Multi-Omics Characterization of Type 2 Diabetes Mellitus-Induced Cognitive Impairment in the db/db Mouse Model

Gene-environment interactions within a precision environmental health framework

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Overlooked cases of mild cognitive impairment: Implications to early Alzheimer's disease

Mild cognitive impairment (MCI) marks the initial phase of memory decline or other cognitive functions like language or spatial perception, while individuals typically retain the capacity to carry out everyday tasks independently. Our comprehensive article investigates the intricate landscape of cognitive disorders, focusing on MCI and Alzheimer's disease (AD) and Alzheimer's disease-related dementias (ADRD). The study aims to understand the signs of MCI, early Alzheimer's disease, and healthy brain aging while assessing factors influencing disease progression, pathology development and susceptibility. A systematic literature review of over 100 articles was conducted, emphasizing MCI, AD and ADRD within the elderly populations. The synthesis of results reveals significant findings regarding ethnicity, gender, lifestyle, comorbidities, and diagnostic tools. Ethnicity was found to influence MCI prevalence, with disparities observed across diverse populations. Gender differences were evident in cognitive performance and decline, highlighting the need for personalized management strategies. Lifestyle factors and comorbidities were identified as crucial influencers of cognitive health. Regarding diagnostic tools, the Montreal Cognitive Assessment (MoCA) emerged as superior to the Mini-Mental State Examination (MMSE) in early MCI detection. Overall, our article provides insights into the multifaceted nature of cognitive disorders, emphasizing the importance of tailored interventions and comprehensive assessment strategies for effective cognitive health management.

Are Hyperglycemia-Induced Changes in the Retina Associated with Diabetes-Correlated Changes in the Brain? A Review from Zebrafish and Rodent Type 2 Diabetes Models

Diabetes is prevalent worldwide, with >90% of the cases identified as Type 2 diabetes. High blood sugar (hyperglycemia) is the hallmark symptom of diabetes, with prolonged and uncontrolled levels contributing to subsequent complications. Animal models have been used to study these complications, which include retinopathy, nephropathy, and peripheral neuropathy. More recent studies have focused on cognitive behaviors due to the increased risk of dementia/cognitive deficits that are reported to occur in older Type 2 diabetic patients. In this review, we collate the data reported from specific animal models (i.e., mouse, rat, zebrafish) that have been examined for changes in both retina/vision (retinopathy) and brain/cognition, including db/db mice, Goto-Kakizaki rats, Zucker Diabetic Fatty rats, high-fat diet-fed rodents and zebrafish, and hyperglycemic zebrafish induced by glucose immersion. These models were selected because rodents are widely recognized as established models for studying diabetic complications, while zebrafish represent a newer model in this field. Our goal is to (1) summarize the published findings relevant to these models, (2) identify similarities in cellular mechanisms underlying the disease progression that occur in both tissues, and (3) address the hypothesis that hyperglycemic-induced changes in retina precede or predict later complications in brain.

The Effects of Ketogenic Diet on Brain Gene Expressions in Type 2 Diabetes Background

Type 2 diabetes mellitus (T2DM) is a major risk factor of a number of neurodegenerative diseases (NDDs). Ketogenic diet (KD) has significant beneficial effects on glycemic control and may act effectively against NDDs, but the mechanism remains unclear. In this study, we aimed to investigate the potential effects of KD on gene expressions in the brains of T2DM model mice. Male db/db mice at the age of 9 weeks were fed with KD or normal diet to the age of 6 months, and the whole brains were subjected to mRNA-seq analysis for differentially expressed genes. KD significantly lowered fasting glucose and body weights in db/db mice (P < 0.05), and the expression of 189 genes in the brain were significantly changed (P < 0.05, |log2| > 1). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that the differentially expressed genes upon KD are involved in inflammatory responses and the functions of biosynthesis. In inflammatory responses, NF-κB signaling pathway, viral protein interaction with cytokine and cytokine receptor, and cytokine-cytokine receptor interaction pathways were enriched, and in biosynthesis pathways, genes functioning in lipid and amino acid metabolism, protein synthesis, and energy metabolism were enriched. Moreover, consistent with the gene set enrichment analysis results, proteasomal activity measured biochemically were enhanced in KD-fed T2DM mice. These data may facilitate the understanding of how KD can be protective to the brain in T2DM background. KD could be a new strategy for the prevention of NDDs in T2DM patients.

Modulating the Gut Microbiota and Metabolites with Traditional Chinese Medicines: An Emerging Therapy for Type 2 Diabetes Mellitus and Its Complications

Currently, an estimated 537 million individuals are affected by type 2 diabetes mellitus (T2DM), the occurrence of which is invariably associated with complications. Glucose-lowering therapy remains the main treatment for alleviating T2DM. However, conventional antidiabetic agents are fraught with numerous adverse effects, notably elevations in blood pressure and lipid levels. Recently, the use of traditional Chinese medicines (TCMs) and their constituents has emerged as a preferred management strategy aimed at curtailing the progression of diabetes and its associated complications with fewer adverse effects. Increasing evidence indicates that gut microbiome disturbances are involved in the development of T2DM and its complications. This regulation depends on various metabolites produced by gut microbes and their interactions with host organs. TCMs' interventions have demonstrated the ability to modulate the intestinal bacterial microbiota, thereby restoring host homeostasis and ameliorating metabolic disorders. This review delves into the alterations in the gut microbiota and metabolites in T2DM patients and how TCMs treatment regulates the gut microbiota, facilitating the management of T2DM and its complications. Additionally, we also discuss prospective avenues for research on natural products to advance diabetes therapy.

