Medium-chain plasma acylcarnitines, ketone levels, cognition, and gray matter volumes in healthy elderly, mildly cognitively impaired, or Alzheimer's disease subjects

Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease

The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.

Large-Scale Population-Based Studies of Blood Metabolome and Brain Health

Metabolomics technologies enable the quantification of multiple metabolomic measures simultaneously, which provides novel insights into molecular aspects of human health and disease. In large-scale, population-based studies, blood is often the preferred biospecimen. Circulating metabolome may relate to brain health either by affecting or reflecting brain metabolism. Peripheral metabolites may act at or cross the blood-brain barrier and, subsequently, influence brain metabolism, or they may reflect brain metabolism if similar pathways are engaged. Peripheral metabolites may also include those penetrating the circulation from the brain, indicating, for example, brain damage. Most brain health-related metabolomics studies have been conducted in the context of neurodegenerative disorders and cognition, but some studies have also focused on neuroimaging markers of these disorders. Moreover, several metabolomics studies of neurodevelopmental disorders have been performed. Here, we provide a brief background on the types of blood metabolites commonly assessed, and we review the literature describing the relationships between human blood metabolome (n > 50 metabolites) and brain health reported in large-scale studies (n > 500 individuals).

Plasma Acylcarnitines as Metabolic Signatures of Declining Health-Related Quality of Life Measure in Community-Dwelling Older Adults: A Combined Cross-sectional and Longitudinal Pilot Study

BACKGROUND

Health-related quality of life (HRQoL) measures are predictors of adverse health outcomes in older adults. Studies have demonstrated cross-sectional associations between HRQoL measures and blood-based biochemical markers. Acylcarnitines (ACs) are a class of metabolites generated in the mitochondria and are predictive of multiple geriatric syndromes. Changes in ACs reflect alterations in central carbon metabolic pathways. However, the prospective relationship between plasma ACs and declining HRQoL has not been examined. This study aimed to investigate both cross-sectional and longitudinal associations of baseline ACs with baseline and declining EuroQol-5 Dimension/EuroQol Visual Analogue Scale (EQ-5D/EQ-VAS) in community-dwelling older adults.

METHODS

One hundred and twenty community-dwelling older adults with EQ-5D/EQ-VAS measurements at baseline and follow-up were included. We quantified ACs at baseline using targeted plasma metabolomics profiling. Multivariate regressions were performed to examine cross-sectional and longitudinal associations between the measures.

RESULTS

Cross-sectionally, ACs showed no significant associations with either EQ-5D index or EQ-VAS scores. Longitudinally, multiple baseline short-chain ACs were significantly and inversely associated with declining EQ-5D index score, explaining up to 8.5% of variance in the decline.

CONCLUSIONS

Within a cohort of community-dwelling older adults who had high HRQoL at baseline, we showed that higher levels of short-chain ACs are longitudinally associated with declining HRQoL. These findings reveal a novel association between central carbon metabolic pathways and declining HRQoL. Notably, dysregulation in mitochondrial central carbon metabolism could be detected prior to clinically important decline in HRQoL, providing the first evidence of objective biomarkers as novel predictors to monitor HRQoL in nonpharmacological interventions and epidemiology.

Individual bioenergetic capacity as a potential source of resilience to Alzheimer's disease

Impaired glucose uptake in the brain is one of the earliest presymptomatic manifestations of Alzheimer's disease (AD). The absence of symptoms for extended periods of time suggests that compensatory metabolic mechanisms can provide resilience. Here, we introduce the concept of a systemic 'bioenergetic capacity' as the innate ability to maintain energy homeostasis under pathological conditions, potentially serving as such a compensatory mechanism. We argue that fasting blood acylcarnitine profiles provide an approximate peripheral measure for this capacity that mirrors bioenergetic dysregulation in the brain. Using unsupervised subgroup identification, we show that fasting serum acylcarnitine profiles of participants from the AD Neuroimaging Initiative yields bioenergetically distinct subgroups with significant differences in AD biomarker profiles and cognitive function. To assess the potential clinical relevance of this finding, we examined factors that may offer diagnostic and therapeutic opportunities. First, we identified a genotype affecting the bioenergetic capacity which was linked to succinylcarnitine metabolism and significantly modulated the rate of future cognitive decline. Second, a potentially modifiable influence of beta-oxidation efficiency seemed to decelerate bioenergetic aging and disease progression. Our findings, which are supported by data from more than 9,000 individuals, suggest that interventions tailored to enhance energetic health and to slow bioenergetic aging could mitigate the risk of symptomatic AD, especially in individuals with specific mitochondrial genotypes.

Carnitine metabolites and cognitive improvement in patients with schizophrenia treated with olanzapine: a prospective longitudinal study

Objective: Cognitive impairment is one of the core symptoms of schizophrenia, which is stable and lifelong. L-carnitine has been shown to improve cognitive function and decrease the rate of cognitive deterioration in patients with Alzheimer's disease. However, it remains unclear regarding the role of L-carnitine and its metabolites in cognitive functions in schizophrenia after treatment with olanzapine. The purpose of this study was to evaluate the relationship between changes in plasma levels of L-carnitine metabolites and cognitive improvement after olanzapine treatment. Methods: This was a prospective longitudinal study. In this study, we recruited 25 female patients with first episode schizophrenia (FES) who were drug naïve at baseline and received 4 weeks of olanzapine monotherapy. Cognitive function was assessed at baseline and 4-week follow-up using the RBANS. Plasma L-carnitine metabolite levels were determined by a metabolomics technology based on untargeted ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Results: We found that the immediate memory index, delayed memory index and RBANS composite score were significantly increased at the 4-week follow-up after treatment. A total of 7 differential L-carnitine metabolites were identified in FES patients after olanzapine monotherapy. In addition, we found that changes in butyrylcarnitine were positively correlated with improvements in language index and RBANS composite score. Further regression analyses confirmed the association between reduced butyrylcarnitine levels and cognitive improvement after olanzapine monotherapy in FES patients. Conclusion: Our study shows that cognitive improvement after olanzapine treatment was associated with changes in L-carnitine metabolite levels in patients with FES, suggesting a key role of L-carnitine in cognition in schizophrenia.

Medium-chain fatty acids for the prevention or treatment of Alzheimer's disease: a systematic review and meta-analysis

CONTEXT

In preclinical Alzheimer's disease (AD), the brain gradually becomes insulin resistant. As a result, brain glucose utilization is compromised, causing a cellular energy deficit that leads to the accumulation of free radicals, which increases inflammation and damages neurons. When glucose utilization is impaired, ketone bodies offer an alternative energy source. Ketone bodies are synthesized from fats, obtained from either the diet or adipose tissue. Dietary medium-chain fatty acids (MCFAs), which are preferentially metabolized into ketone bodies, have the potential to supply the insulin-resistant brain with energy.

OBJECTIVE

This systematic review and meta-analysis aims to review the effect of MCFA supplements on circulating ketone bodies and cognition in individuals with subjective cognitive decline, mild cognitive impairment, and AD.

DATA SOURCES

A comprehensive search of electronic databases was performed on August 12, 2019, to retrieve all publications meeting the inclusion criteria. Alerts were then set to identify any publications after the search date up until January 31, 2021.

DATA EXTRACTION

Data were extracted by 2 authors and assessed by a third. In total, 410 publications were identified, of which 16 (n = 17 studies) met the inclusion criteria.

DATA ANALYSIS

All studies assessing change in levels of blood ketone bodies due to MCFA supplementation (n = 12) reported a significant increase. Cognition outcomes (measured in 13 studies), however, varied, ranging from no improvement (n = 4 studies) to improvement (n = 8 studies) or improvement only in apolipoprotein E allele 4 (APOE ε4) noncarriers (n = 2 studies). One study reported an increase in regional cerebral blood flow in APOE ε4 noncarriers and another reported an increase in energy metabolism in the brain.

