Genetic dissection of hippocampal sclerosis of ageing using magnetic resonance imaging surrogates

Genetic dissection of hippocampal sclerosis of ageing using magnetic resonance imaging surrogates | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genetic dissection of hippocampal sclerosis of ageing using magnetic resonance imaging surrogates Clàudia Olivé, Itziar de Rojas, Linda Zhang, Oscar Sotolongo-Grau, and 25 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6429978/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKROUND : Hippocampal sclerosis of aging (HS-aging) is frequently present in individuals over 85 who die with dementia. Recent studies suggest that some loci associated with Alzheimer’s disease (AD) may be more related to HS-aging. We aimed to find AD-associated SNPs potentially related to HS-aging. METHODS : We used different regression models to assess the relation of the AD polygenic risk score (AD-PRS) with hippocampal subfield volumes assessed by magnetic resonance imaging (MRI) as HS-by-proxy in 1,130 participants without dementia. We meta-analyzed 1,708 individuals to associate their AD-PRS (83 variants) with AD alongside HS-aging. We also performed co-regulatory network analyses and over-representation enrichment analyses in order to identify biological pathways enriched with co-regulatory networks of genes associated with HS-aging. RESULTS : HS-by-proxy measures of fimbria and hippocampal body and head show association with AD-PRS, SHARPIN , GRN and TNIP1 , also after replication. Our results also show an association of the LUBAC complex with our proxy phenotype. We replicated the stronger AD-PRS association with AD in the presence of HS-aging compared to AD alone. CONCLUSIONS : Results show association between some AD-SNPs and HS-proxy, enriched in immune-brain axis pathways, differentiating HS-aging from AD. This insight aids in understanding their interrelationships and identifying specific therapeutic targets. Hippocampal sclerosis of aging Alzheimer’s disease Magnetic Resonance Imaging Polygenic risk score Figures Figure 1 Figure 2 Figure 3 Figure 4 1. BACKGROUND Hippocampal sclerosis of aging (HS-aging) is present in a considerable proportion of people over 85 years old who die with dementia [ 1 ], although magnetic resonance imaging (MRI) correlates of this disease can be detected over a decade prior to death [ 2 ]. The main pathological features of HS-aging include severe neuronal loss and gliosis in specific regions of the hippocampus, leading to cognitive and memory symptoms that appear to mimic Alzheimer’s disease (AD). Due to the similarity of their clinical profiles, and considering the current lack of biomarkers for HS-aging detection, this condition is often misdiagnosed as AD [ 1 , 3 , 4 ]. However, HS-aging has usually less severe symptoms and longer evolution than AD [ 5 ] because it does not progress outside the hippocampus. HS-aging is also strongly associated with TDP-43 pathology and is encompassed in limbic-predominant age-related TDP-43 encephalopathy (LATE), which tends to co-occur with AD [ 1 , 6 ]. The etiology of HS-aging remains mostly unknown, and the absence of reliable biological markers for its detection makes it a subject of ongoing research. Recently, atrophy in hippocampal subfields detected by magnetic resonance imaging (MRI) has been reported to be a promising in vivo biomarker specific to HS-aging [ 2 , 3 ]. Moreover, various studies have reported some evidence of a genetic predisposition to HS-aging [ 7 , 8 ]. Nevertheless, further investigation is essential to dissect the genetic component and specific biomarkers for HS-aging, as well as its relationships with AD, LATE and other dementias. Common diseases, such as AD or HS-aging, are complex traits which are rarely caused by the dysfunction of a single gene, but rather affected by the additive contribution of several genetic variants together [ 9 ]. Through genome-wide association studies (GWAS), it is possible to identify genetic variants significantly present in case individuals and map its polygenic architecture [ 9 ], and use them to construct polygenic risk scores (PRS) to identify individuals at risk, improve diagnostic tools and develop new drug targets [ 10 ]. Over the last decade, GWAS have grown significantly in sample size and in the number of traits under investigation. Recently, thanks to large international collaborative efforts [ 10 , 11 ], the genomic landscape of the AD risk alleles has doubled, with more than 80 AD-associated loci identified [ 11 ]. However, adding a larger sample size of clinically labeled individuals may result in the addition of cases with co-pathologies, and often misdiagnosis [ 4 , 12 ]. In fact, more than half of individuals with AD brain pathology are found to also have one or more other brain abnormalities that may cause dementia, such as vascular lesions, Lewy bodies, LATE, argyrophilic grains or HS-aging [ 13 ]. Hippocampal atrophy is observed in both healthy individuals and patients with mild cognitive impairment (MCI) as prodromal stage of AD dementia [ 14 , 15 ]. Thus, previous studies have associated the AD polygenic risk score (AD-PRS) with reduced volume in different hippocampal subfields in healthy individuals [ 16 , 17 ]. Nevertheless, some studies suggest that hippocampal atrophy before dementia onset manifests earlier in individuals with HS-aging compared to those with AD [ 18 ], supporting its role as an early biomarker for HS-aging [ 3 , 18 , 19 ]. Therefore, we hypothesize that certain variants included in the AD-PRS might be specifically associated with HS-aging. In this study we aimed to assess the association of AD risk alleles with HS-aging. However, since HS-aging cannot be accurately diagnosed ante mortem , collecting data from individuals with HS-aging for research purposes is currently challenging. Therefore, for the analyses presented here, we used a proxy for HS-aging based on different hippocampal subfield volumes measured with MRI, in participants likely to develop dementia but without a current dementia diagnosis. Since the genomic architecture has not been as extensively studied in HS-aging as in AD, we designed our analyses to detect which AD-related SNPs [ 11 ] might be more specifically associated with HS-aging. To achieve this, we examined both the individual and combined effects of AD-related SNPs included in the AD-PRS by Bellenguez et al. [ 11 ] and investigated their correlation with HS-aging. 2. METHODS 2.1. Participants For this study we leveraged data from Ace Alzheimer Center Barcelona (ACE) [ 20 ], Alzheimer’s Disease Neuroimaging Initiative (ADNI, adni.loni.usc.edu, [ 21 , 22 ]) and the Vallecas Project (VP) [ 23 ]. We combined data from ACE and ADNI for our discovery analyses whereas VP was used for replication purposes. All samples for this study have available MRI, GWAS and clinical data. Only participants without a clinical diagnosis of dementia were included, consisting of individuals with MCI, who are at increased risk of developing dementia, and cognitively normal controls. By focusing on this preclinical stage, we aim to identify genetic variants associated with lower hippocampal volumes before the onset of dementia symptoms, which may represent early markers of HS-aging. Participants from ACE (N = 263) were collected through the BIOFACE project [ 24 ], Fundació ACE Healthy Brain Initiative (FACEHBI) [ 25 ], European Prevention of Alzheimer's Dementia (EPAD) [ 26 ] and Models of Patient Engagement for Alzheimer’s Disease (MOPEAD) [ 27 ]. From ADNI we selected participants from either ADNI1 or ADNI2 who had a diagnosis of MCI (N = 665) or did not have dementia (N = 465). For our replication analyses, we leveraged a cohort of individuals without dementia (N = 729) from the VP [ 23 ]. A summary of demographic and clinical information for the participants included in this study by project is shown in Table 1 , and further information about each individual project is provided in the Supplementary Methods. All projects passed the ethics committee and written informed consent was obtained from all participants. Table 1 Demographic and clinical data for the subjects included in this study by project. Projects N Females (%) MCI (%) CN (%) Age (± SD) Education (± SD) Discovery ADNI-1 562 221(39.32) 353(62.81) 209(37.19) 75.12(6.51) 15.83(2.97) ADNI-2 305 141(46.23) 212(69.51) 93(30.49) 71.89(7.24) 16.16(2.58) BIOFACE 56 37(66.07) 56(100) 00(00.00) 61.46(3.48) 9.77(3.05) FACEHBI 158 99(62.66) 2(1.27) 156(98.73) 67.27(6.90) 12.98(4.59) EPAD 29 15(51.72) 23(79.31) 6(20.69) 70.68(5.82) 9.79(3.48) MOPEAD 20 9(45.00) 19(95.00) 1(5.00) 73.79(4.63) 9.10(4.06) Total 1,130 522(46.19) 665(58.84) 465(41.15) 72.34(7.55) 14.95(3.75) Replication VP 729 496(68.04) 00(00.00) 729(100) 74.74(3.84) 10.30(5.77) Total 1,859 1,018(54.76) 665(35.77) 1,194(64.22) 73.28(6.47) 13.13(5.17) * N: number of samples; MCI: Mild cognitive impairment; CN: Controls; Age: average age at MRI; SD: Standard error. Moreover, the EADB/ACE/BTN-Clinic-FRCB-IDIBAPS cohort (Supplementary Methods) was used for the replication of the association between AD-PRS and AD concomitant pathologies previously published by de Rojas et al. [ 10 ]. 