ABSTRACT
Background and Aims: With the global rise in type 2 diabetes, predictive modeling has become crucial for early detection, particularly in populations with low routine medical checkup profiles. This study aimed to develop a predictive model for type 2 diabetes using health check-up data focusing on clinical details, demographic features, biochemical markers, and diabetes knowledge. Methods: Data from 444 Nigerian patients were collected and analysed. We used 80% of this data set for training, and the remaining 20% for testing. Multivariable penalized logistic regression was employed to predict the disease onset, incorporating waist-hip ratio (WHR), triglycerides (TG), catalase, and atherogenic indices of plasma (AIP). Results: The predictive model demonstrated high accuracy, with an area under the curve of 99% (95% CI = 97%-100%) for the training set and 94% (95% CI = 89%-99%) for the test set. Notably, an increase in WHR (adjusted odds ratio [AOR] = 70.35; 95% CI = 10.04-493.1, p-value < 0.001) and elevated AIP (AOR = 4.55; 95% CI = 1.48-13.95, p-value = 0.008) levels were significantly associated with a higher risk of type 2 diabetes, while higher catalase levels (AOR = 0.33; 95% CI = 0.22-0.49, p < 0.001) correlated with a decreased risk. In contrast, TG levels (AOR = 1.04; 95% CI = 0.40-2.71, p-value = 0.94) were not associated with the disease. Conclusion: This study emphasizes the importance of using distinct clinical and biochemical markers for early type 2 diabetes detection in Nigeria, reflecting global trends in diabetes modeling, and highlighting the need for context-specific methods. The development of a web application based on these results aims to facilitate the early identification of individuals at risk, potentially reducing health complications, and improving diabetes management strategies in diverse settings.
ABSTRACT
BACKGROUND: Drug repositioning is becoming popular due to the development of resistance to almost all the recommended antimalarials. Pregabalin and gabapentin are chemical analogs of gamma- aminobutyric acid (GABA) approved for the treatment of epilepsy and neuropathic pain. OBJECTIVE: This study investigates acute toxicities and antimalarial activities of pregabalin and gabapentin in the murine malarial model. METHODS: Acute toxicities were assessed using the method of Lorke, while curative activities were assessed by the administration of serial doses of pregabalin and gabapentin to Plasmodium berghei infected mice. Pregabalin was further investigated for its prophylactic activity, and curative potential when combined with either artesunate or amodiaquine. All drugs were freshly prepared and administered orally. Thin films were collected, stained, and observed under the microscope for the estimation of parasitemia and calculation of percentage chemoinhibition or chemoprevention. In pregabalin -artesunate or -amodiaquine combination aspect of this study, survival day post-infection (SDPI) was recorded, while parasitemia was re-estimated for animals that survived till day 28. RESULTS: The oral LD50 of gabapentin, as well as pregabalin, was >5,000 mg/kg. Gabapentin at 100 and 200 mg/Kg demonstrated 35.64% and -12.78% chemoinhibition, respectively, while pregabalin demonstrated 75.60% and 100.00% chemoinhibition at doses of 12.5 and 25 mg/Kg, respectively. Moreover, pregabalin at individual doses of 25, 50 mg/Kg, and in combination with either artesunate or amodiaquine demonstrated 100.00% chemoinhibition. In its prophylactic study, pregabalin was found to be 100% chemopreventive at individual doses of 12.5 and 25 mg/Kg. CONCLUSION: Both GABA analogs have antimalarial properties, but pregabalin proved to be more efficacious.
