Metabolic Alteration in Oxylipins and Endocannabinoids Point to an Important Role for Soluble Epoxide Hydrolase and Inflammation in Alzheimer’s Disease - Finding from Alzheimer’s Disease Neuroimaging Initiative

Metabolic Alteration in Oxylipins and Endocannabinoids Point to an Important Role for Soluble Epoxide Hydrolase and Inflammation in Alzheimer’s Disease - Finding from Alzheimer’s Disease Neuroimaging Initiative | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Metabolic Alteration in Oxylipins and Endocannabinoids Point to an Important Role for Soluble Epoxide Hydrolase and Inflammation in Alzheimer’s Disease - Finding from Alzheimer’s Disease Neuroimaging Initiative View ORCID Profile Kamil Borkowski , Chunyuan Yin , Alida Kindt , Nuanyi Liang , Elizabeth de Lange , Colette Blach , John W. Newman , Rima Kaddurah-Daouk , Thomas Hankemeier , Alzheimer’s Disease Metabolomics Consortium , the Alzheimer’s Disease Neuroimaging Initiative doi: https://doi.org/10.1101/2025.04.01.646677 Kamil Borkowski 1 West Coast Metabolomics Center, Genome Center, University of California Davis , Davis, CA 95616, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kamil Borkowski For correspondence: kborkowski{at}ucdavis.edu Chunyuan Yin 2 Metabolomics and Analytics Centre, Leiden Academic Center for Drug Research, Leiden University , The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alida Kindt 2 Metabolomics and Analytics Centre, Leiden Academic Center for Drug Research, Leiden University , The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nuanyi Liang 1 West Coast Metabolomics Center, Genome Center, University of California Davis , Davis, CA 95616, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Elizabeth de Lange 3 Research Division of Systems Pharmacology and Pharmacy , LACDR, Leiden University , The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Colette Blach 4 Department of Psychiatry and Behavioral Sciences, Duke Institute for Brain Sciences and Department of Medicine, Duke University , Durham, NC 27708, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site John W. Newman 1 West Coast Metabolomics Center, Genome Center, University of California Davis , Davis, CA 95616, USA 5 Western Human Nutrition Research Center, United States Department of Agriculture - Agricultural Research Service , Davis, CA 95616, USA 6 Department of Nutrition, University of California - Davis , Davis, CA 95616, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rima Kaddurah-Daouk 4 Department of Psychiatry and Behavioral Sciences, Duke Institute for Brain Sciences and Department of Medicine, Duke University , Durham, NC 27708, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Thomas Hankemeier 2 Metabolomics and Analytics Centre, Leiden Academic Center for Drug Research, Leiden University , The Netherlands 7 Predictive Pharmacology, Division of Systems Pharmacology and Pharmacy, Leiden Academic Center for Drug Research, Leiden University , The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Mounting evidence implicates inflammation as a key factor in Alzheimer’s disease (AD) development. We previously identified pro-inflammatory soluble epoxide hydrolase (sEH) metabolites to be elevated in plasma and CSF of AD patients and to be associated with lower cognition in non-AD subjects. Soluble epoxide hydrolase is a key enzyme converting anti-inflammatory epoxy fatty acids to pro-inflammatory diols, reported to be elevated in multiple cardiometabolic disorders. Here we analyzed over 700 fasting plasma samples from the baseline of Alzheimer’s Disease Neuroimaging Initiative (ADNI) 2/GO study. We applied targeted mass spectrometry method to provide absolute quantifications of over 150 metabolites from oxylipin and endocannabinoids pathway, interrogating the role for inflammation/immune dysregulation and the key enzyme soluble epoxide hydrolase in AD. We provide further insights into the regulation of this pathway in different disease stages, APOE genotypes and between sexes. Additionally, we investigated in mild cognitive impaired (MCI) patients, metabolic signatures that inform about resilience to progression and conversion to AD. Key findings include I) confirmed disruption in this key central pathway of inflammation and pointed to dysregulation of sEH in AD with sex and disease stage differences; II) identified markers of disease progression and cognitive resilience using sex and ApoE genotype stratified analysis highlighting an important role for bile acids, lipid peroxidation and stress response hormone cortisol. In conclusion, we provide molecular insights into a central pathway of inflammation and links to cognitive dysfunction, suggesting novel therapeutic approaches that are based on targeting inflammation tailored for subgroups of individuals based on their sex, APOE genotype and their metabolic profile. Introduction Inflammation is recognized as an important contributor to Alzheimer’s disease (AD) pathology and cognitive decline and as a crucial risk factor for AD development that exacerbates disease progression ( 1 , 2 ) .Oxylipin and endocannabinoid (OxyL-EC) pathways are key regulators of inflammation, with changes in their levels being a hallmark of inflammation-related disorders ( 3 – 13 ). The oxylipins including fatty acid alcohols, diols, epoxides, ketones, and prostanoids are derived from multiple polyunsaturated fatty acids (PUFA) by the action of cyclooxygenases (COX), lipoxygenases (LOX), cytochrome P450 (CYP), soluble epoxide hydrolase (sEH) or reactive oxygen species (ROS) and various downstream enzymatic processes ( 14 ). Circulating endocannabinoids are produced either by acylation and release of acyl ethanolamides from phosphatidylethanolamine and further degraded by fatty acid amide hydrolase (FAAH), or as a product of glycerol-lipid metabolism like monoacylglycerols (MAGL) ( 15 ). Increasing evidence has demonstrated OxyL-EC dysregulation in AD patients. Disturbances at the protein expression and metabolome level of these pathways are observed in both the periphery ( 16 , 17 ) and central nervous system ( 18 – 20 ) in humans. In rodent models of AD, AD-related pathologies can be altered by manipulating OxyL-EC related enzymes, including 5-LOX ( 21 , 22 ), 12/15-LOX ( 23 – 25 ), COX ( 26 , 27 ), sEH ( 28 , 29 ), CYP ( 30 , 31 ), FAAH ( 32 ), MAGL ( 33 ), and their combination ( 34 ). Moreover, genetic polymorphisms in many of these pathways have been linked to AD risk, supporting causative effects from these pathways ( 35 – 37 ). The Alzheimer’s Disease Metabolomics Consortium (ADMC) has shown that peripheral metabolic changes informed about cognitive changes, brain imaging changes, and ATN markers for disease ( 38 – 41 ). We have recently generated a comprehensive profile of OxyL-EC pathways in subsets of well-characterized AD cohorts, including the Religious Orders Study and the Rush Memory and Aging Project (ROS-MAP) ( 42 ) and the Emory cohort (the Emory Healthy Brain Study, Cognitive Neurology Research, and Memory) ( 43 ). We have used a state-of-the-art validated, quantitative, targeted mass spectrometry platform, providing absolute quantification of ∼ 150 OxyL-EC, covering multiple metabolic pathways and possible substrates ( 44 , 45 ). This pioneer work identified metabolites of CYP/sEH and acyl ethanolamides in plasma and CSF to be associated with AD pathology, and plasma sEH metabolites to be associated with perceptual speed in cognitively normal and mild cognitively impaired subjects. Furthermore, utilizing the metabolomic-proteomic integration approach, we have shown association of the sEH pathway with CSF proteins related to glycolysis, vascular inflammation and neuronal outgrowth ( 46 ), with changes in CSF levels of those proteins being reflective of AD-related changes in the brain ( 47 ). In the current manuscript we seek to confirm and expand on our previous findings of the dysregulation in OxyL-EC pathways in AD, utilizing 763 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 2/GO cohort. The ADNI cohort enables us to further investigate the dysregulation of OxyL-EC pathways at different disease stages, ranging from cognitively normal (CN), mild cognitively impaired (MCI) to AD, and the association of baseline OxyL-EC levels with future cognitive decline. Additionally, to uncover disease heterogeneity, we investigate the influence of sex and APOE genotype on the OxyL-EC interaction with AD-related outcomes. Methods Study Participants The Alzheimer’s Disease Neuroimaging Initiative (ADNI) study recruited 763 individuals over the age of 70 years into longitudinal study. At baseline (the time when patients enrolled in the project), this comprised 178 cognitively normal controls (CN), 445 with mild cognitive impairment (MCI; 290 early-stage and 155 late-stage MCI) and 136 with Alzheimer’s Disease (AD). The early-stage and late-stage MCI patients were grouped together as the distinction between these two groups is minimal. Follow-up cognition tests were performed at 1, 2 and 4 years after baseline. We analyzed 763 plasma samples from baseline, from which 23 non-fasting participants were excluded. Additional participant group classification was based on the experienced diagnostic change, i.e., progression from CN to MCI or AD, or from MCI to AD within 1, 2 or 4 years after baseline. “Stable CN” comprises CN baseline samples that did not convert to either MCI or AD within 4 years follow up, and “stable MCI” comprises MCI baseline patients that did not convert to AD within 2 years follow up. Patients whose diagnosis reverted, i.e., changed from AD to MCI or CN, or from MCI to CN were excluded (n=39). See