Circulating metabolites and general cognitive ability and dementia: Evidence from 11 cohort studies

Causal association between plasma metabolites and neurodegenerative diseases

BACKGROUND

Establishing causal relationships between metabolic biomarkers and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD) is a challenge faced by observational studies. In this study, our aim was to investigate the causal associations between plasma metabolites and neurodegenerative diseases using Mendelian Randomization (MR) methods.

METHODS

We utilized genetic associations with 1400 plasma metabolic traits as exposures. We used large-scale genome-wide association study (GWAS) summary statistics for AD and PD as our discovery datasets. For validation, we performed repeated analyses using different GWAS datasets. The main statistical method employed was inverse variance-weighted (IVW). We also conducted enrichment pathway analysis for IVW-identified metabolites.

RESULTS

In the discovered dataset, there are a total of 69 metabolites (36 negatively, 33 positively) potentially associated with AD, and 47 metabolites (24 negatively， 23 positively) potentially associated with PD. Among these, 4 significant metabolites overlap with significant metabolites (PIVW < 0.05)in the validation dataset for AD, and 1 metabolite overlaps with significant metabolites in the validation dataset for PD. Three metabolites serve as common potential metabolic markers for both AD and PD, including Tryptophan betaine, Palmitoleoylcarnitine (C16:1), and X-23655 levels. Further pathway enrichment analysis suggests that the SLC-mediated transmembrane transport pathway, involving tryptophan betaine and carnitine metabolites, may represent potential intervention targets for treating AD and PD.

CONCLUSION

This study offers novel insights into the causal effects of plasma metabolites on degenerative diseases through the integration of genomics and metabolomics. The identification of metabolites and metabolic pathways linked to AD and PD enhances our comprehension of the underlying biological mechanisms and presents promising targets for future therapeutic interventions in AD and PD.

Vascular cognitive impairment: Advances in clinical research and management

ABSTRACT

Vascular cognitive impairment (VCI) encompasses a wide spectrum of cognitive disorders, ranging from mild cognitive impairment to vascular dementia. Its diagnosis relies on thorough clinical evaluations and neuroimaging. VCI predominately arises from vascular risk factors (VRFs) and cerebrovascular disease, either independently or in conjunction with neurodegeneration. Growing evidence underscores the prevalence of VRFs, highlighting their potential for early prediction of cognitive impairment and dementia in later life. The precise mechanisms linking vascular pathologies to cognitive deficits remain elusive. Chronic cerebrovascular pathology is the most common neuropathological feature of VCI, often interacting synergistically with neurodegenerative processes. Current research efforts are focused on developing and validating reliable biomarkers to unravel the etiology of vascular brain changes in VCI. The collaborative integration of these biomarkers into clinical practice, alongside routine incorporation into neuropathological assessments, presents a promising strategy for predicting and stratifying VCI. The cornerstone of VCI prevention remains the control of VRFs, which includes multi-domain lifestyle modifications. Identifying appropriate pharmacological approaches is also of paramount importance. In this review, we synthesize recent advancements in the field of VCI, including its definition, determinants of vascular risk, pathophysiology, neuroimaging and fluid-correlated biomarkers, predictive methodologies, and current intervention strategies. Increasingly evident is the notion that more rigorous research for VCI, which arises from a complex interplay of physiological events, is still needed to pave the way for better clinical outcomes and enhanced quality of life for affected individuals.

Clinical trajectories preceding incident dementia up to 15 years before diagnosis: a large prospective cohort study

BACKGROUND

Dementia has a long prodromal stage with various pathophysiological manifestations; however, the progression of pre-diagnostic changes remains unclear. We aimed to determine the evolutional trajectories of multiple-domain clinical assessments and health conditions up to 15 years before the diagnosis of dementia.

METHODS

Data was extracted from the UK-Biobank, a longitudinal cohort that recruited over 500,000 participants from March 2006 to October 2010. Each demented subject was matched with 10 healthy controls. We performed logistic regressions on 400 predictors covering a comprehensive range of clinical assessments or health conditions. Their evolutional trajectories were quantified using adjusted odds ratios (ORs) and FDR-corrected p-values under consecutive timeframes preceding the diagnosis of dementia.

FINDINGS

During a median follow-up of 13.7 [Interquartile range, IQR 12.9-14.2] years until July 2022, 7620 subjects were diagnosed with dementia. In general, upon approaching the diagnosis, demented subjects witnessed worse functional assessments and a higher prevalence of health conditions. Associations up to 15 years preceding the diagnosis comprised declined physical strength (hand grip strength, OR 0.65 [0.63-0.67]), lung dysfunction (peak expiratory flow, OR 0.78 [0.76-0.81]) and kidney dysfunction (cystatin C, OR 1.13 [1.11-1.16]), comorbidities of coronary heart disease (OR 1.78 [1.67-1.91]), stroke (OR 2.34 [2.1-1.37]), diabetes (OR 2.03 [1.89-2.18]) and a series of mental disorders. Cognitive functions in multiple tests also demonstrate decline over a decade before the diagnosis. Inadequate activity (3-5 year, overall time of activity, OR 0.82 [0.73-0.92]), drowsiness (3-5 year, sleep duration, OR 1.13 [1.04-1.24]) and weight loss (0-5 year, weight, OR 0.9 [0.83-0.98]) only exhibited associations within five years before the diagnosis. In addition, serum biomarkers of enriched endocrine, dysregulations of ketones, deficiency of brand-chain amino acids and polyunsaturated fatty acids were found in a similar prodromal time window and can be witnessed as the last pre-symptomatic conditions before the diagnosis.

INTERPRETATION

Our findings present a comprehensive temporal-diagnostic landscape preceding incident dementia, which could improve selection for preventive and early disease-modifying treatment trials.

Unraveling the link between neuropathy target esterase NTE/SWS, lysosomal storage diseases, inflammation, abnormal fatty acid metabolism, and leaky brain barrier

Mutations in Drosophila Swiss cheese (SWS) gene or its vertebrate orthologue neuropathy target esterase (NTE) lead to progressive neuronal degeneration in flies and humans. Despite its enzymatic function as a phospholipase is well established, the molecular mechanism responsible for maintaining nervous system integrity remains unclear. In this study, we found that NTE/SWS is present in surface glia that forms the blood-brain barrier (BBB) and that NTE/SWS is important to maintain its structure and permeability. Importantly, BBB glia-specific expression of Drosophila NTE/SWS or human NTE in the sws mutant background fully rescues surface glial organization and partially restores BBB integrity, suggesting a conserved function of NTE/SWS. Interestingly, sws mutant glia showed abnormal organization of plasma membrane domains and tight junction rafts accompanied by the accumulation of lipid droplets, lysosomes, and multilamellar bodies. Since the observed cellular phenotypes closely resemble the characteristics described in a group of metabolic disorders known as lysosomal storage diseases (LSDs), our data established a novel connection between NTE/SWS and these conditions. We found that mutants with defective BBB exhibit elevated levels of fatty acids, which are precursors of eicosanoids and are involved in the inflammatory response. Also, as a consequence of a permeable BBB, several innate immunity factors are upregulated in an age-dependent manner, while BBB glia-specific expression of NTE/SWS normalizes inflammatory response. Treatment with anti-inflammatory agents prevents the abnormal architecture of the BBB, suggesting that inflammation contributes to the maintenance of a healthy brain barrier. Considering the link between a malfunctioning BBB and various neurodegenerative diseases, gaining a deeper understanding of the molecular mechanisms causing inflammation due to a defective BBB could help to promote the use of anti-inflammatory therapies for age-related neurodegeneration.

Identification of blood metabolites associated with risk of Alzheimer's disease by integrating genomics and metabolomics data

Specific metabolites have been reported to be potentially associated with Alzheimer's disease (AD) risk. However, the comprehensive understanding of roles of metabolite biomarkers in AD etiology remains elusive. We performed a large AD metabolome-wide association study (MWAS) by developing blood metabolite genetic prediction models. We evaluated associations between genetically predicted levels of metabolites and AD risk in 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia (proxy-ADD) cases, and 401,577 controls. We further conducted analyses to determine microbiome features associated with the detected metabolites and characterize associations between predicted microbiome feature levels and AD risk. We identified fourteen metabolites showing an association with AD risk. Five microbiome features were further identified to be potentially related to associations of five of the metabolites. Our study provides new insights into the etiology of AD that involves blood metabolites and gut microbiome, which warrants further investigation.

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).

Metabolomics and Risk of Dementia: A Systematic Review of Prospective Studies

BACKGROUND

The projected increase in the prevalence of dementia has sparked interest in understanding the pathophysiology and underlying causal factors in its development and progression. Identifying novel biomarkers in the preclinical or prodromal phase of dementia may be important for predicting early disease risk. Applying metabolomic techniques to prediagnostic samples in prospective studies provides the opportunity to identify potential disease biomarkers.

OBJECTIVE

The objective of this systematic review was to summarize the evidence on the associations between metabolite markers and risk of dementia and related dementia subtypes in human studies with a prospective design.

DESIGN

We searched PubMed, PsycINFO, and Web of Science databases from inception through December 8, 2023. Thirteen studies (mean/median follow-up years: 2.1-21.0 y) were included in the review.

