TY - JOUR
T1 - Growth hormone assay-adjusted standardization reveals distinct clinical phenotypes in acromegaly
AU - Biagetti, Betina
AU - Marques, Pedro
AU - Ferrer, Roser
AU - Cardoso, Luís Miguel
AU - Moreno, Eva Venegas
AU - Fajardo-Montañana, Carmen
AU - Gonzalez-Fernandez, Laura
AU - Pérez Pena, Marta María
AU - García-Centeno, Rogelio
AU - Lozano-Aida, Claudia
AU - Novoa-Testa, Iría
AU - Pascual-Corrales, Eider
AU - Sánchón, Raúl
AU - Guerrero-Pérez, Fernando
AU - Rodríguez, Rosario Oliva
AU - Jiménez, Beatriz Rodríguez
AU - García, María Dolores Ollero
AU - Echarri, Ana Irigaray
AU - Simó-Servat, Andreu
AU - Rodríguez, María Dolores Moure
AU - Calatayud, María
AU - Villar-Taibo, Rocío
AU - Tenorio-Jimenéz, Carmen
AU - Novo-Rodríguez, Cristina
AU - Molero, Inmaculada González
AU - Iglesias, Pedro
AU - Blanco, Concepción
AU - Lara, Fernando Vidal-Ostos de
AU - Aulinas, Anna
AU - Roca, Queralt Asla
AU - Paja, Miguel
AU - Galiana, Pablo Abellán
AU - Cordido, Fernando
AU - Torre, Edelmiro Menéndez
AU - Cámara, Rosa
AU - Sarria-Estrada, Silvana
AU - Rodríguez, Silvia Aznar
AU - Lamas, Cristina
AU - Alvarez-Escola, Cristina
AU - Bernabéu, Ignacio
AU - Hanzu, Felicia
AU - Marazuela, Mónica
AU - Puig-Domingo, Manel
AU - Araujo-Castro, Marta
N1 - Publisher Copyright:
© 2025 AACE. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2025/10
Y1 - 2025/10
N2 - Objective: To identify distinct clinical phenotypes in acromegaly based on growth hormone (GH) assay standardization and unsupervised machine learning. Methods: This was a multicenter cross-sectional analysis of 416 patients diagnosed with acromegaly from 2010 onward. Patients were stratified according to baseline serum GH levels standardized to the assay-specific upper limit of normal (GHxULN) using a binary classification (GH-B: <1.0×ULN vs ≥1.0×ULN) and a four-tier classification (GH-4: <0.25, 0.25-0.99, 1.0-9.9, ≥10×ULN). Unsupervised cluster analysis included age, GHxULN, insulin-like growth factor 1 (IGF-1)xULN, tumor diameter, and T2-weighted signal intensity. Results: Overall, 36% of patients had GH levels within the normal reference range for their assay (GH-B <1.0×ULN). Microadenomas (23.1%) were more frequent in older patients and associated with lower GH/IGF-1 levels. Across GH-4 categories, significant gradients were observed for age (z = −5.34, P < .001), tumor size (z = 8.01, P < .001), IGF-1 (z = 9.00, P < .001), and symptom duration (z = 4.34, P < .001). Higher GH categories were associated with greater odds of arthropathy (odds ratio 3.5, P = .015 for 1.0-9.9×ULN and odds ratio 6.58, P = .002 for ≥10×ULN). Cluster analysis revealed 3 phenotypes: cluster 1 (49.0%) [older age, lower GH/IGF-1, intermediate tumor size]; cluster 2 (44.4%) [intermediate age, moderate biochemical activity, smaller tumors]; cluster 3 (6.6%) [younger age, markedly elevated GH/IGF-1, large aggressive tumors]. Conclusion: GH standardization to assay-specific ULN reveals clinically meaningful phenotypes in acromegaly that correlate with age, tumor characteristics, and disease severity (particularly arthropathy). GHxULN complements IGF-1 by capturing tumor secretory activity, and this stratification approach may support more individualized clinical decision-making.
AB - Objective: To identify distinct clinical phenotypes in acromegaly based on growth hormone (GH) assay standardization and unsupervised machine learning. Methods: This was a multicenter cross-sectional analysis of 416 patients diagnosed with acromegaly from 2010 onward. Patients were stratified according to baseline serum GH levels standardized to the assay-specific upper limit of normal (GHxULN) using a binary classification (GH-B: <1.0×ULN vs ≥1.0×ULN) and a four-tier classification (GH-4: <0.25, 0.25-0.99, 1.0-9.9, ≥10×ULN). Unsupervised cluster analysis included age, GHxULN, insulin-like growth factor 1 (IGF-1)xULN, tumor diameter, and T2-weighted signal intensity. Results: Overall, 36% of patients had GH levels within the normal reference range for their assay (GH-B <1.0×ULN). Microadenomas (23.1%) were more frequent in older patients and associated with lower GH/IGF-1 levels. Across GH-4 categories, significant gradients were observed for age (z = −5.34, P < .001), tumor size (z = 8.01, P < .001), IGF-1 (z = 9.00, P < .001), and symptom duration (z = 4.34, P < .001). Higher GH categories were associated with greater odds of arthropathy (odds ratio 3.5, P = .015 for 1.0-9.9×ULN and odds ratio 6.58, P = .002 for ≥10×ULN). Cluster analysis revealed 3 phenotypes: cluster 1 (49.0%) [older age, lower GH/IGF-1, intermediate tumor size]; cluster 2 (44.4%) [intermediate age, moderate biochemical activity, smaller tumors]; cluster 3 (6.6%) [younger age, markedly elevated GH/IGF-1, large aggressive tumors]. Conclusion: GH standardization to assay-specific ULN reveals clinically meaningful phenotypes in acromegaly that correlate with age, tumor characteristics, and disease severity (particularly arthropathy). GHxULN complements IGF-1 by capturing tumor secretory activity, and this stratification approach may support more individualized clinical decision-making.
KW - Acromegaly
KW - Growth hormone
KW - IGF-1
KW - Micromegaly
KW - Phenotypes
KW - Pituitary
UR - https://www.scopus.com/pages/publications/105023900098
U2 - 10.1016/j.eprac.2025.10.006
DO - 10.1016/j.eprac.2025.10.006
M3 - Article
C2 - 41101706
AN - SCOPUS:105023900098
SN - 1530-891X
JO - Endocrine Practice
JF - Endocrine Practice
ER -