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Dr. Dominika Jamioł-Milc

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Guest Editor

Department of Human Nutrition and Metabolomics, Pomeranian Medical University in Szczecin, 71-460 Szczecin, Poland
Interests: eating behavior; chronic diseases; dietary interventions; health promotion; dietary patterns

Special Issue Information

Dear Colleagues,

Our well-being and good health depend as much as 50% on our lifestyle, including our diet. Numerous studies have proven that incorrect eating behavior is a risk factor for the development of a number of diseases, including cardiovascular diseases, obesity, diabetes, insulin resistance as well as cancer. Unfortunately, chronic diseases are one of the most common causes of death in the world, and they mostly affect young and middle-aged people.

Modification of broadly understood eating behavior is crucial both in the prevention and treatment of chronic diseases. It is well known that dietary interventions effectively support the treatment of diseases such as hypertension or type II diabetes, and in many cases may even replace pharmacotherapy. To date, the best-documented models of nutrition with a broad therapeutic spectrum are the Mediterranean diet and Dietary Approaches to Stop Hypertension(DASH). Although the preventive and therapeutic potential is best demonstrated by studies on the influence of holistic dietary regimens, the role of selected food products and biologically active food ingredients are also invaluable.

We invite you to share the results of your research and opinions on the impact of food ingredients or overall nutritional regimens on the development, prevention, and treatment of chronic diseases in the Special Issue "Dietary Intake and Nutritional Support for Patients with Chronic Diseases".

Dr. Dominika Jamioł-Milc
Guest Editor

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Keywords

prevention of chronic diseases
diet therapy of chronic diseases
nutrition models
nutrients and chronic diseases
diabetes
insulin resistance
obesity

Published Papers (1 paper)

Research

12 pages, 862 KiB

Open AccessArticle

Machine Learning Model in Predicting Sarcopenia in Crohn’s Disease Based on Simple Clinical and Anthropometric Measures

by Yujen Tseng, Shaocong Mo, Yanwei Zeng, Wanwei Zheng, Huan Song, Bing Zhong, Feifei Luo, Lan Rong, Jie Liu and Zhongguang Luo

Int. J. Environ. Res. Public Health 2023, 20(1), 656; https://doi.org/10.3390/ijerph20010656 - 30 Dec 2022

Cited by 3 | Viewed by 1823

Abstract

Sarcopenia is associated with increased morbidity and mortality in Crohn’s disease. The present study is aimed at investigating the different diagnostic performance of different machine learning models in identifying sarcopenia in Crohn’s disease. Patients diagnosed with Crohn’s disease at our center provided clinical, anthropometric, and radiological data. The cross-sectional CT slice at L3 was used for segmentation and the calculation of body composition. The prevalence of sarcopenia was calculated, and the clinical parameters were compared. A total of 167 patients were included in the present study, of which 127 (76.0%) were male and 40 (24.0%) were female, with an average age of 36.1 ± 14.3 years old. Based on the previously defined cut-off value of sarcopenia, 118 (70.7%) patients had sarcopenia. Seven machine learning models were trained with the randomly allocated training cohort (80%) then evaluated on the validation cohort (20%). A comprehensive comparison showed that LightGBM was the most ideal diagnostic model, with an AUC of 0.933, AUCPR of 0.970, sensitivity of 72.7%, and specificity of 87.0%. The LightGBM model may facilitate a population management strategy with early identification of sarcopenia in Crohn’s disease, while providing guidance for nutritional support and an alternative surveillance modality for long-term patient follow-up. Full article

(This article belongs to the Special Issue Dietary Intake and Nutritional Support for Patients with Chronic Diseases)

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