The impact of maternal vulnerability on stress biomarkers and first-trimester growth: the Rotterdam Periconceptional Cohort (Predict Study).

STUDY QUESTION: Is the degree of maternal vulnerability positively associated with stress biomarkers (stress hormones, C-reactive protein, tryptophan metabolites, and one-carbon metabolites), and does long-term exposure to stress hormones reduce first-trimester growth? SUMMARY ANSWER: The maternal vulnerability risk score is positively associated with concentrations of hair cortisol and cortisone and negatively with tryptophan, while higher hair cortisol concentrations are associated with reduced first-trimester growth without mediation of tryptophan. WHAT IS KNOWN ALREADY: A high degree of maternal vulnerability during the periconception period is associated with impaired first-trimester growth and pregnancy complications, with consequences for long-term health of the child and future life course. However, due to the challenges of early identification of vulnerable women, the uptake of periconception care is low in this target group. STUDY DESIGN, SIZE, DURATION: Between June 2022 and June 2023, this study was conducted in a sub-cohort of 160 pregnant women participating in the Rotterdam Periconceptional Cohort (Predict Study), an ongoing prospective tertiary hospital-based cohort. PARTICIPANTS/MATERIALS, SETTING, METHODS: One hundred and thirty-two women with ongoing pregnancies and available stress biomarker data were included in the analysis. Data on periconceptional social, lifestyle, and medical risk factors were collected via self-administered questionnaires, and these factors were used for the development of a composite maternal vulnerability risk score. Stress biomarkers, including stress hormones (hair cortisol and cortisone) and inflammatory and oxidative stress biomarkers (C-reactive protein, total homocysteine, and tryptophan metabolites) were determined in the first trimester of pregnancy. First-trimester growth was assessed by crown-rump length (CRL) and embryonic volume (EV) measurements at 7, 9, and 11 weeks gestation by making use of an artificial intelligence algorithm and virtual reality techniques using 3D ultrasound data sets. The associations between the maternal vulnerability risk score and stress biomarkers were identified using linear regression models, and between stress hormones and CRL- and EV-trajectories using mixed models. A mediation analysis was performed to assess the contribution of tryptophan. All associations were adjusted for potential confounders, which were identified using a data-driven approach. Several sensitivity analyses were performed to check the robustness of the findings. MAIN RESULTS AND THE ROLE OF CHANCE: The maternal vulnerability risk score was positively associated with concentrations of hair cortisol and cortisone (pg/mg) (ß = 0.366, 95% CI = 0.010-0.722; ß = 0.897, 95% CI = 0.102-1.691, respectively), and negatively with tryptophan concentrations (µmol/L) (ß = -1.637, 95% CI = -2.693 to -0.582). No associations revealed for C-reactive protein and total homocysteine. Higher hair cortisol concentrations were associated with reduced EV-trajectories (3√EV: ß = -0.010, 95% CI = -0.017 to -0.002), while no associations were found with CRL-trajectories. Mediation by tryptophan was not shown. LIMITATIONS, REASONS FOR CAUTION: Residual confounding cannot be ruled out, and the external validity may be limited due to the study's single-center observational design in a tertiary hospital. WIDER IMPLICATIONS OF THE FINDINGS: There is mounting evidence that a high degree of maternal vulnerability negatively affects maternal and perinatal health, and that of the future life course. The results of our study emphasize the need to identify highly vulnerable women as early as possible, at least before conception. Our findings suggest that the chronic stress response and alterations of the maternal tryptophan metabolism are involved in maternal vulnerability, affecting first-trimester growth, with potential impact on the long-term health of the offspring. STUDY FUNDING/COMPETING INTEREST(S): This study was funded by the Departments of Obstetrics and Gynecology and Clinical Chemistry of the Erasmus MC, University Medical Center, Rotterdam, the Netherlands, and the Junior Award granted by the De Snoo-van 't Hoogerhuijs Foundation in March 2022. There are no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.

Computational methods for the analysis of early-pregnancy brain ultrasonography: a systematic review.

BACKGROUND: Early screening of the brain is becoming routine clinical practice. Currently, this screening is performed by manual measurements and visual analysis, which is time-consuming and prone to errors. Computational methods may support this screening. Hence, the aim of this systematic review is to gain insight into future research directions needed to bring automated early-pregnancy ultrasound analysis of the human brain to clinical practice. METHODS: We searched PubMed (Medline ALL Ovid), EMBASE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar, from inception until June 2022. This study is registered in PROSPERO at CRD42020189888. Studies about computational methods for the analysis of human brain ultrasonography acquired before the 20th week of pregnancy were included. The key reported attributes were: level of automation, learning-based or not, the usage of clinical routine data depicting normal and abnormal brain development, public sharing of program source code and data, and analysis of the confounding factors. FINDINGS: Our search identified 2575 studies, of which 55 were included. 76% used an automatic method, 62% a learning-based method, 45% used clinical routine data and in addition, for 13% the data depicted abnormal development. None of the studies shared publicly the program source code and only two studies shared the data. Finally, 35% did not analyse the influence of confounding factors. INTERPRETATION: Our review showed an interest in automatic, learning-based methods. To bring these methods to clinical practice we recommend that studies: use routine clinical data depicting both normal and abnormal development, make their dataset and program source code publicly available, and be attentive to the influence of confounding factors. Introduction of automated computational methods for early-pregnancy brain ultrasonography will save valuable time during screening, and ultimately lead to better detection, treatment and prevention of neuro-developmental disorders. FUNDING: The Erasmus MC Medical Research Advisor Committee (grant number: FB 379283).