Lack of association between CAG repeat polymorphism in the androgen receptor gene and the outcome of rheumatoid arthritis treatment with leflunomide

Dziedziejko, Violetta; Kurzawski, Mateusz; Safranow, Krzysztof; Ossowski, Andrzej; Piątek, Jarosław J.; Parafiniuk, Mirosław; Chlubek, Dariusz; Pawlik, Andrzej

doi:10.1007/s00228-011-1131-3

Cited by 5 publications

(4 citation statements)

References 29 publications

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“…e study was approved by the ethics committee of the Medicine College of Inner Mongolia University for the Nationalities (IMUNMCEC20210412 [1]). Male Wistar rats (200 ± 10 g) were provided by YiSi Laboratory Animal Technology Co., Ltd. (Changchun, China).…”

Section: Adjuvant-induced Arthritis Model Establishment Andmentioning

confidence: 99%

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An Improved Stacked Autoencoder for Metabolomic Data Classification

Fan

Wang

Jiang

et al. 2021

Computational Intelligence and Neuroscience

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Naru3 (NR) is a traditional Mongolian medicine with high clinical efficacy and low incidence of side effects. Metabolomics is an approach that can facilitate the development of traditional drugs. However, metabolomic data have a high throughput, sparse, high-dimensional, and small sample nature, and their classification is challenging. Although deep learning methods have a wide range of applications, deep learning-based metabolomic studies have not been widely performed. We aimed to develop an improved stacked autoencoder (SAE) for metabolomic data classification. We established an NR-treated rheumatoid arthritis (RA) mouse model and classified the obtained metabolomic data using the Hessian-free SAE (HF-SAE) algorithm. During training, the unlabeled data were used for pretraining, and the labeled data were used for fine-tuning based on the HF algorithm for gradient descent optimization. The hybrid algorithm successfully classified the data. The results were compared with those of the support vector machine (SVM), k-nearest neighbor (KNN), and gradient descent SAE (GD-SAE) algorithms. A five-fold cross-validation was used to complete the classification experiment. In each fine-tuning process, the mean square error (MSE) and misclassification rates of the training and test data were recorded. We successfully established an NR animal model and an improved SAE for metabolomic data classification.

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Section: Adjuvant-induced Arthritis Model Establishment Andmentioning

confidence: 99%

“…Rheumatoid arthritis (RA) is a common systemic autoimmune disease characterized by symmetric polyarthritis and joint destruction [1]. It is traditionally treated with methotrexate combined with the botanical preparation of Tripterygium wilfordii.…”

Section: Introductionmentioning

confidence: 99%

An Improved Stacked Autoencoder for Metabolomic Data Classification

Fan

Wang

Jiang

et al. 2021

Computational Intelligence and Neuroscience

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“…The authors found that 17b-estradiol and testosterone respectively seem to contrast and to synergize the LEF downregulatory activity [ 110 ]. In another study, Dziedziejko et al [ 111 ] examined the association between CAG repeat polymorphism in the androgen receptor (AR) gene and response to therapy in women with RA treated with LEF. The AR gene, present on the X chromosome, contains a highly polymorphic CAG (glutamine) repeat in the region encoding NH2-terminal transactivation domain, which normally varies between 9 and 37 repeats (approximately 10–30 in Caucasians) [ 112 , 113 ].…”

Section: Pharmacogenetic Biomarkersmentioning

confidence: 99%

“…The authors studied 114 women diagnosed with RA and treated with LEF 20 mg daily, with a 12-month follow-up. No statistically significant associations between CAG repeat polymorphism in the AR gene and improvement of disease activity parameters was found, suggesting no correlation between CAG repeat polymorphism in the AR gene and response to treatment with LEF in women with RA [ 111 ].…”

Section: Pharmacogenetic Biomarkersmentioning

confidence: 99%

Biomarkers to Personalize the Treatment of Rheumatoid Arthritis: Focus on Autoantibodies and Pharmacogenetics

et al. 2020

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Rheumatoid arthritis (RA) is a chronic inflammatory disease that is very complex and heterogeneous. If not adequately treated, RA patients are likely to manifest excess of morbidity and disability with an important impact on the quality of life. Pharmacological treatment is based on the administration of the disease-modifying antirheumatic drugs (DMARDs), subdivided into conventional synthetic (csDMARDs), targeted synthetic (tsDMARDs), and biological (bDMARDs). bDMARDs are now frequently administered in patients, both as alternative treatment and together with csDMARDs. Unfortunately, there is a therapeutic response variability both to old and new drugs. Therefore, to identify pre-therapeutic and on-treatment predictors of response is a priority. This review aims to summarize recent advances in understanding the causes of the variability in treatment response in RA, with particular attention to predictive potential of autoantibodies and DMARD pharmacogenetics. In recent years, several biomarkers have been proposed to personalize the therapy. Unfortunately, a magic bullet does not exist, as many factors concur to disease susceptibility and treatment outcomes, acting around the patient’s congenital background. Models integrating demographic, clinical, biochemical, and genetic data are needed to enhance the predictive capacity of specific factors singularly considered to optimize RA treatment in light of multidisciplinary patient management.

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