[resting HR])) and HR recovery (HR CPET end – 1 minute post CPET) were evaluated and 18-Dyspnea Descriptors re-assessed post-CPET. T-tests, Wilcoxon and Fisher's exact test were used to compare the hEDS/G-HSD group with control participants. Results: 27 hEDS/G-HSD (30±8 years, 93% female, BMI: 25.3±4.6 kg/m2) and 17 healthy controls (29 ± 8 years, 88% female, BMI: 21.2±2.9 kg/m2) were evaluated. Participants with hEDS/G-HSD had lower SF-36 Physical Component Scores (33±9 vs. 58±6), GLTEQ (32±19 vs. 51±30), and greater MRC dyspnea (2.0±0.7 vs. 1.0±0), p < 0.05 for all comparisons. Spirometry, lung volumes and maximal inspiratory pressures were normal for both groups, Table 1. hEDS/G/HSD participants had a lower aerobic capacity (86±19% vs. 103±27% predicted, p< 0.001), reduced chronotropic response index (79±15 vs. 89±8 %, p=0.045), but no difference in peak minute ventilation (58±14 vs. 62±16%, p=0.32), change in inspiratory capacity or peak workload achieved (Table 1). Borg dyspnea and leg fatigue were moderate to severe in both groups upon CPET completion. The top three dyspnea descriptors pre- and post-CPET are described in Table 1 with hEDS/G-HSD reporting a greater number of dyspnea limitations. Conclusion: hEDS/G-HSD participants exhibited a lower aerobic capacity and chronotropic response index with a trend towards greater proportion of distressing dyspnea descriptors with exercise, despite preservation of their breathing reserve. Future work aims to explore other contributors to dyspnea such as cardiac autonomic dysfunction, thoracic musculoskeletal changes, and multidimensional perception of dyspnea.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.342
Teacher spread0.325 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2025
Admission routes1
Has abstractyes

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