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Background Cognitive dysfunction is increasingly recognized as an important comorbidity of diabetes mellitus (DM). Objective The purpose of this study was to determine the prevalence and predictors of cognitive decline in individuals with type 2 diabetes mellitus (T2DM). Methods This cohort study included patients with type 2 diabetes mellitus aged between 40 and 75 years and with a duration of the evolution of diabetes that is greater than five years admitted in endocrinology consultation of the Sheikh Khalifa ibn Zaid Hospital in Casablanca, Morocco. For each patient, we collected clinical characteristics and biological assessments. All subjects provided screening test results as defined by the Mini-Mental State Examination (MMSE). Results We included a total of 100 patients with diabetes between May and September 2021. The median age of the patients was 65 years (interquartile range (IQR): 59-70 years), 65% were males, and the median duration of diabetes was 15 years (IQR: 9-20 years). The most common cardiovascular risk factors (CVRFs) were hypertension (72.7%) and dyslipidemia (53%). The most common complications of diabetes were peripheral neuropathy (50%), diabetic retinopathy (DR) (39%), peripheral artery disease (33%), and coronary artery disease (27%). Cognitive impairment was present in 47.5% of our patients. For the multivariate analysis, we found that the decrease in the MMSE score is associated with the increase in age (p-value = 0.004) and the occurrence of diabetic retinopathy (p-value < 0.001), dyslipidemia (p-value = 0.006), and elevated creatinine (p-value < 0.001). Conclusion It is necessary to consider the cognitive decline of patients with diabetes as one of the most important complications of this disease because of its impact on the evolution and compliance of these patients., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2022, Rhmari Tlemçani et al.)

Aims: This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models., Methods: First, we conducted three consultations, before, during, and after Ramadan to assess demographics, diabetes history, caloric intake, anthropometric and metabolic parameters. Second, machine learning techniques (Logistic Regression, Support Vector Machine, Naive Bayes, K-nearest neighbor, Decision Tree, Random Forest, Extra Trees Classifier and Catboost) were trained using the data to predict poor glycemic control among patients. Then, we conducted several simulations with the best performing machine learning model using variables that were found as main predictors of poor glycemic control., Results: The prevalence of poor glycemic control among patients was 52.6%. Extra tree Classifier was the best performing model for glycemic deterioration (accuracy = 0.87, AUC = 0,87). Caloric intake evolution, gender, baseline caloric intake, baseline weight, BMI variation, waist circumference evolution and Total Cholesterol serum level after Ramadan were selected as the most significant for the prediction of poor glycemic control. We determined thresholds for each predicting factor among which this risk is present., Conclusions: The clinical use of our findings may help to improve glycemic control during Ramadan among patients who do not fast by targeting risk factors of poor glycemic control., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Background: The management of diabetes during Ramadan is well codified. International guidelines recommend avoiding fasting for patients with the risk of complications. However, during Ramadan drastic changes occur in lifestyles habits. Our study aims to evaluate the impact of the month of Ramadan on the lifestyle habits and metabolic profile of non-fasting patients with diabetes., Patients and Methods: This observational cross-sectional study was carried out during 3 months of Ramadan in 2018, Ramadan 2019, and Ramadan 2021. We conducted 3 consultations (before, during, and after Ramadan). Before Ramadan, we collected anthropometric and metabolic parameters, and we assessed physical activity level and dietary intake. During Ramadan, we evaluated the occurrence of complications such as hyperglycemia and hypoglycemia, as well as we assessed physical activity level, dietary intake, and the number of meals. After Ramadan re-evaluate anthropometric and metabolic parameters., Results: We included 155 patients, 93.5% had type 2 diabetes and 6.5% had type 1 diabetes. We found that glycated hemoglobin, LDL cholesterol, and Triglyceride increased significantly after Ramadan (p-value <0.001). We also found that weight, body mass index, waist circumference. Caloric intake increased significantly during Ramadan (p-value <0.001); this increase concerned 61.3% of patients. In terms of metabolic parameters, diabetes was unbalanced in 52.6% of patients, hypoglycemia occurs in 20.9% of patients, and hyperglycemia was experienced by 37% of patients during Ramadan. We found that LDL cholesterol increased in 48.4% of patients, triglycerides increased in 60.6% of patients and serum level of total cholesterol increased in 55% of patients., Conclusion: Our study showed that during Ramadan risk of complications in patients with diabetes is not only related to fasting., Competing Interests: The authors report no conflicts of interest in this work., (© 2022 Motaib et al.)