Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Surgical site infections (SSIs) are common surgical complications that lead to increased costs. Depending on payer type, however, they do not necessarily translate into deficits for every hospital.

We investigated how surgical site infections (SSIs) influence the contribution margin in 2 reimbursement systems based on diagnosis-related groups (DRGs).

This preplanned observational health cost analysis was nested within a Swiss multicenter randomized controlled trial on the timing of preoperative antibiotic prophylaxis in general surgery between February 2013 and August 2015. A simulation of cost and income in the National Health Service (NHS) England reimbursement system was conducted.

Of 5,175 patients initially enrolled, 4,556 had complete cost and income data as well as SSI status available for analysis. SSI occurred in 228 of 4,556 of patients (5%). Patients with SSIs were older, more often male, had higher BMIs, compulsory insurance, longer operations, and more frequent ICU admissions. SSIs led to higher hospital cost and income. The median contribution margin was negative in cases of SSI. In SSI cases, median contribution margin was Swiss francs (CHF) −2045 (IQR, −12,800 to 4,848) versus CHF 895 (IQR, −2,190 to 4,158) in non-SSI cases. Higher ASA class and private insurance were associated with higher contribution margins in SSI cases, and ICU admission led to greater deficits. Private insurance had a strong increasing effect on contribution margin at the 10th, 50th (median), and 90th percentiles of its distribution, leading to overall positive contribution margins for SSIs in Switzerland. The NHS England simulation with 3,893 patients revealed similar but less pronounced effects of SSI on contribution margin.

Depending on payer type, reimbursement systems with DRGs offer only minor financial incentives to the prevention of SSI.

To describe the average consumption of carbohydrate-providing food groups among study centres of the European Prospective Investigation into Cancer and Nutrition (EPIC).

Of the 27 redefined EPIC study centres, 19 contributed subjects of both genders and eight centres female participants only (men, n=13 031; women, n=22924, after exclusion of subjects under 35 and over 74 years of age from the original 36 900 total). Dietary data were obtained using the 24-hour recall methodology using the EPIC-SOFT software. The major sources of dietary carbohydrate were identified, and 16 food groups were examined.

The 10 food groups contributing most carbohydrate were bread; fruit; milk and milk products; sweet buns, cakes and pies; potato; sugar and jam; pasta and rice; vegetables and legumes; crispbread; and fruit and vegetable juices. Consumption of fruits as well as vegetables and legumes was higher in southern compared with northern centres, while soft drinks consumption was higher in the north. Italian centres had high pasta and rice consumption, but breakfast cereal, potato, and sweet buns, cakes and pies were higher in northern centres. In Sweden, lower bread consumption was balanced with a higher consumption of crispbread, and with sweet buns, cakes and pies. Overall, men consumed higher amounts of vegetables and legumes, bread, soft drinks, potatoes, pasta and rice, breakfast cereal and sugar and jam than women, but fruit consumption appeared more frequent in women.

The study supports the established idea that carbohydrate-rich foods chosen in northern Europe are different from those in the Mediterranean region. When comparing and interpreting diet–disease relationships across populations, researchers need to consider all types of foods.

The aim of this study was to describe the variation of soy product intake in 10 European countries by using a standardised reference dietary method. A subsidiary aim was to characterise the pattern of soy consumption among a sub-group of participants with a habitual health-conscious lifestyle (HHL), i.e. non-meat eaters who are fish eaters, vegetarians and vegans.

A 24-hour dietary recall interview (24-HDR) was conducted among a sample (5–12%) of all cohorts (n = 36 900) in the European Prospective Investigation into Cancer and Nutrition (EPIC). Study participants totalled 35 955 after exclusion of subjects younger than 35 or older than 74 years of age. Soy products were subdivided into seven sub-groups by similarity. Distribution of consumption and crude and adjusted means of intake were computed per soy product group across countries. Intake of soy products was also investigated among participants with an HHL.

In total, 195 men and 486 women reported consuming soy products in the 24-HDR interview. Although soy product intake was generally low across all countries, the highest intake level was observed in the UK, due to over-sampling of a large number of participants with an HHL. The most frequently consumed soy foods were dairy substitutes in the UK and France and beans and sprouts among mid-European countries. For both genders, the sub-group of soy dairy substitutes was consumed in the highest quantities (1.2 g day−1 for men; 1.9 g day−1 for women). Participants with an HHL differed substantially from others with regard to demographic, anthropometric and nutritional factors. They consumed higher quantities of almost all soy product groups.

Consumption of soy products is low in centres in Western Europe. Soy dairy substitutes are most frequently consumed. Participants with an HHL form a distinct sub-group with higher consumptions of fruit, vegetables, legumes, cereals and soy products compared with the other participants.

To evaluate the consumption of added fats and oils across the European centres and countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC).

24-Hour dietary recalls were collected by means of standardised computer-guided interviews in 27 redefined EPIC centres across 10 European countries.

From an initial number of 36 900 subjects, single dietary recalls from 22 924 women and 13 031 men in the age range of 35–74 years were included.

Mean daily intake of added fats and oils varied between 16.2 g (Varese, Italy) and 41.1 g (Malmö, Sweden) in women and between 24.7 g (Ragusa, Italy) and 66.0 g (Potsdam, Germany) in men. Total mean lipid intake by consumption of added fats and oils, including those used for sauce preparation, ranged between 18.3 (Norway) and 37.2 g day−1 (Greece) in women and 28.4 (Heidelberg, Germany) and 51.2 g day−1 (Greece) in men. The Mediterranean EPIC centres with high olive oil consumption combined with low animal fat intake contrasted with the central and northern European centres where fewer vegetable oils, more animal fats and a high proportion of margarine were consumed. The consumption of added fats and oils of animal origin was highest in the German EPIC centres, followed by the French. The contribution of added fats and oils to total energy intake ranged from 8% in Norway to 22% in Greece.

The results demonstrate a high variation in dietary intake of added fats and oils in EPIC, providing a good opportunity to elucidate the role of dietary fats in cancer aetiology.