Serum brain-derived neurotrophic factor levels in type 2 diabetes mellitus patients and its association with cognitive impairment: A meta-analysis

OBJECTIVE

To compare the serum levels of brain-derived neurotrophic factor (BDNF) in type 2 diabetes mellitus (T2DM) patients with healthy controls (HC) and evaluate the BDNF levels in T2DM patients with/without cognitive impairment.

METHODS

PubMed, EMBASE, and the Cochrane Library databases were searched for the published English literature on BDNF in T2DM patients from inception to December 2022. The BDNF data in the T2DM and HC groups were extracted, and the study quality was evaluated using the Agency for Healthcare Research and Quality. A meta-analysis of the pooled data was conducted using Review Manager 5.3 and Stata 12.0 software.

RESULTS

A total of 18 English articles fulfilled with inclusion criteria. The standard mean difference of the serum BDNF level was significantly lower in T2DM than that in the HC group (SMD: -2.04, z = 11.19, P <0.001). Besides, T2DM cognitive impairment group had a slightly lower serum BDNF level compared to the non-cognitive impairment group (SMD: -2.59, z = 1.87, P = 0.06).

CONCLUSION

BDNF might be involved in the neuropathophysiology of cerebral damage in T2DM, especially cognitive impairment in T2DM.

Single nuclei transcriptomics in diabetic mice reveals altered brain hippocampal endothelial cell function, permeability, and behavior

Type 2 diabetes mellitus (T2DM) is a metabolic disorder with cerebrovascular and cardiovascular sequelae. Yet, a clear pattern of gene dysregulation by T2DM in dementia has yet to be defined. We used single nuclei RNA sequencing technology to profile the transcriptome of endothelial cells (EC) from anatomically defined hippocampus of db/db mice to identify differentially expressed (DE) genes, gene pathways and networks, predicted regulating transcription factors, and targets of DE long noncoding RNAs. We also applied gadolinium (Gd) enhanced magnetic resonance imaging (MRI) to assess blood brain barrier (BBB) permeability, and functionally assessed cognitive behavior. The murine gene expression profiles were then integrated with those of persons with Alzheimer's disease (AD) and vascular dementia (VaD). We reveal that the transcriptome of the diabetic hippocampal murine brain endothelium differs substantially from control wild types with molecular changes characterized by differential RNA coding and noncoding pathways enriched for EC signaling and for endothelial functions for neuroinflammation, endothelial barrier disruption, and neurodegeneration. Gd enhanced structural brain MRI linked endothelial molecular alterations to BBB dysfunction by neuroimaging. Integrated multiomics of hippocampal endothelial gene dysregulation associated with impairments in cognitive adaptive capacity. In addition, the diabetic transcriptome significantly and positively correlated with that of persons with AD and VaD. Taken together, our results from comprehensive, multilevel, integrated, single nuclei transcriptomics support the hypothesis of T2DM-mediated neuroinflammation and endothelial cell and barrier disruption as key mechanisms in cognitive decline in T2DM, thereby suggesting potential endothelial-specific molecular therapeutic targets.

Revolutionizing Blood Collection: Innovations, Applications, and the Potential of Microsampling Technologies for Monitoring Metabolites and Lipids

Blood serves as the primary global biological matrix for health surveillance, disease diagnosis, and response to drug treatment, holding significant promise for personalized medicine. The diverse array of lipids and metabolites in the blood provides a snapshot of both physiological and pathological processes, with many routinely monitored during conventional wellness checks. The conventional method involves intravenous blood collection, extracting a few milliliters via venipuncture, a technique limited to clinical settings due to its dependence on trained personnel. Microsampling methods have evolved to be less invasive (collecting ≤150 µL of capillary blood), user-friendly (enabling self-collection), and suitable for remote collection in longitudinal studies. Dried blood spot (DBS), a pioneering microsampling technique, dominates clinical and research domains. Recent advancements in device technology address critical limitations of classical DBS, specifically variations in hematocrit and volume. This review presents a comprehensive overview of state-of-the-art microsampling devices, emphasizing their applications and potential for monitoring metabolites and lipids in blood. The scope extends to diverse areas, encompassing population studies, nutritional investigations, drug discovery, sports medicine, and multi-omics research.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

The gut microbiota-astrocyte axis: Implications for type 2 diabetic cognitive dysfunction

BACKGROUND

Diabetic cognitive dysfunction (DCD) is one of the most insidious complications of type 2 diabetes mellitus, which can seriously affect the ability to self-monitoring of blood glucose and the quality of life in the elderly. Previous pathological studies of cognitive dysfunction have focused on neuronal dysfunction, characterized by extracellular beta-amyloid deposition and intracellular tau hyperphosphorylation. In recent years, astrocytes have been recognized as a potential therapeutic target for cognitive dysfunction and important participants in the central control of metabolism. The disorder of gut microbiota and their metabolites have been linked to a series of metabolic diseases such as diabetes mellitus. The imbalance of intestinal flora has the effect of promoting the occurrence and deterioration of several diabetes-related complications. Gut microbes and their metabolites can drive astrocyte activation.

AIMS

We reviewed the pathological progress of DCD related to the "gut microbiota-astrocyte" axis in terms of peripheral and central inflammation, intestinal and blood-brain barrier (BBB) dysfunction, systemic and brain energy metabolism disorders to deepen the pathological research progress of DCD and explore the potential therapeutic targets.

CONCLUSION

"Gut microbiota-astrocyte" axis, unique bidirectional crosstalk in the brain-gut axis, mediates the intermediate pathological process of neurocognitive dysfunction secondary to metabolic disorders in diabetes mellitus.