Nonesterified Fatty Acids and Depression in Cancer Patients and Caregivers

Abstract

Background Non-esterified fatty acids (NEFAs) are known to have inflammatory effects. The inflammatory hypothesis of depression suggests that omega-3 and omega-6 fatty acids might be negatively and positively correlated with depression, respectively. Objective An exploratory study was conducted to determine the association between dietary free fatty acids and depressive symptoms in cancer patients and caregivers. Methods Associations between depression and the NEFAs pool were investigated in 56 cancer patients and 23 caregivers using a combination of non-parametric tests and regularized regression. Plasma NEFAs were measured using thin layer and gas chromatography with flame ionization detection. Depression was characterized both as a continuous severity score using the GRID Ham-D, and as a categorical diagnosis of major depression by structured clinical interview. Results Initial hypotheses regarding the relationship between depression and omega-3 or omega-6 fatty acids were not well supported. However, elaidic acid, a trans-unsaturated fatty acid found in hydrogenated vegetable oils, was found to be negatively correlated with continuous depression scores in cancer patients. No significant associations were found in caregivers. Conclusions An unexpected negative association between elaidic acid and depression was identified, supporting recent literature on the role of G protein-coupled receptors in depression. Further research is needed to confirm this result and to evaluate the potential role of G protein agonists as therapeutic agents for depression in cancer patients.

Exploring Depression and Nutritional Covariates Amongst US Adults using Shapely Additive Explanations

“…Many authors have noted statistically significant associations between Vitamin B6, Dietary Fiber, Potassium Intake, Vitamin E, Low Fat and Low Cholesterol Diet, Copper, Caffeine, Lutein Zeaxanthin, Phosphorous, Food Folate, Folate, Vitamin K, Iron, Theobromine, Total Choline, Magnesium, Sodium, Grams of Protein, Vitamin B12, Fiber, and Zinc similarly to our study 38–43 . Since visualizations for risk factors match literature relationships, we have increased confidence that the machine learning model is able to capture the actual physiological relationships of these covariates 44–51 . These transparent machine‐learning tools allow for increased confidence that these algorithms are picking up true signal within these covariates to predict the presence of depression rather than just replicating potential biases stemming from systemic data‐quality errors that are present within the data set.…”