CONCLUSION

MCFA supplementation increases circulating ketone body levels, resulting in increased brain energy metabolism. Further research is required to determine whether this MCFA-mediated increase in brain energy metabolism improves cognition.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number CRD42019146967.

Longitudinal lipidomic signatures of all-cause and CVD mortality in American Indians: findings from the Strong Heart Study

Dyslipidemia is an independent and modifiable risk factor for aging and age-related disorders. Routine lipid panel cannot capture all individual lipid species in blood (i.e., blood lipidome). To date, a comprehensive assessment of the blood lipidome associated with mortality is lacking in large-scale community-dwelling individuals, especially in a longitudinal setting. Using liquid chromatograph-mass spectrometry, we repeatedly measured individual lipid species in 3,821 plasma samples collected at two visits (~ 5.5 years apart) from 1,930 unique American Indians in the Strong Heart Family Study. We first identified baseline lipids associated with risks for all-cause mortality and CVD mortality (mean follow-up period: 17.8 years) in American Indians, followed by replication of top hits in European Caucasians in the Malmö Diet and Cancer-Cardiovascular Cohort (n = 3,943, mean follow-up period: 23.7 years). The model adjusted age, sex, BMI, smoking, hypertension, diabetes, and LDL-c at baseline. We then examined the associations between changes in lipid species and risk of mortality. Multiple testing was controlled by false discovery rate (FDR). We found that baseline levels and longitudinal changes of multiple lipid species, e.g., cholesterol esters, glycerophospholipids, sphingomyelins, and triacylglycerols, were significantly associated with risks of all-cause or CVD mortality. Many lipids identified in American Indians could be replicated in European Caucasians. Network analysis identified differential lipid networks associated with risk of mortality. Our findings provide novel insight into the role of dyslipidemia in disease mortality and offer potential biomarkers for early prediction and risk reduction in American Indians and other ethnic groups.

Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Lipid metabolism and Alzheimer's disease: clinical evidence, mechanistic link and therapeutic promise

Alzheimer's disease (AD) is an age-associated neurodegenerative disorder with multifactorial etiology, intersecting genetic and environmental risk factors, and a lack of disease-modifying therapeutics. While the abnormal accumulation of lipids was described in the very first report of AD neuropathology, it was not until recent decades that lipid dyshomeostasis became a focus of AD research. Clinically, lipidomic and metabolomic studies have consistently shown alterations in the levels of various lipid classes emerging in early stages of AD brains. Mechanistically, decades of discovery research have revealed multifaceted interactions between lipid metabolism and key AD pathogenic mechanisms including amyloidogenesis, bioenergetic deficit, oxidative stress, neuroinflammation, and myelin degeneration. In the present review, converging evidence defining lipid dyshomeostasis in AD is summarized, followed by discussions on mechanisms by which lipid metabolism contributes to pathogenesis and modifies disease risk. Furthermore, lipid-targeting therapeutic strategies, and the modification of their efficacy by disease stage, ApoE status, and metabolic and vascular profiles, are reviewed.

Predictive metabolic networks reveal sex- and APOE genotype-specific metabolic signatures and drivers for precision medicine in Alzheimer's disease

INTRODUCTION

Late-onset Alzheimer's disease (LOAD) is a complex neurodegenerative disease characterized by multiple progressive stages, glucose metabolic dysregulation, Alzheimer's disease (AD) pathology, and inexorable cognitive decline. Discovery of metabolic profiles unique to sex, apolipoprotein E (APOE) genotype, and stage of disease progression could provide critical insights for personalized LOAD medicine.

METHODS

Sex- and APOE-specific metabolic networks were constructed based on changes in 127 metabolites of 656 serum samples from the Alzheimer's Disease Neuroimaging Initiative cohort.

RESULTS

Application of an advanced analytical platform identified metabolic drivers and signatures clustered with sex and/or APOE ɛ4, establishing patient-specific biomarkers predictive of disease state that significantly associated with cognitive function. Presence of the APOE ɛ4 shifts metabolic signatures to a phosphatidylcholine-focused profile overriding sex-specific differences in serum metabolites of AD patients.

DISCUSSION

These findings provide an initial but critical step in developing a diagnostic platform for personalized medicine by integrating metabolomic profiling and cognitive assessments to identify targeted precision therapeutics for AD patient subgroups through computational network modeling.

Combined metabolic activators improve cognitive functions in Alzheimer's disease patients: a randomised, double-blinded, placebo-controlled phase-II trial

BACKGROUND

Alzheimer's disease (AD) is associated with metabolic abnormalities linked to critical elements of neurodegeneration. We recently administered combined metabolic activators (CMA) to the AD rat model and observed that CMA improves the AD-associated histological parameters in the animals. CMA promotes mitochondrial fatty acid uptake from the cytosol, facilitates fatty acid oxidation in the mitochondria, and alleviates oxidative stress.

METHODS

Here, we designed a randomised, double-blinded, placebo-controlled phase-II clinical trial and studied the effect of CMA administration on the global metabolism of AD patients. One-dose CMA included 12.35 g L-serine (61.75%), 1 g nicotinamide riboside (5%), 2.55 g N-acetyl-L-cysteine (12.75%), and 3.73 g L-carnitine tartrate (18.65%). AD patients received one dose of CMA or placebo daily during the first 28 days and twice daily between day 28 and day 84. The primary endpoint was the difference in the cognitive function and daily living activity scores between the placebo and the treatment arms. The secondary aim of this study was to evaluate the safety and tolerability of CMA. A comprehensive plasma metabolome and proteome analysis was also performed to evaluate the efficacy of the CMA in AD patients.

RESULTS

We showed a significant decrease of AD Assessment Scale-cognitive subscale (ADAS-Cog) score on day 84 vs day 0 (P = 0.00001, 29% improvement) in the CMA group. Moreover, there was a significant decline (P = 0.0073) in ADAS-Cog scores (improvement of cognitive functions) in the CMA compared to the placebo group in patients with higher ADAS-Cog scores. Improved cognitive functions in AD patients were supported by the relevant alterations in the hippocampal volumes and cortical thickness based on imaging analysis. Moreover, the plasma levels of proteins and metabolites associated with NAD + and glutathione metabolism were significantly improved after CMA treatment.

CONCLUSION

Our results indicate that treatment of AD patients with CMA can lead to enhanced cognitive functions and improved clinical parameters associated with phenomics, metabolomics, proteomics and imaging analysis. Trial registration ClinicalTrials.gov NCT04044131 Registered 17 July 2019, https://clinicaltrials.gov/ct2/show/NCT04044131.

Age and APOE affect L-carnitine system metabolites in the brain in the APOE-TR model

With age the apolipoprotein E (APOE) E4 allele (involved in lipid homeostasis) is associated with perturbation of bioenergetics pathways in Alzheimer's disease (AD). We therefore hypothesized that in aging mice APOE genotype would affect the L-carnitine system (central to lipid bioenergetics), in the brain and in the periphery. Using liquid chromatography-mass spectrometry, levels of L-carnitine and associated metabolites: γ-butyrobetaine (GBB), crotonobetaine, as well as acylcarnitines, were evaluated at 10-, 25-, and 50-weeks, in the brain and the periphery, in a targeted replacement mouse model of human APOE (APOE-TR). Aged APOE-TR mice were also orally administered 125 mg/kg of L-carnitine daily for 7 days followed by evaluation of brain, liver, and plasma L-carnitine system metabolites. Compared to E4-TR, an age-dependent increase among E2- and E3-TR mice was detected for medium- and long-chain acylcarnitines (MCA and LCA, respectively) within the cerebrovasculature and brain parenchyma. While following L-carnitine oral challenge, E4-TR mice had higher increases in the L-carnitine metabolites, GBB and crotonobetaine in the brain and a reduction of plasma to brain total acylcarnitine ratios compared to other genotypes. These studies suggest that with aging, the presence of the E4 allele may contribute to alterations in the L-carnitine bioenergetic system and to the generation of L-carnitine metabolites that could have detrimental effects on the vascular system. Collectively the E4 allele and aging may therefore contribute to AD pathogenesis through aging-related lipid bioenergetics as well as cerebrovascular dysfunctions.

APOE ε4 and Alzheimer's disease diagnosis associated differences in L-carnitine, GBB, TMAO, and acylcarnitines in blood and brain

BACKGROUND

The apolipoprotein E (APOE) ε4 allele, involved in fatty acid (FA) metabolism, is a major genetic risk factor for Alzheimer's disease (AD). This study examined the influence of APOE genotypes on blood and brain markers of the L-carnitine system, necessary for fatty acid oxidation (FAO), and their collective influence on the clinical and pathological outcomes of AD.

METHODS

L-carnitine, its metabolites γ-butyrobetaine (GBB) and trimethylamine-n-oxide (TMAO), and its esters (acylcarnitines) were analyzed in blood from predominantly White community/clinic-based individuals (n = 372) and in plasma and brain from the Religious Order Study (ROS) (n = 79) using liquid chromatography tandem mass spectrometry (LC-MS/MS).

FINDINGS

Relative to total blood acylcarnitines, levels of short chain acylcarnitines (SCAs) were higher whereas long chain acylcarnitines (LCAs) were lower in AD, which was observed pre-clinically in APOE ε4s. Plasma medium chain acylcarnitines (MCAs) were higher amongst cognitively healthy APOE ε2 carriers relative to other genotypes. Compared to their respective controls, elevated TMAO and lower L-carnitine and GBB were associated with AD clinical diagnosis and these differences were detected preclinically among APOE ε4 carriers. Plasma and brain GBB, TMAO, and acylcarnitines were also associated with post-mortem brain amyloid, tau, and cerebrovascular pathologies.

INTERPRETATION

Alterations in blood L-carnitine, GBB, TMAO, and acylcarnitines occur early in clinical AD progression and are influenced by APOE genotype. These changes correlate with post-mortem brain AD and cerebrovascular pathologies. Additional studies are required to better understand the role of the FAO disturbances in AD.

Nutritional metabolism and cerebral bioenergetics in Alzheimer's disease and related dementias

Disturbances in the brain's capacity to meet its energy demand increase the risk of synaptic loss, neurodegeneration, and cognitive decline. Nutritional and metabolic interventions that target metabolic pathways combined with diagnostics to identify deficits in cerebral bioenergetics may therefore offer novel therapeutic potential for Alzheimer's disease (AD) prevention and management. Many diet-derived natural bioactive components can govern cellular energy metabolism but their effects on brain aging are not clear. This review examines how nutritional metabolism can regulate brain bioenergetics and mitigate AD risk. We focus on leading mechanisms of cerebral bioenergetic breakdown in the aging brain at the cellular level, as well as the putative causes and consequences of disturbed bioenergetics, particularly at the blood-brain barrier with implications for nutrient brain delivery and nutritional interventions. Novel therapeutic nutrition approaches including diet patterns are provided, integrating studies of the gut microbiome, neuroimaging, and other biomarkers to guide future personalized nutritional interventions.

Dynamic changes of metabolic characteristics in neonatal intrahepatic cholestasis caused by citrin deficiency

Introduction: Neonatal intrahepatic cholestasis caused by citrin deficiency (NICCD) is a pan-ethnic complicated inborn error of metabolism but the specific mechanism is not fully understood.

Methods: A total of 169 patients with NICCD who have biallelic pathogenic SLC25A13 variants detected by targeted next-generation sequencing were collected. They were divided into the “Newborn-screen Group” and “Clinical diagnosed Group” depending on the newborn screening results. Amino acid and acylcarnitine profiles were measured by MS/MS. The total bile acids, blood amino acids and acylcarnitines, general biochemistry, blood count, and coagulation parameters were monitored every 2–3 months. We compared the differences in metabolic indices and their dynamic changes between these two groups. The Mann–Whitney test and orthogonal partial least squares discrimination analysis (OPLS-DA) were used for statistical analysis.

Results: At the onset of NICCD, we found that the “Clinical diagnosed Group” had higher levels of intermediate products of the urea cycle, free carnitine, and short-chain and long-chain acylcarnitines than those in the “Newborn-screen Group,” but the levels of ketogenic/glucogenic amino acids and several medium-chain acylcarnitines were lower. Furthermore, concentrations of direct bilirubin, total bile acid, lactate, prothrombin time, and several liver enzymes were significantly higher while total protein, amylase, and hemoglobin were lower in the “Clinical diagnosed Group” than in the “Newborn-screen Group.” Dynamic change analysis showed that direct bilirubin, albumin, arginine, and citrulline were the earliest metabolic derangements to reach peak levels in NICCD groups, followed by acylcarnitine profiles, and finally with the elevation of liver enzymes. All abnormal characteristic metabolic indicators in the “Newborn-screen Group” came back to normal levels at earlier ages than the “Clinical diagnosed Group.” c.852_855del (41.2%), IVS16ins3kb (17.6%), c.615 + 5G>A (9.6%), 1638_1660dup (4.4%), and c.1177 + 1G>A (3.7%) accounted for 76.5% of all the mutated SLC25A13 alleles in our population.

Conclusion: Argininosuccinate synthesis, gluconeogenesis, ketogenesis, fatty acid oxidation, liver function, and cholestasis were more severely affected in the “Clinical diagnosed Group.” The “Newborn-screen Group” had a better prognosis which highlighted the importance of newborn screening of NICCD.

Identification of Serum Biomarkers Associated With Emergence Agitation After General Anesthesia in Adult Patients: A Metabolomics Analysis

Background

Emergence agitation (EA) is a conscious disturbance after general anesthesia in adult patients that can lead to severe respiratory or circulatory complications and serious physical injury to patients and caregivers. However, the pathophysiological mechanisms underlying EA remain unclear. The present study aimed to identify serum metabolites with significant alterations in EA patients after general anesthesia and enable inferences on their associations with EA.

Methods

EA patients were identified by Richmond Agitation-Sedation Scale (RASS) ≥ + 2 among a cohort of adult patients who received elective surgery under general anesthesia in Peking University Third Hospital between 01 June 2020 and 30 December 2020. We further selected sex-, age-, and surgery type-matched non-EA control patients at a 1:1.5 ratio. Postoperative serum samples were collected from both groups of patients. An untargeted metabolic method was used to identify differences in serum metabolomic profiles between the EA patients and the non-EA patients.

Results

A total of 19 EA patients and 32 matched non-EA patients were included in the study. After screening and mapping with a database, 12 metabolites showed significant postoperative alterations in EA patients compared with non-EA patients, and were mainly involved in lipid, fatty acid and amino acid metabolism pathways. Receiver operating characteristic curve analyses indicated that vanillic acid, candoxatril, tiglylglycine, 5-methoxysalicylic acid, decanoylcarnitine, and 24-epibrassinolide may be involved in EA pathogenesis after general anesthesia.

Conclusion

In this study, we found differences in the serum levels of vanillic acid, candoxatril, tiglylglycine, 5-methoxysalicylic acid, decanoylcarnitine, and 24-epibrassinolide involved in fatty acid metabolism, lipid metabolism, and amino acid metabolism pathways in EA patients compared with non-EA patients, which may demonstrate an EA pathogenesis-associated molecular pattern and contribute toward better understanding of EA occurrence.

Predictive Modeling of Alzheimer's and Parkinson's Disease Using Metabolomic and Lipidomic Profiles from Cerebrospinal Fluid

In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer's disease (AD), Parkinson's disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.

Recent Advances in Understanding of Alzheimer's Disease Progression through Mass Spectrometry-Based Metabolomics

Alzheimer's disease (AD) is the leading cause of dementia in the aging population, but despite extensive research, there is no consensus on the biological cause of AD. While AD research is dominated by protein/peptide-centric research based on the amyloid hypothesis, a theory that designates dysfunction in beta-amyloid production, accumulation, or disposal as the primary cause of AD, many studies focus on metabolomics as a means of understanding the biological processes behind AD progression. In this review, we discuss mass spectrometry (MS)-based AD metabolomics studies, including sample type and preparation, mass spectrometry specifications, and data analysis, as well as biological insights gleaned from these studies, with the hope of informing future AD metabolomic studies.

A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.

OBJECTIVE

To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.

METHODS

We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.

RESULTS

The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.

CONCLUSION

Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.

Metabolic correlates of late midlife cognitive outcomes: findings from the 1946 British Birth Cohort

Investigating associations between metabolites and late midlife cognitive function could reveal potential markers and mechanisms relevant to early dementia. Here, we systematically explored the metabolic correlates of cognitive outcomes measured across the seventh decade of life, while untangling influencing life course factors. Using levels of 1019 metabolites profiled by liquid chromatography-mass spectrometry (age 60-64), we evaluated relationships between metabolites and cognitive outcomes in the British 1946 Birth Cohort (N = 1740). We additionally conducted pathway and network analyses to allow for greater insight into potential mechanisms, and sequentially adjusted for life course factors across four models, including sex and blood collection (Model 1), Model 1 + body mass index and lipid medication (Model 2), Model 2 + social factors and childhood cognition (Model 3) and Model 3 + lifestyle influences (Model 4). After adjusting for multiple tests, 155 metabolites, 10 pathways and 5 network modules were associated with cognitive outcomes. Of the 155, 35 metabolites were highly connected in their network module (termed 'hub' metabolites), presenting as promising marker candidates. Notably, we report relationships between a module comprised of acylcarnitines and processing speed which remained robust to life course adjustment, revealing palmitoylcarnitine (C16) as a hub (Model 4: β = -0.10, 95% confidence interval = -0.15 to -0.052, P = 5.99 × 10-5). Most associations were sensitive to adjustment for social factors and childhood cognition; in the final model, four metabolites remained after multiple testing correction, and 80 at P < 0.05. Two modules demonstrated associations that were partly or largely attenuated by life course factors: one enriched in modified nucleosides and amino acids (overall attenuation = 39.2-55.5%), and another in vitamin A and C metabolites (overall attenuation = 68.6-92.6%). Our other findings, including a module enriched in sphingolipid pathways, were entirely explained by life course factors, particularly childhood cognition and education. Using a large birth cohort study with information across the life course, we highlighted potential metabolic mechanisms associated with cognitive function in late midlife, suggesting marker candidates and life course relationships for further study.

Alterations in Glycerolipid and Fatty Acid Metabolic Pathways in Alzheimer's Disease Identified by Urinary Metabolic Profiling: A Pilot Study

An easily accessible and non-invasive biomarker for the early detection of Alzheimer's disease (AD) is needed. Evidence suggests that metabolic dysfunction underlies the pathophysiology of AD. While urine is a non-invasively collectable biofluid and a good source for metabolomics analysis, it is not yet widely used for this purpose. This small-scale pilot study aimed to examine whether the metabolic profile of urine from AD patients reflects the metabolic dysfunction reported to underlie AD pathology, and to identify metabolites that could distinguish AD patients from cognitively healthy controls. Spot urine of 18 AD patients (AD group) and 18 age- and sex-matched, cognitively normal controls (control group) were analyzed by mass spectrometry (MS). Capillary electrophoresis time-of-flight MS and liquid chromatography–Fourier transform MS were used to cover a larger range of molecules with ionic as well as lipid characteristics. A total of 304 ionic molecules and 81 lipid compounds of 12 lipid classes were identified. Of these, 26 molecules showed significantly different relative concentrations between the AD and control groups (Wilcoxon's rank-sum test). Moreover, orthogonal partial least-squares discriminant analysis revealed significant discrimination between the two groups. Pathway searches using the KEGG database, and pathway enrichment and topology analysis using Metaboanalyst software, suggested alterations in molecules relevant to pathways of glycerolipid and glycerophospholipid metabolism, thermogenesis, and caffeine metabolism in AD patients. Further studies of urinary metabolites will contribute to the early detection of AD and understanding of its pathogenesis.

Myo-Inositol Levels in the Dorsal Hippocampus Serve as Glial Prognostic Marker of Mild Cognitive Impairment in Mice

Dementia is a devastating age-related disorder. Its therapy would largely benefit from the identification of susceptible subjects at early, prodromal stages of the disease. To search for such prognostic markers of cognitive impairment, we studied spatial navigation in male BALBc vs. B6N mice in combination with in vivo magnetic resonance spectroscopy (1H-MRS). BALBc mice consistently showed higher escape latencies than B6N mice, both in the Water Cross Maze (WCM) and the Morris water maze (MWM). These performance deficits coincided with higher levels of myo-inositol (mIns) in the dorsal hippocampus before and after training. Subsequent biochemical analyses of hippocampal specimens by capillary immunodetection and liquid chromatography mass spectrometry-based (LC/MS) metabolomics revealed a higher abundance of glial markers (IBA-1, S100B, and GFAP) as well as distinct alterations in metabolites including a decrease in vitamins (pantothenic acid and nicotinamide), neurotransmitters (acetylcholine), their metabolites (glutamine), and acetyl-L-carnitine. Supplementation of low abundant acetyl-L-carnitine via the drinking water, however, failed to revert the behavioral deficits shown by BALBc mice. Based on our data we suggest (i) BALBc mice as an animal model and (ii) hippocampal mIns levels as a prognostic marker of mild cognitive impairment (MCI), due to (iii) local changes in microglia and astrocyte activity, which may (iv) result in decreased concentrations of promnesic molecules.

Metabolomic Alterations in the Blood and Brain in Association with Alzheimer's Disease: Evidence from in vivo to Clinical Studies

Alzheimer's disease (AD) has become a major health problem among the elderly population. Some evidence suggests that metabolic disturbance possibly plays a role in the pathophysiology of AD. Currently, the study of metabolomics has been used to explore changes in multiple metabolites in several diseases, including AD. Thus, the metabolomics research in AD might provide some information regarding metabolic dysregulations, and their possible associated pathophysiology. This review summarizes the information discovered regarding the metabolites in the brain and the blood from the metabolomics research of AD from both animal and clinical studies. Additionally, the correlation between the changes in metabolites and outcomes, such as pathological findings in the brain and cognitive impairment are discussed. We also deliberate on the findings of cohort studies, demonstrating the alterations in metabolites before changes of cognitive function. All of these findings can be used to inform the potential identity of specific metabolites as possible biomarkers for AD.

An elaborative NMR based plasma metabolomics study revealed metabolic derangements in patients with mild cognitive impairment: a study on north Indian population

Mild cognitive impairment (MCI) is transition phase between cognitive decline and dementia. The current study aims to investigate altered metabolic pattern in plasma of MCI for potential biomarkers. MCI (N = 50) and healthy controls (HC, N = 50) age group 55-75 years were screened based on Mini Mental State Examination Test (MMSE) and diffusion tensor imaging (DTI imaging). The MMSE score of MCI was significantly lower (25.74 ± 1.83) compared to healthy control subjects (29 ± 1). The MCI patients exhibit significant changes in white matter integrity in the right frontal lobe, right temporal lobe, left frontal lobe, forcep major, fornix, corpus callosum. Further, the plasma samples of twenty seven MCI patients (N = 27) and twenty HC subjects (N = 20; having no significant differences in any demographics) were analyzed using ¹H NMR based metabolomics approach. Consistent with many previous reports, the levels of several plasma metabolites were found to be elevated in MCI patients compared to healthy controls. Further univariate and multivariate ROC curve analyses provided three plasma metabolites as a diagnostic panel of biomarker for MCI; which are lysine, glycine, and glutamine. Overall, the results of this study will help to improve the diagnostic and prognostic strategies of MCI in addition to improving our understanding about disease pathogenesis. We believe that the over-nutritional metabolic phenotype of MCI needs to be targeted for developing future dietary interventions so that the progression of MCI can be limited. Metabolic derangements associated with Mild Cognitive Impairment.

Astrocyte-Neuron Metabolic Crosstalk in Neurodegeneration: A Mitochondrial Perspective

Converging evidence made clear that declining brain energetics contribute to aging and are implicated in the initiation and progression of neurodegenerative disorders such as Alzheimer's and Parkinson's disease. Indeed, both pathologies involve instances of hypometabolism of glucose and oxygen in the brain causing mitochondrial dysfunction, energetic failure and oxidative stress. Importantly, recent evidence suggests that astrocytes, which play a key role in supporting neuronal function and metabolism, might contribute to the development of neurodegenerative diseases. Therefore, exploring how the neuro-supportive role of astrocytes may be impaired in the context of these disorders has great therapeutic potential. In the following, we will discuss some of the so far identified features underlining the astrocyte-neuron metabolic crosstalk. Thereby, special focus will be given to the role of mitochondria. Furthermore, we will report on recent advancements concerning iPSC-derived models used to unravel the metabolic contribution of astrocytes to neuronal demise. Finally, we discuss how mitochondrial dysfunction in astrocytes could contribute to inflammatory signaling in neurodegenerative diseases.

Alternative Targets to Fight Alzheimer's Disease: Focus on Astrocytes

The available treatments for patients affected by Alzheimer’s disease (AD) are not curative. Numerous clinical trials have failed during the past decades. Therefore, scientists need to explore new avenues to tackle this disease. In the present review, we briefly summarize the pathological mechanisms of AD known so far, based on which different therapeutic tools have been designed. Then, we focus on a specific approach that is targeting astrocytes. Indeed, these non-neuronal brain cells respond to any insult, injury, or disease of the brain, including AD. The study of astrocytes is complicated by the fact that they exert a plethora of homeostatic functions, and their disease-induced changes could be context-, time-, and disease specific. However, this complex but fervent area of research has produced a large amount of data targeting different astrocytic functions using pharmacological approaches. Here, we review the most recent literature findings that have been published in the last five years to stimulate new hypotheses and ideas to work on, highlighting the peculiar ability of palmitoylethanolamide to modulate astrocytes according to their morpho-functional state, which ultimately suggests a possible potential disease-modifying therapeutic approach for AD.

Alterations in the gut microbiota and metabolite profiles in the context of neuropathic pain

The aim of this study was to explore the relationships among gut microbiota disturbances and serum and spinal cord metabolic disorders in neuropathic pain. 16S rDNA amplicon sequencing and serum and spinal cord metabolomics were used to identify alterations in the microbiota and metabolite profiles in the sham rats and the chronic constriction injury (CCI) model rats. Correlations between the abundances of gut microbiota components at the genus level, the levels of serum metabolites, and pain-related behavioural parameters were analysed. Ingenuity pathway analysis (IPA) was applied to analyse the interaction networks of the differentially expressed serum metabolites. First, we found that the composition of the gut microbiota was different between rats with CCI-induced neuropathic pain and sham controls. At the genus level, the abundances of Helicobacter, Phascolarctobacterium, Christensenella, Blautia, Streptococcus, Rothia and Lactobacillus were significantly increased, whereas the abundances of Ignatzschineria, Butyricimonas, Escherichia, AF12, and Corynebacterium were significantly decreased. Additionally, 72 significantly differentially expressed serum metabolites and 17 significantly differentially expressed spinal cord metabolites were identified between the CCI rats and the sham rats. Finally, correlation analysis showed that changes in the gut microbiota was significantly correlated with changes in serum metabolite levels, suggesting that dysbiosis of the gut microbiota is an important factor in modulating metabolic disturbances in the context of neuropathic pain. In conclusion, our research provides a novel perspective on the potential roles of the gut microbiota and related metabolites in neuropathic pain.

Plasma metabolomics of presymptomatic PSEN1-H163Y mutation carriers: a pilot study

BACKGROUND AND OBJECTIVE

PSEN1-H163Y carriers, at the presymptomatic stage, have reduced ¹⁸ FDG-PET binding in the cerebrum of the brain (Scholl et al., Neurobiol Aging 32:1388-1399, 2011). This could imply dysfunctional energy metabolism in the brain. In this study, plasma of presymptomatic PSEN1 mutation carriers was analyzed to understand associated metabolic changes.

METHODS

We analyzed plasma from noncarriers (NC, n = 8) and presymptomatic PSEN1-H163Y mutation carriers (MC, n = 6) via untargeted metabolomics using gas and liquid chromatography coupled with mass spectrometry, which identified 1199 metabolites. All the metabolites were compared between MC and NC using univariate analysis, as well as correlated with the ratio of Aβ_{1-42/A β 1-40} , using Spearman's correlation. Altered metabolites were subjected to Ingenuity Pathway Analysis (IPA).

RESULTS

Based on principal component analysis the plasma metabolite profiles were divided into dataset A and dataset B. In dataset A, when comparing between presymptomatic MC and NC, the levels of 79 different metabolites were altered. Out of 79, only 14 were annotated metabolites. In dataset B, 37 metabolites were significantly altered between presymptomatic MC and NC and nine metabolites were annotated. In both datasets, annotated metabolites represent amino acids, fatty acyls, bile acids, hexoses, purine nucleosides, carboxylic acids, and glycerophosphatidylcholine species. 1-docosapentaenoyl-GPC was positively correlated, uric acid and glucose were negatively correlated with the ratio of plasma Aβ_1-42 /Aβ_1-40 (P < 0.05).

INTERPRETATION

This study finds dysregulated metabolite classes, which are changed before the disease symptom onset. Also, it provides an opportunity to compare with sporadic Alzheimer's Disease. Observed findings in this study need to be validated in a larger and independent Familial Alzheimer's Disease (FAD) cohort.

A panel of blood lipids associated with cognitive performance, brain atrophy, and Alzheimer's diagnosis: A longitudinal study of elders without dementia

INTRODUCTION

We sought lipid-metabolic biomarkers involved in the processes underlying cognitive decline and detected them in association with Alzheimer's disease (AD) phenotypes.

METHODS

A least absolute shrinkage and selection operator logistic regression model was used to select lipids that best classified cognitive decline defined by a fast-annual rate of cognition. Lipid summary scores were constructed as predictors of cognitive decline by using this model. Multivariable-adjusted models tested the associations of risk score with AD phenotypes.

RESULTS

A model incorporating 17 selected lipids showed good discrimination and calibration. The lipid risk score was positively associated with the baseline Alzheimer Disease Assessment Scale-13-item cognitive subscale (ADAS-Cog13) score and cerebrospinal tau protein level, and predicted cognitive diagnoses. Additional results showing that individuals with increased lipid risk scores had rapid change rates of ADAS-Cog13 and brain atrophy further corroborated the predictive role of lipids.

DISCUSSION

A panel of blood lipids instead of individual lipid molecules could better diagnose and predict cognitive decline.

Plasma acylcarnitine levels increase with healthy aging

Acylcarnitines transport fatty acids into mitochondria and are essential for β-oxidation and energy metabolism. Decreased mitochondrial activity results in increased plasma acylcarnitines, and increased acylcarnitines activate proinflammatory signaling and associate with age-related disease. Changes in acylcarnitines associated with healthy aging, however, are not well characterized. In the present study, we examined the associations of plasma acylcarnitines with age (range: 20-90) in 163 healthy, non-diseased individuals from the predictive medicine research cohort (NCT00336570) and tested for gender-specific differences. The results show that long-chain and very long-chain acylcarnitines increased with age, while many odd-chain acylcarnitines decreased with age. Gender-specific differences were observed for several acylcarnitines, e.g., eicosadienoylcarnitine varied with age in males, and hydroxystearoylcarnitine varied in females. Metabolome-wide association study (MWAS) of age-associated acylcarnitines with all untargeted metabolic features showed little overlap between genders. These results show that plasma concentrations of acylcarnitines vary with age and gender in individuals selected for criteria of health. Whether these variations reflect mitochondrial dysfunction with aging, mitochondrial reprogramming in response to chronic environmental exposures, early pre-disease change, or an adaptive response to healthy aging, is unclear. The results highlight a potential utility for untargeted metabolomics research to elucidate gender-specific mechanisms of aging and age-related disease.

Differences in plasma metabolites related to Alzheimer's disease, APOE ε4 status, and ethnicity

INTRODUCTION

We investigated metabolites in plasma to capture systemic biochemical changes associated with Alzheimer's disease (AD).

METHODS

Metabolites in plasma were measured in 59 AD cases and 60 healthy participants of African American (AA), Caribbean Hispanic (CH), and non-Hispanic white (NHW) ancestry using untargeted liquid-chromatography-based ultra-high-resolution mass spectrometry. Metabolite differences between AD and healthy, ethnic groups and apolipoprotein E gene (APOE) ε4 status were analyzed. Untargeted network analysis identified pathways enriched in AD-associated metabolites.

RESULTS

A total of 5929 annotated metabolites were measured. Partial least squares discriminant analysis (PLS-DA) inferred that AD clustered separately from healthy controls (area under the curve [AUC] = 0.9816); discriminating pathways included glycerophospholipid, sphingolipid, and non-essential amino acid (alanine, aspartate, glutamate) metabolism. Metabolic features in AA clustered differently from CH and NHW (AUC = 0.9275), and differed between APOE ε4 carriers and non-carriers (AUC = 0.9972).

DISCUSSION

Metabolites, specifically lipids, were associated with AD, APOE ε4, and ethnic group. Metabolite profiling can identify perturbed AD pathways, but genetic and ancestral background need to be considered.

Brain and blood metabolome for Alzheimer's dementia: findings from a targeted metabolomics analysis

The development of Alzheimer's dementia (AD) accompanies both central and peripheral metabolic disturbance, but the metabolic basis underlying AD and metabolic markers predictive of AD risk remain to be determined. It is also unclear whether the metabolic changes in the peripheral blood and brain are overlapping in relation to AD. The present study addresses these questions by targeted metabolomics in both antemortem blood and postmortem brain samples in 2 community-based longitudinal cohorts of aging and dementia. We found that higher serum levels of 3 acylcarnitines, including decanoylcarnitine (C10), pimelylcarnitine (C7-DC), and tetradecadienylcarnitine (C14:2), significantly predict a lower risk of incident AD (composite hazard ratio = 0.368, 95% CI [0.207, 0.653]) after an average of 4.5-year follow-up, independent of age, sex, and education. In addition, baseline serum levels of ten glycerophospholipids, one amino acid, and 5 acylcarnitines predict the longitudinal change in cognitive functions. Moreover, 28 brain metabolites were associated with AD phenotypes. Of the putative metabolites identified in the serum and brain, 4 metabolites (3 glycerophospholipids [PC aa C30:0, PC ae C34:0, PC ae C36:1] and 1 acylcarnitine [C14:2]) were present in both the postmortem brain and antemortem blood, but only one metabolite (C14:2) was associated with AD in the same direction (i.e., protective). Partial correlation and network analyses suggest a potential tissue-specific regulation of metabolism, although other alternatives exist. Together, we identified significant associations of both central and peripheral metabolites with AD phenotypes, but there seems to be little overlap between the 2 tissues.

An Exploratory Analysis of Potential New Biomarkers of Cognitive Function

We examined the relationship between serially measured, novel serum biomarkers and a measure of cognitive functioning in older adults. We assayed stored serum samples from two Fels Longitudinal Study visits in N = 100 adult participants (visit 1 ages 59.3 ± 8.5 years; 53% female), and Montreal Cognitive Assessment (MoCA) scores also assessed at the second visit. Assays included acylcarnitines, amino acids, and 2-hydroxybutyric acid (b-HBA). Cross-sectional correlations between acylcarnitines and amino acids and MoCA were identified. Serial change in short-chain acylcarnitines and visit 2 MoCA were also correlated. Participants with MoCA scores <26 were more likely to have an increase in short-chain acylcarnitines between visits 1 and 2 [adjusted odds ratio (OR) = 5.24; 95% confidence interval (CI) 1.07-25.9]. b-HBA was also correlated with acylcarnitines. Several cross-sectional and serial associations between novel serum biomarkers and cognitive functioning were identified. b-HBA may also be a cost-effective marker of dysregulation associated with cognitive decline.

A Review on MS-Based Blood Biomarkers for Alzheimer's Disease

Alzheimer's disease (AD) is the most common form of neurodegenerative dementia and there is no cure to date. Biomarkers in cerebrospinal fluid (CSF) are already included in the diagnostic work-up of symptomatic patients but markers for preclinical diagnosis and disease progression are not available. Furthermore, blood biomarkers are highly appreciated because they are minimally invasive and more accessible in primary care and in clinical studies. Mass spectrometry (MS) is an established tool for the measurement of various analytes in biological fluids such as blood. Its major strength is the high selectivity which is why it is also preferred as a reference method for immunoassays. MS has been used in several studies in the past for blood biomarker discovery and validation in AD using targeted MS such as multiple/selected reaction monitoring (MRM/SRM) or unbiased approaches (proteomics, metabolomics). In this short review, we give an overview on the status of current MS-based biomarker candidates for AD in blood plasma and serum.Plain Language Summary: Plain language summary available for this article.

A novel UHPLC-HRMS-based metabolomics strategy enables the discovery of potential neuroactive metabolites in mice plasma, following i.p. administration of the main Crocus sativus L. bioactive component

Trans-crocin 4 (TC4) is an important carotenoid constituent of saffron showing potential activity against Alzheimer's Disease (AD) due to its antioxidant and antiamyloidogenic properties. Metabolomics is an emerging scientific field that enhances biomarker discovery and reveals underlying biochemical mechanisms aiming towards the early subclinical diagnosis of diseases. To date, there are no reports on the changes induced to mice plasma metabolome after TC4 administration. We report a novel untargeted UHPLC-ESI HRMS metabolomics strategy to determine the alteration of the metabolic fingerprint following i.p. administration of TC4 in male and female mice. Blood samples from fiftysix mice treated with TC4 as well as from control animals were analyzed with UHPLC-ESI HRMS. Statistical evaluation of the results was achieved by multivariate analysis (MVA), i.e., principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA) in order to discover the variables that contributed to the discrimination between treated and untreated groups which were identified by online database searching (e.g., Metlin, HMDB, KEGG) aided by chemometric processing, e.g., covariance searching etc. Due to the high variability imposed by various factors, e.g., sex of the animals participating in the study, administration dose and time-points of sacrifice, multilevel sparse PLS-DA analysis, e.g., splitting variation to each individual component, has been employed as a more efficient approach for such designs. This methodology allowed the identification of the time sequence of metabolome changes due to the administration of TC4, whereas a sex-related effect on the metabolome is indicated, denoting that the administration in both sexes is indispensable in order to acquire safe conclusions as reliable metabolome pictures. The results demonstrated a number of annotated metabolites playing a potential role in neuroprotection while they are closely related to AD. Moreover, five additional annotated metabolites were involved in the steroid biosynthesis pathway while two of them may be considered as putative neuroprotective agents.

Sets of coregulated serum lipids are associated with Alzheimer's disease pathophysiology

Introduction

Comorbidity with metabolic diseases indicates that lipid metabolism plays a role in the etiology of Alzheimer's disease (AD). Comprehensive lipidomic analysis can provide new insights into the altered lipid metabolism in AD.

Method

In this study, a total 349 serum lipids were measured in 806 participants enrolled in the Alzheimer's Disease Neuroimaging Initiative Phase 1 cohort and analyzed using lipid-set enrichment statistics, a data mining method to find coregulated lipid sets.

Results

We found that sets of blood lipids were associated with current AD biomarkers and with AD clinical symptoms. AD diagnosis was associated with 7 of 28 lipid sets of which four also correlated with cognitive decline, including polyunsaturated fatty acids. Cerebrospinal fluid amyloid beta (Aβ_1-42) correlated with glucosylceramides, lysophosphatidylcholines and unsaturated triacylglycerides; cerebrospinal fluid total tau and brain atrophy correlated with monounsaturated sphingomyelins and ceramides, in addition to EPA-containing lipids.

Discussion

AD-associated lipid sets indicated that lipid desaturation, elongation, and acyl chain remodeling processes are disturbed in AD subjects. Monounsaturated lipid metabolism was important in early stages of AD, whereas the polyunsaturated lipid metabolism was associated with later stages of AD. Our study provides several new hypotheses for studying the role of lipid metabolism in AD.

•

Twenty eight coregulatory lipid sets are identified in Alzheimer's Disease Neuroimaging Initiative lipidomics data.

•

Distinct lipid pathways are associated with cerebrospinal fluid biomarkers, brain atrophy, and Alzheimer's disease (AD) diagnosis.

•

Monounsaturated lipids play a role in the AD initiation and early neurodegeneration.

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Polyunsaturated lipids are associated with AD diagnosis.

Inhibition of de novo ceramide biosynthesis affects aging phenotype in an in vitro model of neuronal senescence

Although aging is considered to be an unavoidable event, recent experimental evidence suggests that the process can be counteracted. Intracellular calcium (Ca²⁺_i) dyshomeostasis, mitochondrial dysfunction, oxidative stress, and lipid dysregulation are critical factors that contribute to senescence-related processes. Ceramides, a pleiotropic class of sphingolipids, are important mediators of cellular senescence, but their role in neuronal aging is still largely unexplored. In this study, we investigated the effects of L-cycloserine (L-CS), an inhibitor of thede novoceramide biosynthesis, on the aging phenotype of cortical neurons cultured for 22 days, a setting employed as anin vitromodel of senescence. Our findings indicate that, compared to control cultures, ‘aged’ neurons display dysregulation of [Ca²⁺]_ilevels, mitochondrial dysfunction, increased generation of reactive oxygen species (ROS), altered synaptic activity as well as the activation of neuronal death-related molecules. Treatment with L-CS positively affected the senescent phenotype, a result associated with recovery of neuronal [Ca²⁺]_isignaling and reduction of mitochondrial dysfunction and ROS generation. The results suggest that thede novoceramide biosynthesis represents a critical intermediate in the molecular and functional cascade leading to neuronal senescence and identify ceramide biosynthesis inhibitors as promising pharmacological tools to decrease age-related neuronal dysfunctions.

Multi-Omics Approach for Studying Tears in Treatment-Naïve Glaucoma Patients

Primary open-angle glaucoma (POAG) represents the leading cause of irreversible blindness worldwide and is a multifactorial, chronic neurodegenerative disease characterized by retinal ganglion cell and visual field loss. There are many factors that are associated with the risk of developing POAG, with increased intraocular pressure being one of the most prevalent. Due to the asymptomatic nature of the disease, the diagnosis of POAG often occurs too late, which necessitates development of new effective screening strategies for early diagnosis of the disease. However, this task still remains unfulfilled. In order to provide further insights into the pathophysiology of POAG, we applied a targeted metabolomics strategy based on a high-throughput screening method for the determination of tear amino acids, free carnitine, acylcarnitines, succinylacetone, nucleosides, and lysophospholipids in naïve to therapy glaucomatous patients and normal controls. Also, we conducted proteomic analyses of the whole lacrimal fluid and purified extracellular vesicles obtained from POAG patients and healthy subjects. This multi-omics approach allowed us to conclude that POAG patients had lower levels of certain tear amino acids and lysophospholipids compared with controls. These targeted analyses also highlighted the low amount of acetylcarnitine (C2) in POAG patient which correlated well with proteomics data. Moreover, POAG tear proteins seemed to derive from extracellular vesicles, which carried a specific pro-inflammatory protein cargo.

Diet-Related Metabolites Associated with Cognitive Decline Revealed by Untargeted Metabolomics in a Prospective Cohort

Scope

Untargeted metabolomics may reveal preventive targets in cognitive aging, including within the food metabolome.

Methods and results

A case‐control study nested in the prospective Three‐City study includes participants aged ≥65 years and initially free of dementia. A total of 209 cases of cognitive decline and 209 controls (matched for age, gender, education) with slower cognitive decline over up to 12 years are contrasted. Using untargeted metabolomics and bootstrap‐enhanced penalized regression, a baseline serum signature of 22 metabolites associated with subsequent cognitive decline is identified. The signature includes three coffee metabolites, a biomarker of citrus intake, a cocoa metabolite, two metabolites putatively derived from fish and wine, three medium‐chain acylcarnitines, glycodeoxycholic acid, lysoPC(18:3), trimethyllysine, glucose, cortisol, creatinine, and arginine. Adding the 22 metabolites to a reference predictive model for cognitive decline (conditioned on age, gender, education and including ApoE‐ε4, diabetes, BMI, and number of medications) substantially increases the predictive performance: cross‐validated Area Under the Receiver Operating Curve = 75% [95% CI 70–80%] compared to 62% [95% CI 56–67%].

Conclusions

The untargeted metabolomics study supports a protective role of specific foods (e.g., coffee, cocoa, fish) and various alterations in the endogenous metabolism responsive to diet in cognitive aging.

Integrated Lipidomics and Metabolomics Analysis of Tears in Multiple Sclerosis: An Insight into Diagnostic Potential of Lacrimal Fluid

Metabolomics based on mass spectrometry represents an innovative approach to characterize multifactorial diseases, such as multiple sclerosis (MuS). To date, the most important biomarker source for MuS diagnosis is the cerebrospinal fluid. However, an important goal for research is to identify new molecules in more easily accessible biological fluids. A very interesting biofluid in MuS is represented by tears, considered as an intermediate fluid between the cerebrospinal fluid and serum. In this work, we developed a merged strategy for the analysis of lipids containing choline by Liquid Chromatography coupled to Tandem Mass Spectrometry (LC-MS/MS), as well as for the targeted analysis of free carnitine, acylcarnitines and aminoacids by direct infusion mass spectrometry. Samples for both metabolomics and lipidomics approaches were obtained in a single extraction procedure from tears of patients affected by MuS and healthy controls. Tear lipidomics showed 30 phospholipids significantly modulated and, notably, many sphingomyelins resulted lower in MuS. Moreover, the metabolomics approach carried out both on tears and serum highlighted the diagnostic potential of specific aminoacids and acylcarnitines. In conclusion, the metabolic profiling of tears appears to reflect the pathological conditions of the central nervous system, suggesting that the molecular repository of tears can be considered as a source of potential biomarkers for MuS.

A metabolomic approach to identifying biomarkers in blood of Alzheimer's disease

Objective

This study aims to identify metabolites with altered levels of expression in patients with early and progressive stages of Alzheimer's disease (AD).

Methods

All participants of the study underwent genetic screening and were diagnosed using both neuropsychological assessment and amyloid imaging before metabolome analysis. According to these assessments, the patients were classified as normal (n = 15), with mild cognitive impairment (n = 10), and with AD (n = 15).

Results

Using a targeted metabolomic approach, we found that plasma levels of C3, C5, and C5‐DC acylcarnitines, arginine, phenylalanine, creatinine, symmetric dimethylarginine (SDMA) and phosphatidylcholine ae C38:2 were significantly altered in patients with early and progressive stages of AD. We created a predictive model based on the decision tree that included three main parameters: age, arginine and C5 plasma concentrations. The model distinguished AD patients from other participants with 60% sensitivity and 86.7% specificity. For healthy controls, the sensitivity was 85.7% and specificity was 61.5%. Multivariate ROC analysis to develop a decision tree showed that our model reached moderate diagnostic power in differentiating between older adults who are cognitively normal (AUC = 0.77) and those with AD (AUC = 0.72).

Interpretation

The plasma levels of arginine and valeryl carnitine, together with subject age, are promising as biomarkers for the diagnosis of AD in older adults.

