Abstract:
Gene-environment interactions (G × E) have attracted considerable research interest in the past owing to their scientific and public health implications, but powerful statistical methods are required to successfully track down G × E, particularly at a genome-wide level. Previously, a case-only (CO) design has been proposed as a means to identify G × E with greater efficiency than traditional case-control or cohort studies. However, as with genotype-phenotype association studies themselves, hidden population stratification (PS) can impact the validity of G × E studies using a CO design. Since this problem has been subject to little research to date, we used comprehensive simulation to systematically assess the type I error rate, power and effect size bias of CO studies of G × E in the presence of PS. Three types of PS were considered, namely genetic-only (PSG), environment-only (PSE), and joint genetic and environmental stratification (PSGE). Our results reveal that the type I error rate of an unadjusted Wald test, appropriate for the CO design, would be close to its nominal level (0.05 in our study) as long as PS involves only one interaction partner (i.e., either PSG or PSE). In contrast, if the study population is stratified with respect to both G and E (i.e., if there is PSGE), then the type I error rate is seriously inflated and estimates of the underlying G × E interaction are biased. Comparison of CO to a family-based case-parents design confirmed that the latter is more robust against PSGE, as expected. However, case-parent trios may be particularly unsuitable for G × E studies in view of the fact that they require genotype data from parents and that many diseases with an environmental component are likely to be of late onset. An alternative approach to adjusting for PS is principal component analysis (PCA), which has been widely used for this very purpose in past genome-wide association studies (GWAS). However, resolving genetic PS properly by PCA requires genetic data at the population level, the availability of which would conflict with the basic idea of the CO design. Therefore, we explored three modified Wald test statistics, inspired by the genomic control (GC) approach to GWAS, as an alternative means to allow for PSGE. The modified statistics were benchmarked against a stratified Wald test assuming known population affiliation, which should provide maximum power under PS. Our results confirm that GC is capable of successfully and efficiently correcting the PS-induced inflation of the type I error rate in CO studies of G × E.

Abstract:
Elevated GDF15 levels were superior to NT-proBNP for TAVI risk stratification and provided additional prognostic information. Thus, patient selection for TAVI may benefit from measurement of GDF15.

Abstract:
Motivation to change is an important predictor of short-term outcome with respect to weight gain trajectory during treatment of adolescent AN. As patients with a higher BMI at admission and those with more severe eating disorder-specific symptoms seem to be less motivated to change, the crucial issue of motivation to change should be addressed with these patients during the therapeutic process.

Abstract:
This study reports the outcomes of childhood and adolescent eating-disordered behaviour on the development of body mass index (BMI) and psychological functioning in young adulthood in a population-based sample in Germany (the BELLA study). Information at baseline and follow-up was obtained through a telephone interview and mailed self-report questionnaires. At both measurement points, BMI, eating disorder symptoms (SCOFF questionnaire), and symptoms of depression and anxiety were assessed in the same cohort of 771 participants (n = 420 females, n = 351 males). The age range at baseline was 11-17 years, and the age range at follow-up was 17-23 years. High scores for eating-disordered behaviour in childhood or adolescence significantly predicted eating-disordered behaviour in young adulthood (multiplicative effect estimate: 1.31; 95 % CI: 1.2-1.42, p < 0.0001), although there was a decline in prevalence (from 19.3 to 13.8 %, p = 0.002) and severity (mean decrease in SCOFF 0.07, 95 % CI: -0.01-0.14, p = 0.06). After accounting for potentially confounding variables at baseline (SES, probands' BMI, parental BMI, depressive symptoms), participants with more eating disorder symptoms at baseline had a higher risk of developing overweight (odds ratio (OR): 1.58; 95 % CI: 1.19-2.09, p = 0.001), obesity (OR = 1.67; 95 % CI: 1.03-2.66, p = 0.03), and depressive symptoms at follow-up (additive effect estimate: 0.45; 95 %CI: 0.19-0.7, p = 0.0006). Early symptoms of depression showed a significant relationship with extreme underweight in young adulthood (OR = 1.13; 95 %CI: 1.01-1.25, p = 0.02). The high stability of eating disorder symptoms and the significant association with overweight and worse mental health in adulthood underscore the need for early detection and intervention during childhood and adolescence. Youth with depression should be monitored for the development of restrictive eating disorders.

Abstract:
Body mass index (BMI) is one of the most important outcome predictors in patients with anorexia nervosa (AN). A low premorbid BMI percentile calculated by the patients recalled premorbid weight and the height at first admission has been found to predict the BMI at first inpatient admission. In this study, we sought to confirm this relationship. We additionally analyze the relationship between premorbid BMI percentile and BMI percentile at discharge from the first inpatient treatment and at 1-year follow-up or alternatively if applicable upon readmission within this time period. We included 161 female patients aged 11-18 years of the multisite ANDI-trial with a DSM-IV diagnosis of AN. We used a multivariate statistical model including the independent variables age, duration of illness, duration of treatment, BMI at admission and BMI percentile at discharge. The relationship between premorbid BMI percentile and BMI at admission was solidly confirmed. In addition to premorbid BMI percentile, BMI at admission and age were significant predictors of BMI percentile at discharge. BMI percentile at discharge significantly predicted BMI percentile at 1-year follow-up. An additional analysis that merely included variables available upon referral revealed that premorbid BMI percentile predicts the 1-year follow-up BMI percentile. Further studies are required to identify the underlying biological mechanisms and to address the respective treatment strategies for AN patients with a low or high premorbid BMI percentile.