Explainable machine learning for identifying osteoarthritis comorbidity among adults with diabetes: A nationally representative NHANES study - Summary - MDSpire
To assess dietary nutrient-mixture associations and identify patterns associated with osteoarthritis (OA) comorbidity among U.S. adults with diabetes.
Approach:
Study design and population: Cross-sectional analysis of seven NHANES cycles from 2005–2018. The final sample included 6,716 participants with diabetes.
Dietary assessment: Dietary nutrient intake was assessed using two 24-hour recalls; values were averaged for participants with two valid recalls.
Analytical methods: The study describes using weighted quantile sum (WQS) regression and explainable machine learning, including SHAP and LIME, to examine nutrient mixtures and classification patterns.
Interpretation:
The provided context describes the study rationale and methods but does not include results.
Limitations:
The supplied article context ends during the methods section, so outcome definitions, detailed analyses, results, and study limitations are unavailable.
Harold Burstein, MD, PhD, and Jennifer A. Ligibel, MD discuss new research from the Breast Cancer Weight Loss (BWEL) trial, as well as recommendations for lifestyle, diet and GLP1 agonists.
Adults assigned to nonnutritive sweetened beverages or water maintained weight reductions through 104 weeks, with no statistically significant difference between groups.