Table-1 and Table-2 for more details. View this table: View inline View popup Table-1 Demographics and clinical data of studied ADNI subjects at baseline. View this table: View inline View popup Download powerpoint Table-2 Diagnoses for subjects with available CSF measurements in each visit. Metabolomics profiling All baseline plasma samples (n=763) were assessed using a targeted UHPLC-MS/MS lipidomic profiling strategy as previously described ( 44 ). Briefly, liquid-liquid extraction with 1:1 methyl tertbutyl ether/n-butanol was performed on plasma samples after including isotopically labeled internal standards and targets were measured using reversed-phase liquid chromatography-mass spectrometry (RPLC-MS/MS) under high and low pH conditions after solvent exchange. Specific UHPLC systems and columns were employed for optimal separation and detection and data preprocessing was performed with vendor-specific software to ensure accurate peak integration and internal standard matching. Briefly, for the high pH measurements a SCIEX 6500+ QTRAP mass spectrometer was used with a Kinetex EVO column by Phenomenex followed by analyst data acquisition software (Version V1.7.2, AB Sciex). The triple quadrupole mass spectrometer operated in polarity switching mode and all analytes were monitored in dynamic Multiple Reaction Monitoring (dMRM) mode. While for the low pH analysis, a Sciex 7500 QTRAP mass spectrometer was used with an Acquity UPLC BEH C18 column (Waters) followed by Sciex OS Software V2.0.0.45330 (AB Sciex). This platform enables analyses of 260 metabolites covering oxylipins and their free fatty acid precursors, lysophospholipids, sphingoid bases, PAF, endocannabinoids and bile acids. All samples were processed with rigorous quality control measures including case/control randomization over all batches, inclusion of solvent blanks, pooled sample quality control aliquots, and NIST Standard Reference Material 1950 – Metabolites in Human Plasma (Merck, Darmstadt, Germany). These batches were re-randomized for measurements where method blanks, reference and solvent calibration samples were included at regular intervals. The majority of analytes were quantified against analytical standards, where area counts were recorded, adjusted for deuterated surrogate responses and then calculated into nanomolar concentrations using solvent calibration lines from which the lowest calibration point was subtracted from other calibration points. Next, the calibration lines were checked for outliers which were identified using an outlier test, where only calibration points at the ends were allowed to be removed. A calibration point within the calibration line was removed if the ratio was lower than its previous (lower) calibration point. After these corrections, the model was recalculated. Reported monoacylglycerols (MAGs) are the sum of 1- and 2-acyl isomers and 10-Nitrooleate is the sum of 9-Nitrooleate and 10-Nitrooleate, due to isomerization during sample processing. Data preprocessing After peak integration an inhouse quality assessment tool was used to perform correction of target compounds with internal standards to obtain relative ratios, batch correction, and filtering of targets with low signal to noise ratios. To obtain absolute concentrations, linear regression models were calculated per batch between known concentrations and relative ratios for each target. The ratio of the lowest calibration point (CAL0) was subtracted from all ratios in the calibration line. Baseline data with detectable coverage of over 70% were further analyzed. All data were log2-transformed. The following data pre-processing procedure was performed in JMP® Pro 17.2.0 (SAS Institute Inc, Cary NC): outliers were detected and removed using the Robust Fit Outliers method and missing values were imputed with the Multivariate Normal Imputation method ( 48 ). Ratios of metabolites were calculated based on the base-2 exponential-transformed data after imputation, followed by log2-transformation before further analysis. Non-fasted participants (n =23) and participants of Significant Memory Concern (SMC) were excluded from further analysis. Medication adjustments were made based on the stepwise AIC-backward method previously described ( 49 , 50 ). Statistical analysis Data reduction To reduce the dimensionality and collinearity of the data, gain additional information about associations among variables and to facilitate result interpretation variables showing significant associations with the outcomes (Determined by ANOVA) were converted into cluster components. Variables were clustered using the Jmp Pro implementation of the VARCLUS Procedure, a principal components analysis for variable grouping and cluster component (the linear combination of all variables in each cluster) calculation. This is a data-driven approach where the algorithm defines the optimal number of clusters, with resulting clusters composed of highly correlated variables. Those correlations in terms of metabolomic data mostly align with biological pathways, further confirming integrity of the data. Comparison of lipid mediator levels between diagnosis groups For lipid mediator levels, ANOVA followed by Dunnett’s post hoc test was performed to compare means between CN and other diagnosis groups, stratified by sex. Lipid mediators exhibiting significant differences between groups were grouped into clusters as described above. Cluster components were used in ANOVA analysis with Dunnett post-test, comparing the difference in lipid mediator levels between CN and other diagnosis groups. Supplemental Table S1 and Supplemental Table S2 describe cluster members as well as their correlations within the cluster, least square means (standard error range) and ANOVA p-values. ANOVA p-values for individual metabolites are presented in the Supplemental Table S3 . Presence of the APOE 4 allele, sex, body mass index (BMI), age and years of education were included as covariates in all analyses. This analysis was performed in R studio software (Version 4.3.1). Figures were created in R, cytoscape 3.10.2. Multiple comparison control was accomplished with the false discovery rate (FDR) correction method of Benjamini and Hochberg ( 51 ) MCI conversion and memory (ADNI MEM) predictive minimal models Using JMP 17.0 pro (JMP, SAS institute, Cary, NC), we applied a combination of bootstrap forest and stepwise linear (for ADNI MEM) and logistic (for MCI converters vs stable MCI) regression modeling, with Bayesian Information Criterion (BIC) cutoff. Variable selection by bootstrap forest minimized outlier effects. Variables most frequently appearing in the models were identified by bootstrap forest (logistic or regression, respectively): trees in forest =100; terms sampled per split =5; bootstrap sample rate =1. A variable contribution scree plot was generated using variable rank and the likelihood ratio of chi-square (for categorical fasted/non-fasted prediction) or sum of squares (for continues cognitive scores). The scree plot was used to determine a likelihood ratio of chi-square or sum of squares cutoff value for variables contributing to the model. Selected variables were then subjected to forward stepwise logistic or linear regression modelling. Stepwise regression was used to highlight independent predictors of outcomes, to create the minimal model. Results In the current study we have analyzed plasma from the 763 participants from the ADNI 2/GO, providing absolute quantification of 45 eicosanoids, 5 endocannabinoids, 49 lysophospholipids, 13 monounsaturated and polyunsaturated fatty acid (MUFA&PUFA), 4 sphingolipids, and 1 steroids lipids ( Figure 1 and Supplemental Table S3 ). In addition, our data included 18 bile acids ( Supplemental Figure S1 ). The bile acid levels in ADNI cohort were previously reported ( 40 ) and are used in the current analysis only to show their interaction with lipid mediators. Download figure Open in new tab Figure 1. Quantified oxylipins, endocannabinoids and polyunsaturated fatty acids in plasma samples of ADNI 2/GO participants, projected onto their metabolic pathway. Nod size indicates the average concentration observed across the samples, with chemical classes indicated by node color. 