1375-P: Why Some Patients Tolerate Severe Insulin Resistance Longer—Insights from Familial Partial Lipodystrophy
Notice bibliographique
Résumé
Introduction and Objective: Familial partial lipodystrophy (FPLD) is a rare genetic disorder characterized by selective subcutaneous fat loss alongside metabolic changes like severe insulin resistance. Despite this, not all patients with FPLD develop diabetes, suggesting the presence of further risk modifiers. In common type 2 diabetes, beta cell capacity determined through polygenic risk appears to play a crucial role. We investigated whether a family history of diabetes, which can indicate polygenic risk, determines similar risk in FPLD patients. Methods: We analyzed data from FPLD patients with available information on diabetes status and family history of diabetes within the European Consortium of Lipodystrophies (ECLip) registry. We included all forms of FPLD with at least 5 patients present within ECLip (N = 253, diabetes present in N = 171). Results: Lifetime prevalence of diabetes was significantly different between FPLD subtypes (p < 0.0001) but not between patients with/without a family history of diabetes (p = 0.8). However, having a family history of diabetes was significantly associated with an earlier age of diagnosis of diabetes, which was independent of FPLD subtype (average age of diagnosis with/without a family history = 41.5 and 54.9 years, respectively; 13.4 years earlier diabetes onset, adj.p = 0.0164). Conclusion: Having a family history of diabetes is linked to a much earlier age of diabetes onset, independent of the genetic variant causing FPLD. This suggests that common polygenic diabetes risk may impair beta cell compensatory capacity, accelerating diabetes onset in the severe insulin resistant milieu. Even with low polygenic risk, beta cell capacity appears to exhaust with age, explaining the high lifetime prevalence of diabetes in FPLD. Characterizing these polygenic mechanisms could reveal determinants of beta cell compensation, with potential implications for diabetes risk in more common, less severe insulin-resistant states. Disclosure M. Ennis: Other Relationship; Novo Nordisk. K. Ulrich: None. G. Ufer: Other Relationship; Boehringer-Ingelheim, Lilly Diabetes. R. Sabia: None. M. Beghini: None. G. Ceccarini: Speaker's Bureau; Novo Nordisk, Rhythm Pharmaceuticals, Inc. Consultant; Amryt Pharma. Speaker's Bureau; Chiesi. H.D. Dupuis: None. A. Fernandez-Pombo: None. K. Miehle: Research Support; Amryt Pharma. L. Palladino: None. F. Prodam: Consultant; Novartis Pharmaceuticals Corporation. Speaker's Bureau; Novo Nordisk. Consultant; Amryt Pharma. Speaker's Bureau; Amryt Pharma, Menarini. M. Romanisio: None. A. Stears: None. I. Stotl: None. M. Vantyghem: Other Relationship; Elsevier. Research Support; Amryt Pharma, Takeda Pharmaceutical - Canada. Speaker's Bureau; Sanofi. Advisory Panel; Vertex Pharmaceuticals Incorporated. Research Support; Vertex Pharmaceuticals Incorporated. C. Vatier: Consultant; Abbott, Sanofi, Regeneron Pharmaceuticals, Novo Nordisk, Lilly Diabetes, AstraZeneca, Menarini. C. Vigouroux: Other Relationship; Amryt Pharma, Amryt Pharma, Sanofi. E. Withers: None. B. Akinci: Consultant; Regeneron Pharmaceuticals, Amryt Pharma, Alnylam Pharmaceuticals, Inc. D. Araujo-Vilar: Consultant; Amryt Pharma. J. von Schnurbein: Speaker's Bureau; Amryt Pharma. Advisory Panel; Rhythm Pharmaceuticals, Inc. M. Wabitsch: Consultant; Novo Nordisk, Nestlé Health Science, Chiesi Farmaceutici, Rhythm Pharmaceuticals, Inc, Abbott. Other Relationship; Merck Healthcare Germany, Novo Nordisk, Rhythm Pharmaceuticals, Inc, Chiesi Farmaceutici, SYNLAB, Abbott, Sandoz, InfectoPharm, Mediagnost, Ascendis Pharma A/S, Hexal AG, Novo Nordisk, Merck Healthcare Germany, Abbott, Sandoz, SYNLAB. Board Member; Chiesi Pharmaceutici, Rhythm Pharmaceuticals, Inc, Novo Nordisk, Nestlé Health Science, Abbott. M. Heni: Advisory Panel; Amryt Pharma. Speaker's Bureau; Amryt Pharma, AstraZeneca, Boehringer-Ingelheim. Advisory Panel; Boehringer-Ingelheim. Speaker's Bureau; Lilly Diabetes, Novartis AG, Novo Nordisk, Sanofi.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».