Evidence map›Paper›PMID 42065088›Full record

ArticleFrontiers in sports and active living2026

The exploration of immune system function changes in marathon athletes after high-intensity training by Agent-Based Model.

Hao Tian, Renzheng Zuo, Deng Wang, Guoping Qian, Qiang Ye, Adam Kawczyński, Robert Trybulski, Filipe Manuel Clemente

Abstract read
In one paragraph

Article in Frontiers in sports and active living, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Hao TianDepartment of Physical Culture, Gdansk University of Physical Education and Sport, Gdańsk, Poland.
Renzheng ZuoMacao Polytechnic University, Macao, Macao SAR, China.
Deng WangDepartment of Physical Education, Guangzhou Sport University, Guangzhou, China.
Guoping QianDepartment of Physical Culture, Gdansk University of Physical Education and Sport, Gdańsk, Poland.
Qiang YeNanjing Sport Institute, Nanjing, China.
Adam KawczyńskiFaculty of Medicine, Wrocław University of Science and Technology, Wrocław, Poland.
Robert TrybulskiMedical Department, Wojciech Korfanty Upper Silesian Academy in Katowice, Katowice, Poland.
Filipe Manuel ClementeDepartment of Biomechanics, Gdansk University of Physical Education and Sport, Gdańsk, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Marathon running imposes substantial metabolic demand. While the acute effects of exercise are well-documented, the chronic immunometabolic alterations associated with intensified training blocks in elite populations remain characterized by complex, non-linear dynamics. Contemporary debates persist regarding whether post-exercise lymphopenia represents immunosuppression (the "open window") or a functional redistribution of immune effectors. This study explored the chronic effects of a four-week high-intensity training (HIT) block on immune markers in professional marathon athletes and evaluated the utility of an Agent-Based Model (ABM) for visualizing these system behaviors. Methods: Twenty-two professional marathon athletes (14 male, 8 female) underwent a four-week intensified training protocol characterized by sustained time in the severe-intensity domain (blood lactate > 7.0 mmol/L). Peripheral blood samples were analyzed pre- and post-training for leukocytes, immunoglobulins (Ig), cytokines (IL-6, IL-8, IL-10, TNF-α), and lymphocyte subsets. Concurrently, a NetLogo-based ABM was developed to simulate theoretical immune system dynamics under metabolic constraints. Results: The training period coincided with significant shifts in circulating immune markers. Total leukocyte counts and serum IgG levels were significantly lower post-training ( Conclusions: A four-week block of high-intensity marathon training is associated with a state of immunometabolic perturbation characterized by reduced circulating leukocytes, CD4+/CD8+ imbalance, and an uncoupled inflammatory-resolution cytokine response. While plasma volume expansion may contribute to the observed lower cell concentrations, the specific suppression of IL-10 and CD4+ cells suggests a maladaptive response to chronic load. The agent-based model serves as an exploratory tool for visualizing potential immunological tipping points during intensified training, bridging the gap between reductionist data and complex system dynamics.

Indexed as

Agent-Based Model (ABM)high-intensity trainingimmune system functionmarathon athletesNetLogo

Identifiers

PMID42065088
PMCPMC13126728

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.