A comparison of the International Association of Diabetes and Pregnancy Study Groups Recommendations with Former Criteria for Diagnosing Gestational Diabetes Mellitus: A Retrospective Cohort Study

Association between the history of abortion and gestational diabetes mellitus: A meta-analysis

PURPOSE

Gestational diabetes mellitus (GDM) is a common metabolic disease in pregnant women. The purpose of this study was to determine whether a history of abortion increases the risk of GDM by meta-analysis.

METHODS

A comprehensive literature search was conducted in nine databases of studies on the association between abortion history and GDM up to April 12, 2022. Fixed- or random-effects models were used to estimate the pooled odds ratio (OR) and 95% CI. The I square value (I²) was used to assess heterogeneity. Possible sources of heterogeneity were explored by conducting subgroup analysis and meta-regression. A sensitivity analysis was also performed for this meta-analysis. Publication bias was assessed by funnel plots and Egger's tests.

RESULTS

Thirty-one studies enrolling 311,900 subjects were included in this meta-analysis. The risk of GDM was higher in women who experienced abortion than in those who did not (OR = 1.41 95% CI: 1.28-1.55, I² = 66.8%). The risk of GDM increased with an increasing number of abortions (1 time: OR = 1.67, 95% CI = 1.26-2.22; 2 times: OR = 2.10, 95% CI = 1.26-3.49; ≥3 times: OR = 2.49, 95% CI = 1.24-5.01). Both spontaneous abortion (OR = 1.52, 95% CI = 1.30-1.78) and induced abortion (OR = 1.07, 95% CI = 1.03-1.11) were associated with an increased risk of GDM.

CONCLUSIONS

A history of abortion was associated with an increased risk of GDM in pregnant women, which may be a risk factor for predicting GDM.

Miscarriage, stillbirth and the risk of diabetes in women: A systematic review and meta-analysis

AIMS

Whether women with a history of miscarriage or stillbirth have an increased risk of diabetes is inconclusive. We aimed to systematically assess the association between them.

METHODS

We searched PubMed, Web of Science and Scopus through November 2022. Random-effect model for meta-analysis was applied to calculate pooled odds ratios and corresponding 95 % confidence intervals (CIs) when heterogeneity was > 40 %.

RESULTS

Thirteen cohort studies and eight case-control studies with a total of 529,990 participants were included. Women ever experiencing a miscarriage had a 1.15-fold risk of non-gestational diabetes (95% CI: 1.02-1.28) and a 1.62-fold risk of gestational diabetes (95% CI: 1.32-1.98) compared to those never experiencing a miscarriage. Of them, women with three or more miscarriages had a 1.99-fold risk of non-gestational diabetes (95% CI: 1.36-2.91). The risk of non-gestational diabetes among women ever experiencing a stillbirth was 1.21 times compared with those never experiencing a stillbirth (95% CI: 1.03-1.41). Pooled results did not support a stable association between stillbirth and gestational diabetes risk (odds ratio:1.91, 95% CI: 1.00-3.64).

CONCLUSIONS

A history of miscarriage or stillbirth was associated with an increased risk of diabetes in women. Future studies are needed to explore whether prediabetic metabolic conditions contribute to this association.

Effective Macrosomia Prediction Using Random Forest Algorithm

(1) Background: Macrosomia is prevalent in China and worldwide. The current method of predicting macrosomia is ultrasonography. We aimed to develop new predictive models for recognizing macrosomia using a random forest model to improve the sensitivity and specificity of macrosomia prediction; (2) Methods: Based on the Shandong Multi-Center Healthcare Big Data Platform, we collected the prenatal examination and delivery data from June 2017 to May 2018 in Jinan, including the macrosomia and normal-weight newborns. We constructed a random forest model and a logistic regression model for predicting macrosomia. We compared the validity and predictive value of these two methods and the traditional method; (3) Results: 405 macrosomia cases and 3855 normal-weight newborns fit the selection criteria and 405 pairs of macrosomia and control cases were brought into the random forest model and logistic regression model. On the basis of the average decrease of the Gini coefficient, the order of influencing factors was: interspinal diameter, transverse outlet, intercristal diameter, sacral external diameter, pre-pregnancy body mass index, age, the number of pregnancies, and the parity. The sensitivity, specificity, and area under curve were 91.7%, 91.7%, and 95.3% for the random forest model, and 56.2%, 82.6%, and 72.0% for logistic regression model, respectively; the sensitivity and specificity were 29.6% and 97.5% for the ultrasound; (4) Conclusions: A random forest model based on the maternal information can be used to predict macrosomia accurately during pregnancy, which provides a scientific basis for developing rapid screening and diagnosis tools for macrosomia.

The dual roles of three MMPs and TIMP in innate immunity and metamorphosis in the silkworm, Bombyx mori

The matrix metalloproteinases (MMPs) and their endogenous inhibitory factors, tissue inhibitors of metalloproteinases (TIMPs), are implicated in many diseases. However, the mammalian MMPs (> 20) and TIMPs (> 3) are larger in number, and so little is known about their individual roles in organisms. Hence, we have systematically studied the roles of all three MMPs and one TIMP in silkworm innate immunity and metamorphosis. We observed that MMPs and TIMP are highly expressed during the pupation stage of the silkworms, and TIMP could interact with each MMPs. High-activity MMPs and low-activity TIMP may enhance the infection of B. mori nucleopolyhedrovirus in both in vitro and in vivo. MMPs' knockout and TIMP overexpression delayed silkworm development and even caused death. Interestingly, different MMPs' knockout led to different tubular tissue dysplasia. These findings provide insights into the conserved functions of MMPs and TIMP in human organogenesis and immunoregulation.