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

Abstract

Objective 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 (IR 1,2 ); 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 R 2 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. Results The mean ± SD values for the participants’ age, MFIS, PWV, AC, IR 1,2 , 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 x10 6 cm 5 /din, 85.3 ± 29.7%, 33.9 ± 9.3 kgf, and 603.0 ± 106.1 m, respectively. The best predictive model (R 2 = 0.378, R 2 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). Conclusion Hormonal, vascular, and functional variables can predict general fatigue in patients with acromegaly.