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Pressures and responsibilities of medical school put a strain on medical student's personal wellbeing, leading among all to high rates of anxiety, emotional discomfort and stress. In this work we evaluated the effectiveness of a comprehensive Mindfulness-Based Intervention (MBI) in reducing this load. The intervention comprised 10 twice-a-week Integral Meditation classes, dietary advice, and brief yoga sessions. We performed a randomized trial on two cohort of medical students from Italian universities: 239 in cohort 1 (106 treated and 133 controls), and 123 in cohort 2 (68 treated and 55 control) for a total sample of 362 students. Nine questionnaires for evaluating the effectiveness of our intervention on stress (PSS), state anxiety (STAIX-1), well-being (WEMWBS), mind-wandering (MW-S), overall distress (PANAS), emotion regulation (DERS), resilience (RS-14), and attentional control (ACS-C and ACS-D) were collected both pre and post intervention. Linear mixed effect models were run on the whole sample showing that, after multiple testing correction, our intervention was effective in reducing perceived stress (β = − 2.57 [− 4.02; − 1.12], p = 0.004), improving mental well-being (β = 2.82 [1.02; 4.63], p = 0.008) and emotional regulation (β = − 8.24 [− 12.98; − 3.51], p = 0.004), resilience (β = 3.79 [1.32; 6.26], p = 0.008), reducing the tendency to wander with the mind (β = − 0.70 [− 0.99; − 0.39], p = 0.0001), ameliorating the ability to maintain attention (AC-S (β = − 0.23 [− 0.44; − 0.02], p = 0.04) and AC-D (β = − 0.19 [− 0.36; − 0.01], p = 0.04)), and the overall distress (β = 1.84 [0.45; 3.23], p = 0.02).

Background and Objectives: In the context of disease prevention, interaction on an additive scale is commonly assessed to determine synergistic effects between exposures. While the “Relative Excess Risk due to Interaction” represents the main measure of additive interaction between risk factors, in this study we aimed to extend this approach to assess additive interaction between factors known to prevent the event’s occurrence, such as medical interventions and drugs. Materials and Methods: We introduced and described the “Relative Risk Reduction due to Interaction” (RRRI) as a key measure to assess additive interactions between preventive factors, such as therapeutic interventions and drug combinations. For RRRI values closer to 1, the combination of exposures has a greater impact on reducing the event risk due to their interaction. As a purely illustrative example, we re-evaluated a previous investigation of the synergistic effect between statins and blood pressure-lowering drugs in preventing major adverse cardiovascular events (MACE). Moreover, simulation studies were used to empirically evaluate the performance of a robust Poisson regression model to estimate RRRI across different scenarios. Results: In our example, the drug combination revealed a positive additive interaction in further reducing MACE risk (RRRI > 0), even if not statistically significant. This result is more straightforward to interpret as compared to the original one based on the RERI. Additionally, our simulations highlighted the importance of large sample sizes for detecting significant interaction effects. Conclusion: We recommend RRRI as the main measure to be considered when exploring additive interaction effects between protective exposures, such as the investigation of synergistic effects between drug combinations or preventive treatments.

The diagnosis of Marfan spectrum includes a large number of clinical criteria. Although the identification of pathogenic variants contributes to the diagnostic process, its value to the prediction of clinical outcomes is still limited. An important novelty of the present study is represented by the statistical approach adopted to investigate genotype-phenotype correlation. The analysis has been improved considering the extended genetic information obtained by Next Generation Sequencing (NGS) and combining the effects of both rare and common genetic variants in an inclusive model. To this aim a cohort of 181 patients were analyzed with a NGS panel including 11 genes associated with Marfan spectrum. The genotype-phenotype correlation was also investigated considering the possibility to predict presence of a pathological mutation in Marfan syndrome (MFS) main genes based only on the analysis of phenotypic traits. Results obtained indicate that information about clinical traits can be summarized in a new variable that resulted significantly associated with the probability to find a pathological mutation in MFS main genes. This is important since the choice of the genetic test is often influenced by the phenotypic characterization of patients. Moreover, both rare and common variants were found to significantly contribute to clinical spectrum and their combination allowed to increase the percentage of phenotype variability that could be explained based on genetic factors. Results highlight the opportunity to take advantage of the overall genetic information obtained by NGS data to have a better clinical classification of patients.

