PubMed

Alzheimers Dement. 2024 Dec;20 Suppl 2:e089874. doi: 10.1002/alz.089874.ABSTRACTBACKGROUND: There is growing interest in the role of environmental factors (i.e., exposome) in the pathogenesis of Alzheimer's diseases. The exposome includes three categories: internal (e.g., metabolism, gut microbiome, inflammation), specific external (e.g., environmental pollutants, diet, drugs, occupational), and general external (e.g., socioeconomic status, education, climate). The metabolome provides a readout of the influences of the exposome, capturing the presence of a large number of chemical exposures and allowing an interrogation of the influences of these chemicals on cognition and brain imaging changes.METHOD: Four centers of excellence in metabolomics have used mass spectrometry-based capabilities to provide broad coverage of the chemical exposome. A 'ring trial' was performed to determine the coverage of the metabolome/exposome using state-of-the-art analytical tools. Human serum/plasma standards from diseased individuals - Alzheimer's, COPD, IBS, osteoarthritis, and Type 2 diabetes (BioIVT), and NIST standards (1950 and 1958) were analyzed, using LC-MS/MS (targeted and untargeted), Direct Flow-MS/MS and/or ICP-MS. Similarly, we are measuring the exposome/metabolome in large studies of AD patients, including ADNI, ADRCs and the ROSMAP brain collection.RESULT: Compounds were classified by chemical class (ClassyFire), toxicity/source groups (EPA CompTox DB)), and disease association (Comparative Toxicogenomics DB). Using the available InChiKey and CAS numbers available, ClassyFire sorted compounds into 184 chemical classes, EPA CompTox DB sorted 1179 unique InChiKey descriptors into 93 groups (toxicity class and/or exposure source), including DrugBank, Hazardous Substances DB 2019, BLOOD Toxic Substances Control Act, COSMO cosmetics and FDA Food Substances. These included 7 neurological groups with over 100 ring trial compounds. These compounds were classified as endogenous compounds, foods, medications, industrial chemicals, surfactants, plasticizers, personal care, and pesticides (Figure 1). Identical studies are being performed using large cohorts of brain and blood samples, including from ADRCs and the ROSMAP collection. We will present highlights of the brain chemical exposome and its links to cognition.CONCLUSION: Big data is being generated to capture the influences of the exposome on brain health, connecting these peripheral influences on brain metabolic health and disease, facilitating the development of novel therapeutic approaches targeting the exposome and its effect on brain health.PMID:39786083 | DOI:10.1002/alz.089874

Alzheimers Dement. 2024 Dec;20 Suppl 2:e089889. doi: 10.1002/alz.089889.ABSTRACTBACKGROUND: Diet has been associated with memory, emotion/stress regulation, structure and function of the hippocampus and amygdala and attenuation of cognitive aging. There is a well-recognized lack of reliability in self-reported dietary intake and great interest in objective metabolic readout of dietary patterns. In this study we constructed dietary profiles from untargeted metabolomics data using a novel metadata-based source annotation method developed at the Dorrestein Lab, also referred to as "foodomics". The aim of was to link these objective metabolic readouts of dietary intake to cognition and AD markers of disease.METHOD: Untargeted metabolomic profiling (LC-MS/MS) was applied to blood samples from 872 individuals from the ADNI cohort (182 cognitive normal individuals, 449 MCI, 103 significant memory concern, and 139 AD); for whom brain imaging data was also collected. Principal component analysis (PCA) was to derive a dietary profile that explained the most variance in the dataset. Whole-brain voxel-wise analysis was applied to determine association between dietary intake profiles and brain amyloid-β deposition (amyloid PET), gray matter density (brain atrophy; MRI), and glucose metabolism (FDG PET) using a multivariate regression analysis with SPM12. The general linear model was used to examining the associations of the dietary profile with cognition and AD markers. Covariates included age, education, sex, and BMI.RESULT: Correlation between the first principal component (PC1) and the relative abundances of foods at level 3 of the food ontology showed that PC1 explained variance derived from food intake within poultry, dairy, meat, egg, vegetable, legume and seafood categories (Figure 1). Greater intake of poultry, dairy, and meat was significantly associated with 1) reduced glucose metabolism and brain gray matter atrophy in the frontal, parietal, and temporal (including the hippocampus; Figure 2,3) and 2) difficulties with executive functioning (p=0.03) and memory at baseline (p=0.002), longitudinal decreases in memory functioning over time (p=.02), and increases in baseline plasma levels of neurofilament light chain at baseline (p=.02).CONCLUSION: Dietary intake selectively influences glucose metabolism and gray matter atrophy of brain regions preferentially targeted in AD, cognition and neurofilament light chain. This proof-of-concept study supports the hypotheses that diet influences the progression of the disease. NIA U19 AG063744.PMID:39786075 | DOI:10.1002/alz.089889

