Diet & Nutrition

Predicting Alzheimer’s Disease Diagnosis, a Decade or more Years before Onset using the Electronic Health Record and Random Forest Machine Learning Models

There is need to detect and intervene in pre-clinical phases of Alzheimer’s disease (AD). Electronic health records (EHRs) may help predict AD using machine learning methods We identified EHRs for 19,473 cases with AD and 111,922 controls. Records spanned 10 or more years prior to AD diagnosis. We trained a random forest model (employing 5-fold cross-validation with 2,499 features) to predict AD 1...

Integrating Infection Burden and Multimodal Biomarkers for Early Detection of Alzheimers Disease: A Sheaf-ML Framework

Alzheimers disease (AD) remains a major global health challenge, with growing evidence linking chronic infections, immune aging, and neurodegeneration. Grounded in the Antimicrobial Protection Hypothesis, this study introduces a sheaf-theoretic machine learning framework, Sheaf-ML, for integrating multimodal health data and assessing infection-related cognitive risk. Sheaf-ML constructs a unified ...

Assessment of fatal cardiovascular disease risk using data-driven diabetes subgroups and SCORE2-Diabetes in 24,943 adults in Mexico City

Cardiovascular disease (CVD) is a leading cause of diabetes-related mortality in Mexico. Although diabetes subgroups capture underlying disease hetero...

Incorporating Dietary Information to Enhance Polygenic Prediction Models with Applications to Body Mass Index and Type 2 Diabetes

Polygenic predictors can enhance screening for biomedical conditions, such as metabolism-related traits and diseases, but explain limited phenotypic v...

FusionAge framework for multimodal machine learning-based aging clocks uncovers cardiorespiratory fitness as a major driver of aging and inflammatory drivers of aging in response to spaceflight

Traditional epigenetic aging clocks are limited because they do not incorporate clinical information and functional tests, and rely on DNA samples and...

Nationwide Spatiotemporal Dynamics and Machine Learning Prediction of Anemia Among Women in Lesotho, 2023–2024

Despite substantial efforts, anemia continues to pose a significant public health challenge, disproportionately affecting women of reproductive age. I...

Temporal deep learning with clinically engineered biomarkers for the early prediction of type 2 diabetes

Diabetes mellitus remains a major global health burden, causing an estimated 3.4 million deaths in 2024 and highlighting the need for accurate early i...

Comparing the Treatment and Side Effects of Existing Bariatric Surgery Procedures: An Observational Study

Bariatric surgery is an established treatment for obesity and its associated comorbidities, including diabetes, hypertension, sleep apnea, and hyperch...

Relationships between vitamin C intake and COPD assessed by machine learning approaches from the NHANES (2017-2023).

BACKGROUND: This research aims to explore the possible link between Vitamin C Intake (VCI) and the incidence of Chronic Obstructive Pulmonary Disease ...

Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries.

BACKGROUND: The effectiveness of anti-tumour necrosis factor (TNF) therapy in spondyloarthritis is traditionally associated with factors such as age, ...

The dynamic linkage between covid-19 and nutrition: a review from a probiotics perspective using machine learning and bibliometric analysis.

INTRODUCTION: The pandemic crisis is now a memorable milestone in the history of science, not only for the impacts on the population's health but also...

Perspective on the ethics of AI at the intersection of nutrition and behaviour change.

Artificial intelligence (AI) has emerged as a powerful tool, that has the potential to impact society on multiple levels. Increased adoption as well a...

Exploration in association between vitamin D and cutaneous melanoma and explainable machine learning prediction.

OBJECTIVE: This study aims to examine association between vitamin D with melanoma and develop an explainable machine learning model.

The sports nutrition knowledge of large language model (LLM) artificial intelligence (AI) chatbots: An assessment of accuracy, completeness, clarity, quality of evidence, and test-retest reliability.

BACKGROUND: Generative artificial intelligence (AI) chatbots are increasingly utilised in various domains, including sports nutrition. Despite their g...

Protocol for evaluating the cost-effectiveness of Mongolia's sugar-sweetened beverages tax using double machine learning.

Elevated consumption of sugar-sweetened beverages (SSBs) has been associated with an increase in obesity, type 2 diabetes, and other non-communicable ...

Exploring the effect of the triglyceride-glucose index on bone metabolism in prepubertal children, a retrospective study: insights from traditional methods and machine-learning-based bone remodeling prediction.

BACKGROUND: Childhood obesity poses a significant risk to bone health, but the impact of insulin resistance (IR) on bone metabolism in prepubertal chi...

Effect of artificial intelligence driven therapeutic lifestyle changes (AI-TLC) intervention on health behavior and health among obesity pregnant women in China: a randomized controlled trial protocol.

INTRODUCTION: Obesity has reached epidemic proportions globally, posing significant challenges to public health and economic stability. In China, the ...

Analyzing the impact of clay minerals on the reservoir quality of the Lower Goru Formation using Unsupervised Machine Learning.

The reservoir quality of the Lower Goru Formation is highly variable due to its heterogeneous nature influenced by sea level fluctuations during the E...

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.

Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weigh...

The Metabolic Treatabolome and Inborn Errors of Metabolism Knowledgebase therapy tool: Do not miss the opportunity to treat!

Inborn errors of metabolism (IEMs) are rare genetic conditions with significant morbidity and mortality. Technological advances have increased therape...