2.2. Genomic data processing, QC and AD-PRS Samples from ACE and VP were genotyped as part of the Genome Research at Ace Alzheimer center (GR@ACE) and Dementia Genetics Spanish Consortium (DEGESCO) genetic initiatives [ 10 , 28 ]. Genotyping was conducted using the Axiom 815K Spanish biobank array (according to manufacturer’s instructions - Axiom™ 2.0 Assay Manual Workflow, Thermo Fisher) at the Spanish National Center for Genotyping (CeGEN, Santiago de Compostela, Spain). Details on genotyping and quality control procedures are provided elsewhere [ 28 ]. ADNI samples were genotyped with the Illumina Human 610-Quad BeadChip (Illumina, Inc., San Diego, CA, USA), for ADNI-1 and with the OmniExpress BeadChip for ADNI-GO/2 individuals (ADNI-2) [ 29 , 30 ]. Genomic data quality control (QC) was performed uniformly in three parallel batches for ADNI-1, ADNI-2 and ACE-VP. QC included removal of samples with low-quality genotyping, excess of heterozygosity or high missingness and variants with call rate below 95% or deviation from the Hardy–Weinberg equilibrium ( P value 0.1875), or with a non-European ancestry (as per 1000 Genomes Project) were excluded from the analysis. To maximize the AD-PRS coverage, imputed data was generated with the Trans-Omics for Precision Medicine (TOPMed) reference panel [ 31 , 32 ] on genome build GRCh38. Rare variants (minor allele frequency; MAF < 1%) and variants with low imputation quality (R 2 < 0.3) were excluded. A weighted individual AD-PRS was calculated based on the 83 genome-wide significant (GWS, P value < 5E-08) variants reported by the European Alzheimer’s and Dementia Biobank (EADB) [ 11 ]. AD-PRS was generated by multiplying the genotype dosage of each risk allele for each variant by its respective weight and then summing across all variants. Due to its large effect, we excluded APOE variants (rs429358 and rs7412) from the AD-PRS calculation as well as the ABI3 locus (rs616338) because it was not properly imputed in the ADNI-2 dataset. 2.3. Structural MRI image acquisition The image acquisition process was slightly different for each project of the ACE cohort included in this study. MRI scans from FACEHBI were performed on a 1.5T Siemens MAGNETOM Aera (Erlangen, Germany), while for for BIOFACE, EPAD and MOPEAD images were acquired with a Siemens MAGNETOM VIDA 3T scanner (Erlangen, Germany) using a 32-channel head coil at Clínica Corachan, Barcelona. Anatomical T1-weighted images were acquired using a three-dimensional (3D) magnetization-prepared rapid gradient-echo (MPRAGE) sequence with different parameters for FACEHBI [ 25 ] (repetition time (TR) 2.200 ms, echo time (TE) 2.66 ms, inversion time (TI) 900 ms, slip angle 8°, field of view (FOV) 250 mm, slice thickness 1 mm, and isotropic voxel size 1 × 1 × 1 mm), BIOFACE [ 24 ] (TR 2.200ms, TE 2.23 ms, TI 968ms, 1.2 mm slice thickness, FOV 270 mm, and voxel measurement 1.1 × 1.1 × 1.2mm), MOPEAD [ 27 ] (TR 2.200 ms, TE 2.33 ms, TI 968ms, slip angle 8°, FOV 270 mm, slice thickness 1.2 mm, and isotropic voxel size 1.1 × 1.1 × 1.2 mm) and EPAD [ 26 ] (TR 2.300 ms, TE 2.93 ms, TI 900ms, slip angle 9°, FOV 270 mm, slice thickness 1.2 mm, and isotropic voxel size 1.1 × 1.1 × 1.2 mm). Axial T2-weighted, 3D isotropic fast fluid-attenuated inversion recovery (FLAIR) and axial T2*-weighted sequences were also acquired to detect significant vascular brain damage or microbleeds. All the subjects with existing GWAS information were chosen from ADNI cohorts ( http://adni.loni.usc.edu/ ). The closest MRI to the baseline was selected for each subject. All MRI T1-weighted images were downloaded in NIfTI format [ 33 , 34 ]. Since the ADNI protocol for MRI included one non-accelerated and one accelerated version of the scan, if available, more than one T1-weighted MRI for the same experiment was downloaded, in order to perform movement correction in the Freesurfer image processing pipeline (described below). For the VP cohort, all T1-weighted images (3D fast spoiled gradient echo with inversion recovery preparation) were acquired using a 3T MRI (Signa HDxt GEHC, Waukesha, USA) with a phased array 8 channel head coil and the following parameters: TR 10 ms, TE 4.5 ms, TI 600 ms, FOV 240 mm, matrix 288x288 and slice thickness 1 mm, yielding 0.5x0.5x1 mm voxel size. All MRI scans were reported by a neuroradiologist. 2.4. Hippocampal subfields extraction In order to extract the hippocampal subfield volumes, subjects from ACE and ADNI were processed the same way. First, cortical reconstruction and volumetric segmentation for MRI images was performed with the Freesurfer 7.2 image analysis suite, which is documented and freely available for download online ( https://surfer.nmr.mgh.harvard.edu/ ). The technical details of these procedures are described in prior publications [ 35 – 44 ]. Freesurfer segmentation was performed using both T1 and T2-weighted images for the ACE samples, while only T1-weighted images were used for the ADNI cohort. Then, the hippocampal subfields segmentation (HSF) was carried out using the hippocampal parcellation method included in Freesurfer 7.2 [ 45 ]. Individual hippocampal subfield volumes for cornu ammonis (CA) were extracted out from results and grouped into a table. The following subfields of the hippocampal formation were used for the analyses in this study: CA1 body CA1 head, CA3 body, CA3 head, CA4 body, CA4 head, Granule Cell and Molecular Layer of the Dentate Gyrus (GC-ML-DG) body, GC-ML-DG head, hippocampus-amygdala-transition-area (HATA), whole hippocampal body and head (BH), hippocampal tail, whole hippocampus, fimbria, hippocampal fissure, molecular layer of the hippocampus body, molecular layer of the hippocampal head, parasubiculum, presubiculum body, presubiculum head, subiculum body and subiculum head. Also, estimated total intracranial volumes (TIV) were extracted, from prior Freesurfer analyses, in order to make further volumetric corrections. For the VP cohort, automatic segmentation of the hippocampus was performed on each participant’s T1-weighted image using FreeSurfer v.6.0 ( https://surfer.nmr.mgh.harvard.edu/ ). Technical details of the whole-brain segmentation methods have been described previously [ 40 ]. Hippocampal volumes were extracted using the hippocampal subfields module in FreeSurfer 6.0 [ 45 ], and segmentations for all participants were visually inspected for accuracy. 