Maternal and Neonatal Health Outcomes Associated with the Use of Gliclazide and Metformin for the Treatment of Diabetes in Pregnancy: A Record Linkage Study

The effect of pregnancy-related hormones on hepatic transporters: studies with premenopausal human hepatocytes

Introduction

Pregnancy results in significant changes in drug pharmacokinetics (PK). While previous studies have elucidated the impact of pregnancy-related hormones (PRH) on mRNA or protein expression and activity of major hepatic metabolizing enzymes, their effect on hepatic drug transporters remains largely unexplored. Therefore, we investigated the effect of a cocktail of PRH on the mRNA expression and activity of hepatic transporters.

Methods

Plated human hepatocytes (PHH) from 3 premenopausal donors were incubated, in triplicate, for 72 h, with vehicle (DMSO < 0.01%), rifampin (10 μM; positive control) or a cocktail of PRH consisting of estrone, estradiol, estriol, estetrol, progesterone, cortisol, testosterone, oxytocin, and placental growth hormone. The PRH concentrations replicated 0.1×, 1×, or 10× of the plasma concentrations of these hormones observed during each of the three trimesters of pregnancy. After treatment, mRNA expression (quantified by qPCR) of hepatic influx and efflux transporters as well as the activity of influx transporters was quantified (uptake of a selective substrate ± corresponding transporter inhibitor). The data were expressed relative to that in the control (vehicle) group. Significance was evaluated by ANOVA (followed by Dunn's multiple comparisons) or unpaired t-test when the within-lot data were analyzed, or repeated measures ANOVA (followed by Dunn's multiple comparisons) or paired t-test when data from all 3 lots were analyzed (p < 0.05).

Results and Discussion

In general, a) PRH cocktails significantly induced transporter mRNA expression in the following order OAT2 ≈ NTCP ≈ OCT1 > OATP2B1 and repressed mRNA expression in the following order OATP1B3 > OATP1B1; b) these changes translated into significant induction of OAT2 (T1-T3) and NTCP (T2-T3, in only two lots) activity at the 1× PRH concentration. Compared with the influx transporters, the induction of mRNA expression of efflux transporters was modest, with mRNA expression of MRP2 and BSEP being induced the most.

Conclusion

Once these data are verified through in vivo probe drug PK studies in pregnancy, they can be populated into physiologically based pharmacokinetic (PBPK) models to predict, for all trimesters of pregnancy, transporter-mediated clearance of any drug that is a substrate of the affected transporters.

Achieving comparability in glycemic control between antidiabetic treatment strategies in pregnancy when using real world data

PURPOSE

Healthcare utilization databases often lack information on glycemic control, a key confounder when studying the safety of antidiabetic treatments, since patients with worse control are channeled to second-line agents, in particular insulin, versus first-line agents such as metformin. We evaluated whether adjustment for measured characteristics attains balance in glycemic control when comparing antidiabetic treatment strategies in pregnant women with pregestational type 2 diabetes (T2DM).

METHODS

In a US insurance claims database, we identified 3360 women with T2DM pregnant between 2004 and 2015, of whom a subset of 996 had data on hemoglobin A1c (HbA1c ) levels. We selected insulin only as the comparator group and used propensity score (PS)-matching on comorbidities and proxies of diabetes severity, but not on HbA1c , to adjust for confounding. We used standardized differences (st.diff) to assess balance in claims-based covariates and mean HbA1c (% ± SD) in the subset.

RESULTS

There were imbalances in claims-based covariates before PS-matching, with smaller differences when both treatment strategies included insulin. After PS-matching, balance was achieved in most claims-based covariates (st.diff <0.1). Mean HbA1c was similar before and after PS-matching when both treatments included insulin (e.g., 7.1 ± 1.5 vs. 7.7 ± 1.8 and 7.1 ± 1.5 vs. 7.5 ± 1.7, respectively, for metformin + insulin vs. insulin only). Differences in mean HbA1c remained after PS-matching when non-insulin treatments were compared to treatments including insulin (e.g., 6.3 ± 1.1 vs. 7.6 ± 1.7 for metformin only vs. insulin only).

CONCLUSIONS

Balance in both claims-based characteristics and glycemic control was attained after restricting the population to women with T2DM and comparing treatment strategies indicated for patients with similar diabetes severity. When comparing treatment strategies with versus without insulin, differences in glycemic control persisted after PS-matching even when balance was attained for other measured characteristics.

Major malformations risk following early pregnancy exposure to metformin: a systematic review and meta-analysis

Metformin is considered as first-line treatment for type 2 diabetes and an effective treatment for polycystic ovary syndrome (PCOS). However, evidence regarding its safety in pregnancy is limited. We conducted a systematic review and meta-analysis of major congenital malformations (MCMs) risk after first-trimester exposure to metformin in women with PCOS and pregestational diabetes mellitus (PGDM). Randomized controlled trials (RCTs) and observational cohort studies with a control group investigating risk of MCM after first-trimester pregnancy exposure to metformin were searched until December 2021. ORs and 95% CIs were calculated separately according to indications and study type using Mantel-Haenszel method; outcome data were combined using random-effects model. Eleven studies (two RCTs; nine observational cohorts) met the inclusion criteria: four included pregnant women with PCOS, four included those with PGDM and three evaluated both indications separately and were considered in both indication groups. In PCOS group, there were two RCTs (57 exposed, 52 control infants) and five observational studies (472 exposed, 1892 control infants); point estimates for MCM rates in RCTs and observational studies were OR 0.93 (95% CI 0.09 to 9.21) (I2=0%; Q test=0.31; p value=0.58) and OR 1.35 (95% CI 0.37 to 4.90) (I2=65%; Q test=9.43; p value=0.05), respectively. In PGDM group, all seven studies were observational (1122 exposed, 1851 control infants); the point estimate for MCM rates was OR 1.05 (95% CI 0.50 to 2.18) (I2=59%; Q test=16.34; p value=0.01). Metformin use in first-trimester pregnancy in women with PCOS or PGDM do not meaningfully increase the MCM risk overall. However, further studies are needed to characterize residual safety concerns.

A Comprehensive Systematic Review of Data Linkage Publications on Diabetes in Australia

Aims

Our study aimed to identify the common themes, knowledge gaps and to evaluate the quality of data linkage research on diabetes in Australia.

Methods

This systematic review was developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (the PRISMA Statement). Six biomedical databases and the Australian Population Health Research Network (PHRN) website were searched. A narrative synthesis was conducted to comprehensively identify the common themes and knowledge gaps. The guidelines for studies involving data linkage were used to appraise methodological quality of included studies.

Results

After screening and hand-searching, 118 studies were included in the final analysis. Data linkage publications confirmed negative health outcomes in people with diabetes, reported risk factors for diabetes and its complications, and found an inverse association between primary care use and hospitalization. Linked data were used to validate data sources and diabetes instruments. There were limited publications investigating healthcare expenditure and adverse drug reactions (ADRs) in people with diabetes. Regarding methodological assessment, important information about the linkage performed was under-reported in included studies.

Conclusions

In the future, more up to date data linkage research addressing costs of diabetes and its complications in a contemporary Australian setting, as well as research assessing ADRs of recently approved antidiabetic medications, are required.

Real-World Performance of the MiniMed™ 780G System: First Report of Outcomes from 4120 Users

Background: The MiniMed™ 780G system includes an advanced hybrid closed loop (AHCL) algorithm that provides both automated basal and correction bolus insulin delivery. The preliminary performance of the system in real-world settings was evaluated. Methods: Data uploaded from August 2020 to March 2021 by individuals living in Belgium, Finland, Italy, the Netherlands, Qatar, South Africa, Sweden, Switzerland, and the United Kingdom were aggregated and retrospectively analyzed to determine the mean glucose management indicator (GMI), percentage of time spent within (TIR), below (TBR), and above (TAR) glycemic ranges, system use, and insulin consumption in users having ≥10 days of sensor glucose (SG) data after initiating AHCL. The impact of initiating AHCL was evaluated in a subgroup of users also having ≥10 days of SG data, before AHCL initiation. Results: Users (N = 4120) were observed for a mean of 54 ± 32 days. During this time, they spent a mean of 94.1% ± 11.4% of the time in AHCL and achieved a mean GMI of 6.8% ± 0.3%, TIR of 76.2% ± 9.1%, TBR <70 of 2.5% ± 2.1%, and TAR >180 of 21.3% ± 9.4%, after initiating AHCL. There were 77.3% and 79.0% of users who achieved a TIR >70% and a GMI of <7.0%, respectively. Users for whom comparison with pre-AHCL was possible (N = 812) reduced their GMI by 0.4% ± 0.4% (P = 0.005) and increased their TIR by 12.1% ± 10.5% (P < 0.0001), post-AHCL initiation. More users achieved the glycemic treatment goals of GMI <7.0% (37.6% vs. 75.2%, P < 0.0001) and TIR >70% (34.6% vs. 74.9%, P < 0.0001) when compared with pre-AHCL initiation. Conclusion: Most MiniMed 780G system users achieved TIR >70% and GMI <7%, while minimizing hypoglycemia, in a real-world condition.

Pregestational diabetes in pregnancy: Complications, management, surveillance, and mechanisms of disease-A review

Diabetes is an increasingly common diagnosis among pregnant women. Pregestational diabetes is associated with an increase in many adverse pregnancy outcomes, which impact both on the woman and her fetus. The models of pregnancy care for women with diabetes are based largely on observational data or consensus opinion. Strategies for aneuploidy screening and monitoring for fetal well-being should be modified in women with diabetes. There is an increasing understanding of the mechanisms by which congenital anomalies and disorders of fetal growth occur, involving epigenetic modifications, changes in gene expression in critical developmental pathways, and oxidative stress. This knowledge may lead to pathways for improved care for these high-risk pregnancies.