Effect of an individualised nutritional intervention on gestational diabetes mellitus prevention in a high-risk population screened by a prediction model: study protocol for a multicentre randomised controlled trial

Promoting healthy eating in early pregnancy in individuals at risk of gestational diabetes mellitus: does it improve glucose homeostasis? A study protocol for a randomized control trial

Background

Healthy eating during pregnancy has favorable effects on glycemic control and is associated with a lower risk of gestational diabetes mellitus (GDM). According to Diabetes Canada, there is a need for an effective and acceptable intervention that could improve glucose homeostasis and support pregnant individuals at risk for GDM.

Aims

This unicentric randomized controlled trial (RCT) aims to evaluate the effects of a nutritional intervention initiated early in pregnancy, on glucose homeostasis in 150 pregnant individuals at risk for GDM, compared to usual care.

Methods

Population: 150 pregnant individuals ≥18 years old, at ≤14 weeks of pregnancy, and presenting ≥1 risk factor for GDM according to Diabetes Canada guidelines. Intervention: The nutritional intervention initiated in the first trimester is based on the health behavior change theory during pregnancy and on Canada's Food Guide recommendations. It includes (1) four individual counseling sessions with a registered dietitian using motivational interviewing (12, 18, 24, and 30 weeks), with post-interview phone call follow-ups, aiming to develop and achieve S.M.A.R.T. nutritional objectives (specific, measurable, attainable, relevant, and time-bound); (2) 10 informative video clips on healthy eating during pregnancy developed by our team and based on national guidelines, and (3) a virtual support community via a Facebook group. Control: Usual prenatal care. Protocol: This RCT includes three on-site visits (10-14, 24-26, and 34-36 weeks) during which a 2-h oral glucose tolerance test is done and blood samples are taken. At each trimester and 3 months postpartum, participants complete web-based questionnaires, including three validated 24-h dietary recalls to assess their diet quality using the Healthy Eating Food Index 2019. Primary outcome: Difference in the change in fasting blood glucose (from the first to the third trimester) between groups. This study has been approved by the Ethics Committee of the Centre de recherche du CHU de Québec-Université Laval.

Discussion

This RCT will determine whether a nutritional intervention initiated early in pregnancy can improve glucose homeostasis in individuals at risk for GDM and inform Canadian stakeholders on improving care trajectories and policies for pregnant individuals at risk for GDM.

Clinical trial registration

https://clinicaltrials.gov/study/NCT05299502, NCT05299502.

Protective effect of soy isolate protein against streptozotocin induced gestational diabetes mellitus via TLR4/MyD88/NF-κB signaling pathway

BACKGROUND

Gestational diabetes mellitus (GDM) is a serious complication of pregnancy that is characterized by high blood sugar levels that occur due to insulin resistance and dysfunction in glucose metabolism during pregnancy. It usually develops in the second or third trimester of pregnancy and affects about 7 % of all pregnancies worldwide. In this experimental study, we scrutinized the GDM protective effect of soy isolate protein against streptozotocin (STZ) induced GDM in rats and explore the underlying mechanism.

MATERIAL AND METHODS

Sprague-Dawley (SD) rats were used in this experimental study. A 55 mg/kg intraperitoneal injection of streptozotocin (STZ) was administered to induce diabetes in female rats, followed by oral administration of soy isolate protein for 18 days. Body weight, glucose levels, and insulin were measured at different time intervals (0, 9, and 18 days). Lipid profiles, antioxidant levels, inflammatory cytokines, apoptosis parameters, and mRNA expression were also assessed. Pancreatic and liver tissues were collected for histopathological examination during the experimental study.

RESULTS

Soy isolate protein significantly (P < 0.001) reduced the glucose level and enhanced the insulin level and body weight. Soy isolate protein remarkably decreased the placental weight and increased the fetal weight. Soy isolate protein significantly (P < 0.001) decreased the HbA1c, hepatic glycogen, serum C-peptide and increased the level of free fatty acid. Soy isolate protein significantly (P < 0.001) altered the level of lipid, antioxidant and inflammatory cytokines. Soy isolate protein significantly (P < 0.001) improved the level of adiponectin, visfatin and suppressed the level of leptin and ICAM-1. Soy isolate protein significantly (P < 0.001) altered the mRNA expression and also restored the alteration of histopathology.

CONCLUSION

Based on the result, soy isolate protein exhibited the GDM protective effect against the STZ induced GDM in rats via alteration of TLR4/MyD88/NF-κB signaling pathway.

