AIMC Topic: Nutrition Surveys

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Impact of the oxidative balance score on cardiovascular-kidney-metabolic syndrome: A cross-sectional study with machine learning prediction.

PloS one
BACKGROUND AND AIM: The antioxidant diet and lifestyle are widely believed to prevent and even treat various diseases; however, their applicability to cardiovascular-kidney-metabolic (CKM) syndrome remains unknown. In this study, the correlation betw...

Estimated glucose disposal rate predicts frailty through diabetes: Evidence from machine learning and mediation models in NHANES.

PloS one
OBJECTIVE: As an emerging insulin resistance marker, the relationship between estimated glucose disposal rate (eGDR) and frailty needs further exploration. This study examines the eGDR-frailty link, develops a machine learning predictive model to add...

Development and validation of a machine learning model for cardiovascular disease risk prediction in type 2 diabetes patients.

Scientific reports
Patients with type 2 diabetes mellitus (T2DM) have a significantly higher risk of cardiovascular disease (CVD) compared to the general population. Accurately predicting this risk is crucial for developing personalized treatment plans and public healt...

Association between geriatric nutritional risk index (GNRI) and asthma in elderly individuals aged 60 and above: a cross-sectional study of the NHANES 2005-2018.

BMC pulmonary medicine
OBJECTIVE: The geriatric nutritional risk index (GNRI) is a promising tool for predicting nutrition-related complications in older adults. This study aimed to explore the association between GNRI and asthma in individuals aged 60 and above.

Construct prediction models for low muscle mass with metabolic syndrome using machine learning.

PloS one
BACKGROUND: Metabolic syndrome (MetS) and sarcopenia are major global public health problems, and their coexistence significantly increases the risk of death. In recent years, this trend has become increasingly prominent in younger populations, posin...

Detecting, Characterizing, and Mitigating Implicit and Explicit Racial Biases in Health Care Datasets With Subgroup Learnability: Algorithm Development and Validation Study.

Journal of medical Internet research
BACKGROUND: The growing adoption of diagnostic and prognostic algorithms in health care has led to concerns about the perpetuation of algorithmic bias against disadvantaged groups of individuals. Deep learning methods to detect and mitigate bias have...

Data-driven identification of key predictors of uncontrolled hypertension: A cross-sectional study.

PloS one
Uncontrolled hypertension (HTN) increases the risk of adverse health events. This study aimed to identify key predictors of uncontrolled HTN in 1,308 Mexican adults with a prior diagnosis of HTN who were undergoing pharmacological treatment. We utili...

A Machine Learning Model for Predicting Sarcopenia Among Middle-Aged Adults: Development and External Validation.

JMIR medical informatics
BACKGROUND: Sarcopenia is a common muscle disorder in older adults, and its early identification and management in middle-aged populations are essential for ensuring a healthier later life. Detecting sarcopenia at an earlier stage may reduce the futu...

Exploring the potential relationship between kidney disease index and cognitive dysfunction: a machine learning approach with NHANES data.

BMC geriatrics
OBJECTIVE: This study investigates the relationship between the Kidney Disease Index (KDI) and cognitive function, evaluating its potential as a predictive marker for cognitive impairment in older adults. We also compare the performance of KDI with t...

The additive effect of the estimated glucose disposal rate and a body shape index on cardiovascular disease: A cross-sectional study.

PloS one
BACKGROUND: The glucose disposal rate (eGDR) and a body shape index (ABSI) are predictors strongly associated with cardiovascular disease (CVD) and outcomes. However, whether they have additive effects on CVD risk is unknown. This study aimed to inve...