Omics-based Biomarkers for the Early Alzheimer Disease Diagnosis and Reliable Therapeutic Targets Development

BACKGROUND

Alzheimer's disease (AD), the most common cause of dementia in adulthood, has great medical, social, and economic impact worldwide. Available treatments result in symptomatic relief, and most of them are indicated from the early stages of the disease. Therefore, there is an increasing body of research developing accurate and early diagnoses, as well as diseasemodifying therapies.

OBJECTIVE

Advancing the knowledge of AD physiopathological mechanisms, improving early diagnosis and developing effective treatments from omics-based biomarkers.

METHODS

Studies using omics technologies to detect early AD, were reviewed with a particular focus on the metabolites/lipids, micro-RNAs and proteins, which are identified as potential biomarkers in non-invasive samples.

RESULTS

This review summarizes recent research on metabolomics/lipidomics, epigenomics and proteomics, applied to early AD detection. Main research lines are the study of metabolites from pathways, such as lipid, amino acid and neurotransmitter metabolisms, cholesterol biosynthesis, and Krebs and urea cycles. In addition, some microRNAs and proteins (microglobulins, interleukins), related to a common network with amyloid precursor protein and tau, have been also identified as potential biomarkers. Nevertheless, the reproducibility of results among studies is not good enough and a standard methodological approach is needed in order to obtain accurate information.

CONCLUSION

The assessment of metabolomic/lipidomic, epigenomic and proteomic changes associated with AD to identify early biomarkers in non-invasive samples from well-defined participants groups will potentially allow the advancement in the early diagnosis and improvement of therapeutic interventions.

Mass spectrometry is a multifaceted weapon to be used in the battle against Alzheimer's disease: Amyloid beta peptides and beyond

Amyloid-β peptide (Aβ) accumulation and aggregation have been considered for many years the main cause of Alzheimer's disease (AD), and therefore have been the principal target of investigation as well as of the proposed therapeutic approaches (Grasso [2011] Mass Spectrom Rev. 30: 347-365). However, the amyloid cascade hypothesis, which considers Aβ accumulation the only causative agent of the disease, has proven to be incomplete if not wrong. In recent years, actors such as metal ions, oxidative stress, and other cofactors have been proposed as possible co-agents or, in some cases, main causative factors of AD. In this scenario, MS investigation has proven to be fundamental to design possible diagnostic strategies of this elusive disease, as well as to understand the biomolecular mechanisms involved, in the attempt to find a possible therapeutic solution. We review the current applications of MS in the search for possible Aβ biomarkers of AD to help the diagnosis of the disease. Recent examples of the important contributions that MS has given to prove or build theories on the molecular pathways involved with such terrible disease are also reviewed.

PPARβ/δ-agonist GW0742 ameliorates dysfunction in fatty acid oxidation in PSEN1ΔE9 astrocytes

Astrocytes are the gatekeepers of neuronal energy supply. In neurodegenerative diseases, bioenergetics demand increases and becomes reliant upon fatty acid oxidation as a source of energy. Defective fatty acid oxidation and mitochondrial dysfunctions correlate with hippocampal neurodegeneration and memory deficits in Alzheimer's disease (AD), but it is unclear whether energy metabolism can be targeted to prevent or treat the disease. Here we show for the first time an impairment in fatty acid oxidation in human astrocytes derived from induced pluripotent stem cells of AD patients. The impairment was corrected by treatment with a synthetic peroxisome proliferator activated receptor delta (PPARβ/δ) agonist GW0742 which acts to regulate an array of genes governing cellular metabolism. GW0742 enhanced the expression of CPT1a, the gene encoding for a rate-limiting enzyme of fatty acid oxidation. Similarly, treatment of a mouse model of AD, the APP/PS1-mice, with GW0742 increased the expression of Cpt1a and concomitantly reversed memory deficits in a fear conditioning test. Although the GW0742-treated mice did not show altered astrocytic glial fibrillary acidic protein-immunoreactivity or reduction in amyloid beta (Aβ) load, GW0742 treatment increased hippocampal neurogenesis and enhanced neuronal differentiation of neuronal progenitor cells. Furthermore, GW0742 prevented Aβ-induced impairment of long-term potentiation in hippocampal slices. Collectively, these data suggest that PPARβ/δ-agonism alleviates AD related deficits through increasing fatty acid oxidation in astrocytes and improves cognition in a transgenic mouse model of AD.

β-Hydroxybutyrate Elicits Favorable Mitochondrial Changes in Skeletal Muscle

The clinical benefit of ketosis has historically and almost exclusively centered on neurological conditions, lending insight into how ketones alter mitochondrial function in neurons. However, there is a gap in our understanding of how ketones influence mitochondria within skeletal muscle cells. The purpose of this study was to elucidate the specific effects of β-hydroxybutyrate (β-HB) on muscle cell mitochondrial physiology. In addition to increased cell viability, murine myotubes displayed beneficial mitochondrial changes evident in reduced H₂O₂ emission and less mitochondrial fission, which may be a result of a β-HB-induced reduction in ceramides. Furthermore, muscle from rats in sustained ketosis similarly produced less H₂O₂ despite an increase in mitochondrial respiration and no apparent change in mitochondrial quantity. In sum, these results indicate a general improvement in muscle cell mitochondrial function when β-HB is provided as a fuel.

Proteomic and metabolomic characterization of streptozotocin-induced diabetic nephropathy in TIMP3-deficient mice

AIMS

The tissue inhibitor of metalloproteinase TIMP3 is a stromal protein that restrains the activity of both protease and receptor in the extracellular matrix and has been found to be down-regulated in diabetic nephropathy (DN), the leading cause of end-stage renal disease in developed countries.

METHODS

In order to gain deeper insights on the association of loss of TIMP3 and DN, we performed differential proteomic analysis of kidney and blood metabolic profiling of wild-type and Timp3-knockout mice before and after streptozotocin (STZ) treatment, widely used to induce insulin deficiency and hyperglycemia.

RESULTS

Kidney proteomic data and blood metabolic profiles suggest significant alterations of peroxisomal and mitochondrial fatty acids β-oxidation in Timp3-knockout mice compared to wild-type mice under basal condition. These alterations were exacerbated in response to STZ treatment.

CONCLUSIONS

Proteomic and metabolomic approaches showed that loss of TIMP3 alone or in combination with STZ treatment results in significant alterations of kidney lipid metabolism and peripheral acylcarnitine levels, supporting the idea that loss of TIMP3 may generate a phenotype more prone to DN.

Metabolic Biomarkers of Aging and Aging-related Diseases in Chinese Middle-Aged and Elderly Men

BACKGROUND

Aging is an acknowledged risk factor for most chronic diseases and functional impairments. The practicability of potential biomarkers of aging remains unsure. Moreover, biomarkers related to certain geriatric diseases, such as carotid atherosclerosis and multiple co-morbidities are less understood. The purpose of this study was to investigate the definite relationship between metabolic biomarkers and aging-related diseases.

METHODS

Eighty-five male adults aged fifty years or older from the general population were enrolled. Plasma metabolic biomarkers, including fourteen amino acids and thirty-six acylcarnitines, were measured by liquid chromatography mass spectrometry. Bivariate correlation analysis was employed to estimate the correlations between variables and age, and also to evaluate the relationship between metabolic biomarkers and aging-related diseases. Receiver operating characteristic (ROC) curve was conducted to judge the diagnostic efficiency of potential metabolic biomarkers for co-morbidities.

RESULTS

Certain metabolic biomarkers were strongly positively correlated with age, such as tetradecenoylcarnitine (C14:1), microalbumin-urine creatinine ratio (UACR), dodecenoylcarnitine (C12:1) and citrulline (p < 0.001). Carotid atherosclerosis and co-morbidities were positively correlated with aging (p < 0.001). After adjustment for age, hydroxytetradecanoylcarnitine (C14OH) remained positively correlated with carotid plague area. Besides, citrulline had diagnostic power for co-morbidities.

CONCLUSIONS

Citrulline may be a promising metabolic biomarker in the middle-aged and elderly men. Larger-scale and long-term studies are needed to confirm our findings.