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Objective: To investigate causal associations between blood-based inflammatory markers and female infertility using Mendelian randomization (MR).

Design: Mendelian randomization using genome-wide association study data.

Setting: Publicly available genome-wide association study data. Patient(s): Large female-only cohorts of European ancestry.

Intervention(s): Blood-based inflammatory markers (C-reactive protein, interleukins, monocyte chemoattractant protein-1, tumor necrosis factor-α, interferon-γ).

Main Outcomes Measure(s): Anovulatory infertility (1,054 cases and 117,098 controls); female infertility of other/unspecified origin (5,667 cases and 117,098 controls); and medical treatment for female infertility (2,706 cases and 120,873 controls). Total causal effects were assessed using univariable two-sample methods including inverse variance weighted (IVW) as the primary analysis, as well as other secondary analyses (MR-Egger, weighted median, etc.), with relevant quality assessments.

Result(s): Interleukin-8 demonstrated a positive association with anovulatory infertility via IVW (odds ratio, 95% confidence interval; 1.51, 1.04–2.21) and weighted median (1.64, 1.05–2.57) methods. Monocyte chemoattractant protein-1 was associated with anovulatory infertility via MR-Egger (2.06, 1.13–3.77). Inverse associations were found for interleukins-12 and -18 via IVW, with higher interleukin-12 being associated with lower medical treatment for female infertility (0.75, 0.59–0.94), whereas higher interleukin-18 was associated with lower female infertility of other/unspecified origin (0.90, 0.83–0.97).

Conclusion(s): This is the first study to examine causal relationships between inflammation and female infertility using MR. Monocyte chemoattractant protein-1 and interleukin-8 are implicated in anovulatory infertility; however, only the relationship with interleukin-8 was evident in the primary analysis. Interleukins-12 and -18 demonstrated inverse associations with infertility outcomes. Further research is needed to uncover the mechanistic functions of these markers to confirm causality and examine their therapeutic potential for female infertility.

Problem/ Background: The acceptability of providing women with personalised cardiometabolic risk information using risk prediction tools early in pregnancy is not well understood.

Aim: To explore women's and healthcare professionals’ perspectives of the acceptability of a prognostic, composite risk prediction tool for cardiometabolic risk (gestational diabetes and/or hypertensive disorders of pregnancy) for use in early pregnancy.

Methods: Semi-structured interviews were conducted to explore the acceptability of cardiometabolic risk prediction tools, preferences for risk communication and considerations for implementation into antenatal care. The Theoretical Framework of Acceptability informed interview questions. Transcripts were thematically analysed.

Findings: Women ≤24 weeks’ gestation (n = 13) and healthcare professionals (n = 8), including midwives (n = 2), general practitioners (n = 2), obstetricians (n = 2), an endocrinologist (n = 1) and cardiologist (n = 1) participated. Participants indicated that providing personalised risk information is only appropriate when preventative measures can be initiated to mitigate risks. Differentiating the risk for each condition (single risk outputs) was often preferred to composite risk outputs to enable targeted monitoring and management. Defining conditions and risks to mother/baby, visually depicting personalised risk scores, and providing clear, patient-centred clinical management plans were recommended. Supportive clinical policy changes, staff engagement/training, and integration into electronic health records were suggested to facilitate uptake into routine antenatal care.

Conclusion: Women and healthcare professionals suggested that early pregnancy cardiometabolic risk prediction tools may be acceptable when preventative interventions are available to reduce risks. Risk prediction tools with integrated patient-centred education materials may promote timely access and engagement with preventative interventions to optimise women's current and future health.

