ArticleNutrients2026
Food- and Nutrient-Based Dietary Patterns and Depression in Korean Adults: A Machine Learning Approach Using KNHANES 2016-2021.
Article in Nutrients, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
BACKGROUND/
objectivesDietary patterns may influence depression, yet findings remain inconsistent, partly due to methodological variation in dietary pattern identification. As data-driven approaches may help reduce subjectivity and improve reproducibility in dietary pattern identification, this study aimed to identify dietary patterns using a machine learning approach and examine their associations with depression among Korean adults.
methodsUsing data from 21,321 Korean adults aged 19-64 years from the Korea National Health and Nutrition Examination Survey (2016-2021), we applied K-means clustering to identify dietary patterns based on both food group and nutrient intake. Dietary intake was assessed using a 24 h dietary recall, and depression status was based on physician diagnosis.
resultsThree distinct patterns were identified in both food group-based and nutrient-based analyses. In the food group-based analysis, a balanced and diverse dietary pattern (Cluster 3) was associated with lower odds of depression compared with a pattern characterized by overall low food intake (Cluster 1) (OR 0.64; 95% CI, 0.47-0.88;
conclusionsOur findings suggest that adherence to balanced and diverse dietary patterns based on whole foods is associated with lower odds of depression. Food group-based clustering approaches may offer more reproducible and interpretable insights than nutrient-based approaches, supporting their potential utility in epidemiological research and public health strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.