El-Shiekh, Riham A, Mohamed, Ahmed F, Mandour, Asmaa A et al. · Chemistry & biodiversity · 2025 · DOI
This review examines hesperidin, a natural compound found in citrus fruits like oranges and lemons, and explores whether it might help people with ME/CFS. The authors describe hesperidin's known anti-inflammatory and antioxidant properties, then use computer algorithms to predict how well it might work for chronic fatigue. They suggest hesperidin could be a promising natural supplement worth studying further.
ME/CFS patients experience debilitating fatigue and oxidative stress; identifying safe, natural compounds with anti-inflammatory and antioxidant properties could expand therapeutic options. This work bridges nutrition science and computational medicine, offering a data-driven approach to evaluating nutraceuticals that warrants clinical investigation.
This is a review article, not a clinical trial, so it does not prove that hesperidin actually improves ME/CFS symptoms in patients. The machine learning predictions have not been validated in real-world clinical settings, and no direct efficacy data in ME/CFS populations are presented. The abstract does not clarify whether the authors conducted original experiments or solely synthesized existing literature.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
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Primary citation
El-Shiekh, Riham A, Mohamed, Ahmed F, Mandour, Asmaa A, Adel, Islam M, Atwa, Ahmed M, Elgindy, Ali M, et al. (2025). Hesperidin in Chronic Fatigue Syndrome: An Integrated Analysis of Traditional Pharmacology and Machine Learning-Based Therapeutic Predictions.. Chemistry & biodiversity. https://doi.org/10.1002/cbdv.202403506
BibTeX
@article{mecfsatlas-el-shiekh-2025-hesperidin-chronic,
author = {El-Shiekh, Riham A and Mohamed, Ahmed F and Mandour, Asmaa A and Adel, Islam M and Atwa, Ahmed M and Elgindy, Ali M and Esmail, Manar M and Senna, Mohamed Magdy and Ebid, Nouran and Mustafa, Aya M},
title = {Hesperidin in Chronic Fatigue Syndrome: An Integrated Analysis of Traditional Pharmacology and Machine Learning-Based Therapeutic Predictions.},
journal = {Chemistry & biodiversity},
year = {2025},
doi = {10.1002/cbdv.202403506},
note = {PubMed: 40234200},
url = {https://www.mecfsatlas.com/evidence/el-shiekh-2025-hesperidin-chronic},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-27. https://www.mecfsatlas.com/evidence/el-shiekh-2025-hesperidin-chronic
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