Clinical and functional variables can predict general fatigue in patients with acromegaly: an explanatory model approach
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 ho...
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| Published in: | Archives of Endocrinology and Metabolism Vol. 63; no. 3; pp. 235 - 240 |
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Sociedade Brasileira de Endocrinologia e Metabologia
01-05-2019
Brazilian Society of Endocrinology and Metabolism |
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| Abstract | 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. |
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| AbstractList | 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 (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. Results 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). Conclusion Hormonal, vascular, and functional variables can predict general fatigue in patients with acromegaly. 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. |
| Author | Lopes, Agnaldo José Gadelha, Monica R Guimarães, Fernando Silva Ferreira, Arthur de Sá Kasuki, Leandro Michalski, André da Cunha |
| AuthorAffiliation | 1 Centro Universitário Augusto Motta Programa de Pós-Graduação em Ciências da Reabilitação Centro Universitário Augusto Motta Rio de Janeiro RJ Brasil Programa de Pós-Graduação em Ciências da Reabilitação, Centro Universitário Augusto Motta, Rio de Janeiro, RJ, Brasil 2 Universidade Federal do Rio de Janeiro Faculdade de Medicina Hospital Universitário Clementino Fraga Filho Universidade Federal do Rio de Janeiro Rio de Janeiro RJ Brasil Centro de Pesquisa em Neuroendocrinologia, Serviço de Endocrinologia, Faculdade de Medicina, Hospital Universitário Clementino Fraga Filho, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brasil 4 Universidade Federal do Rio de Janeiro Departamento de Fisioterapia Universidade Federal do Rio de Janeiro Rio de Janeiro RJ Brasil Departamento de Fisioterapia, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brasil 3 Instituto Estadual do Cérebro Paulo Niemeyer Secretaria Estadual de Saúde do Rio de Janeiro Rio de Janeiro |
| AuthorAffiliation_xml | – name: 2 Universidade Federal do Rio de Janeiro Faculdade de Medicina Hospital Universitário Clementino Fraga Filho Universidade Federal do Rio de Janeiro Rio de Janeiro RJ Brasil Centro de Pesquisa em Neuroendocrinologia, Serviço de Endocrinologia, Faculdade de Medicina, Hospital Universitário Clementino Fraga Filho, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brasil – name: 3 Instituto Estadual do Cérebro Paulo Niemeyer Secretaria Estadual de Saúde do Rio de Janeiro Rio de Janeiro RJ Brasil Divisão de Neuroendocrinologia, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde do Rio de Janeiro, Rio de Janeiro, RJ, Brasil – name: 1 Centro Universitário Augusto Motta Programa de Pós-Graduação em Ciências da Reabilitação Centro Universitário Augusto Motta Rio de Janeiro RJ Brasil Programa de Pós-Graduação em Ciências da Reabilitação, Centro Universitário Augusto Motta, Rio de Janeiro, RJ, Brasil – name: 4 Universidade Federal do Rio de Janeiro Departamento de Fisioterapia Universidade Federal do Rio de Janeiro Rio de Janeiro RJ Brasil Departamento de Fisioterapia, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brasil |
| Author_xml | – sequence: 1 givenname: André da Cunha surname: Michalski fullname: Michalski, André da Cunha organization: Programa de Pós-Graduação em Ciências da Reabilitação, Centro Universitário Augusto Motta, Rio de Janeiro, RJ, Brasil – sequence: 2 givenname: Arthur de Sá surname: Ferreira fullname: Ferreira, Arthur de Sá organization: Programa de Pós-Graduação em Ciências da Reabilitação, Centro Universitário Augusto Motta, Rio de Janeiro, RJ, Brasil – sequence: 3 givenname: Leandro surname: Kasuki fullname: Kasuki, Leandro organization: Divisão de Neuroendocrinologia, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde do Rio de Janeiro, Rio de Janeiro, RJ, Brasil – sequence: 4 givenname: Monica R surname: Gadelha fullname: Gadelha, Monica R organization: Divisão de Neuroendocrinologia, Instituto Estadual do Cérebro Paulo Niemeyer, Secretaria Estadual de Saúde do Rio de Janeiro, Rio de Janeiro, RJ, Brasil – sequence: 5 givenname: Agnaldo José surname: Lopes fullname: Lopes, Agnaldo José organization: Programa de Pós-Graduação em Ciências da Reabilitação, Centro Universitário Augusto Motta, Rio de Janeiro, RJ, Brasil – sequence: 6 givenname: Fernando Silva surname: Guimarães fullname: Guimarães, Fernando Silva organization: Departamento de Fisioterapia, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brasil |
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| Snippet | To evaluate whether hormonal profile, arterial function, and physical capacity are predictors of fatigue in patients with acromegaly. Subjects and methods:... ABSTRACT Objective To evaluate whether hormonal profile, arterial function, and physical capacity are predictors of fatigue in patients with acromegaly.... |
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| SubjectTerms | 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 |
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| Title | Clinical and functional variables can predict general fatigue in patients with acromegaly: an explanatory model approach |
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