AIMC Topic: Nutrition Surveys

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Physical Activity Recommendations Tailored by a Predictive Model for Adults With High Blood Pressure: Observational Study.

Journal of medical Internet research
BACKGROUND: Whether the benefits of identical physical activity (PA) patterns for adults with high blood pressure (BP) vary according to an individual's characteristics has not been adequately studied.

Lymphocytes and related inflammatory factors as predictors of metabolic syndrome risk in shift workers: A machine learning approach based on large-scale population data.

PloS one
BACKGROUND: Metabolic syndrome (MetS) is characterized by chronic inflammation and can be worsened by circadian disruption, which is common among shift work. Machine learning can predict the risk of MetS in shift workers using inflammatory biomarkers...

Association of the endothelial activation and stress index with cognitive function in older adults: a cross-sectional study with machine learning.

European journal of medical research
BACKGROUND: Age-associated memory impairment (AAMI) is a predementia state linked to endothelial dysfunction. The endothelial activation and stress index (EASIX) quantifies endothelial injury, yet its association with cognitive function remains unval...

Multi-omics integrated analysis identifies causal risk factors and therapeutic targets for diabetic retinopathy.

Journal of translational medicine
BACKGROUND: Diabetic retinopathy (DR) is the main cause of blindness worldwide, and its prevalence rate is constantly rising. More in-depth exploration of its risk factors and pathogenic mechanisms is needed.

Integrating inflammatory biomarkers and demographic variables with machine learning to predict endometriosis risk.

Scientific reports
This study explores the relationship between inflammatory biomarkers and the risk of endometriosis, aiming to develop a predictive model using National Health and Nutrition Examination Survey (1999-2006) data. The dataset included 4,089 females with ...

Association of the dietary index for gut microbiota with metabolic syndrome and its components combining interpretable machine learning algorithms.

Journal of health, population, and nutrition
BACKGROUND: Previous studies have emphasized the critical role of diet and gut microbiome in Metabolic syndrome (MetS). The dietary index for gut microbiota (DI-GM) represents a novel dietary index that effectively reflects the diversity of gut micro...

Relationship between C-reactive protein triglyceride glucose index and cardiovascular disease risk: a cross-sectional analysis with machine learning.

BMC medical informatics and decision making
BACKGROUND: Cardiovascular disease (CVD) continues to be a leading cause of disease burden and mortality worldwide. Identifying reliable biomarkers for CVD risk assessment is essential. This study investigates the association between the C-reactive p...

The association between estimated glucose disposal rate and the prevalence and mortality of chronic kidney disease: a cross-sectional study with linked mortality follow-up.

European journal of medical research
BACKGROUND: Metabolic disorders represented by insulin resistance (IR) are at risk of chronic kidney disease (CKD). Estimated glucose disposal rate (eGDR) reflects IR. The relationship between eGDR and CKD was unclear. This study aimed at discussing ...

Machine learning-based prediction of metabolic dysfunction-associated steatotic liver disease using National Health and Nutrition Examination Survey (NHANES) data.

PloS one
OBJECTIVE: With the global increase in obesity rates and lifestyle changes, metabolic dysfunction-associated steatotic liver disease (MASLD) has become a prevalent chronic liver disorder, affecting approximately 25% of the global population. This dis...

Interpretable machine learning for cardiovascular risk prediction: Insights from NHANES dietary and health data.

PloS one
BACKGROUND: Cardiovascular diseases (CVD) are one of the leading global causes of death, which requires an accurate early prediction. This study aimed to develop transparent machine learning (ML) models using National Health and Nutrition Examination...