RESULTS

Several metabolites detected in biological samples, including amino acids, fatty acids, acylcarnitines, lipid and lipoprotein variations, hormones, and other related metabolites, were associated with risk of developing dementia. Our systematic review summarized the adjusted associations between metabolites and dementia risk; however, our findings should be interpreted with caution because of the heterogeneity across the included studies and potential sources of bias. Further studies are warranted with well-designed prospective cohort studies that have defined study populations, longer follow-up durations, the inclusion of additional diverse biological samples, standardization of techniques in metabolomics and ascertainment methods for diagnosing dementia, and inclusion of other related dementia subtypes.

CONCLUSIONS

This study contributes to the limited systematic reviews on metabolomics and dementia by summarizing the prospective associations between metabolites in prediagnostic biological samples with dementia risk. Our review discovered additional metabolite markers associated with the onset of developing dementia and may help aid in the understanding of dementia etiology. The protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (https://www.crd.york.ac.uk/prospero/; registration ID: CRD42022357521).

Investigating metabolic dysregulation in serum of triple transgenic Alzheimer's disease male mice: implications for pathogenesis and potential biomarkers

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease that lacks convenient and accessible peripheral blood diagnostic markers and effective drugs. Metabolic dysfunction is one of AD risk factors, which leaded to alterations of various metabolites in the body. Pathological changes of the brain can be reflected in blood metabolites that are expected to explain the disease mechanisms or be candidate biomarkers. The aim of this study was to investigate the changes of targeted metabolites within peripheral blood of AD mouse model, with the purpose of exploring the disease mechanism and potential biomarkers. Targeted metabolomics was used to quantify 256 metabolites in serum of triple transgenic AD (3 × Tg-AD) male mice. Compared with controls, 49 differential metabolites represented dysregulation in purine, pyrimidine, tryptophan, cysteine and methionine and glycerophospholipid metabolism. Among them, adenosine, serotonin, N-acetyl-5-hydroxytryptamine, and acetylcholine play a key role in regulating neural transmitter network. The alteration of S-adenosine-L-homocysteine, S-adenosine-L-methionine, and trimethylamine-N-oxide in AD mice serum can served as indicator of AD risk. The results revealed the changes of metabolites in serum, suggesting that metabolic dysregulation in periphery in AD mice may be related to the disturbances in neuroinhibition, the serotonergic system, sleep function, the cholinergic system, and the gut microbiota. This study provides novel insights into the dysregulation of several key metabolites and metabolic pathways in AD, presenting potential avenues for future research and the development of peripheral biomarkers.

Circulating Lipoproteins Mediate the Association Between Cardiovascular Risk Factors and Cognitive Decline: A Community-Based Cohort Study

Cardiovascular health metrics are now widely recognized as modifiable risk factors for cognitive decline and dementia. Metabolic perturbations might play roles in the linkage of cardiovascular diseases and dementia. Circulating metabolites profiling by metabolomics may improve understanding of the potential mechanism by which cardiovascular risk factors contribute to cognitive decline. In a prospective community-based cohort in China (n = 725), 312 serum metabolic phenotypes were quantified, and cardiovascular health score was calculated including smoking, exercise, sleep, diet, body mass index, blood pressure, and blood glucose. Cognitive function assessments were conducted in baseline and follow-up visits to identify longitudinal cognitive decline. A better cardiovascular health was significantly associated with lower risk of concentration decline and orientation decline (hazard ratio (HR): 0.84-0.90; p < 0.05). Apolipoprotein-A1, high-density lipoprotein (HDL) cholesterol, cholesterol ester, and phospholipid concentrations were significantly associated with a lower risk of longitudinal memory and orientation decline (p < 0.05 and adjusted-p < 0.20). Mediation analysis suggested that the negative association between health status and the risk of orientation decline was partly mediated by cholesterol ester and total lipids in HDL-2 and -3 (proportion of mediation: 7.68-8.21%, both p < 0.05). Cardiovascular risk factors were associated with greater risks of cognitive decline, which were found to be mediated by circulating lipoproteins, particularly the medium-size HDL components. These findings underscore the potential of utilizing lipoproteins as targets for early stage dementia screening and intervention.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00120-2.

NMR analysis seeking for cognitive decline and dementia metabolic markers in plasma from aged individuals

BACKGROUND

Blood biomarkers can improve the ability to diagnose dementia, providing new information to better understand the pathophysiology and causes of the disease. Some studies with patients have already shown changes in metabolic profiles among patients with pathological cognitive decline or Alzheimer's disease, when compared to individuals with normal cognition.

METHODS

To search for new metabolic biomarkers of dementia, we analyzed serum levels of several metabolites, measured by nuclear magnetic resonance spectroscopy, in elderly individuals, a group with normal cognitive decline (control), and three other groups with cognitive decline. pathological (low, moderate, and severe).

RESULTS

Decreased plasma levels of tyrosine, glutamate, valine, leucine, and isoleucine are associated with worsening of pathological cognitive decline. However, the area under analysis of receptor operating characteristics suggests that tyrosine and glutamate have low specificity and sensitivity. Valine, leucine, and isoleucine are influenced by blood glucose or diabetes, but these conditions do not seem to be of great influence in the differences observed. Isobutyrate, histidine, acetone and unknown-1 metabolite also decrease their plasma levels with increasing CD. Isobutyrate ad histidine could have neuroprotective and antioxidant actions, respectively. To elucidate the role of decreased unknown metabolite-1 as a CD biomarker, it will be necessary to previously investigate its identity. To define and elucidate the role of acetone in pathological CD, additional laboratory and clinical studies must be performed. All these metabolites together may constitute a set of biomarkers with capability to identify pathological CD or dementia.

SIGNIFICANCE AND NOVELTY

Decrease of glutamate, tyrosine, valine, leucine, isoleucine, histidine, isobutyrate, acetone and unknown-1 metabolite together are a set of biomarkers able to identify pathological CD or dementia. Histidine, isobutyrate, acetone and unknown-1 metabolite are more specific biomarkers of CD.

Plasma metabolites distinguish dementia with Lewy bodies from Alzheimer's disease: a cross-sectional metabolomic analysis

Background

In multifactorial diseases, alterations in the concentration of metabolites can identify novel pathological mechanisms at the intersection between genetic and environmental influences. This study aimed to profile the plasma metabolome of patients with dementia with Lewy bodies (DLB) and Alzheimer's disease (AD), two neurodegenerative disorders for which our understanding of the pathophysiology is incomplete. In the clinical setting, DLB is often mistaken for AD, highlighting a need for accurate diagnostic biomarkers. We therefore also aimed to determine the overlapping and differentiating metabolite patterns associated with each and establish whether identification of these patterns could be leveraged as biomarkers to support clinical diagnosis.

Methods

A panel of 630 metabolites (Biocrates MxP Quant 500) and a further 232 metabolism indicators (biologically informative sums and ratios calculated from measured metabolites, each indicative for a specific pathway or synthesis; MetaboINDICATOR) were analyzed in plasma from patients with probable DLB (n = 15; age 77.6 ± 8.2 years), probable AD (n = 15; 76.1 ± 6.4 years), and age-matched cognitively healthy controls (HC; n = 15; 75.2 ± 6.9 years). Metabolites were quantified using a reversed-phase ultra-performance liquid chromatography column and triple-quadrupole mass spectrometer in multiple reaction monitoring (MRM) mode, or by using flow injection analysis in MRM mode. Data underwent multivariate (PCA analysis), univariate and receiving operator characteristic (ROC) analysis. Metabolite data were also correlated (Spearman r) with the collected clinical neuroimaging and protein biomarker data.

Results

The PCA plot separated DLB, AD and HC groups (R2 = 0.518, Q2 = 0.348). Significant alterations in 17 detected metabolite parameters were identified (q ≤ 0.05), including neurotransmitters, amino acids and glycerophospholipids. Glutamine (Glu; q = 0.045) concentrations and indicators of sphingomyelin hydroxylation (q = 0.039) distinguished AD and DLB, and these significantly correlated with semi-quantitative measurement of cardiac sympathetic denervation. The most promising biomarker differentiating AD from DLB was Glu:lysophosphatidylcholine (lysoPC a 24:0) ratio (AUC = 0.92; 95%CI 0.809-0.996; sensitivity = 0.90; specificity = 0.90).

Discussion

Several plasma metabolomic aberrations are shared by both DLB and AD, but a rise in plasma glutamine was specific to DLB. When measured against plasma lysoPC a C24:0, glutamine could differentiate DLB from AD, and the reproducibility of this biomarker should be investigated in larger cohorts.

High-Fat Diets in Animal Models of Alzheimer's Disease: How Can Eating Too Much Fat Increase Alzheimer's Disease Risk?

