Author
He Sun
Bio: He Sun is an academic researcher from China Medical University (PRC). The author has contributed to research in topics: Pregnancy & Gestational diabetes. The author has co-authored 1 publications.
Topics: Pregnancy, Gestational diabetes
Papers
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TL;DR: In this article, the authors summarized the studies in this field based on the recent literature and provided evidence for the value of these novel and practical serological markers in early identification of GDM and the prevention and its adverse outcomes.
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TL;DR: In this paper , the authors investigated the associations of inflammatory blood cell parameters in both early and middle pregnancy and their change patterns from early to middle pregnancy with gestational diabetes mellitus (GDM) risk.
Abstract: CONTEXT
Chronic low-grade inflammation may play a crucial role in the pathogenesis of gestational diabetes mellitus (GDM). However, prospective studies on the relations of inflammatory blood cell parameters during pregnancy with GDM are lacking.
OBJECTIVE
To prospectively investigate the associations of inflammatory blood cell parameters in both early and middle pregnancy and their change patterns from early to middle pregnancy with GDM risk.
METHODS
We used data from the Tongji-Shuangliu Birth Cohort. Inflammatory blood cell parameters (white blood cells, neutrophils, lymphocytes, monocytes, neutrophil to lymphocyte ratio [NLR], and platelets) were assayed before 15 weeks and 16-28 gestational age. Logistic regression was used to evaluate the associations between inflammatory blood cell parameters and GDM.
RESULTS
Of the 6354 pregnant women, 445 were diagnosed with GDM. After adjustment for potential confounders, white blood cells, neutrophils, lymphocytes, monocytes, and NLR in early pregnancy were positively associated with GDM risk (odds ratios [OR] [95% CI] for extreme-quartile comparison were 2.38 [1.76-3.20], 2.47 [1.82-3.36], 1.40 [1.06-1.85], 1.69 [1.27-2.24], and 1.51 [1.12-2.02], respectively, all P for trend ≤.010). Higher level of white blood cells, neutrophils, monocytes, and NLR in middle pregnancy were associated with an increased risk of GDM (all P for trend ≤.014). Stable high levels (≥median in both early and middle pregnancy) of white blood cells, neutrophils, monocytes, and NLR were positively associated with GDM risk (all P ≤.001).
CONCLUSION
Elevated white blood cells, neutrophils, monocytes, and NLR in both early and middle pregnancy and their stable high levels from early to middle pregnancy were associated with a higher GDM risk, highlighting that they might be clinically relevant for identifying individuals at high risk for GDM.
TL;DR: Wang et al. as discussed by the authors developed a visual nomogram model for predicting the probability of preterm delivery in women with GDM at the time of diagnosis, and validated the model on the training and validation cohort.
Abstract: Background The incidence of preterm delivery (<37 weeks’ gestation) is increased due to gestational diabetes mellitus (GDM). The preterm delivery is the leading cause of death in children. If potential preterm delivery can be diagnosed early and then prevented, adverse pregnancy outcomes can be improved. Therefore, effective methods are needed for early prediction of preterm delivery in women with GDM. Methods Patients with GDM defined as the presence of at least 1 plasma glucose abnormality at 24–28 weeks of pregnancy [fasting plasma glucose ≥5.1 mmol/L, 60-min ≥10.0 mmol/L, 120-min ≥8.5 mmol/L by 75 g oral glucose tolerance test (OGTT)] from the First Affiliated Hospital of Wenzhou Medical University were enrolled. The data (564 patients) recorded from January 2017 to June 2020 were named the training cohort, and the data (242 patients) obtained from patients with GDM, from July 2020 to January 2022, were named the validation cohort. Mann-Whitney U test and chi-square test were used to compare the skewed distributed and categorical data, respectively. According to the results of univariate logistic regression analysis, the multivariate logistic regression model was developed in the training cohort. Then, the nomogram was established. The validation of the nomogram was conducted on the training and validation cohort. Results No significant differences in baseline characteristics were detected between the 2 cohorts (all P>0.05). The multivariate analysis suggested that maternal age, insulin use, NLR, and monocyte count were the independent predictors of preterm delivery. A nomogram for predicting the probability of preterm delivery was developed. The model suggested good discrimination [areas under the curve (AUC) =0.885, 95% confidence interval (95% CI): 0.855–0.910, sensitivity =83.0%, specificity =83.1% in the training cohort; AUC =0.919, 95% CI: 0.858–0.980, sensitivity =90.6%, specificity =84.8% in the validation cohort] and good calibration [Hosmer-Lemeshow (HL) test: χ2=3.618, P=0.306 in the training cohort; χ2=6.012, P=0.111 in the validation cohort]. Conclusions The visual nomogram model appears to be a reliable approach for the prediction of preterm delivery, allowing clinicians to take timely measures to prevent the occurrence of preterm delivery in women with GDM at the time of GDM diagnosis, and deserves further investigation.