Associations of the Lipidome with Ageing, Cognitive Decline and Exercise Behaviours

One of the most recognisable features of ageing is a decline in brain health and cognitive dysfunction, which is associated with perturbations to regular lipid homeostasis. Although ageing is the largest risk factor for several neurodegenerative diseases such as dementia, a loss in cognitive function is commonly observed in adults over the age of 65. Despite the prevalence of normal age-related cognitive decline, there is a lack of effective methods to improve the health of the ageing brain. In light of this, exercise has shown promise for positively influencing neurocognitive health and associated lipid profiles. This review summarises age-related changes in several lipid classes that are found in the brain, including fatty acyls, glycerolipids, phospholipids, sphingolipids and sterols, and explores the consequences of age-associated pathological cognitive decline on these lipid classes. Evidence of the positive effects of exercise on the affected lipid profiles are also discussed to highlight the potential for exercise to be used therapeutically to mitigate age-related changes to lipid metabolism and prevent cognitive decline in later life.

Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease

Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.

Diagnostic Model of Alzheimer's Disease in the Elderly Based on Protein and Metabolic Biomarkers

BACKGROUND

With the accelerating aging process, the number of participants with Alzheimer's disease (AD) is rising sharply, causing a huge economic burden.

OBJECTIVE

This study aimed to identify blood protein and metabolic biomarkers and explore the diagnostic model for AD among elderly in southeast China.

METHODS

We established a cohort among population with high risk AD in Zhejiang Province in 2018. Case and control groups each consisting of 45 subjects, matched for gender and age, were randomly selected from the cohort. Based on bioinformatics research, PRM/MRM technology was used to detect candidate biomarkers. Ensemble-based feature selection and machine learning methods was used to screen important variables as risk indicators for AD. Based on the risk biomarkers, the risk diagnostic model of AD in the elderly was constructed and evaluated.

RESULTS

Cystine and CPB2 were evaluated as biomarkers. The diagnostic model is constructed using logistic regression algorithm with the best cutoff value, sensitivity, specificity, and accuracy of 0.554, 0.895, 0.976, and 0.938, respectively, which determined by Youden's index. The results showed that the model with protein and metabolite had a high efficiency.

CONCLUSION

It showed that the diagnostic model constructed by Cystine and CPB2 had a good performance on sample classification. This study was of great significance for the early screening and diagnosis of AD, timely intervention, control and delay the development of dementia in southeast China.

A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort

Introduction

Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers.

Methods

This study analyzed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n = 883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV).

Results

On the test data, DL produced the AUC of 0.85 (0.80–0.89), XGBoost produced 0.88 (0.86–0.89) and RF produced 0.85 (0.83–0.87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively.

Discussion

This study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders.

Primary fatty amides in plasma associated with brain amyloid burden, hippocampal volume, and memory in the European Medical Information Framework for Alzheimer's Disease biomarker discovery cohort

Introduction:

A critical and as-yet unmet need in Alzheimer’s disease (AD) is the discovery of peripheral small molecule biomarkers. Given that brain pathology precedes clinical symptom onset, we set out to test whether metabolites in blood associated with pathology as indexed by cerebrospinal fluid (CSF) AD biomarkers.

Methods:

This study analyzed 593 plasma samples selected from the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery study, of individuals who were cognitively healthy (n = 242), had mild cognitive impairment (n = 236), or had AD-type dementia (n = 115). Logistic regressions were carried out between plasma metabolites (n = 883) and CSF markers, magnetic resonance imaging, cognition, and clinical diagnosis.

Results:

Eight metabolites were associated with amyloid b and one with t-tau in CSF, these were primary fatty acid amides (PFAMs), lipokines, and amino acids. From these, PFAMs, glutamate, and aspartate also associated with hippocampal volume and memory.

Discussion:

PFAMs have been found increased and associated with amyloid b burden in CSF and clinical measures.

FTIR and Raman Spectroscopy Applied to Dementia Diagnosis Through Analysis of Biological Fluids

To date, it is still difficult to perform an early and accurate diagnosis of dementia, therefore significant research has focused on finding new dementia biomarkers that can aid in this respect. There is an urgent need for non-invasive, rapid, and relatively inexpensive procedures for early diagnostics. Studies have demonstrated that of spectroscopic techniques, such as Fourier Transform Infrared Spectroscopy (FTIR) and Raman Spectroscopy could be a useful and accurate procedure to diagnose dementia. Given that several biochemical mechanisms related to neurodegeneration and dementia can lead to changes in plasma components and others peripheral body fluids; blood-based samples coupled to spectroscopic analyses can be used as a simple and less invasive approach.

Association of blood lipids with Alzheimer's disease: A comprehensive lipidomics analysis

INTRODUCTION

The aim of this study was to (1) replicate previous associations between six blood lipids and Alzheimer's disease (AD) (Proitsi et al 2015) and (2) identify novel associations between lipids, clinical AD diagnosis, disease progression and brain atrophy (left/right hippocampus/entorhinal cortex).

METHODS

We performed untargeted lipidomic analysis on 148 AD and 152 elderly control plasma samples and used univariate and multivariate analysis methods.

RESULTS

We replicated our previous lipids associations and reported novel associations between lipids molecules and all phenotypes. A combination of 24 molecules classified AD patients with >70% accuracy in a test and a validation data set, and we identified lipid signatures that predicted disease progression (R² = 0.10, test data set) and brain atrophy (R² ≥ 0.14, all test data sets except left entorhinal cortex). We putatively identified a number of metabolic features including cholesteryl esters/triglycerides and phosphatidylcholines.