2.5. Association of HS-by-proxy with AD-PRS Hippocampal subfield volumes and AD-PRS data were standardized by project using the scale function in R (Supplementary Fig. 1) and independent t-tests were conducted to assess differences between AD-PRS means in individuals classified by diagnosis and APOE ɛ4 carriers. For hippocampal subfield volumes, outliers differing ± 3 SD from the mean were removed. As has been frequently applied to MRI data analysis [ 46 – 49 ], we conducted hierarchical agglomerative clustering to group highly correlated hippocampal subfields in order to increase statistical power. We used the average agglomeration method on the Euclidean distance matrix of hippocampal subfield volumes (Supplementary Figs. 2 & 3). After dimension reduction of these variables, analyses were conducted using the volumes of the following four hippocampal subfields used as HS-by-proxy: hippocampal fissure, parasubiculum, fimbria and whole hippocampal BH. All analyses were performed using R version 4.1.2 software. Association of AD-PRS with HS-by-proxy was assessed using linear regression for each project. HS-by-proxy and AD-PRS were set as dependent and independent variables respectively, with sex, age, age 2 (to account for the non-linear effect of age), years of education, diagnosis (when applicable), TIV and first ten genetic principal components (PC1-10; to adjust genetic variability for population structure) as covariates in the following regression model: $$\:HS\:by\:proxy\:\sim\:PRS+Sex+\:Age+{Age}^{2}+Education+Diagnosis+TIV+PCs$$ Pearson’s correlation analysis was conducted to study the association between each SNP included in the AD-PRS calculation and HS-by-proxy phenotype previously found to be significantly associated with AD-PRS. SNPs presenting higher and significant correlations ( P value .05) were later employed as independent variants for the estimation of linear regression models adjusted by the same covariates as before: $$\:HS\:by\:proxy\:\sim\:SNP\:dose+Age+{Age}^{2}+Sex+Education+Diagnosis+TIV+PCs$$ Results for both regression models were then combined across projects with a fixed-effect meta-analysis (I 2 < 75%) for each hippocampal subfield representing HS-by-proxy phenotype, using the inverse variance weighted approach implemented with the meta package in R [ 50 ]. 2.6. Co-regulatory network analysis To identify co-regulatory networks of genes associated with HS-aging, we used GeneFriends [ 51 ], a bioinformatics pipeline previously reported by our team [ 52 ]. These lists of genes were based on the Sequence Read Archive (SRA) [ 53 ] and only genes highly co-expressed (co-expression value > 0.5) in neuron and/or brain tissue were included in further analyses. Next, WebGestalt [ 54 ] was used to identify potential enrichments in the previously identified co-regulated gene lists. We used over-representation enrichment analysis (ORA) [ 55 ] of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in humans using protein-coding regions of the genome as reference set. Those lists of co-expressed genes showing significantly enriched KEGG pathways were further analyzed using STRING v11.5 [ 56 ] in order to find known and predicted protein-protein interactions. 3. RESULTS 3.1. AD-PRS and HS-by-proxy phenotype In the meta-analysis presented here, we included data from six different projects, which consisted of 1,130 participants without dementia with MRI and GWAS data. Their demographic composition was as follows: 46.2% were female, 58.9% had MCI and 41.1% were individuals without dementia (Table 1 ). The average age of the participants was 72.3 (± 3.84) years. Additionally, an AD-PRS was calculated for these participants, which ranged from − 3.60 to 3.73 on a scaled min-max basis (Supplementary Fig. 4). MCI showed a tendency toward a higher AD genetic load compared to controls (Supplementary Fig. 4A). This trend may be due to significant differences in AD-PRS observed between APOE ɛ4 MCI carriers and controls ( P value = .000084; Supplementary Fig. 4B & C). We found a study-wise significant association between the AD-PRS and hippocampal BH (OR [95% CI] = 0.91 [0.87–0.96]; P value = .000142) and fimbria (OR [95% CI] = 0.92 [0.87–0.97]; P value = .00213) as HS-by-proxy phenotypes after Bonferroni correction ( P value < .0125; Table 2 ). Table 2 Meta-analysis results of the hippocampal subfield volumes association with AD-PRS [ 76 ]. Hippocampal subfield OR CI (95%) P value Discovery (N = 1,128) Parasubiculum 0.96 0.91–1.02 1.67 × 10 − 01 Hippocampal fissure 1.02 0.96–1.08 5.16 × 10 − 01 Fimbria 0.92 0.87–0.97 2.13 × 10 − 03 Hippocampal BH 0.91 0.87–0.96 1.42 × 10 − 04 Replication (N = 728) Fimbria 0.95 0.89–1.02 1.76×10 − 01 Hippocampal BH 0.94 0.88-1.00 3.55×10 − 02 Final (N = 1,856) Fimbria 0.93 0.89–0.97 1.12 × 10 − 03 Hippocampal BH 0.92 0.89–0.96 1.77 × 10 − 05 * Significant P values after Bonferroni correction in bold ( P value < .0125; replication P value < .05). † OR: Odds ratio; CI: Confidence interval; BH: Body and head. Next, the Vallecas replication cohort (N = 728 healthy controls, 68.1% females and mean age 74.7 years with an AD-PRS range scaled min−max from − 3.11 to 2.96) confirmed a significant and independent association between AD-PRS and hippocampal BH (OR [95% CI] = 0.94 [0.88–1.00]; P value = .0355) validating our discovery finding (Table 2 ). Combining all three cohorts in an extended meta-analysis (N = 1,856) showed the same effect size and improved the statistical significance of the association between the AD-PRS and the HS-by-proxy phenotypes (hippocampal BH, OR [95% CI] = 0.92 [0.89–0.96]; P value = 1.77×10 − 05 ; fimbria, OR [95% CI] = 0.93 [0.89–0.97]; P value = .00112; Fig. 1 ; Table 2 ) suggesting that participants without dementia with higher genetic burden of AD have less volume in these hippocampal subfields. All subsequent values reported here correspond to results of analyses of all cohorts combined. The impact of covariates in each model is presented in Supplementary Table 1 for hippocampal BH and Supplementary Table 2 for fimbria. 3.2. AD SNPs and HS-by-proxy phenotype For those HS-by-proxy phenotypes for which we found a significant association with AD-PRS (hippocampal BH and fimbria) we tested for correlation with the AD loci reported by Bellenguez et al. [ 11 ] composing the AD-PRS. The purpose of this analysis is to reduce the dimensionality of gene data associated with AD for a more specific assessment of its relation with HS-aging (Table 3 ). Variants in SHARPIN (rs34173062), GRN (rs5848) and TNIP1 (rs871269) loci were found to be significantly associated with hippocampal BH (rs34173062, OR [95% CI] = 0.84 [0.77–0.92], P value = 1.51×10 − 04 ; rs871269, OR [95% CI] = 0.90 [0.85–0.96], P value = 3.57×10 − 04 ) and/or fimbria (rs34173062, OR [95% CI] = 0.81 [0.73–0.90], P value = 6.63×10 − 05 ; rs5848, OR[95% CI] = 0.89 [0.83–0.95], P value = 2.84×10 − 04 ; Fig. 3 ) after Bonferroni correction ( P value < 6.02×10 − 04 ) in the meta-analysis. Based on the correlation of AD-PRS loci with HS-by-proxy, we found three clusters of AD loci showing an unequal association profile to HS-aging which supports the existence of a loci set more related to specific hippocampal pathobiology (Fig. 2 ). The SNPs in cluster A (blue cluster) show an overall negative correlation with our HS-by-proxy phenotype, meaning that they are generally correlated with smaller fimbria and hippocampal BH volumes, thus potentially specific to tissue atrophy. What is more, all SNPs found significantly associated with HS-by-proxy ( SHARPIN , GRN , TNIP1 ) gather together in cluster A. SNPs in cluster B and C were not significantly associated with our HS-by-proxy phenotype, with an overall positive correlation among the SNPs in these clusters and hippocampal volume. This suggests a tendency toward greater hippocampal volumes for carriers of these cluster B and C variants. Table 3 Meta-analysis results of the HS-by-proxy association with the selected SNPs in the AD-PRS [ 76 ]. Loci SNP OR CI (95%) P value Hippocampal BH ADAM17 rs72777026 0.90 0.84–0.97 .0049 BCKDK rs889555 1.07 1.00-1.13 .03 GRN rs5848 0.92 0.87–0.98 .0068 HS3ST5 rs785129 1.01 0.96–1.07 .07 MAPT rs199515 0.92 0.87–0.98 .01 PRDM7 rs56407236 1.11 0.99–1.23 .06 SHARPIN rs34173062 0.84 0.77–0.92 1.51 × 10 − 04 TNIP1 rs871269 0.90 0.85–0.96 3.57 × 10 − 04 Fimbria ADAM17 rs72777026 0.95 0.88–1.03 .25 BCKDK rs889555 1.00 0.93–1.07 .99 GRN rs5848 0.89 0.83–0.95 2.84 × 10 − 04 HS3ST5 rs785129 1.00 0.94–1.06 .89 MAPT rs199515 0.89 0.83–0.96 .0013 PRDM7 rs56407236 1.19 1.05–1.34 .0049 SHARPIN rs34173062 0.81 0.73–0.90 6.63 × 10 − 05 TNIP1 rs871269 0.94 0.89-1.00 .07 * Significant P values after Bonferroni correction in bold ( P value < 6.02 ×10 − 04 ). † OR: Odds ratio; CI: Confidence interval; BH: Body and head. 3.3. The LUBAC complex as represented pathway in the AD SNPs associated with HS-by-proxy phenotype Using the STRING software, we found that cluster A (Fig. 2 ; blue) which includes SHARPIN, GRN and TNIP1 , was significantly enriched in the linear ubiquitin chain assembly complex (LUBAC) cellular component (Gene Ontology (GO) Term: GO:0071797) responsible