Depression is a major health condition during pregnancy. Postnatal depression, defined as depression occurring up to a year after giving birth, is slowly gaining more attention. However, one of its most important risk factors – depression during pregnancy (“antenatal depression”) – is often overlooked and left untreated (Leigh & Milgrom, 2008; Míguez & Vázquez, 2021).

Antenatal depression affects at least one in ten pregnant women, and about one in five experience clinically significant depressive symptoms (Girchenko et al., 2025; Woody et al., 2017; Yin et al., 2021; Molyneux, 2017). It has been linked to birth complications, such as premature delivery and low birth weight (Jarde et al., 2016) and with poorer offspring neuropsychological development (Rogers et al., 2020).

Antenatal depression is as common as other pregnancy-related health conditions typically screened for by clinicians, such as pregnancy induced (“gestational”) diabetes, or high blood pressure (“hypertensive disorders of pregnancy”), both of which are known to increase the risk of medical problems for mother and child during pregnancy, birth, and after delivery (Sweeting et al., 2024; Webster et al., 2019). It is thus vital that similar clinical attention is paid to antenatal depression.

Metabolomic research in psychiatry

Despite increasing evidence for the association between antenatal depression and negative postnatal outcomes, the underlying biological mechanisms remain poorly understood.

Metabolomic research in psychiatry tries to understand mental health problems by looking at molecules circulating through our bodies, specifically those involved in metabolism (Shih, 2019). With this method, researchers hope to discover biomarkers that allow for better detection and treatment of specific mental health problems.

While several studies have investigated metabolic changes in individuals with depression, Girchenko et al.’s is the first large scale study to investigate such changes in pregnant women. The female body is known to undergo significant metabolic changes during pregnancy and the authors hypothesised that antental depression may be linked to additional metabolic changes that explain associations with birth outcomes and child development.

Methods

This Finnish study included pregnant women from the Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) cohort (n=331) and the InTraUterine sampling in early pregnancy (ITU) cohort (n= 416). Measures were collected on:

• Maternal Antenatal Depression using health care records and the Center for Epidemiological Studies Depression Scale.
• Maternal Metabolites: 95 metabolic measures were derived at three timepoints.
• Birth Outcomes: Child gestational age (i.e., the pregnancy week they were born) and birth weight were extracted from medical records.
• Child Neurodevelopment and Mental Health using the Ages and Stages Questionnaire (ASQ), behavioural diagnoses (PREDO only), and additional neurodevelopmental assessments (ITU only).

A penalised elastic net regression was used to identify metabolic measures associated with antenatal depression. These were then used to predict the presence or absence of antenatal depression.

Two nested logistic regression models compared the ability of the selected metabolites to predict antenatal depression over its risk factors. The ability of the selected metabolites to predict birth, neurodevelopmental and child mental health outcomes over antenatal depression was assessed.

All analytical steps using the identified metabolic measures were replicated in the second cohort and models were adjusted for child sex and age of behavioural outcomes.

Results

Metabolic Measures associated with antenatal depression

Fifteen metabolic measures were linked to antenatal depression, including several amino acids (e.g., alanine, glutamine), fatty acids (e.g., ratio of polyunsaturated fatty acids to total fatty acids), and inflammatory markers (i.e., glycoprotein acetylation). These 15 metabolic measures explained 25.0% (p<.0001) of the variance in antenatal depression, suggesting they might partially explain biological pathways associated with antenatal depression.

Metabolic predictions of antenatal depression, birth outcomes, children’s neurodevelopment and mental health

Risk factors for antenatal depression such as body mass index before pregnancy, gestational diabetes, and maternal age, were able to explain 12.6% (p=.004) of the variance in antenatal depression. When the 15 metabolic measures were added, the explained variance increased to 32.3% (p<.0001). This suggests that these metabolic measures asserted a large additional effect on antenatal depression, compared to other risk factors alone.

In the prediction of birth outcomes, antenatal depression and child sex explained 0.7% (p=.34) of variance in child gestational age and 0.7% of variance in birth weight. Through the addition of the metabolic markers, the amount of explained variance increased to 18.5% (p<.0001) for child gestational age and to 9.0% (p=0.03) for birth weight. This suggests that the metabolic measures asserted an additional influence on birth outcomes, beyond the effects of antenatal depression. The measures may thus reflect metabolic alterations that predispose women both to antenatal depression and to adverse birth outcomes.

In the prediction of neuropsychological outcomes, antenatal depression, child sex and child age at testing explained 11.6% (p<.0001) of the variance in the developmental milestones questionnaire (ASQ) results and

[antenatal depression], child sex and child age at first diagnosis explained 5.0% (p=.11) of the variance in any child mental or behavioural disorder.

