Year-end Sale % OFF 
Abstract
One of the biggest challenges in treating depression is the heterogeneous and qualitative nature of its clinical presentations. This highlights the need to find quantitative molecular markers to tailor existing treatment strategies to the individual’s biological system. In this study, high-resolution metabolic phenotyping of urine and plasma samples from the CAN-BIND study collected before treatment with two common pharmacological strategies, escitalopram and aripiprazole, was performed. Here we show that a panel of LDL and HDL subfractions were negatively correlated with depression in males. For treatment response, lower baseline concentrations of apolipoprotein A1 and HDL were predictive of escitalopram response in males, while higher baseline concentrations of apolipoprotein A2, HDL and VLDL subfractions were predictive of aripiprazole response in females. These findings support the potential of metabolomics in precision medicine and the possibility of identifying personalized interventions for depression.
Highlights
One of the biggest challenges in treating depression is the heterogeneous and qualitative nature of its clinical presentations
There is extensive evidence supporting a link between dyslipidemia and MDD20–24, to the best of our knowledge, only one metabolomic study to date has defined differences in lipoprotein profile between Major depressive disorder (MDD) and healthy control (HC) participants[25], and none has tested the potential of lipoproteins to predict treatment outcome
PCA models constructed on the urine metabolic profiles and plasma lipoprotein profiles of all participants showed that no clustering was apparent based on the site that the samples were collected from, demonstrating that the metabolic profile was not affected by study location (Supplementary Fig. 1)
Summary
One of the biggest challenges in treating depression is the heterogeneous and qualitative nature of its clinical presentations. Lower baseline concentrations of apolipoprotein A1 and HDL were predictive of escitalopram response in males, while higher baseline concentrations of apolipoprotein A2, HDL and VLDL subfractions were predictive of aripiprazole response in females These findings support the potential of metabolomics in precision medicine and the possibility of identifying personalized interventions for depression. There have been reports of females being more responsive to selective serotonin reuptake inhibitors (SSRIs) than males[16], possibly due to hormones like progesterone and estradiol, lower rates of gastric emptying (leading to quicker absorption17), and higher adiposity (leading to greater distribution[18,19]) For this reason, the present study investigated sex differences in metabolic associations with treatment outcome, while accounting for the confounding effects of BMI, as well as age and inflammation. There is extensive evidence supporting a link between dyslipidemia and MDD20–24, to the best of our knowledge, only one metabolomic study to date has defined differences in lipoprotein profile between MDD and healthy control (HC) participants[25], and none has tested the potential of lipoproteins to predict treatment outcome
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
