protocolSection identificationModule nctId: NCT06365320, orgStudyIdInfo id: PROTEOMET, briefTitle: Association Between Training Load and Lactate and Other Metabolites Analyzed by Metabolomic and Proteomic Techniques, acronym: PROTEOMET, statusModule overallStatus: NOT_YET_RECRUITING, startDateStruct date: 2024-04, primaryCompletionDateStruct date: 2024-07, completionDateStruct date: 2024-07, studyFirstPostDateStruct date: 2024-04-15, lastUpdatePostDateStruct date: 2024-04-15, sponsorCollaboratorsModule leadSponsor name: Fundació Eurecat, class: OTHER, collaborators name: University Rovira i Virgili, descriptionModule briefSummary: 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., conditionsModule conditions: Lactate, conditions: Sports Performance, conditions: Metabolomics, conditions: Proteomics, designModule studyType: INTERVENTIONAL, phases: NA, designInfo allocation: NA, interventionModel: SINGLE_GROUP, interventionModelDescription: Single-group pretest-posttest design (quasi- experimental), primaryPurpose: OTHER, maskingInfo masking: NONE, maskingDescription: Given the nature of the study (pretest-posttest design), masking will not be possible., enrollmentInfo count: 70, type: ESTIMATED, armsInterventionsModule interventions name: Single basketball training, outcomesModule primaryOutcomes measure: Correlation between blood lactate levels and the subjective sensation of perceived effort, secondaryOutcomes measure: Change in saliva lactate levels, secondaryOutcomes measure: Change in blood lactate levels, secondaryOutcomes measure: Change in levels of lipid metabolic markers determined in capillary blood samples, secondaryOutcomes measure: Change in levels of other lipid metabolic markers determined in capillary blood samples, secondaryOutcomes measure: Change in levels of polar metabolites determined in capillary blood samples, secondaryOutcomes measure: Change in levels of lipid metabolic markers determined in saliva samples, secondaryOutcomes measure: Change in levels of other lipid metabolic markers determined in saliva samples, secondaryOutcomes measure: Change in levels of polar metabolites determined in saliva samples, secondaryOutcomes measure: Change in levels of proteomic markers determined in capillary blood samples, secondaryOutcomes measure: Change in levels of proteomic markers determined in saliva samples, secondaryOutcomes measure: Pittsburgh Sleep Quality Index, secondaryOutcomes measure: Heart rate variations, secondaryOutcomes measure: Sociodemographic data: age and birth date, secondaryOutcomes measure: Sociodemographic data: sex, secondaryOutcomes measure: Lifestyle data: weekly training load, secondaryOutcomes measure: Lifestyle data: playing position, secondaryOutcomes measure: Clinical data: use of supplementation, secondaryOutcomes measure: Clinical data: use of medication, secondaryOutcomes measure: Clinical data: previous muscle injuries, secondaryOutcomes measure: Physiological data, secondaryOutcomes measure: Anthropometric data: weight, secondaryOutcomes measure: Anthropometric data: height, secondaryOutcomes measure: Anthropometric data: body mass index, secondaryOutcomes measure: Anthropometric data: fat mass percentage, secondaryOutcomes measure: Anthropometric data: muscle mass percentage, eligibilityModule sex: ALL, minimumAge: 18 Years, maximumAge: 40 Years, stdAges: ADULT, contactsLocationsModule locations facility: Fundació Eurecat, Center for Omic Sciences, city: Reus, state: Tarragona, zip: 43204, country: Spain, contacts name: Nuria Canela, PhD, role: CONTACT, phone: +34977300431, email: nuria.canela@eurecat.org, contacts name: Anna Crescenti, PhD, role: CONTACT, phone: +34977770958, phoneExt: 4832, email: anna.crescenti@eurecat.org, geoPoint lat: 41.15612, lon: 1.10687, hasResults: False