1. Sex-specific differences in sEH metabolites along disease trajectories To assess the changes in oxylipin metabolism along disease trajectory, we compared the differences in their levels between diagnosis groups, in the analysis stratified by sex. The following groups were used: cognitively normal (CN), mild cognitively impaired (MCI) and dementia (AD). We excluded subjects who were cognitively normal at baseline who change their status at the 4-year follow-up. The differences between the groups were compared using ANOVA with significant metabolites further converted into cluster components ( Figure 2 ). Factorial analysis with sex x group interaction was applied to identify metabolites affected differently by diagnosis group between males and females. Out of 224 metabolites and informative ratios and summations, 21 showed differences between groups. The 21 significant variables were condensed into 6 cluster components by variable clustering and reanalyzed by ANOVA with a Dunnett post-test against CN in a sex-stratified analysis ( Figure 2 ). Individual cluster members, their contribution to the cluster and their mean differences between groups are described in the Supplemental Table S1 and Supplemental Table S2 . The main differences were observed in fatty acid vicinal diols (products of sEH metabolism, Clusters 1 for eicosapentanoic acid (EPA) and docosahexanoic acid (DHA) and Cluster 2 for the arachidonic acid (AA) derivatives) with sex differences. In Females, both omega-3 (EPA and DHA) and omega-6 (AA) derivatives were higher in AD when compared to the CN. However, the MCI was not different from the CN. Additionally, differences were observed in the levels of DHA-derived LOX metabolites (HDoHEs) and LPAs (Clusters 3 and 4) with MCI showing higher levels than CN and cortisol with AD showing higher levels than HC. Download figure Open in new tab Figure 2. Soluble epoxide hydrolase metabolites are elevated along AD trajectory in a sex specific manner. Differences in means of lipid mediators between diagnosis groups, stratified by sex. To reduce data dimensionality and to facilitate interpretation and presentation, lipid mediators manifesting significant diagnosis group differences were converted into cluster components. Individual cluster members are defined in the cluster description, with the more detailed description of individual contribution to the cluster and differences in means between the treatment groups provided in the Supplemental Table S1 and Supplemental Table S2 . Colors indicate diagnosis group: green – cognitively normal (CN; 83 females, 75 males) defined as healthy subjects who remind healthy after 4 years follow up; yellow - mild cognitively impaired (MCI; 179 females, 215 males); red – Alzheimer’s disease (AD; 53 females, 79 males). P values are derived from ANOVA with Dunnett post-test and indicate the differences from the CN. In males, omega-3 diols differences did not reach significance (e.g., AD vs stable CN p =0.07). On the other hand, AA diols were higher in MCI and AD, compared to CN. This diagnosis group effect for both omega-3 and omega-6 vicinal diols was different from the one observed in females, with the diagnosis group x sex interaction p =0.078 (for cluster 1, omega-3 diols) and p =0.022 (for cluster 2, omega-6 diols) in the diagnosis group and sex factorial analysis. Additionally, 10-Nitrooleate was lower in MCI than CN and cortisol was higher in AD then CN. 2. Lipid mediators and APOE4 discriminate between stable MCI and MCI converters Metabolic signatures associated with AD resilience, i.e., remaining MCI in comparison to MCI that progresses further (within 2 years) into AD, are poorly understood. Utilizing ADNI longitudinal information about patient diagnosis status, we defined the minimal number of factors (both clinical outcomes and lipid mediators, including bile acids) that can describe the difference between converters and non-converters in a sex-stratified analysis ( Figure 3 ). In both sexes, combining APOE4 status with the plasma lipid mediator levels yielded the best discriminatory model: only APOE4 AUC = 64 for females and 62 for males; only lipid mediators AUC = 80 for females and 73 for males; APOE4 and lipid mediators AUC = 85 for females and 78 for males. Download figure Open in new tab Figure 3: Lipid mediators and APOE genotype define AD resilient phenotype. Minimal model discriminating between stable MCI and MCI-AD converters (within 2 years), generated using stepwise logistic regression, stratified by sex. Biometrics (BMI, age,), presence of APOE4 allele and lipid mediators, including oxylipins, endocannabinoids and bile acids were used. The Receiver operation characteristics (ROC) curve is shown for the best model consisting of APOE4 information together with lipid mediators (red line) as well as for APOE4 alone (yellow line) and lipid mediators alone (orange line). The estimate and p values for each factor in the model are displayed in the ROC graph. The number of subjects: stable MCI = 153 females and 182 males; MCI converters = 32 females and 41 males. In females, the likelihood of remaining MCI without converting to AD (AD resilience) was defined by the lack of an APOE4 allele, lower glycine conjugation of chenodeoxycholic acid (define as GCDCA/CDCA ratio) and lower levels of EPA-derived 8iso-prostaglanding F3 alpha (8-iso PGF3a). In males, AD resilience was defined by the lack of APOE4 allele, lower cortisol and hyodeoxycholic acid (HDCA) and higher levels of lysophosphatidylinostitol palmitate (i.e., LPI16.0). Further factorial analysis with group x sex interaction (model adjusted by APOE, age and BMI) confirm sex-specific effect for GCDCA/CDCA (p interaction =0.04), 8iso_PGF3a (p interaction =0.003) and cortisol (p interaction =0.04). The interaction terms for HDCA and LPI16:0 were not significant. The derived minimal models had similar statistical power to discriminate converter vs non-converters whether the model was applied for the 1, 2 or 4 years follow up conversion criteria. 3. Markers of cognitive resilience among differential Tau and amyloid pathology, mediated by APOE genotypes Next, we sought to define peripheral markers of memory function, measured by ADNI composite memory score( 52 ) within the cohort. We are particularly interested in those markers in subjects manifesting high Tau and amyloid pathology, as indicators of cognitive resilience. Tau and amyloid pathology are measured by the ratio of total Tau to AB42 in CSF. In the ADNI GO/2 population, the Tau/AB42 ratio in CSF is influenced by APOE genotype, with e2 allele manifesting the least and e4 allele manifesting the highest level ( Supplemental Figure S2 ). Each genotype manifested a range of memory scores, although e2e2 and e2e4 genotypes did not have sufficient number of participants for further analysis (n =3 and =14, respectively). To define the relationship between peripheral metabolism, Tau and amyloid pathology and memory, we applied stratification strategies for correlation analysis between plasma lipid mediators and the memory score. For correlation analysis, we applied a combination of random forest for features selection and stepwise linear regression to define a minimal model that explains the relation between memory and plasma lipid mediators (see materials and methods). We identified APOE genotype stratification to yield significant models for associations of plasma metabolites and memory scores ( Figure 4 ) in comparison to stratification by CSF Tau/AB42 (using previously published cutoff value ( 53 )), which did not produce significant models. Each APOE genotype manifested unique sets of variables contributing to the model. Memory in APOE 2/3 was mainly defined by the age (negative association, p =0.0004) and 10-Nitrooleate (positive association, p =0.0005), in addition to lesser contribution of the ratio of GCDCA to GLCA (positive association, p =0.018) and LPG16:1 (negative association, p =0.015). Those 4 variables generated a memory model with the RSq =0.49 with the n =71. Memory in APOE3/3 genotype subjects was defined by age, the ratio of 19,20-DiHDPA/19,20-EpDPE, LPE22:5 (negative association, p =0.0001, 0.0079, 0.0028 respectively) and the years of education, LPI20:4 and the ratio of 5-HETE/AA (positive association, p =0.0001, 0.0013 and 0.015 respectively). Those 6 variables generated a memory model with the RSq =0.18 with the n =387. Memory in APOE3/4 genotype subjects was defined by age, 19,20-DiHDPA and LPE16:0 (negative association, p<0.0001, 0.005, and 0.019 respectively). Those 3 variables generated a memory model with the RSq=0.16 with the n=283. Memory in APOE4/4 genotype subjects was defined by an unknown (unidentified) oxylipin, lysophosphatidylinositol (LPI)16:1, 8HDoHE (positive association, p=0.0004, 0.0075 and 0.0096 respectively) and cortisol, leukotriene E4 (LTE4) and GDCA (negative association, p=0.0015, 0.0021, 0.0027 respectively). Those 6 variables generated a memory model with the RSq=0.46 with the n=78. The unknown (unidentified) oxylipin manifested mass transition of Resolvin E2 (m/z 333 -> m/z 115, the 115 fragment is for carboxyl group until 4’ carbon, as it breaks by 5’ hydroxyl group), but the chromatography retention time was different from the analytical standard. Additional correlative analysis shows a high correlation of this unknown oxylipin with EPA-derived vicinal diols ( Supplemental Figure S3 ). Considering the importance of this metabolite in our predictive model and poor understanding of Resolvin biology, the full identification of this compound is of interest and is underway. Download figure Open in new tab Figure 4. Lipid mediators reveal heterogeneity among APOE genotypes, an important AD risk factor. Multilinear model showing differential contribution of key peripheral metabolites and other factors such as age and education to memory scores (MEM) across APOE genotypes. The number of subjects: APOE2/3 = 60; APOE3/3 = 334; APOE3/4 = 254; APOE4/4 = 77. To further demonstrate the APOE genotype specificity of those associations, we applied a factorial analysis for the key factors identified in the stepwise model (age, education, 10-Nitrooleate, 19,20-DiHDPA, unknown oxylipin, LTE4 and cortisol) to test the memory score interaction with APOE genotype. Following p interactions were achieved with other key factors used as covariates in the model: age p interaction = 0.06 for e4e4 vs e3e4; 0.35 for e4e4 vs e3e3 and 0.09 for e4e4 vs e2e3; education p interaction = 0.67 for e3e3 vs e3e4; 0.45 for e3e3 vs e4e4 and 0.8 for e3e3 vs e2e3; 10-Nitrooleate p interaction = 0.037 for e2e3 vs e3e3; 0.098 for e2e3 vs e3e4 and 0.74 for e2e3 vs e4e4; 19,20-DiHDPA p interaction = 0.04 for e3e4 vs e4e4; 0.015 for e3e4 vs e3e3 and 0.05 for e3e4 vs e2e3; unknown oxylipin p interaction = 0.12 for e4e4 vs e3e4; 0.07 for e4e4 vs e3e3 and 0.28 for e4e4 vs e2e3; LTE4 p interaction = 0.05 for e4e4 vs e3e4; 0.0052 for e4e4 vs e3e3 and 0.0043 for e4e4 vs e2e3; cortisol p interaction = 0.11 for e4e4 vs e3e4; 0.062 for e4e4 vs e3e3 and 0.17 for e4e4 vs e2e3; Stepwise regression model identifies a minimal number of factors for predicted values and eliminates collinear variables. For example, 19,20-DiHDPA is highly correlated with other omega-3 diols, like EPA derived DiHETEs, and replacing 19,20-DiHDPA with closely correlated DiHETEs would yield similar predictive power. To illustrate the collinear relationship between measured lipid mediators and to facilitate minimal model interpretation, metabolite partial correlation network is presented in Figure S3 . Discussion Increased inflammatory responses have been reported in previous AD investigations but how these systems change over the progression of this disease have not been clearly delineated. Here we show a dysregulation of lipid mediators throughout AD progression and uncover disease heterogeneity influenced by sex and APOE genotype. We confirmed and extended our previous findings, from two independent cohorts, of increased soluble epoxide hydrolase activity during AD development. We also provide evidence implicating the APOE4 allele, bile acid metabolism, lipid peroxidation and stress response as sex-specific factors influencing the resistance of MCI subjects to progress to ADAPOE. Additionally, we showed APOE4/4 genotype-specific associations between plasma inflammatory markers and memory, identifying populations for precision medicine approach for potential therapeutic treatment. Growing evidence points towards the sEH as a novel therapeutic target for AD ( 28 , 54 ). Furthermore, plasma sEH metabolites (derived from AA, EPA and DHA) increase with AD pathology ( 53 ) and these metabolites are associated with cognition (i.e. perceptual speed) in healthy and MCI individuals ( 48 ). A strong connection of sEH-associated biochemistry with AD-affected vascular inflammation and energy metabolism in the central nervous system was also observed ( 55 ). Increased sEH expression in the AD-afflicted brain at both the transcript and protein levels are also seen ( 56 ). In rodent models of AD, AD-related pathologies can be altered by manipulating both sEH ( 28 , 29 ) and CYP ( 30 , 31 ) metabolism. Moreover, several genetic polymorphisms in the CYP/sEH pathway have consistently pointed towards reduced CYP-dependent epoxy fatty acid production and increased sEH activity or expression to be a risk for AD and increase AD-related pathologies ( 57 – 60 ), supporting causative effects of this critical inflammatory switch in the progression of this disease. The current work provides additional evidence from a third independent AD cohort to support the dysregulation of she-dependent metabolism during AD development. Furthermore, the current study provides additional granularity regarding sex, APOE genotype and pre-AD stages. In particular, we saw an increase in EPA, DHA and AA-derived sEH products in females with AD, but not with MCI, compared to HC participants. In contrast, in males, we only saw an increase in AA-derived sEH metabolites, but they were present in subjects with MCI, suggesting that dysregulation of sEH in males may appear earlier in disease progression than in females. Similarly, differences in sEH metabolism have been reported to contribute to the sex difference in the vulnerability to other dementia risk factors, such as ischemic brain injury ( 61 , 62 ), high glycemic diet ( 63 – 66 ) and high-fat diet ( 67 , 68 ). At the molecular level, the possible involvement of sEH in sex-specific AD development includes a wide range fatty acid diol cellular functions (or depletion of fatty acids epoxide), e.g., ferroptosis-mediated neurodegeneration ( 69 ), exciting neurons ( 70 ), immune regulation ( 71 , 72 ), and regulating mitochondrial respiration ( 73 ). For the epoxy fatty acids, their action is beneficial or detrimental depending on factors such as precursor fatty acids, concentration, targeting cells, and biological processes ( 71 , 74 ). Together, this evidence suggests that reducing sEH activity may be a promising therapeutic approach in patients with both MCI and AD. These may include the use of sEH inhibitors currently under development ( 75 , 76 ), but also include current therapies such as estrogen replacement therapy which can both down regulate sEH ( 77 ) and reduce AD risk in post-menopausal women ( 78 ). Our results also support a sex-specific dysregulation of cortisol metabolism in AD, as seen by others ( 79 , 80 ). Particularly we observed, increased cortisol concentrations in AD subjects when compared to CN in both sexes; increased levels in males MCI subjects that further convert to AD within 2 years, when compared to stable MCI phenotype; and negative association with memory in APOE 4/4 carriers. Cortisol is a stress response hormone that facilitates glucose dependent energy metabolism. This hormone crosses blood brain barrier via P-glycoprotein transporter ( 81 ) consistent with the strong correlation between plasma and CSF cortisol levels ( 53 ). Cortisol was previously implicated in neurodegeneration ( 82 ) and cognition ( 83 ) via regulation of the Hypothalamic-Pituitary-Adrenal (HPA) axis ( 84 , 85 ) and was suggested by others as potential therapeutic target for AD ( 86 ). Sex-related differences were also reported, with plasma cortisol associations with brain volumes and memory performance being more pronounced in females than in males ( 87 ). We also show greater difference between CN and AD in females then in males. Our analysis here also adds to those findings by extending our understanding of cortisol and stress involvement in cognition and AD in specific subpopulations. In particular, we noted that males with MCI have elevated cortisol prior to conversion to AD, an effect not observed in females. This appears to contradict the previous findings suggesting a greater importance of this stress hormone in females with AD. Additionally in subjects with APOE 4/4 genotype, we show evidence that cortisol may impact memory performance, together with immune cells regulators. Our finding adds granularity to the known associations of stress hormones, cognition and AD development, identifying subpopulations for further potential therapeutic targeting of cortisol pathway in AD. The current work also indicates the differential relationship between markers of immune cells activation and memory performance among various APOE genotype carriers. Notably in the presence of the APOE 4/4 genotype, memory was negatively associated with the pro-inflammatory cysteinyl leukotriene metabolism reported by LTE4, generated via 5-LOX-dependent metabolism. LTE4 is a monocyte activator with vasoactive properties, activating vascular permeability and edema ( 88 ), and its levels were associated with cardiovascular disorders ( 89 , 90 ). Previously, 5-LOX expression was found to be upregulated in the peripheral blood mononuclear cells from AD patients ( 91 , 92 ). Furthermore, a gain of function 5-LOX polymorphism was also associated with risk of AD ( 93 ), indicating a potential causative relationship of the dysregulation of this pathway to AD. Meanwhile in rodent models of AD upregulation of hippocampal and cortex 5-LOX expression was associated with elevated γ-secretase activity and Aβ peptide formation ( 92 , 94 – 97 ). APOE genotypes-specific oxylipin profile in postmortem human dorsolateral prefrontal cortex found that 5-LOX-derived EPA oxylipins LXA5 and LXB5 in brain were negatively associated with cognitive performance, and the association was stronger among APOE4 carriers compared to APOE3 carriers ( 98 ). This research also found that the levels of a 5-LOX product, LXA4 was negatively associated with sEH activity. On the basis of this, our results here further indicate that the dynamics between APOE genotypes, 5-LOX, sEH, fatty acid precursors and cognition can be captured in the blood lipidome, which can be monitored less invasively and more routinely for longitudinal cohorts. The functional interaction between APOE, 5-LOX and sEH can be supported by various mechanisms: for example, the higher level of APOE4 carriers in activation of Ca 2+ influx and thus Ca 2+ -dependent cPLA2 and 5-LOX compared to APOE2 or E3 ( 98 , 99 ); for another, the presence of prone-to-aggregation lipid-poor APOE4 protein can affect the lipid efflux functions of ABCA1( 100 ) and change the lipid composition of cells ( 101 ), which serve as substrates to release free fatty acids subject to 5-LOX and sEH metabolism ( 102 , 103 ). A possibility also remains that APOE4 leads to the altered ability of trafficking inflammation-regulating lipid mediators between lipoprotein and lipoprotein-metabolizing cells, which includes microglia, a critical player in AD pathogenesis ( 102 ). Further investigation is therefore warranted to clarify if the combination of altering 5-LOX metabolism and fatty acid composition together can be a specific modifiable target for APOE4 carriers to improve cognition. Limitations This study is not big enough to explore APOE genotypes and sex combined stratification. ADNI 1 and GO/2 cohorts do not represent full demographics of US and world population. This shortcoming is addressed in ADNI 3 cohort. Longitudinal data are needed to further connect dysregulation in lipid mediators, sEH among others and disease progression, define subgroups to identify population for targeted interventions. Data availability statement All metabolomics data is publicly available at https://ida.loni.usc.edu/login.jsp Conflict of interest R.K-D is an inventor on several patents in the field of metabolomics and holds equity in Metabolon Inc., Chymia LCC, and Metabosensor LLC which were not involved in this study. She holds several patents on applications of metabolomics in diseases of the central nervous system. M.A. is