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

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.

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.

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.

The study involved binary variables based on minimum daily intakes and did not account for positive effects of exceeding minimum recommended doses.

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.