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Background: According to previous reports, very high percentages of individuals in Saudi Arabia are undiagnosed for type 2 diabetes mellitus (T2DM). Despite conducting several screening and awareness campaigns, these efforts lacked full accessibility and consumed extensive human and material resources. Thus, developing machine learning (ML) models could enhance the population-based screening process. The study aims to compare a newly developed ML model's outcomes with the validated American Diabetes Association's (ADA) risk assessment regarding predicting people with high risk for T2DM. Research design and methods: Patients' age, gender, and risk factors that were obtained from the National Health Information Center's dataset were used to build and train the ML model. To evaluate the developed ML model, an external validation study was conducted in three primary health care centers. A random sample (N = 3400) was selected from the non-diabetic individuals. Results: The results showed the plotted data of sensitivity/100-specificity represented in the Receiver Operating Characteristic (ROC) curve with an AROC value of 0.803, 95% CI: 0.779–0.826. Conclusions: The current study reveals a new ML model proposed for population-level classification that can be an adequate tool for identifying those at high risk of T2DM or who already have T2DM but have not been diagnosed. [ABSTRACT FROM AUTHOR]

Background: Several reports indicate that extended stay at home for online school as a result of the coronavirus disease 2019 (COVID-19) pandemic or other reasons may change the normal physical activity and body weight of school-age students. Previous studies revealed that almost 50% of school students complained of an increase or decrease of more than 3 kg of weight due to lower physical activity. This study aims to provide an insight into the body mass index (BMI) of school students during the COVID-19 pandemic. Methods: A retrospective matched-longitudinal study was conducted in 2022 based on previous data on the BMI-for-age percentile in school students aged 6–18 years. These data were collected the year before and during the COVID-19 pandemic (2019–2021). Results: All the included students were Arabic (n = 53,580) and were not diagnosed with chronic illnesses. The outcomes showed that the overall percentage of students with normal weight was 54%, 56%, and 53% during 2019–2021 years, respectively. The frequencies of students with optimal BMI increased significantly in 2020 compared to 2019 (P < 0.01); however, there was no statistical significance with regard to normal body weight trends (P > 0.05) between 2019 and 2021. Overweight among students increased during the COVID-19 pandemic (2020–2021), as almost 10% of the students were overweight in 2019 compared to 12% in 2020 and 2021 (P < 0.01). In addition, obese students increased in 2020–2021 compared to 2019 (15–17% vs. 10%, P < 0.01). In contrast, underweight trends among students declined from 26% reported in 2019 compared to 17–18% in 2020 and 2021 (P < 0.01). Conclusion: There are significant variations in Arabic school-age students regarding their BMI values. Obesity was highly detected among children and adolescents during the COVID-19 pandemic years compared to that in 2019, while underweight cases declined. [ABSTRACT FROM AUTHOR]

Byline: Abdullah. Alkattan, Yousef. Almutairi, Eman. Alsalameen, Ahmed. Alkhalifah, Fatimah. Alghanim Sir, Patients with cardiovascular diseases (CVDs) include stroke, coronary artery disease, deep-vein thrombosis, and pulmonary embolism are at high [...]