Adaptive Precision Nutrition: Integrating Dynamic Phenotyping, Multi-Omics, Artificial Intelligence, and the Allostatic Viability Principle
- 1Department of Nutritional Sciences, Faculty of Life Sciences, University of Vienna, Josef-Holaubek-Platz 2 (UZA II), 1090 Vienna, Austria
Abstract
Precision nutrition increasingly combines functional foods, multi-omics, digital phenotyping, and artificial intelligence, yet the incremental decision value of additional data layers remains uncertain. This critical narrative review and hypothesis-generating conceptual proposal examines evidence on personalized nutrition, dynamic response phenotyping, functional-food heterogeneity, multi-omics integration, and AI-supported decisions. Personalized advice can improve dietary behavior and selected cardiometabolic outcomes, whereas the additional clinical benefit attributable specifically to elaborate molecular profiling has been inconsistent. Dynamic challenge studies suggest that response amplitude, timing, coordination, recovery, and conditional repeatability may provide information beyond static profiles, but reproducibility is required before these features can be treated as person-specific traits. The Allostatic Viability Principle (AVP) is proposed as a conceptual physiological and decision framework rather than a validated predictor or biomarker. Its intended contribution is to organize existing approaches around viable, context-appropriate regulation, proportional use of additional measurements, explicit uncertainty, an outcome hierarchy that does not equate molecular change with health benefit, and a response-and-revision loop. Whether this organization improves decisions or outcomes beyond high-quality conventional or adaptive personalized nutrition remains an empirical question. The framework is therefore presented as a testable structure for determining when additional individual-level information is reproducible, relevant, and sufficiently decision changing to justify use.
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BibTeX
@article{haslberger2026adaptive,
title = {{Adaptive Precision Nutrition: Integrating Dynamic Phenotyping, Multi-Omics, Artificial Intelligence, and the Allostatic Viability Principle}},
author = {Alexander G. Haslberger},
journal = {Nutrients},
year = {2026},
month = oct,
doi = {10.3390/nu18203373},
url = {https://www.mdpi.com/2072-6643/18/20/3373},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Adaptive Precision Nutrition: Integrating Dynamic Phenotyping, Multi-Omics, Artificial Intelligence, and the Allostatic Viability Principle
AU - Alexander G. Haslberger
PY - 2026
DA - 2026/10/14
JO - Nutrients
DO - 10.3390/nu18203373
UR - https://www.mdpi.com/2072-6643/18/20/3373
AB - Precision nutrition increasingly combines functional foods, multi-omics, digital phenotyping, and artificial intelligence, yet the incremental decision value of additional data layers remains uncertain. This critical narrative review and hypothesis-generating conceptual proposal examines evidence on personalized nutrition, dynamic response phenotyping, functional-food heterogeneity, multi-omics integration, and AI-supported decisions. Personalized advice can improve dietary behavior and selected cardiometabolic outcomes, whereas the additional clinical benefit attributable specifically to elaborate molecular profiling has been inconsistent. Dynamic challenge studies suggest that response amplitude, timing, coordination, recovery, and conditional repeatability may provide information beyond static profiles, but reproducibility is required before these features can be treated as person-specific traits. The Allostatic Viability Principle (AVP) is proposed as a conceptual physiological and decision framework rather than a validated predictor or biomarker. Its intended contribution is to organize existing approaches around viable, context-appropriate regulation, proportional use of additional measurements, explicit uncertainty, an outcome hierarchy that does not equate molecular change with health benefit, and a response-and-revision loop. Whether this organization improves decisions or outcomes beyond high-quality conventional or adaptive personalized nutrition remains an empirical question. The framework is therefore presented as a testable structure for determining when additional individual-level information is reproducible, relevant, and sufficiently decision changing to justify use.
SN - 2072-6643
ER -