Individuals at risk for bipolar disorder (BD) have a wide range of genetic and non-genetic risk factors, like a positive family history of BD or (sub)threshold affective symptoms. Yet, it is unclear whether these individuals at risk and those diagnosed with BD share similar gray matter brain alterations.

In 410 male and female participants aged 17–35 years, we compared gray matter volume (3T MRI) between individuals at risk for BD (as assessed using the EPIbipolar scale; n = 208), patients with a DSM-IV-TR diagnosis of BD (n = 87), and healthy controls (n = 115) using voxel-based morphometry in SPM12/CAT12. We applied conjunction analyses to identify similarities in gray matter volume alterations in individuals at risk and BD patients, relative to healthy controls. We also performed exploratory whole-brain analyses to identify differences in gray matter volume among groups. ComBat was used to harmonize imaging data from seven sites.

Both individuals at risk and BD patients showed larger volumes in the right putamen than healthy controls. Furthermore, individuals at risk had smaller volumes in the right inferior occipital gyrus, and BD patients had larger volumes in the left precuneus, compared to healthy controls. These findings were independent of course of illness (number of lifetime manic and depressive episodes, number of hospitalizations), comorbid diagnoses (major depressive disorder, attention-deficit hyperactivity disorder, anxiety disorder, eating disorder), familial risk, current disease severity (global functioning, remission status), and current medication intake.

Our findings indicate that alterations in the right putamen might constitute a vulnerability marker for BD.

Individuals with bipolar disorder are commonly correctly diagnosed a decade after symptom onset. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging may provide relevant classification features.

Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness of help-seeking individuals from seven study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS, EPIbipolar).

For BPSS-P, SVM achieved a fair performance of Cohen's κ of 0.235 (95% CI 0.11–0.361) and a balanced accuracy of 63.1% (95% CI 55.9–70.3) in the 10-fold cross-validation. In the leave-one-site-out cross-validation, the model performed with a Cohen's κ of 0.128 (95% CI −0.069 to 0.325) and a balanced accuracy of 56.2% (95% CI 44.6–67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance.

Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out cross-validation. Whole-brain cortical thickness seems to be superior to other structural brain features.

Patients with severe psychotic disorders exhibit a severely reduced quality of life (QoL) at all stages of the disease. Integrated care often led to an improvement in QoL. However, the specific mediators of QoL change are not yet well understood.

The ACCESS II study is a prospective, long-term study investigating the effectiveness of an integrated care program for people with severe psychotic disorders (IC-TACT) that includes Therapeutic Assertive Community Treatment within a care network of in- and outpatient services at the University Medical Center Hamburg-Eppendorf, Germany. We examined longitudinal associations between QoL and the hypothesized mediators of change (i.e., negative symptoms, depression, and anxiety), using cross-lagged panel models.

The sample includes 418 severely ill patients treated in IC-TACT for at least 1 year. QoL increased, whereas symptom severity decreased significantly from baseline to 6-month follow-up (p-values ≤ 0.001), and remained stable until 12-month follow-up. QoL and symptom severity demonstrated significant auto-correlated effects and significant cross-lagged effects from QoL at baseline to negative symptoms (6 months, β = −0.20, p < 0.001) to QoL (12 months, β = −0.19, p < 0.01) resulting in a significant indirect, mediated effect. Additionally, negative symptoms after 6 months had a significant effect on the severity of depression after 12 months (β = 0.13, p < 0.05).

Negative symptoms appear to represent an important mechanism of change in IC-TACT indicating that improvement of QoL could potentially be achieved through optimized intervention on negative symptoms. Moreover, this may lead to a reduction in the severity of depression after 12 months.

Considering the dramatically increasing incidence of type 2 diabetes (T2D), decreasing glycemic variability in T2D patients is a key challenge to limit the occurrence of diabetic complications. Diet appears as one potential lever that can be set up above medications. Particularly, the ingestion of foods with a high content in slowly digestible starch (SDS) demonstrated both lower postprandial glycemic and insulin responses in healthy and insulin resistant subjects. This study aimed at designing a full high-SDS diet by selecting high-SDS starchy food products and at studying its impact on glycemic response and variability in T2D.

This pilot randomized controlled cross-over study included eight T2D patients (HbA1c = 7.0 ± 0.2%, BMI = 31.7 ± 2.1 kg/m2, treated by Metformin & Sitagliptin) who consumed twice, for one week a controlled diet containing starchy food products screened and selected to be either High (High-SDS) or Low (Low-SDS) in SDS, as determined by the SDS in-vitro method developed by Englyst et al. During each diet period, the glycemic profile was monitored for 6 days using a Continuous Glucose Monitoring System (CGMS). Multiple metrics related to variability and glycemic responses were calculated.

