AIMC Topic: Nutrition Surveys

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Dietary pattern, serum magnesium, ferritin, C-reactive protein and anaemia among older people.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: Epidemiological data of dietary patterns and anaemia among older Chinese remains extremely scarce. We examined the association between dietary patterns and anaemia in older Chinese, and to assess whether biomarkers of serum magnesi...

Vitamin D status in youth with type 1 and type 2 diabetes enrolled in the Pediatric Diabetes Consortium (PDC) is not worse than in youth without diabetes.

Pediatric diabetes
OBJECTIVE: To describe vitamin D levels and prevalence of vitamin D sufficiency, insufficiency and deficiency in a large, ethnically/racially diverse population of youth with type 1 diabetes (T1D) and type 2 diabetes (T2D) in comparison to national d...

Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
INTRODUCTION: Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that ...

Advanced prediction of heart failure risk in elderly diabetic and hypertensive patients using nine machine learning models and novel composite indices: insights from NHANES 2003-2016.

European journal of preventive cardiology
AIMS: As the global population ages, cardiovascular diseases, particularly heart failure (HF), have become leading causes of mortality and disability among elderly patients. Diabetes and hypertension are major risk factors for cardiovascular diseases...

The non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) as predictors of hypertensive patients: Analyses of NHANES data with machine learning.

Medicine
Elevated values of the non-HDL/HDL cholesterol ratio (NHHR) have been associated with increased hypertension risk, indicating its potential as a pathogenic factor, but its assessment remains challenging. We analyzed data from 22,562 hypertensive part...

Machine Learning With Ingredient-Level Food Trees Reveals Contributors to Systemic Inflammation Among Adults in the National Health and Nutrition Examination Survey, 2001-2010 and 2015-2018.

Journal of the Academy of Nutrition and Dietetics
BACKGROUND: Methods for modeling the relationship between self-reported 24-hour dietary recalls and health outcomes are traditionally based on nutrients and/or dietary patterns. Machine learning (ML), combined with hierarchical representations of die...

Exploring the influencing factors of abdominal aortic calcification events in chronic kidney disease (CKD) and non-CKD patients based on interpretable machine learning methods.

International urology and nephrology
BACKGROUND: Calcification is prevalent in CKD patients, with abdominal aortic calcification (AAC) being a strong predictor of coronary calcification. We aimed to identify key calcification factors in CKD and non-CKD populations using machine learning...

Risk Prediction of Low Bone Density in Elderly Patients with Supervised Machine Learning Algorithms.

Balkan medical journal
BACKGROUND: Low bone mineral density (BMD) is a common age-related condition that elevates the risk of fractures and mortality. Machine learning (ML) techniques offer a promising approach for early prediction using readily available clinical, biochem...

Machine learning prediction of thrombolysis efficacy using hs-CRP and inflammatory markers in stroke.

Medicine
The aim of this study was to investigate the relationship between serum ultrasensitive C-reactive protein (hs-CRP) levels and stroke incidence and to assess its potential role in decision-making for thrombolytic therapy in stroke. Given that hs-CRP i...