AIMC Topic: Nutrition Surveys
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Machine learning-based online web calculator predicts the risk of sarcopenic obesity in older adults.
Aging clinical and experimental research
Jul 9, 2025
BACKGROUND: Sarcopenic obesity (SO) has a higher risk of adverse health events compared to having obesity or sarcopenia alone due to the dual burden of both muscle loss and fat gain. The prevalence of SO is progressively increasing as the population ...
Association of dietary quality, biological aging, progression and mortality of cardiovascular-kidney-metabolic syndrome: insights from mediation and machine learning approaches.
Nutrition journal
Jul 7, 2025
BACKGROUND: To investigate the association between the Dietary Inflammatory Index (DII), biological aging, and the staging and mortality of cardiovascular-kidney-metabolic (CKM) syndrome.
The value of triglyceride-glucose index-related indices in evaluating migraine: perspectives from multi-centre cross-sectional studies and machine learning models.
Lipids in health and disease
Jul 3, 2025
BACKGROUND: This study employed representative data from the U.S. and China to delve into the correlation among migraine prevalence, the triglyceride‒glucose index, a marker of insulin resistance, and the composite indicator of obesity.
Construction and validation of a risk prediction model for chronic obstructive pulmonary disease (COPD): a cross-sectional study based on the NHANES database from 2009 to 2018.
BMC pulmonary medicine
Jul 3, 2025
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major global public health concern, and early screening and identification of high-risk populations are critical for reducing the disease burden. Although several studies have explored the...
Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015-2018.
BMC public health
Jul 3, 2025
Cardiometabolic Multimorbidity (CMM) has emerged as one of the primary threats to human health globally due to its high incidence, disability, and mortality rates. Accurate identification of CMM patterns is crucial for CMM classification and health m...
Development and validation of a risk prediction model for depression in patients with chronic obstructive pulmonary disease.
BMC psychiatry
Jul 3, 2025
BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory condition often accompanied by depression, which exacerbates disease burden and impairs quality of life. Early identification of depression risk in COPD patients rema...
Exploring the link between the ZJU index and sarcopenia in adults aged 20-59 using NHANES and machine learning.
Scientific reports
Jul 2, 2025
Sarcopenia, characterized by progressive loss of muscle mass and function, is a growing public health concern. The ZJU index, a novel metabolic marker, integrates lipid metabolism and glucose regulation parameters. While its association with metaboli...
Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES.
BMC medical informatics and decision making
Jul 1, 2025
OBJECTIVE: Using 2005-2018 NHANES data, this study examined the association between the visceral fat metabolism score (METS-VF) and heart failure (HF) prevalence in U.S. adults, leveraging machine learning (LightGBM/XGBoost) and SHAP for classficatio...
Effect of the exposure to brominated flame retardants on hyperuricemia using interpretable machine learning algorithms based on the SHAP methodology.
PloS one
Jun 26, 2025
BACKGROUND: Brominated flame retardants (BFRs) are classified as important endocrine disruptors and persistent organic pollutants; nevertheless, there is no comprehensive investigation to evaluate the association between BFRs and hyperuricemia, and t...
Exploring the association between volatile organic compound exposure and chronic kidney disease: evidence from explainable machine learning methods.
Renal failure
Jun 23, 2025
BACKGROUND: Chronic Kidney Disease (CKD) affects approximately 697.5 million people worldwide. Volatile organic compounds (VOCs) are emerging as potential risk factors, but their complex relationships with CKD may be underestimated by traditional lin...
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