Diet & Nutrition

Explainable AI to predict a complex multifactorial outcome, childhood obesity: Application to clinical epidemiology

Childhood obesity, driven by genetic and epidemiological factors, poses significant health risks, yet traditional machine learning models lack interpretability for clinical use. This study aims to apply Kolmogorov-Arnold Networks (KAN), an explainable machine learning model, to predict body mass index (BMI) at age 8 as an indicator of obesity risk and to develop a publicly accessible prediction to...

Metabolic Subphenotypes of Obstructive Sleep Apnea: NHANES 2017-2020 (pre-pandemic)

OSA and MetS have a bidirectional relationship but increasing evidence suggests metabolic heterogeneity in OSA, systematic phenotyping of metabolic drivers in OSA are lack. To identify metabolic subphenotypes of OSA and elucidate potential pathophysiological mechanisms using population-level data. To analyze the data related to OSA and MetS from 2,260 participants in the NHANES database (2017–2020...

Machine learning algorithm to predict fragility fractures and identification of important features – an explainable approach

In this study, we developed ML algorithms to predict fragility fractures, considering the occurrence of fractures at different skeletal sites. We inve...

Clinically meaningful combined improvements of sleep, physical activity, and nutrition (SPAN) in relation to major adverse cardiovascular events

Sleep, physical activity, and nutrition (SPAN) are major modifiable risk factors for cardiovascular disease, yet the minimum and optimal combined impr...

Protocol for update of food-based dietary guidelines for Czechia

Current Czech national food-based dietary guidelines are outdated and do not reflect the most recent scientific evidence, nor considerations of sustai...

Personalized AI Prompt Generator and ChatGPT for Weight Loss: Randomized Controlled Trial in Adults with Overweight and Obesity

The global prevalence of overweight and obesity continues escalating, driven by environmental factors and lifestyle behaviors leading to cardiovascula...

The human milk microbiome varies by environmental factors and is associated with infant growth: findings from the IMiC Consortium

Human milk (HM) is a complex ecological matrix that connects mothers and infants to the surrounding environment, and promotes infant growth and health...

Compartment-specific Fat Distribution Profiles have Distinct Relationships with Cardiovascular Ageing and Future Cardiovascular Events

Obesity is a global public health priority and a major risk factor for cardiovascular disease (CVD). Emerging evidence indicates variation in patholog...

Dramatic increases in redundant publications in the Generative AI era

Redundant publication, the practice of submitting the same or substantially overlapping manuscripts multiple times, distorts the scientific record and...

Using deep learning to improve genetic studies of osteoporosis

To evaluate how recent advances in deep learning can improve the construction of quantitative phenotypes for genome-wide association studies (GWAS), w...

Non-Traditional Lipid Ratios Predict Cardiovascular-Kidney-Metabolic Syndrome: Insights from Machine Learning Model Using NHANES Data

Cardiovascular-kidney-metabolic (CKM) syndrome is a newly defined multisystem disease continuum characterized by the coexistence of metabolic dysfunct...

Development of Self-Assessment Tools for Osteoporosis among Postmenopausal Vietnamese Women: A Machine Learning Approach

Osteoporosis is a major health concern in Vietnam due to a rise in aging rates. However, cost-effective early screening tools tailored to the Vietname...

Dietary Macronutrient Intake and the Gut Microbiome in Adults Undergoing Bariatric Surgery for Obesity

Limited information linking dietary intake to gut metagenomic data in bariatric surgery patients is available. We examined whether there were correlat...

Prediction of Nutritional Content in Peruvian Lunch Meals by Large Language Models: A One-Shot Evaluation

Various artificial intelligence applications have been developed to predict the nutrient content of meals. However, none have been evaluated in the co...

A Metabolic-Inflammatory Phenotype of Pelvic Floor Dysfunction: A Machine Learning-Based Discovery in a Nationally Representative U.S. Cohort

Pelvic floor dysfunction (PFD) is a highly prevalent and heterogeneous condition among women. The traditional view of PFD as a single clinical entity ...

Modeling nonlinear and interaction effects of spatiotemporal and other non-genetic factors improves phenotypic prediction for complex traits

Adjusting for non-genetic factors can improve genetic association testing and polygenic prediction, yet most studies rely on linear adjustments for a ...

Circulating Metabolites are Linked to Dementia and Brain Imaging Phenotypes, and Mediate Modifiable Risk Pathways

Dementia poses an escalating global health burden, yet its underlying mechanisms remain incompletely understood. In this large-scale, targeted metabol...

Prevalence and Predictors of Silent Vertebral Compression Fractures: A Cross-Sectional Population-Based Study Using UK Biobank Imaging Data

To estimate the prevalence of silent vertebral compression fractures (VCF) in an asymptomatic population and to assess the demographic and clinical pr...

Characterize Disease Progression Subphenotypes in Real World Populations with Overweight and Obesity using a Graph-based Neural Network Framework

Obesity is a chronic, heterogeneous condition, with risks, trajectories, and treatment responses that vary widely among individuals. However, research...

Evaluating Large Language Models for Colonoscopy Preparation Assistance: Correctness and Diversity in Synthetic Dialogues

Colorectal cancer is the third leading cause of cancer-related deaths in the United States, and colonoscopy remains the gold standard for early detect...