Back

Mediating effect of pro-inflammatory cytokines in the association between depression, anxiety, and cardiometabolic disorders in an ethnically diverse middle-aged and older population

Hallab, A.; The Health and Aging Brain Study (HABS-HD) Study Team,

2025-04-16 cardiovascular medicine
10.1101/2025.04.14.25325836 medRxiv
Show abstract

IntroductionNeuroinflammation is associated with depression and anxiety risk, both of which demonstrate a bilateral relationship with cardiometabolic disorders. Systemic inflammation is also commonly described in patients with cardiometabolic disorders. It is, thus, unclear whether pro-inflammatory cytokines might mediate the relationship between depression, anxiety, and cardiometabolic disorders, particularly in advanced ages. MethodsThe multiethnic [&ge;] 50-year-old study population is a subset of the Health and Aging Brain Study: Health Disparities (HABS-HD). Adjusted logistic and linear regression models were applied to assess associations. Non-linearity was evaluated using restricted cubic splines. Statistical mediation analysis was used to determine the role of inflammation (Tumor Necrosis Factor-alpha (TNF-alpha) and Interleukin-6 (IL-6)). Models were corrected for multiple testing using the False Discovery Rate (FDR)-method. ResultsIn the 2,093 included cases, depression and/or anxiety were significantly associated with 62% higher odds of Cardiovascular Disorder (CVD) (OR=1.62 [95% CI: 1.22-2.15]), 54% of type 2 diabetes (T2DM) (OR=1.54 [95% CI: 1.29-1.85]), 26% of hypertension (OR=26% [95% CI: 1.07-1.48]), and 29% of obesity (OR=1.29 [95% CI: 1.11-1.51]). Only IL-6 showed a significant mediating role in the association of depression and/or anxiety with CVD (10%, p-valueFDR=0.016), T2DM (13%, p-valueFDR<0.001), hypertension (16%, p-valueFDR<0.001), and obesity (23%, p-valueFDR<0.001). ConclusionsDepression and anxiety were significantly associated with higher odds of major cardiometabolic disorders, and IL-6 partly mediated these associations. Clinical studies are needed to replicate the findings and specifically cluster high-risk profiles.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.