[The adaptation and validation to Spanish of the questionnaire Aid to Capacity Evaluation (ACE), for the assessment of the ability of patients in medical decision-making]. / Adaptación y validación al español del cuestionario Aid to Capacity Evaluation (ACE) , para la valoración de la capacidad del paciente en la toma de decisiones médicas.

OBJECTIVE: To adapt and validate the Spanish version of the Aid to Capacity Evaluation scale, designed to assess the capacity of the adult in medical decision-making, both in diagnosis and treatment processes. DESIGN: Observational study of prospective validation. SETTING: Primary and hospital care of the basic health area of Jaen. PARTICIPANTS: One hundred twenty-nine patients. MAIN MEASUREMENTS: Questionnaire which included sociodemographic variables, concerning the decision (scope, type of decision, the need for written informed consent), assessment of the capacity to the Aid to Capacity Evaluation scale and other related comorbidity (hearing loss, alcoholism, cognitive level variables with the Mini-Mental State Examination and depression by Goldberg or Yesavage test). RESULTS: The tool is considered viable. The conclusions of the expert panel were favorable. The result of the criteria' validity, comparing the results with the assessment of the experts (forensic and psychiatrist) was very satisfying (P<.001). The intra-observer reliability was low (kappa=0,135). Interobserver reliability remained high (kappa=0.74). The internal consistency was awarded an alpha of Cronbach's 0,645 for the reduced model of 6 items. CONCLUSIONS: The Aid to Capacity Evaluation scale was adapted to Spanish, demonstrating adequate internal consistency and construct validity. Its use in clinical practice could contribute to the identification of patients unable to make a particular medical decision and/or to give an informed consent.

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.