for producing linear polyubiquitin chains and regulating the NF-κB pathway [ 57 ], which plays a critical role in inflammatory and immune responses. The biological processes enriched by this cluster are: protein linear polyubiquitination (GO:0097039) and the negative regulation of biological process (GO:0048519). Next, the top three significantly enriched biological process in cluster B (Fig. 2 B) were the regulation of neurofibrillary tangle assembly (GO:1902996), microglial cell proliferation (GO:0061518) and positive regulation of dendritic cell cytokine production (GO:0002732). In cluster C, the top three significantly enriched biological processes were the positive regulation of engulfment of apoptotic cell (GO:1901076), neuropeptide catabolic process (GO:0010813) and negative regulation of aspartic-type endopeptidase activity involved in amyloid precursor protein catabolic process (GO:1902960). To point out, the B and C clusters are both significantly enriched in their top ten pathways by the positive regulation of immune system process (GO:0002684). Another 23 biological processes are enriched by loci within both clusters B and C, with some of them being related to amyloid β (Aβ) formation or clearance. However, none of the pathways enriched for genes in cluster A are also enriched for loci in cluster B or C. Summaries of the biological processes enriched by each cluster are reported on Supplementary Tables 3, 4 & 5. Regarding disease-gene associations found using STRING, genes in clusters B and C, which are associated with higher hippocampal subfield volumes, showed a significant enrichment for AD (DOID:10652; B cluster FDR = 5.7×10 − 03 ; C cluster FDR = 8.8×10 − 04 ). In contrast to the cluster A, which includes genes correlated with smaller hippocampal subfield volumes and is only significantly enriched in nominal aphasia (DOID:4541; FDR = 1.84×10 − 02 ). 3.4. Network of genes associated with SNPs related to HS-by-proxy phenotype Looking for a gene-network and pathways enriched for the genes associated with hippocampal BH and fimbria, we extracted the genes co-expressed with SHARPIN, GRN and TNIP1 in brain tissue and neurons using Genefriends. For this purpose, only transcripts displaying a high expression correlation between them were selected (Pearson’s r > 0.5). We detected a 3.57% overlap among genes co-expressed with SHARPIN, GRN and TNIP1 which were further tested in a pathway enrichment analysis (Supplementary Fig. 5). Nine significantly enriched KEGG pathways were identified by the enrichment analysis with WebGestalt and 10 pathways (FDR ≤ 0.05) with STRING in brain (Supplementary Table 6–7), No significant pathways were detected in neurons (Supplementary Table 8). Four of the most significantly enriched pathways concordant between the STRING and the WebGestalt strategies could be connected to biological mechanisms of HS-aging: protein processing in endoplasmic reticulum (hsa04141), other glycan degradation (hsa00511), B cell receptor signaling pathway (hsa04662) and lysosome (hsa04142). 4. DISCUSSION Characterization of the genetic landscape of complex diseases provides a unique opportunity for a better understanding of their associated physiopathological processes. Although GWAS analyses have significantly helped improve our current understanding of AD genetic architecture, the fact that AD often co-occurs with other pathologies, and the massive sample sizes required for this kind of studies comes with the increased risk of adding higher rates of individuals with co-pathologies or doubtful diagnoses in these analyses. Approximately 75% of older adults examined post mortem exhibit multiple brain pathologies, commonly referred to as mixed neuropathologies [ 58 , 59 ]. Our recent study of AD in a Spanish histopathological cohort [ 10 ], suggested a stronger association of the AD-PRS with AD-mixed pathologies than with AD alone. The replication of this analysis using an updated version of the AD-PRS with the 83 SNPs from Bellenguez et al. [ 11 ], reveals an increase of the effect of this trend (Fig. 4 ). These results indicate that the AD association of some AD-PRS variants might be due to their relation with other neuropathologies, like HS-aging, rather than AD alone. Our hypothesis arose from our previous findings [ 10 ] where AD-PRS was found to be more strongly associated with mixed pathologies (AD + HS-aging) than with pure AD pathology. We hypothesized that some SNPs associated with AD in large GWAS might be more specifically associated to HS-aging hippocampal atrophy patterns than AD. For this purpose, we used the AD-related SNPs extracted from the recently published meta-GWAS for AD risk [ 11 ] to assess their association with hippocampal subfield volumes as HS-by-proxy phenotype in individuals without dementia, since previous studies suggest that hippocampal atrophy seen in HS-aging begins early prior to dementia compared to AD and support its use as a biomarker for HS-aging [ 18 , 19 ]. In line with previous research that associate AD-PRS with hippocampal atrophy in individuals without dementia [ 16 , 17 , 60 ], the main findings of this study agree with our hypothesis: higher values of AD-PRS are associated not only with AD but more specifically with HS-by-proxy represented by small hippocampal subfields volumes in fimbria and hippocampus BH regions in individuals without clinical AD dementia. This is also in keeping with the recent demonstration [ 61 ] that in vivo MRI correlates of HS-aging, derived from postmortem histopathology, are primarily reductions in grey matter in anterior hippocampus (i.e., head extending into body). Regarding the loss of volume of with matter (i.e., the fimbria), we speculate that this occurs secondary to degeneration of the CA1 and subiculum. Moreover, our results show two new variants that are part of the AD-PRS but might be more specific to HS-aging (rs34173062 and rs871269). These variants drive the effect of the association between HS-aging and AD-PRS together with rs5848 in GRN , for which we replicate the association with HS-aging. Previous HS-aging studies have consistently identified four loci ( GRN , TMEM106B , ABCC9 , and KCNMB2 ) [ 62 ]. Specifically, variant rs5848 in GRN has been associated with HS-aging [ 8 , 63 ]. GRN (Granulin Precursor) is a gene that encodes for granulins, a family of glycosylated peptides. Glycosylation happens in the endoplasmic reticulum (ER) and disruptions on the protein processing in the ER (KEGG pathway has04141, enriched by genes commonly co-expressed with SHARPIN, GRN and TNIP1 ) have been associated with neurodegenerative diseases. After being attached to proteins in the ER, glycans are degraded (KEGG pathway hsa00511) in the lysosome (KEGG pathway hsa04142) by autophagy, both pathways enriched by genes co-expressed with SHARPIN and TNIP1 . Autophagy eliminates inflammatory triggers (i.e., cytokines) and regulates the organelle function in immune cells (i.e., B cells). The B cell receptor signaling pathway (KEGG pathway has04662), involved in inflammation [ 64 ], is enriched in our results by genes co-expressed with the loci linked with HS-aging. Consistent with our findings, variant rs34173062 in SHARPIN has been suggested to be a genetic modifier of neuroanatomical variation in the limbic system through a GWAS of imaging that used a much larger sample size (N = 8,428) of younger individuals (age 49–69) [ 65 ]. Additionally, a rare variant in the SHARPIN gene (rs77359862), which is in linkage disequilibrium with the variant examined in this study (rs34173062, R 2 = 0.003, D’= 1, and Minor Allele Frequency (MAF) = 3.5×10 − 04 ) and located 4,325 base pairs away, has been previously identified to have a genome-wide significant association with MRI traits in a Korean cohort [ 66 ]. SHARPIN (SHANK Associated RH Domain Interactor) is a gene coding for a postsynaptic density protein of excitatory synapses which is part of the NF-κB-activating the LUBAC complex in the nervous system[ 66 ]. NF-κB induces the expression of various pro-inflammatory genes, including those encoding cytokines and chemokines, and participates in inflammasome regulation [ 67 ]. Further supporting our results, variant rs871269 in TNIP1 has been formerly associated with