an inventor on several patents on applications of metabolomics in diseases of the central nervous system; he holds equity in Chymia LLC and IP in PsyProtix and Atai, which were not involved in this study. G.B. is a consultant for SciNeuro Pharmaceuticals and Kisbee Therapeutics. He is also the Editor-in-Chief of Molecular Neurodegeneration. All other authors declare no competing interests. Acknowledgement The Alzheimer’s Disease Metabolomics Consortium (ADMC) is funded wholly or in part by the following National Institute on Aging grants and supplements, which are components of the Accelerating Medicines Partnership for AD (AMP-AD) and/or Molecular Mechanisms of the Vascular Etiology of AD (M2OVE-AD): NIA R01AG046171, RF1AG051550, RF1AG057452, R01AG059093, RF1AG058942, U01AG061359, U19AG063744, 3U19AG063744-04S1, 1R01AG081322, U01AG088562, and FNIH: #DAOU16AMPA awarded to Dr. Rima Kaddurah-Daouk at Duke University in partnership with a large number of academic institutions. Chunyuan Yin acknowledges support from the China Scholarship Council fellowship (No. 202006550003). TH acknowledges funding for the Exposome-Scan infrastructure by the Dutch Research Council (project number 175.2019.032). Reference 1. ↵ Akiyama H , Barger S , Barnum S , Bradt B , Bauer J , Cole GM , Cooper NR , Eikelenboom P , Emmerling M , Fiebich BL , Finch CE , Frautschy S , Griffin WS , Hampel H , Hull M , Landreth G , Lue L , Mrak R , Mackenzie IR , McGeer PL , O’Banion MK , Pachter J , Pasinetti G , Plata-Salaman C , Rogers J , Rydel R , Shen Y , Streit W , Strohmeyer R , Tooyoma I , Van Muiswinkel FL , Veerhuis R , Walker D , Webster S , Wegrzyniak B , Wenk G , Wyss-Coray T. Inflammation and Alzheimer’s disease . Neurobiol Aging. 2000 ; 21 ( 3 ): 383 – 421 . doi: 10.1016/s0197-4580(00)00124-x . PubMed PMID: 10858586 ; PMCID: PMC3887148 . OpenUrl CrossRef PubMed Web of Science 2. ↵ Tan S , Chen W , Kong G , Wei L , Xie Y . Peripheral inflammation and neurocognitive impairment: correlations, underlying mechanisms, and therapeutic implications . Front Aging Neurosci . 2023 ; 15 : 1305790 . doi: 10.3389/fnagi.2023.1305790 . PubMed PMID: 38094503 ; PMCID: PMC10716308 . OpenUrl CrossRef PubMed 3. ↵ Chistyakov DV , Guryleva MV , Stepanova ES , Makarenkova LM , Ptitsyna EV , Goriainov SV , Nikolskaya AI , Astakhova AA , Klimenko AS , Bezborodova OA , Rasskazova EA , Potanina OG , Abramovich RA , Nemtsova ER , Sergeeva MG . Multi-Omics Approach Points to the Importance of Oxylipins Metabolism in Early-Stage Breast Cancer . Cancers (Basel ). 2022 ; 14 ( 8 ). doi: 10.3390/cancers14082041 . PubMed PMID: 35454947 ; PMCID: PMC9032865 . OpenUrl CrossRef PubMed 4. Grapov D , Adams SH , Pedersen TL , Garvey WT , Newman JW . Type 2 diabetes associated changes in the plasma non-esterified fatty acids, oxylipins and endocannabinoids . PLoS One . 2012 ; 7 ( 11 ): e48852 . doi: 10.1371/journal.pone.0048852 . PubMed PMID: 23144998 ; PMCID: 3493609 . OpenUrl CrossRef PubMed 5. Jurado-Fasoli L , Di X , Kohler I , Osuna-Prieto FJ , Hankemeier T , Krekels E , Harms AC , Yang W , Garcia-Lario JV , Fernandez-Veledo S , Ruiz JR , Rensen PCN , Martinez-Tellez B . Omega-6 and omega-3 oxylipins as potential markers of cardiometabolic risk in young adults . Obesity (Silver Spring ). 2022 ; 30 ( 1 ): 50 – 61 . doi: 10.1002/oby.23282 . PubMed PMID: 34898010 ; PMCID: PMC9299871 . OpenUrl CrossRef PubMed 6. Shearer GC , Borkowski K , Puumala SL , Harris WS , Pedersen TL , Newman JW . Abnormal lipoprotein oxylipins in metabolic syndrome and partial correction by omega-3 fatty acids . Prostaglandins Leukot Essent Fatty Acids . 2018 ; 128 : 1 – 10 . doi: 10.1016/j.plefa.2017.10.006 . PubMed PMID: 29413356 . OpenUrl CrossRef PubMed 7. Nayeem MA . Role of oxylipins in cardiovascular diseases . Acta Pharmacol Sin . 2018 ; 39 ( 7 ): 1142 – 54 . doi: 10.1038/aps.2018.24 . PubMed PMID: 29877318 ; PMCID: PMC6289399 . OpenUrl CrossRef PubMed 8. Le DE , Garcia-Jaramillo M , Bobe G , Alcazar Magana A , Vaswani A , Minnier J , Jump DB , Rinkevich D , Alkayed NJ , Maier CS , Kaul S . Plasma Oxylipins: A Potential Risk Assessment Tool in Atherosclerotic Coronary Artery Disease . Front Cardiovasc Med . 2021 ; 8 : 645786 . doi: 10.3389/fcvm.2021.645786 . PubMed PMID: 33969011 ; PMCID: PMC8097092 . OpenUrl CrossRef PubMed 9. McReynolds CB , Cortes-Puch I , Ravindran R , Khan IH , Hammock BG , Shih P-aB , Hammock BD , Yang J . Plasma Linoleate Diols Are Potential Biomarkers for Severe COVID-19 Infections . Frontiers in physiology . 2021 ; 12 . 10. Biagini D , Franzini M , Oliveri P , Lomonaco T , Ghimenti S , Bonini A , Vivaldi F , Macera L , Balas L , Durand T . MS-based targeted profiling of oxylipins in COVID-19: A new insight into inflammation regulation . Free Radical Biology and Medicine . 2022 ; 180 : 236 – 43 . OpenUrl CrossRef PubMed 11. Karu N , Kindt A , Lamont L , van Gammeren AJ , Ermens AAM , Harms AC , Portengen L , Vermeulen RCH , Dik WA , Langerak AW , van der Velden VHJ , Hankemeier T . Plasma Oxylipins and Their Precursors Are Strongly Associated with COVID-19 Severity and with Immune Response Markers . Metabolites [Internet ]. 2022 ; 12 7 . 12. Borkowski K , Newman JW , Aghaeepour N , Mayo JA , Blazenovic I , Fiehn O , Stevenson DK , Shaw GM , Carmichael SL . Mid-gestation serum lipidomic profile associations with spontaneous preterm birth are influenced by body mass index . PLoS One . 2020 ; 15 ( 11 ): e0239115 . doi: 10.1371/journal.pone.0239115 . PubMed PMID: 33201881 ; PMCID: PMC7671555 . OpenUrl CrossRef PubMed 13. ↵ Svenvik M , Raffetseder J , Brudin L , Lindberg R , Blomberg M , Axelsson D , Jenmalm MC , Ernerudh J , Nording ML . Plasma oxylipin levels associated with preterm birth in preterm labor() . Prostaglandins Leukot Essent Fatty Acids . 2021 ; 166 : 102251 . doi: 10.1016/j.plefa.2021.102251 . PubMed PMID: 33626402 . OpenUrl CrossRef PubMed 14. ↵ Gabbs M , Leng S , Devassy JG , Monirujjaman M , Aukema HM . Advances in Our Understanding of Oxylipins Derived from Dietary PUFAs . Adv Nutr . 2015 ; 6 ( 5 ): 513 – 40 . doi: 10.3945/an.114.007732 . PubMed PMID: 26374175 ; PMCID: PMC4561827 . OpenUrl Abstract / FREE Full Text 15. ↵ Biernacki M , Skrzydlewska E . Metabolism of endocannabinoids . Postepy Hig Med Dosw (Online ). 2016 ; 70 ( 0 ): 830 – 43 . doi: 10.5604/17322693.1213898 . PubMed PMID: 27516570 . OpenUrl CrossRef PubMed 16. ↵ Yoshida Y , Yoshikawa A , Kinumi T , Ogawa Y , Saito Y , Ohara K , Yamamoto H , Imai Y , Niki E . Hydroxyoctadecadienoic acid and oxidatively modified peroxiredoxins in the blood of Alzheimer’s disease patients and their potential as biomarkers . Neurobiology of aging . 2009 ; 30 ( 2 ): 174 – 85 . OpenUrl CrossRef PubMed Web of Science 17. ↵ Shekhar S , Yadav SK , Rai N , Kumar R , Yadav Y , Tripathi M , Dey AB , Dey S . 5-LOX in Alzheimer’s Disease: Potential Serum Marker and In Vitro Evidences for Rescue of Neurotoxicity by Its Inhibitor YWCS . Mol Neurobiol . 2018 ; 55 ( 4 ): 2754 – 62 . Epub 20170427. doi: 10.1007/s12035-017-0527-1 . PubMed PMID: 28451886 . OpenUrl CrossRef PubMed 18. ↵ Pasinetti G , Aisen P . Cyclooxygenase-2 expression is increased in frontal cortex of Alzheimer’s disease brain . Neuroscience . 1998 ; 87 ( 2 ): 319 – 24 . OpenUrl CrossRef PubMed Web of Science 19. Ebright B , Assante I , Poblete RA , Wang S , Duro MV , Bennett DA , Arvanitakis Z , Louie SG , Yassine HN . Eicosanoid lipidome activation in post-mortem brain tissues of individuals with APOE4 and Alzheimer’s dementia . Alzheimer’s Research & Therapy . 2022 ; 14 ( 1 ): 152 . doi: 10.1186/s13195-022-01084-7 . OpenUrl CrossRef PubMed 20. ↵ Ikonomovic MD , Abrahamson Ee Fau - Uz T, Uz T Fau - Manev H, Manev H Fau - Dekosky ST , Dekosky ST . Increased 5-lipoxygenase immunoreactivity in the hippocampus of patients with Alzheimer’s disease(0022-1554 (Print)) . 21. ↵ Firuzi O , Zhuo J , Chinnici CM , Wisniewski T , Praticò D . 5-Lipoxygenase gene disruption reduces amyloid-β pathology in a mouse model of Alzheimer’s disease . The FASEB journal: official publication of the Federation of American Societies for Experimental Biology . 2008 ; 22 ( 4 ): 1169 . OpenUrl PubMed 22. ↵ Chu J , Praticò D . The 5-Lipoxygenase as modulator of Alzheimer’s γ-secretase and therapeutic target . Brain research bulletin . 2016 ; 126 : 207 – 12 . OpenUrl PubMed 23. ↵ Succol F , Praticò D . A role for 12/15 lipoxygenase in the amyloid β precursor protein metabolism . Journal of Neurochemistry . 2007 ; 103 ( 1 ): 380 – 7 . doi: 10.1111/j.1471-4159.2007.04742.x . OpenUrl CrossRef PubMed 24. Chu J , Zhuo JM , Praticò D . Transcriptional regulation of βsecretase-1 by 12/15-lipoxygenase results in enhanced amyloidogenesis and cognitive impairments . Annals of neurology . 2012 ; 71 ( 1 ): 57 – 67 . OpenUrl CrossRef PubMed 25. ↵ Joshi YB , Giannopoulos PF , Praticò D . The 12/15-lipoxygenase as an emerging therapeutic target for Alzheimer’s disease . Trends in Pharmacological Sciences . 2015 ; 36 ( 3 ): 181 – 6 . doi: 10.1016/j.tips.2015.01.005 . OpenUrl CrossRef 26. ↵ Xiang Z , Ho L Fau - Yemul S, Yemul S Fau - Zhao Z, Zhao Z Fau - Qing W, Qing W Fau - Pompl P, Pompl P Fau - Kelley K, Kelley K Fau - Dang A, Dang A Fau - Qing W, Qing W Fau - Teplow D, Teplow D Fau - Pasinetti GM , Pasinetti GM . Cyclooxygenase-2 promotes amyloid plaque deposition in a mouse model of Alzheimer’s disease neuropathology(1052-2166 (Print)) . 27. ↵ Woodling NS , Colas D , Wang Q , Minhas P , Panchal M , Liang X , Mhatre SD , Brown H , Ko N , Zagol-Ikapitte I . Cyclooxygenase inhibition targets neurons to prevent early behavioural decline in Alzheimer’s disease model mice . Brain . 