The addition of the 15 metabolic measures increased the amount of explained variance in the ASQ score to 21.0% (p=.002) and in any mental or behavioural disorder to 25.2% (p=.03). This suggests that the same metabolic alterations in pregnant women that are linked to antenatal depression and unfavourable birth outcomes, may predispose their offspring to impaired neurodevelopment and mental health problems.

Conclusions

The authors concluded that antenatal depression is associated with altered concentrations of certain metabolites, such as amino acids, fatty acids, glucose, lipids, and inflammatory proteins. These metabolic measures predicted antenatal depression better than other known risk factors on their own. They also improved the prediction of birth outcomes, child neurodevelopment and offspring mental health over antenatal depression. The authors stated that:

these metabolic measures may become biomarkers that could be used to identify at-risk mothers and their children for targeted interventions.

Strengths and limitations

Girchenko et al. (2025) have published the largest and most comprehensive study to date on metabolomic pathways to antenatal depression, birth outcomes and offspring development. It is laudable that the assessment of metabolic markers and of antenatal depression occurred at several points of pregnancy. It is also impressive that the children’s neurodevelopmental outcomes were followed up to 16 years. In terms of statistical design, the penalised elastic net regression approach avoided overfitting the metabolic measures to antenatal depression in the main sample. The replication of the prediction models in a second cohort adds to the validity of the findings.

It would have been very interesting to include assessments of maternal mental health after delivery as an outcome, since women are particularly vulnerable to the development of mental health problems in the postpartum period. This vulnerability arises at a moment where new mothers no longer receive the medical attention they experienced during pregnancy. Better prediction tools for postpartum mental health conditions are thus needed, ideally from antenatal measures, so that women can already be identified and supported during pregnancy. Furthermore, postpartum mental health problems have been linked to behavioural problems in children and should thus have been included as a risk factor in the regression model.

Moreover, it would have been interesting to include further risk factors for antenatal depression, such as social support, recent stressful live events, migration, whether the pregnancy was planned and whether conception occurred with the help of artificial reproductive technologies. It also would have been good to include information on maternal comorbidities other than gestational diabetes and hypertensive disorders of pregnancy, since health conditions can be linked to variations in metabolic measures.

It remains unclear from the study whether the link between antenatal depression and metabolic alterations is one of causation or correlation. Longitudinal data including metabolic measures and mental health assessments before and during pregnancies would be useful to infer whether metabolic alterations precede antenatal depression or vice versa.

Furthermore, the statistical analysis of the study did not investigate specific interaction effects between individual risk factors and metabolic markers. Such analyses could determine whether the influence of metabolic markers on antenatal depression is independent from other known risk factors.

Implications for practice

The present paper opens interesting perspectives on shared metabolic pathways and potential biomarkers for antenatal depression, birth outcomes and adverse neuropsychological child development. Before such biomarkers can be implemented into clinical practice, more research is needed to determine the precise role of metabolic alterations in predicting specific risks at specific time points.

My clinical experience has taught me that biomarkers predicting pregnancy, birth, and child outcomes must be reliable and specific to guide further monitoring and treatment decisions. A particularly successful example for such a blood-based measure is the non-invasive-prenatal test (NIPT) which tests for foetal chromosomal abnormalities and has a 99% detection rate for trisomy-21 (Down-Syndrome), making it superior to all other clinical screening tools for this outcome (Gil et al., 2015). Another example is the sFlt-1/PIGF ratio, a blood marker that predicts whether women with clinical risk factors for the pregnancy complication pre-eclampsia, have a very high or low risk of developing it at specific points of pregnancy (Stepan et al., 2023). I have drawn on these examples to illustrate that, with further research and validation, metabolic biomarkers could complement existing screening tools and enhance risk assessment.

Meanwhile, the growing evidence on shared biological connections between maternal mental health, pregnancy and child outcomes should be used to educate health care professionals involved in the care of women and children, particularly obstetricians and midwives. It is important that maternal health services implement routine screening practices for antenatal depression so that mental health professionals can become involved directly when needed and long-term adverse consequences for women and children can be prevented through joint efforts.

Statement of interests

None to declare

Links

Primary paper

Girchenko, P., Lahti-Pulkkinen, M., Laivuori, H., Kajantie, E., & Räikkönen, K. (2025). Maternal Antenatal Depression Is Associated With Metabolic Alterations That Predict Birth Outcomes and Child Neurodevelopment and Mental Health. Biological Psychiatry, 97(3), 269–278. https://doi.org/10.1016/j.biopsych.2024.07.023

Other references

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Girchenko, P., Lahti-Pulkkinen, M., Laivuori, H., Kajantie, E., & Räikkönen, K. (2025). Maternal Antenatal Depression Is Associated With Metabolic Alterations That Predict Birth Outcomes and Child Neurodevelopment and Mental Health. Biological Psychiatry, 97(3), 269–278. https://doi.org/10.1016/j.biopsych.2024.07.023

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