Alzheimers Dement. 2024 Dec;20 Suppl 2:e089880. doi: 10.1002/alz.089880.ABSTRACTBACKGROUND: It is now widely acknowledged that diet, lifestyle, and environmental exposures largely affect an individual's metabolic state in health and disease, including the brain. Metabolomics has demonstrated its potential to enable exciting discoveries in brain health, facilitated by advances in analytical and informatics techniques. Here, we highlighted the use of MS/MS-based untargeted metabolomics to study the diet and medication exposure of cognitively declined cohorts through the newly developed FoodMASST and DrugMASST tools.METHODS: A food metabolomics reference dataset was created with tandem mass spectrometry data (MS/MS) alongwith detailed and structured metadata for ∼3,500 foods. We used this resource to trace the food origin of important Alzheimer's disease metabolite biomarkers as well as to investigate the empirical diet patterns of subjects in a Mediterranean diet intervention study (MIND). An MS/MS spectral library for drugs, metabolites, and related molecules was developed by spectral similarity search against ∼1.2 billion public metabolomics spectra. We utilized this resource to perform empirical medication read-out in 501 brain samples from the Rush Religious Orders and Memory and Aging Project (ROS/MAP) and 637 fecal samples from the Aging and Disability Resource Centers (ADRCs).RESULTS: We demonstrated the utility of the FoodMASST and DrugMASST tools through several examples. Using FoodMASST we demonstrated that lentil is an important food source of tryptophan betaine, an important AD marker. FoodMASST-based diet score separated the metabolomics profiles of MIND subjects in the PCoA cohort. DrugMASST detected 102 drugs in brain samples from the ROS/MAP and revealed that neurology/psychiatry and cardiology drugs were the most important categories. The computationally derived drug metabolite library enabled additional annotation of 15 drug metabolites with high confidence. We observed significant separation in the metabolome of patients with and without antibiotic detection. In the ADRC fecal samples, we detected 190 drugs and the neurology/psychiatry and cardiology categories had the highest number of drugs observed. Carbidopa, a dopamine promoter drug given to treat PD, was detected in 62% of the patients.CONCLUSIONS: Untargeted metabolomics tools, such as metadata-driven metabolomics exemplified by FoodMASST and DrugMASST, create insights into patients' chemical exposure and their role in gut microbiome and Alzheimer's disease.PMID:39786044 | DOI:10.1002/alz.089880

Alzheimers Dement. 2024 Dec;20 Suppl 2:e089130. doi: 10.1002/alz.089130.ABSTRACTBACKGROUND: Alzheimer's disease (AD) is a devastating disease at an individual level and for the wider society. Despite huge research efforts the underlaying causes of AD is still not well understood. We know that lipid metabolism is fundamental for maintaining a heathy brain and that some of the strongest risk factors for AD, such as APOE4, affect lipids. In this study we set out to measure the lipid changes associated with AD.METHOD: We used a total of 841 participants from the AddNeuroMed and Dementia Case Register cohorts: 306 people with AD, 165 people with mild cognitive impairment (MCI) (n = 165), and 370 healthy controls. Mass spectrometry based lipidomics was carried out on plasma samples using a Waters ACQUITY UPLC and XEVO QTOF-MS. Lipid molecules were clustered into modules based on correlation networks using Weighted Gene Correlation Network Analysis (WGCNA). Module eigengenes were used to investigate the association of each module with MCI and AD. Key lipid drivers ('Hub') associated with AD were selected and investigated further. Linear regression analyses were used to investigate the association of modules and key lipids with AD and MCI status after adjustment for sampling site, age, sex and presence of APOE4 allele. The False Discovery Rate (FDR) was applied to control for multiple testing.RESULT: Lipidomics analysis resulted in 261 identified lipid molecules, which were analyzed with WGCNA resulting in 10 robust modules. Four modules were significantly associated with AD vs. healthy control after adjusting for covariates and following correction for multiple testing. Investigating the lipid drivers of these modules highlighted 55 lipid hubs, 26 of which were significantly associated with AD vs. control, after adjustment for confounders and multiple testing. These lipids included triglycerides, phosphatidylcholines, phosphatidylethanolamines and lysophosphatidylcholines.CONCLUSION: Here, we present a workflow for robust identification of lipid molecules associated with Alzheimer's disease. Further work will establish how these lipids relate to AD risk factors, clinical measures, and genetic risk for AD.PMID:39786025 | DOI:10.1002/alz.089130

Alzheimers Dement. 2024 Dec;20 Suppl 2:e088514. doi: 10.1002/alz.088514.ABSTRACTBACKGROUND: Growing evidence indicates that people with neurodegenerative diseases have altered metabolic status, but the association between metabolic age (MetAge), as assessed by circulating plasma metabolomics, and dementia remains unclear. We aimed to investigate the association between MetAge and risk of dementia and to explore whether genetic background plays a role in these associations.METHOD: From the UK Biobank, 153,436 dementia-free adults aged ≥55 (mean age 62.08±4.07 years; 52.54% female) at baseline were followed up to 16 years to detect incident dementia. Dementia was diagnosed based on information from patient registry following international criteria. Nuclear magnetic resonance spectroscopy was used to measure 249 metabolites from plasma samples collected at baseline. MetAge (in years) was calculated based on these metabolites using the XGBoost machine learning algorithm (25.04 to 93.11 years) and tertiled (younger 25.0-55.7 years, middle-aged 55.7-62.7 years, and older 62.7-93.1 years). AD-related polygenic risk score (PRSAD) was categorized as low, moderate, and high. Data were analyzed using Cox regression and Laplace regression.RESULT: During the follow-up (median: 14.5, interquartile range: 13.6 - 15.3 years), 4,521 (3.0%) participants developed dementia. MetAge (as continuous variable) was dose-dependently associated with dementia risk (hazard ratio [HR] and 95% confidence interval [CI] per 5-year increase: 1.04 [1.02, 1.06]). Compared with younger MetAge, the HR (95% CI) for dementia was 1.13 [1.03, 1.23]. Older MetAge was also associated with earlier onset [10th percentile difference [95% CI]: -0.89 [-1.27, -0.50] years) of dementia. In joint-effect analysis, the HR of dementia was 2.43 (95% CI 2.08, 2.83) among participants with older MetAge and moderate-to-high PRSAD, compared to those with younger MetAge and low PRSAD. There was an additive interaction between older MetAge and moderate-to-high PRSAD on dementia (attributable proportion: 0.16 [0.01, 0.31]).CONCLUSION: Older MetAge is associated with a moderately increased risk of dementia and accelerates dementia onset by nearly 1 year, particularly among people with a high genetic susceptibility for dementia.PMID:39786010 | DOI:10.1002/alz.088514

Alzheimers Dement. 2024 Dec;20 Suppl 2:e091901. doi: 10.1002/alz.091901.ABSTRACTBACKGROUND: Alzheimer's disease (AD) is associated with impaired lipid metabolism in the brain. To identify the specific regions where pathological change to cell functionality occurs, a spatial investigation of regional lipid dysregulation is needed.METHOD: We measured untargeted spatial lipidomics using Desorption Electrospray Ionization (DESI) mass spectrometry in the brains of mice from two genotypes, wild type (WT) and APPsw, an AD mouse model overexpression amyloid precursor protein (APP). We generated a longitudinal profile of these mice at ages 6 months, 12 months, and 22 months. We computationally segmented brain regions defined by the Allen Mouse Brain Atlas to discover region-specific differential lipid biomarkers across age and genotype. We developed "Spatial Lipidomics Analysis Tool" (SLAT), a computational framework which identifies statistically differential ions between age and genotype in a region-specific manner, and applied this to our data. Independently of SLAT, we applied K-means clustering to lipidomics as an unbiased approach to find regions in whole brains that may drive the development of disease. We applied K-means clustering to log2 fold changes (log2FC) to find groups of significant ions reported by SLAT characterized by similar changes in abundance in APPsw mice over time compared to 6 months WT mice.RESULT: We identified the globus pallidus (GP) as having multiple significantly differential lipids between WT and APPsw samples. However, whole brain analysis was unable to identify significant differential ions between genotypes, demonstrating the advantage of region-specific-analysis. Additionally, the GP was found to drive clustering for specific ages and genotypes. Longitudinal analysis of significant GP ions revealed multiple groups of ions with coordinated changes in ion intensity over time. The group of ions with the greatest temporal changes had a 0.50 log2FC increase from 6 to 12 months, followed by a 0.63 log2FC decrease from 12 to 22 months, on average.CONCLUSION: Our results illustrate lipid biomarkers specific to the GP which may have potential use for the diagnosis and prognosis of AD. The development of SLAT as a partially automated pipeline to analyze longitudinal data with different phenotypes can be utilized as a tool for discovery of disease-specific mechanisms from spatial lipidomics and metabolomics data.PMID:39785850 | DOI:10.1002/alz.091901

Crit Rev Toxicol. 2025 Jan 9:1-26. doi: 10.1080/10408444.2024.2422890. Online ahead of print.ABSTRACTXylene substances have wide industrial and consumer uses and are currently undergoing dossier and substance evaluation under Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) for further toxicological testing including consideration of an additional neurotoxicological testing cohort to an extended one-generation reproduction toxicity (EOGRT) study. New repeated dose study data on xylenes identify the thyroid as a potential target tissue, and therefore a weight of evidence review is provided to investigate whether or not xylene-mediated changes on the hypothalamus-pituitary-thyroid (HPT) axis are secondary to liver enzymatic induction and are of a magnitude that is relevant for neurological human health concerns. Multiple published studies confirm xylene-mediated increases in liver weight, hepatocellular hypertrophy, and liver enzymatic induction via the oral or inhalation routes, including an increase in uridine 5'-diphospho-glucuronosyltransferase (UDP-GT) activity, the key step in thyroid hormone metabolism in rodents. Only minimal to slight increases in thyroid follicular cell hypertrophy have been observed in some xylene repeated dose studies, with no associated robust or consistent perturbance of thyroid hormone changes across the studies or carried through to offspring indicating adaptive homeostatic maintenance of the HPT axis. Also importantly, in vitro human cell line data from the United States Environmental Protection Agency (US EPA) Toxicity Forecasting (ToxCast) provides supporting evidence of xylene's inability to directly perturb thyroidal functionality. A further supplemental in-depth metabolomics analysis (MetaMap®Tox) of xylene showed a tentative match to compounds that also demonstrate extra-thyroidal effects on the HPT axis as a consequence of liver enzyme induction. Lastly, the slight HPT axis changes mediated by xylene were well-below the published literature thresholds for developmental neurotoxicological outcomes established for thyroidal changes in animals and humans. In summary, the data and various lines of scientific evidence presented herein individually and collectively demonstrate that xylene's mediated changes in the HPT axis, via a secondary extra-thyroidal MOA (i.e. liver enzyme induction), do not raise a human health concern with regards to developmental neurotoxicity. As such, the available toxicological data do not support the classification of xylene as a known or suspected endocrine disruptor, specifically through the thyroid modality, per Regulations Commission Delegated Regulation (EU) 2023/707 of 19 December 2022 amending Regulation (EC) No 1272/2008 and do not support the need for a neurotoxicological cohort evaluation in any subsequent EOGRTS.PMID:39785829 | DOI:10.1080/10408444.2024.2422890

