Nutritional patterns as machine learning predictors of liver health in a population of elderly subjects

Lampignano, Luisa; Tatoli, Rossella; Donghia, Rossella; Bortone, Ilaria; Castellana, Fabio; Zupo, Roberta; Lozupone, Madia; Conte, Caterina; Sardone, Rodolfo

doi:10.1016/j.numecd.2023.07.009

Cited by 3 publications

(2 citation statements)

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“…This review provides a snapshot of a rapidly evolving field, 73,74 with the involvement of an interdisciplinary team of researchers lending to a robust consideration of emerging methods in dietary patterns research. While prior reviews have provided perspectives on the potential applications of machine learning within the field of nutrition, [19][20][21] this review considered dietary patterns in particular, as well as considering approaches beyond machine learning that have not been traditionally used in this area, broadening the scope compared to prior reviews.…”

Section: Discussionmentioning

confidence: 99%

Advances in methods for characterizing dietary patterns: A scoping review

Hutchinson,

Raffoul,

Pepetone

et al. 2024

Preprint

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There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns.

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Section: Discussionmentioning

confidence: 99%

Advances in methods for characterizing dietary patterns: A scoping review

Hutchinson,

Raffoul,

Pepetone

et al. 2024

Preprint

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“…Diet and eating habits play a central role in the prevention and management of both SLD and HTN. They are considered modifiable risk factors for the development of HTN [10] and the only recommended treatment for SLD [11].…”

Section: Introductionmentioning

confidence: 99%

Effects of Egg Consumption on Subjects with SLD or Hypertension: A MICOL Study

Tatoli,

Bonfiglio,

Cuccaro

et al. 2024

Nutrients

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Background: Steatotic liver disease (SLD) is defined as a fat accumulation in more than 5% of hepatocytes; it can progress to non-alcoholic steatohepatitis (NASH), associated with an increased state of inflammation. The aim of this study was to explore the protective effects of eating eggs and any association with SLD and hypertension (HTN). Methods: The study cohort included 908 participants assessed in the fourth recall of the MICOL study, grouped into four groups, based on NALFD and/or HTN. Results: The prevalence of HTN and SLD among participants was 31.61%. Overall, the results indicated a statistical significance of egg consumption, showing a protective role against the two disease conditions, in both the raw and adjusted models (RRR = 0.34, p = 0.009, 0.15 to 0.76 95% C.I.). Conclusions: Many differences were found among the groups, and the protective role of eating eggs was amply demonstrated. We can conclude that it is unwise to demonize the intake of this food and its nutritional properties, in contrast with previous reports in the literature.

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