Since the recruitment process, Italian Mafias impose on their members a strict code of conduct. These rigid rules regulate their private and public behavior, implying a total adhesion to the group’s values. Such juridical and social aspects substantially distinguish organized crime (OC) from ordinary crime. It is still unknown whether these two categories of offenders also show distinctive cognitive traits. Here we investigated the frontal lobe cognitive functions of 50 OC prisoners from the Mafia and 50 non-OC prisoners based on the performance of 50 non-prisoner controls. We found that OC members were more likely to show pathological risk-propensity than non-OC prisoners. We interpret this finding as the result of the internal dynamics of Mafia groups. OC is a worldwide threat, and the identification of cognitive traits behind criminal behavior will help in devising focused prevention policies.

Background Multiple sclerosis (MS) is an autoimmune condition characterized by inflammatory and neurodegenerative traits. Recently, DNA methylation has emerged as a promising field of investigation for elucidating dynamics characterizing MS development and progression. Objectives This study aimed to comprehensively investigate the role of epigenetics in MS by analyzing the methylation profiles from blood and brain tissues from public datasets. Methods Employing a meta-analytical framework for differential methylation analyses, the study extended beyond conventional analyses to explore additional dimensions of epigenetic regulation, including epigenetic drift, age acceleration, and rare epivariations. Results Results of the differential methylation analysis were in line with previously reported findings. No significant differences were observed in age acceleration or global epigenetic drift between MS cases and controls. However, upon closer analysis at the gene level, distinctive patterns of epigenetic drift emerged, particularly within genes implicated in neural biological functions. Conclusions These findings underscore the role of epigenetic modifications in shaping MS pathology. Furthermore, the study unveiled the exclusive presence of rare epivariations within the MS cases, some of which involved genes previously linked to MS or other autoimmune diseases. This highlights the potential significance of rare genetic aberrations in driving MS susceptibility and progression.

Background: Previous research examining the effects of omnivorous and plant-based diets on cardiovascular disease (CVD) outcomes has produced inconsistent findings, and the importance of diet quality is overlooked. Our study aimed to develop a novel heart-protective diet index to assess the association of a high-quality, predominantly plant-based diet—including fish, eggs, and low-fat dairy products—with CVD incidence and mortality. Methods: This study included 192,274 participants in the UK Biobank (mean age: 56.3 ± 7.9 years) without CVD at baseline who completed a 24 h recall Oxford WebQ questionnaire. Using the Oxford WebQ questionnaire, we developed and validated a novel heart-protective diet score (HPDS) based on 22 food groups. Cox proportional hazard models were used to study the associations between HPDS and outcomes. Results: During a median follow-up of 12.3 years, 20,692 CVD events and 1131 CVD deaths were observed. After adjusting for demographics, Townsend deprivation index, lifestyle, and history of chronic diseases, participants in the top HPDS quartile were at lower risk, compared to those in the bottom quartile, of overall CVD (HR: 0.92 [95%CI: 0.88, 0.95]), IHD (HR: 0.89 [95%CI: 0.84, 0.94]), MI (HR: 0.85 [95%CI: 0.77, 0.94]), and HF (HR: 0.86 [95%CI: 0.77, 0.95]). Conclusions: Adherence to a healthy, predominantly plant-based heart-protective diet rich in non-starchy vegetables, fruits, wholegrains, fish, eggs, and low-fat dairy products is associated with a reduced cardiovascular disease incidence and mortality. Further research in diverse ethnic populations is necessary to examine the reproducibility of our findings and enhance generalizability.

