A Standardized Metabolomics Protocol For Analyzing Exercise-Induced Metabolic Shifts In Elite Boxers By Liquid Chromatography-Mass Spectrometry

Summary

This protocol describes a standardized serum metabolomics workflow based on liquid chromatography coupled to quadrupole time-of-flight tandem mass spectrometry (LC-QTOF-MS/MS) and multivariate data analysis for profiling acute and short-term recovery-related metabolic shifts in elite male boxers.

Abstract

Elite boxing induces rapid metabolic changes that are not fully captured by conventional physiological measurements. A standardized untargeted serum metabolomics workflow based on liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-QTOF-MS/MS) was applied to samples collected before sparring, immediately after sparring, and 24 h after sparring in seven elite male boxers. The workflow included standardized sample collection, pooled quality-control monitoring, metabolite profiling, multivariate statistical analysis, and pathway interpretation. Acute sparring was associated with changes in metabolites related to glycolysis and gluconeogenesis, whereas the 24-h timepoint was associated with sulfur metabolism.

Phosphatidylinositol PI(16:0/18:2(9Z,12Z)) showed strong discrimination of the immediate post-sparring state, and thiosulfate was associated with the 24-h recovery state. These findings support the use of this workflow for reproducible profiling of exercise-related metabolic changes in this cohort of seven elite male boxers. The protocol is intended for controlled small-cohort studies of acute exercise and short-term recovery and emphasizes reproducible sample handling, pooled quality-control monitoring, and interpretable downstream analysis.

Introduction

Elite boxing, characterized by three rounds of 2 min of high-intensity sparring, relies on a combination of anaerobic and aerobic metabolism and triggers rapid shifts in energy substrates, oxidative stress mediators, and tissue repair-related metabolites. Traditional assessments (blood lactate and heart rate) only capture isolated physiological endpoints, failing to resolve the systemic, interconnected metabolic changes that span “pre-sparring baseline → post-sparring acute stress → 24-h recovery.”

Untargeted metabolomics, enabled by liquid chromatography-quadrupole time-of-flight tandem mass spectrometry, addresses this limitation by comprehensively profiling small-molecule metabolites in biological samples. It typically detects thousands of metabolic peaks and annotates hundreds to thousands of metabolites through integration with curated databases^1,2,3. This approach can reveal nuanced metabolic dynamics that traditional methods miss, but existing boxing-focused metabolomic studies lack standardization: they often omit controlled sparring protocols, rigorous quality control (QC) steps, or long-term recovery timepoints, leading to irreproducible results and limited translation to training or recovery strategies^4,5.

A standardized metabolomic workflow based on liquid chromatography-quadrupole time-of-flight tandem mass spectrometry and optimized for elite boxers is presented to capture both acute exercise responses and delayed recovery. The protocol includes three key timepoints (pre-sparring [T0], immediate post-sparring [T1], 24 h post-sparring [T2]) to distinguish transient stress from sustained adaptation^6,7,8. Critical technical features include: (1) controlled sparring (80–85% maximum heart rate) to ensure consistent exercise intensity^7,9; (2) high-resolution LC-QTOF for sensitive detection of low-abundance serum metabolites^2,3,10; (3) multivariate and univariate analyses, including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and pairwise statistical testing, to identify exercise-responsive metabolites^6,8,11; and (4) a single pooled serum QC sample (equal-volume mixing of study samples) with multiple injections to evaluate instrument stability against signal drift¹².

This protocol may be adaptable to other high-intensity, short-duration sports (e.g., sprinting and wrestling), but such transferability requires validation in sport-specific cohorts. In the present study, the workflow provides a preliminary framework for identifying metabolic markers of stress and recovery in elite male boxers. This workflow is most appropriate for controlled studies of acute exercise and short-term recovery in small, well-characterized athletic cohorts using serum-based untargeted metabolomics. It may be less suitable for direct extrapolation to other sports, female athletes, larger heterogeneous populations, or long-term adaptation studies without additional validation.

The study aimed to characterize serum metabolic changes induced by elite boxing sparring and to distinguish acute post-sparring responses from 24 h recovery-related changes. It was hypothesized that acute sparring would predominantly affect energy metabolism, whereas 24-h recovery would be associated with metabolites linked to redox balance and tissue repair. The overall workflow is shown in Figure 1.

Protocol

Perform all procedures in accordance with approval from the Institutional Ethics Committee for Human Research of Capital University of Physical Education and Sports (approval no. 2025A005).

CAUTION: Treat all human blood specimens as potentially infectious. Wear gloves, a laboratory coat, and eye protection. Perform venous blood collection, serum handling, and waste disposal in accordance with institutional biosafety procedures. Decontaminate work surfaces after sample handling.

