Blood DNA methylation signature of diet quality and association with cardiometabolic traits.

AIMS: Diet quality might influence cardiometabolic health through epigenetic changes, but this has been little investigated in adults. Our aims were to identify cytosine-phosphate-guanine (CpG) dinucleotides associated with diet quality by conducting an epigenome-wide association study (EWAS) based on blood DNA methylation (DNAm) and to assess how diet-related CpGs associate with inherited susceptibility to cardiometabolic traits: body mass index (BMI), systolic blood pressure (SBP), triglycerides, type 2 diabetes (T2D), and coronary heart disease (CHD). METHODS AND RESULTS: Meta-EWAS including 5274 participants in four cohorts from Spain, the USA, and the UK. We derived three dietary scores (exposures) to measure adherence to a Mediterranean diet, to a healthy plant-based diet, and to the Dietary Approaches to Stop Hypertension. Blood DNAm (outcome) was assessed with the Infinium arrays Human Methylation 450K BeadChip and MethylationEPIC BeadChip. For each diet score, we performed linear EWAS adjusted for age, sex, blood cells, smoking and technical variables, and BMI in a second set of models. We also conducted Mendelian randomization analyses to assess the potential causal relationship between diet-related CpGs and cardiometabolic traits. We found 18 differentially methylated CpGs associated with dietary scores (P < 1.08 × 10-7; Bonferroni correction), of which 12 were previously associated with cardiometabolic traits. Enrichment analysis revealed overrepresentation of diet-associated genes in pathways involved in inflammation and cardiovascular disease. Mendelian randomization analyses suggested that genetically determined methylation levels corresponding to lower diet quality at cg02079413 (SNORA54), cg02107842 (MAST4), and cg23761815 (SLC29A3) were causally associated with higher BMI and at cg05399785 (WDR8) with greater SBP, and methylation levels associated with higher diet quality at cg00711496 (PRMT1) with lower BMI, T2D risk, and CHD risk and at cg0557921 (AHRR) with lower CHD risk. CONCLUSION: Diet quality in adults was related to differential methylation in blood at 18 CpGs, some of which related to cardiometabolic health.

We conducted a study to investigate the connection between diet quality, epigenetic changes, and cardiovascular health in adults. The study included 5274 participants from Spain, the USA, and the UK, combining data from four different cohorts. We assessed adherence to different healthy diets: Mediterranean style diet, plant-based diet, and Dietary Approaches to Stop Hypertension diet. We used advanced technology to analyse blood DNA methylation, which refers to chemical modifications in the DNA that can affect gene activity.We discovered 18 CpGs that showed differential methylation patterns related to the dietary scores. Importantly, 12 of these CpGs had previously been associated with cardiovascular disease or risk factors, suggesting a potential link between diet, epigenetic changes, and heart health. Some of the diet-related CpGs mapped to genes involved in pathways associated with cardiovascular disease. Moreover, using a method called Mendelian randomization, we found that several CpGs may have a causal association with body mass index, systolic blood pressure, and risk of type 2 diabetes and coronary heart disease.

An integrated precision medicine approach in major depressive disorder: a study protocol to create a new algorithm for the prediction of treatment response.

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the most common option among the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. Here we describe the methodology of the PROMPT project that aims at the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. To address this, the project will be organized in 2 phases. Phase 1 will involve 300 patients with MDD already recruited, comprising 150 TRD and 150 responders, considered as extremes phenotypes of response. A deep clinical stratification will be performed for all patients; moreover, a genomic, transcriptomic and miRNomic profiling will be conducted. The data generated will be exploited to develop an innovative algorithm integrating clinical, omics and sex-related data, in order to predict treatment response and TRD development. In phase 2, a new naturalistic cohort of 300 MDD patients will be recruited to assess, under real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase we will investigate shared decision making (SDM) in the context of pharmacogenetic testing and evaluate various needs and perspectives of different stakeholders toward the use of predictive tools for MDD treatment to foster active participation and patients' empowerment. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.