Effect of individualised nutritional intervention on the postpartum nutritional status of patients with gestational diabetes mellitus and the growth and development of their offspring: a quasi-experimental study

This study explored the effect of individualised nutritional intervention on the postpartum nutritional status of patients with the growth and development of their offspring. This study included pregnant women with gestational diabetes mellitus (GDM) at Hangzhou Women's Hospital in 2019. At 42 days after childbirth, the HbA1c (95% CI: 0.44-0.56%, p < 0.001), the FPG (95% CI: 0.01-0.26 mmol/L, p < 0.05), 2HPG (95% CI: -0.01-0.73 mmol/L, p < 0.05) and TCH (95% CI: -0.34-0.00 mmol/L, p < 0.05) level of the control group were 0.14, 0.36, and 0.17 mmol/L higher than in the intervention group. There were no differences in TG and HGB between the two groups (all p > 0.05). There were significant differences in the number of macrosomia and neonatal weight between the two groups (both p < 0.05). Differences in WHZ after childbirth were not statistically significant between the two groups (all p > 0.05). Individualised nutritional intervention could improve blood glucose levels 42 days after childbirth and reduce macrosomia incidence in pregnant women with GDM.Impact statementWhat is already known on this subject? Individualised nutrition intervention can improve blood glucose status and complications during pregnancy, thus improving pregnancy outcomes.What the results of this study add? Individual nutrition intervention improved the blood glucose and nutritional status of patients at 42 days postpartum, but there was no difference in the growth and development indicators of their offspring at 0-24 months.What the implications are of these findings for clinical practice and/or further research? Improve nutritional intervention programs for gestational diabetes, improve blood glucose during pregnancy and postpartum, to improve pregnancy outcomes and reduce the occurrence of type 2 diabetes and other metabolic diseases; Extend the monitoring range of the growth and development of the offspring of gestational diabetes, find the problems and timely carry out the nutritional intervention, to improve the development of the offspring.

Relationship between gut microbiome characteristics and the effect of nutritional therapy on glycemic control in pregnant women with gestational diabetes mellitus

The purpose of this study was to explore the relationship between the characteristics of gut microbiome and the effect of medical nutrition therapy (MNT) on glycemic control in pregnant women with gestational diabetes mellitus (GDM). Seventy-four pregnant women newly diagnosed with GDM received MNT for one-week. The effect of glycemic control was evaluated by fasting and 2-hour postprandial blood glucose; and stool samples of pregnant women were collected to detect the gut microbiome before and after MNT. We used a nested case-control study design, with pregnant women with GDM who did not meet glycemic standards after MNT as the ineffective group and those with an age difference of ≤5 years, matched for pre-pregnancy body mass index (BMI) 1:1, and meeting glycemic control criteria as the effective group. Comparison of the gut microbiome characteristics before MNT showed that the ineffective group was enriched in Desulfovibrio, Aeromonadales, Leuconostocaceae, Weissella, Prevotella, Bacillales_Incertae Sedis XI, Gemella and Bacillales, while the effective group was enriched in Roseburia, Clostridium, Bifidobacterium, Bifidobacteriales, Bifidobacteriaceae, Holdemania and Proteus. After treatment, the effective group was enriched in Bifidobacterium and Actinomycete, while the ineffective group was enriched in Holdemania, Proteus, Carnobacteriaceae and Granulicatella. In conclusion, the decrease in the abundance of characteristic gut microbiome positively correlated with blood glucose may be a factor influencing the poor hypoglycemic effect of MNT in pregnant women with GDM. Abundance of more characteristic gut microbiome negatively correlated with blood glucose could help control blood glucose in pregnant women with GDM.

An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus

Gestational Diabetes Mellitus (GDM), a common pregnancy complication associated with many maternal and neonatal consequences, is increased in mothers with overweight and obesity. Interventions initiated early in pregnancy can reduce the rate of GDM in these women, however, untargeted interventions can be costly and time-consuming. We have developed an explainable machine learning-based clinical decision support system (CDSS) to identify at-risk women in need of targeted pregnancy intervention. Maternal characteristics and blood biomarkers at baseline from the PEARS study were used. After appropriate data preparation, synthetic minority oversampling technique and feature selection, five machine learning algorithms were applied with five-fold cross-validated grid search optimising the balanced accuracy. Our models were explained with Shapley additive explanations to increase the trustworthiness and acceptability of the system. We developed multiple models for different use cases: theoretical (AUC-PR 0.485, AUC-ROC 0.792), GDM screening during a normal antenatal visit (AUC-PR 0.208, AUC-ROC 0.659), and remote GDM risk assessment (AUC-PR 0.199, AUC-ROC 0.656). Our models have been implemented as a web server that is publicly available for academic use. Our explainable CDSS demonstrates the potential to assist clinicians in screening at risk patients who may benefit from early pregnancy GDM prevention strategies.