FASEB J. 2025 Jan 15;39(1):e70259. doi: 10.1096/fj.202401512R.ABSTRACTPregnant ewes mobilize body fat to increase energy supply for fetal growth and development upon undernutrition, which disrupts the metabolic homeostasis of the body. However, the comprehensive metabolic changes in subcutaneous adipose tissue upon undernutrition are poorly understood. In this study, an undernutrition sheep model was established to investigate the effects of undernutrition on metabolic changes, immune response, and inflammation in subcutaneous fat through transcriptome, RT-qPCR, and metabolome analysis. Results showed that undernutrition changed the total transcriptional and metabolic profiles of adipose tissue. Compared to the controls, differentially expressed genes (DEGs) involved in fatty acid synthesis, triglyceride genesis, and lipid transport were downregulated in undernourished ewes, while DEGs related to fatty acid and triglyceride degradation were upregulated. Almost all lipid-related differential metabolites (DMs) were downregulated. DEGs and DMs involved in glucose metabolism and glycogen degradation were downregulated, while glycogen synthesis and carbohydrate transport were upregulated. DEGs linked to amino acid degradation were upregulated and some amino acids and derivatives were downregulated. KEGG pathway analysis showed complement and coagulation cascades were enriched significantly by DEGs, and DEGs related to coagulation, macrophage, and inflammation were upregulated while DEGs associated with the complement system were downregulated. Undernutrition during late gestation disrupted the metabolism of lipids, carbohydrates, and amino acids in adipose tissue, which weakened the complement system and immune response and may have ultimately led to inflammation.PMID:39785680 | DOI:10.1096/fj.202401512R

Alzheimers Dement. 2024 Dec;20 Suppl 2:e089181. doi: 10.1002/alz.089181.ABSTRACTBACKGROUND: Cognitive resilience (CR) refers to the continuum from worse to better-than-expected cognition, given the degree of neuropathology. Understanding mechanisms underlying CR could inform discovery of novel targets for dementia prevention; however, specific metabolic pathways underlying CR are yet to be elucidated.METHODS: Our study included 484 deceased participants (mean age at death =91 years, 70.2% women) from the Religious Orders Study and Rush Memory and Aging Project. Metabolomics profiling was conducted at Metabolon Inc. by a chromatography-mass spectrometry in postmortem dorsolateral prefrontal cortex tissue, yielding 600 known metabolites quantified for analysis. Antemortem cognitive function was assessed annually by 17 neuropsychologic tests through death, and neuropathologies were evaluated in postmortem brain. CR was defined as the residual slope of cognitive decline after controlling for age at death, sex, education, and nine Alzheimer's disease, other neurodegenerative, and cerebrovascular neuropathologies. We analyzed the associations between individual brain metabolites and CR score using linear regressions. We also constructed co-regulatory networks for metabolites significantly related to CR, and identified key metabolites for CR within each network using recursive conditional analysis.RESULTS: We identified 72 metabolites whose levels in the prefrontal cortex were associated with CR (FDR<0.05), encompassing diverse biochemical categories including 33 amino acids and peptides, 21 lipids, 7 cofactors and vitamins, 6 carbohydrates/energy metabolites, and 5 others. Based on partial correlations, the 72 CR-related metabolites were constructed into 6 co-regulatory networks, each linked to specific biological functions including oxidative stress (key metabolites driving the associations were N-acetylmethionine sulfoxide, ergothioneine, and carotene diol), synaptic function (led by myo-inositol, glycerate, and docosahexaenoyl ethanolamide), neuroinflammation (led by 7-hydroxycholesterol), cell signaling (led by inositol 1-phosphate and gamma-glutamylthreonine), phospholipid metabolism (led by glycerophosphorylcholine), and energy/glucose metabolism (led by UDP-glucose and adenosine monophosphate). The eigenvectors of all 6 co-functional networks were significantly associated with CR (FDR<0.05).CONCLUSION: We identified brain metabolites implicated in oxidative stress and necroinflammation, synaptic function and cell signaling, and lipid/energy metabolism, associated with rates of cognitive decline after controlling for neuropathologies. These CR-related metabolites may inform the discovery of novel targets for improving cognitive resilience to neuropathology.PMID:39785567 | DOI:10.1002/alz.089181

