Integrated metabolomics and machine learning approach to predict hypertensive disorders of pregnancy.

BACKGROUND: Hypertensive disorders of pregnancy account for 3% to 10% of maternal-fetal morbidity and mortality worldwide. This condition has been considered one of the leading causes of maternal deaths in developing countries, such as India. OBJECTIVE: This study aimed to discover hypertensive disorders of pregnancy-specific candidate urine metabolites as markers for hypertensive disorders of pregnancy by applying integrated metabolomics and machine learning approaches. STUDY DESIGN: The targeted urinary metabolomics study was conducted in 70 healthy pregnant controls and 133 pregnant patients having hypertension as cases. Hypertensive disorders of pregnancy-specific metabolites for disease prediction were further extracted using univariate and multivariate statistical analyses. For machine learning analysis, 80% of the data were used for training (79 for hypertensive disorders of pregnancy and 42 for healthy pregnancy) and validation (27 for hypertensive disorders of pregnancy and 14 for healthy pregnancy), and 20% of the data were used for test sets (27 for hypertensive disorders of pregnancy and 14 for healthy pregnancy). RESULTS: The statistical analysis using an unpaired t test revealed 44 differential metabolites. Pathway analysis showed mainly that purine and thiamine metabolism were altered in the group with hypertensive disorders of pregnancy compared with the healthy pregnancy group. The area under the receiver operating characteristic curves of the 5 most predominant metabolites were 0.98 (adenosine), 0.92 (adenosine monophosphate), 0.89 (deoxyadenosine), 0.81 (thiamine), and 0.81 (thiamine monophosphate). The best prediction accuracies were obtained using 2 machine learning models (95% for the gradient boost model and 98% for the decision tree) among the 5 used models. The machine learning models showed higher predictive performance for 3 metabolites (ie, thiamine monophosphate, adenosine monophosphate, and thiamine) among 5 metabolites. The combined accuracies of adenosine from all models were 98.6 in the training set and 95.6 in the test set. Moreover, the predictive performance of adenosine was higher than other metabolites. The relative feature importance of adenosine was also observed in the decision tree and the gradient boost model. CONCLUSION: Among other metabolites, adenosine and thiamine metabolites were found to differentiate participants with hypertensive disorders of pregnancy from participants with healthy pregnancies; hence, these metabolites can serve as a promising noninvasive marker for the detection of hypertensive disorders of pregnancy.

Circulatory hepcidin levels association with gestational diabetes mellitus: a meta-analysis of observational studies.

OBJECTIVES: Hepcidin is linked to glucose metabolism in women with gestational diabetes mellitus (GDM). This systematic review and meta-analysis was conducted to determine the association between hepcidin levels and GDM. A literature search was performed using different databases to identify potential studies investigating hepcidin association in GDM patients. The effect sizes were calculated based on the standardized mean difference (SMD) and Fisher's Z value with a 95% confidence interval (CI). KEY FINDINGS: Out of 827 articles, only 7 case-control studies satisfied the inclusion and exclusion criteria. The pooled SMD of circulatory hepcidin levels in GDM patients was considerably higher than normal pregnant women (SMD = 1.69; 95% CI, 0.86 to 2.53; P < 0.0001). This study also observed that hepcidin levels were positively correlated with ferritin levels (r = 0.264; Z = 0.27; P < 0.0001). Furthermore, a subgroup analysis of serum and plasma groups revealed significantly higher hepcidin levels in serum (SMD = 2.12; 95% CI, 0.44 to3.79; P = 0.001) than in the plasma group (SMD = 1.28; 95% CI, 0.32 to 2.2; I2 = 96%). SUMMARY: Our findings suggest that hepcidin levels may be elevated in GDM patients, making it a viable marker for GDM diagnosis, and regular monitoring of its levels could be helpful in aiding clinical decisions.