Association of Quantity and Quality of Protein Intake with Depression and Anxiety Symptoms among Adolescent Boys and Girls (13–15 Years) Studying in Public Schools of Delhi

Khanna, Preeti; Aeri, Bani Tamber

doi:10.3177/jnsv.66.s141

Cited by 13 publications

(12 citation statements)

References 20 publications

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“…All nutrition variables were negatively correlated with depression; however, among them, fiber quantile, fiber, and protein were the top three highly correlated diet-based features with depression. This is in line with the results of other reports in which the low protein intake groups had a significantly higher risk of depression than the standard protein intake groups ( 60 – 62 ). Several studies have demonstrated a potential relationship between depression and dietary fiber intake, in which an increased intake of total dietary fiber is associated with a lower likelihood of depression ( 63 , 64 ).…”

Section: Resultssupporting

confidence: 93%

Evaluation of nutritional status and clinical depression classification using an explainable machine learning method

et al. 2023

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IntroductionDepression is a prevalent disorder worldwide, with potentially severe implications. It contributes significantly to an increased risk of diseases associated with multiple risk factors. Early accurate diagnosis of depressive symptoms is a critical first step toward management, intervention, and prevention. Various nutritional and dietary compounds have been suggested to be involved in the onset, maintenance, and severity of depressive disorders. Despite the challenges to better understanding the association between nutritional risk factors and the occurrence of depression, assessing the interplay of these markers through supervised machine learning remains to be fully explored.MethodsThis study aimed to determine the ability of machine learning-based decision support methods to identify the presence of depression using publicly available health data from the Korean National Health and Nutrition Examination Survey. Two exploration techniques, namely, uniform manifold approximation and projection and Pearson correlation, were performed for explanatory analysis among datasets. A grid search optimization with cross-validation was performed to fine-tune the models for classifying depression with the highest accuracy. Several performance measures, including accuracy, precision, recall, F1 score, confusion matrix, areas under the precision-recall and receiver operating characteristic curves, and calibration plot, were used to compare classifier performances. We further investigated the importance of the features provided: visualized interpretation using ELI5, partial dependence plots, and local interpretable using model-agnostic explanations and Shapley additive explanation for the prediction at both the population and individual levels.ResultsThe best model achieved an accuracy of 86.18% for XGBoost and an area under the curve of 84.96% for the random forest model in original dataset and the XGBoost algorithm with an accuracy of 86.02% and an area under the curve of 85.34% in the quantile-based dataset. The explainable results revealed a complementary observation of the relative changes in feature values, and, thus, the importance of emergent depression risks could be identified.DiscussionThe strength of our approach is the large sample size used for training with a fine-tuned model. The machine learning-based analysis showed that the hyper-tuned model has empirically higher accuracy in classifying patients with depressive disorder, as evidenced by the set of interpretable experiments, and can be an effective solution for disease control.

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Section: Resultssupporting

confidence: 93%

Evaluation of nutritional status and clinical depression classification using an explainable machine learning method

et al. 2023

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“…The results showed a significant low proportion of protein intake associated with the prevalence of MDD ( 87 ). Food frequency questionnaires have shown the important issue of quality and quantity of protein intake, being low consumption of protein-rich foods such as milk, and legumes significantly associated with higher mean scores of depression and anxiety symptoms ( 88 , 89 ). Diet is also known to be the greatest shaper of gut microbiota, and this complex “organ” is even involved in the synthesis of vitamin B and may affect host vitamin B usage ( 90 , 91 ).…”

Section: Nutrition Epigenetics and Mdd: Is There A Link?mentioning

confidence: 99%

Nutrition, Epigenetics, and Major Depressive Disorder: Understanding the Connection

et al. 2022

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Major depressive disorder (MDD) is a complex, multifactorial disorder of rising prevalence and incidence worldwide. Nearly, 280 million of people suffer from this leading cause of disability in the world. Moreover, patients with this condition are frequently co-affected by essential nutrient deficiency. The typical scene with stress and hustle in developed countries tends to be accompanied by eating disorders implying overnutrition from high-carbohydrates and high-fat diets with low micronutrients intake. In fact, currently, coronavirus disease 2019 (COVID-19) pandemic has drawn more attention to this underdiagnosed condition, besides the importance of the nutritional status in shaping immunomodulation, in which minerals, vitamins, or omega 3 polyunsaturated fatty acids (ω-3 PUFA) play an important role. The awareness of nutritional assessment is greater and greater in the patients with depression since antidepressant treatments have such a significant probability of failing. As diet is considered a crucial environmental factor, underlying epigenetic mechanisms that experience an adaptation or consequence on their signaling and expression mechanisms are reviewed. In this study, we included metabolic changes derived from an impairment in cellular processes due to lacking some essential nutrients in diet and therefore in the organism. Finally, aspects related to nutritional interventions and recommendations are also addressed.

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“…Therefore, increasing protein intake at breakfast and lunch may enhance muscle mass, as well as promote physical and mental health. Indeed, among adolescent males and females, those with lower protein intake, especially those with lower milk intake at breakfast, have been reported to have lower scores for depression and anxiety symptoms [ 32 ]. Another possibility is that individuals with higher physical activity levels are more health conscious and typically consume more protein in the morning and afternoon.…”

Section: Discussionmentioning

confidence: 99%

Relationship Between Protein Intake in Each Traditional Meal and Physical Activity: Cross-sectional Study

Shinto¹,

Makino²,

Tahara³

et al. 2022

JMIR Public Health Surveill

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Background Protein intake plays an important role in the synthesis and maintenance of skeletal muscles for the prevention of health risks. It is also widely known that physical activity influences muscle function. However, no large-scale studies have examined the relationship between daily dietary habits, especially the timing of protein intake, and daily physical activity. Objective The purpose of this cross-sectional study was to investigate how protein intake and composition (involving the 3 major nutrients protein, fat, and carbohydrate) in the 3 traditional meals (breakfast, lunch, and dinner) are associated with physical activity. Methods Using daily dietary data accumulated in the smartphone food log app “Asken” and a web-based cross-sectional survey involving Asken users (N=8458), we analyzed nutrient intake and composition, as well as daily activity levels. As very few individuals skipped breakfast (1102/19,319 responses, 5.7%), we analyzed data for 3 meals per day. Results Spearman rank correlation analysis revealed that breakfast and lunch protein intakes had higher positive correlations with daily physical activity among the 3 major macronutrients (P<.001). These findings were confirmed by multivariate logistic regression analysis with confounding factors. Moreover, participants with higher protein intake and composition at breakfast or lunch tended to exhibit significantly greater physical activity than those with higher protein intake at dinner (P<.001). Conclusions Among the 3 macronutrients, protein intake during breakfast and lunch was closely associated with daily physical activity.

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Association of Quantity and Quality of Protein Intake with Depression and Anxiety Symptoms among Adolescent Boys and Girls (13–15 Years) Studying in Public Schools of Delhi

Cited by 13 publications

References 20 publications

Evaluation of nutritional status and clinical depression classification using an explainable machine learning method

Evaluation of nutritional status and clinical depression classification using an explainable machine learning method

Nutrition, Epigenetics, and Major Depressive Disorder: Understanding the Connection

Relationship Between Protein Intake in Each Traditional Meal and Physical Activity: Cross-sectional Study

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