Endocr Connect
Department of Clinical Nutrition, Xin Hua Hospital Affiliated to School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Published: December 2024
Introduction: Gestational diabetes mellitus (GDM) significantly affects pregnancy outcomes. Therefore, it is crucial to develop prediction models since they can guide timely interventions to reduce the incidence of GDM and its associated adverse effects.
Methods: A total of 554 pregnant women were selected and their sociodemographic characteristics, clinical data and dietary data were collected. Dietary data were investigated by a validated semi-quantitative food frequency questionnaire (FFQ). We applied random forest mean decrease impurity for feature selection and the models are built using logistic regression, XGBoost, and LightGBM algorithms. The prediction performance of different models was compared by accuracy, sensitivity, specificity, area under curve (AUC) and Hosmer-Lemeshow test.
Results: Blood glucose, age, pre-pregnancy body mass index (BMI), triglycerides and high-density lipoprotein cholesterol (HDL) were the top five features according to the feature selection. Among the three algorithms, XGBoost performed best with an AUC of 0.788, LightGBM came second (AUC = 0.749), and logistic regression performed the worst (AUC = 0.712). In addition, XGBoost and LightGBM both achieved a fairly good performance when dietary information was included, surpassing their performance on the non-dietary dataset (0.788 vs 0.718 in XGBoost; 0.749 vs 0.726 in LightGBM).
Conclusion: XGBoost and LightGBM algorithms outperform logistic regression in predicting GDM among Chinese pregnant women. In addition, dietary data may have a positive effect on improving model performance, which deserves more in-depth investigation with larger sample size.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11623027 | PMC |
http://dx.doi.org/10.1530/EC-24-0169 | DOI Listing |
Cureus
December 2024
Community Medicine, Dhanalakshmi Srinivasan Medical College and Hospital, Siruvachur, IND.
Background The escalating global obesity epidemic requires comprehensive investigations for effective weight management strategies. Understanding the patterns, barriers, and facilitators of dietary interventions is crucial for developing effective weight management protocols. This research aims to assess dietary modification interventions among weight loss subjects in Tamilnadu, South India.
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January 2025
Department of Ophthalmology, The Second Affiliated Hospital, Nanchang University, Nanchang, China.
Objective: To investigate the association between Oxidative Balance Score (OBS) and glaucoma risk.
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BMJ Oncol
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Department of Big Data in Health Science, School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Objective: This study aimed to explore the global burden of early-onset cancer based on the Global Burden of Disease (GBD) 2019 study for 29 cancers worldwid.
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Results: Global incidence of early-onset cancer increased by 79.
Curr Dev Nutr
October 2024
Clinical Nutrition Research Centre (CNRC), Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore.
Complementing discourse following a February 2023 event on dietary protein needs in Southeast Asia (SEA), this symposium report summarizes the region's protein intake, while simultaneously examining the impact of dietary shift toward complementary and alternative proteins and their health implications. It highlights the importance of protein quality in dietary evaluations, optimal intake, and sustainability, advocating for environmentally conscious protein production and innovation in future foods. Discussion points, expert opinions, national nutrition data, and relevant literature, addressing protein intake and quality, their impact on human health, and various technologies for future foods production, have been included.
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October 2024
Center for Nutrition, Healthy Lifestyle and Disease Prevention, Loma Linda University School of Public Health, Loma Linda, CA, United States.
Background: Avocado intake has been associated with improvements in diet quality. Whether this response is because of avocado intake, , or combined with a food and/or nutrient displacement (D) has yet to be determined.
Objectives: This secondary analysis, conducted using dietary data from the Habitual Diet and Avocado Trial, sought to assess the effect of consuming a large avocado (168 g, 281 kcal) daily in the avocado-supplemented diet (AD) group compared with the habitual diet (HD) group on food and nutrient D.
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