High dietary intake of saturated fatty acids is a suspected risk factor for neurodegenerative diseases, including Alzheimer's disease (AD). To decipher the causal link behind these associations, high-fat diets (HFD) have been repeatedly investigated in animal models. Preclinical studies allow full control over dietary composition, avoiding ethical concerns in clinical trials. The goal of the present article is to provide a narrative review of reports on HFD in animal models of AD. Eligibility criteria included mouse models of AD fed a HFD defined as > 35% of fat/weight and western diets containing > 1% cholesterol or > 15% sugar. MEDLINE and Embase databases were searched from 1946 to August 2022, and 32 preclinical studies were included in the review. HFD-induced obesity and metabolic disturbances such as insulin resistance and glucose intolerance have been replicated in most studies, but with methodological variability. Most studies have found an aggravating effect of HFD on brain Aβ pathology, whereas tau pathology has been much less studied, and results are more equivocal. While most reports show HFD-induced impairment on cognitive behavior, confounding factors may blur their interpretation. In summary, despite conflicting results, exposing rodents to diets highly enriched in saturated fat induces not only metabolic defects, but also cognitive impairment often accompanied by aggravated neuropathological markers, most notably Aβ burden. Although there are important variations between methods, particularly the lack of diet characterization, these studies collectively suggest that excessive intake of saturated fat should be avoided in order to lower the incidence of AD.

Gut microbiota-driven metabolic alterations reveal gut-brain communication in Alzheimer's disease model mice

The gut microbiota (GM) and its metabolites affect the host nervous system and are involved in the pathogeneses of various neurological diseases. However, the specific GM alterations under pathogenetic pressure and their contributions to the "microbiota - metabolite - brain axis" in Alzheimer's disease (AD) remain unclear. Here, we investigated the GM and the fecal, serum, cortical metabolomes in APP/PS1 and wild-type (WT) mice, revealing distinct hub bacteria in AD mice within scale-free GM networks shared by both groups. Moreover, we identified diverse peripheral - central metabolic landscapes between AD and WT mice that featured bile acids (e.g. deoxycholic and isodeoxycholic acid) and unsaturated fatty acids (e.g. 11Z-eicosenoic and palmitoleic acid). Machine-learning models revealed the relationships between the differential/hub bacteria and these metabolic signatures from the periphery to the brain. Notably, AD-enriched Dubosiella affected AD occurrence via cortical palmitoleic acid and vice versa. Considering the transgenic background of the AD mice, we propose that Dubosiella enrichment impedes AD progression via the synthesis of palmitoleic acid, which has protective properties against inflammation and metabolic disorders. We identified another association involving fecal deoxycholic acid-mediated interactions between the AD hub bacteria Erysipelatoclostridium and AD occurrence, which was corroborated by the correlation between deoxycholate levels and cognitive scores in humans. Overall, this study elucidated the GM network alterations, contributions of the GM to peripheral - central metabolic landscapes, and mediatory roles of metabolites between the GM and AD occurrence, thus revealing the critical roles of bacteria in AD pathogenesis and gut - brain communications under pathogenetic pressure.

Mendelian Randomization of Blood Metabolites Suggests Circulating Glutamine Protects Against Late-Onset Alzheimer's Disease

Background

Late-onset Alzheimer's disease (LOAD) represents a growing health burden. Previous studies suggest that blood metabolite levels influence risk of LOAD.

Objective

We used a genetics-based study design which may overcome limitations of other epidemiological studies to assess the influence of metabolite levels on LOAD risk.

Methods

We applied Mendelian randomization (MR) to evaluate bi-directional causal effects using summary statistics from the largest genome-wide association studies (GWAS) of 249 blood metabolites (n = 115,082) and GWAS of LOAD (ncase = 21,982, ncontrol = 41,944).

Results

MR analysis of metabolites as exposures revealed a negative association of genetically-predicted glutamine levels with LOAD (Odds Ratio (OR) = 0.83, 95% CI = 0.73, 0.92) that was consistent in multiple sensitivity analyses. We also identified a positive association of genetically-predicted free cholesterol levels in small LDL (OR = 1.79, 95% CI = 1.36, 2.22) on LOAD. Using genetically-predicted LOAD as the exposure, we identified associations with phospholipids to total lipids ratio in large LDL (OR = 0.96, 95% CI = 0.94, 0.98), but not with glutamine, suggesting that the relationship between glutamine and LOAD is unidirectional.

Conclusions

Our findings support previous evidence that higher circulating levels of glutamine may be a target for protection against LOAD.

Metabolites and Cognitive Decline in a Puerto Rican Cohort

Background

Recent studies have identified plasma metabolites associated with cognitive decline and Alzheimer's disease; however, little research on this topic has been conducted in Latinos, especially Puerto Ricans.

Objective

This study aims to add to the growing body of metabolomics research in Latinos to better understand and improve the health of this population.

Methods

We assessed the association between plasma metabolites and global cognition over 12 years of follow-up in 736 participants of the Boston Puerto Rican Health Study (BPRHS). Metabolites were measured with untargeted metabolomic profiling (Metabolon, Inc) at baseline. We used covariable adjusted linear mixed models (LMM) with a metabolite * time interaction term to identify metabolites (of 621 measured) associated with ∼12 years cognitive trajectory.

Results

We observed strong inverse associations between medium-chain fatty acids, caproic acid, and the dicarboxylic acids, azelaic and sebacic acid, and global cognition. N-formylphenylalanine, a tyrosine pathway metabolite, was associated with improvement in cognitive trajectory.

Conclusions

The metabolites identified in this study are generally consistent with prior literature and highlight a role medium chain fatty acid and tyrosine metabolism in cognitive decline.

Circulating metabolites and risk of incident dementia: A prospective cohort study

Identifying circulating metabolites associated with dementia, cognition, and brain volume may improve the understanding of dementia pathogenesis and provide novel insights for preventive and therapeutic interventions. This cohort study included a total of 87 885 participants (median follow-up of 9.1 years, 54% female) without dementia at baseline from the UK Biobank. A total of 249 plasma metabolites were measured using nuclear magnetic resonance spectroscopy at baseline. Cox proportional regression was used to examine the associations of each metabolite with incident dementia (cases = 1134), Alzheimer's disease (AD; cases = 488), and vascular dementia (VD; cases = 257) during follow-up. Dementia-associated metabolites were further analyzed for association with cognitive deficits (N = 87 885) and brain volume (N = 7756) using logistic regression and linear regression. We identified 26 metabolites associated with incident dementia, of which 6 were associated with incident AD and 5 were associated with incident VD. These 26 dementia-related metabolites were subfractions of intermediate-density lipoprotein, large low-density lipoprotein (L-LDL), small high-density lipoprotein (S-HDL), very-low-density lipoprotein, fatty acids, ketone bodies, citrate, glucose, and valine. Among them, the cholesterol percentage in L-LDL (L-LDL-C%) was associated with lower risk of AD (HR [95% CI] = 0.92 [0.87-0.97], p = 0.002), higher brain cortical (β = 0.047, p = 3.91 × 10-6 ), and hippocampal (β = 0.043, p = 1.93 × 10-4 ) volume. Cholesteryl ester-to-total lipid ratio in L-LDL (L-LDL-CE%) was associated with lower risk of AD (HR [95% CI] = 0.93 [0.90-0.96], p = 1.48 × 10-4 ), cognitive deficits (odds ratio = 0.98, p = 0.009), and higher hippocampal volume (β = 0.027, p = 0.009). Cholesteryl esters in S-HDL (S-HDL-CE) were associated with lower risk of VD (HR [95% CI] = 0.81 [0.71-0.93], p = 0.002), but not AD. Taken together, circulating levels of L-LDL-CE% and L-LDL-C% were robustly associated with risk of AD and AD phenotypes, but not with VD. S-HDL-CE was associated with lower risk of VD, but not with AD or AD phenotypes. These metabolites may play a role in the advancement of future intervention trials. Additional research is necessary to gain a complete comprehension of the molecular mechanisms behind these associations.

Cerebrospinal and Blood Biomarkers in Alzheimer's Disease: Did Mild Cognitive Impairment Definition Affect Their Clinical Usefulness?

Despite Alzheimer's Disease (AD) being known from the times of Alois Alzheimer, who lived more than one century ago, many aspects of the disease are still obscure, including the pathogenesis, the clinical spectrum definition, and the therapeutic approach. Well-established biomarkers for AD come from the histopathological hallmarks of the disease, which are Aβ and phosphorylated Tau protein aggregates. Consistently, cerebrospinal fluid (CSF) Amyloid β (Aβ) and phosphorylated Tau level measurements are currently used to detect AD presence. However, two central biases affect these biomarkers. Firstly, incomplete knowledge of the pathogenesis of diseases legitimates the search for novel molecules that, reasonably, could be expressed by neurons and microglia and could be detected in blood simpler and earlier than the classical markers and in a higher amount. Further, studies have been performed to evaluate whether CSF biomarkers can predict AD onset in Mild Cognitive Impairment (MCI) patients. However, the MCI definition has changed over time. Hence, the studies on MCI patients seem to be biased at the beginning due to the imprecise enrollment and heterogeneous composition of the miscellaneous MCI subgroup. Plasma biomarkers and novel candidate molecules, such as microglia biomarkers, have been tentatively investigated and could represent valuable targets for diagnosing and monitoring AD. Also, novel AD markers are urgently needed to identify molecular targets for treatment strategies. This review article summarizes the main CSF and blood AD biomarkers, underpins their advantages and flaws, and mentions novel molecules that can be used as potential biomarkers for AD.

