ArticleHealth science reports2023
Exploring Depression and Nutritional Covariates Amongst US Adults using Shapely Additive Explanations.
Article in Health science reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Move your body, stay away from depression: a systematic review and meta-analysis of exercise-based prevention of depression in middle-aged and older adults.Frontiers in public health · 2025Pooled it
- Artificial Intelligence in Malnutrition: A Systematic Literature Review.Advances in nutrition (Bethesda, Md.) · 2024Pooled it
- SYNERGY-VAE: An explainable generative deep learning framework for discovering depression subgroups from multimodal population health data.PLOS digital health · 2026Article
- Integrating epidemiologic modeling and explainable machine learning to predict and identify factors associated with self-reported depression among adults in Tennessee, United States.Discover mental health · 2026Article
- Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care.Health science reports · 2026Article
- Genetic Causal Association Between Vitamin E and Depression: A Two-Sample Mendelian Randomization Study.Brain and behavior · 2026Article
- An interpretable machine learning model predicts frailty risk in middle-aged and older adults with gastrointestinal disease: a longitudinal study.Scientific reports · 2026Article
- Multimodal prediction of future depressive symptoms in adolescents.BMC psychiatry · 2025Article
- Multimodal Prediction of Future Depressive Symptoms in Adolescents.Research square · 2025Article
- Deep learning based optimal fish species identification to maximize production in fish ponds.Scientific reports · 2025Article
- Article
- A scoping review of the barriers and facilitators in the use of traditional, complementary, and integrative medicine: insights for health policy development.Journal of health, population, and nutrition · 2025Article
- Age-dependent mechanisms of exercise in the treatment of depression: a comprehensive review of physiological and psychological pathways.Frontiers in psychology · 2025Review
- Associations of physical activity volume and intensity with depression symptoms among US adults.Frontiers in public health · 2025Article
- The Associations between Depression and Sugar Consumption Are Mediated by Emotional Eating and Craving Control in Multi-Ethnic Young Adults.Healthcare (Basel, Switzerland) · 2024Article
- Key risk factors of generalized anxiety disorder in adolescents: machine learning study.Frontiers in public health · 2024Article
- Sex-specific associations of serum cotinine levels with depressive symptoms and sleep disorders in American adults: NHANES 2007-2014.Frontiers in psychiatry · 2024Article
- Exploring Depression and Nutritional Covariates Amongst US Adults using Shapely Additive Explanations.Health science reports · 2023Article
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: Depression affects personal and public well-being and identification of natural therapeutics such as nutrition is necessary to help alleviate this public health concern. Objective: The study aimed to identify feature importance in a machine learning model using solely nutrition covariates. Methods: A retrospective analysis was conducted using a modern, nationally representative cohort, the National Health and Nutrition Examination Surveys (NHANES 2017-2020). Depressive symptoms were evaluated using the validated 9-item Patient Health Questionnaire (PHQ-9), and all adult patients (total of 7929 individuals) who completed the PHQ-9 and total nutritional intake questionnaire were included in the study. Univariable regression was used to identify significant nutritional covariates to be included in a machine learning model and feature importance was reported. The acquisition and analysis of the data were authorized by the National Center for Health Statistics Ethics Review Board. Results: 7929 patients met the inclusion criteria in this study. The machine learning model had 24 out of a total of 60 features that were found to be significant on univariate analysis ( Conclusion: Machine learning models with feature importance can be utilized to identify nutritional covariates for further study in patients with clinical symptoms of depression.
Indexed as
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What Socratic 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 Socratic graph.