Exploration for biomarkers of postpartum depression based on metabolomics: A systematic review

Li, Shiming; Yang, Zhuoqiao; Yao, Mengxin; Shen, Ying; Zhu, Hongyi; Jiang, Ying; Ji, Yingying; Yin, Jieyun

doi:10.1016/j.jad.2022.08.043

Cited by 8 publications

(3 citation statements)

References 94 publications

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“…Therefore, we constructed SHAP dependence plots to enhance our understanding of the relationship between calories BMR and PPD. Plots during pre-pregnancy/PPD suggested that elevated calories BMR is predictive of PPD, which could be attributed to a variety of factors, such as heightened stress levels or metabolic alterations associated with PPD (Figure 7A) 2,34 . During pregnancy/PPD and postpartum/PPD time periods, the relationship between Shapley and actual values of calories BMR flipped, signifying an increased number of calories BMR was inversely associated with PPD (Figure 7A).…”

Section: Discussionmentioning

confidence: 99%

Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using theAll of UsResearch Program dataset

Hurwitz,

Butzin-Dozier,

Master

et al. 2023

Preprint

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Postpartum depression (PPD), afflicting one in seven women, poses a major challenge in maternal health. Existing approaches to detect PPD heavily depend on in-person postpartum visits, leading to cases of the condition being overlooked and untreated. We explored the potential of consumer wearable-derived digital biomarkers for PPD recognition to address this gap. Our study demonstrated that intra-individual machine learning (ML) models developed using these digital biomarkers can discern between pre-pregnancy, pregnancy, postpartum without depression, and postpartum with depression time periods (i.e., PPD diagnosis). When evaluating variable importance, calories burned from the basal metabolic rate (calories BMR) emerged as the digital biomarker most predictive of PPD. To confirm the specificity of our method, we demonstrated that models developed in women without PPD could not accurately classify the PPD-equivalent phase. Prior depression history did not alter model efficacy for PPD recognition. Furthermore, the individualized models demonstrated superior performance compared to a conventional cohort-based model for the detection of PPD, underscoring the effectiveness of our individualized ML approach. This work establishes consumer wearables as a promising avenue for PPD identification. More importantly, it also emphasizes the utility of individualized ML model methodology, potentially transforming early disease detection strategies.

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Section: Discussionmentioning

confidence: 99%

Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using theAll of UsResearch Program dataset

Hurwitz,

Butzin-Dozier,

Master

et al. 2023

Preprint

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“…However, the underlying mechanisms of depression remain unclear. Previous studies have indicated that depression is closely associated with purine metabolism [15], energy-related metabolism [16,17], and amino acids [18,19]. Research on depression in various brain regions has been conducted.…”

Section: Introductionmentioning

confidence: 99%

Desorption Electrospray Ionization Mass Spectrometry Imaging Techniques Depict a Reprogramming of Energy and Purine Metabolism in the Core Brain Regions of Chronic Social Defeat Stress Mice

Song,

Xiao,

et al. 2024

Metabolites

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Depression is associated with pathological changes and metabolic abnormalities in multiple brain regions. The simultaneous comprehensive and in situ detection of endogenous molecules in all brain regions is essential for a comprehensive understanding of depression pathology, which is described in this paper. A method based on desorption electrospray ionization mass spectrometry imaging (DESI-MSI) technology was developed to classify mouse brain regions using characteristic lipid molecules and to detect the metabolites in mouse brain tissue samples simultaneously. The results showed that characteristic lipid molecules can be used to clearly distinguish each subdivision of the mouse brain, and the accuracy of this method is higher than that of the conventional staining method. The cerebellar cortex, medial prefrontal cortex, hippocampus, striatum, nucleus accumbens-core, and nucleus accumbens-shell exhibited the most significant differences in the chronic social defeat stress model. An analysis of metabolic pathways revealed that 13 kinds of molecules related to energy metabolism and purine metabolism exhibited significant changes. A DESI-MSI method was developed for the detection of pathological brain sections. We found, for the first time, that there are characteristic changes in the energy metabolism in the cortex and purine metabolism in the striatum, which is highly important for obtaining a deeper and more comprehensive understanding of the pathology of depression and discovering regulatory targets.

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“…To capture the full path of the cross-generational transmission of child development, the identification of early risk factors for peripartum depressive symptoms has been an important topic in the field. Previous research has shown potential factors like inflammatory biomarkers (eg, increased levels of interleukin (IL)-6, IL-8, C-reactive protein (CRP) and tumour necrosis factor-α (TNF-α), see review by Silva-Fernandes et al 22), genetic factors (eg, serotonin transporter gene (5-HTT), catechol-O-methyl-transferase, mono-amine-oxidase type A and so on),23 and psychosocial stressors (eg, high life stress, lack of social support, history of depression, and so on) for peripartum depression 24–28. It is essential to further elucidate the longitudinal and dynamic relationship between these potential risk factors, maternal depressive symptoms and child development.…”

Section: Introductionmentioning

confidence: 99%

Cohort profile: the U-BIRTH study on peripartum depression and child development in Sweden

Tu,

Fransson,

Kunovac Kallak

et al. 2023

BMJ Open

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PurposeThe current U-BIRTH cohort (Uppsala Birth Cohort) extends our previous cohort Biology, Affect, Stress, Imaging and Cognition (BASIC), assessing the development of children up to 11 years after birth. The U-BIRTH study aims to (1) assess the impact of exposure to peripartum mental illness on the children’s development taking into account biological and environmental factors during intrauterine life and childhood; (2) identify early predictors of child neurodevelopmental and psychological problems using biophysiological, psychosocial and environmental variables available during pregnancy and early post partum.ParticipantsAll mothers participating in the previous BASIC cohort are invited, and mother–child dyads recruited in the U-BIRTH study are consecutively invited to questionnaire assessments and biological sampling when the child is 18 months, 6 years and 11 years old. Data collection at 18 months (n=2882) has been completed. Consent for participation has been obtained from 1946 families of children having reached age 6 and from 698 families of children having reached age 11 years.Findings to dateBased on the complete data from pregnancy to 18 months post partum, peripartum mental health was significantly associated with the development of attentional control and gaze-following behaviours, which are critical to cognitive and social learning later in life. Moreover, infants of depressed mothers had an elevated risk of difficult temperament and behavioural problems compared with infants of non-depressed mothers. Analyses of biological samples showed that peripartum depression and anxiety were related to DNA methylation differences in infants. However, there were no methylation differences in relation to infants’ behavioural problems at 18 months of age.Future plansGiven that the data collection at 18 months is complete, analyses are now being undertaken. Currently, assessments for children reaching 6 and 11 years are ongoing.

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Exploration for biomarkers of postpartum depression based on metabolomics: A systematic review

Cited by 8 publications

References 94 publications

Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using theAll of UsResearch Program dataset

Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using theAll of UsResearch Program dataset

Desorption Electrospray Ionization Mass Spectrometry Imaging Techniques Depict a Reprogramming of Energy and Purine Metabolism in the Core Brain Regions of Chronic Social Defeat Stress Mice

Cohort profile: the U-BIRTH study on peripartum depression and child development in Sweden

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