Plasma linoelaidyl carnitine levels positively correlated with symptom improvement in olanzapine-treated first-episode drug-naïve schizophrenia

Age related gut microbiota regulates energy-related metabolism to influence natural aging phenotypes in the heart

As the population ages, problems pertaining to health and life expectancy due to the aging heart have become increasingly prominent. The gut microbiota has become a potential therapeutic target in several diseases, including cardiovascular diseases. Current studies on the roles of the gut microbiota in the cardiovascular system have focused mainly on cardiovascular diseases; therefore, the effects of the gut microbiota on the natural aging of myocardial tissue remain unclear. The present study aimed to explore the roles and mechanisms of the gut microbiota and related metabolites in the natural aging of the heart. Animal models of fecal microbiota transplantation (FMT) were established in elderly and young rats. 16S rRNA sequencing revealed that the gut microbiota of the recipients shifted toward the profile of the donors, with concomitant cardiac structure and diastolic function changes detected via ultrasound and positron emission tomography-computed tomography (PET-CT). A group of significantly enriched myocardial metabolites detected by LC/MS were involved in the fatty acid β-oxidation process. Together with altered glucose uptake, as revealed by PET-CT, changes in ATP content and mitochondrial structure further verified a metabolic difference related to energy among rats transplanted with the gut microbiota from donors of different ages. This study demonstrated that gut microbes may participate in the physiological aging process of the rat heart by regulating oxidative stress and autophagy. The gut microbiota has been shown to be involved in the natural aging of the heart at multiple levels, from the organ level to the metabolically plastic myocardiocytes and associated molecules.

Metabolomics, Lipidomics, and Antipsychotics: A Systematic Review

Antipsychotics are an important pharmacotherapy option for the treatment of many mental illnesses. Unfortunately, selecting antipsychotics is often a trial-and-error process due to a lack of understanding as to which medications an individual patient will find most effective and best tolerated. Metabolomics, or the study of small molecules in a biosample, is an increasingly used omics platform that has the potential to identify biomarkers for medication efficacy and toxicity. This systematic review was conducted to identify metabolites and metabolomic pathways associated with antipsychotic use in humans. Ultimately, 42 studies were identified for inclusion in this review, with all but three studies being performed in blood sources such as plasma or serum. A total of 14 metabolite classes and 12 lipid classes were assessed across studies. Although the studies were highly heterogeneous in approach and mixed in their findings, increases in phosphatidylcholines, decreases in carboxylic acids, and decreases in acylcarnitines were most consistently noted as perturbed in patients exposed to antipsychotics. Furthermore, for the targeted metabolomic and lipidomic studies, seven metabolites and three lipid species had findings that were replicated. The most consistent finding for targeted studies was an identification of a decrease in aspartate with antipsychotic treatment. Studies varied in depth of detail provided for their study participants and in study design. For example, in some cases, there was a lack of detail on specific antipsychotics used or concomitant medications, and the depth of detail on sample handling and analysis varied widely. The conclusions here demonstrate that there is a large foundation of metabolomic work with antipsychotics that requires more complete reporting so that an objective synthesis such as a meta-analysis can take place. This will then allow for validation and clinical application of the most robust findings to move the field forward. Future studies should be carefully controlled to take advantage of the sensitivity of metabolomics while limiting potential confounders that may result from participant heterogeneity and varied analysis approaches.