Assessing the Biological Mechanisms Linking Smoking Behavior and Cognitive Function: A Mediation Analysis of Untargeted Metabolomics

(1) Smoking is the most significant preventable health hazard in the modern world. It increases the risk of vascular problems, which are also risk factors for dementia. In addition, toxins in cigarettes increase oxidative stress and inflammation, which have both been linked to the development of Alzheimer's disease and related dementias (ADRD). This study identified potential mechanisms of the smoking-cognitive function relationship using metabolomics data from the longitudinal Wisconsin Registry for Alzheimer's Prevention (WRAP). (2) 1266 WRAP participants were included to assess the association between smoking status and four cognitive composite scores. Next, untargeted metabolomic data were used to assess the relationships between smoking and metabolites. Metabolites significantly associated with smoking were then tested for association with cognitive composite scores. Total effect models and mediation models were used to explore the role of metabolites in smoking-cognitive function pathways. (3) Plasma N-acetylneuraminate was associated with smoking status Preclinical Alzheimer Cognitive Composite 3 (PACC3) and Immediate Learning (IMM). N-acetylneuraminate mediated 12% of the smoking-PACC3 relationship and 13% of the smoking-IMM relationship. (4) These findings provide links between previous studies that can enhance our understanding of potential biological pathways between smoking and cognitive function.

Development of a diagnostic and risk prediction model for Alzheimer's disease through integration of single-cell and bulk transcriptomic analysis of glutamine metabolism

Background

In this study, we present a novel system for quantifying glutamine metabolism (GM) to enhance the effectiveness of Alzheimer's disease (AD) diagnosis and risk prediction.

Methods

Single-cell RNA sequencing (scRNA-seq) analysis was utilized to comprehensively assess the expression patterns of GM. The WGCNA algorithm was applied to investigate the most significant genes related to GM. Subsequently, three machine learning algorithms (Boruta, LASSO, and SVM-RFE) were employed to identify GM-associated characteristic genes and develop a risk model. Patients were divided into high- and low-risk groups based on this model. Moreover, we explored biological properties, distinct signaling pathways, and immunological characteristics of AD patients at different risk levels. Finally, in vitro and in vivo models of AD were constructed to validate the characteristics of the feature genes.

Results

Both scRNA-seq and bulk transcriptomic analyses revealed increased GM activity in AD patients, specifically in certain cell subsets (pDC, Tem/Effector helper T cells (LTB), and plasma cells). Cells with higher GM scores demonstrated more significant numbers and strengths of interactions with other cell types. The WGCNA algorithm identified 360 genes related to GM, and a risk score was constructed based on nine characteristic genes (ATP13A4, PIK3C2A, CD164, PHF1, CES2, PDGFB, LCOR, TMEM30A, and PLXNA1) identified through multiple machine learning algorithms displayed reliable diagnostic efficacy for AD onset. Nomograms, calibration curves, and decision curve analysis (DCA) based on these characteristic genes provided significant clinical benefits for AD patients. High-risk AD patients exhibited higher levels of immune-related functions and pathways, increased immune cell infiltration, and elevated expressions of immune modulators. RT-qPCR analysis revealed that the majority of the nine characteristic genes were differentially expressed in AD-induced rat neurons. Knocking down PHF1 could protect against neurite loss and alleviate cell injury in AD neurons. In vivo, down-regulation of PHF1 in AD models decreases GM metabolism levels and modulates the immunoinflammatory response in the brain.

Conclusion

This comprehensive identification of gene expression patterns contributes to a deeper understanding of the underlying pathological mechanisms driving AD pathogenesis. Furthermore, the risk model based on the nine-gene signature offers a promising theoretical foundation for developing individualized treatments for AD patients.

Alteration of Metabolic Profiles during the Progression of Alzheimer's Disease

(1) Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder that threatens the population health of older adults. However, the mechanisms of the altered metabolism involved in AD pathology are poorly understood. The aim of the study was to identify the potential biomarkers of AD and discover the metabolomic changes produced during the progression of the disease. (2) Methods: Gas chromatography-mass spectrometry (GC-MS) was used to measure the concentrations of the serum metabolites in a cohort of subjects with AD (n = 88) and a cognitively normal control (CN) group (n = 85). The patients were classified as very mild (n = 25), mild (n = 27), moderate (n = 25), and severe (n = 11). The serum metabolic profiles were analyzed using multivariate and univariate approaches. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify the potential biomarkers of AD. Biofunctional enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes. (3) Results: Our results revealed considerable separation between the AD and CN groups. Six metabolites were identified as potential biomarkers of AD (AUC > 0.85), and the diagnostic model of three metabolites could predict the risk of AD with high accuracy (AUC = 0.984). The metabolic enrichment analysis revealed that carbohydrate metabolism deficiency and the disturbance of amino acid, fatty acid, and lipid metabolism were involved in AD progression. Especially, the pathway analysis highlighted that l-glutamate participated in four crucial nervous system pathways (including the GABAergic synapse, the glutamatergic synapse, retrograde endocannabinoid signaling, and the synaptic vesicle cycle). (4) Conclusions: Carbohydrate metabolism deficiency and amino acid dysregulation, fatty acid, and lipid metabolism disorders were pivotal events in AD progression. Our study may provide novel insights into the role of metabolic disorders in AD pathogenesis and identify new markers for AD diagnosis.

Association of Baseline Metabolomic Profiles With Incident Stroke and Dementia and With Imaging Markers of Cerebral Small Vessel Disease

BACKGROUND AND OBJECTIVES

Cerebral small vessel disease is a major cause of stroke and dementia. Metabolomics can help identify novel risk factors to better understand pathogenesis and predict disease progression and severity.

METHODS

We analyzed baseline metabolomic profiles from 118,021 UK Biobank participants. We examined cross-sectional associations of 325 metabolites with MRI markers of small vessel disease, evaluated longitudinal associations with incident stroke and dementia, and ascertained causal relationships using Mendelian randomization.

RESULTS

In cross-sectional analyses, lower levels of apolipoproteins, free cholesterol, cholesteryl esters, fatty acids, lipoprotein particle concentrations, phospholipids, and triglycerides were associated with increased white matter microstructural damage on diffusion tensor MRI. In longitudinal analyses, lipoprotein subclasses of very large high-density lipoprotein cholesterol (HDL) were associated with an increased risk of stroke, and acetate and 3-hydroxybutyrate were associated with an increased risk of dementia. Mendelian randomization analyses identified strong evidence supporting causal relationships for many findings. A few metabolites had consistent associations across multiple analysis types. Increased total lipids in very large HDL and increased HDL particle size were associated with increased white matter damage (lower fractional anisotropy: OR: 1.44, 95% CI 1.07-1.95, and OR: 1.19, 95% CI 1.06-1.34, respectively; mean diffusivity: OR: 1.49, 95% CI 1.11-2.01, and OR: 1.24, 95% CI 1.11-1.40, respectively) and an increased risk of incident all stroke (HR: 4.04, 95% CI 2.13-7.64, and HR: 1.54, 95% CI 1.20-1.98, respectively) and ischemic stroke (HR: 3.12, 95% CI 1.53-6.38; HR: 1.37, 95% CI 1.04-1.81). Valine was associated with decreased mean diffusivity (OR: 0.51, 95% CI 0.30-0.88) and had a protective association with all-cause dementia (HR: 0.008, 95% CI 0.002-0.035). Increased levels of cholesterol in small HDL were associated with a decreased risk of incident all stroke (HR: 0.17, 95% CI 0.08-0.39) and ischemic stroke (HR: 0.19, 95% CI 0.08-0.46) and were supported by evidence of a causal association with MRI-confirmed lacunar stroke (OR: 0.96, 95% CI 0.93-0.99).

DISCUSSION

In this large-scale metabolomics study, we found multiple metabolites associated with stroke, dementia, and MRI markers of small vessel disease. Further studies may help inform the development of personalized prediction models and provide insights into mechanistic pathways and future treatment approaches.

Association between inflammation and cognition: Triangulation of evidence using a population-based cohort and Mendelian randomization analyses

BACKGROUND

Inflammation is associated with cognitive functioning and dementia in older adults, but whether inflammation is related to cognitive functioning in youth and whether these associations are causal remains unclear.

METHODS

In a population-based cohort (Avon Longitudinal Study of Parents and Children; ALSPAC), we investigated cross-sectional associations of inflammatory markers (C-reactive protein [CRP], Interleukin-6 [IL-6] and Glycoprotein acetyls [GlycA]) with measures of cold (working memory, response inhibition) and hot (emotion recognition) cognition at age 24 (N = 3,305 in multiple imputation models). Furthermore, we conducted one-sample and two-sample bidirectional Mendelian randomization (MR) analyses to examine potential causal effects of genetically-proxied inflammatory markers (CRP, GlycA, IL-6, IL-6 receptor, soluble IL-6 receptor) on cognitive measures (above) and on general cognitive ability.

RESULTS

In the ALSPAC cohort, there was limited evidence of an association between standardised inflammatory markers and standardised cognitive measures at age 24 after adjusting for potential confounders (N = 3,305; beta range, -0.02 [95 % confidence interval (CI) -0.06 to 0.02, p = 0.27] to 0.02 [95 % CI -0.02 to 0.05, p = 0.33]). Similarly, we found limited evidence of potential effects of 1-unit increase in genetically-proxied inflammatory markers on standardised working memory, emotion recognition or response inhibition in one-sample MR using ALSPAC data (beta range, -0.73 [95 % CI -2.47 to 1.01, p = 0.41] to 0.21 [95 % CI -1.42 to 1.84, p = 0.80]; or on standardised general cognitive ability in two-sample MR using the latest Genome-Wide Association Study (GWAS) datasets (inverse-variance weighted beta range, -0.02 [95 % CI -0.05 to 0.01, p = 0.12] to 0.03 [95 % CI -0.01 to 0.07, p = 0.19]).

CONCLUSIONS

Our MR findings do not provide strong evidence of a potential causal effect of inflammatory markers (CRP, IL-6, IL-6 receptor, GlycA) on the cognitive functions examined here. Given the large confidence intervals in the one-sample MR, larger GWAS of specific cognitive measures are needed to enable well-powered MR analyses to investigate whether inflammation causally influences specific cognitive domains.

Circulating polyunsaturated fatty acids, fish oil supplementation, and risk of incident dementia: a prospective cohort study of 440,750 participants

Cohort studies report inconsistent associations between omega-3 polyunsaturated fatty acids (n-3 PUFA) or fish oil and dementia risk. Furthermore, evidence relating omega-6 polyunsaturated fatty acids (n-6 PUFA) with dementia is scarce. Here, we included 440,750 dementia-free participants from UK Biobank to comprehensively investigate the associations between plasma levels of different types of PUFA, fish oil supplementation, and dementia risk. During a median follow-up of 9.25 years, 7768 incident dementia events occurred. Higher plasma levels of five PUFA measures showed consistent associations with lower dementia risk (hazard ratios [95% confidence intervals] for per standard deviation increment of plasma concentrations 0.85 [0.81-0.89] for total PUFAs; 0.90 [0.86-0.95] for omega-3 PUFAs; 0.92 [0.87-0.96] for docosahexaenoic acid (DHA); 0.86 [0.82-0.90] for omega-6 PUFAs; 0.86 [0.82-0.90] for linoleic acid (LA); all p < 0.001). Compared with non-users, fish oil supplement users had a 7% decreased risk of developing all-cause dementia (0.93 [0.89-0.97], p = 0.002), and the relationship was partially mediated by plasma n-3 PUFA levels (omega-3 PUFAs: proportion of mediation = 57.99%; DHA: proportion of mediation = 56.95%). Furthermore, we observed significant associations of plasma n-3 PUFA levels and fish oil supplementation with peripheral immune markers that were related to dementia risk, as well as the positive associations of plasma PUFA levels with brain gray matter volumes and white matter microstructural integrity, suggesting they may affect dementia risk by affecting peripheral immunity and brain structure. Taken together, higher plasma PUFA levels and fish oil supplementation were associated with lower risk of incident dementia. This study may support the value of interventions to target PUFAs (specifically n-3 PUFAs) to prevent dementia.

Plasma metabolites associated with cognitive function across race/ethnicities affirming the importance of healthy nutrition

INTRODUCTION

We studied the replication and generalization of previously identified metabolites potentially associated with global cognitive function in multiple race/ethnicities and assessed the contribution of diet to these associations.

METHODS

We tested metabolite-cognitive function associations in U.S.A. Hispanic/Latino adults (n = 2222) from the Community Health Study/ Study of Latinos (HCHS/SOL) and in European (n = 1365) and African (n = 478) Americans from the Atherosclerosis Risk In Communities (ARIC) Study. We applied Mendelian Randomization (MR) analyses to assess causal associations between the metabolites and cognitive function and between Mediterranean diet and cognitive function.

RESULTS

Six metabolites were consistently associated with lower global cognitive function across all studies. Of these, four were sugar-related (e.g., ribitol). MR analyses provided weak evidence for a potential causal effect of ribitol on cognitive function and bi-directional effects of cognitive performance on diet.

DISCUSSION

Several diet-related metabolites were associated with global cognitive function across studies with different race/ethnicities.

HIGHLIGHTS

Metabolites associated with cognitive function in Puerto Rican adults were recently identified. We demonstrate the generalizability of these associations across diverse race/ethnicities. Most identified metabolites are related to sugars. Mendelian Randomization (MR) provides weak evidence for a causal effect of ribitol on cognitive function. Beta-cryptoxanthin and other metabolites highlight the importance of a healthy diet.

Glial Glutamine Homeostasis in Health and Disease

Glutamine is an essential cerebral metabolite. Several critical brain processes are directly linked to glutamine, including ammonia homeostasis, energy metabolism and neurotransmitter recycling. Astrocytes synthesize and release large quantities of glutamine, which is taken up by neurons to replenish the glutamate and GABA neurotransmitter pools. Astrocyte glutamine hereby sustains the glutamate/GABA-glutamine cycle, synaptic transmission and general brain function. Cerebral glutamine homeostasis is linked to the metabolic coupling of neurons and astrocytes, and relies on multiple cellular processes, including TCA cycle function, synaptic transmission and neurotransmitter uptake. Dysregulations of processes related to glutamine homeostasis are associated with several neurological diseases and may mediate excitotoxicity and neurodegeneration. In particular, diminished astrocyte glutamine synthesis is a common neuropathological component, depriving neurons of an essential metabolic substrate and precursor for neurotransmitter synthesis, hereby leading to synaptic dysfunction. While astrocyte glutamine synthesis is quantitatively dominant in the brain, oligodendrocyte-derived glutamine may serve important functions in white matter structures. In this review, the crucial roles of glial glutamine homeostasis in the healthy and diseased brain are discussed. First, we provide an overview of cellular recycling, transport, synthesis and metabolism of glutamine in the brain. These cellular aspects are subsequently discussed in relation to pathological glutamine homeostasis of hepatic encephalopathy, epilepsy, Alzheimer's disease, Huntington's disease and amyotrophic lateral sclerosis. Further studies on the multifaceted roles of cerebral glutamine will not only increase our understanding of the metabolic collaboration between brain cells, but may also aid to reveal much needed therapeutic targets of several neurological pathologies.

Metabolomic analysis of vascular cognitive impairment due to hepatocellular carcinoma

Introduction

Screening for metabolically relevant differentially expressed genes (DEGs) shared by hepatocellular carcinoma (HCC) and vascular cognitive impairment (VCI) to explore the possible mechanisms of HCC-induced VCI.

Methods

Based on metabolomic and gene expression data for HCC and VCI, 14 genes were identified as being associated with changes in HCC metabolites, and 71 genes were associated with changes in VCI metabolites. Multi-omics analysis was used to screen 360 DEGs associated with HCC metabolism and 63 DEGs associated with VCI metabolism.

Results

According to the Cancer Genome Atlas (TCGA) database, 882 HCC-associated DEGs were identified and 343 VCI-associated DEGs were identified. Eight genes were found at the intersection of these two gene sets: NNMT, PHGDH, NR1I2, CYP2J2, PON1, APOC2, CCL2, and SOCS3. The HCC metabolomics prognostic model was constructed and proved to have a good prognostic effect. The HCC metabolomics prognostic model was constructed and proved to have a good prognostic effect. Following principal component analyses (PCA), functional enrichment analyses, immune function analyses, and TMB analyses, these eight DEGs were identified as possibly affecting HCC-induced VCI and the immune microenvironment. As well as gene expression and gene set enrichment analyses (GSEA), a potential drug screen was conducted to investigate the possible mechanisms involved in HCC-induced VCI. The drug screening revealed the potential clinical efficacy of A-443654, A-770041, AP-24534, BI-2536, BMS- 509744, CGP-60474, and CGP-082996.

Conclusion

HCC-associated metabolic DEGs may influence the development of VCI in HCC patients.

