Clinical trial NCT06365320: Association Between Training Load and Lactate and Other Metabolites Analyzed by Metabolomic and Proteomic Techniques
Official title: 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
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Lactate
Sports Performance
Metabolomics
Proteomics
Keywords: Physical training
Lactate
Dried Blood Spot Method
Basketball
Lead sponsor: Fundació Eurecat
Sponsor class: OTHER
Collaborators: name: University Rovira i Virgili
First posted: 2024-04-15
Enrollment: 60
Sex: ALL
Minimum age (years): 18
Maximum age (years): 40
Standard ages: ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: Single-group pretest-posttest design (quasi- experimental)
Primary purpose: OTHER

Brief summary: 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.

Detailed description: 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:

* A recruitment and pre-selection visit (to check the eligibility criteria and sign the informed consent). If the inclusion criteria are met, will be scheduled to:
* A single study visit on the same day agreed for training.

The main variable of the study is the correlation between lactate levels, measured in capillary blood pre- and post-training, and the subjective sensation of perceived effort.

Eligibility criteria: Inclusion Criteria:

* 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.

Exclusion Criteria:

* 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/m2.
* 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.

Primary outcomes:
measure: Correlation between blood lactate levels and the subjective sensation of perceived effort
timeFrame: Pre-training (baseline) and post-training (immediately after the training)
Secondary outcomes:
measure: Change in saliva lactate levels
timeFrame: Pre-training (baseline) and post-training (immediately after the training)
measure: Change in blood lactate levels
timeFrame: Pre-training (baseline) and post-training (immediately after the training)
measure: Change in levels of lipid metabolic markers determined in capillary blood samples
timeFrame: Pre-training (baseline) and post-training (immediately after the training)
measure: Change in levels of other lipid metabolic markers determined in capillary blood samples
timeFrame: Pre-training (baseline) and post-training (immediately after the training)
measure: Change in levels of polar metabolites determined in capillary blood samples
timeFrame: Pre-training (baseline) and post-training (immediately after the training)
measure: Change in levels of lipid metabolic markers determined in saliva samples
timeFrame: Pre-training (baseline) and post-training (immediately after the training)
measure: Change in levels of other lipid metabolic markers determined in saliva samples
timeFrame: Pre-training (baseline) and post-training (immediately after the training)
measure: Change in levels of polar metabolites determined in saliva samples
timeFrame: Pre-training (baseline) and post-training (immediately after the training)
measure: Change in levels of proteomic markers determined in capillary blood samples
timeFrame: Pre-training (baseline) and post-training (immediately after the training)
measure: Change in levels of proteomic markers determined in saliva samples
timeFrame: Pre-training (baseline) and post-training (immediately after the training)
measure: Pittsburgh Sleep Quality Index
timeFrame: Pre-training (baseline)
measure: Heart rate variations
timeFrame: During the training
measure: Sociodemographic data: age and birth date
timeFrame: Pre-training (baseline)
measure: Sociodemographic data: sex
timeFrame: Pre-training (baseline)
measure: Lifestyle data: weekly training load
timeFrame: Pre-training (baseline)
measure: Lifestyle data: playing position
timeFrame: Pre-training (baseline)
measure: Clinical data: use of supplementation
timeFrame: Pre-training (baseline)
measure: Clinical data: use of medication
timeFrame: Pre-training (baseline)
measure: Clinical data: previous muscle injuries
timeFrame: Pre-training (baseline)
measure: Physiological data
timeFrame: Pre-training (baseline)
measure: Anthropometric data: weight
timeFrame: Pre-training (baseline)
measure: Anthropometric data: height
timeFrame: Pre-training (baseline)
measure: Anthropometric data: body mass index
timeFrame: Pre-training (baseline)
measure: Anthropometric data: fat mass percentage
timeFrame: Pre-training (baseline)
measure: Anthropometric data: muscle mass percentage
timeFrame: Pre-training (baseline)
Other outcomes:
Locations:
city: Reus
country: Spain
facility: Fundació Eurecat, Center for Omic Sciences
state: Tarragona
city: Reus
country: Spain
facility: Fundació Eurecat
state: Tarragona
Study officials:
affiliation: Fundació Eurecat, Center for Omic Sciences
name: Nuria Canela, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Fundació Eurecat, Biotechnology Area
name: Antoni Caimari, PhD
role: STUDY_DIRECTOR
References: