The serum metabolomics signature of type 2 diabetes is obscured in Alzheimer's disease

Insulin Resistance and Impaired Branched-Chain Amino Acid Metabolism in Alzheimer's Disease

The pathogenesis of Alzheimer's disease (AD) is complicated and involves multiple contributing factors. Mounting evidence supports the concept that AD is an age-related metabolic neurodegenerative disease mediated in part by brain insulin resistance, and sharing similar metabolic dysfunctions and brain pathological characteristics that occur in type 2 diabetes mellitus (T2DM) and other insulin resistance disorders. Brain insulin signal pathway is a major regulator of branched-chain amino acid (BCAA) metabolism. In the past several years, impaired BCAA metabolism has been described in several insulin resistant states such as obesity, T2DM and cardiovascular disease. Disrupted BCAA metabolism leading to elevation in circulating BCAAs and related metabolites is an early metabolic phenotype of insulin resistance and correlated with future onset of T2DM. Brain is a major site for BCAA metabolism. BCAAs play pivotal roles in normal brain function, especially in signal transduction, nitrogen homeostasis, and neurotransmitter cycling. Evidence from animal models and patients support the involvement of BCAA dysmetabolism in neurodegenerative diseases including Huntington's disease, Parkinson's disease, and maple syrup urine disease. More recently, growing studies have revealed altered BCAA metabolism in AD, but the relationship between them is poorly understood. This review is focused on the recent findings regarding BCAA metabolism and its role in AD. Moreover, we will explore how impaired BCAA metabolism influences brain function and participates in the pathogenesis of AD.

Disturbances in Metabolic Pathways and the Identification of a Potential Biomarker Panel for Early Cartilage Degeneration in a Rabbit Anterior Cruciate Ligament Transection Model

OBJECTIVE

This study aimed to assess the association between synovial fluid (SF) metabolites and magnetic resonance imaging (MRI) measurements of cartilage biochemical composition to identify potential SF biomarkers for detecting the early onset of cartilage degeneration in a rabbit model.

METHODS

Both knees of 12 New Zealand White rabbits were used. The anterior cruciate ligament transection (ACLT) model was performed on right knees, and the sham surgery on left knees. MRI UTE-T2* scanning and SF sample collection were performed on ACLT knees at 4 and 8 weeks postsurgery and on sham surgery knees at 4 weeks postsurgery. Ultra-performance liquid chromatography-mass spectrometry and multivariate statistical analysis were used to distinguish samples in three groups. Pathway and receiver operating characteristic analyses were utilized to identify potential metabolite biomarkers.

RESULTS

There were 12 knees in sham surgery models, 11 in ACLT models at 4 weeks postsurgery, and 10 in ACLT models at 8 weeks postsurgery. UTE-T2* values for the lateral tibia cartilage showed significant decreases over the study period. Levels of 103 identified metabolites in SF were markedly different among three groups. Furthermore, 24 metabolites were inversely correlated with UTE-T2* values of the lateral tibia cartilage, while hippuric acid was positively correlated with UTE-T2* values of the lateral tibia cartilage. Among 25 potential markers, N1-acetylspermidine, 2-amino-1,3,4-octadecanetriol, l-phenylalanine, 5-hydroxy-l-tryptophan, and l-tryptophan were identified as potential biomarkers with high area under the curve values and Pearson correlation coefficients.

CONCLUSION

Five differential metabolites in SF were found as potential biomarkers for the early detection of cartilage degeneration in the rabbit ACLT model.