CAUTION: Methanol, acetonitrile, and formic acid are hazardous chemicals. Handle solvent preparation, extraction, and reconstitution steps in a certified chemical fume hood. Avoid skin contact and inhalation. Collect solvent waste in approved containers.

1. Study participant recruitment and preparation

1. Recruit seven elite male boxers who are 20–28 years old and compete in the 60–75 kg weight classes.
2. Confirm that each participant has at least 5 years of professional training experience and no history of metabolic disease.
3. Exclude participants with musculoskeletal injury during the previous 6 months.
4. Instruct participants to follow a standardized diet containing 55% carbohydrate, 25% protein, and 20% fat for 3 days before testing.
5. Instruct participants to avoid strenuous exercise at or above 60% of maximum heart rate for 48 h before testing.
6. Instruct participants to fast for 12 h before blood collection. Permit water intake during the fasting period.
7. Determine maximum heart rate by graded exercise testing 1 week before sparring.
8. Increase treadmill speed by 1 km/h every 3 min until exhaustion. Use the measured maximum heart rate to define the sparring target intensity.

2. Sparring protocol and serum sample collection

1. Prepare a standard 6 × 6 m boxing ring, 16 oz gloves, and a chest-strap heart-rate monitor.
2. Conduct a standardized 10 min warm-up consisting of 5 min of jogging at 8 km/h, 3 min of dynamic stretching, and 2 min of shadowboxing.
3. Execute the sparring protocol as follows.
   1. Conduct three rounds of sparring of 2 min each.
   2. Match opponents by weight class and technical level.
   3. Allow a 1-min rest interval between rounds.
   4. Monitor heart rate every 30 s throughout the session.
   5. Adjust sparring intensity to maintain heart rate at 80%–85% of MHR.
   6. Pause sparring if heart rate exceeds 90% of MHR. Resume sparring only after heart rate returns to the target range.
4. Collect serum samples at three timepoints as follows.
   1. Collect 5 mL of venous blood from the antecubital vein 30 min after warm-up for T0 by using one 8 mL serum separation tube (SST).
   2. Collect 5 mL of venous blood within 5 min after the final round for T1 by using a new SST.
   3. Collect 5 mL of venous blood at the same time of day as T0 for T2 by using a new SST. Ensure that participants follow the same fasting and activity restrictions used for T0.
5. Process blood samples as follows.
   1. Centrifuge each SST at 11000 × g. for 15 min at 4 °C within 1 h after collection.
   2. Transfer 2–3 mL of the upper serum layer into 1.5 mL cryovials.
   3. Store the cryovials immediately at -80 °C.
   4. Limit each sample to no more than two freeze-thaw cycles.
6. Prepare a pooled QC sample.
   1. Thaw all 21 study serum samples (T0, T1, and T2 from seven participants) on ice before pooled QC preparation.
   2. Transfer an equal aliquot from each study sample into one 1.5 mL tube.
   3. Transfer 20 µL from each sample to obtain a final pooled QC volume of 420 µL.
   4. Vortex the pooled QC sample for 10 s.
   5. Use the pooled QC sample for repeated injections during LC-QTOF-MS analysis to monitor instrumental stability and signal drift.

3. Metabolite extraction from serum samples

1. Remove frozen serum samples (T0, T1, T2, and QC) from -80 °C. Thaw the samples on ice for 30 min and vortex each sample for 10 s.
2. Pipette 100 µL of serum into a 1.5 mL low-protein-binding microcentrifuge tube. Add 500 µL of ice-cold extraction solvent consisting of methanol:acetonitrile (1:1, v/v). Vortex for 30 s.
   1. Do not add an internal standard during extraction. Do not apply internal-standard-based extraction correction or downstream normalization in the finalized workflow.
3. Sonicate the tube in an ice-water bath at 4 °C for 10 min at 300 W and 40 kHz. Incubate the tube at -20 °C for 1 h to precipitate protein.
4. Centrifuge the tube at 13200 × g. for 20 min at 4 °C. Transfer approximately 450 µL of supernatant to a new microcentrifuge tube and discard the pellet as hazardous biological waste.
5. Dry the supernatant in a vacuum concentrator at 37 °C and -0.09 MPa for 45 min or until complete dryness is achieved.
6. Reconstitute the dried extract in 100 µL of acetonitrile:water (1:1, v/v). Vortex for 30 s.
   Note: Acetonitrile:water (1:1, v/v) was used because it was compatible with the reversed-phase chromatography column used in this workflow and did not produce visible precipitation in preliminary testing.
7. Centrifuge the reconstituted solution at 13200 × g. for 15 min at 4 °C. Transfer approximately 90 µL of supernatant to an LC-MS glass vial fitted with a 200 µL insert and seal the vial.

