MO041URINE PROTEOMICS FOR PREDICTION OF DISEASE PROGRESSION IN PATIENTS WITH IGA NEPHROPATHY
Notice bibliographique
Résumé
Abstract Background and Aims Although IgA nephropathy (IgAN) is the most common primary glomerulonephritis in many parts of the world, risk of progressive kidney function decline is significant. Furthermore, the effect of immunosuppressive treatment in IgAN currently remains uncertain. There is no validated tool to predict disease progression or response to treatment. To serve this unmet need, we aimed at developing a urinary biomarker-based algorithm that predicts fast disease progression in patients with IgAN, thus enabling a personalized risk stratification and potentially improving patient management. Method In this multicenter study in 7 centers in Europe and in Canada urine samples were collected as part of clinical routine at time of biopsy in n= 300 patients (63% male, age 42±14 years) with biopsy proven IgAN. The follow-up data were collected for at least one year. Progressive disease was defined as an annual loss of kidney function (estimated glomerular filtration rate, eGFR) of more than -5ml/min/1.73m2 per year or when end-stage kidney disease (ESKD) was reached. The institutional review boards of each of the participating centers approved this study. Urine samples were analyzed using capillary electrophoresis coupled mass spectrometry (CE-MS). Whole proteome/peptidome profile was obtained for each sample. This study received funding from the European Union´s ERA PerMed program. Results Urine proteome/peptidome profiles were obtained from n=294 patients. On average, 2383 peptides were detected per sample. The data were subsequently divided into a discovery (n=154) and validation cohort (n=140). The comparison of the progressors (n=35) and non-progressors (n=119) in the discovery cohort resulted in the definition of more than 100 significant peptides. These included mainly fragments from collagen, mostly type 1 (decreased in progressors) and from different blood derived proteins like alpha-1-antitrypsin, alpha-2-HS-glycoprotein and apolipoproteins (increased in progressors). The distribution of the 100 most significant peptides in the progressor and non-progressor group is shown in the figure. The peptides were combined into a classifier using support vector machine. After optimizing the classifier employing a take-one-out procedure combined with n-1 cross-validation, the urine-peptide based algorithm enabled separation of progressors versus no progressors with an accuracy of 90% in take-one-out cross-validation. This classifier was subsequently applied to the validation cohort and resulted in highly significant separation of progressive from non-progressive IgAN patients. Furthermore, this classifier will be further applied blinded in an independent well characterized multicenter cohort of 267 IgAN patients. Conclusion We identified a urinary proteome profile which was associated with progressive loss of GFR in patients with IgAN. Further validation of this profile in an independent cohort is ongoing. The data indicate that CE-MS-based urinary proteomics enables identifying IgAN patients at high risk of disease progression. These patients may benefit from aggressive immunosuppressive treatment. Upon validation of the classifier in an independent cohort, its value in predicting response to immunosuppression will be assessed, aiming at establishing an innovative strategy that could improve patient management and personalize treatment of IgAN patients.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».