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Collagen and Elastin Degradation Products as Potential Biomarkers for Chronic Graft-Versus-Host Disease (cGVHD).

2009· article· en· W2979769416 on OpenAlexaff
Imran Ahmad, Michel Boutin, Pierre Thibault, Lambert Busque, Sandra Cohen, Thomas Kiss, Silvy Lachance, Denis‐Claude Roy, Guy Sauvageau, Jean Roy

Bibliographic record

VenueBlood · 2009
Typearticle
Languageen
FieldMedicine
TopicBone and Joint Diseases
Canadian institutionsInstitute for Research in Immunology and CancerUniversité de MontréalHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineTransplantationInternal medicineGastroenterologyGraft-versus-host diseaseProteinuriaUrinary systemImmunologySurgeryKidney

Abstract

fetched live from OpenAlex

Abstract Abstract 1156 Poster Board I-178 Introduction cGVHD is the main cause of impaired quality of life, morbidity and mortality in patients surviving after allogeneic transplantation. To date, no validated biomarkers for diagnosis and follow-up of cGVHD have been established. Among proposed pathophysiological mechanisms, one involves increased turnover of extracellular matrix components as a result of immune-mediated tissue destruction. We hypothesized that analysis of urinary excretion of degradation peptides from collagen (hydroxylysylpyridinoline [HP] and lysylpyridinoline [LP]) and elastin (desmosine [DES]) using high-sensitivity nano-flow liquid chromatography tandem mass spectrometry (nanoLC-MS/MS) might lead to identification of potential biomarkers in patients with cGVHD. Patients and Methods We elected to compare 3 groups: 16 allogeneic transplant recipients with newly diagnosed or relapsing cGVHD before initiation of systemic immunosuppressive therapy (group A), 13 pts with lymphoma who underwent autologous transplantation, in order to measure the impact of high-dose chemotherapy (group B) and 10 healthy volunteers (group C). Clinical characterization of cGVHD was performed according to NIH criteria. Pts already on glucocorticoids and those with bloodstream infection or proteinuria were excluded. Morning urine samples were collected in fasting subjects, then processed and frozen at -80°C until analysis by nanoLC-MS/MS. Samples were purified using mixed-mode strong cation exchange cartridges, derivatized using propionic anhydride and analyzed with nanoLC-MS/MS. Results Pts with cGVHD (group A) were collected at a median of 229 (107-2966) days post transplant; 8 pts had de novo cGVHD, 8 presented with obvious clinical flare-up and 3 had previously suffered from acute GVHD. cGVHD was mild in 1 (6%), moderate in 8 (50%) and severe in 7 (44%) pts. Affected organs included mouth in 12 pts, skin in 11, liver in 11, eyes in 5, GI tract in 5, joints/fascia in 2, genital tract in 1 and lungs in 1. Thrombocytopenia was present in 4 (25%) pts and eosinophilia in 7 (44%). All transplants had been performed for hematological malignancies, using myeloablative (7 pts) or reduced-intensity conditioning (9 pts). Donors were HLA-identical siblings (13 pts), 10/10 (2 pts) or 9/10 (1 pt) unrelated volunteers. Stem cell source was peripheral blood in all but one pts. Pts from group B were collected at a median of 123 (90-377) days post transplant. As shown in enclosed figure, urinary concentrations of DES and HP in group A and B were similar, but both groups showed a marked increase in levels of these compounds compared to group C (p<0.0001, except HP in group B vs C: p=0.0003). We observed no difference in concentrations of LP, a bone collagen cross-linker, between studied groups. Total and free forms of studied peptides showed excellent correlation. Conclusions We found an increase in the urinary excretion of extracellular matrix components in patients with cGVHD. Our results also suggest that the conditioning regimen has significant early effects on the turnover of elastin and collagen. Prospective sampling in a cohort of allogeneic transplant recipients is currently underway to further elucidate the kinetics and the value of HP and DES as potential biomarkers for cGVHD. Disclosures No relevant conflicts of interest to declare.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.784
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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
Published2009
Admission routes1
Has abstractyes

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