Evidence map›Paper›PMID 41618909›Full record

Observational studyJournal of the International Society of Sports Nutrition2026

Development of an individualized prediction model for dynamic adaptations in performance and immune function associated with dietary patterns in endurance athletes using machine learning.

Yun Hou, Meijia Chen, Gang Qin, Ziyu Wang

Abstract readObservational Study
In one paragraph

Observational study in Journal of the International Society of Sports Nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yun HouSchool of Physical Education, Shandong Sport University, Rizhao, Shandong, People's Republic of China.
Meijia ChenDepartment of Physical Education, The Graduate School of, Sangmyung University, Seoul, South Korea.
Gang QinDepartment of Sports Science, Hanyang University, Seoul, Republic of Korea.
Ziyu WangCollege of Sports, Sejong University, Gwangjin-gu, Seoul, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPsychological resilience significantly influences immune function and health outcomes in high-stress populations, yet mechanisms underlying nutrition-psychology-immunity interactions remain poorly understood. This study developed an individualized prediction model integrating dietary patterns with psychological and immune adaptations to inform personalized therapeutic approaches.

methodsA retrospective cohort analysis examined 200 endurance athletes over 12 months using integrated datasets from NHANES athletic subcohort, UK Biobank, and training monitoring databases. Athletes were categorized into three dietary pattern groups (high-carbohydrate, high-protein, balanced micronutrient) based on their naturalistic dietary intake. This observational design examined associations between dietary patterns and health outcomes without manipulating participant diets. A hybrid LSTM-XGBoost machine learning architecture with SHAP analysis predicted individual responses based on psychological variables, immune markers (IL-6, TNF-

resultsPsychological resilience emerged as the primary predictor of dietary pattern response (SHAP importance = 0.342), with psychological improvements consistently preceding immune function recovery by 1-2 months. Three distinct resilience-based subgroups demonstrated different response trajectories: high resilience athletes achieved superior improvement rates (0.43 vs. 0.10 points/month) and reached plateau phases earlier (6.8 vs. 11.2 months) compared to low resilience individuals. The predictive model achieved exceptional performance metrics (91.2% sensitivity, 87.6% specificity) for identifying non-responders to dietary patterns. Mediation analysis revealed that 42.4% of the associations between dietary patterns and immune function operated through psychological pathways, with cortisol reduction serving as a critical mechanism.

conclusionsPsychological resilience predicts responsiveness to dietary patterns through psychoneuroimmunological pathways. Baseline psychological assessment should guide personalized nutrition strategies in clinical populations experiencing chronic stress and immune dysfunction.

Indexed as

AthletesAthletic PerformanceDietMachine LearningPhysical EnduranceResilience, PsychologicalAdaptation, PhysiologicalAdultC-Reactive ProteinFemaleHumansImmunoglobulin AInterleukin-6MalePrediction AlgorithmsPredictive Learning ModelsC-Reactive ProteinImmunoglobulin AInterleukin-6Tumor Necrosis Factor-alphadietary patternsimmune functionmachine learningPsychological resiliencepsychoneuroimmunology

Identifiers

PMID41618909
PMCPMC12862862

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LicenceCC BY
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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.