Metabolic profiling of blood plasma in depression in pregnant women during early pregnancy

Abstract

Objective

The study aimed to perform metabolic profiling of serum samples using liquid chromatography with mass spectroscopy (LC-MS) and to explore potential biomarkers of early trimester depression.

Method

Using the Edinburgh Postnatal Depression Scale (EPDS), participants were randomly divided into study and control groups. Serum metabolic profiles of the two groups were analysed by using LC-MS. Differential metabolite and pathway analysis were identified by using orthogonal projections to latent structure-discriminant analysis (OPLS-DA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Additionally, least absolute shrinkage and selection operator (LASSO) logistic and receiver operating characteristic (ROC) curve analyses were also conducted to explore potential biomarkers of antenatal depression (AD).

Results

The study included 41 participants, consisting of 16 subjects with AD and 25 controls. A total of 22 different metabolites were identified (p < .005), mainly affecting glycerophospholipid metabolism, linoleic acid metabolism, synthesis and degradation of ketone bodies, phenylalanine metabolism, and butanoate metabolism. The area under the ROC curve (AUC) for the LysoPC (24:0) was 0.858. This suggests that LysoPC (24:0) may be a potentially effective predictor of risk factors for AD.

Conclusions

The study suggests that LysoPC (24:0) may be an effective and specific lipid biomarker for early trimester depression.

Keywords

antenatal depression serum metabolic analysis early pregnancy

Get full access to this article

View all access options for this article.

References

Bennett

Einarson

Taddio

, et al. Prevalence of depression during pregnancy: systematic review. Obstet Gynecol 2004; 103(4): 698–709.

Cox

. Thirty years with the Edinburgh Postnatal Depression Scale: voices from the past and recommendations for the future. Br J Psychiatr 2019; 214(3): 127–129.

Zhang

, et al. Antenatal depressive symptoms and the risk of preeclampsia or operative deliveries: a meta-analysis. PLoS One 2015; 10(3): e119018.

de Paz

Sanchez

Huaman

, et al. Risk of placental abruption in relation to maternal depressive, anxiety and stress symptoms. J Affect Disord 2011; 130(1-2): 280–284.

Chung

Lau

Yip

, et al. Antepartum depressive symptomatology is associated with adverse obstetric and neonatal outcomes. Psychosom Med 2001; 63(5): 830–834.

Ghimire

Papabathini

Kawuki

, et al. Depression during pregnancy and the risk of low birth weight, preterm birth and intrauterine growth restriction- an updated meta-analysis. Early Hum Dev 2021; 152: 105243.

Goodman

Rouse

Connell

, et al. Maternal depression and child psychopathology: a meta-analytic review. Clin Child Fam Psychol Rev 2011; 14(1): 1–27.

Kingston

Tough

Whitfield

. Prenatal and postpartum maternal psychological distress and infant development: a systematic review. Child Psychiatry Hum Dev 2012; 43(5): 683–714.

Austin

. Antenatal screening and early intervention for “perinatal” distress, depression and anxiety: where to from here? Arch Womens Ment Health 2004; 7(1): 1–6.

10.

Murray

Cox

. Screening for depression during pregnancy with the Edinburgh depression scale (EPDS). J REPROD INFANT PSYC 1990; 8(2): 99–107.

11.

MacDonald

Krishnan

Cervenka

, et al. Biomarkers for major depressive and bipolar disorders using metabolomics: a systematic review. Am J Med Genet B Neuropsychiatr Genet 2019; 180(2): 122–137.

12.

Modzelewski

Oracz

Ilendo

, et al. Biomarkers of postpartum depression: a narrative review. J Clin Med 2023; 12(20).

13.

Zhao

Long

, et al. Biomarker screening for antenatal depression in women who underwent caesarean section: a matched observational study with plasma Lipidomics. BMC Psychiatr 2019; 19(1): 1–12.

14.

Mao

Tian

Chen

, et al. Serum metabolic profiling of late-pregnant women with antenatal depressive symptoms. Front Psychiatry 2021; 12.

15.

Levis

Negeri

Sun

, et al. Accuracy of the Edinburgh Postnatal Depression Scale (EPDS) for screening to detect major depression among pregnant and postpartum women: systematic review and meta-analysis of individual participant data. BMJ 2020; 371.

16.

Matthey

Henshaw

Elliott

, et al. Variability in use of cut-off scores and formats on the Edinburgh Postnatal Depression Scale: implications for clinical and research practice. Arch Womens Ment Health 2006; 9(6): 309–315.

17.

Davies

Howells

Jenkins

. Early detection and treatment of postnatal depression in primary care. J Adv Nurs 2003; 44(3): 248–255.

18.

Gibson

McKenzie-McHarg

Shakespeare

, et al. A systematic review of studies validating the Edinburgh Postnatal Depression Scale in antepartum and postpartum women. Acta Psychiatr Scand 2009; 119(5): 350–364.

19.

Cox

Holden

Sagovsky

. Detection of postnatal depression. Development of the 10-item Edinburgh postnatal depression scale. Br J Psychiatry 1987; 150: 782–786.

20.

Bharti

Bhardwaj

Hood

, et al. A systematic review and meta-analysis of lipid metabolomic signatures of Major Depressive Disorder. J Psychiatr Res 2021; 139: 197–205.

21.

Duan

Xie

. The potential for metabolomics in the study and treatment of major depressive disorder and related conditions. Expert Rev Proteomics 2020; 17(4): 309–322.

22.

GCL

Carneiro

Alves

, et al. Metabolomics of major depressive disorder: a systematic review of clinical studies. Cureus 2022; 14(3): e23009.

23.

Zhou

Liu

Lan

, et al. Polyunsaturated fatty acids metabolism, purine metabolism and inosine as potential independent diagnostic biomarkers for major depressive disorder in children and adolescents. Mol Psychiatr 2019; 24(10): 1478–1488.

24.

Xie

Zhong

, et al. Multi-omics data reveals the important role of glycerophospholipid metabolism in the crosstalk between gut and brain in depression. J Transl Med 2023; 21(1): 93.

25.

Miranda

Oliveira

. Lipids under stress – a lipidomic approach for the study of mood disorders. Bioessays 2015; 37(11).

26.

Nyamundanda

Gormley

Fan

, et al. MetSizeR: selecting the optimal sample size for metabolomic studies using an analysis based approach. BMC Bioinf 2013; 14: 338.