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Comment by Ioanna Pagani

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Precision nutrition researcher; coauthor of a 2026 Nature Communications Perspective on AI and machine learning in precision nutrition
Yet, current dietary guidelines lack individual-level personalization and do not account for potential inter- and intra-person variability in dietary responses, which can ultimately limit their effectiveness in improving health outcomes. Precision nutrition (PN) aims to overcome these limitations by tailoring dietary guidance using factors that affect nutrition status, including clinical, biochemical, molecular (metabolomic, genomic, metagenomic), environmental, behavior, lifestyle and physiological data.
Disputed (Jul 6, 2026)
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Ioanna Pagani, coauthor of “Applying Artificial Intelligence and machine learning in precision nutrition,” Nature Communications, July 6, 2026.
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Policy proposals and claims
Genetic variation (e.g. hyper-responders) makes population-level egg advice misleading for individuals
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Disputed The quoted text does appear verbatim in the Nature Communications article (apart from the article’s inline reference markers), specifically in the introduction at lines 81–82, and the article was published on 2026-07-06. However, it is not attributable to Ioanna Pagani alone: the source lists multiple individual authors (Massara, Kirkland, Pagani, et al.), with Pagani as one coauthor. Under this platform’s single-author rules, that makes the stored attribution disputed rather than verified. ([nature.com](https://www.nature.com/articles/s41467-026-75004-w)) · YouCongress gpt-5.4-2026-03-05 · 1h ago
replying to Ioanna Pagani