Hippocampal transcriptome-wide association analysis of shared genetic risks between posttraumatic stress disorder and Alzheimer’s disease

Hippocampal transcriptome-wide association analysis of shared genetic risks between posttraumatic stress disorder and Alzheimer’s disease | medRxiv /* */ /* */ <!-- <!-- /*! * 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-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Hippocampal transcriptome-wide association analysis of shared genetic risks between posttraumatic stress disorder and Alzheimer’s disease Eric Du , View ORCID Profile Jing Tian doi: https://doi.org/10.1101/2025.08.11.25333445 Eric Du 1 Blue Valley West High School , Overland Park, Kansas Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jing Tian 2 Department of Pharmacology and Toxicology, University of Kansas , Lawrence, Kansas Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jing Tian For correspondence: jingtian{at}ku.edu Abstract Full Text Info/History Metrics Preview PDF Abstract The etiopathogenesis of Alzheimer’s disease (AD) is puzzled by the heterogeneous nature of this neurodegenerative disorder. In recent years, clinical and basic research has accentuated a relationship between post-traumatic stress disorder (PTSD) and AD risk. Despite several pathways related to neuroinflammation, metabolic disturbances, and stress, emerging evidence implicates genetic risks at the nexus of PTSD and AD. However, the genetic link between the two conditions is relatively understudied. In this study, we adopted tissue-specific transcriptome-wide association studies (TWAS) with a special emphasis on the hippocampus, a shared vulnerable brain region between AD and PTSD to investigate common genetic risks between the two brain disorders. By leveraging large-scale GWAS summary statistics from large-scale AD and PTSD cohorts, we applied FUSION TWAS and identified susceptibility genes common to both disorders. Further functional annotation mounted TWAS-identified cross-disease susceptibility genes to multiple pivotal biological pathways, especially those related to cell metabolism. Metabolic pathway analysis topped lipid metabolism-related pathways such as “acyl-CoA hydrolysis” that overlaps AD and PTSD risk. The simple interpretation of our results is that AD and PTSD share common susceptibility genes. Deregulation of these common susceptibility genes in the hippocampus may potentiate PTSD and AD in sequence through hippocampal dysmetabolism. These findings from computational analysis shed light on the genetic association between AD and PTSD, which will endorse further investigation through experimental approaches for a better understanding of the etiopathogenesis of AD and PTSD as well as the link between the two conditions from a perspective of precision medicine. Introduction Characterized by insidious onset and progressive cognitive impairment, Alzheimer’s disease (AD) constitutes one of the leading causes of death in the elderly[ 1 ]. However, the etiopathogenesis of this devastating neurodegenerative disorder so far remains elusive. In addition to the well-documented interactions between aging process and toxic protein aggregates in AD pathogenesis[ 1 , 2 ], a large body of evidence has accentuated other risk factors and health conditions that predispose older adults to the development of Alzheimer’s dementia[ 3 , 4 ]. As a result, the heterogeneous and multifactorial nature of this complex disease is increasingly appreciated. Post-traumatic stress disorder (PTSD) is a psychological disorder that arises from exposure to traumatic events such as combat, assault, or natural disasters[ 5 ]. The characteristic clinical manifestations of PTSD cover mode disturbances, behavioral changes, somatic symptoms, and cognitive impairment[ 5 ]. Although PTSD and AD are distinct neuropsychological diseases, recent clinical studies and basic research have implicated potential mechanistic links between the two conditions. Epidemiological studies reported that subjects vulnerable to PTSD are also susceptibility to AD in their later life[ 6 ]. In addition, neuroimaging data showed that PTSD and AD overlap in structural changes in multiple brain regions including the hippocampus, which are pivotal for emotional processing and cognitive function[ 7 , 8 ]. Moreover, shared disturbances in key neurotransmitters such as glutamate and serotonin as well as common deregulation of several molecular pathways such as the actin nucleator Formin 2 (Fmn2)-ERK1/2 signaling and the transcription factor nuclear factor-κB (NF-κB) signaling further support the biological basis linking PTSD and AD[ 9 – 14 ]. Of note, a previous genome-wide pleiotropy analysis using genome-wide association studies (GWAS) datasets from patients with AD and PTSD has identified several shared loci between the two conditions, implicating a role of genetic risks in predisposing AD development from PTSD[ 15 ]. GWAS analysis has been widely used to identify the association of pathogenic variants with diseases. However, its limitation to inform the consequences of the identified genetic variants has created chasm that constrains the use of GWAS data to understand the pathogenesis of complex diseases[ 16 ]. Transcriptome-wide association Studies (TWAS) is a newly developed tool in computational biology[ 17 ]. Tissue-specific TWAS gene expression modeling is dictated by expression quantitative loci (eQLT) data to establish associations between single-nucleotide polymorphism (SNPs) and gene expression changes to account for gene expression differences in addition to the variety of expressed sequences from a tissue type-specific