IGF-I variability over repeated measures in patients with acromegaly under long-acting somatostatin receptor ligands

肢端肥大症 生长抑素 医学 内科学 内分泌学 受体 生长抑素受体 胃肠病学
作者
Luigi Maione,Cristina Albrici,Solange Grunenwald,Céline Mouly,Vincenzo Cimino,Anne-Lise Lecoq,Jean Claude Souberbielle,Philippe Caron,Philippe Chanson
出处
期刊:The Journal of Clinical Endocrinology and Metabolism [Oxford University Press]
卷期号:107 (9): e3644-e3653
标识
DOI:10.1210/clinem/dgac385
摘要

In patients with acromegaly on long-term treatment with long-acting somatostatin receptor ligands (SRLs), the time of blood collection for IGF-I measurement after injection is not well defined.We aimed to assess serum IGF-I dynamics and variability in SRL-treated patients compared with surgically cured patients and healthy controls.Thirty patients under SRLs considered controlled based on a normal previous IGF-I level, 10 patients cured by pituitary surgery, and 7 healthy subjects underwent 4 weekly IGF-I determinations.In SRL-treated patients, the IGF-I SDS (mean ± SD) was higher just before injection (0.34 ± 0.66) than at Day 7 (-0.33 ± 0.61; P = 0.0041) and Day 14 (-0.23 ± 0.60; P = 0.047) after injection, but it did not significantly vary in cured patients and healthy controls. The IGF-I CV was higher in SRL-treated patients than in cured patients or healthy controls (14.4 ± 7.6% vs 7.9 ± 4.4% and 8.3 ± 3.2%, respectively; P < 0.05 for both). Among SRL-treated patients, IGF-I CV was higher in "nonoptimally controlled patients"-i.e., patients with at least one elevated IGF-I value out of 4 (n = 9) compared with "optimally controlled" patients for whom all 4 IGF-I SDS values were < 2.0 (21.3 ± 9.3 vs 11.6 ± 6.0%; P = 0.0019). The latter did not differ from surgically cured patients and healthy controls. The measurement at the farthest distance from the SRL injection was the most predictive of patients with nonoptimally controlled disease.In patients treated with long-acting SRLs, IGF-I sampling at the farthest distance from SRL injection is the most informative and best predictor of optimal disease control.
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