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Student Research

You Are What You Eat: Folate and Depression

by Stephen Gonzalez

laboratory

Depression is a major source of mental health problems in the U.S., effecting persons of all ages and cultures. Many studies have found associations between lower folate nutrition and high depressive symptoms. Additionally, an association have been shown between physical activity and depression, such that depression increases as physical activity decreases over time. The primary goal of this research is to provide further support for the above findings. The main question of this research is “what are predictors depressive symptoms?”. I hypothesize that depressive symptoms can be predicted from serum folate levels and about of physical activity.

Data will be obtained from the National Health and Nutrition Examination Survey (NHANES) dataset, ranging from collection years 2007-2012. Depression was measured via the Patient Health Questionnaire (PHQ-9), which is tool used for diagnosing and measuring the severity of depression. Cut-points recommended by the PHQ-9 itself will be used to define minimal symptoms (5-9), minor depression (10-14), moderately severe major depression (15-19), and sever major depression (>20). Physical activity (PA) was measured using a survey asking subjects about the amount of moderate and vigorous physical activity they engaged in on a typical day. Using the US Department of Health & Human Services’ Physical Activity Guidelines for adults, S’s reporting engaging in at least 150 minutes of moderate-intensity physical activity per week or 75 minutes of vigorous-intensity physical activity per week, are considered highly physically active, those reporting less than this are moderately physically active, and those reporting 0 minutes are considered as not physically active.

Blood serum folate was reported in both nanograms & milliliters (ng/mL), as well as nanomoles & liters (nmol/L). Blood serum folate will be categorized by performing a median spit on the blood serum folate data, and defining the upper and lower quartiles for comparison. Multiple regression will be used for evaluating the data, using blood serum folate levels and physical activity as predictors and depression as the criterion in the model. Because there are multiple predictors there is the possibility of collinearity. To solve this Ridge regression or Lasso may be necessary, and more appropriate then multiple regression.

Checking that the data align with the assumptions of multiple regression has revealed that the data is positively skewed, violating the assumption of normality. Additionally, running a correlation and Chi-Square analysis has shown that the data for serum folate and depression screener scores are not related. This is troubling as it violates the assumption that there are linear relationships between the predictors and the criterion. More work is needed before conclusions can be made.

 

Big Data Discovery & Diversity

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Dr. Archana McEligot 
amceligot@fullerton.edu

 

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Mary Aboud
maboud@fullerton.edu 

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