perspective. In view of this feature, TWAS functions as a critical complement to GWAS to demonstrate the causal effects of specific sequences towards phenotypic discrepancies, especially those with established genetic risks. Additional to this benefit, TWAS enables tissue specificity and lowers the computational burden of genome analysis by targeting its analysis towards highly relevant gene sequences. As a result of these factors, TWAS is a highly adaptive and effective genome analysis tool, which has been used in investigations of various complex traits and diseases. In this study, we performed hippocampal TWAS analysis by using GWAS summary statistics from large-scale AD and PTSD cohorts. At the nominal significance of 0.05, 120 PTSD/AD- overlapped susceptibility genes were determined in the hippocampus. Among these PTSD/AD- overlapped susceptibility genes, 68 underwent upregulation and 52 were downregulated. Further functional annotation analysis mounted these PTSD/AD-overlapped susceptibility genes to multiple critical biological pathways, among which metabolism-related pathways were listed on the top. The most parsimonious interpretation of our data is that PTSD and AD share common susceptibility genes. The deregulation of these PTSD/AD-overlapped susceptibility genes may promote disease through disturbances in critical pathways in the hippocampus. Further in-depth experimental investigation will help to delineate the molecular pathways that contribute to both PTSD and AD as well as predispose AD onset from PTSD from a perspective of personalized medicine. Results Hippocampal TWAS analysis of AD and PTSD cohorts To explore whether PTSD and AD share common deregulation of gene expression in the hippocampus, we performed hippocampal FUSION transcriptome-wide association studies (TWAS) using GWAS summary statistics from an AD cohort (90,338 cases; 1,036,225 controls) and a PTSD cohort (137,136 cases; 1,085,746 controls)[ 18 – 20 ] ( Fig. 1 ). We integrated the expression quantitative trait loci (eQTL) reference panels and linkage disequilibrium (LD) scores. Manhattan plots generated from FUSION TWAS analysis ( supplementary table 1 ) summarized susceptibility genes that were associated with PTSD ( Fig. 2A ) and AD ( Fig. 2B ), respectively. Through the analysis using nominal significance at 0.05, we identified 1,422 AD- associated genes ( Fig. 2C ) and 2,145 PTSD-associated genes ( Fig. 2D ). Download figure Open in new tab Figure 1. Schematic graph. GWAS: genome-wide association studies, AD: Alzheimer’s disease, PTSD: Post-traumatic stress disorder, wFisher: weight Fisher method. Download figure Open in new tab Figure 2. Hippocampal FUSION-TWAS analysis of AD and PTSD GWAS summary data. ( A ) Manhattan plot of AD hippocampal FUSION-TWAS result. n = 71,880 AD, 383,378 nonAD controls. ( B ) Clustered heatmap of AD hippocampal TWAS identified genes with p < 0.05. ( C ) Manhattan plot of AD hippocampal FUSION-TWAS result. n = 137,136 PTSD, 1,085,746 controls. ( D ) Clustered heatmap of PTSD hippocampal TWAS identified genes with p < 0.05. Cross-disease association analysis of PTSD/AD-overlapped susceptibility genes To determine the susceptibility genes overlapped in both AD and PTSD, we performed cross-disease association analyses. The FUSION TWAS results were sequentially filtered as shown in the Sankey plot ( Fig. 3A ). At the nominal significance of 0.05, 230 PTSD/AD- overlapped susceptibility genes ( supplementary table 2 ) were determined. Afterwards, we performed further filtering process to keep genes with consistent direction of regulation in both diseases. A total of 120 PTSD/AD-overlapped susceptibility genes were determined, among which 68 underwent upregulation and 52 were downregulated ( Fig. 3B - 2C ). To enhance statistical power for the combined p -values, we performed a meta-analysis using the weighted Fisher’s method (wFisher) based on TWAS summary statistics from the AD and PTSD cohorts ( Fig. 3D ). These findings indicate that PTSD and AD have shared genetic risks that affect the transcriptomic landscape in brain regions vulnerable to both diseases. Download figure Open in new tab Figure 3. Cross-disease susceptibility genes identification. ( A ) Sankey plot to represent hippocampal AD/PTSD TWAS analysis result filtration for susceptibility genes. ( B&C ) Lollipop graphs for AD ( B ) and PTSD ( C ) overlapped significant gene with same expression direction. P < 0.05. ( D ) wFisher meta analysis for AD/PTSD susceptibility genes. Upper panel represents upregulated genes, lower panel represents downregulated genes. Functional annotation of cross-disease susceptibility genes To further investigate potential molecular links between the two diseases, we employed ingenuity pathway analysis (IPA) for comparative analysis of canonical pathways using the AD/PTSD-overlapped susceptibility genes as identified in Fig. 3B&C . In the hippocampus, these 120 genes were enriched across several overlapping pathways, especially those related to metabolic functions ( Fig. 4 ). We performed canonical pathway analysis based on the wFisher meta-analysis results and that revealed enrichment in several critical metabolic processes such as “Acyl-CoA Hydrolysis”, “Fatty Acid Biosynthesis Initiation”, “Glutamine Degradation”, and “Fatty Acid Activation”. ( Fig. 4A ). For a better understanding of the impacts of AD and PTSD overlapped susceptibility genes on hippocampal functions, we employed the hippocampal functional gene network analysis in HumanBase ( https://hb.flatironinstitute.org )[ 21 ] and mounted the 120 AD and PTSD shared susceptibility genes to key biological processes in the hippocampus, including “developmental growth” (Module 2) and “protein localization” (Module 3) ( Fig. 4B ). Put together, these results highlight shared alterations in critical pathways related to pivotal biological processes, especially cell metabolism in the hippocampus across AD and PTSD. Download figure Open in new tab Figure 4. Cross-disease functional analysis. ( A ) Cross-disease IPA canonical pathway identification using AD/PTSD wFisher meta analysis result. ( B ) AD and PTSD shared biological process analysis. Expression of AD/PTSD-sensitive metabolic pathway-related genes in the hippocampus Different cell types express distinct transcriptomic landscape. Therefore, it is of significance to examine cell type-specific gene expression. To further investigate how metabolic pathways influence brain function in AD and PTSD, we examined the expression patterns of key genes involved in the pathways identified in Fig. 4B using the CZ CELLxGENE platform ( https://cellxgene.cziscience.com/docs/01CellxGene )[ 22 ]. The results showed that these genes were broadly expressed across various brain cell types, including neurons, neural stem cells, oligodendrocytes, microglia, astrocytes, and brain vascular cells ( Fig. 5 ). In the hippocampus, the genes were also expressed in multiple hippocampal cell types such as hippocampal pyramidal neurons, granule cells, interneurons, and astrocytes except for Acyl-CoA Thioesterase 4 (ACOT4), which was not expressed in hippocampal pyramidal neurons. The widespread expression of these AD/PTSD-sensitive genes highlights the broad impacts of altered expression of these genes on hippocampal function. Download figure Open in new tab Figure 5. Cell-type-specific expression profiling of shared AD/PTSD metabolic pathway– related genes in the brain. Expression pattern of IPA-identified AD/PTSD overlapped metabolic pathway-related genes in multiple brain cell types and different hippocampal cells. Discussion An association between post-traumatic stress disorder (PTSD) and Alzheimer’s disease (AD) has been repeatedly implicated in previous clinical and basic research. However, the precise mechanisms linking the two neuropsychiatric conditions have not yet been elucidated. A previous study using GWAS dataset has identified multiple loci and pathogenic variants overlapping in both PTSD and AD[ 15 ], which provides critical evidence of the genetic link between the two diseases. Tissue-specific TWAS is a recently developed method that has its capacity to complement GWAS analysis in a better understanding of the impact of pathogenic variants on gene regulation in a tissue-specific manner. Inspired by the advantages of tissue-specific TWAS, in this study, we performed TWAS analysis using GWAS summary statistics from large-scale cohorts of PTSD and AD and examined the shared genetic risks between PTSD and AD and their impacts on hippocampus gene expression in these two conditions. In addition to the identified PTSD/AD-overlapped susceptibility genes, our further functional annotation mounted these genetic risks to multiple biological pathways, especially those related to key metabolic regulations. It is consensually accepted that metabolic homeostasis is important to synaptic strength and brain function[ 23 , 24 ]. As a result, disruptions in metabolism have been consistently observed patients with neurological disorders including PTSD and AD[ 25 – 28 ]. AD patients frequently report metabolic changes before cognitive decline[ 29 ]. Previous studies have firmly established the pivotal role of metabolic perturbations including insulin resistance, mitochondrial dysfunction and cholesterol and sphingolipid dysmetabolism in the etiopathogenesis of AD, which promotes the appraisal of metabolic pathways of this neurodegenerative disorder[ 30 – 34 ]. Of note, metabolic conditions such as obesity, dyslipidemia, type 2 diabetes, and mitochondrial dysfunction are also well-documented risks associated with PTSD[ 27 , 35 – 37 ]. A previous study showed a close association between metabolic syndrome and cortical atrophy in military veterans with PTSD[ 38 ], implicating a contribution of dysmetabolism to PTSD-associated brain structural changes. Despite the agreement that systematic and brain dysmetabolism are involved in the pathogenesis of both AD and PTSD, there exists a debate over the causal role of dysmetabolism in the development of these disorders. Previous studies frequently define dysmetabolism in PTSD and AD as secondary responses to other disease- promoting factors such as chronic stress, inflammatory reactions, and the hypothalamic-pituitary-adrenal (HPA) axis instability[ 39 – 42 ]. Although we cannot refute the possibility that dysmetabolism is an aftereffect or adaptive changes in patients with PTSD and AD, our findings of the deleterious impact of PTSD/AD-overlapped susceptibility gene regulation on key metabolic pathways, at least in the hippocampus, implicate an alternative mechanism. In view of the detrimental effect of dysmetabolism on brain function and the pivotal role of the hippocampus in cognition and emotion[ 43 – 45 ], we thus cautiously formulate a hypothesis that subjects carrying genetic risks may gradually develop hippocampal dysmetabolism, which compromises neuronal function, rendering susceptibility to failed defense strategies in the context of stress, culminating in increased risk of PTSD as well as AD in their later life. To this end, at least a subgroup of PTSD and AD patients, who carry the determined susceptibility genes, converge in hippocampal dysmetabolism in their disease development as well as the vicious sequence of PTSD to AD. Further in-depth investigations using experimental approaches are in immediate need to address this critical scientific question. Another critical finding merits discussion is that our TWAS analysis determined alterations in the expression of ACOT2 and ACOT4 in the hippocampus of both AD and PTSD, resulting in acyl-CoA hydrolysis as the top pathway affected in both AD and PTSD. Acyl-CoA hydrolysis involves the breakdown of acyl-CoA into free fatty acids and coenzyme A, a reaction catalyzed by acyl-CoA thioesterases (ACOTs) [ 46 , 47 ]. Our findings of the association of hippocampal ACOT2 and ACOT4 deregulation with the risk of both AD and PTSD implicates a contribution of fatty acid dysmetabolism to the development of the two disorders, corroborating previous reports of lipid dysmetabolism in the pathogenesis of both AD and PTSD [ 48 – 50 ]. Noteworthy, both ACOT2 and ACOT4, the two key enzymes in fatty acid metabolism, are expressed in various types of brain cells. Previous studies indicate that fatty acid oxidation is pivotal for the functions of glial cells including astrocytes, microglia, and oligodendrocytes. Consistent with the role of astrocytes as the frontline of fatty acid transport from the peripheral circulation into the brain, astrocytes are active in using fatty acids to produce energy, synthesize proteins as well as facilitate neurotransmitter production via astrocyte-neuron interactions [ 51 , 52 ]. Disrupted lipid clearance, particularly in the astrocytes, results in lipid accumulation, contributing to the formation of amyloid-beta (Aβ) plaques and tau tangles, the defining pathological features of AD [ 28 , 53 ]. Moreover, microglial cells use fatty acids as an alternative energy source to meet the high energy demand during microglial activation, and deregulated lipid usage may underlie microglial abnormalities [ 54 ]. In oligodendrocytes, which generate the lipid-rich myelin sheath, impaired lipid metabolism can lead to white matter abnormalities, a common feature observed in both AD and PTSD [ 55 – 57 ]. Furthermore, abnormal lipid accumulation can compromise brain vasculature, induce insulin resistance, and disrupt neuronal membrane integrity, further contributing to neurodegeneration[ 58 , 59 ]. However, despite the importance of fatty acid oxidation through acyl-CoA hydrolysis to glial and vascular cell functions, the current opinion is that neurons have limited capability in using fatty acids to fuel neuronal activity[ 60 ], which brings up a scientific question of whether and how altered acyl-CoA hydrolysis affects neuronal function. Of note, neural stem/progenitor cells give rise to granule neurons[ 60 ]. Previous studies indicate that mitochondrial fatty acid β-oxidation is important to the development of neural progenitor cells, and the defects of mitochondrial fatty acid β-oxidation may underlie developmental brain disorders such as Autism [ 61 ]. To this end, if we could perform a thought experiment, there exists a possibility that downregulation of ACOT2 in neural progenitor cells may impair the development and maturation of hippocampal pyramidal neurons, which dampens the reserve of neuronal activity, culminating in augmented sensitivity of affected subjects to the development of PTSD and AD in sequence. This hypothesis is further supported by our findings from the hippocampal functional gene network analysis, which enriched in “developmental growth”. Lastly, our TWAS analysis showed a negative relationship between ACOT2 expression and AD/PTSD risk. In contrast, AD and PTSD are positively associated with ACOT4 expression. ACOT2 primarily resides in mitochondria and is involved in mitochondrial β-oxidation of branched chain fatty acid, while ACOT4 is abundant in peroxisomes and contributes to peroxisomal degradation of very long chain fatty acids [ 47 ]. In this regard, the opposing effects of ACOT2 and ACOT4 deregulation on brain dysfunction in PTSD and AD remain unresolved. This discrepancy may arise from the versatile metabolic products of peroxisomal very long chain fatty acids [ 47 ]. On a related note, elevated very long chain fatty acids and dysregulated peroxisomal very long chain fatty acid metabolism has been previously linked to neurotransmission defects and pathological characteristics of AD including brain amyloidosis and tauopathy [ 62 , 63 ], further supporting impaired homeostasis of very long chain fatty acid metabolism in potentiating neuronal damages. These outstanding questions warrant further experimental investigations. The limitation of this study should also be noted, that is, the underrepresentation of racially and ethnically diverse populations. The large-scale GWAS summary statistics for AD and PTSD used in our TWAS analysis were primarily derived from individuals of European ancestry, which may constrain the generalizability of our findings to other populations. This lack of diversity can mask population-specific genetic risk factors, environmental exposures, and disease trajectories. Future studies will incorporate cohorts from a broader range of ancestral backgrounds to better elucidate the genetic links between AD and PTSD across diverse populations. Another limitation of this proof-of-concept study is its reliance solely on computational analysis to identify genetic links between AD and PTSD, without experimental validation. As a next step, we plan to use these findings to inform experimental designs that will test and validate computational predictions. In addition, longitudinal studies using PTSD cohorts to assess the role of these identified susceptibility genes and pathogenic variants in promoting AD development will be informative to assess the potential of the computational findings in early diagnosis and prevention of PTSD, AD, as well as AD conversion from PTSD patients. In summary, we identified shared gene variants in brain regions vulnerable to both AD and PTSD, specifically the hippocampus. These genes were enriched in key metabolic pathways, especially in the key pathway of fatty acid metabolism, which align with existing evidence from both patient studies and animal models. Although in vivo experimental studies are needed to further explore the detailed impact of these genes on neuronal and brain function, our results reveal gene variant–regulated metabolic pathways as a novel molecular link between AD and PTSD. These pathways not only shed light on shared disease mechanisms but also hold promise as biomarkers and potential personalized therapeutic targets, particularly for PTSD-associated risk of developing AD. Materials and Methods Hippocampal FUSION TWAS analysis To explore the relationship between single nucleotide polymorphism (SNP)-associated phenotypes and gene expression in the hippocampus, we performed a transcriptome-wide association study (TWAS) using the FUSION-TWAS method [ 18 ]. We obtained freely accessible genome-wide association study (GWAS) summary data for both Alzheimer’s disease cohort including 71,880 AD cases and 383,378 non-AD controls and Post-traumatic stress disorder (PTSD) including 137,136 cases and 1,085,746 controls from the original publication [ 19 , 20 ]. Cis-expression quantitative trait loci (cis-eQTL) weights for the hippocampus and reference linkage disequilibrium (LD) covariance data for chromosomes 1–22 calculated based on GTEx V8 dataset were obtained via the FUSION-TWAS github [ 18 ]. Hippocampal Gene expression imputation was carried out in accordance with the FUSION association test protocol. Functional annotation of the identified genes QIAGEN Ingenuity Pathway Analysis (IPA) was used to perform comparative analysis and identify canonical pathways. To identify shared susceptibility genes, we filtered the AD/PTSD TWAS results for genes with P < 0.05 in both disorders and used this list for IPA analysis. We first conducted a comparison analysis to uncover shared disease-related pathways and functional associations using genes exhibiting the same direction of expression changes (based on FUSION TWAS z-scores) and significant p value ( P < 0.05) in both AD and PTSD. We then applied wFisher meta-analysis p-values to further explore canonical pathways implicated in both AD and PTSD. Lastly, to further investigate the biological processes regulated by these overlapping genes, we carried out hippocampal gene network analysis using HumanBase ( https://hb.flatironinstitute.org ), focusing on genes with consistent expression direction between AD and PTSD patients[ 21 ]. Tissue and cell gene expression screening To investigate the expression patterns of genes that are involved in IPA determined AD/PTSD shared metabolic pathways, we leveraged open-access human single cell gene expression data available through the Cell Types Database on CZ CELL x GENE [ 22 ]. Genes that are essential to AD/PTSD-related metabolic pathways were obtained from IPA analysis result. The screening included multiple brain cell types, such as neurons, neural stem cells, oligodendrocytes, microglia, astrocytes, brain vascular cells, as well as hippocampal pyramidal neurons, granule cells, interneurons, and astrocytes. Both gene expression levels and the percentage of expressing cells were assessed in this analysis. Statistical analysis and meta-analysis All data were analyzed using Prism graphpad 9 statistical software unless otherwise indicated. The meta-analysis of TWAS statistics from the two cohorts was performed with the use of the weighted Fisher’s method (wFisher) in metapro R package [ 64 ]. P -values from the TWAS analysis, along with corresponding sample sizes and effect directions, were used as input for the meta-analysis to calculate combined p-values. Genes with a combined p -value less than 0.05 and consistent z -score direction across both cohorts were considered transcriptome-wide significant. Graphs are generated using R packages and HumanBase, CZ CELL x GENE web services. Funding This work was supported by research fundings from National Institutes of Health (NIH) P30 AG072973 to the University of Kansas Alzheimer’s Disease Research Center’s Research Education Component, and REC Fellowship to JT. Conflict of Interest The authors have no conflict of interest to report. Data Availability All original data are available upon request from the corresponding author. References [1]. ↵ ( 2024 ) 2024 Alzheimer’s disease facts and figures . Alzheimers Dement 20 , 3708 – 3821 . OpenUrl CrossRef PubMed [2]. ↵ Hampel H , Hardy J , Blennow K , Chen C , Perry G , Kim SH , Villemagne VL , Aisen P , Vendruscolo M , Iwatsubo T , Masters CL , Cho M , Lannfelt L , Cummings JL , Vergallo A ( 2021 ) The Amyloid-beta Pathway in Alzheimer’s Disease . Mol Psychiatry 26 , 5481 – 5503 . OpenUrl CrossRef PubMed [3]. ↵ Litke R , Garcharna LC , Jiwani S , Neugroschl J ( 2021 ) Modifiable Risk Factors in Alzheimer Disease and Related Dementias: A Review . Clin Ther 43 , 953 – 965 . OpenUrl CrossRef PubMed [4]. ↵ Baumgart M , Snyder HM , Carrillo MC , Fazio S , Kim H , Johns H ( 2015 ) Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population-based perspective . Alzheimers Dement 11 , 718 – 726 . OpenUrl CrossRef PubMed [5]. ↵ Mann SK , Marwaha R , Torrico TJ ( 2025 ) Posttraumatic Stress Disorder In StatPearls , Treasure Island (FL). [6]. ↵ Qureshi SU , Kimbrell T , Pyne JM , Magruder KM , Hudson TJ , Petersen NJ , Yu HJ , Schulz PE , Kunik ME ( 2010 ) Greater prevalence and incidence of dementia in older veterans with posttraumatic stress disorder . J Am Geriatr Soc 58 , 1627 – 1633 . OpenUrl CrossRef PubMed [7]. ↵ Weiner MW , Veitch DP , Hayes J , Neylan T , Grafman J , Aisen PS , Petersen RC , Jack C , Jagust W , Trojanowski JQ , Shaw LM , Saykin AJ , Green RC , Harvey D , Toga AW , Friedl KE , Pacifico A , Sheline Y , Yaffe K , Mohlenoff B , Department of Defense Alzheimer’s Disease Neuroimaging I ( 2014 ) Effects of traumatic brain injury and posttraumatic stress disorder on Alzheimer’s disease in veterans, using the Alzheimer’s Disease Neuroimaging Initiative . Alzheimers Dement 10 , S226 – 235 . OpenUrl CrossRef PubMed [8]. ↵ Bird CM , Burgess N ( 2008 ) The hippocampus and memory: insights from spatial processing . Nat Rev Neurosci 9 , 182 – 194 . OpenUrl CrossRef PubMed Web of Science [9]. ↵ Averill LA , Purohit P , Averill CL , Boesl MA , Krystal JH , Abdallah CG ( 2017 ) Glutamate dysregulation and glutamatergic therapeutics for PTSD: Evidence from human studies . Neurosci Lett 649 , 147 – 155 . OpenUrl CrossRef PubMed [10]. Wang R , Reddy PH ( 2017 ) Role of Glutamate and NMDA Receptors in Alzheimer’s Disease . J Alzheimers Dis 57 , 1041 – 1048 . OpenUrl PubMed [11]. Azargoonjahromi A , Alzheimer’s Disease Neuroimaging I ( 2024 ) Serotonin enhances neurogenesis biomarkers, hippocampal volumes, and cognitive functions in Alzheimer’s disease . Mol Brain 17 , 93 . [12]. Murrough JW , Huang Y , Hu J , Henry S , Williams W , Gallezot JD , Bailey CR , Krystal JH , Carson RE , Neumeister A ( 2011 ) Reduced amygdala serotonin transporter binding in posttraumatic stress disorder . Biol Psychiatry 70 , 1033 – 1038 . OpenUrl CrossRef PubMed Web of Science [13]. Agis-Balboa RC , Pinheiro PS , Rebola N , Kerimoglu C , Benito E , Gertig M , Bahari-Javan S , Jain G , Burkhardt S , Delalle I , Jatzko A , Dettenhofer M , Zunszain PA , Schmitt A , Falkai P , Pape JC , Binder EB , Mulle C , Fischer A , Sananbenesi F ( 2017 ) Formin 2 links neuropsychiatric phenotypes at young age to an increased risk for dementia . EMBO J 36 , 2815 – 2828 . OpenUrl Abstract / FREE Full Text [14]. ↵ Sun E , Motolani A , Campos L , Lu T ( 2022 ) The Pivotal Role of NF-kB in the Pathogenesis and Therapeutics of Alzheimer’s Disease . Int J Mol Sci 23 . [15]. ↵ Lutz MW , Luo S , Williamson DE , Chiba-Falek O ( 2020 ) Shared genetic etiology underlying late-onset Alzheimer’s disease and posttraumatic stress syndrome . Alzheimers Dement 16 , 1280 – 1292 . OpenUrl PubMed [16]. ↵ Tam V , Patel N , Turcotte M , Bosse Y , Pare G , Meyre D ( 2019 ) Benefits and limitations of genome-wide association studies . Nat Rev Genet 20 , 467 – 484 . OpenUrl CrossRef PubMed [17]. ↵ Evans P , Nagai T , Konkashbaev A , Zhou D , Knapik EW , Gamazon ER ( 2024 ) Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges . Curr Protoc 4 , e981 . OpenUrl [18]. ↵ Gusev A , Ko A , Shi H , Bhatia G , Chung W , Penninx BW , Jansen R , de Geus EJ , Boomsma DI , Wright FA , Sullivan PF , Nikkola E , Alvarez M , Civelek M , Lusis AJ , Lehtimaki T , Raitoharju E , Kahonen M , Seppala I , Raitakari OT , Kuusisto J , Laakso M , Price AL , Pajukanta P , Pasaniuc B ( 2016 ) Integrative approaches for large-scale transcriptome-wide association studies . Nat Genet 48 , 245 – 252 . OpenUrl CrossRef PubMed [19]. ↵ Jansen IE , Savage JE , Watanabe K , Bryois J , Williams DM , Steinberg S , Sealock J , Karlsson IK , Hagg S , Athanasiu L , Voyle N , Proitsi P , Witoelar A , Stringer S , Aarsland D , Almdahl IS , Andersen F , Bergh S , Bettella F , Bjornsson S , Braekhus A , Brathen G , de Leeuw C , Desikan RS , Djurovic S , Dumitrescu L , Fladby T , Hohman TJ , Jonsson PV , Kiddle SJ , Rongve A , Saltvedt I , Sando SB , Selbaek G , Shoai M , Skene NG , Snaedal J , Stordal E , Ulstein ID , Wang Y , White LR , Hardy J , Hjerling-Leffler J , Sullivan PF , van der Flier WM , Dobson R , Davis LK , Stefansson H , Stefansson K , Pedersen NL , Ripke S , Andreassen OA , Posthuma D ( 2019 ) Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk . Nat Genet 51 , 404 – 413 . OpenUrl CrossRef PubMed [20]. ↵ Nievergelt CM , Maihofer AX , Atkinson EG , Chen CY , Choi KW , Coleman JRI , Daskalakis NP , Duncan LE , Polimanti R , Aaronson C , Amstadter AB , Andersen SB , Andreassen OA , Arbisi PA , Ashley-Koch AE , Austin SB , Avdibegovic E , Babic D , Bacanu SA , Baker DG , Batzler A , Beckham JC , Belangero S , Benjet C , Bergner C , Bierer LM , Biernacka JM , Bierut LJ , Bisson JI , Boks MP , Bolger EA , Brandolino A , Breen G , Bressan RA , Bryant RA , Bustamante AC , Bybjerg-Grauholm J , Baekvad-Hansen M , Borglum AD , Borte S , Cahn L , Calabrese JR , Caldas-de-Almeida JM , Chatzinakos C , Cheema S , Clouston SAP , Colodro-Conde L , Coombes BJ , Cruz-Fuentes CS , Dale AM , Dalvie S , Davis LK , Deckert J , Delahanty DL , Dennis MF , Desarnaud F , DiPietro CP , Disner SG , Docherty AR , Domschke K , Dyb G , Kulenovic AD , Edenberg HJ , Evans A , Fabbri C , Fani N , Farrer LA , Feder A , Feeny NC , Flory JD , Forbes D , Franz CE , Galea S , Garrett ME , Gelaye B , Gelernter J , Geuze E , Gillespie CF , Goleva SB , Gordon SD , Goci A , Grasser LR , Guindalini C , Haas M , Hagenaars S , Hauser MA , Heath AC , Hemmings SMJ , Hesselbrock V , Hickie IB , Hogan K , Hougaard DM , Huang H , Huckins LM , Hveem K , Jakovljevic M , Javanbakht A , Jenkins GD , Johnson J , Jones I , Jovanovic T , Karstoft KI , Kaufman ML , Kennedy JL , Kessler RC , Khan A , Kimbrel NA , King AP , Koen N , Kotov R , Kranzler HR , Krebs K , Kremen WS , Kuan PF , Lawford BR , Lebois LAM , Lehto K , Levey DF , Lewis C , Liberzon I , Linnstaedt SD , Logue MW , Lori A , Lu Y , Luft BJ , Lupton MK , Luykx JJ , Makotkine I , Maples-Keller JL , Marchese S , Marmar C , Martin NG , Martinez-Levy GA , McAloney K , McFarlane A , McLaughlin KA , McLean SA , Medland SE , Mehta D , Meyers J , Michopoulos V , Mikita EA , Milani L , Milberg W , Miller MW , Morey RA , Morris CP , Mors O , Mortensen PB , Mufford MS , Nelson EC , Nordentoft M , Norman SB , Nugent NR , O’Donnell M , Orcutt HK , Pan PM , Panizzon MS , Pathak GA , Peters ES , Peterson AL , Peverill M , Pietrzak RH , Polusny MA , Porjesz B , Powers A , Qin XJ , Ratanatharathorn A , Risbrough VB , Roberts AL , Rothbaum AO , Rothbaum BO , Roy-Byrne P , Ruggiero KJ , Rung A , Runz H , Rutten BPF , de Viteri SS , Salum GA , Sampson L , Sanchez SE , Santoro M , Seah C , Seedat S , Seng JS , Shabalin A , Sheerin CM , Silove D , Smith AK , Smoller JW , Sponheim SR , Stein DJ , Stensland S , Stevens JS , Sumner JA , Teicher MH , Thompson WK , Tiwari AK , Trapido E , Uddin M , Ursano RJ , Valdimarsdottir U , Van Hooff M , Vermetten E , Vinkers CH , Voisey J , Wang Y , Wang Z , Waszczuk M , Weber H , Wendt FR , Werge T , Williams MA , Williamson DE , Winsvold BS , Winternitz S , Wolf C , Wolf EJ , Xia Y , Xiong Y , Yehuda R , Young KA , Young RM , Zai CC , Zai GC , Zervas M , Zhao H , Zoellner LA , Zwart JA , deRoon-Cassini T , van Rooij SJH , van den Heuvel LL , Study A , Estonian Biobank Research T , FinnGen I , Psychiatry HA-I , Stein MB , Ressler KJ , Koenen KC ( 2024 ) Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder . Nat Genet 56 , 792 – 808 . OpenUrl CrossRef PubMed [21]. ↵ Greene CS , Krishnan A , Wong AK , Ricciotti E , Zelaya RA , Himmelstein DS , Zhang R , Hartmann BM , Zaslavsky E , Sealfon SC , Chasman DI , FitzGerald GA , Dolinski K , Grosser T , Troyanskaya OG ( 2015 ) Understanding multicellular function and disease with human tissue-specific networks . Nat Genet 47 , 569 – 576 . OpenUrl CrossRef PubMed [22]. ↵ Biology CS-C ( 2023 ) CZ CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data . bioRxiv . [23]. ↵ Zhang Y , Liu G , Yan J , Zhang Y , Li B , Cai D ( 2015 ) Metabolic learning and memory formation by the brain influence systemic metabolic homeostasis . Nat Commun 6 , 6704 . OpenUrl PubMed [24]. ↵ Carneiro L , Knauf C , Mansuy-Aubert V ( 2022 ) Editorial: Neural Control of Energy Homeostasis and Energy Homeostasis Regulation of Brain Function . Front Neurosci 16 , 872296 . OpenUrl PubMed [25]. ↵ Michopoulos V , Vester A , Neigh G ( 2016 ) Posttraumatic stress disorder: A metabolic disorder in disguise? Exp Neurol 284 , 220 – 229 . OpenUrl CrossRef PubMed [26]. Mellon SH , Gautam A , Hammamieh R , Jett M , Wolkowitz OM ( 2018 ) Metabolism, Metabolomics, and Inflammation in Posttraumatic Stress Disorder . Biol Psychiatry 83 , 866 – 875 . OpenUrl CrossRef PubMed [27]. ↵ Yuan Y , Zhao G , Zhao Y ( 2024 ) Dysregulation of energy metabolism in Alzheimer’s disease . J Neurol 272 , 2 . OpenUrl CrossRef PubMed [28]. ↵ Yin F , Sancheti H , Patil I , Cadenas E ( 2016 ) Energy metabolism and inflammation in brain aging and Alzheimer’s disease . Free Radic Biol Med 100 , 108 – 122 . OpenUrl CrossRef PubMed [29]. ↵ Bano D , Ehninger D , Bagetta G ( 2023 ) Decoding metabolic signatures in Alzheimer’s disease: a mitochondrial perspective . Cell Death Discov 9 , 432 . OpenUrl PubMed [30]. ↵ Wei Z , Koya J , Reznik SE ( 2021 ) Insulin Resistance Exacerbates Alzheimer Disease via Multiple Mechanisms . Front Neurosci 15 , 687157 . OpenUrl CrossRef PubMed [31]. Feringa FM , van der Kant R ( 2021 ) Cholesterol and Alzheimer’s Disease; From Risk Genes to Pathological Effects . Front Aging Neurosci 13 , 690372 . OpenUrl CrossRef PubMed [32]. Bhatia S , Rawal R , Sharma P , Singh T , Singh M , Singh V ( 2022 ) Mitochondrial Dysfunction in Alzheimer’s Disease: Opportunities for Drug Development . Curr Neuropharmacol 20 , 675 – 692 . OpenUrl PubMed [33]. Pszczolowska M , Walczak K , Miskow W , Mroziak M , Chojdak-Lukasiewicz J , Leszek J ( 2024 ) Mitochondrial disorders leading to Alzheimer’s disease-perspectives of diagnosis and treatment . Geroscience 46 , 2977 – 2988 . OpenUrl PubMed [34]. ↵ Jia K , Tian J , Wang T , Guo L , Xuan Z , Swerdlow RH , Du H ( 2023 ) Mitochondria-sequestered Abeta renders synaptic mitochondria vulnerable in the elderly with a risk of Alzheimer disease . JCI Insight 8 . [35]. ↵ Roberts AL , Agnew-Blais JC , Spiegelman D , Kubzansky LD , Mason SM , Galea S , Hu FB , Rich-Edwards JW , Koenen KC ( 2015 ) Posttraumatic stress disorder and incidence of type 2 diabetes mellitus in a sample of women: a 22-year longitudinal study . JAMA Psychiatry 72 , 203 – 210 . OpenUrl PubMed [36]. Farr OM , Sloan DM , Keane TM , Mantzoros CS ( 2014 ) Stress- and PTSD-associated obesity and metabolic dysfunction: a growing problem requiring further research and novel treatments . Metabolism 63 , 1463 – 1468 . OpenUrl CrossRef PubMed [37]. ↵ Ogawa S , Hori H , Niwa M , Itoh M , Lin M , Yoshida F , Ino K , Kawanishi H , Narita M , Nakano W , Imai R , Matsui M , Kamo T , Kunugi H , Hattori K , Kim Y ( 2025 ) Serum lipid and plasma fatty acid profiles in PTSD patients and healthy individuals: Associations with symptoms, cognitive function, and inflammatory markers . Prog Neuropsychopharmacol Biol Psychiatry 138 , 111298 . OpenUrl PubMed [38]. ↵ Heppner PS , Crawford EF , Haji UA , Afari N , Hauger RL , Dashevsky BA , Horn PS , Nunnink SE , Baker DG ( 2009 ) The association of posttraumatic stress disorder and metabolic syndrome: a study of increased health risk in veterans . BMC Med 7 , 1 . OpenUrl CrossRef PubMed [39]. ↵ Mohlenhoff BS , O’Donovan A , Weiner MW , Neylan TC ( 2017 ) Dementia Risk in Posttraumatic Stress Disorder: the Relevance of Sleep-Related Abnormalities in Brain Structure, Amyloid, and Inflammation . Curr Psychiatry Rep 19 , 89 . OpenUrl PubMed [40]. Delic V , Ratliff WA , Citron BA ( 2021 ) Sleep Deprivation, a Link Between Post-Traumatic Stress Disorder and Alzheimer’s Disease . J Alzheimers Dis 79 , 1443 – 1449 . OpenUrl PubMed [41]. Duarte-Zambrano F , Barrero JA , Mockus I ( 2023 ) Cerebrospinal fluid levels of hypothalamic-pituitary-adrenal axis hormones in MCI and dementia due to Alzheimer’s disease: a systematic review . Dement Neuropsychol 17 , e20230031 . OpenUrl [42]. ↵ Dunlop BW , Wong A ( 2019 ) The hypothalamic-pituitary-adrenal axis in PTSD: Pathophysiology and treatment interventions . Prog Neuropsychopharmacol Biol Psychiatry 89 , 361 – 379 . OpenUrl CrossRef PubMed [43]. ↵ Zhu Y , Gao H , Tong L , Li Z , Wang L , Zhang C , Yang Q , Yan B ( 2019 ) Emotion Regulation of Hippocampus Using Real-Time fMRI Neurofeedback in Healthy Human . Front Hum Neurosci 13 , 242 . OpenUrl CrossRef PubMed [44]. Barkus C , McHugh SB , Sprengel R , Seeburg PH , Rawlins JN , Bannerman DM ( 2010 ) Hippocampal NMDA receptors and anxiety: at the interface between cognition and emotion . Eur J Pharmacol 626 , 49 – 56 . OpenUrl CrossRef PubMed Web of Science [45]. ↵ Sweatt JD ( 2004 ) Hippocampal function in cognition . Psychopharmacology (Berl ) 174 , 99 – 110 . OpenUrl CrossRef PubMed [46]. ↵ Hunt MC , Alexson SE ( 2002 ) The role Acyl-CoA thioesterases play in mediating intracellular lipid metabolism . Prog Lipid Res 41 , 99 – 130 . OpenUrl CrossRef PubMed Web of Science [47]. ↵ Brocker C , Carpenter C , Nebert DW , Vasiliou V ( 2010 ) Evolutionary divergence and functions of the human acyl-CoA thioesterase gene (ACOT) family . Hum Genomics 4 , 411 – 420 . OpenUrl CrossRef PubMed [48]. ↵ Zeng Q , Gong Y , Zhu N , Shi Y , Zhang C , Qin L ( 2024 ) Lipids and lipid metabolism in cellular senescence: Emerging targets for age-related diseases . Ageing Res Rev 97 , 102294 . OpenUrl CrossRef PubMed [49]. Mutlu AS , Duffy J , Wang MC ( 2021 ) Lipid metabolism and lipid signals in aging and longevity . Dev Cell 56 , 1394 – 1407 . OpenUrl CrossRef PubMed [50]. ↵ Yoon H , Shaw JL , Haigis MC , Greka A ( 2021 ) Lipid metabolism in sickness and in health: Emerging regulators of lipotoxicity . Mol Cell 81 , 3708 – 3730 . OpenUrl CrossRef PubMed [51]. ↵ Ebert D , Haller RG , Walton ME ( 2003 ) Energy contribution of octanoate to intact rat brain metabolism measured by 13C nuclear magnetic resonance spectroscopy . J Neurosci 23 , 5928 – 5935 . OpenUrl Abstract / FREE Full Text [52]. ↵ Kreft M , Bak LK , Waagepetersen HS , Schousboe A ( 2012 ) Aspects of astrocyte energy metabolism, amino acid neurotransmitter homoeostasis and metabolic compartmentation . ASN Neuro 4 . [53]. ↵ Di Paolo G , Kim TW ( 2011 ) Linking lipids to Alzheimer’s disease: cholesterol and beyond . Nat Rev Neurosci 12 , 284 – 296 . OpenUrl CrossRef PubMed [54]. ↵ Flowers A , Bell-Temin H , Jalloh A , Stevens SM , Jr. , Bickford PC ( 2017 ) Proteomic anaysis of aged microglia: shifts in transcription, bioenergetics, and nutrient response . J Neuroinflammation 14 , 96 . OpenUrl CrossRef PubMed [55]. ↵ Montani L ( 2021 ) Lipids in regulating oligodendrocyte structure and function . Semin Cell Dev Biol 112 , 114 – 122 . OpenUrl PubMed [56]. Nasrabady SE , Rizvi B , Goldman JE , Brickman AM ( 2018 ) White matter changes in Alzheimer’s disease: a focus on myelin and oligodendrocytes . Acta Neuropathol Commun 6 , 22 . OpenUrl PubMed [57]. ↵ Li L , Lei D , Li L , Huang X , Suo X , Xiao F , Kuang W , Li J , Bi F , Lui S , Kemp GJ , Sweeney JA , Gong Q ( 2016 ) White Matter Abnormalities in Post-traumatic Stress Disorder Following a Specific Traumatic Event . EBioMedicine 4 , 176 – 183 . OpenUrl PubMed [58]. ↵ Qin Q , Yin Y , Xing Y , Wang X , Wang Y , Wang F , Tang Y ( 2021 ) Lipid Metabolism in the Development and Progression of Vascular Cognitive Impairment: A Systematic Review . Front Neurol 12 , 709134 . OpenUrl PubMed [59]. ↵ Pifferi F , Laurent B , Plourde M ( 2021 ) Lipid Transport and Metabolism at the Blood-Brain Interface: Implications in Health and Disease . Front Physiol 12 , 645646 . OpenUrl CrossRef PubMed [60]. ↵ Cleland NRW , Bruce KD ( 2024 ) Fatty acid sensing in the brain: The role of glial-neuronal metabolic crosstalk and horizontal lipid flux . Biochimie 223 , 166 – 178 . OpenUrl PubMed [61]. ↵ Xie Z , Jones A , Deeney JT , Hur SK , Bankaitis VA ( 2016 ) Inborn Errors of Long-Chain Fatty Acid beta-Oxidation Link Neural Stem Cell Self-Renewal to Autism . Cell Rep 14 , 991 – 999 . OpenUrl CrossRef PubMed [62]. ↵ Kou J , Kovacs GG , Hoftberger R , Kulik W , Brodde A , Forss-Petter S , Honigschnabl S , Gleiss A , Brugger B , Wanders R , Just W , Budka H , Jungwirth S , Fischer P , Berger J ( 2011 ) Peroxisomal alterations in Alzheimer’s disease . Acta Neuropathol 122 , 271 – 283 . OpenUrl CrossRef PubMed Web of Science [63]. ↵ Yin F ( 2023 ) Lipid metabolism and Alzheimer’s disease: clinical evidence, mechanistic link and therapeutic promise . FEBS J 290 , 1420 – 1453 . OpenUrl CrossRef PubMed [64]. ↵ Yoon S , Baik B , Park T , Nam D ( 2021 ) Powerful p-value combination methods to detect incomplete association . Sci Rep 11 , 6980 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted August 13, 2025. Download PDF Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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