HS-aging pathology in 2,831 individuals with European ancestry [ 68 ]. TNIP1 (TNFAIP3 Interacting Protein 1) is also a gene implicated in NF-κB activation and NF-κB-dependent gene expression involved in the anti-inflammatory response. Functionally, TNIP1 protein is an inflammation modulatory protein that exerts its influence by regulating nuclear factor κB activation [ 69 ]. All three variants in SHARPIN , GRN and TNIP1 disclosed in this study, cluster with SNPs showing a negative correlation with hippocampal subfield volumes representing HS-by-proxy (Fig. 2 A). In addition, the only pathway enriched for this cluster, the LUBAC complex pathway, is associated with neurodegeneration via inflammatory pathways but neither with Aβ nor with Tau pathology. This, implies that these loci are potentially more specific to brain atrophy in general or hippocampal pathology in particular than to other AD pathological hallmarks. It is known that ubiquitination by the LUBAC complex is a key checkpoint in death receptor signaling [ 70 ]. Moreover, recent investigations demonstrate the impact of LUBAC-mediated linear polyubiquitination on the aggregation of disease associated proteins linked to various neurodegenerative diseases, such as TDP-43 proteinpathy which has been seen to improve after LUBAC inhibition [ 71 ]. Moreover, linear polyubiquitination by LUBAC complex leads the abnormal TDP-43 aggregates to autophagic proteolysis, via failed protein degradation system and subsequent NF-κB activation [ 72 ]. Given that SHARPIN is part of LUBAC and TNIP1 is implicated in NF-κB activation, we speculate that these loci might be more prominently associated with TDP-43 and HS pathologies. A previous study [ 73 ] adds evidence to the relation of these variants with the immune-mediated component of hippocampal atrophy. This study reported a pathway enrichment analysis of AD-loci by Bellenguez et al. [ 11 ] to the clusters obtained as a result of associating these AD SNPs with levels of Aβ42 and phosphorylated Tau (pTau) in cerebrospinal fluid (CSF). Variants in GRN and TNIP1 cluster together separated from the variant located in SHARPIN . No GO-terms were enriched by the SNPs that cluster with SHARPIN , but SHARPIN alone shows a significant association with pTau in CSF [ 73 ], which may be related to pathologies other than AD. A total of eight GO-terms were enriched by GRN and TNIP1 cluster. The common denominator of the names of the pathways enriched by this cluster is “immune”, of which GRN is one of the most frequents contributor. Aside from GRN , a high number of genes included in the “immune” cluster have been related to dementia types other than AD [ 73 ]. This aligns with the results presented in this study, which further confirm that these loci may be more closely related to dysfunction in the immune response involved in multiple neurodegenerative diseases rather than specifically to AD pathology [ 74 ]. The concept of the immune-brain axis has gained attention, highlighting bidirectional communication between the immune system and the central nervous system [ 75 ]. Since both age and neurodegeneration are associated with inflammatory processes, we speculate that in HS-aging, inflammatory responses in the hippocampus may influence autophagy events [ 74 , 75 ]. A limitation of our study is the heterogeneity of the MRI data due to varying acquisition parameters and Freesurfer versions across centers. T2-weighted images were used mainly to exclude individuals with vascular damage or microbleeding, while hippocampal segmentation was performed using only T1-weighted images. Moreover, despite combining cohorts, our sample size remains limited, and a larger one is needed to increase statistical power and validate the present findings. Nevertheless, the main limitation of this study is the reliance on a proxy for HS-aging, as direct pathological confirmation is currently not possible in vivo . While hippocampal atrophy measured with MRI has consistently been proposed as a promising in vivo biomarker for HS-aging, it is not as accurate as post mortem diagnosis of the pathology. However, disentangling the genetic risk factors associated different neuropathologies remains a necessary area of investigation. The findings presented here contribute to this effort, and suggest promising new directions for future research. A longitudinal follow-up of the present study to identify patients developing HS-aging, or future validations in HS-aging autopsy cohorts, are required to confirm our results. Nevertheless, this study yielded significant results and include independent and consistent replication results supporting our hypothesis. 5. CONCLUSIONS In conclusion, AD-PRS and some AD-variants showed correlation with hippocampal subfields volume which might be used as a proxy for HS-aging. By studying preclinical hippocampal subfield atrophy we identified predominant genetic signals that might be instrumental for the detection of premature dementia. Specifically, variants in SHARPIN, GRN and TNIP1 might be more related to hippocampal subfield atrophy caused by HS-aging rather than to AD alone. Our study highlights the importance of precise phenotyping in genetic studies to generate disease-specific PRS. Dissecting the molecular pathways, cell types, and brain regions associated with each AD locus is crucial to translate genetic observations into clinical benefits. Abbreviations Ace Alzheimer Center Barcelona (ACE) AD polygenic risk score (AD-PRS) Alzheimer’s disease (AD) Alzheimer’s Disease Neuroimaging Initiative (ADNI) Amyloid β (Aβ) Body and head (BH) Cornu Ammonis (CA) Dementia Genetics Spanish Consortium (DEGESCO) Echo time (TE) European Alzheimer’s and Dementia Biobank (EADB) European Prevention of Alzheimer's Dementia (EPAD) False Discovery Rate (FDR) Field of view (FOV) Fundació ACE Healthy Brain Initiative (FACEHBI) Gene Ontology (GO) Genome Research at Ace Alzheimer center (GR@ACE) Genome-wide association studies (GWAS) Genome-wide significant (GWS) Granule Cell and Molecular Layer of the Dentate Gyrus (GC-ML-DG) Hippocampal sclerosis of aging (HS-aging) Hippocampal subfields segmentation (HSF) Hippocampus-amygdala-transition-area (HATA) Inversion time (TI) Kyoto Encyclopedia of Genes and Genomes (KEGG) Limbic-predominant age-related TDP-43 encephalopathy (LATE) Linear ubiquitin chain assembly complex (LUBAC) Magnetic resonance imaging (MRI) Magnetization-prepared rapid gradient-echo (MPRAGE) Mild cognitive impairment (MCI) Minor allele frequency (MAF) Models of Patient Engagement for Alzheimer’s Disease (MOPEAD) Odd ratio (OR) Over-representation enrichment analysis (ORA) Polygenic risk scores (PRS) Principal Component (PC) Quality Control (QC) Repetition time (TR) Sequence Read Archive (SRA) Spanish National Center for Genotyping (CeGEN) Three-dimensional (3D) Total intracranial volumes (TIV) Trans-Omics for Precision Medicine (TOPMed) Vallecas Project (VP) Declarations Ethics approval and consent to participate All projects passed the ethics committee and written informed consent was obtained from all human participants. Institutional Review Board Statement All protocols were approved by the Clinical Research Ethics Commission of the Hospital Clinic, Barcelona, Spain (reference: HCB/2014/0494) in accordance with the current Spanish regulations in the field of biomedical research and the Declaration of Helsinki. Consent for publication All authors critically revised the manuscript for important intellectual content and approved the final manuscript. Availability of data and materials The datasets used in this study from ADNI (ADNI-1 and ADNI-2) can be accessed upon request through their respective repositories. Datasets from ACE (BIOFACE, FACEHBI, EPAD and MOPEAD) and VP dataset can be accessed upon request through the corresponding author of this paper. Competing interests The authors declare that the research was conducted in the absence of any commercial or potential conflict of interest. Funding The Genome Research @ Ace Alzheimer Center Barcelona project (GR@ACE) is supported by Grifols SA, Fundacion bancaria La Caixa, Ace Alzheimer Center Barcelona and CIBERNED. Ace Alzheimer Center Barcelona is one of the participating centers of the Dementia Genetics Spanish Consortium (DEGESCO). The FACEHBI study is supported by funds from Ace Alzheimer Center Barcelona, Grifols, Life Molecular Imaging, Araclon Biotech, Alkahest, Laboratorio de analisis Echevarne and IrsiCaixa. MB, AR, MM acknowledge the support of the Spanish Ministry of Science and Innovation, Proyectos de Generacion de Conocimiento grants PID2021-122473OA-I00, PID2021-123462OB-I00 and PID2019-106625RB-I00. ISCIII, Accion Estrategica en Salud, integrated in the Spanish National R+D+I Plan and financed by ISCIII Subdireccion General de Evaluacion and the Fondo Europeo de Desarrollo Regional (FEDER Una manera de hacer Europa) grants PI13/02434, PI16/01861, PI17/01474, PI19/00335, PI19/01240, PI19/01301, PI12019/08-1, PI22/01403, PI22/00258 and the ISCIII national grant PMP22/00022, funded by the European Union (NextGenerationEU). The support of CIBERNED (ISCIII) under the grants CB06/05/2004 and CB18/05/00010. The support from the ADAPTED and MOPEAD projects, European Union/EFPIA Innovative Medicines Initiative Joint (grant numbers 115975 and 115985, respectively); from PREADAPT project, Joint Program for Neurodegenerative Diseases (JPND) grant No AC19/00097 and No AC23_2/00038; from HARPONE project, Agency for Innovation and Entrepreneurship (VLAIO) grant No PR067/21 and Janssen. DESCARTES project is funded by German Research Foundation (DFG). Additionally, IdR and CO are supported by the Instituto de Salud Carlos III (ISCIII) under the grant FI20/00215 and FI24/00029 respectively. PGG is supported by CIBERNED employment plan (CNV-304-PRF-866). ACF received support from the ISCIII under the grant Sara Borrell (CD22/00125). Author’s contributions C.O. and I.dR. contributed to data acquisition, analysis, interpreted the data and co-wrote the manuscript, supplementary materials and prepared figures and tables. O.S.G. and L.Z. contributed to MRI data analysis. A.Ru., M.V.F. and I.dR. designed, conceptualized, supervised the study and interpreted the data. L.T., S.V., M.Ma., P.S.J., M.B., B.S., M.V.F. and A.Ru. contributed to the critical revision of the paper. Data generation, sample contribution: C.O., I.dR., L.Z., O.S.G., I.Q., P.G.G., R.P., F.G.G., L.M., M.C.B., A.Can., A.M., J.B.F., M.C., A.Ra., M.T.A., A.B.P., T.dS., M.Me. and A.Car. All authors reviewed the manuscript. 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Nat Genet. 2022. https://doi.org/10.1101/2020.10.01.20200659 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6429978","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454337909,"identity":"1e0b7b2a-9fc8-46f1-8086-7d350d4c98a1","order_by":0,"name":"Clàudia Olivé","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Clàudia","middleName":"","lastName":"Olivé","suffix":""},{"id":454337910,"identity":"a0fa057f-2741-4c65-bc7d-0268524fe8f7","order_by":1,"name":"Itziar de Rojas","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Itziar","middleName":"","lastName":"de Rojas","suffix":""},{"id":454337911,"identity":"0116d132-7552-4386-9a91-aacd2969326c","order_by":2,"name":"Linda Zhang","email":"","orcid":"","institution":"Alzheimer’s Centre Reina Sofia-CIEN Foundation-ISCIII","correspondingAuthor":false,"prefix":"","firstName":"Linda","middleName":"","lastName":"Zhang","suffix":""},{"id":454337912,"identity":"a1e4e52b-a133-4084-bdf3-30636052f1e0","order_by":3,"name":"Oscar Sotolongo-Grau","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Oscar","middleName":"","lastName":"Sotolongo-Grau","suffix":""},{"id":454337913,"identity":"96a4f9f2-7694-4a78-b484-22615f97aa74","order_by":4,"name":"Inés Quintela","email":"","orcid":"","institution":"Grupo de Medicina Xenómica,Centro Nacional de Genotipado (CEGEN-PRB3-ISCIII). Universidad de Santiago de Compostela","correspondingAuthor":false,"prefix":"","firstName":"Inés","middleName":"","lastName":"Quintela","suffix":""},{"id":454337914,"identity":"7b5ae65f-fae4-4623-bd92-c3d28470130f","order_by":5,"name":"Pablo García-González","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"García-González","suffix":""},{"id":454337915,"identity":"afa9d3d4-f2e8-40ab-ad62-f9b863e4854c","order_by":6,"name":"Raquel Puerta","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Raquel","middleName":"","lastName":"Puerta","suffix":""},{"id":454337916,"identity":"fe438628-b0d0-4ec7-a7aa-c19f4bd3f45e","order_by":7,"name":"Fernando García-Gutiérrez","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"García-Gutiérrez","suffix":""},{"id":454337917,"identity":"1d9e2eee-28e3-47ba-b3d6-fdbf4a38780f","order_by":8,"name":"Laura Montrreal","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Montrreal","suffix":""},{"id":454337918,"identity":"c478100d-c6bc-4d58-ab22-b66cbdd6d12e","order_by":9,"name":"Maria Capdevila-Bayo","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Capdevila-Bayo","suffix":""},{"id":454337919,"identity":"c28405ff-ac2b-486f-af34-6a47164999cc","order_by":10,"name":"Andrea Miguel","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Miguel","suffix":""},{"id":454337920,"identity":"14486ce7-a938-42e9-a83d-4d5f27133fc1","order_by":11,"name":"Josep Blazquez-Folch","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Josep","middleName":"","lastName":"Blazquez-Folch","suffix":""},{"id":454337921,"identity":"08a6334f-2cdd-480d-9088-b675a76b2d0b","order_by":12,"name":"Miguel Calero","email":"","orcid":"","institution":"Alzheimer’s Centre Reina Sofia-CIEN Foundation-ISCIII","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Calero","suffix":""},{"id":454337922,"identity":"40b32e3a-6fc7-4a42-b66d-b63f2972d96d","order_by":13,"name":"Alberto Rábano","email":"","orcid":"","institution":"Alzheimer’s Centre Reina Sofia-CIEN Foundation-ISCIII","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Rábano","suffix":""},{"id":454337923,"identity":"7c8c3c46-64ab-4ecf-b6e4-f687d57ac43c","order_by":14,"name":"Ana Belén Pastor","email":"","orcid":"","institution":"Alzheimer’s Centre Reina Sofia-CIEN Foundation-ISCIII","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Belén","lastName":"Pastor","suffix":""},{"id":454337924,"identity":"fc1f658e-2450-48e3-96d2-4811a043c7d5","order_by":15,"name":"Teodoro del Ser","email":"","orcid":"","institution":"Alzheimer’s Centre Reina Sofia-CIEN Foundation-ISCIII","correspondingAuthor":false,"prefix":"","firstName":"Teodoro","middleName":"del","lastName":"Ser","suffix":""},{"id":454337925,"identity":"c5c6deca-6b6b-4188-8dd5-8b497c9f2bd2","order_by":16,"name":"Miguel Medina","email":"","orcid":"","institution":"Alzheimer’s Centre Reina Sofia-CIEN Foundation-ISCIII","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Medina","suffix":""},{"id":454337926,"identity":"aee1bb4c-c3ef-4738-b3c6-594915f18980","order_by":17,"name":"Ángel Carracedo","email":"","orcid":"","institution":"Grupo de Medicina Xenómica,Centro Nacional de Genotipado (CEGEN-PRB3-ISCIII). 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Neurology Service, Hospital Clínic, FRCB-IDIBAPS and Institute of Neurosciences, University of Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Molina-Porcel","suffix":""},{"id":454337929,"identity":"4c7a384a-778b-414b-8f68-3f1a94c934df","order_by":20,"name":"Lluís Tàrraga","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Lluís","middleName":"","lastName":"Tàrraga","suffix":""},{"id":454337930,"identity":"6464e5ba-eef7-4625-af87-ad3e4def591c","order_by":21,"name":"Amanda Cano","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"","lastName":"Cano","suffix":""},{"id":454337931,"identity":"6ee081ef-c5b4-40db-b992-4dd26bae6fc3","order_by":22,"name":"Sergi Valero","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Sergi","middleName":"","lastName":"Valero","suffix":""},{"id":454337932,"identity":"8d20adce-bd86-4833-9a92-b2c7b7891c85","order_by":23,"name":"Marta Marquié","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Marta","middleName":"","lastName":"Marquié","suffix":""},{"id":454337933,"identity":"94ac388d-b4a6-4db8-911e-d43fbf506a96","order_by":24,"name":"Pascual Sánchez-Juan","email":"","orcid":"","institution":"Alzheimer’s Centre Reina Sofia-CIEN Foundation-ISCIII","correspondingAuthor":false,"prefix":"","firstName":"Pascual","middleName":"","lastName":"Sánchez-Juan","suffix":""},{"id":454337934,"identity":"02222125-55b0-4331-91eb-ad0ac69235e7","order_by":25,"name":"Mercè