Alzheimers Dement. 2024 Dec;20 Suppl 2:e088693. doi: 10.1002/alz.088693.ABSTRACTBACKGROUND: Alzheimer's Disease (AD) is a complex disorder and much of its etiopathology is still unknown. Here, we applied dimensionality reduction methods to disentangle cyptic patterns in CSF proteomic and lipidomic data.METHOD: We studied 1121 CSF samples using targeted lipidomics based on liquid chromatography (LC)-MS/MS (mass spectrometry), generated by Lipometrix (Lueven, Belgium), and proteomic data generated by Somalogic (Boulder, Colorado) using the SOMAscan 7k Assay. We independently computed the principal components for the proteomic and lipidomic datasets using good quality lipids (N=388) and proteins (N=2469). CSF samples were obtained by lumbar punctures at ACE Alzheimer Center (Barcelona, Spain), including patients at different points of the dementia continuum (SCD, MCI and dementia). Principal components were calculated using the princomp() function in R.RESULT: PC1 explained a substantial fraction of the variance in lipidomic (∼40%) and proteomic (∼60%) datasets and was highly correlated between both omics (R2=0.43; p=2.3·10-138, Figure 1). We explored the association profile of the PCs to AD risk factors (age, sex, APOE, PRS, diabetes, hypertension, dyslipidemia, BMI), endophenotypes (abeta, tau), disease progression and total protein in the CSF. PC1, as well as the individual lipid species, were strongly associated with abeta, tau and total CSF protein levels (Figure 2). Finally, we conducted a GWAS of the first 20 lipidomic and proteomic PCs. PC1 displayed a GWS signal at chr3q28 in both omics. This region has been previously linked with CSF tau levels and brain morphology. Subsequent lipidomic PCs were associated with variants in the FADS1/FADS2 locus, while other proteomic PCs were associated with variants in the APOE locus (Figure 3).CONCLUSION: Our findings revealed a shared major contributor to the variance in CSF between lipidomic and proteomic data, which is related to the tau, abeta and total protein signature, and identified a QTL associated to both the lipidomic and proteomic PC1. Accounting for this major variance contributor may enhance future studies involving CSF biomarkers. Subsequent principal components had specific association profiles to AD risk factors and endophenotypes.PMID:39785560 | DOI:10.1002/alz.088693

Alzheimers Dement. 2024 Dec;20 Suppl 2:e092268. doi: 10.1002/alz.092268.ABSTRACTBACKGROUND: There is evidence indicating that disruptions in lipid metabolism are implicated in the pathophysiology of Alzheimer's disease (AD), with systemic repercussions that can be identified in peripheral blood. Recent studies conducted by our group have identified abnormalities in lipid metabolism among patients with mild cognitive impairment (MCI) and dementia (probable AD), through the investigation of a specific panel of lipid metabolites in plasma. Although much remains to be elucidated about the complex interaction between disturbances in lipid metabolites and the pathogenesis of AD, this promising research area offers exciting opportunities for the development of new strategies for disease diagnosis, treatment, and prevention.METHOD: Blood plasma samples were collected from the following groups: individuals with Alzheimer's disease (AD, n=17), individuals with Mild Cognitive Impairment (MCI) showing pathological AD signature (n=17), individuals with MCI without pathological AD signature (n=17), individuals with Down syndrome (n=17), elderly (n=17), and young (n=17) cognitively healthy individuals. We used liquid chromatography coupled with mass spectrometry (LC-MS/MS) for lipidomic analysis.RESULT: A total of 29 lipids were identified by principal component analysis (PCA) from a dataset consisting of 208 lipids. The lipid modules (attached image) composed of lysophosphatidylcholine, lysophosphatidylethanolamine, phosphatidylcholine, phosphatidylethanolamine, phosphatidylserine, phosphatidylglycerol and sphingomyelin are differentially expressed in the samples. PCA1 (74.92%) and PCA2 (27.6%) account for the majority of variance in the sample (attached table).CONCLUSION: Our preliminary findings suggest that the use of PCA analysis may aid in discriminating differently expressed lipids in the plasma of individuals with dementia syndromes.PMID:39785405 | DOI:10.1002/alz.092268

Alzheimers Dement. 2024 Dec;20 Suppl 2:e093365. doi: 10.1002/alz.093365.ABSTRACTBACKGROUND: The study of new blood biomarkers in addition to amyloid beta and hyperphosphorylated tau is fundamental to expanding the understanding of the pathophysiology of dementia, especially Alzheimer`s disease, in search of therapeutic approaches.METHOD: In this study, we included individuals diagnosed with Alzheimer disease (AD), Mixed-type dementia (MD), Vascular dementia (VD) and a control group of cognitively healthy individuals. Demographics, clinical characteristics, neuroimages and plasma samples were collected. We performed a metabolomic aminoacid analysis using High-Performance Liquid Chromatography-Mass Spectrometry (HPLC-MS/MS). Analyses were performed with GraphPad Prism 9.0 (GraphPad Software, San Diego, USA) or SPSS v. 18 software. Depending on the group comparison type, one-way analysis of variances with post-hoc Tukey-HSD test or independent samples t-tests. We compared aminoacid levels between the different groups (controls, AD, MD or VD) and dementia severity, assessed by Clinical Dementia Rating (CDR) score.RESULT: 135 individuals were clinically assessed as 33 controls, 42 AD, 41 MD and 19 VD. There was a statistically significant difference between controls and individuals with dementia in the following amino acids: aspartic acid (p<0.0001), arginine (p<0.0001), glutamic acid (p<0.0001), glutamine (p<0.0001), isoleucine (p<0.0008), leucine (p<0.0001). However, only arginine proved to be a biomarker capable of differentiating AD from DV (p<0.0003) CONCLUSION: These preliminary analyses show that leucine, isoleucine and glutamine were reduced in the plasma of patients with dementia, while aspartic acid, glutamic acid and arginine were increased. These markers can help us identify the underlying metabolic dysfunctions of different signaling pathways relevant to neuroprotection and vascular function.PMID:39785298 | DOI:10.1002/alz.093365

