The relationship between BDNF and physical activity on depression.

BACKGROUND/OBJECTIVE: Major depressive disorder (MDD) is one of the leading causes of disease burden and disability worldwide. Brain-derived neurotrophic factor (BDNF) seems to have an important role in the molecular mechanisms underlying MDD aetiology, given its implication in regulating neuronal plasticity. There is evidence that physical activity (PA) improves depressive symptoms, with a key role of BDNF in this effect. We aim to perform a systematic review examining the relationship between the BDNF Val66Met polymorphism and the BDNF protein, PA and MDD. METHODS: Both observational and experimental design original articles or systematic reviews were selected, according to the PRISMA statement. RESULTS: Six studies evaluated the Val66Met polymorphism, suggesting a greater impact of physical activity on depression depending on the Val66Met genotype. More discordant findings were observed among the 13 studies assessing BDNF levels with acute or chronic exercise interventions, mainly due to the high heterogeneity found among intervention designs, limited sample size, and potential bias. CONCLUSIONS: Overall, there is cumulative evidence supporting the potential role of BDNF in the interaction between PA and MDD. However, this review highlights the need for further research with more homogeneous and standardised criteria, and pinpoints important confounding factors that must be considered in future studies to provide robust conclusions.

Mutational landscape of risk variants in comorbid depression and obesity: a next-generation sequencing approach.

Major depression (MD) and obesity are complex genetic disorders that are frequently comorbid. However, the study of both diseases concurrently remains poorly addressed and therefore the underlying genetic mechanisms involved in this comorbidity remain largely unknown. Here we examine the contribution of common and rare variants to this comorbidity through a next-generation sequencing (NGS) approach. Specific genomic regions of interest in MD and obesity were sequenced in a group of 654 individuals from the PISMA-ep epidemiological study. We obtained variants across the entire frequency spectrum and assessed their association with comorbid MD and obesity, both at variant and gene levels. We identified 55 independent common variants and a burden of rare variants in 4 genes (PARK2, FGF21, HIST1H3D and RSRC1) associated with the comorbid phenotype. Follow-up analyses revealed significantly enriched gene-sets associated with biological processes and pathways involved in metabolic dysregulation, hormone signaling and cell cycle regulation. Our results suggest that, while risk variants specific to the comorbid phenotype have been identified, the genes functionally impacted by the risk variants share cell biological processes and signaling pathways with MD and obesity phenotypes separately. To the best of our knowledge, this is the first study involving a targeted sequencing approach toward the study of the comorbid MD and obesity. The framework presented here allowed a deep characterization of the genetics of the co-occurring MD and obesity, revealing insights into the mutational and functional profile that underlies this comorbidity and contributing to a better understanding of the relationship between these two disabling disorders.

Interaction Effect between Physical Activity and the BDNF Val66Met Polymorphism on Depression in Women from the PISMA-ep Study.

The relationship between depression and the Val66Met polymorphism at the brain-derived neurotrophic factor gene (BDNF), has been largely studied. It has also been related to physical activity, although the results remain inconclusive. The aim of this study is to investigate the relationship between this polymorphism, depression and physical activity in a thoroughly characterised sample of community-based individuals from the PISMA-ep study. A total of 3123 participants from the PISMA-ep study were genotyped for the BDNF Val66Met polymorphism, of which 209 had depression. Our results are in line with previous studies reporting a protective effect of physical activity on depression, specifically in light intensity. Interestingly, we report a gene-environment interaction effect in which Met allele carriers of the BDNF Val66Met polymorphism who reported more hours of physical activity showed a decreased prevalence of depression. This effect was observed in the total sample (OR = 0.95, 95%CI = 0.90-0.99, p = 0.027) and was strengthened in women (OR = 0.93, 95%CI = 0.87-0.98, p = 0.019). These results highlight the potential role of physical activity as a promising therapeutic strategy for preventing and adjuvant treatment of depression and suggest molecular and genetic particularities of depression between sexes.

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals.

Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals.