Metabolic disorder in Alzheimer's disease

Association between depression and diabetes mellitus and the impact of their comorbidity on mortality: Evidence from a nationally representative study

BACKGROUND

Depression and diabetes mellitus (DM) are major chronic noncommunicable diseases that impair one's mental and physical well-being and impose substantial burdens on the health system. Depressed individuals have an increased risk of impaired blood glucose, weight gain and dyslipidemia which could induce poorer long-term survival.

METHODS

37,040 individuals from the National Health and Nutrition Examination Survey (NHANES) were included. Depressive symptoms were assessed by the Patient Health Questionnaire (PHQ-9) and classified by the total scores as no (0-4), mild (5-9), moderate (10-14), and severe (15-27). DM was determined based on self-reported medical history, clinical test results, and medication use. Logistic and Cox regression were the main statistical models. All analyses were based on weighted data from complex sampling.

RESULTS

The prevalence of DM was higher in depressed than non-depressed individuals (21.26 % vs. 13.75 %). The adjusted odds ratio (OR) (95 % CI) of comorbid DM increased with depression severity, from 1.00 (reference) for no depression, to 1.22 (1.09,1.36) for mild, 1.62 (1.37,1.92) for moderate, and 1.52(1.28,1.82) for severe depression. Comorbidity of DM and depression significantly associated with a higher risk of all-cause mortality, with a hazard ratio (HR) (95 % CI) = 2.09 (1.64,2.66).

LIMITATIONS

Dynamic demographic and metabolic data were not available.

CONCLUSION

Depression is associated with a higher risk of DM, which may be related to biological, socioeconomic, and medication-related factors. Comorbidity of the two worsens long-term survival. Therefore, blood glucose management and prevention of DM should be emphasized in depressed patients.

Adipose tissue as a therapeutic target for vascular damage in Alzheimer's disease

Adipose tissue has recently been recognized as an important endocrine organ that plays a crucial role in energy metabolism and in the immune response in many metabolic tissues. With this regard, emerging evidence indicates that an important crosstalk exists between the adipose tissue and the brain. However, the contribution of adipose tissue to the development of age-related diseases, including Alzheimer's disease, remains poorly defined. New studies suggest that the adipose tissue modulates brain function through a range of endogenous biologically active factors known as adipokines, which can cross the blood-brain barrier to reach the target areas in the brain or to regulate the function of the blood-brain barrier. In this review, we discuss the effects of several adipokines on the physiology of the blood-brain barrier, their contribution to the development of Alzheimer's disease and their therapeutic potential. LINKED ARTICLES: This article is part of a themed issue From Alzheimer's Disease to Vascular Dementia: Different Roads Leading to Cognitive Decline. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.6/issuetoc.

Tauroursodeoxycholic acid: a bile acid that may be used for the prevention and treatment of Alzheimer's disease

Alzheimer's disease (AD) is a prevalent neurodegenerative disease that has become one of the main factors affecting human health. It has serious impacts on individuals, families, and society. With the development of population aging, the incidence of AD will further increase worldwide. Emerging evidence suggests that many physiological metabolic processes, such as lipid metabolism, are implicated in the pathogenesis of AD. Bile acids, as the main undertakers of lipid metabolism, play an important role in the occurrence and development of Alzheimer's disease. Tauroursodeoxycholic acid, an endogenous bile acid, has been proven to possess therapeutic effects in different neurodegenerative diseases, including Alzheimer's disease. This review tries to find the relationship between bile acid metabolism and AD, as well as explore the therapeutic potential of bile acid taurocursodeoxycholic acid for this disease. The potential mechanisms of taurocursodeoxycholic acid may include reducing the deposition of Amyloid-β protein, regulating apoptotic pathways, preventing tau hyperphosphorylation and aggregation, protecting neuronal synapses, exhibiting anti-inflammatory properties, and improving metabolic disorders. The objective of this study is to shed light on the use of tauroursodeoxycholic acid preparations in the prevention and treatment of AD, with the aim of identifying effective treatment targets and clarifying various treatment mechanisms involved in this disease.

Electroacupuncture stimulation improves cognitive ability and regulates metabolic disorders in Alzheimer's disease model mice: new insights from brown adipose tissue thermogenesis

Background

Metabolic defects play a crucial role in Alzheimer's disease (AD) development. Brown adipose tissue (BAT) has been identified as a novel potential therapeutic target for AD due to its unique role in energy metabolism. Electroacupuncture (EA) shows promise in improving cognitive ability and brain glucose metabolism in AD, but its effects on peripheral and central metabolism are unclear.

Methods

In this study, SAMP8 mice (AD model) received EA stimulation at specific acupoints. Cognitive abilities were evaluated using the Morris water maze test, while neuronal morphology and tau pathology were assessed through Nissl staining and immunofluorescence staining, respectively. Metabolic variations and BAT thermogenesis were measured using ELISA, HE staining, Western blotting, and infrared thermal imaging.

Results

Compared to SAMR1 mice, SAMP8 mice showed impaired cognitive ability, neuronal damage, disrupted thermoregulation, and metabolic disorders with low BAT activity. Both the EA and DD groups improved cognitive ability and decreased tau phosphorylation (p<0.01 or p<0.05). However, only the EA group had a significant effect on metabolic disorders and BAT thermogenesis (p<0.01 or p<0.05), while the DD group did not.

Conclusion

These findings indicate that EA not only improves the cognitive ability of SAMP8 mice, but also effectively regulates peripheral and central metabolic disorders, with this effect being significantly related to the activation of BAT thermogenesis.

Association of metabolic dysfunction with cognitive decline and Alzheimer's disease: A review of metabolomic evidence

The discovery of new biomarkers that can distinguish Alzheimer's disease (AD) from mild cognitive impairment (MCI) in the early stages will help to provide new diagnostic and therapeutic strategies and slow the transition from MCI to AD. Patients with AD may present with a concomitant metabolic disorder, such as diabetes, obesity, and dyslipidemia, as a risk factor for AD that may be involved in the onset of both AD pathology and cognitive impairment. Therefore, metabolite profiling, or metabolomics, can be very useful in diagnosing AD, developing new therapeutic targets, and evaluating both the course of treatment and the clinical course of the disease. In addition, studying the relationship between nutritional behavior and AD requires investigation of the role of conditions such as obesity, hypertension, dyslipidemia, and elevated glucose level. Based on this literature review, nutritional recommendations, including weight loss by reducing calorie and cholesterol intake and omega-3 fatty acid supplementation can prevent cognitive decline and dementia in the elderly. The underlying metabolic causes of the pathology and cognitive decline caused by AD and MCI are not well understood. In this review article, metabolomics biomarkers for diagnosis of AD and MCI and metabolic risk factors for cognitive decline in AD were evaluated.

Urine metabolomics phenotyping and urinary biomarker exploratory in mild cognitive impairment and Alzheimer's disease

Introduction

Alzheimer's disease is a prevalent disease with a heavy global burden and is suggested to be a metabolic disease in the brain in recent years. The metabolome is considered to be the most promising phenotype which reflects changes in genetic, transcript, and protein profiles as well as environmental effects. Aiming to obtain a comprehensive understanding and convenient diagnosis of MCI and AD from another perspective, researchers are working on AD metabolomics. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels.

Methods

We first enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples and conducted an LC-MS/MS analysis. In parallel, clinical data were collected and clinical examinations were performed. After statistical and bioinformatics analyzes, significant risk factors and differential urinary metabolites were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM.

Results

Fifty-seven AD patients, 43 MCI patients and 62 CN subjects were enrolled. A total of 2,140 metabolites were identified among which 125 significantly differed between the AD and CN groups, including 46 upregulated ones and 79 downregulated ones. In parallel, there were 93 significant differential metabolites between the MCI and CN groups, including 23 upregulated ones and 70 downregulated ones. AD diagnostic panel (30 metabolites+ age + APOE) achieved an AUC of 0.9575 in the test set while MCI diagnostic panel (45 metabolites+ age + APOE) achieved an AUC of 0.7333 in the test set. Atropine, S-Methyl-L-cysteine-S-oxide, D-Mannose 6-phosphate (M6P), Spiculisporic Acid, N-Acetyl-L-methionine, 13,14-dihydro-15-keto-tetranor Prostaglandin D2, Pyridoxal 5'-Phosphate (PLP) and 17(S)-HpDHA were considered valuable for both AD and MCI diagnosis and defined as hub metabolites. Besides, diagnostic metabolites were weakly correlated with cognitive functions.

Discussion

In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN. Atropine, M6P and PLP were evidence-based hub metabolites in AD.

Identification of metabolism-related subtypes and feature genes in Alzheimer's disease

BACKGROUND

Owing to the heterogeneity of Alzheimer's disease (AD), its pathogenic mechanisms are yet to be fully elucidated. Evidence suggests an important role of metabolism in the pathophysiology of AD. Herein, we identified the metabolism-related AD subtypes and feature genes.

METHODS

The AD datasets were obtained from the Gene Expression Omnibus database and the metabolism-relevant genes were downloaded from a previously published compilation. Consensus clustering was performed to identify the AD subclasses. The clinical characteristics, correlations with metabolic signatures, and immune infiltration of the AD subclasses were evaluated. Feature genes were screened using weighted correlation network analysis (WGCNA) and processed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Furthermore, three machine-learning algorithms were used to narrow down the selection of the feature genes. Finally, we identified the diagnostic value and expression of the feature genes using the AD dataset and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis.

RESULTS

Three AD subclasses were identified, namely Metabolism Correlated (MC) A (MCA), MCB, and MCC subclasses. MCA contained signatures associated with high AD progression and may represent a high-risk subclass compared with the other two subclasses. MCA exhibited a high expression of genes related to glycolysis, fructose, and galactose metabolism, whereas genes associated with the citrate cycle and pyruvate metabolism were downregulated and associated with high immune infiltration. Conversely, MCB was associated with citrate cycle genes and exhibited elevated expression of immune checkpoint genes. Using WGCNA, 101 metabolic genes were identified to exhibit the strongest association with poor AD progression. Finally, the application of machine-learning algorithms enabled us to successfully identify eight feature genes, which were employed to develop a nomogram model that could bring distinct clinical benefits for patients with AD. As indicated by the AD datasets and qRT-PCR analysis, these genes were intimately associated with AD progression.

CONCLUSION

Metabolic dysfunction is associated with AD. Hypothetical molecular subclasses of AD based on metabolic genes may provide new insights for developing individualized therapy for AD. The feature genes highly correlated with AD progression included GFAP, CYB5R3, DARS, KIAA0513, EZR, KCNC1, COLEC12, and TST.

Metabolic Pathway Pairwise-Based Signature as a Potential Non-Invasive Diagnostic Marker in Alzheimer's Disease Patients

Alzheimer's disease (AD) is an incurable neurodegenerative disorder. Early screening, particularly in blood plasma, has been demonstrated as a promising approach to the diagnosis and prevention of AD. In addition, metabolic dysfunction has been demonstrated to be closely related to AD, which might be reflected in the whole blood transcriptome. Hence, we hypothesized that the establishment of a diagnostic model based on the metabolic signatures of blood is a workable strategy. To that end, we initially constructed metabolic pathway pairwise (MPP) signatures to characterize the interplay among metabolic pathways. Then, a series of bioinformatic methodologies, e.g., differential expression analysis, functional enrichment analysis, network analysis, etc., were used to investigate the molecular mechanism behind AD. Moreover, an unsupervised clustering analysis based on the MPP signature profile via the Non-Negative Matrix Factorization (NMF) algorithm was utilized to stratify AD patients. Finally, aimed at distinguishing AD patients from non-AD groups, a metabolic pathway-pairwise scoring system (MPPSS) was established using multi-machine learning methods. As a result, many metabolic pathways correlated to AD were disclosed, including oxidative phosphorylation, fatty acid biosynthesis, etc. NMF clustering analysis divided AD patients into two subgroups (S1 and S2), which exhibit distinct activities of metabolism and immunity. Typically, oxidative phosphorylation in S2 exhibits a lower activity than that in S1 and non-AD group, suggesting the patients in S2 might possess a more compromised brain metabolism. Additionally, immune infiltration analysis showed that the patients in S2 might have phenomena of immune suppression compared with S1 and the non-AD group. These findings indicated that S2 probably has a more severe progression of AD. Finally, MPPSS could achieve an AUC of 0.73 (95%CI: 0.70, 0.77) in the training dataset, 0.71 (95%CI: 0.65, 0.77) in the testing dataset, and an AUC of 0.99 (95%CI: 0.96, 1.00) in one external validation dataset. Overall, our study successfully established a novel metabolism-based scoring system for AD diagnosis using the blood transcriptome and provided new insight into the molecular mechanism of metabolic dysfunction implicated in AD.

Simultaneous neuronal expression of human amyloid-β and Tau genes drives global phenotypic and multi-omic changes in C. elegans

Alzheimer's disease and Alzheimer's related diseases (ADRD) are a class of prevalent age-related neurodegenerative disorders characterized by the accumulation of amyloid- β (Aβ) plaques and Tau neurofibrillary tangles. The intricate interplay between Aβ and Tau proteins requires further investigation to better understand the precise mechanisms underlying disease pathology. The nematode Caenorhabditis elegans ( C. elegans ) serves as an invaluable model organism for studying aging and neurodegenerative diseases. Here we performed an unbiased systems analysis of a C. elegans strain expressing both Aβ and Tau proteins within neurons. Intriguingly, even at an early stage of adulthood, we observed reproductive impairments and mitochondrial dysfunction consistent with substantial disruptions in mRNA transcript abundance, protein solubility, and metabolite levels. Notably, the simultaneous expression of these two neurotoxic proteins exhibited a synergistic effect, leading to accelerated aging in the model organism. Our comprehensive findings shed new light on the intricate relationship between normal aging processes and the etiology of ADRD. Specifically, we demonstrate the alterations to metabolic functions precede age-related neurotoxicity, offering critical insights into potential therapeutic strategies.

Comprehensive metabolomics and lipidomics profiling uncovering neuroprotective effects of Ginkgo biloba L. leaf extract on Alzheimer's disease

Introduction: Ginkgo biloba L. leaf extract (GBLE) has been reported to be effective for alleviating cognitive and memory impairment in Alzheimer's disease (AD). Nevertheless, the potential mechanism remains unclear. Herein, this study aimed to explore the neuroprotective effects of GBLE on AD and elaborate the underlying therapeutic mechanism. Methods: Donepezil, the most widely prescribed drug for AD, was used as a positive control. An integrated metabolomics and lipidomics approach was adopted to characterize plasma metabolic phenotype of APP/PS1 double transgenic mice and describe the metabolomic and lipidomic fingerprint changes after GBLE intervention. The Morris water maze test and immunohistochemistry were applied to evaluate the efficacy of GBLE. Results: As a result, administration of GBLE significantly improved the cognitive function and alleviated amyloid beta (Aβ) deposition in APP/PS1 mice, showing similar effects to donepezil. Significant alterations were observed in metabolic signatures of APP/PS1 mice compared with wild type (WT) mice by metabolomic analysis. A total of 60 markedly altered differential metabolites were identified, including 28 lipid and lipid-like molecules, 13 organic acids and derivatives, 11 organic nitrogen compounds, and 8 other compounds, indicative of significant changes in lipid metabolism of AD. Further lipidomic profiling showed that the differential expressed lipid metabolites between APP/PS1 and WT mice mainly consisted of phosphatidylcholines, lysophosphatidylcholines, triglycerides, and ceramides. Taking together all the data, the plasma metabolic signature of APP/PS1 mice was primarily characterized by disrupted sphingolipid metabolism, glycerophospholipid metabolism, glycerolipid metabolism, and amino acid metabolism. Most of the disordered metabolites were ameliorated after GBLE treatment, 19 metabolites and 24 lipids of which were significantly reversely regulated (adjusted-p<0.05), which were considered as potential therapeutic targets of GBLE on AD. The response of APP/PS1 mice to GBLE was similar to that of donepezil, which significantly reversed the levels of 23 disturbed metabolites and 30 lipids. Discussion: Our data suggested that lipid metabolism was dramatically perturbed in the plasma of APP/PS1 mice, and GBLE might exert its neuroprotective effects by restoring lipid metabolic balance. This work provided a basis for better understanding the potential pathogenesis of AD and shed new light on the therapeutic mechanism of GBLE in the treatment of AD.

Aberrant Energy Metabolism in Alzheimer's Disease

To maintain energy supply to the brain, a direct energy source called adenosine triphosphate (ATP) is produced by oxidative phosphorylation and aerobic glycolysis of glucose in the mitochondria and cytoplasm. Brain glucose metabolism is reduced in many neurodegenerative diseases, including Alzheimer's disease (AD), where it appears presymptomatically in a progressive and region-specific manner. Following dysregulation of energy metabolism in AD, many cellular repair/regenerative processes are activated to conserve the energy required for cell viability. Glucose metabolism plays an important role in the pathology of AD and is closely associated with the tricarboxylic acid cycle, type 2 diabetes mellitus, and insulin resistance. The glucose intake in neurons is from endothelial cells, astrocytes, and microglia. Damage to neurocentric glucose also damages the energy transport systems in AD. Gut microbiota is necessary to modulate bidirectional communication between the gastrointestinal tract and brain. Gut microbiota may influence the process of AD by regulating the immune system and maintaining the integrity of the intestinal barrier. Furthermore, some therapeutic strategies have shown promising therapeutic effects in the treatment of AD at different stages, including the use of antidiabetic drugs, rescuing mitochondrial dysfunction, and epigenetic and dietary intervention. This review discusses the underlying mechanisms of alterations in energy metabolism in AD and provides potential therapeutic strategies in the treatment of AD.

SOX2 modulated astrocytic process plasticity is involved in arsenic-induced metabolic disorders

Metabolic disorders induced by arsenic exposure have attracted great public concern. However, it remains unclear whether hypothalamus-based central regulation mechanisms are involved in this process. Here, we exposed mice to 100 μg/L arsenic in drinking water and established a chronic arsenic exposure model. Our study revealed that chronic arsenic exposure caused metabolic disorders in mice including impaired glucose metabolism and decreased energy expenditure. Arsenic exposure also impaired glucose sensing and the activation of proopiomelanocortin (POMC) neurons in the hypothalamus. In particular, arsenic exposure damaged the plasticity of hypothalamic astrocytic process. Further research revealed that arsenic exposure inhibited the expression of sex-determining region Y-Box 2 (SOX2), which decreased the expression level of insulin receptors (INSRs) and the phosphorylation of AKT. The conditional deletion of astrocytic SOX2 exacerbated arsenic-induced effects on metabolic disorders, the impairment of hypothalamic astrocytic processes, and the inhibition of INSR/AKT signaling. Furthermore, the arsenic-induced impairment of astrocytic processes and inhibitory effects on INSR/AKT signaling were reversed by SOX2 overexpression in primary hypothalamic astrocytes. Together, we demonstrated here that chronic arsenic exposure caused metabolic disorders by impairing SOX2-modulated hypothalamic astrocytic process plasticity in mice. Our study provides evidence of novel central regulatory mechanisms underlying arsenic-induced metabolic disorders and emphasizes the crucial role of SOX2 in regulating the process plasticity of adult astrocytes.

Glucose enrichment impair neurotransmission and induce Aβ oligomerization that cannot be reversed by manipulating O-β-GlcNAcylation in the C. elegans model of Alzheimer's disease

Amyloid beta (Aβ) plaques formation and impaired neurotransmission and neuronal behaviors are primary hallmarks of Alzheimer's disease (AD) that are further associated with impaired glucose metabolism in elderly AD's patients. However, the exact role of glucose metabolism on disease progression has not been elucidated yet. In this study, the effect of glucose on Aβ-mediated toxicity, neurotransmission and neuronal behaviors has been investigated using a C. elegans model system expressing human Aβ. In addition to regular diet, worms expressing Aβ were supplemented with different concentrations of glucose and glycerol and 5 mM 2-deoxyglucose to draw any conclusions. Addition of glucose to the growth medium delayed Aβ-associated paralysis, promoted abnormal body shapes and movement, unable to restore impaired acetylcholine neurotransmission, inhibited egg laying and hatching in pre-existing Aβ-mediated pathology. The harmful effects of glucose may associate with an increase in toxic Aβ oligomers and impaired neurotransmission. O-β-GlcNAcylation (O-GlcNAc), a well-known post-translational modification is directly associated with glucose metabolism and has been found to ameliorates the Aβ- toxicity. We reasoned that glucose addition might induce O-GlcNAc, thereby protect against Aβ. Contrary to our expectations, induced glucose levels were not protective. Increasing O-GlcNAc, either with Thiamet-G (TMG) or by suppressing the O-GlcNAcase (oga-1) gene does interfere with and, therefore, reduce Aβ- toxicity but not in the presence of high glucose. The effects of glucose cannot be effectively managed by manipulating O-GlcNAc in AD models of C. elegans. Our observations suggest that glucose enrichment is unlikely to be an appropriate therapy to minimize AD progression.

Urinary and faecal metabolic characteristics in APP/PS1 transgenic mouse model of Alzheimer's disease with and without cognitive decline

Alzheimer's disease (AD) has been considered to be a systematic metabolic disorder, but little information is available about metabolic changes in the urine and feces. In this study, we investigated urinary and faecal metabolic profiles in amyloid precursor protein/presenilin 1 (APP/PS1) mice at 3 and 9 months of age (3 M and 9 M) and age-matched wild-type (WT) mice by using ¹H NMR-based metabolomics, and aimed to explore changes in metabolic pathways during amyloid pathology progression and identify potential metabolite biomarkers at earlier stage of AD. The results show that learning and memory abilities were impaired in APP/PS1 mice relative to WT mice at 9 M, but not at 3 M. However, metabolomics analysis demonstrates that AD disrupted metabolic phenotypes in the urine and feces of APP/PS1 mice at both 3 M and 9 M, including amino acid metabolism, microbial metabolism and energy metabolism. In addition, several potential metabolite biomarkers were identified for discriminating AD and WT mice prior to cognitive decline with the AUC values from 0.755 to 0.971, such as taurine, hippurate, urea and methylamine in the urine as well as alanine, leucine and valine in the feces. Therefore, our results not only confirmed AD as a metabolic disorder, but also contributed to the identification of potential biomarkers at earlier stage of AD.

The mechanism and efficacy of GLP-1 receptor agonists in the treatment of Alzheimer's disease

Since type 2 diabetes mellitus (T2DM) is a risk factor for Alzheimer's disease (AD) and both have the same pathogenesis (e.g., insulin resistance), drugs used to treat T2DM have been gradually found to reduce the progression of AD in AD models. Of these drugs, glucagon-like peptide 1 receptor (GLP-1R) agonists are more effective and have fewer side effects. GLP-1R agonists have reducing neuroinflammation and oxidative stress, neurotrophic effects, decreasing Aβ deposition and tau hyperphosphorylation in AD models, which may be a potential drug for the treatment of AD. However, this needs to be verified by further clinical trials. This study aims to summarize the current information on the mechanisms and effects of GLP-1R agonists in AD.

Polydatin protects neuronal cells from hydrogen peroxide damage by activating CREB/Ngb signaling

Oxidative stress‑induced neuronal cell death contributes significantly to the physiological processes of a number of neurological disorders. Polydatin (PD) has been reported to protect against Alzheimer's disease (AD), ischemic stroke and traumatic brain injury. However, the underlying neuroprotective mechanisms remain to be elucidated. The current study suggested that PD activates AKT/cAMP response element‑binding protein (CREB) signaling and induces neuroglobin (Ngb) to protect neuronal cells from hydrogen peroxide (H₂O₂) in vitro. PD inhibited the H₂O₂‑induced neuronal cell death of primary mouse cortical neurons and N2a cells. Functional studies showed that PD attenuated H₂O₂‑induced mitochondrial dysfunction and mitochondrial reactive oxygen species production. Mechanistically, PD was verified to induce the phosphorylation of AKT and CREB and increase the protein level of Ngb. The luciferase assay results showed that Ngb transcriptional activity was activated by CREB, especially after PD treatment. It was further indicated that PD increased the transcription of Ngb by enhancing the binding of CREB to the promoter region of Ngb. Finally, Ngb knockdown largely attenuated the neuroprotective role of PD against H₂O₂. The results indicated that PD protected neuronal cells from H₂O₂ by activating CREB/Ngb signaling in neuronal cells, indicating that PD has a neuroprotective effect against neurodegenerative diseases.

Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database

Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity at cholinergic synapses in various regions of the nervous system. The inhibition of acetylcholinesterase is frequently used to treat Alzheimer's disease. In this study, a merged BindingDB and ChEMBL dataset containing molecules with reported half-maximal inhibitory concentration (IC₅₀) values for AChE (7032 molecules) was used to build machine learning classification models for selecting potential AChE inhibitors from the SistematX dataset (8593 secondary metabolites). A total of seven fivefold models with accuracy above 80% after cross-validation were obtained using three types of molecular descriptors (VolSurf, DRAGON 5.0, and bit-based fingerprints). A total of 521 secondary metabolites (6.1%) were classified as active in this stage. Subsequently, virtual screening was performed, and 25 secondary metabolites were identified as potential inhibitors of AChE. Separately, the crystal structure of AChE in complex with (-)-galantamine was used to perform molecular docking calculations with the entire SistematX dataset. Consensus analysis of both methodologies was performed. Only eight structures achieved combined probability values above 0.5. Finally, two sesquiterpene lactones, structures 15 and 24, were predicted to be able to cross the blood-brain barrier, which was confirmed in the VolSurf+ quantitative model, revealing these two structures as the most promising secondary metabolites for AChE inhibition among the 8593 molecules tested. A consensus analysis of classification models and molecular docking calculations identified four potential inhibitors of acetylcholinesterase from the SistematX dataset (8593 structures).