2016 ; 139 ( 7 ): 2063 – 81 . OpenUrl CrossRef PubMed 28. ↵ Grinan-Ferre C , Codony S , Pujol E , Yang J , Leiva R , Escolano C , Puigoriol-Illamola D , Companys-Alemany J , Corpas R , Sanfeliu C , Perez B , Loza MI , Brea J , Morisseau C , Hammock BD , Vazquez S , Pallas M , Galdeano C . Pharmacological Inhibition of Soluble Epoxide Hydrolase as a New Therapy for Alzheimer’s Disease . Neurotherapeutics . 2020 ; 17 ( 4 ): 1825 – 35 . doi: 10.1007/s13311-020-00854-1 . PubMed PMID: 32488482 ; PMCID: PMC7851240 . OpenUrl CrossRef PubMed 29. ↵ Ghosh AA-O , Comerota MM , Wan D , Chen F , Propson NE , Hwang SA-OX , Hammock BD , Zheng HA-O . An epoxide hydrolase inhibitor reduces neuroinflammation in a mouse model of Alzheimer’s disease . LID - doi: 10.1126/scitranslmed.abb1206 [doi] LID - eabb1206(1946-6242 (Electronic)). OpenUrl Abstract / FREE Full Text 30. ↵ Mast N , Saadane A , Valencia-Olvera A , Constans J , Maxfield E , Arakawa H , Li Y , Landreth G , Pikuleva IA . Cholesterol-metabolizing enzyme cytochrome P450 46A1 as a pharmacologic target for Alzheimer’s disease(1873-7064 (Electronic)) . 31. ↵ Mast N , Li Y , Linger M , Clark M , Wiseman J , Pikuleva IA . Pharmacologic Stimulation of Cytochrome P450 46A1 and Cerebral Cholesterol Turnover in Mice* . Journal of Biological Chemistry . 2014 ; 289 ( 6 ): 3529 – 38 . doi: 10.1074/jbc.M113.532846 . OpenUrl Abstract / FREE Full Text 32. ↵ Ruiz-Pérez G , Ruiz de Martín Esteban S, Marqués S, Aparicio N, Grande MT, Benito-Cuesta I, Martínez-Relimpio AM, Arnanz MA, Tolón RM, Posada-Ayala M, Cravatt BF, Esteban JA, Romero J , Palenzuela R. Potentiation of amyloid beta phagocytosis and amelioration of synaptic dysfunction upon FAAH deletion in a mouse model of Alzheimer’s disease. Journal of Neuroinflammation . 2021 ; 18 ( 1 ): 223 . doi: 10.1186/s12974-021-02276-y . OpenUrl CrossRef 33. ↵ Yan W , Yun Y , Ku T , Li G , Sang N . NO 2 inhalation promotes Alzheimer’s disease-like progression: cyclooxygenase-2-derived prostaglandin E 2 modulation and monoacylglycerol lipase inhibition-targeted medication . Scientific Reports . 2016 ; 6 ( 1 ): 22429 . doi: 10.1038/srep22429 . OpenUrl CrossRef PubMed 34. ↵ Bajaj SA-O , Zameer S , Jain S , Yadav V , Vohora D . Effect of the MAGL/FAAH Dual Inhibitor JZL-195 on Streptozotocin-Induced Alzheimer’s Disease-like Sporadic Dementia in Mice with an Emphasis on Aβ, HSP-70, Neuroinflammation, and Oxidative Stress(1948-7193 (Electronic)) . 35. ↵ Yan H , Kong Y , He B , Huang M , Li J , Zheng J , Liang L , Bi J , Zhao S , Shi L . CYP2J2 rs890293 polymorphism is associated with susceptibility to Alzheimer’s disease in the Chinese Han population . Neurosci Lett . 2015 ; 593 : 56 – 60 . doi: 10.1016/j.neulet.2015.03.024 . PubMed PMID: 25796175 . OpenUrl CrossRef PubMed 36. Qu T , Manev R , Manev H . 5-Lipoxygenase (5-LOX) promoter polymorphism in patients with early-onset and late-onset Alzheimer’s disease . J Neuropsychiatry Clin Neurosci . 2001 ; 13 ( 2 ): 304 – 5 . doi: 10.1176/jnp.13.2.304 . PubMed PMID: 11449041 . OpenUrl CrossRef PubMed 37. ↵ Tang W , He M , Yang B , Wei K , Yin M , Zhang L . Association study of polymorphisms in the cyclooxygenase-2 gene and Alzheimer’s disease risk in Chinese . Neurol Sci . 2013 ; 34 ( 5 ): 695 – 9 . doi: 10.1007/s10072-012-1115-9 . PubMed PMID: 22622868 . OpenUrl CrossRef PubMed 38. ↵ Toledo JB , Arnold M , Kastenmuller G , Chang R , Baillie RA , Han X , Thambisetty M , Tenenbaum JD , Suhre K , Thompson JW , John-Williams LS , MahmoudianDehkordi S , Rotroff DM , Jack JR , Motsinger-Reif A , Risacher SL , Blach C , Lucas JE , Massaro T , Louie G , Zhu H , Dallmann G , Klavins K , Koal T , Kim S , Nho K , Shen L , Casanova R , Varma S , Legido-Quigley C , Moseley MA , Zhu K , Henrion MYR , van der Lee SJ , Harms AC , Demirkan A , Hankemeier T , van Duijn CM , Trojanowski JQ , Shaw LM , Saykin AJ , Weiner MW , Doraiswamy PM , Kaddurah-Daouk R , Alzheimer’s Disease Neuroimaging I, the Alzheimer Disease Metabolomics C. Metabolic network failures in Alzheimer’s disease: A biochemical road map . Alzheimers Dement . 2017 ; 13 ( 9 ): 965 – 84 . doi: 10.1016/j.jalz.2017.01.020 . PubMed PMID: 28341160 ; PMCID: PMC5866045 . OpenUrl CrossRef PubMed 39. Liang N , Nho K , Newman JW , Arnold M , Huynh K , Meikle PJ , Borkowski K , Kaddurah-Daouk R , Alzheimer’s Disease Metabolomics C. Peripheral inflammation is associated with brain atrophy and cognitive decline linked to mild cognitive impairment and Alzheimer’s disease . Sci Rep . 2024 ; 14 ( 1 ): 17423 . doi: 10.1038/s41598-024-67177-5 . PubMed PMID: 39075118 ; PMCID: PMC11286782 . OpenUrl CrossRef PubMed 40. ↵ Chen T , Wang L , Xie G , Kristal BS , Zheng X , Sun T , Arnold M , Louie G , Li M , Wu L , Mahmoudiandehkordi S , Sniatynski MJ , Borkowski K , Guo Q , Kuang J , Wang J , Nho K , Ren Z , Kueider-Paisley A , Blach C , Kaddurah-Daouk R , Jia W , Alzheimer’s Disease Neuroimaging I, the Alzheimer Disease Metabolomics C . Serum Bile Acids Improve Prediction of Alzheimer’s Progression in a Sex-Dependent Manner. Adv Sci (Weinh ). 2024 ; 11 ( 9 ): e2306576 . doi: 10.1002/advs.202306576 . PubMed PMID: 38093507 ; PMCID: PMC10916590 . OpenUrl CrossRef PubMed 41. ↵ Amin N , Liu J , Bonnechere B , MahmoudianDehkordi S , Arnold M , Batra R , Chiou YJ , Fernandes M , Ikram MA , Kraaij R , Krumsiek J , Newby D , Nho K , Radjabzadeh D , Saykin AJ , Shi L , Sproviero W , Winchester L , Yang Y , Nevado-Holgado AJ , Kastenmuller G , Kaddurah-Daouk R , van Duijn CM . Interplay of Metabolome and Gut Microbiome in Individuals With Major Depressive Disorder vs Control Individuals . JAMA Psychiatry . 2023 ; 80 ( 6 ): 597 – 609 . doi: 10.1001/jamapsychiatry.2023.0685 . PubMed PMID: 37074710 ; PMCID: PMC10116384 . OpenUrl CrossRef PubMed 42. ↵ Borkowski K , Taha AY , Pedersen TL , De Jager PL , Bennett DA , Arnold M , Kaddurah-Daouk R , Newman JW . Serum metabolomic biomarkers of perceptual speed in cognitively normal and mildly impaired subjects with fasting state stratification . Scientific Reports . 2021 ; 11 ( 1 ): 18964 . doi: 10.1038/s41598-021-98640-2 . OpenUrl CrossRef PubMed 43. ↵ Borkowski K , Pedersen TL , Seyfried NT , Lah JJ , Levey AI , Hales CM , Dammer EB , Blach C , Louie G , Kaddurah-Daouk R , Newman JW , Alzheimer’s Disease Metabolomics C . Association of plasma and CSF cytochrome P450, soluble epoxide hydrolase, and ethanolamide metabolism with Alzheimer’s disease . Alzheimer’s Research & Therapy . 2021 ; 13 ( 1 ): 149 . doi: 10.1186/s13195-021-00893-6 . OpenUrl CrossRef PubMed 44. ↵ Yang W , Schoeman JC , Di X , Lamont L , Harms AC , Hankemeier T . A comprehensive UHPLC-MS/MS method for metabolomics profiling of signaling lipids: Markers of oxidative stress, immunity and inflammation . Anal Chim Acta . 2024 ; 1297 : 342348 . doi: 10.1016/j.aca.2024.342348 . PubMed PMID: 38438234 . OpenUrl CrossRef PubMed 45. ↵ Pedersen TL , Gray IJ , Newman JW . Plasma and serum oxylipin, endocannabinoid, bile acid, steroid, fatty acid and nonsteroidal anti-inflammatory drug quantification in a 96-well plate format . Anal Chim Acta . 2021 ; 1143 : 189 – 200 . doi: 10.1016/j.aca.2020.11.019 . PubMed PMID: 33384117 . OpenUrl CrossRef PubMed 46. ↵ Borkowski K , Seyfried NT , Arnold M , Lah JJ , Levey AI , Hales CM , Dammer EB , Blach C , Louie G , Kaddurah-Daouk R . Integration of plasma and CSF metabolomics with CSF proteomic reveals novel associations between lipid mediators and central nervous system vascular and energy metabolism . Scientific Reports . 2023 ; 13 ( 1 ): 13752 . OpenUrl PubMed 47. ↵ Higginbotham L , Ping L , Dammer EB , Duong DM , Zhou M , Gearing M , Hurst C , Glass JD , Factor SA , Johnson ECB , Hajjar I , Lah JJ , Levey AI , Seyfried NT . Integrated proteomics reveals brain-based cerebrospinal fluid biomarkers in asymptomatic and symptomatic Alzheimer’s disease . Sci Adv . 2020 ; 6 ( 43 ). doi: 10.1126/sciadv.aaz9360 . PubMed PMID: 33087358 ; PMCID: PMC7577712 . OpenUrl FREE Full Text 48. ↵ Borkowski K , Taha AY , Pedersen TL , De Jager PL , Bennett DA , Arnold M , Kaddurah-Daouk R , Newman JW . Serum metabolomic biomarkers of perceptual speed in cognitively normal and mildly impaired subjects with fasting state stratification . Sci Rep . 2021 ; 11 ( 1 ): 18964 . doi: 10.1038/s41598-021-98640-2 . PubMed PMID: 34556796 ; PMCID: PMC8460824 . OpenUrl CrossRef PubMed 49. ↵ Batra R , Arnold M , Worheide MA , Allen M , Wang X , Blach C , Levey AI , Seyfried NT , Ertekin-Taner N , Bennett DA , Kastenmuller G , Kaddurah-Daouk RF , Krumsiek J , Alzheimer’s Disease Metabolomics C. The landscape of metabolic brain alterations in Alzheimer’s disease . Alzheimers Dement . 2023 ; 19 ( 3 ): 980 – 98 . doi: 10.1002/alz.12714 . PubMed PMID: 35829654 ; PMCID: PMC9837312 . OpenUrl CrossRef PubMed 50. ↵ Chetnik K , Benedetti E , Gomari DP , Schweickart A , Batra R , Buyukozkan M , Wang Z , Arnold M , Zierer J , Suhre K , Krumsiek J. maplet: an extensible R toolbox for modular and reproducible metabolomics pipelines . Bioinformatics . 2022 ; 38 ( 4 ): 1168 – 70 . doi: 10.1093/bioinformatics/btab741 . PubMed PMID: 34694386 ; PMCID: PMC8796365 . OpenUrl CrossRef PubMed 51. ↵ Benjamini Y , Yekutieli D . Quantitative trait Loci analysis using the false discovery rate . Genetics . 2005 ; 171 ( 2 ): 783 – 90 . doi: 10.1534/genetics.104.036699 . PubMed PMID: 15956674 ; PMCID: PMC1456787 . OpenUrl Abstract / FREE Full Text 52. ↵ Crane PK , Carle A , Gibbons LE , Insel P , Mackin RS , Gross A , Jones RN , Mukherjee S , Curtis SM , Harvey D , Weiner M , Mungas D , Alzheimer’s Disease Neuroimaging I. Development and assessment of a composite score for memory in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) . Brain Imaging Behav . 