Plasma metabolic disturbances during pregnancy and postpartum in women with depression

Blood metabolomic and postpartum depression: a mendelian randomization study

BACKGROUND

Postpartum depression is a complex mental health condition that often occurs after childbirth and is characterized by persistent sadness, anxiety, and fatigue. Recent research suggests a metabolic component to the disorder. This study aims to investigate the causal relationship between blood metabolites and postpartum depression using mendelian randomization (MR).

METHODS

This study used a bi-directional MR framework to investigate the causal relationship between 1,400 metabolic biomarkers and postpartum depression. We used two specific genome-wide association studies datasets: one with single nucleotide polymorphisms data from mothers diagnosed with postpartum depression and another with blood metabolite data, both of which focused on people of European ancestry. Genetic variants were chosen as instrumental variables from both datasets using strict criteria to improve the robustness of the MR analysis. The combination of these datasets enabled a thorough examination of genetic influences on metabolic profiles associated with postpartum depression. Statistical analyses were conducted using techniques such as inverse variance weighting, weighted median, and model-based estimation, which enabled rigorous causal inference from the observed associations. postpartum depression was defined using endpoint definitions approved by the FinnGen study's clinical expert groups, which included leading experts in their respective medical fields.

RESULTS

The MR analysis identified seven metabolites that could be linked to postpartum depression. Out of these, one metabolite was found to be protective, while six were associated with an increased risk of developing the condition. The results were consistent across multiple MR methods, indicating a significant correlation.

CONCLUSIONS

This study emphasizes the potential of metabolomics for understanding postpartum depression. The discovery of specific metabolites associated with the condition sheds new insights on its pathophysiology and opens up possibilities for future research into targeted treatment strategies.

Optimal risk and diagnosis assessment strategies in perinatal depression: A machine learning approach from the life-ON study cohort

This study aimed to assess the concordance of various psychometric scales in detecting Perinatal Depression (PND) risk and diagnosis. A cohort of 432 women was assessed at 10-15th and 23-25th gestational weeks, 33-40 days and 180-195 days after delivery using the Edinburgh Postnatal Depression Scale (EPDS), Visual Analogue Scale (VAS), Hamilton Depression Rating Scale (HDRS), Montgomery-Åsberg Depression Rating Scale (MADRS), and Mini International Neuropsychiatric Interview (MINI). Spearman's rank correlation coefficient was used to assess agreement across instruments, and multivariable classification models were developed to predict the values of a binary scale using the other scales. Moderate agreement was shown between the EPDS and VAS and between the HDRS and MADRS throughout the perinatal period. However, agreement between the EPDS and HDRS decreased postpartum. A well-performing model for the estimation of current depression risk (EPDS > 9) was obtained with the VAS and MADRS, and a less robust one for the estimation of current major depressive episode (MDE) diagnosis (MINI) with the VAS and HDRS. When the EPDS is not feasible, the VAS may be used for rapid and comprehensive postpartum screening with reliability. However, a thorough structured interview or clinical examination remains necessary to diagnose a MDE.

Overview of metabolomic aspects in postpartum depression

Along with the typical biochemical alterations that occur during pregnancy, certain metabolic changes might be associated with the development of several psychiatric disorders, including postpartum depression (PPD), which is the most common type of psychiatric disorder during pregnancy or first postpartum year, and it develops in about 15% of women. Metabolomics is a rapidly developing discipline that deals with the metabolites as the final products of all genetically controlled biochemical pathways, highly influenced by external and internal changes. The aim of this paper was to review the published studies whose results suggest or deny a possible association between the fine regulation of the metabolome and PPD, enabling conclusions about whether metabolomics could be a useful tool in defining the biochemical pathways directly involved in the etiology, diagnosis and course of PPD. Beside numerous hormonal changes, a lot of different metabolic pathways have been discovered to be affected in women with PPD or associated with its development, including alterations in the energy metabolism, tryptophan and amino acid metabolism, steroid metabolism, purine cycle, as well as neurotransmitter metabolism. Additionally, metabolomics helped in defining the association between PPD and the exposure to various endocrine disrupting metabolites during pregnancy. Finally, metabolome reflects different PPD therapies and exposure of fetus or breastfed infants to pharmacotherapy prescribed to a mother suffering from PPD. This review can help in creating the picture about metabolomics' broad application in PPD studies, but it also implies that its potential is still not completely used.