Association Between Triglycerides and Incident Cognitive Impairment in Black and White Adults in the Reasons for Geographic and Racial Differences in Stroke Study

Background Elevated nonfasting triglycerides were associated with non-Alzheimer dementia in a recent study. However, this study neither evaluated the association of fasting triglycerides with incident cognitive impairment (ICI) nor adjusted for high-density lipoprotein cholesterol or hs-CRP (high-sensitivity C-reactive protein), known risk markers for ICI and dementia. Methods and Results We examined the association between fasting triglycerides and ICI among 16 170 participants in the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study without cognitive impairment or a history of stroke at baseline in 2003 to 2007 and who had no stroke events during follow-up through September 2018. Overall, 1151 participants developed ICI during the median follow-up of 9.6 years. The relative risk for ICI associated with fasting triglycerides of ≥150 mg/dL versus <100 mg/dL including adjustment for age and geographic region of residence was 1.59 (95% CI, 1.20-2.11) among White women and 1.27 (95% CI, 1.00-1.62) among Black women. After multivariable adjustment, including adjustment for high-density lipoprotein cholesterol and hs-CRP, the relative risk for ICI associated with fasting triglycerides ≥150 mg/dL versus <100 mg/dL was 1.50 (95% CI, 1.09-2.06) among White women and 1.21 (95% CI, 0.93-1.57) among Black women. There was no evidence of an association between triglycerides and ICI among White or Black men. Conclusions Elevated fasting triglycerides were associated with ICI in White women after full adjustment including high-density lipoprotein cholesterol and hs-CRP. The current results suggest that the association between triglycerides and ICI is stronger in women than men.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

Ferulic acid alleviates high fat diet-induced cognitive impairment by inhibiting oxidative stress and apoptosis

Cognitive impairment has become a major public health problem. Growing evidence suggests that high-fat diet (HFD) can cause cognitive dysfunction and increase the risk of dementia. However, effective treatment for cognitive impairment is not available. Ferulic acid (FA) is a single phenolic compound with anti-inflammatory and antioxidant properties. Nevertheless, its role in regulating learning and memory in HFD-fed mice and the underlying mechanism remains unclear. In this study, we aimed to identify the neuroprotective mechanisms of FA in HFD induced cognitive impairment. We found that FA improved the survival rate of HT22 cells treated with palmitic acid (PA), inhibited cell apoptosis, and reduced oxidative stress via the IRS1/PI3K/AKT/GSK3β signaling pathway; Furthermore, FA treatment for 24 weeks improved the learning and memory of HFD-fed mice and decreased hyperlipidemia. Moreover, the expression of Nrf2 and Gpx4 proteins were decreased in HFD-fed mice. After FA treatment, the decline of these proteins was reversed. Our study showed that the neuroprotective effect of FA on cognitive impairment was related to the inhibition of oxidative stress and apoptosis and regulation of glucose and lipid metabolism. These findings suggested that FA can be developed as a potential agent for the treatment of HFD-induced cognitive impairment.

Investigating the plasma-liver-brain axis of omega-3 fatty acid metabolism in mouse knock-in for the human apolipoprotein E epsilon 4 allele

The metabolism of docosahexaenoic acid (DHA), an omega-3 fatty acid, is different in carriers of APOE4, the main genetic risk factor for late-onset Alzheimer's disease. The brain relies on the plasma DHA pool for its need, but the plasma-liver-brain axis in relation to cognition remains obscure. We hypothesized that this relationship is compromised in APOE4 mice considering the differences in fatty acid metabolism between APOE3 and APOE4 mice. Male and female APOE3 and APOE4 mice were fed either a diet enriched with DHA (0.7 g DHA/100 g diet) or a control diet for 8 months. There was a significant genotype × diet interaction for DHA concentration in the liver and adipose tissue. In the cortex, a genotype effect was found where APOE4 mice had a higher concentration of DHA than APOE3 mice fed the control diet. There was a significant genotype × diet interaction for the liver and hippocampal arachidonic acid (AA). APOE4 mice had 20-30% lower plasma DHA and AA concentrations than APOE3 mice, independent of diet. Plasma and liver DHA levels were significantly correlated in APOE3 and APOE4 mice. In APOE4 mice, there was a significant correlation between plasma, adipose tissues, cortex DHA and the Barnes maze and/or with a better recognition index. Moreover, higher AA levels in the liver and the hippocampus of APOE4 mice were correlated with lower cognitive performance. Our results suggest that there is a plasma-liver-brain axis of DHA that is modified in APOE4 mice. Moreover, our data support that APOE4 mice rely more on plasma DHA than APOE3 mice, especially in cognitive performance. Any disturbance in plasma DHA metabolism might have a greater impact on cognition in APOE4 carriers.

Does ethnicity influence dementia, stroke and mortality risk? Evidence from the UK Biobank

Introduction

The number of people with dementia and stroke is increasing worldwide. There is increasing evidence that there are clinically relevant genetic differences across ethnicities. This study aims to quantify risk factors of dementia, stroke, and mortality in Asian and black participants compared to whites.

Methods

272,660 participants from the UK Biobank were included in the final analysis, among whom the vast majority are white (n = 266,671, 97.80%), followed by Asian (n = 3,790, 1.35%), and black (n = 2,358, 0.84%) participants. Cumulative incidence risk was calculated based on all incident cases occurring during the follow-up of the individuals without dementia and stroke at baseline. We compared the allele frequency of variants in Asian and black participants with the referent ethnicity, whites, by chi-square test. Hierarchical cluster analysis was used in the clustering analysis. Significance level corrected for the false discovery rate was considered.

Results

After adjusting for risk factors, black participants have an increased risk of dementia and stroke compared to white participants, while Asians has similar odds to the white. The risk of mortality is not different in blacks and white participants but Asians have a decreased risk.

Discussion

The study provides important insights into the potential differences in the risk of dementia and stroke among different ethnic groups. Specifically, the study found that black individuals had a higher incidence of dementia and stroke compared to white individuals living in the UK. These findings are particularly significant as they suggest that there may be underlying factors that contribute to these differences, including genetic, environmental, and social factors. By identifying these differences, the study helps to inform interventions and policies aimed at reducing the risk of dementia and stroke, particularly among high-risk populations.

Association of Red Blood Cell Omega-3 Fatty Acids With MRI Markers and Cognitive Function in Midlife: The Framingham Heart Study

BACKGROUND AND OBJECTIVES

Diet may be a key contributor to brain health in midlife. In particular, omega-3 fatty acids have been related to better neurologic outcomes in older adults. However, studies focusing on midlife are lacking. We investigated the cross-sectional association of red blood cell (RBC) omega-3 fatty acid concentrations with MRI and cognitive markers of brain aging in a community-based sample of predominantly middle-aged adults and further explore effect modification by APOE genotype.

METHODS

We included participants from the Third-Generation and Omni 2 cohorts of the Framingham Heart Study attending their second examination. Docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) concentrations were measured from RBC using gas chromatography, and the Omega-3 index was calculated as EPA + DHA. We used linear regression models to relate omega-3 fatty acid concentrations to brain MRI measures (i.e., total brain, total gray matter, hippocampal, and white matter hyperintensity volumes) and cognitive function (i.e., episodic memory, processing speed, executive function, and abstract reasoning) adjusting for potential confounders. We further tested for interactions between omega-3 fatty acid levels and APOE genotype (e4 carrier vs noncarrier) on MRI and cognitive outcomes.

RESULTS

We included 2,183 dementia-free and stroke-free participants (mean age of 46 years, 53% women, 22% APOE-e4 carriers). In multivariable models, higher Omega-3 index was associated with larger hippocampal volumes (standard deviation unit beta ±standard error; 0.003 ± 0.001, p = 0.013) and better abstract reasoning (0.17 ± 0.07, p = 0.013). Similar results were obtained for DHA or EPA concentrations individually. Stratification by APOE-e4 status showed associations between higher DHA concentrations or Omega-3 index and larger hippocampal volumes in APOE-e4 noncarriers, whereas higher EPA concentrations were related to better abstract reasoning in APOE-e4 carriers. Finally, higher levels of all omega-3 predictors were related to lower white matter hyperintensity burden but only in APOE-e4 carriers.

DISCUSSION

Our results, albeit exploratory, suggest that higher omega-3 fatty acid concentrations are related to better brain structure and cognitive function in a predominantly middle-aged cohort free of clinical dementia. These associations differed by APOE genotype, suggesting potentially different metabolic patterns by APOE status. Additional studies in middle-aged populations are warranted to confirm these findings.

Metabolomic profiles predict individual multidisease outcomes

Risk stratification is critical for the early identification of high-risk individuals and disease prevention. Here we explored the potential of nuclear magnetic resonance (NMR) spectroscopy-derived metabolomic profiles to inform on multidisease risk beyond conventional clinical predictors for the onset of 24 common conditions, including metabolic, vascular, respiratory, musculoskeletal and neurological diseases and cancers. Specifically, we trained a neural network to learn disease-specific metabolomic states from 168 circulating metabolic markers measured in 117,981 participants with ~1.4 million person-years of follow-up from the UK Biobank and validated the model in four independent cohorts. We found metabolomic states to be associated with incident event rates in all the investigated conditions, except breast cancer. For 10-year outcome prediction for 15 endpoints, with and without established metabolic contribution, a combination of age and sex and the metabolomic state equaled or outperformed established predictors. Moreover, metabolomic state added predictive information over comprehensive clinical variables for eight common diseases, including type 2 diabetes, dementia and heart failure. Decision curve analyses showed that predictive improvements translated into clinical utility for a wide range of potential decision thresholds. Taken together, our study demonstrates both the potential and limitations of NMR-derived metabolomic profiles as a multidisease assay to inform on the risk of many common diseases simultaneously.

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study

BACKGROUND

Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach and determined whether the selected metabolites improved prediction accuracy beyond the effect of age.

METHODS

Five thousand three hundred seventy-four participants from the Whitehall II study, mean age 55.8 (standard deviation (SD) 6.0) years in 1997-1999 when 233 metabolites were quantified using nuclear magnetic resonance metabolomics. Participants were followed for a median 21.0 (IQR 20.4, 21.7) years for clinically-diagnosed dementia (N=329). Elastic net penalized Cox regression with 100 repetitions of nested cross-validation was used to select models that improved prediction accuracy for incident dementia compared to an age-only model. Risk scores reflecting the frequency with which predictors appeared in the selected models were constructed, and their predictive accuracy was examined using Royston's R², Akaike's information criterion, sensitivity, specificity, C-statistic and calibration.

RESULTS

Sixteen of the 100 models had a better c-statistic compared to an age-only model and 15 metabolites were selected at least once in all 16 models with glucose present in all models. Five risk scores, reflecting the frequency of selection of metabolites, and a 1-SD increment in all five risk scores was associated with higher dementia risk (HR between 3.13 and 3.26). Three of these, constituted of 4, 5 and 15 metabolites, had better prediction accuracy (c-statistic from 0.788 to 0.796) compared to an age-only model (c-statistic 0.780), all p<0.05.

CONCLUSIONS

Although there was robust evidence for the role of glucose in dementia, metabolites measured in midlife made only a modest contribution to dementia prediction once age was taken into account.

Metabolomic alterations in the blood plasma of older adults with mild cognitive impairment and Alzheimer's disease (from the Nakayama Study)

Alzheimer's disease (AD) is a progressive disease, and the number of AD patients is increasing every year as the population ages. One of the pathophysiological mechanisms of AD is thought to be the effect of metabolomic abnormalities. There have been several studies of metabolomic abnormalities of AD, and new biomarkers are being investigated. Metabolomic studies have been attracting attention, and the aim of this study was to identify metabolomic biomarkers associated with AD and mild cognitive impairment (MCI). Of the 927 participants in the Nakayama Study conducted in Iyo City, Ehime Prefecture, 106 were selected for this study as Control (n = 40), MCI (n = 26), and AD (n = 40) groups, matched by age and sex. Metabolomic comparisons were made across the three groups. Then, correlations between metabolites and clinical symptoms were examined. The blood mRNA levels of the ornithine metabolic enzymes were also measured. Of the plasma metabolites, significant differences were found in ornithine, uracil, and lysine. Ornithine was significantly decreased in the AD group compared to the Control and MCI groups (Control vs. AD: 97.2 vs. 77.4; P = 0.01, MCI vs. AD: 92.5 vs. 77.4; P = 0.02). Uracil and lysine were also significantly decreased in the AD group compared to the Control group (uracil, Control vs. AD: 272 vs. 235; P = 0.04, lysine, Control vs. AD: 208 vs. 176; P = 0.03). In the total sample, the MMSE score was significantly correlated with lysine, ornithine, thymine, and uracil. The Barthel index score was significantly correlated with lysine. The instrumental activities of daily living (IADL) score were significantly correlated with lysine, betaine, creatine, and thymine. In the ornithine metabolism pathway, the spermine synthase mRNA level was significantly decreased in AD. Ornithine was decreased, and mRNA expressions related to its metabolism were changed in the AD group compared to the Control and MCI groups, suggesting an association between abnormal ornithine metabolism and AD. Increased betaine and decreased methionine may also have the potential to serve as markers of higher IADL in elderly persons. Plasma metabolites may be useful for predicting the progression of AD.

Plasma metabolomics and lipidomics signatures of motoric cognitive risk syndrome in community-dwelling older adults

Introduction

Motoric cognitive risk syndrome (MCR) is characterized by subjective cognitive complaints (SCCs) and slow gait (SG). Metabolomics and lipidomics may potentiate disclosure of the underlying mechanisms of MCR.

Methods

This was a cross-sectional study from the West China Health and Aging Trend cohort study (WCHAT). The operational definition of MCR is the presence of SCCs and SG without dementia or mobility disability. The test and analysis were based on untargeted metabolomics and lipidomics, consensus clustering, lasso regression and 10-fold cross-validation.

Results

This study enrolled 6,031 individuals for clinical analysis and 577 plasma samples for omics analysis. The overall prevalence of MCR was 9.7%, and the prevalence of MCR-only, assessed cognitive impairment-only (CI-only) and MCR-CI were 7.5, 13.3, and 2.1%, respectively. By consensus clustering analysis, MCR-only was clustered into three metabolic subtypes, MCR-I, MCR-II and MCR-III. Clinically, body fat mass (OR = 0.89, CI = 0.82–0.96) was negatively correlated with MCR-I, and comorbidity (OR = 2.19, CI = 1.10–4.38) was positively correlated with MCR-III. Diabetes mellitus had the highest ORs above 1 in MCR-II and MCR-III (OR = 3.18, CI = 1.02–9.91; OR = 2.83, CI = 1.33–6.04, respectively). The risk metabolites of MCR-III showed relatively high similarity with those of cognitive impairment. Notably, L-proline, L-cystine, ADMA, and N1-acetylspermidine were significantly changed in MCR-only, and PC(40:3), SM(32:1), TG(51:3), eicosanoic acid(20:1), methyl-D-galactoside and TG(50:3) contributed most to the prediction model for MCR-III.

Interpretation

Pre-dementia syndrome of MCR has distinct metabolic subtypes, and SCCs and SG may cause different metabolic changes to develop MCR.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Nutrition state of science and dementia prevention: recommendations of the Nutrition for Dementia Prevention Working Group

Observational studies suggest that nutritional factors have a potential cognitive benefit. However, systematic reviews of randomised trials of dietary and nutritional supplements have reported largely null effects on cognitive outcomes and have highlighted study inconsistencies and other limitations. In this Personal View, the Nutrition for Dementia Prevention Working Group presents what we consider to be limitations in the existing nutrition clinical trials for dementia prevention. On the basis of this evidence, we propose recommendations for incorporating dietary patterns and the use of genetic, and nutrition assessment tools, biomarkers, and novel clinical trial designs to guide future trial developments. Nutrition-based research has unique challenges that could require testing both more personalised interventions in targeted risk subgroups, identified by nutritional and other biomarkers, and large-scale and pragmatic study designs for more generalisable public health interventions across diverse populations.

Red Blood Cell DHA Is Inversely Associated with Risk of Incident Alzheimer's Disease and All-Cause Dementia: Framingham Offspring Study

Docosahexaenoic acid (DHA) might help prevent Alzheimer's disease (AD). Red blood cell (RBC) status of DHA is an objective measure of long-term dietary DHA intake. In this prospective observational study conducted within the Framingham Offspring Cohort (1490 dementia-free participants aged ≥65 years old), we examined the association of RBC DHA with incident AD, testing for an interaction with APOE-ε4 carriership. During the follow-up (median, 7.2 years), 131 cases of AD were documented. In fully adjusted models, risk for incident AD in the highest RBC DHA quintile (Q5) was 49% lower compared with the lowest quintile (Q1) (Hazard ratio [HR]: 0.51, 95% confidence interval [CI]: 0.27, 0.96). An increase in RBC DHA from Q1 to Q5 was predicted to provide an estimated 4.7 additional years of life free of AD. We observed an interaction DHA × APOE-ε4 carriership for AD. Borderline statistical significance for a lower risk of AD was observed per standard deviation increase in RBC DHA (HR: 0.71, 95% CI: 0.51, 1.00, p = 0.053) in APOE-ε4 carriers, but not in non-carriers (HR: 0.85, 95% CI: 0.65, 1.11, p = 0.240). These findings add to the increasing body of literature suggesting a robust association worth exploring dietary DHA as one strategy to prevent or delay AD.

Healthcare services relaxing natural selection may contribute to increase of dementia incidence

Ageing and genetic traits can only explain the increasing dementia incidence partially. Advanced healthcare services allow dementia patients to survive natural selection and pass their genes onto the next generation. Country-specific estimates of dementia incidence rates (all ages and 15-49 years old), Biological State Index expressing reduced natural selection (I_s), ageing indexed by life expectancy e₍₆₅₎, GDP PPP and urbanization were obtained for analysing the global and regional correlations between reduced natural selection and dementia incidence with SPSS v. 27. Worldwide, I_s significantly, but inversely, correlates with dementia incidence rates for both all ages and 15-49 years old in bivariate correlations. These relationships remain inversely correlated regardless of the competing contributing effects from ageing, GDP and urbanization in partial correlation model. Results of multiple linear regression (enter) have shown that I_s is the significant predictor of dementia incidence among all ages and 15-49 years old. Subsequently, I_s was selected as the variable having the greatest influence on dementia incidence in stepwise multiple linear regression. The I_s correlated with dementia incidence more strongly in developed population groupings. Worldwide, reduced natural selection may be yet another significant contributor to dementia incidence with special regard to developed populations.

Microstructural Evidence of Neuroinflammation for Psychological Symptoms and Pain in Patients with Fibromyalgia

Objective

In patients with fibromyalgia (FM), the brain shows altered structure and functional connectivity, but the mechanisms underlying these changes remain unclear. This study investigated the associated changes in brain microstructures and neuroinflammation of patients with FM.

Methods

We recruited 14 patients with FM and 14 healthy controls. Visual analog scale (VAS), Beck's Anxiety Inventory (BAI), and Beck's Depression Inventory-II (BDI-II) were used for assessing their pain, anxiety, and depression levels, respectively. Diffusion kurtosis imaging (DKI) was used to visualize microstructural alterations associated with neuroinflammation in specific brain regions. The biomarkers for the neuron damage, including serum tau and amyloid β protein fragment 1-42 (Aβ1-42) levels, were assessed. Spearman correlation of DKI parameters with VAS, BAI, and BDI-II scores and tau and Aβ1-42 levels were assessed.