2012 ; 6 ( 4 ): 502 – 16 . doi: 10.1007/s11682-012-9186-z . PubMed PMID: 22782295 ; PMCID: PMC3806057 . OpenUrl CrossRef PubMed 53. ↵ Borkowski K , Pedersen TL , Seyfried NT , Lah JJ , Levey AI , Hales CM , Dammer EB , Blach C , Louie G , Kaddurah-Daouk R , Newman JW , Alzheimer’s Disease Metabolomics C . Association of plasma and CSF cytochrome P450, soluble epoxide hydrolase, and ethanolamide metabolism with Alzheimer’s disease . Alzheimers Res Ther. 2021 ; 13 ( 1 ): 149 . doi: 10.1186/s13195-021-00893-6 . PubMed PMID: 34488866 ; PMCID: PMC8422756 . OpenUrl CrossRef PubMed 54. ↵ Inoue Y , Bamkole M , Kanekiyo T . Hepatic soluble epoxide hydrolase: A promising target for unveiling the liver-brain axis in Alzheimer’s disease . Neuron . 2023 ; 111 ( 18 ): 2775 – 7 . doi: 10.1016/j.neuron.2023.08.019 . PubMed PMID: 37734320 . OpenUrl CrossRef PubMed 55. ↵ Borkowski K , Seyfried NT , Arnold M , Lah JJ , Levey AI , Hales CM , Dammer EB , Blach C , Louie G , Kaddurah-Daouk R , Newman JW . Integration of plasma and CSF metabolomics with CSF proteomic reveals novel associations between lipid mediators and central nervous system vascular and energy metabolism . Sci Rep . 2023 ; 13 ( 1 ): 13752 . doi: 10.1038/s41598-023-39737-8 . PubMed PMID: 37612324 ; PMCID: PMC10447532 . OpenUrl CrossRef PubMed 56. ↵ Wingo AP , Liu Y , Gerasimov ES , Gockley J , Logsdon BA , Duong DM , Dammer EB , Robins C , Beach TG , Reiman EM , Epstein MP , De Jager PL , Lah JJ , Bennett DA , Seyfried NT , Levey AI , Wingo TS . Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis . Nat Genet . 2021 ; 53 ( 2 ): 143 – 6 . doi: 10.1038/s41588-020-00773-z . PubMed PMID: 33510477 ; PMCID: PMC8130821 . OpenUrl CrossRef PubMed 57. ↵ Benedet AL , Yu L , Labbe A , Mathotaarachchi S , Pascoal TA , Shin M , Kang MS , Gauthier S , Rouleau GA , Poirier J , Bennett DA , Rosa-Neto P , Alzheimer’s Disease Neuroimaging I . CYP2C19 variant mitigates Alzheimer disease pathophysiology in vivo and postmortem . Neurol Genet. 2018 ; 4 ( 1 ): e216 . doi: 10.1212/NXG.0000000000000216 . PubMed PMID: 29473050 ; PMCID: PMC5820598 . OpenUrl Abstract / FREE Full Text 58. Stingl JC , Scholl C , Bosch JE , Viviani R . Genetic polymorphism of CYP2C19 and subcortical variability in the human adult brain . Transl Psychiatry . 2021 ; 11 ( 1 ): 467 . doi: 10.1038/s41398-021-01591-5 . PubMed PMID: 34497262 ; PMCID: PMC8426391 . OpenUrl CrossRef PubMed 59. Ou YN , Yang YX , Deng YT , Zhang C , Hu H , Wu BS , Liu Y , Wang YJ , Zhu Y , Suckling J , Tan L , Yu JT . Identification of novel drug targets for Alzheimer’s disease by integrating genetics and proteomes from brain and blood . Mol Psychiatry . 2021 ; 26 ( 10 ): 6065 – 73 . doi: 10.1038/s41380-021-01251-6 . PubMed PMID: 34381170 . OpenUrl CrossRef PubMed 60. ↵ Padhy B , Hayat B , Nanda GG , Mohanty PP , Alone DP . Pseudoexfoliation and Alzheimer’s associated CLU risk variant, rs2279590, lies within an enhancer element and regulates CLU, EPHX2 and PTK2B gene expression . Hum Mol Genet. 2017 ; 26 ( 22 ): 4519 – 29 . doi: 10.1093/hmg/ddx329 . PubMed PMID: 28973302 . OpenUrl CrossRef PubMed 61. ↵ Gupta NC , Davis CM , Nelson JW , Young JM , Alkayed NJ . Soluble epoxide hydrolase: sex differences and role in endothelial cell survival . Arterioscler Thromb Vasc Biol . 2012 ; 32 ( 8 ): 1936 – 42 . doi: 10.1161/ATVBAHA.112.251520 . PubMed PMID: 22723436 ; PMCID: PMC3417749 . OpenUrl Abstract / FREE Full Text 62. ↵ Corraini P , Henderson VW , Ording AG , Pedersen L , Horvath-Puho E , Sorensen HT . Long-Term Risk of Dementia Among Survivors of Ischemic or Hemorrhagic Stroke . Stroke . 2017 ; 48 ( 1 ): 180 – 6 . doi: 10.1161/STROKEAHA.116.015242 . PubMed PMID: 27899749 ; PMCID: PMC5796652 . OpenUrl Abstract / FREE Full Text 63. ↵ Nuthikattu S , Milenkovic D , Norman JE , Rutledge J , Villablanca A . High Glycemia and Soluble Epoxide Hydrolase in Females: Differential Multiomics in Murine Brain Microvasculature . Int J Mol Sci . 2022 ; 23 ( 21 ). doi: 10.3390/ijms232113044 . PubMed PMID: 36361847 ; PMCID: PMC9655872 . OpenUrl CrossRef PubMed 64. Nuthikattu S , Milenkovic D , Norman JE , Rutledge J , Villablanca A . The Brain’s Microvascular Response to High Glycemia and to the Inhibition of Soluble Epoxide Hydrolase Is Sexually Dimorphic . Nutrients . 2022 ; 14 ( 17 ). doi: 10.3390/nu14173451 . PubMed PMID: 36079709 ; PMCID: PMC9460226 . OpenUrl CrossRef PubMed 65. Gentreau M , Raymond M , Chuy V , Samieri C , Feart C , Berticat C , Artero S . High Glycemic Load Is Associated with Cognitive Decline in Apolipoprotein E epsilon4 Allele Carriers . Nutrients . 2020 ; 12 ( 12 ). doi: 10.3390/nu12123619 . PubMed PMID: 33255701 ; PMCID: PMC7761247 . OpenUrl CrossRef PubMed 66. ↵ Taylor MK , Sullivan DK , Swerdlow RH , Vidoni ED , Morris JK , Mahnken JD , Burns JM . A high-glycemic diet is associated with cerebral amyloid burden in cognitively normal older adults . Am J Clin Nutr . 2017 ; 106 ( 6 ): 1463 – 70 . doi: 10.3945/ajcn.117.162263 . PubMed PMID: 29070566 ; PMCID: PMC5698843 . OpenUrl Abstract / FREE Full Text 67. ↵ Wagner KM , Yang J , Morisseau C , Hammock BD . Soluble Epoxide Hydrolase Deletion Limits High-Fat Diet-Induced Inflammation . Front Pharmacol . 2021 ; 12 : 778470 . doi: 10.3389/fphar.2021.778470 . PubMed PMID: 34975481 ; PMCID: PMC8719166 . OpenUrl CrossRef PubMed 68. ↵ Freeman LR , Haley-Zitlin V , Rosenberger DS , Granholm AC . Damaging effects of a high-fat diet to the brain and cognition: a review of proposed mechanisms . Nutr Neurosci . 2014 ; 17 ( 6 ): 241 – 51 . doi: 10.1179/1476830513Y.0000000092 . PubMed PMID: 24192577 ; PMCID: PMC4074256 . OpenUrl CrossRef PubMed 69. ↵ Sarparast M , Pourmand E , Hinman J , Vonarx D , Reason T , Zhang F , Paithankar S , Chen B , Borhan B , Watts JL , Alan J , Lee KSS . Dihydroxy-Metabolites of Dihomo-gamma-linolenic Acid Drive Ferroptosis-Mediated Neurodegeneration . ACS Cent Sci . 2023 ; 9 ( 5 ): 870 – 82 . doi: 10.1021/acscentsci.3c00052 . PubMed PMID: 37252355 ; PMCID: PMC10214511 . OpenUrl CrossRef PubMed 70. ↵ Zimmer B , Angioni C , Osthues T , Toewe A , Thomas D , Pierre SC , Geisslinger G , Scholich K , Sisignano M . The oxidized linoleic acid metabolite 12,13-DiHOME mediates thermal hyperalgesia during inflammatory pain . Biochim Biophys Acta Mol Cell Biol Lipids . 2018 ; 1863 ( 7 ): 669 – 78 . doi: 10.1016/j.bbalip.2018.03.012 . PubMed PMID: 29625231 . OpenUrl CrossRef PubMed 71. ↵ Bergmann CB , McReynolds CB , Wan D , Singh N , Goetzman H , Caldwell CC , Supp DM , Hammock BD . sEH-derived metabolites of linoleic acid drive pathologic inflammation while impairing key innate immune cell function in burn injury . Proc Natl Acad Sci U S A . 2022 ; 119 ( 13 ): e2120691119 . doi: 10.1073/pnas.2120691119 . PubMed PMID: 35312372 ; PMCID: PMC9060469 . OpenUrl CrossRef PubMed 72. ↵ Bergmann CB , Hammock BD , Wan D , Gogolla F , Goetzman H , Caldwell CC , Supp DM . TPPU treatment of burned mice dampens inflammation and generation of bioactive DHET which impairs neutrophil function . Sci Rep . 2021 ; 11 ( 1 ): 16555 . doi: 10.1038/s41598-021-96014-2 . PubMed PMID: 34400718 ; PMCID: PMC8368302 . OpenUrl CrossRef PubMed 73. ↵ Pinckard KM , Shettigar VK , Wright KR , Abay E , Baer LA , Vidal P , Dewal RS , Das D , Duarte-Sanmiguel S , Hernandez-Saavedra D , Arts PJ , Lehnig AC , Bussberg V , Narain NR , Kiebish MA , Yi F , Sparks LM , Goodpaster BH , Smith SR , Pratley RE , Lewandowski ED , Raman SV , Wold LE , Gallego-Perez D , Coen PM , Ziolo MT , Stanford KI . A Novel Endocrine Role for the BAT-Released Lipokine 12,13-diHOME to Mediate Cardiac Function . Circulation . 2021 ; 143 ( 2 ): 145 – 59 . doi: 10.1161/CIRCULATIONAHA.120.049813 . PubMed PMID: 33106031 ; PMCID: PMC7856257 . OpenUrl CrossRef PubMed 74. ↵ McReynolds C , Hammock B , Morisseau C . Regulatory lipid vicinal diols counteract the biological activity of epoxy fatty acids and can act as biomarkers and mechanisms for disease progression . Pharmacol Ther . 2023 ; 248 : 108454 . doi: 10.1016/j.pharmthera.2023.108454 . PubMed PMID: 37268114 ; PMCID: PMC10529401 . OpenUrl CrossRef PubMed 75. ↵ Lee HT , Lee KI , Chen CH , Lee TS . Genetic deletion of soluble epoxide hydrolase delays the progression of Alzheimer’s disease . J Neuroinflammation . 2019 ; 16 ( 1 ): 267 . doi: 10.1186/s12974-019-1635-9 . PubMed PMID: 31847859 ; PMCID: PMC6916033 . OpenUrl CrossRef PubMed 76. ↵ Sun CP , Zhang XY , Zhou JJ , Huo XK , Yu ZL , Morisseau C , Hammock BD , Ma XC . Inhibition of sEH via stabilizing the level of EETs alleviated Alzheimer’s disease through GSK3beta signaling pathway . Food Chem Toxicol . 2021 ; 156 : 112516 . doi: 10.1016/j.fct.2021.112516 . PubMed PMID: 34411643 ; PMCID: PMC8889936 . OpenUrl CrossRef PubMed 77. ↵ Yang YM , Sun D , Kandhi S , Froogh G , Zhuge J , Huang W , Hammock BD , Huang A . Estrogen-dependent epigenetic regulation of soluble epoxide hydrolase via DNA methylation . Proc Natl Acad Sci U S A . 2018 ; 115 ( 3 ): 613 – 8 . doi: 10.1073/pnas.1716016115 . PubMed PMID: 29295935 ; PMCID: PMC5776989 . OpenUrl Abstract / FREE Full Text 78. ↵ Song YJ , Li SR , Li XW , Chen X , Wei ZX , Liu QS , Cheng Y . The Effect of Estrogen Replacement Therapy on Alzheimer’s Disease and Parkinson’s Disease in Postmenopausal Women: A Meta-Analysis . Front Neurosci . 2020 ; 14 : 157 . doi: 10.3389/fnins.2020.00157 . PubMed PMID: 32210745 ; PMCID: PMC7076111 . OpenUrl CrossRef PubMed 79. ↵ Ennis GE , An Y , Resnick SM , Ferrucci L , O’Brien RJ , Moffat SD . Long-term cortisol measures predict Alzheimer disease risk . Neurology . 2017 ; 88 ( 4 ): 371 – 8 . doi: 10.1212/WNL.0000000000003537 . PubMed PMID: 27986873 ; PMCID: PMC5272965 . OpenUrl CrossRef PubMed 80. ↵ Zheng B , Tal R , Yang Z , Middleton L , Udeh-Momoh C . Cortisol hypersecretion and the risk of Alzheimer’s disease: A systematic review and meta-analysis . Ageing Res Rev . 