Alzheimers Dement. 2024 Dec;20 Suppl 7:e088163. doi: 10.1002/alz.088163.ABSTRACTBACKGROUND: Dietary factors are modifiable risk factors for dementia. In particular, the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet has been associated with better cognitive function and lower risk of dementia. However, circulating metabolomic characteristics of the MIND diet and its associations with cognitive function remains unclear.METHOD: In the current study, 45906 UK Biobank (UKB) participants (mean age: 56.4 years) were separated into a discovery cohort and an internal prospective validation cohort, and 6193 Whitehall II (WHII) study participants (mean age: 56.8 years) constituted an external validation cohort. We identified the metabolites associated with the alternate MIND diet score (aMIND) using linear regression models, and Benjamini-Hochberg method was applied to control false discovery rate. We constructed a metabolomic signature score of the MIND diet using elastic net model and assessed the potential mediating role of the metabolomic signature in the associations of the aMIND with cognitive outcomes in the two cohorts.RESULT: The aMIND showed significant associations with 149 out of 168 (89%) metabolites in the UKB discovery cohort, and 47 of these associations were also replicated in both the internal validation cohort and the external validation cohort, including 38 lipoprotein subclass, 4 fatty acid, 1 cholesterol, 1 inflammation, 2 lipoprotein particle size, and 1 triglyceride measures. The metabolomic signature score based on the selected metabolites was significantly correlated with the aMIND (Pearson's r = 0.36, 0.31, and 0.27 in the discovery, internal validation and external validation cohort). In the UKB (491 incident dementia cases during a median follow-up of 11.8 years), but not WHII (169 in 17.8 years), the MIND metabolomic signature score significantly mediated the association of the aMIND and incident dementia by 45%. In the WHII, association between the aMIND and cognitive function was partially mediated by the metabolomic signature (proportion = 19%, P for mediation = 0.039).CONCLUSION: MIND diet was favourably associated with a panel of metabolites, and a MIND diet metabolomic signature score partially mediated the observed associations between MIND diet adherence and incident dementia and cognitive function.PMID:39784744 | DOI:10.1002/alz.088163

Alzheimers Dement. 2024 Dec;20 Suppl 7:e087444. doi: 10.1002/alz.087444.ABSTRACTExposure to environmental chemicals has been associated with Alzheimer's disease (AD); however, most studies have used a targeted approach to study this relationship. While targeted approaches have been critical to understand mechanisms, they do not reflect real world exposures where an individual is exposed to multiple chemicals at the same time. Exposomics provides the opportunity to use an -omics level approach to understand the environmental drivers of disease by measuring the burden of multiple chemicals at the same time. Using an exposomic approach, we found a metabolite of the legacy pesticide DDT to be associated with AD in an observational study. We then investigated whether DDT can exacerbate AD-related pathology using a transgenic strain of the nematode model Caenorhabditis elegans (worm) that expresses a mutant tau protein fragment that is prone to aggregation. Exposure to 3µM DDT resulted in internal levels of 0.5 pg (SD: 0.01) p, p'-DDT per worm and 0.1 pg (SD: 0.01) of its metabolite p, p'-DDE. We found that exposure DDT significantly restricted growth in the transgenic strain more than it does in wildtype worms. Further, DDT significantly lowered mitochondrial respiration rates in both strains. High-resolution mass spectrometry-based metabolomics (HRMS) analysis using the whole worm showed reduced levels of several amino acids and an increase in adenosylselenohomocysteine in DDT exposed worms. DDT exposure in the transgenic strain significantly increased the amount of time spent curling. The curling phenotype has been previously associated with mitochondrial dysfunction. Our data suggest that low level exposure to DDT likely exacerbates the mitochondrial inhibitory effects of aggregating tau protein in C. elegans. We are investigating the relationship between persistent organic pollutants and age-related cognitive decline in the Reference Ability Neural Network study. This study, conducted in healthy volunteers, allows us to consider the relationship between exposure and poor cognitive function without confounding by underlying disease. We plan to investigate the potential mechanism through which these chemicals affect neuronal health while considering them as a mixture using C. elegans. Recent advancements in predictive toxicology provide a starting point to consider the appropriate health and functional end points that should be investigated.PMID:39784694 | DOI:10.1002/alz.087444

Alzheimers Dement. 2024 Dec;20 Suppl 7:e091942. doi: 10.1002/alz.091942.ABSTRACTBACKGROUND: The Coaching for Cognition in Alzheimer's (COCOA) Trial was a prospective RCT testing a remotely coached multimodal lifestyle intervention for participants early on the Alzheimer's disease spectrum. Intervention focused on diet, exercise, cognitive training, sleep, stress, and social engagement. Enrollment criteria targeted individuals with cognitive decline who were able to engage remotely with a professional coach. COCOA demonstrated cognitive and functional benefits. Dense omics data were collected on 53 individuals (≥ 58 years).METHODS: We sought to identify blood analytes that mediated the effects of specific elements of the multimodal intervention on specific outcomes. Outcomes were assessed with the MCI Screen (MCIS) and the Functional Assessment Staging Tool (FAST). We combined these and other measures with proteomics and metabolomics data. We analyzed the resulting dataset of over 300,000 distinct molecular data points-reflecting over 1400 measures- assayed over a period of two years. We used MEGENA to hierarchically multiscale cluster analytes based on correlated responses and identified individual metabolites and functional clusters associated with each intervention and outcome. We analyzed individual time courses of key analyte mediators to illustrate personalized effects of interventions and individualized functional and cognitive outcomes.RESULTS: Distinct sets of correlated serum analytes ("communities") convey effects to functional (FAST) outcome and to cognitive (MCIS) outcome. Distinct communities respond to different modalities of intervention. Participants followed different aspects of the multimodal recommendations to different extents, and the analytes in their blood also responded idiosyncratically; analyte trajectories in different individuals show distinct dynamics. We made personalized predictions of future inflections in outcome based on observed changes in key serum mediators. We validated results with data from the Precision Recommendations for Environmental Variables, Exercise, Nutrition and Training Interventions to Optimize Neurocognition (PREVENTION) Trial.CONCLUSIONS: Lifestyle interventions have profound effects on blood metabolites (Figure 1). These in turn convey subtler specific effects to cognition and broad-based effects to function. Pathways that ameliorate the impact of AD via lifestyle interventions in some individuals include nitrogen subsystems, kidney function, and mitochondrial metabolism. These highlight the importance of clinical attention to overall health spanning multiple organ systems in individuals across the Alzheimer's disease spectrum.PMID:39784630 | DOI:10.1002/alz.091942

