Diet quality, inflammation and affective reactivity in mental health



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There is growing evidence for the influence of diet, inflammation and affective reactivity on mental health. It is important to consider whether diet quality impacts expression of anxiety and depression symptoms. Inflammation has been linked to mental health, yet the relationship is ambiguous. For example, the link between inflammation and depression only appears to exist for a subset of individuals. Differences in affective reactivity are also linked to anxiety and depression, however the role affective reactivity plays is unclear. Although anxiety and depression are often co-occurring conditions, they may have distinct relationships with diet, inflammation and affective reactivity. To distinguish whether anxiety and depression have different biopsychosocial influencers, we separate them into two regression models. We seek to clarify whether diet, inflammation and affective reactivity are determinants of anxiety and depression symptoms. Methods Using data from the Midlife in the United States study (MIDUS), including two sample groups representing a predominantly European American sample (Sample 1; S1) and a predominantly African American sample (Sample 2; S2), we perform multiple regression analyses to examine the biopsychosocial influences of diet, inflammation and affective reactivity on anxiety and depression. Overall diet quality is scored using an adaptation of the Alternative Healthy Eating Index 2010 (AHEI-2010), on self-report of average intake of fruits and vegetables, fish, non-meat protein, high fat meat and sugar sweetened beverages. An inflammation summary score is calculated including C-reactive protein (CRP), fibrinogen, E-selectin, tumor necrosis factor alpha (TNF-⍺), and interleukin 6 (IL-6) and interleukin 8 (IL-8). Affective reactivity is measured from corrugator supercilii activity (COR) in response to pictures from the International Affective Picture System (IAPS). A recovery residual is calculated to specifically highlight COR after picture presentation, controlling for initial response differences during picture display. Depression and anxiety symptoms were measured using the Mood and Symptom Questionnaire Short Form (MASQ-SF). Results. Multiple regression analyses for S1 and S2 were conducted separately. Multiple regression results for S1 indicated that neither diet, nor affective reactivity were able to predict depression or anxiety. However, age was a significant predictor of depression and anxiety for S1, with younger age predicting more depression and anxiety. While controlling for age and income, inflammation was also a significant predictor of anxiety for S1. Affective reactivity was able to predict depression and anxiety for S2, showing an inverse relationship. Also, better diet quality predicted more anxiety for S2. Conclusion. Results from this study suggest that anxiety and depression should be considered separately when examining their biopsychosocial influences. A key finding in this study is that biopsychosocial factors predicting depression and anxiety may differ for individuals of different race groups. S1 is a predominantly European American sample group with higher income, while S2 is a predominantly African-American group with lower income. For S1, younger age and higher inflammation were significant predictors of anxiety. However for S2, better quality diet and lower affective reactivity were significant predictors of anxiety. Concerning depression, younger age was the only significant predictor for S1, and lower affective reactivity was the only significant predictor for S2. These results have implications for understanding the unique biopsychosocial influences on depression and anxiety for individuals from different race groups.



Anxiety, Depression, Biological, Inflammation, Diet quality, Affective reactivity, Affective recovery, Emotional reactivity, Emotional recovery, Age, Race, African American, Middle age, Adulthood, Psychological resilience, Biopsychosocial, Mental health, Health disparities