Results

The patients with FM had significantly higher levels of Aβ1-42 levels compared with the controls. Compared with the controls, the patients showed significantly lower DKI parameters in the bilateral dorsal–lateral prefrontal cortex and orbital–frontal cortex. The patients showed a significant correlation between the axial kurtosis values of the amygdala and VAS scores (left: rho = -0.603, p = 0.022; right: rho = -7.04, p = 0.005).

Conclusion

To the best of our knowledge, this is the first study to use DKI to examine the brain of FM patients. We noted significant DKI changes at specific areas associated with neuroinflammation in patients with FM. Our results provide valuable information on brain neuroinflammation and pathophysiological changes in patients with FM.

Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ_1–42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

Circulating Metabolome and White Matter Hyperintensities in Women and Men

BACKGROUND

White matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites.

METHODS

We studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment-stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance-weighted method. Associations with false discovery rate (FDR)-adjusted P values (PFDR)<0.05 were considered significant.

RESULTS

In the meta-analysis of results from the basic models, we identified 30 metabolomic measures associated with WMH (PFDR<0.05), 7 of which remained significant in the fully adjusted models. The most significant association was with higher level of hydroxyphenylpyruvate in men (PFDR.full.adj=1.40×10-7) and in both the pooled sample (PFDR.full.adj=1.66×10-4) and statin-untreated (PFDR.full.adj=1.65×10-6) subsample. In men, hydroxyphenylpyruvate explained 3% to 14% of variance in WMH. In men and the pooled sample, WMH were also associated with lower levels of lysophosphatidylcholines and hydroxysphingomyelins and a larger diameter of low-density lipoprotein particles, likely arising from higher triglyceride to total lipids and lower cholesteryl ester to total lipids ratios within these particles. In women, the only significant association was with higher level of glucuronate (PFDR=0.047).

CONCLUSIONS

Circulating metabolomic measures, including multiple lipid measures (eg, lysophosphatidylcholines, hydroxysphingomyelins, low-density lipoprotein size and composition) and nonlipid metabolites (eg, hydroxyphenylpyruvate, glucuronate), associate with WMH in a general population of middle-aged and older adults. Some metabolomic measures show marked sex specificities and explain a sizable proportion of WMH variance.

Disentangling Independent and Mediated Causal Relationships Between Blood Metabolites, Cognitive Factors, and Alzheimer's Disease

Background

Education and cognition demonstrate consistent inverse associations with Alzheimer's disease (AD). The biological underpinnings, however, remain unclear. Blood metabolites reflect the end point of biological processes and are accessible and malleable. Identifying metabolites with etiological relevance to AD and disentangling how these relate to cognitive factors along the AD causal pathway could, therefore, offer unique insights into underlying causal mechanisms.

Methods

Using data from the largest metabolomics genome-wide association study (N ≈ 24,925) and three independent AD cohorts (N = 4725), cross-trait polygenic scores were generated and meta-analyzed. Metabolites genetically associated with AD were taken forward for causal analyses. Bidirectional two-sample Mendelian randomization interrogated univariable causal relationships between 1) metabolites and AD; 2) education and cognition; 3) metabolites, education, and cognition; and 4) education, cognition, and AD. Mediating relationships were computed using multivariable Mendelian randomization.

Results

Thirty-four metabolites were genetically associated with AD at p < .05. Of these, glutamine and free cholesterol in extra-large high-density lipoproteins demonstrated a protective causal effect (glutamine: 95% confidence interval [CI], 0.70 to 0.92; free cholesterol in extra-large high-density lipoproteins: 95% CI, 0.75 to 0.92). An AD-protective effect was also observed for education (95% CI, 0.61 to 0.85) and cognition (95% CI, 0.60 to 0.89), with bidirectional mediation evident. Cognition as a mediator of the education-AD relationship was stronger than vice versa, however. No evidence of mediation via any metabolite was found.

Conclusions

Glutamine and free cholesterol in extra-large high-density lipoproteins show protective causal effects on AD. Education and cognition also demonstrate protection, though education's effect is almost entirely mediated by cognition. These insights provide key pieces of the AD causal puzzle, important for informing future multimodal work and progressing toward effective intervention strategies.

Higher Serum DHA and Slower Cognitive Decline in Patients with Alzheimer's Disease: Two-Year Follow-Up

Omega-3 polyunsaturated fatty acids (PUFAs), especially eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have been associated with slower rates of cognitive decline. We investigated the association between omega-3 PUFAs and cognitive function in patients with Alzheimer’s disease (AD) receiving acetylcholinesterase inhibitors (AChEIs). This was a prospective cohort study using registered data. Patients with AD receiving AChEIs were recruited from 1 May 2016 to 30 April 2019 and were followed up for two years. Their daily diet record and blood concentration of omega-3 PUFAs were analyzed. Multiple linear and binary logistic regression was used to determine the factors associated with cognitive decline (continuous and dichotomized cognitive change). In the research, 129 patients with AD were identified with a mean age of 76.5 ± 6.6. Patients with AD with lower baseline omega-3 PUFAs levels were associated with a higher risk of cognitive decline than those with higher levels (odds ratio [OR] = 1.067, 95% confidence interval [CI]: 1.012, 1.125; p = 0.016) after adjustment. Patients with AD with a lower baseline DHA (OR = 1.131, 95% CI: 1.020, 1.254; p = 0.020), but not EPA, were associated with a higher risk of cognitive decline. We found that higher Mini-Nutritional Assessment scores (beta = −0.383, 95% CI = −0.182–−0.048, p = 0.001) and total fat (beta = −0.248, 95% CI = −0.067–−0.003, p = 0.031) were independently associated with slow cognitive decline in patients with AD receiving AChEIs. The baseline blood levels of omega-3 PUFAs were associated with cognitive decline in patients with AD receiving AChEIs. Future randomized controlled trials are needed to clarify whether this association is causal.

Long-chain Omega-3 fatty acids supplementation and cognitive performance throughout adulthood: A 6-month randomized controlled trial

OBJECTIVE

To investigate whether omega-3 polyunsaturated fatty acids (n-3 PUFA) supplementation improve cognitive performance and if apolipoprotein E (APOE) genotype or age were effect modifiers.

METHODS

Healthy adults of 20 to 80 years old (n = 193) were completed a 6-month double-blind randomized controlled trial with two groups: 2.5 g/day of n-3 PUFA or a placebo. Primary outcomes were visuospatial ability and working memory and secondary outcomes were episodic memory and executive function, measured at baseline and 6 months.

RESULTS

Cognitive performances did not significantly differ between groups on primary or secondary outcomes after 6 months of treatment. APOE carriers and age were not effect modifiers for any outcomes. Those with low episodic memory scores and taking the n-3 PUFA supplement, significantly improved their scores (p = 0.043).

CONCLUSIONS

A 6-month n-3 PUFA supplementation did not improve cognitive performance in cognitively healthy adults and APOE status or age were not effect modifiers.

A Nuclear Magnetic Resonance Spectroscopy Method in Characterization of Blood Metabolomics for Alzheimer's Disease

There is currently a crucial need for improved diagnostic techniques and targeted treatment methods for Alzheimer's disease (AD), a disease which impacts millions of elderly individuals each year. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease. In this report, we present our AD metabolomics results measured with high resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) on human blood plasma samples obtained from AD and non-AD subjects. Our study centers on developments of AD and non-AD metabolomics differentiating models with procedures of quality assurance (QA) and quality control (QC) through pooled samples. Our findings suggest that analysis of blood plasma samples using HRMAS NMR has the potential to differentiate between diseased and healthy subjects, which has important clinical implications for future improvements in AD diagnosis methodologies.

Development of a Novel Nutrition-Related Multivariate Biomarker for Mild Cognitive Impairment Based on the Plasma Free Amino Acid Profile

Nutritional epidemiology has shown the importance of protein intake for maintaining brain function in the elderly population. Mild cognitive impairment (MCI) may be associated with malnutrition, especially protein intake. We explored blood-based biomarkers linking protein nutritional status with MCI in a multicenter study. In total, 219 individuals with MCI (79.5 ± 5.7 year) from 10 institutions and 220 individuals who were cognitively normal (CN, 76.3 ± 6.6 year) in four different cities in Japan were recruited. They were divided into the training (120 MCI and 120 CN) and validation (99 MCI and 100 CN) groups. A model involving concentrations of PFAAs and albumin to discriminate MCI from CN individuals was constructed by multivariate logistic regression analysis in the training dataset, and the performance was evaluated in the validation dataset. The concentrations of some essential amino acids and albumin were significantly lower in MCI group than CN group. An index incorporating albumin and PFAA discriminated MCI from CN participants with the AUC of 0.705 (95% CI: 0.632–0.778), and the sensitivities at specificities of 90% and 60% were 25.3% and 76.8%, respectively. No significant association with BMI or APOE status was observed. This cross-sectional study suggests that the biomarker changes in MCI group may be associated with protein nutrition.