Mediation effects of diabetes and inflammation on the relationship of obesity to cognitive impairment in African Americans

Interleukin-5: an indicator of mild cognitive impairment in patients with type 2 diabetes mellitus - a comprehensive investigation ranging from bioinformatics analysis to clinical research

PURPOSE

Neuroinflammation constitutes an underlying mechanism for cognitive impairment. Here, we endeavor to scrutinize the potential contribution of interleukin-5 (IL-5) towards mild cognitive impairment (MCI), and to assess its diagnostic value for MCI in patients with type 2 diabetes mellitus (T2DM).

METHODS

RNA-seq was used to explore the potential neuroinflammation factors in the hippocampus of diabetic mice with cognitive decline. Additionally, the promising risk factor was verified in animals. Finally, the association between IL-5 levels and cognitive function and its diagnostic value for MCI were assessed.

RESULTS

In animals, up-regulated IL-5 mRNA and protein levels were detected by RNA-seq and (or) verified experiments in the hippocampus of diabetic db/db mice with cognitive decline, compared to those of db/m mice without diabetes. In human, compared to diabetic patients without MCI, those with MCI demonstrate elevated levels of IL-5. It is natively associated with Montreal Cognitive Assessment (MoCA) scores, reflecting global cognitive function, and positively correlated with Trail Making Test A (TMTA) scores, reflecting information processing speed. Furthermore, an elevated level of IL-5 is identified as a risk factor for MCI, and a factor that influences TMTA scores. Finally, it is recommended that the cut-off value for IL-5 in the diagnosis of MCI is 22.98 pg/mL, with a sensitivity of 68.6% and specificity of 72.9%.

CONCLUSIONS

IL-5 is considered a risk factor for MCI in T2DM patients and is associated with their performance in information processing speed. Moreover, an elevated level of IL-5 is a plausible biomarker for MCI in T2DM patients.

Exploring galectin-3's role in predicting mild cognitive impairment in type 2 diabetes and its regulation by miRNAs

Objective

This study aimed to investigate the role of galectin-3 (Gal-3; coded by LGALS3 gene), as a biomarker for MCI in T2DM patients and to develop and validate a predictive nomogram integrating galectin-3 with clinical risk factors for MCI prediction. Additionally, microRNA regulation of LGALS3 was explored.

Methods

The study employed a cross-sectional design. A total of 329 hospitalized T2DM patients were recruited and randomly allocated into a training cohort (n = 231) and a validation cohort (n = 98) using 7:3 ratio. Demographic data and neuropsychological assessments were recorded for all participants. Plasma levels of galectin-3 were measured using ELISA assay. We employed Spearman's correlation and multivariable linear regression to analyze the relationship between galectin-3 levels and cognitive performance. Furthermore, univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for MCI in T2DM patients. Based on these analyses, a predictive nomogram incorporating galectin-3 and clinical predictors was developed. The model's performance was evaluated in terms of discrimination, calibration, and clinical utility. Regulatory miRNAs were identified using bioinformatics and their interactions with LGALS3 were confirmed through qRT-PCR and luciferase reporter assays.

Results

Galectin-3 was identified as an independent risk factor for MCI, with significant correlations to cognitive decline in T2DM patients. The developed nomogram, incorporating Gal-3, age, and education levels, demonstrated excellent predictive performance with an AUC of 0.813 in the training cohort and 0.775 in the validation cohort. The model outperformed the baseline galectin-3 model and showed a higher net benefit in clinical decision-making. Hsa-miR-128-3p was significantly downregulated in MCI patients, correlating with increased Gal-3 levels, while Luciferase assays confirmed miR-128-3p's specific binding and influence on LGALS3.

Conclusion

Our findings emphasize the utility of Gal-3 as a viable biomarker for early detection of MCI in T2DM patients. The validated nomogram offers a practical tool for clinical decision-making, facilitating early interventions to potentially delay the progression of cognitive impairment. Additionally, further research on miRNA128's regulation of Gal-3 levels is essential to substantiate our results.

