A first trimester prediction model and nomogram for gestational diabetes mellitus based on maternal clinical risk factors in a resource-poor setting.

BACKGROUND: The implementation of universal screening for Gestational Diabetes Mellitus (GDM) is challenged by several factors key amongst which is limited resources, hence the continued reliance on risk factor-based screening. Effective identification of high-risk women early in pregnancy may enable preventive intervention. This study aimed at developing a GDM prediction model based on maternal clinical risk factors that are easily assessable in the first trimester of pregnancy in a population of Nigerian women. METHODS: This was a multi-hospital prospective observational cohort study of 253 consecutively selected pregnant women from which maternal clinical data was collected at 8-12 weeks gestational age. Diagnosis of GDM was made via a one-step 75-gram Oral Glucose Tolerance Test (OGTT) at 24-28 weeks of gestation. A GDM prediction model and nomogram based on selected maternal clinical risk factors was developed using multiple logistic regression analysis, and its performance was assessed by Receiver Operator Curve (ROC) analysis. Data analysis was carried out using Statistical Package for Social Sciences (SPSS) version 25 and Python programming language (version 3.0). RESULTS: Increasing maternal age, higher body mass index (BMI), a family history of diabetes mellitus in first-degree relative and previous history of foetal macrosomia were the major predictors of GDM. The model equation was: LogitP = 6.358 - 0.066 × Age - 0.075 × First trimester BMI - 1.879 × First-degree relative with diabetes mellitus - 0.522 × History of foetal macrosomia. It had an area under the receiver operator characteristic (ROC) curve (AUC) of 0.814 (95% CI: 0.751-0.877; p-value < 0.001), and at a predicted probability threshold of 0.745, it had a sensitivity of 79.2% and specificity of 74.5%. CONCLUSION: This first trimester prediction model reliably identifies women at high risk for GDM development in the first trimester, and the nomogram enhances its practical applicability, contributing to improved clinical outcomes in the study population.

Oral innate immunity in patients with type 2 diabetes mellitus in a tertiary hospital in Ibadan Nigeria: a cross-sectional study.

Introduction: diabetes mellitus is associated with a high prevalence of oral infections. However, it is unclear how diabetes impacts oral innate antimicrobial proteins. This study evaluated salivary lysozyme and histatins, two major innate antimicrobial proteins, in patients with diabetes and non-diabetic controls. Methods: a cross-sectional study where salivary lysozyme and histatins were measured alongside plasma glucose levels. Values of the salivary proteins were compared between the two groups; their association with glucose levels was also established using correlation and regression analysis. Results: one hundred and fifty-one participants were recruited for this study, 85 (56.3%) of them had type 2 diabetes mellitus with a median fasting plasma glucose of 108.8 mg/dl (IQR 91.2-134.8) while the remaining 66 (43.7%) healthy non-diabetic controls had a median random plasma glucose of 101 mg/dl (IQR 89-112). The median salivary lysozyme was 32.5 ng/ml (IQR 25.0-39.6) in the group with diabetes and 36.4 ng/ml (IQR 31.4-42.1; p=0.01) in the non-diabetic control group. The median salivary histatins was 9.2 ng/ml (IQR 7.6 -10.2) in the group with diabetes and 14.7 ng/ml (IQR12.8-16.5; p<0.001) in the non-diabetic control group. Salivary lysozyme (r = -0.127; p =0.163) and histatins (r = -0.025; p = 0.424) were both negatively correlated with plasma glucose levels, and logistic regression showed that patients with diabetes are more likely to have lower levels of salivary lysozyme (0.957; p=0.013) and histatins (0.527; p<0.001). Conclusion: patients with diabetes had reduced levels of salivary lysozyme and histatins, this could provide an insight into the associated high oral infection rates.