Boada","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Mercè","middleName":"","lastName":"Boada","suffix":""},{"id":454337935,"identity":"a4c7d4a1-25e0-40a2-a62e-7cbc70b0c05a","order_by":26,"name":"Bryan Strange","email":"","orcid":"","institution":"Alzheimer’s Centre Reina Sofia-CIEN Foundation-ISCIII","correspondingAuthor":false,"prefix":"","firstName":"Bryan","middleName":"","lastName":"Strange","suffix":""},{"id":454337936,"identity":"ae927a8a-7420-4cd6-8eed-91be93177bb9","order_by":27,"name":"Maria Victoria Fernández","email":"data:image/png;base64,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","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":true,"prefix":"","firstName":"Maria","middleName":"Victoria","lastName":"Fernández","suffix":""},{"id":454337937,"identity":"fedb907a-1b75-44f0-9ffb-e55a396abf34","order_by":28,"name":"Agustín Ruiz","email":"","orcid":"","institution":"Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya (UIC)","correspondingAuthor":false,"prefix":"","firstName":"Agustín","middleName":"","lastName":"Ruiz","suffix":""}],"badges":[],"createdAt":"2025-04-11 16:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6429978/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6429978/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82605849,"identity":"a582a2d8-3388-47a5-bb68-bdc2914df6ba","added_by":"auto","created_at":"2025-05-13 10:03:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeta-analysis for the association between AD-PRS and HS-by-proxy phenotypes.\u003c/strong\u003e Forest plots for (\u003cstrong\u003eA\u003c/strong\u003e) hippocampal BH and (\u003cstrong\u003eB\u003c/strong\u003e) fimbria, including patients from discovery (ACE and ADNI) and replication (VP) cohorts.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6429978/v1/bd47b88ccc7c3ae9e486db11.png"},{"id":82605851,"identity":"b0fd6394-7b9c-4fb1-8442-d4020e697518","added_by":"auto","created_at":"2025-05-13 10:03:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":378124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between Hippocampal BH and Fimbria and AD SNPs included in the PRS in all study cohorts.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Framed in blue, cluster with a predominant negative Pearson’s correlation with HS-by-proxy phenotype represented in blue tones; (\u003cstrong\u003eB\u003c/strong\u003e) framed in green, cluster with a mixed positive (red) and negative (blue) Pearson’s correlation with HS-by-proxy phenotype; (\u003cstrong\u003eC\u003c/strong\u003e) cluster with a predominant positive correlation with HS-by-proxy phenotype represented in orange and red tones. All SNPs are represented by their risk allele for AD found by Bellenguez et al. [11]. HPC BH: Hippocampal body and head. Sig. level = 0.001***, 0.01**, 0.05*.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6429978/v1/3fd9a25a8738705189e33250.png"},{"id":82605855,"identity":"e5d658c4-a87a-442d-8f3f-08fdc9368877","added_by":"auto","created_at":"2025-05-13 10:03:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":421458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeta-analysis for the association between the significative AD loci and HS-by-proxy phenotypes. \u003c/strong\u003eForest plots for \u003cem\u003eSHARPIN\u003c/em\u003e (\u003cstrong\u003e1\u003c/strong\u003e), \u003cem\u003eTNIP1\u003c/em\u003e (\u003cstrong\u003e2\u003c/strong\u003e) and \u003cem\u003eGRN\u003c/em\u003e (\u003cstrong\u003e3\u003c/strong\u003e) loci with hippocampal BH (\u003cstrong\u003eA\u003c/strong\u003e) and fimbria (\u003cstrong\u003eB\u003c/strong\u003e) in all study cohorts.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6429978/v1/515b1ef64e1f0ca88c94cf97.png"},{"id":82607099,"identity":"bb6dbfb3-28d4-4218-8e27-7de974996400","added_by":"auto","created_at":"2025-05-13 10:11:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":115955,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeta-analysis for the association between AD-PRS and AD in the presence of concomitant brain pathologies. \u003c/strong\u003eAD-PRS based on Bellenguez et al. 2022. Forest plots including data from EADB/ACE/BTN-Clinic-FRCB-IDIBAPS datasets from de Rojas et al. [10]. Data presented as OR per 1-SD increase in PRS (95% CI).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6429978/v1/ddeb73c7e76a19b2995be784.png"},{"id":85195339,"identity":"96c693fb-75fb-4f85-ae10-028c6009b173","added_by":"auto","created_at":"2025-06-23 09:23:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2570389,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6429978/v1/c49102a5-848e-4cae-b352-e00b509d9534.pdf"},{"id":82605850,"identity":"d91f453c-0f4f-41ab-b6b7-c8f7c9663c52","added_by":"auto","created_at":"2025-05-13 10:03:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":482950,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6429978/v1/579a26593cd964dadae96fbd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic dissection of hippocampal sclerosis of ageing using magnetic resonance imaging surrogates","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003eHippocampal sclerosis of aging (HS-aging) is present in a considerable proportion of people over 85 years old who die with dementia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], although magnetic resonance imaging (MRI) correlates of this disease can be detected over a decade prior to death [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The main pathological features of HS-aging include severe neuronal loss and gliosis in specific regions of the hippocampus, leading to cognitive and memory symptoms that appear to mimic Alzheimer\u0026rsquo;s disease (AD).\u003c/p\u003e \u003cp\u003eDue to the similarity of their clinical profiles, and considering the current lack of biomarkers for HS-aging detection, this condition is often misdiagnosed as AD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, HS-aging has usually less severe symptoms and longer evolution than AD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] because it does not progress outside the hippocampus. HS-aging is also strongly associated with TDP-43 pathology and is encompassed in limbic-predominant age-related TDP-43 encephalopathy (LATE), which tends to co-occur with AD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe etiology of HS-aging remains mostly unknown, and the absence of reliable biological markers for its detection makes it a subject of ongoing research. Recently, atrophy in hippocampal subfields detected by magnetic resonance imaging (MRI) has been reported to be a promising \u003cem\u003ein vivo\u003c/em\u003e biomarker specific to HS-aging [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Moreover, various studies have reported some evidence of a genetic predisposition to HS-aging [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nevertheless, further investigation is essential to dissect the genetic component and specific biomarkers for HS-aging, as well as its relationships with AD, LATE and other dementias.