222 SDS analyses were realized on commercial food products as consumed. 23 High-SDS and 20 Low-SDS food items with associated specific cooking instructions were selected to design two diets consistent with local T2D recommendations. The High-SDS diet demonstrated a significantly higher SDS content compared to the Low-SDS diet (61.6 vs 11.6 g/day; p < 0.0001), mainly driven by selected pasta, rice and high-SDS biscuits (75.6% of the consumed SDS content). The % of total daily energy intake (TDEI) for all macronutrients remained similar between diets (p > 0.05) and the carbohydrate content specifically represented 49 ± 1 % and 47 ± 2 % of the TDEI for High-SDS and Low-SDS diets, respectively. With the high-SDS diet, the Mean Amplitude of Glycemic Excursion, a key parameter of glycemic variability, was significantly decreased (79.6 for Low-SDS vs 61.6 mg/dL for High-SDS; p = 0.0067). The significant correlation between the meals SDS contents and various glycemic parameters such as postprandial iAUC, tAUC (up to 180 min) or peak value strengthen this finding (p < 0.05 for all).

It was the first demonstration that a diet including selected starchy food items and cooking recommendations designed to favor products’ high SDS content beneficially impacts glycemic profile in T2D subjects. Carefully selecting starchy food may be a simple and valuable tool to improve glycemic control in T2D.

Increased adiposity, dyslipidemia and insulin resistance are associated with increased risk of developing cardiometabolic diseases (CM). Such deleterious phenotypes have been shown to be associated with a low gene-richness microbiota that can partly be restored by a short-term dietary intervention (energy-restricted high-protein diet, low glycemic index, enrichment with fibers) in parallel to an improvement of CM profile. In this study, we aimed at increasing fiber intake in quantity and diversity through a two-month consumption of bread enriched with a mix of selected fibers and evaluated the impact of this dietary intervention on gut microbiota gene richness and CM risk profile in subjects at risk of developing CM.

In a randomized double blind cross-over design, thirty-nine subjects with CM risk profile (18–70 years old, BMI: 25–35 kg/m2, waist circumference > 80 cm for women and > 96 cm for men, fiber intake < 20g/day, low fiber diversity) consumed daily for 8 weeks 150 g of standard bread vs. 150 g of bread enriched with a 7-selected fibers mix (5.55 g vs. 16.35 g of fiber respectively; 4-week washout). Gut microbiota and CM risk factors’ analyzes were conducted before and after intervention. Stool samples were analyzed by shotgun metagenomics; microbial genes and metagenomics species (MSP) profiles were generated by mapping reads on a reference genes catalog (1529 MSP).

The included dyslipidemic subjects with CM risk profile presented a lower microbiota gene richness compared to reference healthy cohorts. The two-month consumption of fiber-rich bread did not alter microbiota gene richness but modified microbiota composition with a significant decrease of Bacteroides vulgatus (q = 1.7e-4) and a significant increase of Parabacteroides distasonis (q = 2.8e-6), Fusicatenibacter saccharivorans (q = 5e-5) and Clostridiales (q = 3.8e-2). We observed in parallel a significant decrease in total cholesterol (- 0.26 mmol/L; - 5%; p = 0.021), LDL-cholesterol (- 0.2 mmol/L; - 6%, p = 0.0061) and an improvement of insulin sensibility estimated by HOMA index (3.23–2.54 mUI/L; - 21%; p = 0.0079).These effects were even significantly more pronounced for subjects presenting the higher waist circumference. Anthropometric parameters were not altered.

The enrichment of the diet with a mix of selected fibers for 2 months altered microbiota composition by modifying the relative abundance of specific gut bacterial species, in parallel to a significant improvement of cholesterol and insulin sensitivity parameters. Increasing the quantity and diversity of dietary fiber intake could be used as an efficient tool to favorably impact CM profile.

Six healthy women (29.7 ± 1.7 years old, BMI: 23.2 ± 0.9kg/m2, fiber intake: 23 ± 1g/d) consumed in research settings a controlled breakfast containing eight 13C-labelled wheat bran biscuits (50 g of labelled wheat bran, 3.0At%13C). 13C-labelled wheat bran was obtained from wheat cultivated under 13CO2 enriched atmosphere. Samples of expired gases were collected during 24 h after ingestion in order to measure H2 and CH4 by gas chromatography (GC) with piezoelectric detection and 13CO2 and 13CH4 by gas chromatography coupled with an isotope ratio mass spectrometer (GC-IRMS). Apart test breakfast, subjects only consumed standardized meals without fibers.

