RT Journal Article
T1 Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals.
A1 Anguita-Ruiz, Augusto
A1 Zarza-Rebollo, Juan Antonio
A1 Perez-Gutierrez, Ana M
A1 Molina, Esther
A1 Gutierrez, Blanca
A1 Bellon, Juan Angel
A1 Moreno-Peral, Patricia
A1 Conejo-Ceron, Sonia
A1 Aiarzagüena, Jose Maria
A1 Ballesta-Rodriguez, M Isabel
A1 Fernandez, Anna
A1 Fernandez-Alonso, Carmen
A1 Martin-Perez, Carlos
A1 Monton-Franco, Carmen
A1 Rodriguez-Bayon, Antonina
A1 Torres-Martos, Alvaro
A1 Lopez-Isac, Elena
A1 Cervilla, Jorge
A1 Rivera, Margarita
K1 Depression
K1 Genomics
AB 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.
PB Nature Publishing Group
YR 2022
FD 2022-01-04
LK http://hdl.handle.net/10668/19512
UL http://hdl.handle.net/10668/19512
LA en
NO Anguita-Ruiz A, Zarza-Rebollo JA, Pérez-Gutiérrez AM, Molina E, Gutiérrez B, Bellón JÁ, 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. 2022 Jan 24;12(1):30.
NO This study was funded by the Spanish Ministry of Health, the Institute of Health Carlos III (ISCIII), and the European Regional Development Fund (grants PS09/02272, PS09/02147, PS09/01095, PS09/00849, PS09/00461, and PI12-02755); the Andalusian Council of Health (grant PI-0569-2010); the Spanish Network of Primary Care Research, redIAPP (grant RD06/0018); the Aragón group (grant RD06/0018/0020); the Bizkaya group (grant RD06/0018/0018); the Castilla-León group (grant RD06/0018/0027); the Mental Health Barcelona Group (grant RD06/0018/0017); the Mental Health, Services and Primary Care Málaga group (grant RD06/0018/0039); and the projects “PI18/00238” and “PI18/00467” funded by the Institute of Health Carlos III (Co-funded by European Regional Development Fund/European Social Fund “A way to make Europe”/“Investing in your future”). This study was performed as part of a PhD thesis conducted within the Official Doctoral Programme in Biomedicine of the University of Granada, Spain. Augusto Anguita-Ruiz was supported by a Ministry of Economy and Competitiveness and Institute of Health Carlos III fellowship (IFI17/00048). Juan Antonio Zarza-Rebollo received financial support from the Spanish Ministry of Economy and Competitiveness (BES-2017-082698). Ana M. Pérez-Gutiérrez was supported by a grant from the Ministry of Economy and Competitiveness and Institute of Health Carlos III (FI19/00228). Elena López-Isac received financial support from the Spanish Ministry of Science and Innovation Juan de la Cierva Incorporación Program (IJC2019-040080-I), and Margarita Rivera was supported by the Ministry of Economy and Competitiveness Ramón y Cajal Program (RYC-2014-15774). The authors thank the Institute of Health Carlos III (ISCIII), the European Regional Development Fund (FEDER), the Andalusian Council of Health and Andalusian Health Service (SAS), the Primary Care Prevention and Health Promotion Research Network (redIAPP), the Biomedical Research Institute of Málaga (IBIMA), and the Biomedical Research Centre (CIBM) from the University of Granada for their economic and logistic support. The authors thank all the patients and General Practitioners who participated in the trial.
DS RISalud
RD Mar 17, 2025