Evaluation of the Relationship Between Training Load and the Levels of Lactate and Other Metabolites Analyzed in Blood and Saliva Samples Using Metabolomic and Proteomic Techniques in Federated Basketball Players: Quasi-experimental Study
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 60
- 试验地点
- 4
- 主要终点
- Correlation between blood lactate levels and the subjective sensation of perceived effort
研究概览
简要总结
Physical exercise induces numerous changes in the body in a complex signalling network caused by or in response to increased metabolic activity of contracting skeletal muscles.
The application of omics analytical techniques such as proteomics and metabolomics in the field of sport allows us to understand how the human body responds to exercise and how sports results can be improved by optimising nutrition and training. Both omics techniques offer a quantitative measurement of the metabolic profiles associated with exercise and are able to identify metabolic signatures of athletes from different sports disciplines.
Basketball is a high-intensity exercise modality interspersed with low-intensity. The performance requirements of basketball include aerobic and anaerobic metabolism, with anaerobic metabolism being considered the main energy system. Therefore, basketball players need great athletic ability to produce a successful performance during competition.
For optimal sports performance it is important to adjust the training load, i.e. the degree of effort that the player can withstand in a single training session. Coaches require effective and objective load monitoring tools that allow them to make decisions about training plans based on the needs of each player.
Microsampling systems emerge as an alternative to venipuncture by facilitating self-sampling, which can be carried out outside healthcare centres, in a comfortable and precise way from a small finger prick that the user can perform. These systems are less expensive and can be effective in measuring the levels of glucose metabolism products, such as lactate, through the application of metabolomics and proteomics. On the other hand, the use of non-invasive methods of measuring lactate levels is becoming increasingly popular in sports medicine. The use of saliva as an alternative fluid to the blood shows promise for identifying the concentrations of metabolites that occur during and after sports training.
详细描述
The study hypothesizes that the use of minimally invasive microsampling systems, and subsequent application of metabolomics and proteomics, will allow the detection of differences in the levels of lactate and other metabolites and proteins produced by the greater energy demand of the musculoskeletal system after a single collective training on the court, in federated basketball players. In addition, lactate levels will be correlated with the subjective sensation of perceived exertion.
The main objective of the study is to apply metabolomics techniques to analyze lactate levels in capillary blood samples collected by a dried blood spot (DBS) microsampling device, and to study their correlation with the subjective sensation of perceived effort in federated basketball players before and after performing a single collective training session on the court.
The secondary objectives of the study are to measure the change in lactate levels in capillary blood samples collected by a DBS device, and in saliva samples collected by a collector, before and after performing a single collective training on the court. In addition, in these samples, the change in the levels of other metabolomic and proteomic markers related to energy, lipid and amino acid metabolism will be measured. The correlation between salivary and blood lactate levels will also be studied; subjective sensation of perceived exertion and salivary lactate levels; the subjective sensation of perceived exertion and the levels of other metabolomic and proteomic markers will be also studied.
A single-group quasi-experimental (or pre-post) study will be carried out on 70 basketball players between the ages of 18 and 40.
Each participant will attend 2 visits to the sports facilities of their basketball club:
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Other
- 盲法
- None
盲法说明
Given the nature of the study (pretest-posttest design), masking will not be possible.
入排标准
- 年龄范围
- 18 Years 至 40 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Men and women, active players of the Catalan Basketball Federation and the Spanish Basketball Federation between 18 and 40 years old (both included).
- •Sign the informed consent.
排除标准
- •Present any metabolic disorder that may interfere with the objectives of the study (high blood pressure, diabetes, hypercholesterolemia or obesity; Body Mass Index (BMI) values ≥ 35 Kg/m
- •Suffer from disorders of glucose metabolism that may alter lactate synthesis, such as lactic acidosis, hyperlactatemia, or other metabolic acidosis.
- •Taking any type of medication that may alter metabolite or lactate levels.
- •Having belonephobia (phobia of needles).
- •Take ergogenic aids or supplements based on sucrose or glucose polymers before, during, or after training.
- •Being a smoker.
- •Being pregnant.
- •Breastfeeding.
结局指标
主要结局
Correlation between blood lactate levels and the subjective sensation of perceived effort
时间窗: Pre-training (baseline) and post-training (immediately after the training)
Lactate concentration (μM) measured in capillary blood pre- and post- training. Four drops of capillary blood will be collected pre- and post-training by means of a puncture with a retractable lancet on the index, middle or ring finger, and deposited on a dried blood spot (DBS) "HemaXis DB10" card for analysis. The subjective sensation of perceived effort will be assessed with the Perceived Exertion Index (RPE) measured post-training. This tool is used to monitor perceived effort during sports practice. It consists of a graduated scale from 0 to 10 where 0 is rest and 10 is maximum perceived effort.
次要结局
- Change in blood lactate levels(Pre-training (baseline) and post-training (immediately after the training))
- Change in levels of lipid metabolic markers determined in saliva samples(Pre-training (baseline) and post-training (immediately after the training))
- Change in levels of other lipid metabolic markers determined in saliva samples(Pre-training (baseline) and post-training (immediately after the training))
- Clinical data: use of supplementation(Pre-training (baseline))
- Clinical data: previous muscle injuries(Pre-training (baseline))
- Lifestyle data: playing position(Pre-training (baseline))
- Anthropometric data: muscle mass percentage(Pre-training (baseline))
- Change in levels of lipid metabolic markers determined in capillary blood samples(Pre-training (baseline) and post-training (immediately after the training))
- Sociodemographic data: sex(Pre-training (baseline))
- Lifestyle data: weekly training load(Pre-training (baseline))
- Clinical data: use of medication(Pre-training (baseline))
- Change in levels of other lipid metabolic markers determined in capillary blood samples(Pre-training (baseline) and post-training (immediately after the training))
- Change in levels of polar metabolites determined in capillary blood samples(Pre-training (baseline) and post-training (immediately after the training))
- Change in levels of polar metabolites determined in saliva samples(Pre-training (baseline) and post-training (immediately after the training))
- Change in saliva lactate levels(Pre-training (baseline) and post-training (immediately after the training))
- Pittsburgh Sleep Quality Index(Pre-training (baseline))
- Heart rate variations(During the training)
- Anthropometric data: fat mass percentage(Pre-training (baseline))
- Change in levels of proteomic markers determined in capillary blood samples(Pre-training (baseline) and post-training (immediately after the training))
- Change in levels of proteomic markers determined in saliva samples(Pre-training (baseline) and post-training (immediately after the training))
- Sociodemographic data: age and birth date(Pre-training (baseline))
- Physiological data(Pre-training (baseline))
- Anthropometric data: weight(Pre-training (baseline))
- Anthropometric data: height(Pre-training (baseline))
- Anthropometric data: body mass index(Pre-training (baseline))