Genotype imputation has become an essential prerequisite when performing association analysis. It is a computational technique that allows us to infer genetic markers that have not been directly genotyped, thereby increasing statistical power in subsequent association studies, which consequently has a crucial impact on the identification of causal variants. Many features need to be considered when choosing the proper algorithm for imputation, including the target sample on which it is performed, i.e., related individuals, unrelated individuals, or both. Problems could arise when dealing with a target sample made up of mixed data, composed of both related and unrelated individuals, especially since the scientific literature on this topic is not sufficiently clear. To shed light on this issue, we examined existing algorithms and software for performing phasing and imputation on mixed human data from SNP arrays, specifically when related subjects belong to trios. By discussing the advantages and limitations of the current algorithms, we identified LD-based methods as being the most suitable for reconstruction of haplotypes in this specific context, and we proposed a feasible pipeline that can be used for imputing genotypes in both phased and unphased human data.

Objectives Multiple sclerosis (MS) patients often experience a higher incidence of clinical conditions before diagnosis, suggesting a prodromal phase. However, their predictive value and temporal trajectories remain underexplored. We investigated these aspects using the large UK Biobank's population‐based cohort, which provided clinical history through ICD‐10 diagnosis codes. Methods We assessed associations between 600 clinical conditions and MS risk in 477,421 individuals using Cox models adjusted for demographics, smoking, and MS polygenic risk score (MS‐PRS). Statistically significant conditions were included in a LASSO Cox regression (five‐fold cross‐validation on 70% training set) to identify key predictors, with performance assessed by the C‐index and age‐dependent area under the curve (AUC) in the 30% test set. Lastly, temporal trajectories of MS‐associated conditions were analyzed in MS cases. Results We identified 192 conditions associated with MS, of which only ~20% were onset symptoms. Integrating these conditions into a predictive model already including demographics and smoking, improved the C‐index from 0.65 to 0.71. Among the 30 model‐selected best predictors, ~25% were prodromal conditions, including neuromuscular diseases, thromboembolism, and depression which typically occurred more than five years before MS diagnosis. Including MS‐PRS further increased the C‐index to 0.78, with an age‐dependent AUC exceeding 0.80 in individuals over 50 years. Trajectory analysis highlighted migraine as a common early diagnosis, often followed by hypertension, depression, and dorsalgia. Interpretation Our findings highlight early conditions and diagnostic trajectories of MS, supporting the existence of a prodromal phase. These insights could improve MS prediction and facilitate earlier detection, particularly for late‐onset cases.

Background Recent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researcher’s ability to infer cause-effect links from observational data. Now is the time to gauge the potential of these methods within specific areas of biomedical research. In this paper, we choose a study in metabolomics as an illustrative testbed. We apply Mendelian Randomisation methods in the analysis of data from the DILGOM (Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome) study, in the context of an effort to identify molecular pathways of cardiovascular disease. In particular, our illustrative analysis addresses the question whether body mass, as measured by body mass index (BMI), exerts a causal effect on the concentrations of a collection of 137 cardiometabolic markers with different degrees of atherogenic power, such as the (highly atherogenic) lipoprotein metabolites with very low density (VLDLs) and the (protective) high density lipoprotein metabolites. Results We found strongest evidence of a positive BMI effect (that is, evidence that an increase in BMI causes an increase in the metabolite concentration) on those metabolites known to represent strong risk factors for coronary artery disease, such as the VLDLs, and evidence of a negative effect on protective biomarkers. Conclusions The methods discussed represent a useful scientific tool, although they assume the validity of conditions that are (at best) only partially verifiable. This paper provides a rigorous account of such conditions. The results of our analysis provide a proof-of-concept illustration of the potential usefulness of Mendelian Randomisation in genomic biobank studies aiming to dissect the molecular causes of disease, and to identify candidate pharmacological targets.

ObjectivesBeing able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying ‘state-of-the-art’ statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19.DesignThe data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework.Setting3 secondary and tertiary level centres in Greater Manchester, the UK.Participants392 hospitalised patients with a diagnosis of COVID-19.Results392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome.ConclusionsThis study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.