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.

Pharmacological Considerations in the Interpretation of Biochemical Results in Diabetic Patients with Cardiovascular Complications.

Diabetes mellitus with cardiovascular diseases is often a multi-systemic disease that requires a multi-therapeutic approach which mostly poses a challenge to laboratory result interpretation. The non-availability of information on many patients due to poor referral, documentation and record keeping has resulted in isolated interpretation of laboratory result of diabetic patients with multisystemic complications. This has led to both analytical and post-analytical errors which has a negative impact on total quality of results. Therefore, this review showed the possible therapeutic treatment of a diabetic patient with cardiovascular disease and how their pharmacological role could affect laboratory result.

First trimester sex hormone-binding globulin predicts gestational diabetes mellitus in a population of Nigerian women.

There has been a steady rise in the disease burden of Gestational Diabetes Mellitus (GDM) in the sub-Saharan African region over time. Diagnostic testing for GDM is currently recommended at 24 - 28 weeks of gestation, leaving a narrow window for intervention before delivery. Hence the need for early prediction and preventive intervention. The performance of first trimester serum sex hormone-binding globulin (SHBG) assay as a predictor of GDM was determined by binary logistic regression. Women with GDM (n = 49) had a significantly lower mean first trimester SHBG level (104.7 ± 61.6 nmol/L) than did those without GDM (n = 180; 265.2 ± 141.5 nmol/L; p < .001). First trimester SHBG was significantly negatively correlated (rpb = -0.460, p value = <.001) with subsequent development of GDM and an area under receiver operator characteristics (ROC) curve of 0.874 (p < .001). A cut-off value of 158.0 nmol/L predictive of GDM had a diagnostic sensitivity of 81.5%, a specificity of 80.1%, and an overall diagnostic efficiency of 80.3%.IMPACT STATEMENTWhat is already known on this subject? GDM is associated with high risk of various complications and is commonly diagnosed at 24-28 weeks of gestation, leaving a narrow window for intervention. The performance of current maternal clinical and demographic risk factor-based prediction approaches is unreliable. Thus, more favourable prediction approaches need to be developed. Previous studies have suggested that SHBG, a readily assessable marker, has potential to predict GDM; however, these studies have mostly involved Caucasian and other non-African populations.What the results of this study add? SHBG may serve as a reliable first trimester screening tool for GDM development in Nigerian women with singleton pregnancies. This study demonstrates that first trimester SHBG can predict GDM development in sub-Saharan African women despite racial, ethnic and geographical differences.What are the implications of these findings for clinical practice and/or further research? Effective first trimester prediction of GDM using SHBG may enable preventive interventions, thereby mitigating the high burden of the disease in the sub-Saharan African region. It may also provide relevant information that may guide adaptation of current management guidelines to ensure effective management of GDM in the region.