DISCUSSION

Blood lipids are promising AD biomarkers that may lead to new treatment strategies.

Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer's disease

There is an urgent need for the identification of Alzheimer's disease (AD) biomarkers. Studies have now suggested the promising use of associations with blood metabolites as functional intermediate phenotypes in biomedical and pharmaceutical research. The aim of this study was to use lipidomics to identify a battery of plasma metabolite molecules that could predict AD patients from controls. We performed a comprehensive untargeted lipidomic analysis, using ultra-performance liquid chromatography/mass spectrometry on plasma samples from 35 AD patients, 40 elderly controls and 48 individuals with mild cognitive impairment (MCI) and used multivariate analysis methods to identify metabolites associated with AD status. A combination of 10 metabolites could discriminate AD patients from controls with 79.2% accuracy (81.8% sensitivity, 76.9% specificity and an area under curve of 0.792) in a novel test set. Six of the metabolites were identified as long chain cholesteryl esters (ChEs) and were reduced in AD (ChE 32:0, odds ratio (OR)=0.237, 95% confidence interval (CI)=0.10-0.48, P=4.19E-04; ChE 34:0, OR=0.152, 95% CI=0.05-0.37, P=2.90E-04; ChE 34:6, OR=0.126, 95% CI=0.03-0.35, P=5.40E-04; ChE 32:4, OR=0.056, 95% CI=0.01-0.24, P=6.56E-04 and ChE 33:6, OR=0.205, 95% CI=0.06-0.50, P=2.21E-03, per (log2) metabolite unit). The levels of these metabolites followed the trend control>MCI>AD. We, additionally, found no association between cholesterol, the precursor of ChE and AD. This study identified new ChE molecules, involved in cholesterol metabolism, implicated in AD, which may help identify new therapeutic targets; although, these findings need to be replicated in larger well-phenotyped cohorts.

Recent Advances in Metabolic Profiling And Imaging of Prostate Cancer

Cancer is a metabolic disease. Cancer cells, being highly proliferative, show significant alterations in metabolic pathways such as glycolysis, respiration, the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, lipid metabolism, and amino acid metabolism. Metabolites like peptides, nucleotides, products of glycolysis, the TCA cycle, fatty acids, and steroids can be an important read out of disease when characterized in biological samples such as tissues and body fluids like urine, serum, etc. The cancer metabolome has been studied since the 1960s by analytical techniques such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Current research is focused on the identification and validation of biomarkers in the cancer metabolome that can stratify high-risk patients and distinguish between benign and advanced metastatic forms of the disease. In this review, we discuss the current state of prostate cancer metabolomics, the biomarkers that show promise in distinguishing indolent from aggressive forms of the disease, the strengths and limitations of the analytical techniques being employed, and future applications of metabolomics in diagnostic imaging and personalized medicine of prostate cancer.

Evidence of altered phosphatidylcholine metabolism in Alzheimer's disease

Abberant lipid metabolism is implicated in Alzheimer's disease (AD) pathophysiology, but the connections between AD and lipid metabolic pathways are not fully understood. To investigate plasma lipids in AD, a multiplatform screen (n = 35 by liquid chromatography-mass spectrometry and n = 35 by nuclear magnetic resonance) was developed, which enabled the comprehensive analysis of plasma from 3 groups (individuals with AD, individuals with mild cognitive impairment (MCI), and age-matched controls). This screen identified 3 phosphatidylcholine (PC) molecules that were significantly diminished in AD cases. In a subsequent validation study (n = 141), PC variation in a bigger sample set was investigated, and the same 3 PCs were found to be significantly lower in AD patients: PC 16:0/20:5 (p < 0.001), 16:0/22:6 (p < 0.05), and 18:0/22:6 (p < 0.01). A receiver operating characteristic (ROC) analysis of the PCs, combined with apolipoprotein E (ApoE) data, produced an area under the curve predictive value of 0.828. Confirmatory investigations into the background biochemistry indiciated no significant change in plasma levels of 3 additional PCs of similar structure, total choline containing compounds or total plasma omega fatty acids, adding to the evidence that specific PCs play a role in AD pathology.

Plasma biomarkers for Alzheimer's disease: much needed but tough to find

Alzheimer's disease is a complex age-dependent neurodegenerative disease where definitive diagnosis is only possible after autopsy and where there is a long prodromal or preclinical phase. Biomarkers for both early diagnosis and prediction of disease progression are needed and extensive efforts to discover them have been undertaken. In this article, we have attempted to summarize the findings of current studies using proteomics and metabolomics approaches. We are also discussing how the use of emerging technologies and better study designs can support the identification of the much-needed Alzheimer's disease plasma biomarkers.

Recent advances in metabolomics in oncology

The burden of cancer is growing worldwide and with it a more desperate need for better tools to detect, diagnose and monitor the disease is required. It is well recognized that cancer cells are characterized by distinct metabolic perturbations. The metabolomics approach involves the comprehensive profiling of the full complement of low MW compounds in a biological system. By applying advanced analytical and statistical tools, the 'metabolome' is mined for biomarkers that are associated with the state of cancer. This review presents an introduction to the main analytical platforms used in metabolomics analyses, such as NMR spectroscopy and MS, as well as the statistical tools used to mine these datasets. The discussion focuses on 'state-of-the-art' investigations on the four cancer types that have received the most study by metabolomics, namely breast, prostate, colorectal and liver cancer.