Important roles of linoleic acid and α-linolenic acid in regulating cognitive impairment and neuropsychiatric issues in metabolic-related dementia

Metabolic syndrome (MetS), which is characterized by insulin resistance, high blood glucose, obesity, and dyslipidemia, is known to increase the risk of dementia accompanied by memory loss and depression. The direct pathways and specific mechanisms in the central nervous system (CNS) for addressing fatty acid imbalances in MetS have not yet been fully elucidated. Among polyunsaturated acids, linoleic acid (LA, n6-PUFA) and α-linolenic acid (ALA, n3-PUFA), which are two essential fatty acids that should be provided by food sources (e.g., vegetable oils and seeds), have been reported to regulate various cellular mechanisms including apoptosis, inflammatory responses, mitochondrial biogenesis, and insulin signaling. Furthermore, inadequate intake of LA and ALA is reported to be involved in neuropathology and neuropsychiatric diseases as well as imbalanced metabolic conditions. Herein, we review the roles of LA and ALA on metabolic-related dementia focusing on insulin resistance, dyslipidemia, synaptic plasticity, cognitive function, and neuropsychiatric issues. This review suggests that LA and ALA are important fatty acids for concurrent treatment of both MetS and neurological problems.

Risk factors for cognitive impairment in middle-aged type 2 diabetic patients: a cross-sectional study

OBJECTIVE

The aim of this study was to investigate risk factors for cognitive impairment (CI) and explore the relationship between obesity and cognition in hospitalised middle-aged patients with type 2 diabetes (T2DM).

METHODS

Subjects were divided into normal cognitive function (NCF) (n=320) and CI (n=204) groups based on the results of the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). The risk factors for CI were determined by logistic regression analysis and generalised linear modelling. The associations between obesity parameters (body mass index (BMI) and waist circumference (WC)) and cognitive ability were studied with the use of linear regression analysis, piecewise regression modelling and interaction analysis. The receiver operating characteristic curve analysis was used to examine the diagnostic value of influencing factors for cc RESULTS: The prevalence of CI was 38.9% in hospitalised middle-aged T2DM patients (median age, 58 years). Age, WC, hypoglycaemic episode within past 3 months and cerebrovascular disease (CVD) were identified as independent risk factors for CI, while the independent protective factors were education, diabetic dietary pattern, overweight and obesity. BMI was a protective factor for the MoCA score within a certain range, whereas WC was a risk factor for the MMSE and MoCA scores. The area under the curve for the combination of BMI and WC was 0.754 (p<0.001).

CONCLUSION

Age, education, diabetic dietary pattern, WC, overweight, obesity, hypoglycaemic episode in 3 months and CVD may be potential influencing factors for the occurrence of CI in hospitalised middle-aged population with T2DM. The combination of BMI and WC may represent a good predictor for early screening of CI in this population. Nevertheless, more relevant prospective studies are still needed.

A risk prediction model based on machine learning for early cognitive impairment in hypertension: Development and validation study

Background

Clinical practice guidelines recommend early identification of cognitive impairment in individuals with hypertension with the help of risk prediction tools based on risk factors.

Objective

The aim of this study was to develop a superior machine learning model based on easily collected variables to predict the risk of early cognitive impairment in hypertensive individuals, which could be used to optimize early cognitive impairment risk assessment strategies.

Methods

For this cross-sectional study, 733 patients with hypertension (aged 30-85, 48.98% male) enrolled in multi-center hospitals in China were divided into a training group (70%) and a validation group (30%). After least absolute shrinkage and selection operator (LASSO) regression analysis with 5-fold cross-validation determined the modeling variables, three machine learning classifiers, logistic regression (LR), XGBoost (XGB), and gaussian naive bayes (GNB), were developed. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, and F1 score were used to evaluate the model performance. Shape Additive explanation (SHAP) analysis was performed to rank feature importance. Further decision curve analysis (DCA) assessed the clinical performance of the established model and visualized it by nomogram.

Results

Hip circumference, age, education levels, and physical activity were considered significant predictors of early cognitive impairment in hypertension. The AUC (0.88), F1 score (0.59), accuracy (0.81), sensitivity (0.84), and specificity (0.80) of the XGB model were superior to LR and GNB classifiers.

Conclusion

The XGB model based on hip circumference, age, educational level, and physical activity has superior predictive performance and it shows promise in predicting the risk of cognitive impairment in hypertensive clinical settings.