Background: Lipids and adipokines including leptin, resistin and visfatin play various roles in the pathophysiology of Gestational Diabetes Mellitus (GDM). This study was aimed at determining whether serum leptin, resistin and visfatin are significantly altered during the first trimester of pregnancies that subsequently develop GDM and whether such changes are useful in predicting the disease. Methods: This was a case-case control study which compared first trimester biochemical and anthropometric parameters in 70 pregnant women who subsequently developed GDM and 70 pregnant women without GDM at the Volta Regional Hospital, Ho, Ghana. Lipid profile and some selected adipokines were analyzed and first trimester body mass index (BMI) was determined. Results: There were significant differences (p < 0.05) in leptin, resistin, and visfatin as well as significant dyslipidemia among those with GDM compared to those without GDM. Furthermore, the area under the Receiver Operating Characteristic Curves (AUCs) for leptin, resistin and visfatin were; 0.812, 0.836 and 0.799 respectively. Increased first trimester leptin (OR = 1.166; CI = 1.104–1.233; p < 0.0001), resistin (p < 0.0001) and visfatin (p < 0.0001) were associated with GDM. Conclusion: Hyperleptinemia, hyperesistinemia and hypervisfatinemia precede GDM and can serve as good predictive indices for gestational diabetes mellitus.

In this study, Secant Kumaraswamy family of distributions is proposed and studied. This is motivated by the fact that no one distribution can model all types of data from different fields. Therefore, there is the need to develop distributions with desirable properties and flexible enough for modelling data exhibiting different characteristics. Some properties of the new family of distributions, including the quantile function, moments, moment generating function, and mean residual life function, are derived. Five special cases of the family of distributions are presented, and their flexibility is shown by the varying degrees of skewness and kurtosis and nonmonotonic hazard rates. The maximum likelihood estimation method is used to obtain estimators of the family of distributions. Two location-scale regression models are developed for the Secant Kumaraswamy Weibull distribution, which is a special case of the family of distributions. Six different real datasets are used to demonstrate the usefulness of the family of distributions and the regression models. The results show that the family of distributions can be used to model real datasets.