LC-MS-Based Untargeted Metabolomics Reveals Early Biomarkers in STZ-Induced Diabetic Rats With Cognitive Impairment

Diabetes in the elderly increases cognitive impairment, but the underlying mechanisms are still far from fully understood. A non-targeted metabolomics approach based on liquid chromatography-mass spectrometry (LC-MS) was performed to screen out the serum biomarkers of diabetic mild cognitive impairment (DMMCI) in rats. Total 48 SD rats were divided into three groups, Normal control (NC) group, high-fat diet (HFD) fed group and type 2 diabetes mellitus (T2DM) group. The T2DM rat model was induced by intraperitoneal administration of streptozotocin (STZ, 35 mg/kg) after 6 weeks of high-fat diet (HFD) feeding. Then each group was further divided into 4-week and 8-week subgroups, which were calculated from the time point of T2DM rat model establishment. The novel object recognition test (NORT) and the Morris water maze (MWM) method were used to evaluate the cognitive deficits in all groups. Compared to the NC-8w and HFD-8w groups, both NOR and MWM tests indicated significant cognitive dysfunction in the T2DM-8w group, which could be used as an animal model of DMMCI. Serum was ultimately collected from the inferior vena cava after laparotomy. Metabolic profiling analysis was conducted using ultra high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) technology. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to verify the stability of the model. According to variable importance in the project (VIP > 1) and the p-value of t-test (P < 0.05) obtained by the OPLS-DA model, the metabolites with significant differences were screened out as potential biomarkers. In total, we identified 94 differentially expressed (44 up-regulated and 50 down-regulated) endogenous metabolites. The 10 top up-regulated and 10 top down-regulated potential biomarkers were screened according to the FDR significance. These biomarkers by pathway topology analysis were primarily involved in the metabolism of sphingolipid (SP) metabolism, tryptophan (Trp) metabolism, Glycerophospholipid (GP) metabolism, etc. Besides, SP metabolism, Trp metabolism and GP metabolism mainly belonging to the lipid metabolism showed marked perturbations over DMMCI and may contribute to the development of disease. Taken collectively, our results revealed that T2DM could cause cognitive impairment by affecting a variety of metabolic pathways especially lipid metabolism. Besides, serum PE, PC, L-Trp, and S1P may be used as the most critical biomarkers for the early diagnosis of DMMCI.

High-resolution metabolomic profiling of Alzheimer's disease in plasma

Background

Alzheimer’s disease (AD) is a complex neurological disorder with contributions from genetic and environmental factors. High‐resolution metabolomics (HRM) has the potential to identify novel endogenous and environmental factors involved in AD. Previous metabolomics studies have identified circulating metabolites linked to AD, but lack of replication and inconsistent diagnostic algorithms have hindered the generalizability of these findings. Here we applied HRM to identify plasma metabolic and environmental factors associated with AD in two study samples, with cerebrospinal fluid (CSF) biomarkers of AD incorporated to achieve high diagnostic accuracy.

Methods

Liquid chromatography‐mass spectrometry (LC–MS)‐based HRM was used to identify plasma and CSF metabolites associated with AD diagnosis and CSF AD biomarkers in two studies of prevalent AD (Study 1: 43 AD cases, 45 mild cognitive impairment [MCI] cases, 41 controls; Study 2: 50 AD cases, 18 controls). AD‐associated metabolites were identified using a metabolome‐wide association study (MWAS) framework.

Results

An MWAS meta‐analysis identified three non‐medication AD‐associated metabolites in plasma, including elevated levels of glutamine and an unknown halogenated compound and lower levels of piperine, a dietary alkaloid. The non‐medication metabolites were correlated with CSF AD biomarkers, and glutamine and the unknown halogenated compound were also detected in CSF. Furthermore, in Study 1, the unknown compound and piperine were altered in MCI patients in the same direction as AD dementia.

Conclusions

In plasma, AD was reproducibly associated with elevated levels of glutamine and a halogen‐containing compound and reduced levels of piperine. These findings provide further evidence that exposures and behavior may modify AD risks.

Interplay between Alzheimer's disease and global glucose metabolism revealed by the metabolic profile alterations of pancreatic tissue and serum in APP/PS1 transgenic mice

Increasing evidence suggests that there is a correlation between type 2 diabetes mellitus (T2D) and Alzheimer's disease (AD). Increased Aβ polypeptide production in AD patients would promote metabolic abnormalities, insulin signaling dysfunction and perturbations in glucose utilization, thus leading to the onset of T2D. However, the metabolic mechanisms underlying the interplay between AD and its diabetes-promoting effects are not fully elucidated. Particularly, systematic metabolomics analysis has not been performed for the pancreas tissues of AD subjects, which play key roles in the glucose metabolism of living systems. In the current study, we characterized the dynamic metabolic profile alterations of the serum and the pancreas of APP/PS1 double-transgenic mice (an AD mouse model) using the untargeted metabolomics approaches. Serum and pancreatic tissues of APP/PS1 transgenic mice and wild-type mice were extracted and subjected to NMR analysis to evaluate the functional state of pancreas in the progress of AD. Multivariate analysis of principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted to define the global and the local (pancreas) metabolic features associated with the possible initiation of T2D in the progress of AD. Our results showed the onset of AD-induced global glucose metabolism disorders in AD mice. Hyperglycemia and its accompanying metabolic disorders including energy metabolism down-regulation and oxidative stress were observed in the serum of AD mice. Meanwhile, global disturbance of branched-chain amino acid (BCAA) metabolism was detected, and the change of BCAA (leucine) was positively correlated to the alteration of glucose. Moreover, increased level of glucose and enhanced energy metabolism were observed in the pancreas of AD mice. The results suggest that the diabetes-promoting effects accompanying the progress of AD are achieved by down-regulating the global utilization of glucose and interfering with the metabolic function of pancreas. Since T2D is a risk factor for the pathogenesis of AD, our findings suggest that targeting the glucose metabolism dysfunctions might serve as a supplementary therapeutic strategy for Alzheimer's disease.

Oxylipin Profiling of Alzheimer's Disease in Nondiabetic and Type 2 Diabetic Elderly

Oxygenated lipids, called “oxylipins,” serve a variety of important signaling roles within the cell. Oxylipins have been linked to inflammation and vascular function, and blood patterns have been shown to differ in type 2 diabetes (T2D). Because these factors (inflammation, vascular function, diabetes) are also associated with Alzheimer’s disease (AD) risk, we set out to characterize the serum oxylipin profile in elderly and AD subjects to understand if there are shared patterns between AD and T2D. We obtained serum from 126 well-characterized, overnight-fasted elderly individuals who underwent a stringent cognitive evaluation and were determined to be cognitively healthy or AD. Because the oxylipin profile may also be influenced by T2D, we assessed nondiabetic and T2D subjects separately. Within nondiabetic individuals, cognitively healthy subjects had higher levels of the nitrolipid 10-nitrooleate (16.8% higher) compared to AD subjects. AD subjects had higher levels of all four dihydroxyeicosatrienoic acid (DiHETrE) species: 14,15-DiHETrE (18% higher), 11,12 DiHETrE (18% higher), 8,9-DiHETrE (23% higher), and 5,6-DiHETrE (15% higher). Within T2D participants, we observed elevations in 14,15-dihydroxyeicosa-5,8,11-trienoic acid (14,15-DiHETE; 66% higher), 17,18-dihydroxyeicosa-5,8,11,14-tetraenoic acid (17,18-DiHETE; 29% higher) and 17-hydroxy-4,7,10,13,15,19-docosahexaenoic acid (17-HDoHE; 105% higher) and summed fatty acid diols (85% higher) in subjects with AD compared to cognitively healthy elderly, with no differences in the DiHETrE species between groups. Although these effects were no longer significant following stringent adjustment for multiple comparisons, the consistent effects on groups of molecules with similar physiological roles, as well as clear differences in the AD-related profiles within nondiabetic and T2D individuals, warrant further research into these molecules in the context of AD.

Novel strategies for clinical investigation and biomarker discovery: a guide to applied metabolomics

Metabolomics is an emerging technology that is increasing both in basic science and in human applications, providing a physiological snapshot. It has been highlighted as one of the most wide ranging and reliable tools for the investigation of physiological status, the discovery of new biomarkers and the analysis of metabolic pathways. Metabolomics uses innovative mass spectrometry (MS) allied to chromatography or nuclear magnetic resonance (NMR). The recent advances in bioinformatics, databases and statistics, have provided a unique perception of metabolites interaction and the dynamics of metabolic pathways at a system level. In this context, several studies have applied metabolomics in physiology- and disease-related works. The application of metabolomics includes, physiological and metabolic evaluation/monitoring, individual response to different exercise, nutritional interventions, pathological processes, responses to pharmacological interventions, biomarker discovery and monitoring for distinct aspects, such as: physiological capacity, fatigue/recovery and aging among other applications. For metabolomic analyses, despite huge improvements in the field, several complex methodological steps must be taken into consideration. In this regard, the present article aims to summarize the novel aspects of metabolomics and provide a guide for metabolomics for professionals related to physiologist and medical applications.

Plasma Lipidomic Profiling and Risk of Type 2 Diabetes in the PREDIMED Trial

OBJECTIVE

Specific lipid molecular changes leading to type 2 diabetes (T2D) are largely unknown. We assessed lipidome factors associated with future occurrence of T2D in a population at high cardiovascular risk.

RESEARCH DESIGN AND METHODS

We conducted a case-cohort study nested within the PREDIMED trial, with 250 incident T2D cases diagnosed during 3.8 years of median follow-up, and a random sample of 692 participants (639 noncases and 53 overlapping cases) without T2D at baseline. We repeatedly measured 207 plasma known lipid metabolites at baseline and after 1 year of follow-up. We built combined factors of lipid species using principal component analysis and assessed the association between these lipid factors (or their 1-year changes) and T2D incidence.

RESULTS

Baseline lysophosphatidylcholines and lysophosphatidylethanolamines (lysophospholipids [LPs]), phosphatidylcholine-plasmalogens (PC-PLs), sphingomyelins (SMs), and cholesterol esters (CEs) were inversely associated with risk of T2D (multivariable-adjusted P for linear trend ≤0.001 for all). Baseline triacylglycerols (TAGs), diacylglycerols (DAGs), and phosphatidylethanolamines (PEs) were positively associated with T2D risk (multivariable-adjusted P for linear trend <0.001 for all). One-year changes in these lipids showed associations in similar directions but were not significant after adjustment for baseline levels. TAGs with odd-chain fatty acids showed inverse associations with T2D after adjusting for total TAGs.

CONCLUSIONS

Two plasma lipid profiles made up of different lipid classes were found to be associated with T2D in participants at high cardiovascular risk. A profile including LPs, PC-PLs, SMs, and CEs was associated with lower T2D risk. Another profile composed of TAGs, DAGs, and PEs was associated with higher T2D risk.

Ion-Pair Selection Method for Pseudotargeted Metabolomics Based on SWATH MS Acquisition and Its Application in Differential Metabolite Discovery of Type 2 Diabetes

The pseudotargeted metabolomics method integrates advantages of nontargeted and targeted analysis because it can acquire data of metabolites in the multireaction monitoring (MRM) mode of mass spectrometry (MS) without needing standards. The key is the ion-pair information collection from samples to be analyzed. It is well-known that sequential windowed acquisition of all theoretical Fragment ion (SWATH) MS mode can acquire MS2 information to a maximum extent. To expediently acquire as many ion-pairs as possible with optimal collision energy (CE), an ion-pair selection approach based on SWATH MS acquisition with variable isolation windows was developed in this study. Initially, nontargeted acquisition of all metabolites information in plasma Standard Reference Material (SRM 1950) was performed by ultra high-performance liquid chromatography (UHPLC)-quadrupole time-of-flight (Q-TOF) MS platform with three CEs. With the help of software tool, the ion-pairs of unique metabolites were gained. Then they were validated in scheduled MRM coupled with UHPLC. After removing false positive, the ion-pairs with an optimal CE was integrated. A total of 1373 unique metabolite ion-pairs were obtained at positive ion mode. And repeatability of the established pseudotargeted approach was evaluated by intraday and interday precision. The results demonstrated the method was stable, reliable, and suitable for metabolomics study. As an application example, alterations of serum metabolites in Type 2 diabetes were investigated by using the established method. This work provides a pseudotargeted ion-pair selection method based on SWATH MS acquisition with the characters of increased metabolite coverage, suitable CE, and convenient processing.