Clinical and functional variables can predict general fatigue in patients with acromegaly: an explanatory model approach

• Published in: Archives of Endocrinology and Metabolism Vol. 63; no. 3; pp. 235 - 240
• Main Authors: Michalski, André da Cunha, Ferreira, Arthur de Sá, Kasuki, Leandro, Gadelha, Monica R, Lopes, Agnaldo José, Guimarães, Fernando Silva
• Format: Journal Article
• Language: English
• Published: Brazil Sociedade Brasileira de Endocrinologia e Metabologia 01-05-2019; Brazilian Society of Endocrinology and Metabolism
• Subjects: Acromegaly; Acromegaly - complications; Adult; Brazil; Cross-Sectional Studies; exercise tests; Exercise Tolerance; fatigue; Fatigue - diagnosis; Fatigue - etiology; Female; Humans; Insulin-Like Growth Factor I - analysis; Male; Middle Aged; Multivariate Analysis; muscle strength; Original; Predictive Value of Tests; Pulse Wave Analysis; Walk Test; Brazil
• ISSN: 2359-3997; 2359-4292; 2359-4292
• DOI: 10.20945/2359-3997000000127

To evaluate whether hormonal profile, arterial function, and physical capacity are predictors of fatigue in patients with acromegaly. Subjects and methods: This is a cross-sectional study including 23 patients. The subjects underwent a Modified Fatigue Impact Scale (MFIS) assessment; serum growth hormones (GH) and IGF-1 measurements; pulse wave analysis comprising pulse wave velocity (PWV), arterial compliance (AC), and the reflection index (IR1,2); dominant upper limb dynamometry (DYN); and the six-minute walking distance test (6MWT). Multiple linear regression models were used to identify predictors for MFIS. The coefficient of determination R2 was used to assess the quality of the models' fit. The best model was further analyzed using a calibration plot and a limits of agreement (LOA) plot.
The mean ± SD values for the participants' age, MFIS, PWV, AC, IR1,2, DYN, and the distance in the 6MWT were 49.4 ± 11.2 years, 31.2 ± 18.9 score, 10.19 ± 2.34 m/s, 1.08 ± 0.46 x106 cm5/din, 85.3 ± 29.7%, 33.9 ± 9.3 kgf, and 603.0 ± 106.1 m, respectively. The best predictive model (R2 = 0.378, R2 adjusted = 0.280, standard error = 16.1, and P = 0.026) comprised the following regression equation: MFIS = 48.85 - (7.913 × IGF-I) + (1.483 × AC) - (23.281 × DYN).
Hormonal, vascular, and functional variables can predict general fatigue in patients with acromegaly.