Contribution of macro- and micronutrients intake to gastrointestinal cancer mortality in the ONCONUT cohort: Classical vs. modern approaches

Donghia, Rossella; Guerra, Vito; Pesole, Pasqua Letizia; Liso, Marina

doi:10.3389/fnut.2023.1066749

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…RF was computed by an ensemble of binary decision trees, which could be used to select the most important variables linked with the outcomes. Variable predictiveness could be assessed using variable importance measures for both single and grouped variables [ 38 ]. The random forest (RF) method is a machine-supervised learning algorithm based on a randomized decisional tree for ranking the prediction power of a set of variables regarding the outcome of interest.…”

Section: Methodsmentioning

confidence: 99%

Retinal Microvasculature and Neural Changes and Dietary Patterns in an Older Population in Southern Italy

Tatoli

Lampignano

Donghia

et al. 2023

IJERPH

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Background: Like other parts of the body, the retina and its neurovascular system are also affected by age-related changes. The rising age of populations worldwide makes it important to study the pathologies related to age and their potential risk factors, such as diet and eating habits. The aim of this study was to investigate the predictive power of food groups versus retinal features among noninstitutionalized older adults from Southern Italy using a machine learning approach. Methods: We recruited 530 subjects, with a mean age of 74 years, who were drawn from the large population of the Salus in Apulia Study. In the present cross-sectional study, eating habits were assessed with a validated food frequency questionnaire. For the visual assessment, a complete ophthalmic examination and optical coherence tomography-angiography analyses were performed. Results: The analyses identified 13 out of the 28 food groups as predictors of all our retinal variables: grains, legumes, olives-vegetable oil, fruiting vegetables, other vegetables, fruits, sweets, fish, dairy, low-fat dairy, red meat, white meat, and processed meat. Conclusions: Eating habits and food consumption may be important risk factors for age-related retinal changes. A diet that provides the optimal intake of specific nutrients with antioxidant and anti-inflammatory powers, including carotenoids and omega-3 fatty acids, could have beneficial effects.

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Section: Methodsmentioning

confidence: 99%

Retinal Microvasculature and Neural Changes and Dietary Patterns in an Older Population in Southern Italy

Tatoli

Lampignano

Donghia

et al. 2023

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…RF was computed by an ensemble of binary decision trees, which could be used to select the most important variables linked to the outcomes. Variable predictiveness could be assessed using variable importance measures for both single and grouped variables [ 25 ]. Variables are considered “more important” if the variable is more frequently used for the first splits across all decision trees grown in the random forest.…”

Section: Methodsmentioning

confidence: 99%

Age-Related Dietary Habits and Blood Biochemical Parameters in Patients with and without Steatosis—MICOL Cohort

Donghia,

Pesole,

Castellaneta

et al. 2023

Nutrients

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Background: Steatosis is now the most common liver disease in the world, present in approximately 25% of the global population. The aim of this study was to study the association between food intake and liver disease and evaluate the differences in blood parameters in age classes and steatosic condition. Methods: The present study included 1483 participants assessed in the fourth recall of the MICOL study. Patients were subdivided by age (</>65 years) and administered a validated food frequency questionnaire (FFQ) with 28 food groups. Results: The prevalence of steatosis was 55.92% in the adult group and 55.88% in the elderly group. Overall, the results indicated many statistically significant blood parameters and dietary habits. Analysis of food choices with a machine learning algorithm revealed that in the adult group, olive oil, grains, processed meat, and sweets were associated with steatosis, while the elderly group preferred red meat, dairy, seafood, and fruiting vegetables. Furthermore, the latter ate less as compared with the adult group. Conclusions: Many differences were found between the two age groups, both in blood parameters and food intake. The random forest also revealed different foods predicted steatosis in the two groups. Future analysis will be useful to understand the molecular basis of these differences and how different food intake causes steatosis in people of different ages.

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

Lampignano¹,

Tatoli²,

Donghia³

et al. 2023

Nutrition, Metabolism and Cardiovascular Diseases

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Contribution of macro- and micronutrients intake to gastrointestinal cancer mortality in the ONCONUT cohort: Classical vs. modern approaches

Cited by 3 publications

References 47 publications

Retinal Microvasculature and Neural Changes and Dietary Patterns in an Older Population in Southern Italy

Retinal Microvasculature and Neural Changes and Dietary Patterns in an Older Population in Southern Italy

Age-Related Dietary Habits and Blood Biochemical Parameters in Patients with and without Steatosis—MICOL Cohort

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

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