Petrovsky Denis V, Pustovoyt Vasiliy I, Nikolsky Kirill S, Malsagova Kristina A, Kopylov Arthur T, Stepanov Alexander A, Rudnev Vladimir R, Balakin Evgenii I, Kaysheva Anna L
Biobanking Group, Branch of Institute of Biomedical Chemistry "Scientific and Education Center", 109028 Moscow, Russia.
State Research Center-Burnasyan Federal Medical Biophysical Center of Federal Medical Biological Agency, 119435 Moscow, Russia.
Sports (Basel). 2022 Oct 19;10(10):160. doi: 10.3390/sports10100160.
Training and competitive periods can temporarily impair the performance of an athlete. This disruption can be short- or long-term, lasting up to several days. We analyzed the health indicators of 3661 athletes during an in-depth medical examination. At the time of inclusion in the study, the athletes were healthy. Instrumental examinations (fluorography, ultrasound examination of the abdominal cavity and pelvic organs, echocardiography, electrocardiography, and stress testing "to failure"), laboratory examinations (general urinalysis and biochemical and general clinical blood analysis), and examinations by specialists (ophthalmologist, otolaryngologist, surgeon, cardiologist, neurologist, dentist, gynecologist (women), endocrinologist, and therapist) were performed. This study analyzed the significance of determining the indicators involved in the implementation of the "catabolism" and "anabolism" phenotypes using the random forest and multinomial logistic regression machine learning methods. The use of decision forest and multinomial regression models made it possible to identify the most significant indicators of blood and urine biochemistry for the analysis of phenotypes as a characterization of the effectiveness of recovery processes in the post-competitive period in athletes. We found that the parameters of muscle metabolism, such as aspartate aminotransferase, creatine kinase, lactate dehydrogenase, and alanine aminotransferase levels, and the parameters of the ornithine cycle, such as creatinine, urea acid, and urea levels, made the most significant contribution to the classification of two types of metabolism: catabolism and anabolism.
训练期和竞赛期可能会暂时损害运动员的表现。这种干扰可能是短期的，也可能是长期的，持续时间可达数天。我们在一次深入的医学检查中分析了3661名运动员的健康指标。在纳入研究时，这些运动员身体健康。进行了仪器检查（荧光透视、腹腔和盆腔器官超声检查、超声心动图、心电图以及“至力竭”的压力测试）、实验室检查（尿常规、生化和一般临床血液分析）以及由专家进行的检查（眼科医生、耳鼻喉科医生、外科医生、心脏病专家、神经科医生、牙医、妇科医生（女性）、内分泌科医生和治疗师）。本研究使用随机森林和多项逻辑回归机器学习方法分析了确定参与“分解代谢”和“合成代谢”表型的指标的意义。决策森林和多项回归模型的使用使得识别血液和尿液生化中最显著的指标成为可能，这些指标用于分析表型，以此作为运动员赛后恢复期恢复过程有效性的一种表征。我们发现，肌肉代谢参数，如天冬氨酸转氨酶、肌酸激酶、乳酸脱氢酶和丙氨酸转氨酶水平，以及鸟氨酸循环参数，如肌酐、尿酸和尿素水平，对分解代谢和合成代谢这两种代谢类型的分类贡献最大。