The analysis of H2 and CH4 24h-kinetic measurements distinguished 2 groups in terms of fermentation related gas excretion: the high-CH4 producers with high baseline CH4 concentrations (42.1 ± 13.7ppm) and low baseline H2 concentrations (7.3 ± 5.8ppm) and the low-CH4 producers with low baseline CH4 concentrations (6.5 ± 3.6ppm) and high baseline H2 concentrations (20.8 ± 16.0ppm). Following the 13C-wheat bran biscuits’ ingestion, postprandial H2 and CH4 concentrations increased more significantly in the high-CH4 producer subjects. 13C enrichment was detectable in expired gases in all subjects. 13CO2 kinetics were similar for all subjects and correspond to the oxidation of the digestible part of the bran. The appearance of 13CH4 was significantly enhanced and prolonged after 180 min in high-CH4 producers compared to low-CH4 producers, suggesting distinct fiber fermentation profile.

This pilot study allowed to consider novel procedures for development of non-invasive breath biomarkers of fiber-gut microbiota interactions. Assessment of expired gas excretion following 13C-labelled fiber ingestion allowed deciphering distinct fermentation profiles: high-CH4 producers vs low-CH4 producers and accordingly provide a related non-invasive breath metabolic signature of the fiber fermentation for each profile. Further gut microbiota and 13C-metabolites analysis will permit to relate the gut bacteria composition with breath gas excretion kinetics according to fiber fermentation profile.

The ZEISIG study (Ziprasidone Experience in Schizophrenia in Germany/Austria) investigated the effectiveness of ziprasidone as measured by discontinuation rates and mean changes of the BPRS total. Secondary objectives included quality of life, subjective well-being, tolerability, and safety.

Two hundred and seventy-six subjects with schizophrenia and schizoaffective disorder were treated within an open-label, 12-week, prospective, flexible-dose observational trial of ziprasidone (40–160 mg/day). Baseline and outcome assessments mainly included Brief Psychiatric Rating Scale (BPRS), Clinical Global Impressions Scale (CGI), Short-Form 12 (SF-12), and Subjective Well-being under Neuroleptic treatment (SWN-K).

Study discontinuation due to any cause was evident in 58% of subjects, most of them within the first 4 weeks after study initiation. In study completers, ziprasidone was associated with improvements in BPRS total (44.8 to 33.6; p < 0.001), CGI, SF-12, and SWN-K total scores (80.5 to 89.5). Ziprasidone was related to reduction of weight, fasting glucose, and serum lipids. No cardiovascular adverse event or significant increase of the QTc interval was observed.

Approximately 60% of subjects discontinued ziprasidone prematurely, probably related to an initial and overall underdose. The present study confirmed previous tolerability and safety data of ziprasidone as well as results of its effectiveness. Independent from reason to switch, previous antipsychotic class, and severity of illness at baseline, the recommended starting dose of 80 mg/day as well as the maximum treatment dose of 160 mg/day may not be sufficient for a selected subgroup of patients.

Against medical advice, head and neck cancer (HNC) patients have been shown to continue to smoke and misuse alcohol post-diagnosis and treatment. This study aimed to better understand the barriers to and facilitators of health behavior change (HBC) in HNC patients.

We conducted nine focus groups following a standard protocol. Eligible patients were diagnosed less than three years previously with a primary HNC and selected using maximum variability sampling (gender, age, cancer stage, smoking, and alcohol misuse). Thematic analysis was conducted using NVivo 10 software.

Participants were mostly men (79%), 65 years of age (SD = 10.1), and married/common-law (52%, n = 15). Mean time from diagnosis was 19 months (SD = 12.3, range = 5.0–44.5), and most had advanced cancer (65.5%, n = 19). Participants provided a larger than anticipated definition of health behaviors, encompassing both traditional (smoking, drinking, diet, exercise, UV protection) and HNC-related (e.g., dental hygiene, skin care, speech exercises, using a PEG, gaining weight). The main emerging theme was patient engagement, that is, being proactive in rehabilitation, informed by the medical team, optimistic, flexible, and seeking support when needed. Patients were primarily motivated to stay proactive and engage in positive health behaviors in order to return to normal life and reclaim function, rather than to prevent a cancer recurrence. Barriers to patient engagement included emotional aspects (e.g., anxiety, depression, trauma, demoralization), symptoms (e.g., fatigue, pain), lack of information about HBC, and healthcare providers' authoritarian approach in counseling on HBC. We found some commonalities in barriers and facilitators according to behavior type (i.e., smoking/drinking/UV protection vs. diet/exercise).

This study underlines the key challenges in addressing health behaviors in head and neck oncology, including treatment-related functional impairments, symptom burden, and the disease's emotional toll. This delicate context requires health promotion strategies involving close rehabilitative support from a multidisciplinary team attentive to the many struggles of patients both during treatments and in the longer-term recovery period. Health promotion in HNC should be integrated into routine clinical care and target both traditional and HNC-related behaviors, emphasizing emotional and functional rehabilitation as key components.