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Cited by 20Diabetes, Cardiovascular Risks, and LipoproteinsAdipokines, Inflammation, and Metabolic DiseasesArtificial Intelligence in Healthcare

Application of a Machine Learning Technology in the Definition of Metabolically Healthy and Unhealthy Status: A Retrospective Study of 2567 Subjects Suffering from Obesity with or without Metabolic Syndrome

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Masi, Davide, Risi, Renata, Biagi, Filippo, Vasquez Barahona, Daniel, Watanabe, Mikiko, Zilich, Rita, Gabrielli, Gabriele, Santin, Pierluigi +3 more·2022·Nutrients
DOI 10.3390/nu14020373PMC PMC8779369
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Abstract

The key factors playing a role in the pathogenesis of metabolic alterations observed in many patients with obesity have not been fully characterized. Their identification is crucial, and it would represent a fundamental step towards better management of this urgent public health issue. This aim could be accomplished by exploiting the potential of machine learning (ML) technology. In a single-centre study (n = 2567), we used an ML analysis to cluster patients with metabolically healthy (MHO) or metabolically unhealthy (MUO) obesity, based on several clinical and biochemical variables. The first model provided by ML was able to predict the presence/absence of MHO with an accuracy of 66.67% and 72.15%, respectively, and included the following parameters: HOMA-IR, upper body fat/lower body fat, glycosylated haemoglobin, red blood cells, age, alanine aminotransferase, uric acid, white blood cells, insulin-like growth factor 1 (IGF-1) and gamma-glutamyl transferase. For each of these parameters, ML provided threshold values identifying either MUO or MHO. A second model including IGF-1 zSDS, a surrogate marker of IGF-1 normalized by age and sex, was even more accurate with a 71.84% and 72.3% precision, respectively. Our results demonstrated high IGF-1 levels in MHO patients, thus highlighting a possible role of IGF-1 as a novel metabolic health parameter to effectively predict the development of MUO using ML technology.

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  • ML methods have increasingly been applied to MetS prediction based on clinical and biochemical data, with encouraging outcomes across various populations.

    “In recent years, ML methods have increasingly been applied to MetS prediction based on clinical and biochemical data, with encouraging outcomes across various populations [46–50].”

    From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [47]

    The Methods (§2.3) and Results (§3.2) describe an ML model using clinical and biochemical variables to distinguish metabolically healthy from unhealthy obesity, with predictive precision of roughly 67–72%. This supports an example of encouraging ML results related to metabolic syndrome, but the study examines one cohort of patients with obesity, not outcomes across various populations or prospective prediction of MetS. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Authors
Masi, D., Risi, R., Biagi, F., Vasquez Barahona, D., Watanabe, M., Zilich, R., Gabrielli, G., Santin, P., Mariani, S., Lubrano, C., & Gnessi, L.
Affiliations
  • Sapienza University of Rome (IT)
  • University of Cambridge (GB)
  • Department of Experimental Medicine, Section of Medical Pathophysiology, Food Science and Endocrinology, Sapienza University of Rome, 00161 Rome, Italy
  • Mix-x Partner, 20153 Milano, Italy
  • Rulex Inc., 16122 Genova, Italy
  • Deimos Engineering, 33100 Udine, Italy
Corresponding author
Masi, Davide
ORCID
Venue
Nutrients
Publisher
MDPI AG
Published
2022-01-15
Volume
14
Issue
2
Pages
373
ISSN
2072-6643 (ISSN-L)
DOI
10.3390/nu14020373
PMC
PMC8779369
OpenAlex
W4205547543
Cited by
20
References
72
Citations by year
2026: 5 · 2025: 4 · 2024: 3 · 2023: 7 · 2022: 1
FWCI
2.78
Type
journal article
Language
en
Open access
Licence
Locations
  • Nutrients (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo
  • Apollo (cc-by)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
metabolically healthy obesity, metabolically unhealthy obesity, metabolic syndrome, obesity, insulin-like growth factor I, HOMA-IR, machine learning clustering, insulin resistance, uric acid, alanine aminotransferase, gamma-glutamyl transferase, glycosylated haemoglobin
MeSH terms
Obesity, Metabolically Benign, Machine Learning, Adipose Tissue, Adult, Alanine Transaminase, Artificial Intelligence, Female, Health Status, Glycated Hemoglobin, Humans, Insulin-Like Growth Factor I, Male, Middle Aged, Obesity, Prognosis, Retrospective Studies, Risk Factors, Biomarkers, Absorptiometry, Photon, Metabolic Syndrome

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