The relationship between type 2 diabetes mellitus with cognitive functions

Cognitive factors associated with hypertension and diabetes control among diagnosed and treated patients; findings from a community cohort in India

Uncontrolled hypertension and diabetes are a challenge for healthcare providers worldwide. The research documenting the underlying risk factors of uncontrolled chronic illnesses in community cohorts from India is negligible. The current cross-sectional household door-knock survey conducted among 759 participants aged 30 and above from a geographically well-defined area examines the cognitive risk factors associated with hypertension and diabetes control in the Indian population. The study used an assessment tool consisting of a socio-demographic questionnaire, items to measure cognitive factors, and onsite hypertension and diabetes measurements. Results suggested that among the participants, more than 36% had hypertension, 26% had diabetes, and of those with diagnosed diabetes and hypertension, more than 22% with hypertension and 48% with diabetes had uncontrolled conditions. Univariate analysis suggests that cognitive functioning was negatively associated with uncontrolled hypertension and psychological impairments of depression and anxiety were positively associated. The associations were not significant for uncontrolled diabetes. Only if treatments integrate psychological and cognitive interventions to ensure adherence to medical and lifestyle modifications will it achieve the WHO target of 80% control of detected conditions. The findings can inform the policies and programs to optimise government spending and modify the current chronic condition management practices.

The Association between Metformin Use and Risk of Developing Severe Dementia among AD Patients with Type 2 Diabetes

This study explores the potential impact of metformin on the development of severe dementia in individuals with Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM). With an emerging interest in the role of the APOE genotype in mediating metformin's effects on cognitive decline in AD patients, we sought to investigate whether metformin usage is associated with a reduced risk of severe dementia. Using data from the National Alzheimer's Coordinating Center (NACC) database (2005-2021), we identified 1306 participants with both AD and T2DM on diabetes medications. These individuals were categorized based on metformin usage, and a propensity score-matched cohort of 1042 participants was analyzed. Over an average follow-up of 3.6 years, 93 cases of severe dementia were observed. A Kaplan-Meier analysis revealed that metformin users and non-users had similar probabilities of remaining severe dementia-free (log-rank p = 0.56). Cox proportional hazards models adjusted for covariates showed no significant association between metformin usage and a lower risk of severe dementia (HR, 0.96; 95% CI, 0.63-1.46; p = 0.85). A subgroup analysis based on APOE ε4 carrier status demonstrated consistent results, with metformin use not correlating with a reduced severe dementia risk. In conclusion, our findings from a substantial cohort of AD and T2DM patients suggest that metformin usage is not significantly associated with a decreased risk of severe dementia. This observation persists across APOE ε4 carriers and non-carriers, indicating a lack of genotype-mediated effect.

Liraglutide attenuates type 2 diabetes mellitus-associated non-alcoholic fatty liver disease by activating AMPK/ACC signaling and inhibiting ferroptosis

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is one of the most common complications of type 2 diabetes mellitus (T2DM). The pathogenesis of NAFLD involves multiple biological changes, including insulin resistance, oxidative stress, inflammation, as well as genetic and environmental factors. Liraglutide has been used to control blood sugar. But the impact of liraglutide on T2DM-associated NAFLD remains unclear. In this study, we investigated the impact and potential molecular mechanisms of inhibiting ferroptosis for liraglutide improves T2DM-associated NAFLD.

METHODS

Mice were fed on high-fat-diet and injected with streptozotocin to mimic T2DM-associated NAFLD and gene expression in liver was analysed by RNA-seq. The fast blood glucose was measured during the period of liraglutide and ferrostatin-1 administration. Hematoxylin and eosin staining was used to evaluate the pathological changes in the liver. The occurrence of hepatic ferroptosis was measured by lipid peroxidation in vivo. The mechanism of liraglutide inhibition ferroptosis was investigated by in vitro cell culture.

RESULTS

Liraglutide not only improved glucose metabolism, but also ameliorated tissue damage in the livers. Transcriptomic analysis indicated that liraglutide regulates lipid metabolism related signaling including AMPK and ACC. Furthermore, ferroptosis inhibitor rather than other cell death inhibitors rescued liver cell viability in the presence of high glucose. Mechanistically, liraglutide-induced activation of AMPK phosphorylated ACC, while AMPK inhibitor compound C blocked the liraglutide-mediated suppression of ferroptosis. Moreover, ferroptosis inhibitor restored liver function in T2DM mice in vivo.

CONCLUSIONS

These findings indicate that liraglutide ameliorates the T2DM-associated NAFLD, which possibly through the activation of AMPK/ACC pathway and inhibition of ferroptosis.

MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus

Background: Type 2 diabetes mellitus (T2DM) is a crucial risk factor for cognitive impairment. Accurate assessment of patients' cognitive function and early intervention is helpful to improve patient's quality of life. At present, neuropsychiatric screening tests is often used to perform this task in clinical practice. However, it may have poor repeatability. Moreover, several studies revealed that machine learning (ML) models can effectively assess cognitive impairment in Alzheimer's disease (AD) patients. We investigated whether we could develop an MRI-based ML model to evaluate the cognitive state of patients with T2DM. Objective: To propose MRI-based ML models and assess their performance to predict cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM). Methods: Fluid Attenuated Inversion Recovery (FLAIR) of magnetic resonance images (MRI) were derived from 122 patients with T2DM. Cognitive function was assessed using the Chinese version of the Montréal Cognitive Assessment Scale-B (MoCA-B). Patients with T2DM were separated into the Dementia (DM) group (n = 40), MCI group (n = 52), and normal cognitive state (N) group (n = 30), according to the MoCA scores. Radiomics features were extracted from MR images with the Radcloud platform. The variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used for the feature selection. Based on the selected features, the ML models were constructed with three classifiers, k-NearestNeighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR), and the validation method was used to improve the effectiveness of the model. The area under the receiver operating characteristic curve (ROC) determined the appearance of the classification. The optimal classifier was determined by the principle of maximizing the Youden index. Results: 1,409 features were extracted and reduced to 13 features as the optimal discriminators to build the radiomics model. In the validation set, ROC curves revealed that the LR classifier had the best predictive performance, with an area under the curve (AUC) of 0.831 in DM, 0.883 in MIC, and 0.904 in the N group, compared with the SVM and KNN classifiers. Conclusion: MRI-based ML models have the potential to predict cognitive dysfunction in patients with T2DM. Compared with the SVM and KNN, the LR algorithm showed the best performance.

Predictors of dementia amongst newly diagnosed non-valvular atrial fibrillation patients

Atrial fibrillation (AF) confers a 2-to-3-fold increased risk of developing cognitive dysfunction and dementia, independent of age and past stroke. The purpose of study was to identify risk factors for developing dementia amongst AF patients in India. This was a single-centre, prospective, observational study wherein recently diagnosed, treatment naïve, persistent non-valvular AF patients were enrolled. All patients were screened for dementia using the Mini-Mental state exam. Amongst a total of 108 patients enrolled, 40 (37%) had dementia. The most common cognitive deficits were in attention and calculation followed by memory deficits. Factors independently contributing to dementia were advanced age, female sex, presence of diabetes, elevated pulmonary artery pressures and a lower serum albumin.

Effects of Berberine on Diabetes and Cognitive Impairment in an Animal Model: The Mechanisms of Action

Diabetes is a group of metabolic disorders with an increased risk of developing cognitive impairment and dementia. The hippocampus in the forebrain contains an abundance of insulin receptors related to cognitive function and plays an important role in the pathophysiology of neurodegenerative disorders. Berberine from traditional Chinese medicine has been used to treat diabetes and diabetic cognitive impairment, although its related mechanisms are largely unknown. In this study, a STZ diabetes rat model feeding with a high-fat diet was used to test the effects of berberine compared with metformin. Oral glucose tolerance and hyperinsulinemic-euglycemic clamp were used for glucose metabolism and insulin resistance. The Morris water maze was used to observe the compound effects on cognitive impairment. Serum and hippocampal [Formula: see text]-amyloid peptide (A[Formula: see text], Tau and phosphorylated Tau protein deposition in the hippocampi were measured. The TUNEL assay was used to detect the neuronal apoptosis, supported by histomorphological changes and transmissional electron microscopy (TEM) image. Our data showed that the diabetic rats had a significantly cognitive impairment. In addition to improving glucose metabolism and reducing insulin resistance, berberine significantly improved the cognitive function in the rat. Berberine also effectively decreased the expression of hippocampal tau protein, phosphorylated Tau, and increased insulin receptor antibodies. Moreover, berberine downregulated the abnormal phosphorylation of A[Formula: see text] and Tau protein and improved hippocampal insulin signaling. The TUNEL assay confirmed that berberine reduced hippocampal neuronal apoptosis supported by TEM. Thus, berberine significantly improved the cognitive function in diabetic rats by changing the peripheral and central insulin resistance. The reduction of neuronal injury, A[Formula: see text] deposition, abnormal phosphorylation of Tau protein, and neuronal apoptosis in the hippocampus were observed as the related mechanisms of action.