Therapies for Alzheimer's disease: a metabolic perspective

Exploring the role of mitochondrial uncoupling protein 4 in brain metabolism: implications for Alzheimer's disease

The brain's high demand for energy necessitates tightly regulated metabolic pathways to sustain physiological activity. Glucose, the primary energy substrate, undergoes complex metabolic transformations, with mitochondria playing a central role in ATP production via oxidative phosphorylation. Dysregulation of this metabolic interplay is implicated in Alzheimer's disease (AD), where compromised glucose metabolism, oxidative stress, and mitochondrial dysfunction contribute to disease progression. This review explores the intricate bioenergetic crosstalk between astrocytes and neurons, highlighting the function of mitochondrial uncoupling proteins (UCPs), particularly UCP4, as important regulators of brain metabolism and neuronal function. Predominantly expressed in the brain, UCP4 reduces the membrane potential in the inner mitochondrial membrane, thereby potentially decreasing the generation of reactive oxygen species. Furthermore, UCP4 mitigates mitochondrial calcium overload and sustains cellular ATP levels through a metabolic shift from mitochondrial respiration to glycolysis. Interestingly, the levels of the neuronal UCPs, UCP2, 4 and 5 are significantly reduced in AD brain tissue and a specific UCP4 variant has been associated to an increased risk of developing AD. Few studies modulating the expression of UCP4 in astrocytes or neurons have highlighted protective effects against neurodegeneration and aging, suggesting that pharmacological strategies aimed at activating UCPs, such as protonophoric uncouplers, hold promise for therapeutic interventions in AD and other neurodegenerative diseases. Despite significant advances, our understanding of UCPs in brain metabolism remains in its early stages, emphasizing the need for further research to unravel their biological functions in the brain and their therapeutic potential.

Advances in Understanding and Managing Alzheimer's Disease: From Pathophysiology to Innovative Therapeutic Strategies

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder characterized by the presence of amyloid-β (Aβ) plaques and tau-containing neurofibrillary tangles, leading to cognitive and physical decline. Representing the majority of dementia cases, AD poses a significant burden on healthcare systems globally, with onset typically occurring after the age of 65. While most cases are sporadic, about 10% exhibit autosomal forms associated with specific gene mutations. Neurofibrillary tangles and Aβ plaques formed by misfolded tau proteins and Aβ peptides contribute to neuronal damage and cognitive impairment. Currently, approved drugs, such as acetylcholinesterase inhibitors and N-methyl D-aspartate receptor agonists, offer only partial symptomatic relief without altering disease progression. A promising development is using lecanemab, a humanized IgG1 monoclonal antibody, as an immune therapeutic approach. Lecanemab demonstrates selectivity for polymorphic Aβ variants and binds to large soluble Aβ aggregates, providing a potential avenue for targeted treatment. This shift in understanding the role of the adaptive immune response in AD pathogenesis opens new possibilities for therapeutic interventions aiming to address the disease's intricate mechanisms. This review aims to summarize recent advancements in understanding Alzheimer's disease pathophysiology and innovative therapeutic approaches, providing valuable insights for both researchers and clinicians.

Metabolic Profile of Alzheimer's Disease: Is 10-Hydroxy-2-decenoic Acid a Pertinent Metabolic Adjuster?

Alzheimer's disease (AD) represents a significant public health concern in modern society. Metabolic syndrome (MetS), which includes diabetes mellitus (DM) and obesity, represents a modifiable risk factor for AD. MetS and AD are interconnected through various mechanisms, such as mitochondrial dysfunction, oxidative stress, insulin resistance (IR), vascular impairment, inflammation, and endoplasmic reticulum (ER) stress. Therefore, it is necessary to seek a multi-targeted and safer approach to intervention. Thus, 10-hydroxy-2-decenoic acid (10-HDA), a unique hydroxy fatty acid in royal jelly, has shown promising anti-neuroinflammatory, blood-brain barrier (BBB)-preserving, and neurogenesis-promoting properties. In this paper, we provide a summary of the relationship between MetS and AD, together with an introduction to 10-HDA as a potential intervention nutrient. In addition, molecular docking is performed to explore the metabolic tuning properties of 10-HDA with associated macromolecules such as GLP-1R, PPARs, GSK-3, and TREM2. In conclusion, there is a close relationship between AD and MetS, and 10-HDA shows potential as a beneficial nutritional intervention for both AD and MetS.

Brain metabolism in Alzheimer's disease: biological mechanisms of exercise

Alzheimer's disease (AD) is a major subtype of neurodegenerative dementia caused by long-term interactions and accumulation of multiple adverse factors, accompanied by dysregulation of numerous intracellular signaling and molecular pathways in the brain. At the cellular and molecular levels, the neuronal cellular milieu of the AD brain exhibits metabolic abnormalities, compromised bioenergetics, impaired lipid metabolism, and reduced overall metabolic capacity, which lead to abnormal neural network activity and impaired neuroplasticity, thus accelerating the formation of extracellular senile plaques and intracellular neurofibrillary tangles. The current absence of effective pharmacological therapies for AD points to the urgent need to investigate the benefits of non-pharmacological approaches such as physical exercise. Despite the evidence that regular physical activity can improve metabolic dysfunction in the AD state, inhibit different pathophysiological molecular pathways associated with AD, influence the pathological process of AD, and exert a protective effect, there is no clear consensus on the specific biological and molecular mechanisms underlying the advantages of physical exercise. Here, we review how physical exercise improves crucial molecular pathways and biological processes associated with metabolic disorders in AD, including glucose metabolism, lipid metabolism, Aβ metabolism and transport, iron metabolism and tau pathology. How metabolic states influence brain health is also presented. A better knowledge on the neurophysiological mechanisms by which exercise improves AD metabolism can contribute to the development of novel drugs and improvement of non-pharmacological interventions.

The convergent and divergent patterns in brain perfusion between Alzheimer's disease and Parkinson's disease with dementia: An ASL MRI study

Background

Aberrant brain blood perfusion changes have been found to play an important role in the progress of Alzheimer's disease (AD) and Parkinson's disease with dementia (PDD). However, the convergent and divergent patterns in brain perfusion between two dementias remain poorly documented.

Objective

To explore the impaired brain perfusion pattern and investigate their overlaps and differences between AD and PDD using normalized cerebral blood flow (CBF).

Methods

The regional perfusion in patients with AD and PDD as well as healthy control (HC) subjects were explored using the three-dimensional arterial spin labeling. The normalized CBF values were compared across the three groups and further explored the potential linkages to clinical assessments.

Results

In total, 24 patients with AD, 26 patients with PDD, and 35 HC subjects were enrolled. Relative to the HC group, both the AD group and the PDD group showed reduced normalized CBF mainly in regions of the temporal and frontal gyrus, whereas preserved perfusion presented in the sensorimotor cortex and basal ganglia area. Compared with the AD group, the PDD group showed decreased perfusion in the right putamen and right supplementary motor area (SMA), while preserved perfusion in the right inferior parietal lobule (IPL) and right precuneus. In the AD group, significant correlations were observed between the normalized CBF values in the right IPL and scores of global cognitive function (P = 0.033, ρ = 0.442), between the normalized CBF values in the right precuneus and the scores of memory function (P = 0.049,ρ = 0.406). The normalized CBF in the right putamen was significantly linked to cores of motor symptoms (P = 0.017, ρ = 0.214) in the PDD group.

Conclusion

Our findings suggested convergent and divergent patterns of brain hemodynamic dysregulation between AD and PDD and contributed to a better understanding of the pathophysiological mechanisms.

Plasma metabolomic profiles of dementia: a prospective study of 110,655 participants in the UK Biobank

BACKGROUND

Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions.

METHODS

Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model.

RESULTS

Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5-12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups.

CONCLUSIONS

Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.

Effects of Involuntary and Voluntary Exercise in Combination with Acousto-Optic Stimulation on Adult Neurogenesis in an Alzheimer's Mouse Model

Single-factor intervention, such as physical exercise and auditory and visual stimulation, plays a positive role on the prevention and treatment of Alzheimer's disease (AD); however, the therapeutic effects of single-factor intervention are limited. The beneficial effects of these multifactor combinations on AD and its molecular mechanism have yet to be elucidated. Here, we investigated the effect of multifactor intervention, voluntary wheel exercise, and involuntary treadmill running in combination with acousto-optic stimulation, on adult neurogenesis and behavioral phenotypes in a mouse model of AD. We found that 4 weeks of multifactor intervention can significantly increase the production of newborn cells (BrdU⁺ cells) and immature neurons (DCX⁺ cells) in the hippocampus and lateral ventricle of Aβ oligomer-induced mice. Importantly, the multifactor intervention could promote BrdU⁺ cells to differentiate into neurons (BrdU⁺ DCX⁺ cells or BrdU⁺ NeuN⁺ cells) and astrocytes (BrdU⁺GFAP⁺ cells) in the hippocampus and ameliorate Aβ oligomer-induced cognitive impairment and anxiety- and depression-like behaviors in mice evaluated by novel object recognition, Morris water maze tests, elevated zero maze, forced swimming test, and tail suspension test, respectively. Moreover, multifactor intervention could lead to an increase in the protein levels of PSD-95, SYP, DCX, NeuN, GFAP, Bcl-2, BDNF, TrkB, and pSer473-Akt and a decrease in the protein levels of BAX and caspase-9 in the hippocampal lysates of Aβ oligomer-induced mice. Furthermore, sequencing analysis of serum metabolites revealed that aberrantly expressed metabolites modulated by multifactor intervention were highly enriched in the biological process associated with keeping neurons functioning and neurobehavioral function. Additionally, the intervention-mediated serum metabolites mainly participated in glutamate metabolism, glucose metabolism, and the tricarboxylic acid cycle in mice. Our findings suggest the potential of multifactor intervention as a non-invasive therapeutic strategy for AD to anti-Aβ oligomer neurotoxicity.

Identification of Blood-Based Glycolysis Gene Associated with Alzheimer's Disease by Integrated Bioinformatics Analysis

BACKGROUND

Alzheimer's disease (AD) is one of many common neurodegenerative diseases without ideal treatment, but early detection and intervention can prevent the disease progression.

OBJECTIVE

This study aimed to identify AD-related glycolysis gene for AD diagnosis and further investigation by integrated bioinformatics analysis.

METHODS

122 subjects were recruited from the affiliated hospitals of Ningbo University between 1 October 2015 and 31 December 2016. Their clinical information and methylation levels of 8 glycolysis genes were assessed. Machine learning algorithms were used to establish an AD prediction model. Receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to assess the model. An AD risk factor model was developed by SHapley Additive exPlanations (SHAP) to extract features that had important impacts on AD. Finally, gene expression of AD-related glycolysis genes were validated by AlzData.

RESULTS

An AD prediction model was developed using random forest algorithm with the best average ROC_AUC (0.969544). The threshold probability of the model was positive in the range of 0∼0.9875 by DCA. Eight glycolysis genes (GAPDHS, PKLR, PFKFB3, LDHC, DLD, ALDOC, LDHB, HK3) were identified by SHAP. Five of these genes (PFKFB3, DLD, ALDOC, LDHB, LDHC) have significant differences in gene expression between AD and control groups by Alzdata, while three of the genes (HK3, ALDOC, PKLR) are related to the pathogenesis of AD. GAPDHS is involved in the regulatory network of AD risk genes.

CONCLUSION

We identified 8 AD-related glycolysis genes (GAPDHS, PFKFB3, LDHC, HK3, ALDOC, LDHB, PKLR, DLD) as promising candidate biomarkers for early diagnosis of AD by integrated bioinformatics analysis. Machine learning has the advantage in identifying genes.