Although inflammation may disrupt immunoendocrine crosstalk essential for female reproductive function, causal links to disorders like polycystic ovary syndrome (PCOS) and endometriosis remain unestablished. This study aimed to utilise Mendelian randomisation (MR) methods to explore causal associations between serum inflammatory markers and common reproductive disorders, aiming to identify novel mechanisms and potential avenues for treatment. Total causal effects of serum inflammatory markers (interleukins, monocyte chemoattractant protein-1, etc.) on female reproductive disorders in large sample cohorts of Finnish ancestry were assessed using univariable two-sample MR methods, including the inverse variance weighted (IVW) method as the primary analysis, with relevant quality assessments (e.g., leave-one out, heterogeneity, and horizontal pleiotropy testing). The main outcome measures were PCOS (642 cases and 118,228 controls) and endometriosis (8288 cases and 68,969 controls) from the FINNGEN cohort. Monocyte chemoattractant protein-1/C-C motif chemokine ligand demonstrated a positive causal association with polycystic ovary syndrome (odds ratio [95% CI]: 1.48 [1.10, 2.00], p = 0.0097), while higher interleukin-9 levels were positively associated with endometriosis (1.15 [1.02, 1.30], p = 0.0277), both via the IVW method. These markers should be investigated as key candidates for future research into the mechanistic pathways underpinning these conditions.

Background: Early identification of pregnant women at high risk of developing gestational diabetes (GDM) is desirable as effective lifestyle interventions are available to prevent GDM and to reduce associated adverse outcomes. Personalised probability of developing GDM during pregnancy can be determined using a risk prediction model. These models extend from traditional statistics to machine learning methods; however, accuracy remains sub-optimal.

Objective: We aimed to compare multiple machine learning algorithms to develop GDM risk prediction models, then to determine the optimal model for predicting GDM.

Methods: A supervised machine learning predictive analysis was performed on data from routine antenatal care at a large health service network from January 2016 to June 2021. Predictor set 1 were sourced from the existing, internationally validated Monash GDM model: GDM history, body mass index, ethnicity, age, family history of diabetes, and past poor obstetric history. New models with different predictors were developed, considering statistical principles with inclusion of more robust continuous and derivative variables. A randomly selected 80% dataset was used for model development, with 20% for validation. Performance measures, including calibration and discrimination metrics, were assessed. Decision curve analysis was performed.

Results: Upon internal validation, the machine learning and logistic regression model's area under the curve (AUC) ranged from 71% to 93% across the different algorithms, with the best being the CatBoost Classifier (CBC). Based on the default cut-off point of 0.32, the performance of CBC on predictor set 4 was: Accuracy (85%), Precision (90%), Recall (78%), F1-score (84%), Sensitivity (81%), Specificity (90%), positive predictive value (92%), negative predictive value (78%), and Brier Score (0.39).

Conclusions: In this study, machine learning approaches achieved the best predictive performance over traditional statistical methods, increasing from 75 to 93%. The CatBoost classifier method achieved the best with the model including continuous variables.

Objective: The original Monash gestational diabetes mellitus (GDM) risk prediction in early pregnancy model is internationally externally validated and clinically implemented. We temporally validate and update this model in a contemporary population with a universal screening context and revised diagnostic criteria and ethnicity categories, thereby improving model performance and generalizability.

Study Design and Setting: The updating dataset comprised of routinely collected health data for singleton pregnancies delivered in Melbourne, Australia from 2016 to 2018. Model predictors included age, body mass index, ethnicity, diabetes family history, GDM history, and poor obstetric outcome history. Model updating methods were recalibration-in-the-large (Model A), intercept and slope re-estimation (Model B), and coefficient revision using logistic regression (Model C1, original ethnicity categories; Model C2, revised ethnicity categories). Analysis included 10-fold cross-validation, assessment of performance measures (c-statistic, calibration-in-the-large, calibration slope, and expected-observed ratio), and a closed-loop testing procedure to compare models' log-likelihood and akaike information criterion scores.

Results: In 26,474 singleton pregnancies (4,756, 18% with GDM), the original model demonstrated reasonable temporal validation (c-statistic = 0.698) but suboptimal calibration (expected-observed ratio = 0.485). Updated model C2 was preferred, with a high c-statistic (0.732) and significantly better performance in closed testing.

Conclusion: We demonstrated updating methods to sustain predictive performance in a contemporary population, highlighting the value and versatility of prediction models for guiding risk-stratified GDM care.