Toward Omics-Based, Systems Biomedicine, and Path and Drug Discovery Methodologies for Depression-Inflammation Research.

Meta-analyses confirm that depression is accompanied by signs of inflammation including increased levels of acute phase proteins, e.g., C-reactive protein, and pro-inflammatory cytokines, e.g., interleukin-6. Supporting the translational significance of this, a meta-analysis showed that anti-inflammatory drugs may have antidepressant effects. Here, we argue that inflammation and depression research needs to get onto a new track. Firstly, the choice of inflammatory biomarkers in depression research was often too selective and did not consider the broader pathways. Secondly, although mild inflammatory responses are present in depression, other immune-related pathways cannot be disregarded as new drug targets, e.g., activation of cell-mediated immunity, oxidative and nitrosative stress (O&NS) pathways, autoimmune responses, bacterial translocation, and activation of the toll-like receptor and neuroprogressive pathways. Thirdly, anti-inflammatory treatments are sometimes used without full understanding of their effects on the broader pathways underpinning depression. Since many of the activated immune-inflammatory pathways in depression actually confer protection against an overzealous inflammatory response, targeting these pathways may result in unpredictable and unwanted results. Furthermore, this paper discusses the required improvements in research strategy, i.e., path and drug discovery processes, omics-based techniques, and systems biomedicine methodologies. Firstly, novel methods should be employed to examine the intracellular networks that control and modulate the immune, O&NS and neuroprogressive pathways using omics-based assays, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, immunoproteomics and metagenomics. Secondly, systems biomedicine analyses are essential to unravel the complex interactions between these cellular networks, pathways, and the multifactorial trigger factors and to delineate new drug targets in the cellular networks or pathways. Drug discovery processes should delineate new drugs targeting the intracellular networks and immune-related pathways.

A path model of psychosocial and health behaviour change predictors of excessive gestational weight gain

This study aimed to evaluate a conceptual model of psychosocial, behaviour change, and behavioural predictors of excessive gestational weight gain (GWG). Background: Excessive GWG can place women and their babies at risk of poor health outcomes, including obesity. Models of psychosocial and behaviour change predictors of excessive GWG have not been extensively explored; understanding the mechanisms leading to excess GWG will provide crucial evidence towards the development of effective interventions. Method: Two hundred and eighty-eight pregnant women (≤18 weeks gestation) were recruited to a prospective study. Demographic, psychosocial, health behaviour change, and behavioural factors were assessed at 17 (Time 1, T1) and 33 weeks (Time 2, T2) gestation. Pre-pregnancy and final pregnancy weight were obtained and women were classified with/without excessive GWG. Logistic regressions refined the list of predictors of excessive GWG; variables with p < .1 were included in a path analysis. Results: Age, family income, T2 depression, T2 pregnancy-specific coping, T1 buttocks dissatisfaction, T2 GWG-specific self-efficacy, T1 dietary readiness, T1 dietary importance, and T1 vegetable intake predicted excessive GWG in the logistic regressions and were included in the path model. The baseline path model demonstrated poor fit. Once statistically and theoretically plausible paths were added, adequate model fit was achieved (χ² = 21.61(9), p < .05; RMSEA = .07; CFI = .93); this revised model explained 19.5% of the variance in excessive GWG. Women with high T1 buttocks dissatisfaction were more likely to exhibit low levels of dietary readiness. Women with low dietary readiness were more likely to have a lower vegetable intake, which predicted excessive GWG. Women with higher T2 depressive symptoms were more likely to report lower GWG self-efficacy and gain excessively. Conclusion: Future behavioural GWG trials should consider combining psychosocial and health behaviour change factors to optimise GWG.