Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals
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AuthorAnguita Ruiz, Augusto; Zarza Rebollo, Juan Antonio; Pérez Gutiérrez, Ana María; Molina Rivas, Esther; Gutiérrez Martínez, Blanca; Martín Pérez, Carlos; Torres Martos, Álvaro; López Isac, Elena; Cervilla Ballesteros, Jorge Antonio; Rivera Sánchez, Margarita
Anguita-Ruiz, A... [et al.]. Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals. Transl Psychiatry 12, 30 (2022). [https://doi.org/10.1038/s41398-022-01783-7]
SponsorshipInstituto de Salud Carlos III Spanish Government Institute of Health Carlos III (ISCIII) European Commission PS09/02272 PS09/02147 PS09/01095 PS09/00849 PS09/00461 PI12-02755; Andalusian Council of Health PI-0569-2010; Spanish Network of Primary Care Research, redIAPP RD06/ 0018; Gobierno de Aragon RD06/0018/0020; Bizkaya group RD06/0018/0018; Castilla-Leon group RD06/0018/0027; Mental Health Barcelona Group RD06/0018/0017; Mental Health, Services and Primary Care Malaga group RD06/0018/0039; Instituto de Salud Carlos III PI18/00238 PI18/00467 FI19/00228; European Regional Development Fund/European Social Fund "A way tomake Europe"/"Investing in your future"; Ministry of Economy and Competitiveness; Institute of Health Carlos III fellowship IFI17/00048; Spanish Government BES-2017-082698; Spanish Ministry of Science and Innovation Juan de la Cierva Incorporacion Program IJC2019040080-I; Ministry of Economy and Competitiveness Ramon y Cajal Program RYC-2014-15774; Andalusian Council of Health; Andalusian Health Service (SAS); Primary Care Prevention and Health Promotion Research Network (redIAPP); Biomedical Research Institute of Malaga (IBIMA); Biomedical Research Centre (CIBM) from the University of Granada; European Commission
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 obesityassociated 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.