Alzheimers Dement. 2024 Dec;20 Suppl 7:e089878. doi: 10.1002/alz.089878.ABSTRACTBACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder with significant environmental factors, including diet, that influence its onset and progression. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored the KD's molecular impact for markers of AD therapeutic potential. The BEAM diet study simultaneously profiled the KD's effect on the lipidome, blood and cerebrospinal metabolome, and microbiome of both cognitively impaired and cognitively normal individuals. The findings summarized here assess the biological impact of a Modified Mediterranean KD in the context of Alzheimer's disease treatment and prevention.METHOD: BEAM involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean-ketogenic diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed HPLC-MS/MS lipidomics profiling in plasma, nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF, and metagenomics profiling on fecal samples before and after each diet to assess dietary-induced changes.RESULT: The MMKD led to significant alterations in the blood, CSF, and microbiome. These changes included a global elevation across all plasmanyl and plasmenyl ether lipid species, improved modifiable risk factors, like increased HDL-C and reduced BMI, the reversal of serum metabolic disturbances linked to AD such as an increase in valine levels, and a reduction in systemic inflammation. Leveraging prior clinical studies on AD (n = 1,912), we found that MMKD was inversely associated with the peripheral lipidomic signature of prevalent and incident AD. In the CSF, the MMKD was linked to modified amino acid levels and the breakdown of branched-chain amino acids (BCAAs). Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. In addition, participants with MCI on the MMKD had lower levels of GABA-producing microbes and GABA, and higher levels of GABA-regulating microbes.CONCLUSION: Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.FUNDING: Alzheimer's Gut Microbiome Project, NIA U19AG063744.PMID:39784512 | DOI:10.1002/alz.089878

Alzheimers Dement. 2024 Dec;20 Suppl 2:e088855. doi: 10.1002/alz.088855.ABSTRACTBACKGROUND: Microbiota is modulated by normal aging, but also by Alzheimer's disease (AD) risk factors as poor diet or alteration of sleep patterns. Patients with AD exhibit a dysbiosis characterized by changes in the relative proportions of specific bacterial phyla. Eventually, fecal microbiota transplants (FMT) can improve cognitive deficits and reduce amyloid-ß deposition, at least in mouse models of AD.METHOD: We generated a cohort of AD patients, with control participants matching on age, sex, body mass index, Mini Nutritional Assessment® and education, to sample and compare microbiota composition in the stool and blood compartments. This metagenomic study will be completed by targeted and non-targeted metabolomic analysis to inform about microbiota impact on the host. We precisely evaluated cognition, sleep parameters and dietary habits of the subjects. Moreover, stool samples from 19 patients were pooled by 4, with similar age and sex, from each group, and were transplanted in a mouse model of AD (5XFAD, n = 94) or their control littermates (n = 113) to evaluate the consequences on gut microbiota composition, memory-related behaviors and molecular and cellular biomarkers of AD physiopathology.RESULT: We generated a cohort of well characterized patients representative of mild to moderate AD patients with 45 AD patients [age 75 (SD 0.9), 51% women, minimental state examination (MMSE) 22 (interquartile range, IQR, 17-25)] and 37 controls [age 72 (SD 1.5), 62% women, MMSE 29 (IQR 27-30)]. Our preliminary results indicate i) a potential dysbiosis in AD patients that translates to mouse microbiota composition following FMT. A specific bacterial genus is increased both in 5XFAD and control mice potentially indicating over-representation in AD patients relative to the controls; ii) an impact of microbiota transplanted from AD patients to mouse model on memory and behavior, with an alteration of novel object recognition but also on biomarkers of AD pathology including an increase in mouse Aß1-42 expression level.CONCLUSION: Our results suggest that gut microbiota dysbiosis is associated with AD status and point to a specific bacterial genus. Moreover, FMT from AD patients in an AD mouse model recapitulates important features of the disease, with memory impairment and Aß1-42 accumulations.PMID:39784396 | DOI:10.1002/alz.088855