2020 ; 64 : 101171 . doi: 10.1016/j.arr.2020.101171 . PubMed PMID: 32971258 . OpenUrl CrossRef PubMed 81. ↵ Witt KA , Sandoval KE . Steroids and the blood-brain barrier: therapeutic implications . Adv Pharmacol . 2014 ; 71 : 361 – 90 . doi: 10.1016/bs.apha.2014.06.018 . PubMed PMID: 25307223 . OpenUrl CrossRef PubMed 82. ↵ Knezevic E , Nenic K , Milanovic V , Knezevic NN . The Role of Cortisol in Chronic Stress, Neurodegenerative Diseases, and Psychological Disorders . Cells . 2023 ; 12 ( 23 ). doi: 10.3390/cells12232726 . PubMed PMID: 38067154 ; PMCID: PMC10706127 . OpenUrl CrossRef PubMed 83. ↵ Holleman J , Adagunodo S , Kareholt I , Hagman G , Aspo M , Udeh-Momoh CT , Solomon A , Kivipelto M , Sindi S. Cortisol , cognition and Alzheimer’s disease biomarkers among memory clinic patients . BMJ Neurol Open . 2022 ; 4 ( 2 ): e000344 . doi: 10.1136/bmjno-2022-000344 . PubMed PMID: 36277478 ; PMCID: PMC9582323 . OpenUrl Abstract / FREE Full Text 84. ↵ Lee BK , Glass TA , Wand GS , McAtee MJ , Bandeen-Roche K , Bolla KI , Schwartz BS . Apolipoprotein e genotype, cortisol, and cognitive function in community-dwelling older adults . Am J Psychiatry . 2008 ; 165 ( 11 ): 1456 – 64 . doi: 10.1176/appi.ajp.2008.07091532 . PubMed PMID: 18593777 ; PMCID: PMC2579316 . OpenUrl CrossRef PubMed Web of Science 85. ↵ Montoliu T , Hidalgo V , Pulopulos MM , Ivorra JL , Martinez MJ , Salvador A . The relationship between cortisol and cognitive function in healthy older people: The moderating role of Apolipoprotein E polymorphism . Neurobiol Learn Mem . 2018 ; 155 : 297 – 305 . doi: 10.1016/j.nlm.2018.08.013 . PubMed PMID: 30130588 . OpenUrl CrossRef PubMed 86. ↵ Futch HS , Croft CL , Truong VQ , Krause EG , Golde TE . Targeting psychologic stress signaling pathways in Alzheimer’s disease . Mol Neurodegener . 2017 ; 12 ( 1 ): 49 . doi: 10.1186/s13024-017-0190-z . PubMed PMID: 28633663 ; PMCID: PMC5479037 . OpenUrl CrossRef PubMed 87. ↵ Mosconi L , Williams S , Carlton C , Zarate C , Boneu C , Fauci F , Ajila T , Nerattini M , Jett S , Andy C , Battista M , Pahlajani S , Osborne J , Brinton RD , Dyke JP . Sex-specific associations of serum cortisol with brain biomarkers of Alzheimer’s risk . Sci Rep . 2024 ; 14 ( 1 ): 5519 . doi: 10.1038/s41598-024-56071-9 . PubMed PMID: 38448497 ; PMCID: PMC10918173 . OpenUrl CrossRef PubMed 88. ↵ Back M , Hansson GK . Leukotriene receptors in atherosclerosis . Ann Med . 2006 ; 38 ( 7 ): 493 – 502 . doi: 10.1080/07853890600982737 . PubMed PMID: 17101540 . OpenUrl CrossRef PubMed Web of Science 89. ↵ Hardy G , Boizel R , Bessard J , Cracowski JL , Bessard G , Halimi S , Stanke-Labesque F . Urinary leukotriene E4 excretion is increased in type 1 diabetic patients: a quantification by liquid chromatography-tandem mass spectrometry . Prostaglandins Other Lipid Mediat . 2005 ; 78 ( 1-4 ): 291 – 9 . doi: 10.1016/j.prostaglandins.2005.10.001 . PubMed PMID: 16303623 . OpenUrl CrossRef PubMed Web of Science 90. ↵ Allen SP , Sampson AP , Piper PJ , Chester AH , Ohri SK , Yacoub MH . Enhanced excretion of urinary leukotriene E4 in coronary artery disease and after coronary artery bypass surgery . Coron Artery Dis . 1993 ; 4 ( 10 ): 899 – 904 . doi: 10.1097/00019501-199310000-00009 . PubMed PMID: 8269196 . OpenUrl CrossRef PubMed 91. ↵ Di Francesco A , Arosio B , Gussago C , Dainese E , Mari D , D’Addario C , Maccarrone M . Involvement of 5-lipoxygenase in Alzheimer’s disease: a role for DNA methylation . J Alzheimers Dis . 2013 ; 37 ( 1 ): 3 – 8 . doi: 10.3233/JAD-130506 . PubMed PMID: 23727898 . OpenUrl CrossRef PubMed 92. ↵ Czapski GA , Czubowicz K , Strosznajder JB , Strosznajder RP . The Lipoxygenases: Their Regulation and Implication in Alzheimer’s Disease . Neurochem Res . 2016 ; 41 ( 1-2 ): 243 – 57 . doi: 10.1007/s11064-015-1776-x . PubMed PMID: 26677076 ; PMCID: PMC4773476 . OpenUrl CrossRef PubMed 93. ↵ Sery O , Hlinecka L , Povova J , Bonczek O , Zeman T , Janout V , Ambroz P , Khan NA , Balcar VJ . Arachidonate 5-lipoxygenase (ALOX5) gene polymorphism is associated with Alzheimer’s disease and body mass index . J Neurol Sci . 2016 ; 362 : 27 – 32 . doi: 10.1016/j.jns.2016.01.022 . PubMed PMID: 26944113 . OpenUrl CrossRef PubMed 94. ↵ Firuzi O , Zhuo J , Chinnici CM , Wisniewski T , Pratico D . 5-Lipoxygenase gene disruption reduces amyloid-beta pathology in a mouse model of Alzheimer’s disease . FASEB J . 2008 ; 22 ( 4 ): 1169 – 78 . doi: 10.1096/fj.07-9131.com . PubMed PMID: 17998412 ; PMCID: PMC2698428 . OpenUrl CrossRef PubMed Web of Science 95. Chu J , Pratico D . 5-lipoxygenase as an endogenous modulator of amyloid beta formation in vivo . Ann Neurol . 2011 ; 69 ( 1 ): 34 – 46 . doi: 10.1002/ana.22234 . PubMed PMID: 21280074 ; PMCID: PMC3051361 . OpenUrl CrossRef PubMed 96. Chu J , Li JG , Joshi YB , Giannopoulos PF , Hoffman NE , Madesh M , Pratico D . Gamma secretase-activating protein is a substrate for caspase-3: implications for Alzheimer’s disease . Biol Psychiatry . 2015 ; 77 ( 8 ): 720 – 8 . doi: 10.1016/j.biopsych.2014.06.003 . PubMed PMID: 25052851 ; PMCID: PMC4268092 . OpenUrl CrossRef PubMed 97. ↵ Joshi YB , Di Meco A , Pratico D . Modulation of amyloid-beta production by leukotriene B4 via the gamma-secretase pathway . J Alzheimers Dis . 2014 ; 38 ( 3 ): 503 – 6 . doi: 10.3233/JAD-131223 . PubMed PMID: 24008686 . OpenUrl CrossRef PubMed 98. ↵ Ebright B , Assante I , Poblete RA , Wang S , Duro MV , Bennett DA , Arvanitakis Z , Louie SG , Yassine HN . Eicosanoid lipidome activation in post-mortem brain tissues of individuals with APOE4 and Alzheimer’s dementia . Alzheimers Res Ther . 2022 ; 14 ( 1 ): 152 . doi: 10.1186/s13195-022-01084-7 . PubMed PMID: 36217192 ; PMCID: PMC9552454 . OpenUrl CrossRef PubMed 99. ↵ Jiang L , Zhong J , Dou X , Cheng C , Huang Z , Sun X . Effects of ApoE on intracellular calcium levels and apoptosis of neurons after mechanical injury . Neuroscience . 2015 ; 301 : 375 – 83 . doi: 10.1016/j.neuroscience.2015.06.005 . PubMed PMID: 26073697 . OpenUrl CrossRef PubMed 100. ↵ Rawat V , Wang S , Sima J , Bar R , Liraz O , Gundimeda U , Parekh T , Chan J , Johansson JO , Tang C , Chui HC , Harrington MG , Michaelson DM , Yassine HN . ApoE4 Alters ABCA1 Membrane Trafficking in Astrocytes . J Neurosci . 2019 ; 39 ( 48 ): 9611 – 22 . doi: 10.1523/JNEUROSCI.1400-19.2019 . PubMed PMID: 31641056 ; PMCID: PMC6880458 . OpenUrl Abstract / FREE Full Text 101. ↵ Sienski G , Narayan P , Bonner JM , Kory N , Boland S , Arczewska AA , Ralvenius WT , Akay L , Lockshin E , He L , Milo B , Graziosi A , Baru V , Lewis CA , Kellis M , Sabatini DM , Tsai LH , Lindquist S . APOE4 disrupts intracellular lipid homeostasis in human iPSC-derived glia . Sci Transl Med . 2021 ; 13 ( 583 ). doi: 10.1126/scitranslmed.aaz4564 . PubMed PMID: 33658354 ; PMCID: PMC8218593 . OpenUrl FREE Full Text 102. ↵ Liang N , Harsch BA , Zhou S , Borkowska A , Shearer GC , Kaddurah-Daouk R , Newman JW , Borkowski K . Oxylipin transport by lipoprotein particles and its functional implications for cardiometabolic and neurological disorders . Prog Lipid Res . 2024 ; 93 : 101265 . doi: 10.1016/j.plipres.2023.101265 . PubMed PMID: 37979798 . OpenUrl CrossRef PubMed 103. ↵ O’Donnell VB , Aldrovandi M , Murphy RC , Kronke G . Enzymatically oxidized phospholipids assume center stage as essential regulators of innate immunity and cell death . Sci Signal . 2019 ; 12 ( 574 ). doi: 10.1126/scisignal.aau2293 . PubMed PMID: 30914483 . OpenUrl Abstract / FREE Full Text View the discussion thread. Back to top Previous Next Posted April 03, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Metabolic Alteration in Oxylipins and Endocannabinoids Point to an Important Role for Soluble Epoxide Hydrolase and Inflammation in Alzheimer’s Disease - Finding from Alzheimer’s Disease Neuroimaging Initiative Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Metabolic Alteration in Oxylipins and Endocannabinoids Point to an Important Role for Soluble Epoxide Hydrolase and Inflammation in Alzheimer’s Disease - Finding from Alzheimer’s Disease Neuroimaging Initiative Kamil Borkowski , Chunyuan Yin , Alida Kindt , Nuanyi Liang , Elizabeth de Lange , Colette Blach , John W. Newman , Rima Kaddurah-Daouk , Thomas Hankemeier , Alzheimer’s Disease Metabolomics Consortium , the Alzheimer’s Disease Neuroimaging Initiative bioRxiv 2025.04.01.646677; doi: https://doi.org/10.1101/2025.04.01.646677 Share This Article: Copy Citation Tools Metabolic Alteration in Oxylipins and Endocannabinoids Point to an Important Role for Soluble Epoxide Hydrolase and Inflammation in Alzheimer’s Disease - Finding from Alzheimer’s Disease Neuroimaging Initiative Kamil Borkowski , Chunyuan Yin , Alida Kindt , Nuanyi Liang , Elizabeth de Lange , Colette Blach , John W. Newman , Rima Kaddurah-Daouk , Thomas Hankemeier , Alzheimer’s Disease Metabolomics Consortium , the Alzheimer’s Disease Neuroimaging Initiative bioRxiv 2025.04.01.646677; doi: https://doi.org/10.1101/2025.04.01.646677 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7618) Biochemistry (17636) Bioengineering (13859) Bioinformatics (41847) Biophysics (21401) Cancer Biology (18535) Cell Biology (25423) Clinical Trials (138) Developmental Biology (13353) Ecology (19860) Epidemiology (2067) Evolutionary Biology (24287) Genetics (15582) Genomics (22463) Immunology (17701) Microbiology (40300) Molecular Biology (17141) Neuroscience (88432) Paleontology (666) Pathology (2825) Pharmacology and Toxicology (4813) Physiology (7633) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4285) Systems Biology (9808) Zoology (2267)