Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors.

Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used.

All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN).

Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

To assess the differences in comorbid lifetime substance use (tobacco, alcohol and drug use) between eating disorder (ED) patients and healthy controls.

Participants were a consecutive series of 779 ED cases, who had been referred to specialised ED units in five European countries. The ED cases were compared to a balanced control group of 785 healthy individuals. Assessment: Participants completed the Substance Use Subscale of the Cross Cultural Questionnaire (CCQ), a measure of lifetime tobacco, alcohol and drug use. In the control group, also the GHQ-28, the SCID-I interview and the EAT-26 were used.

ED patients had higher lifetime consumption of tobacco and drugs (p <0.01). The only insignificant result was obtained for alcohol (OR= 1.29; δ =0.157; N.S.) and cannabis use (OR= 1.21; δ = 0.037, N.S.). Significant differences across ED sub diagnoses also emerged for all of the assessed variables (p<0.01), with the BN and AN-BP patients generally presenting the highest prevalence rates. The only exception was detected for alcohol consumption where EDNOS patients demonstrated the highest values (p=0.008). Only a few cultural differences between countries emerged (p<0.05).

Lifetime tobacco and drug use but not alcohol consumption are more prevalent in ED patients than healthy controls. While alcohol appears to be more common in EDNOS, smoking and drug use are more frequent in patients with bulimic symptomatology. The differential risk observed in patients with bulimic features might be related to differences in temperament or might be the result of increased sensitivity to reward.

To analyse GxE interactions assess non-shared environmental (E) risk factors for the development of AN specific for sisters discordant for an ED, polymorphisms in the serotonin transporter (G),.

We interviewed 128 sister pairs discordant for an eating disorder using the Oxford-RFI as part of the European "Healthy Eating" multicenter study at 3 university centres (Vienna, London, Barcelona) (AN-R: 58; AN-BP: 70; 128 sisters without ED). To examine association between AN, G and E, and G x E-interaction, conditional logistic regression was used with a Cox proportional hazards regression model using the exact method.

Genotype (GT) distributions did not differ between the sister groups. Significant main effects were found for disruptive events, interpersonal problems and family dieting behaviour. The risk for AN increased with higher levels in these variables independently of the genotype. Significant interactions were found for G x parental problems and G x burden by parental psychiatric disorder. The increase of risk for AN with increasing number of problems with parents is larger for the S/S genotype than for L/L. However, a higher burden by parental psychiatric illness (subjective E according to Turkheimer 2000) increased the risk for AN-this was larger for the L/L than for the S/S GT.

This study suggests that there is an interaction between stress (problems with parents) and the ss GT which increases the risk of developing AN.

To examine whether there is an association between individual and family eating patterns during childhood and early adolescence and the likelihood of developing an eating disorder (ED) later in life.

Participants were a consecutive series of 879 ED cases from five different European countries. The ED cases were compared to a control group of 785 healthy individuals. Assessment: Participants completed the Early Eating Environmental Subscale of the Cross-Cultural (Environmental) Questionnaire (CCQ), a retrospective measure, which has been developed to detect dimensions associated with EDs in different countries. In the control group, also the GHQ-28, the SCID-I interview and the EAT-26 were used.

Five individual CatPCA procedures revealed five predetermined dimensions which were labeled: 1.) food as individualization; 2.) control and rules about food; 3.) food as social glue; 4.) healthy eating and 5.) food neglect. Logistic regression analyses indicated that the domains with the strongest effects were: food used as individualization (p=0.001; OR=1.76) and control and rules about food (p=0.001; OR=1.76). Conversely, healthy eating was negatively related to a later ED (p=0.001; OR=0.629). The pattern of associated ED factors was found to very between countries. There was very little difference in early eating behavior on the subtypes of the ED.

The fragmentation of meals within the family and control and rules about food appears to be linked to the development of a subsequent ED. On the other hand mantaining a structured and balanced diet during infancy seems to protect from a later ED.

Gene × environment (G × E) interactions in eating pathology have been increasingly investigated, however studies have been limited by sample size due to the difficulty of obtaining genetic data.

To synthesize existing G × E research in the eating disorders (ED) field and provide a clear picture of the current state of knowledge with analyses of larger samples.

Complete data from seven studies investigating community (n = 1750, 64.5% female) and clinical (n = 426, 100% female) populations, identified via systematic review, were included. Data were combined to perform five analyses: 5-HTTLPR × Traumatic Life Events (0–17 events) to predict ED status (n = 909), 5-HTTLPR × Sexual and Physical Abuse (n = 1097) to predict bulimic symptoms, 5-HTLPR × Depression to predict bulimic symptoms (n = 1256), and 5-HTTLPR × Impulsivity to predict disordered eating (n = 1149).

The low function (s) allele of 5-HTTLPR interacted with number of traumatic life events (P < .01) and sexual and physical abuse (P < .05) to predict increased likelihood of an ED in females but not males (Fig. 1). No other G × E interactions were significant, possibly due to the medium to low compatibility between datasets (Fig. 1).

Early promising results suggest that increased knowledge of G × E interactions could be achieved if studies increased uniformity of measuring ED and environmental variables, allowing for continued collaboration to overcome the restrictions of obtaining genetic samples.

The authors have not supplied their declaration of competing interest.

Cognitive deficits in schizophrenia have major functional impacts. Modafinil is a cognitive enhancer whose effect in healthy volunteers is well-described, but whose effects on the cognitive deficits of schizophrenia appear to be inconsistent. Two possible reasons for this are that cognitive test batteries vary in their sensitivity, or that the phase of illness may be important, with patients early in their illness responding better.

A double-blind, randomised, placebo-controlled single-dose crossover study of modafinil 200 mg examined this with two cognitive batteries [MATRICS Consensus Cognitive Battery (MCCB) and Cambridge Neuropsychological Test Automated Battery (CANTAB)] in 46 participants with under 3 years’ duration of DSM-IV schizophrenia, on stable antipsychotic medication. In parallel, the same design was used in 28 age-, sex-, and education-matched healthy volunteers. Uncorrected p values were calculated using mixed effects models.

In patients, modafinil significantly improved CANTAB Paired Associate Learning, non-significantly improved efficiency and significantly slowed performance of the CANTAB Stockings of Cambridge spatial planning task. There was no significant effect on any MCCB domain. In healthy volunteers, modafinil significantly increased CANTAB Rapid Visual Processing, Intra-Extra Dimensional Set Shifting and verbal recall accuracy, and MCCB social cognition performance. The only significant differences between groups were in MCCB visual learning.

As in earlier chronic schizophrenia studies, modafinil failed to produce changes in cognition in early psychosis as measured by MCCB. CANTAB proved more sensitive to the effects of modafinil in participants with early schizophrenia and in healthy volunteers. This confirms the importance of selecting the appropriate test battery in treatment studies of cognition in schizophrenia.

The Met allele of the catechol-O-methyltransferase (COMT) valine-to-methionine (Val158Met) polymorphism is known to affect dopamine-dependent affective regulation within amygdala–prefrontal cortical (PFC) networks. It is also thought to increase the risk of a number of disorders characterized by affective morbidity including bipolar disorder (BD), major depressive disorder (MDD) and anxiety disorders. The disease risk conferred is small, suggesting that this polymorphism represents a modifier locus. Therefore our aim was to investigate how the COMT Val158Met may contribute to phenotypic variation in clinical diagnosis using sad facial affect processing as a probe for its neural action.

We employed functional magnetic resonance imaging to measure activation in the amygdala, ventromedial PFC (vmPFC) and ventrolateral PFC (vlPFC) during sad facial affect processing in family members with BD (n=40), MDD and anxiety disorders (n=22) or no psychiatric diagnosis (n=25) and 50 healthy controls.

Irrespective of clinical phenotype, the Val158 allele was associated with greater amygdala activation and the Met158 allele with greater signal change in the vmPFC and vlPFC. Signal changes in the amygdala and vmPFC were not associated with disease expression. However, in the right vlPFC the Met158 allele was associated with greater activation in all family members with affective morbidity compared with relatives without a psychiatric diagnosis and healthy controls.

Our results suggest that the COMT Val158Met polymorphism has a pleiotropic effect within the neural networks subserving emotional processing. Furthermore the Met158 allele further reduces cortical efficiency in the vlPFC in individuals with affective morbidity.

Psychiatric phenotypes are currently defined according to sets of descriptive criteria. Although many of these phenotypes are heritable, it would be useful to know whether any of the various diagnostic categories in current use identify cases that are particularly helpful for biological–genetic research.

To use genome-wide genetic association data to explore the relative genetic utility of seven different descriptive operational diagnostic categories relevant to bipolar illness within a large UK case–control bipolar disorder sample.

We analysed our previously published Wellcome Trust Case Control Consortium (WTCCC) bipolar disorder genome-wide association data-set, comprising 1868 individuals with bipolar disorder and 2938 controls genotyped for 276 122 single nucleotide polymorphisms (SNPs) that met stringent criteria for genotype quality. For each SNP we performed a test of association (bipolar disorder group v. control group) and used the number of associated independent SNPs statistically significant atP<0.00001 as a metric for the overall genetic signal in the sample. We next compared this metric with that obtained using each of seven diagnostic subsets of the group with bipolar disorder: Research Diagnostic Criteria (RDC): bipolar I disorder; manic disorder; bipolar II disorder; schizoaffective disorder, bipolar type; DSM–IV: bipolar I disorder; bipolar II disorder; schizoaffective disorder, bipolar type.

The RDC schizoaffective disorder, bipolar type (v. controls) stood out from the other diagnostic subsets as having a significant excess of independent association signals (P<0.003) compared with that expected in samples of the same size selected randomly from the total bipolar disorder group data-set. The strongest association in this subset of participants with bipolar disorder was at rs4818065 (P = 2.42 ×10–7). Biological systems implicated included gamma amniobutyric acid (GABA)A receptors. Genes having at least one associated polymorphism at P<10–4 includedB3GALTS, A2BP1, GABRB1, AUTS2, BSN, PTPRG, GIRK2 andCDH12.

Our findings show that individuals with broadly defined bipolar schizoaffective features have either a particularly strong genetic contribution or that, as a group, are genetically more homogeneous than the other phenotypes tested. The results point to the importance of using diagnostic approaches that recognise this group of individuals. Our approach can be applied to similar data-sets for other psychiatric and non-psychiatric phenotypes.