Alzheimers Dement. 2024 Dec;20 Suppl 2:e088149. doi: 10.1002/alz.088149.ABSTRACTBACKGROUND: Blood-based biomarkers for dementia are gaining attention due to their non-invasive nature and feasibility in regular healthcare settings. Here, we explored the associations between 249 metabolites with all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VaD) and assessed their predictive potential.METHOD: This study included 274,160 participants from the UK Biobank. Cox proportional hazard models were employed to investigate longitudinal associations between metabolites and dementia. The importance of these metabolites was quantified using machine learning algorithms, and a metabolic risk score (MetRS) was subsequently developed for each dementia type. We further investigated how MetRS stratified the risk of dementia onset and assessed its predictive performance, both alone and in combination with demographic and cognitive predictors.RESULT: During a median follow-up of 14.01 years, 5,274 participants developed dementia. Of the 249 metabolites examined, 143 were significantly associated with incident ACD, 130 with AD, and 140 with VaD. Among metabolites significantly associated with dementia, lipoprotein lipid concentrations, linoleic acid, sphingomyelin, glucose, and branched-chain amino acids ranked top in importance. Individuals within the top tertile of MetRS faced a significantly greater risk of developing dementia than those in the lowest tertile. When MetRS was combined with demographic and cognitive predictors, the model yielded the area under the receiver operating characteristic curve (AUC) values of 0.857 for ACD, 0.861 for AD, and 0.873 for VaD.CONCLUSION: We conducted the largest metabolome investigation of dementia to date, for the first time revealed the metabolite importance ranking, and highlighted the contribution of plasma metabolites for dementia prediction.PMID:39784364 | DOI:10.1002/alz.088149

Alzheimers Dement. 2024 Dec;20 Suppl 2:e083975. doi: 10.1002/alz.083975.ABSTRACTBACKGROUND: Disease mechanisms underlying Alzheimer's disease (AD) are heterogenous amongst patients. We recently identified five distinct AD subtypes in cerebrospinal fluid (CSF) proteomic data with data-driven techniques (Figure 1). Two of these subtypes were characterised by brain barrier dysfunction: one with choroid plexus dysfunction, and another with blood-brain barrier dysfunction. Since most lipid transport takes place across these barriers, we compared these two subtypes on CSF lipid levels.METHOD: We included 419 individuals with abnormal amyloid across the clinical spectrum (i.e., AD) and 196 controls with intact cognition and normal AD biomarkers from the Amsterdam Dementia Cohort and related studies with CSF proteomic subtyping available. Next, we performed untargeted complex lipidomics with CSH-QTOF mass spectrometry. Of 3532 lipids detected, 270 could be mapped to known classes. We compared AD barrier subtypes to controls on CSF lipid levels, controlling for sex and age with general linear models.RESULT: Of the 3532 lipids compared to controls, blood-brain barrier dysfunction AD subtype had mostly increased levels of 302 lipids (148 with a known class; Figure 2, all p<0.05), whereas the choroid plexus subtype had mostly decreased levels of 314 lipids (163 known class;, all p<0.05) with an overlap of 150 lipids. These lipids included mostly glycerophospholipids, ceramides, and sphingomyelins. The blood-brain barrier AD subtype further had increased levels of 14 tryglicerides, which were unaltered in the choroid plexus subtype. No specific alterations in CSF levels of known lipid classes were observed for the choroid plexus subtype.CONCLUSION: We previously identified 5 AD subtypes with distinct underlying mechanisms, including two subtypes with involvement of either the blood-brain barrier or the choroid plexus. The subtypes with different type of brain barrier dysfunction had opposite alterations in CSF levels of lipids from specific classes. This implies that these barriers have a distinct role in lipid metabolism and transport alterations in AD, which may require specific treatments.PMID:39784293 | DOI:10.1002/alz.083975

Alzheimers Dement. 2024 Dec;20 Suppl 2:e089899. doi: 10.1002/alz.089899.ABSTRACTBACKGROUND: Metabolomics captures net influences of exposome, diet, gut microbiome, and genome, informing about individuality and how we respond to interventions. Applications of metabolomics in pharmacology are starting to enable a Systems Pharmacology approach, where the outcome of a treatment is considered to evolve from effects on complex molecular networks, enabling insights into response variations. We bring the power of these approaches to the study of the MIND, a Mediterranean DASH diet for prevention of cognitive decline. We evaluate if metabolomics can reveal beneficial metabolic effects linked to improved cognition in all participants or subgroups of individuals.METHODS: Serum samples were collected from participants enrolled in the MIND trial at the Rush University Medical Center site. Participants were randomized to either the MIND diet or control diet group for three years with study visits, cognitive testing, and sample collection occurring at baseline, Years 1, 2, and 3. A total of 746 serum samples from 243 participants were profiled using targeted and non-targeted metabolomics, lipidomics, metagenomics, and foodomics approaches. The longitudinal effects of the diet on the metabolome were evaluated.RESULTS: We identified metabolic signatures of participants on the MIND diet that were unique compared to the control diet. Major changes in lipid metabolism including ceramides, sphingomyelins, PUFAs, and plasmalogens were noted along with changes in energy metabolism and one carbon metabolism (Figure 1). Food components and exposome-related metabolites were changed. For example, tryptophan betaine (lower in cognitive dysfunction) was increased in the MIND diet group with strongest effects in individuals with low levels at baseline. Additionally, glycoprotein acetyls (GlycA, an inflammation marker associated with AD, cognitive decline, reduced brain volume) was decreased in the MIND diet group compared to controls. Detailed mapping of influences on the gut microbiome are being defined and linked to changes in metabolome.CONCLUSION: The metabolomics data highlighted alterations in metabolism in response to MIND diet. These alterations suggest metabolic benefit for cognitive function and inflammation based on big metabolomics data in ADNI and other cohorts. The variation among individuals seen in our analysis warrants stratification of people enrolled in the MIND study before final conclusions are made on its outcome.PMID:39784222 | DOI:10.1002/alz.089899