Nutrient deficiency profiles and depression: A latent class analysis study of American population

Background: Research into the effects of nutrition on depression is often performed by examining the effects of singular nutrients and dietary styles (e.g.: vegan, Mediterranean). The present study is the first one to establish the effects of patterns of nutritional deficiency within the American population and examines their effects on depression. Methods: Data was drawn from National Health and Nutrition Examination Survey (NHANES). Latent class analysis was performed to identify homogeneous groups of nutrient deficiency. A 3-step analysis was performed to establish class-dependant differences in depression severity. BCH analysis revealed unique predictors of depression dependant on most probable class. Results: Analysis revealed 4 classes of nutrient deficiency. Magnesium and dietary fibre were the least endorsed. ‘Nutrient deprived’ individuals showed the highest depression severity (Mean = 4.137, SD = 0.337). Profiles were predicted by different socioeconomic and anthropogenic predictors with meeting minimum calories showing the strongest odds of not being nutrient deprived (OR between 5.44 and 11.11). Overall, age (β = −0.115, p ≤ 0.01) and income (β = −0.147, p ≤ 0.01) were the strongest protecting factors while being female (β = 0.128, p ≤ 0.01) and arthritis (β = 0.130, p ≤ 0.01) were the strongest risk factors. Limitations: The study involved binary variables based on minimum daily intakes and did not account for positive effects of exceeding minimum recommended doses. Conclusions: The study supports the notion of a negative relationship between good nutrition and depression. Finding unique risk factors for depression symptoms supports the utility of nutrient deficiency profiling.