4. Liquid chromatography-mass spectrometry detection

1. Install a reversed-phase chromatography column suitable for serum metabolomics (see Table of Materials) in the liquid chromatography system. Set the column oven to 40 °C.
2. Prepare mobile phase A as 0.1% (v/v) formic acid in ultrapure water. Prepare mobile phase B as 0.1% (v/v) formic acid in acetonitrile.
3. Filter both mobile phases through a 0.22 µm membrane.
4. Set the flow rate to 400 µL/min and the injection volume to 5 µL.
5. Apply the following gradient: 0.0–0.5 min, 95% A and 5% B; 0.5–5.5 min, linear gradient to 50% A and 50% B; 5.5–9.0 min, linear gradient to 5% A and 95% B; 9.0–10.5 min, 5% A and 95% B; 10.5–12.0 min, return to 95% A and 5% B and re-equilibrate
6. Couple the liquid chromatography system to a high-resolution mass spectrometer equipped with an electrospray ionization source.
7. Acquire data in positive and negative ion modes in separate runs.
8. In positive ion mode, set the capillary voltage to 2500 V and the cone voltage to 30 V.
9. In negative ion mode, set the capillary voltage to -2000 V and the cone voltage to 25 V.
10. Set the desolvation gas temperature to 500 °C, the desolvation gas flow to 800 L/h, and the cone gas flow to 50 L/h.
11. Acquire data over m/z 50–1200 with a scan time of 0.2 s.
12. Acquire tandem mass spectrometry data by using low collision energy at 0 V and high collision energy at 10–40 V.
13. Run samples in the following order: One blank injection of reconstitution solvent; Three consecutive injections of the pooled QC sample; All study samples with one blank injection and one QC injection after every five study samples; One final QC injection; and One final blank injection
14. Evaluate data quality and analytical stability in both ion modes by pooled-QC correlation analysis and principal component analysis (PCA) clustering.
15. Interpret pooled-QC correlation coefficients greater than 0.8 as indicating acceptable analytical stability across the analytical sequence.

5. Data preprocessing and statistical analysis

1. Import raw liquid chromatography-mass spectrometry files into data-processing software for retention-time alignment, deconvolution, peak alignment, and feature extraction (see Table of Materials).
   1. Select the first pooled-QC injection as the alignment reference.
   2. Perform retention-time alignment, deconvolution, and peak alignment by using the default workflow of the selected software. Use a retention-time tolerance of ± 0.1 min and a mass tolerance of ± 5 ppm.
   3. Perform peak picking and retain features with intensity greater than 1000 counts that are present in at least 80% of samples in at least one group.
   4. Do not apply additional post-preprocessing normalization in either ion mode.
   5. Export the processed feature table containing m/z, retention time, and peak area values.
   6. Log2-transform all retained peak intensities.
   7. Impute missing values with one-fifth of the minimum positive value for each feature.
   8. Mean-center each feature and divide by its standard deviation before multivariate analysis.
   9. Use the transformed and scaled matrix for principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), clustering, and correlation analysis. Use the original processed feature intensities for abundance plots.
2. Annotate metabolites by matching features against public metabolomics databases, including the Human Metabolome Database (HMDB), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and LIPID MAPS where applicable.
   1. Score each annotation by precursor-ion mass error, isotope distribution, and tandem mass spectrometry fragment matching. Assign a maximum of 20 points to each criterion.
   2. Apply a precursor-ion mass-error window of 100 ppm and a fragment-ion mass-error window of 50 ppm. Do not apply a fixed retention-time window.
   3. Retain annotations with a total score of at least 40 out of 60. Exclude lower-scoring annotations.
3. Define comparison groups explicitly as follows: A = T0 (pre-sparring), B = T1 (immediate post-sparring), and C = T2 (24 h post-sparring).
4. Perform downstream statistical analyses using statistical computing software and associated analysis packages (see Table of Materials).
   1. Run principal component analysis (PCA) after unit-variance scaling and visualize the score plots.
   2. Build partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models for A versus B, B versus C, and A versus C.
   3. Use 7-fold cross-validation when the sample number is at least 7. If the sample number is fewer than 7, set the number of folds equal to the number of samples.
   4. Perform 200 permutation tests to assess model validity.
   5. Screen differential metabolites by applying the following thresholds: fold change (FC) ≥ 2 or FC ≤ 0.5, variable importance in projection (VIP) ≥ 1, and t-test P. < 0.05.
   6. Generate volcano plots and related statistical graphics for pairwise comparisons.
   7. Generate heatmaps by using the log2-transformed and unit-variance-scaled matrix.
   8. Generate correlation heatmaps with a significance threshold of P ≤ 0.05 and display up to 20 metabolites.
   9. Determine the optimal number of clusters before k-means clustering. Perform k-means clustering after unit-variance scaling.
   10. Generate abundance bar plots from the original processed feature intensities.
   11. Generate Venn diagrams to summarize overlap among pairwise comparisons.
   12. Perform receiver operating characteristic (ROC) analysis for selected metabolites. Evaluate diagnostic performance by the area under the curve (AUC).
5. Map differential metabolites to KEGG pathways by using a pathway enrichment function in an appropriate bioinformatics package (see Table of Materials).
   1. Interpret pathways with P. < 0.05. No multiple-testing correction was reported in the finalized analysis record.

Results

Quality Control (QC) Validation of LC-QTOF-MS/MS Data

To evaluate signal stability and reproducibility, repeated injections of the pooled QC sample were analyzed in both ion modes. Spearman correlation coefficients among pooled-QC injections exceeded 0.8 in both ion modes, which indicated stable instrument performance across the analytical sequence. PCA of all study samples and QC injections showed tight clustering of QC injections and no clear outliers, which supported accept...

Discussion

This standardized workflow characterized metabolite changes associated with elite boxing sparring and short-term recovery in a small, controlled cohort. The pooled-QC injections, QC-based correlation analysis, PCA clustering, and multivariate analyses together supported acceptable analytical stability. The protocol's success depended primarily on four factors: maintaining sparring intensity within 80%–85% of maximum heart rate, processing blood samples within 1 h after collection, limiting each sample to no mor...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Reagents	Acetonitrile	Merck	LC-MS grade, CAS 75-05-8
Reagents	Methanol	Merck	LC-MS grade, CAS 67-56-1
Reagents	Formic acid	TCI	LC-MS grade, CAS 64-18-6
Consumables	VACUETTE TUBE CAT Serum Clot Activator	Greiner Bio-One	8 mL serum clot activator tube
Consumables	Nalgene Cryogenic Vial	Thermo Fisher Scientific	1.5 mL, external thread
Consumables	Protein LoBind Tube	Eppendorf	Cat. No. 0030108442, 1.5 mL
Glassware	Screw-top glass vial with insert	Agilent	2 mL vial with 200 μL glass insert; insert Cat. No. 5183-2090
Consumables	Millex-GP Filter	Merck	Cat. No. SLGP033RS, 0.22 μm membrane
Instruments	Custom Boxing Ring 6x6 m	Muay Thai Sport	6 × 6 m training ring
Consumables	Pro Style 2 Boxing Gloves	Everlast	16 oz
Instruments	Polar H10	Polar	Chest-strap heart-rate sensor
Instruments	quasar	h/p/cosmos	Motorized treadmill
Instruments	Centrifuge 5425 R	Eppendorf	Refrigerated microcentrifuge
Instruments	Bransonic CPX2800	Emerson / Branson	40 kHz digital ultrasonic bath
Instruments	Savant SpeedVac SPD1030	Thermo Fisher Scientific	Integrated vacuum concentrator
Instruments	VWR Mini Vortex Mixer	Avantor / VWR	Cat. No. 10153-688
Instruments	TSX ULT Freezer	Thermo Fisher Scientific	Model TSX50086A
Instruments	ACQUITY UPLC I-Class PLUS	Waters	UHPLC system
Instruments	Xevo G2-XS QTof	Waters	QTOF mass spectrometer
Instruments	ACQUITY UPLC HSS T3 Column	Waters	P/N 176001132, 1.8 μm, 2.1 × 100 mm
Software	MassLynx	Waters	Version 4.2
Software	Progenesis QI	Waters	Version 2.3
Software	R	R Foundation for Statistical Computing	Version 3.6.1
Software	prcomp	base R	Base R function
Software	ropls	Bioconductor	R package
Software	pheatmap	CRAN	R package
Software	corrplot	CRAN	R package
Software	kmeans	base R	Base R function
Software	NbClust	CRAN	R package
Software	venn	CRAN	R package
Software	MetaboAnalystR	CRAN / Xia Lab	Version 1.0
Software	pROC	CRAN	Version 1.15.0
Software	clusterProfiler	Bioconductor	Bioconductor package
Databases	Human Metabolome Database (HMDB)	HMDB	Online database
Databases	Kyoto Encyclopedia of Genes and Genomes (KEGG)	KEGG	Online database
Databases	LIPID MAPS	LIPID MAPS	Online database