Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine
Open accessAbstract
Aging is a fundamental biological process characterized by a progressive decline in physiological functions and an increased susceptibility to diseases. Understanding aging at the molecular level is crucial for developing interventions that could delay or reverse its effects. This review explores the integration of machine learning (ML) with multi-omics technologies—including genomics, transcriptomics, epigenomics, proteomics, and metabolomics—in studying the molecular hallmarks of aging to develop personalized medicine interventions. These hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Using ML to analyze big and complex datasets helps uncover detailed molecular interactions and pathways that play a role in aging. The advances of ML can facilitate the discovery of biomarkers and therapeutic targets, offering insights into personalized anti-aging strategies. With these developments, the future points toward a better understanding of the aging process, aiming ultimately to promote healthy aging and extend life expectancy.
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AI systems can integrate and analyze vast complex datasets to predict individual metabolic and behavioral responses to dietary and lifestyle interventions.
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From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [97]
AI algorithms can incorporate circadian rhythms, physical activity levels, and psychological stressors to refine recommendations.
“These algorithms can also incorporate circadian rhythms, physical activity levels, and psychological stressors to refine the recommendations further [96,97,98,99,100,101,102].”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [97]
Machine learning algorithms can identify specific macronutrient ratios that minimize hunger while maximizing fat oxidation and muscle preservation.
“For example, ML algorithms can identify the specific macronutrient ratios that minimize hunger while maximizing fat oxidation and muscle preservation, thereby enhancing the efficacy of precision nutrition strategies.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [97]
AI provides continuous real-time feedback through wearable devices, mobile applications, and connected health platforms.
“One of AI’s most transformative features is its ability to provide continuous, real-time feedback through wearable devices, mobile applications, and connected health platforms.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [97]
AI can integrate data streams to refine predictive models, enabling a dynamic, iterative approach to weight management.
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From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [97]
Wearable devices provide real-time physiological data, while advanced biomarkers offer deeper insights into metabolic states.
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From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [97]
AI-aided platforms optimize caloric intake and macronutrient distribution based on the user's metabolic and hormonal profiles, addressing adaptive responses such as metabolic slowdown during caloric restriction.
“AI-aided platforms optimize caloric intake and macronutrient distribution based on the user’s metabolic and hormonal profiles, addressing individual adaptive responses such as metabolic slowdown during caloric restriction.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [97]
Predictive analytics can anticipate challenges such as low motivation or high stress, offering proactive interventions.
“Through predictive analytics, these systems can anticipate challenges such as periods of low motivation or high stress, offering proactive interventions to maintain consistency.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [97]
AI platforms integrate behavioral psychology principles, tracking adherence and identifying factors contributing to deviations.
“AI platforms also integrate behavioral psychology principles, tracking adherence trends and identifying factors contributing to deviations.”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [97]
AI can adjust meal timing and composition based on detected variations in metabolic rate or hormonal fluctuations.
“For example, AI can adjust meal timing and composition based on detected variations in metabolic rate or hormonal fluctuations [96,97,98,99,100,101,102].”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [97]
AI-powered platforms, wearable devices, and continuous glucose monitors enhance adherence by tracking biometrics and offering actionable insights to users, making weight management dynamic and adaptive to individual needs.
“These tools enhance adherence by tracking biometrics and offering actionable insights to users, making weight management dynamic and adaptive to individual needs [96,97,98,99,100,101,102].”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [97]
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- Authors
- Theodorakis, N., Feretzakis, G., Tzelves, L., Paxinou, E., Hitas, C., Vamvakou, G., Verykios, V. S., & Nikolaou, M.
- Affiliations
- National and Kapodistrian University of Athens (GR)
- Hellenic Open University (GR)
- Simanogleio-Amalia Fleming General Hospital (GR)
- Corresponding author
- Verykios, Vassilios S.
- ORCID
- Venue
- Journal of Personalized Medicine
- Publisher
- MDPI AG
- Published
- 2024-08-31
- Volume
- 14
- Issue
- 9
- Pages
- 931
- ISSN
- 2075-4426 (ISSN-L)
- PMC
- PMC11433587
- OpenAlex
- W4402198203
- Cited by
- 51
- References
- 105
- Citations by year
- 2026: 21 · 2025: 25 · 2024: 5
- FWCI
- 1.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Personalized Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Keywords
- geroscience, aging, machine learning, multi-omics, personalized medicine, anti-aging interventions, biomarker discovery, therapeutic target, cellular senescence, mitochondrial dysfunction, chronic inflammation, dysbiosis, epigenetic modifications, proteostasis, telomere shortening, genomic instability, healthy aging
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