\u003c/p\u003e \u003cp\u003eCommon diseases, such as AD or HS-aging, are complex traits which are rarely caused by the dysfunction of a single gene, but rather affected by the additive contribution of several genetic variants together [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Through genome-wide association studies (GWAS), it is possible to identify genetic variants significantly present in case individuals and map its polygenic architecture [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and use them to construct polygenic risk scores (PRS) to identify individuals at risk, improve diagnostic tools and develop new drug targets [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver the last decade, GWAS have grown significantly in sample size and in the number of traits under investigation. Recently, thanks to large international collaborative efforts [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the genomic landscape of the AD risk alleles has doubled, with more than 80 AD-associated loci identified [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, adding a larger sample size of clinically labeled individuals may result in the addition of cases with co-pathologies, and often misdiagnosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In fact, more than half of individuals with AD brain pathology are found to also have one or more other brain abnormalities that may cause dementia, such as vascular lesions, Lewy bodies, LATE, argyrophilic grains or HS-aging [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHippocampal atrophy is observed in both healthy individuals and patients with mild cognitive impairment (MCI) as prodromal stage of AD dementia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Thus, previous studies have associated the AD polygenic risk score (AD-PRS) with reduced volume in different hippocampal subfields in healthy individuals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nevertheless, some studies suggest that hippocampal atrophy before dementia onset manifests earlier in individuals with HS-aging compared to those with AD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], supporting its role as an early biomarker for HS-aging [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, we hypothesize that certain variants included in the AD-PRS might be specifically associated with HS-aging.\u003c/p\u003e \u003cp\u003eIn this study we aimed to assess the association of AD risk alleles with HS-aging. However, since HS-aging cannot be accurately diagnosed \u003cem\u003eante mortem\u003c/em\u003e, collecting data from individuals with HS-aging for research purposes is currently challenging. Therefore, for the analyses presented here, we used a proxy for HS-aging based on different hippocampal subfield volumes measured with MRI, in participants likely to develop dementia but without a current dementia diagnosis. Since the genomic architecture has not been as extensively studied in HS-aging as in AD, we designed our analyses to detect which AD-related SNPs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] might be more specifically associated with HS-aging. To achieve this, we examined both the individual and combined effects of AD-related SNPs included in the AD-PRS by Bellenguez et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and investigated their correlation with HS-aging.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Participants\u003c/h2\u003e \u003cp\u003eFor this study we leveraged data from Ace Alzheimer Center Barcelona (ACE) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI, adni.loni.usc.edu, [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]) and the Vallecas Project (VP) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We combined data from ACE and ADNI for our discovery analyses whereas VP was used for replication purposes. All samples for this study have available MRI, GWAS and clinical data. Only participants without a clinical diagnosis of dementia were included, consisting of individuals with MCI, who are at increased risk of developing dementia, and cognitively normal controls. By focusing on this preclinical stage, we aim to identify genetic variants associated with lower hippocampal volumes before the onset of dementia symptoms, which may represent early markers of HS-aging.\u003c/p\u003e \u003cp\u003eParticipants from ACE (N\u0026thinsp;=\u0026thinsp;263) were collected through the BIOFACE project [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], Fundaci\u0026oacute; ACE Healthy Brain Initiative (FACEHBI) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], European Prevention of Alzheimer's Dementia (EPAD) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and Models of Patient Engagement for Alzheimer\u0026rsquo;s Disease (MOPEAD) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. From ADNI we selected participants from either ADNI1 or ADNI2 who had a diagnosis of MCI (N\u0026thinsp;=\u0026thinsp;665) or did not have dementia (N\u0026thinsp;=\u0026thinsp;465). For our replication analyses, we leveraged a cohort of individuals without dementia (N\u0026thinsp;=\u0026thinsp;729) from the VP [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A summary of demographic and clinical information for the participants included in this study by project is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and further information about each individual project is provided in the Supplementary Methods. All projects passed the ethics committee and written informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical data for the subjects included in this study by project.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemales (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMCI (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEducation (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscovery\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADNI-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221(39.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e353(62.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e209(37.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.12(6.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.83(2.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADNI-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141(46.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e212(69.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93(30.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e71.89(7.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.16(2.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIOFACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37(66.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e00(00.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.46(3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.77(3.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFACEHBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99(62.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156(98.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67.27(6.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.98(4.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEPAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15(51.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(79.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(20.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.68(5.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.79(3.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOPEAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9(45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(95.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(5.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e73.79(4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.10(4.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e522(46.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e665(58.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e465(41.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72.34(7.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.95(3.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReplication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e496(68.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e00(00.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e729(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e74.74(3.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.30(5.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,018(54.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e665(35.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,194(64.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e73.28(6.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.13(5.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* N: number of samples; MCI: Mild cognitive impairment; CN: Controls; Age: average age at MRI; SD: Standard error.\u003c/p\u003e \u003cp\u003eMoreover, the EADB/ACE/BTN-Clinic-FRCB-IDIBAPS cohort (Supplementary Methods) was used for the replication of the association between AD-PRS and AD concomitant pathologies previously published by de Rojas et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Genomic data processing, QC and AD-PRS\u003c/h2\u003e \u003cp\u003eSamples from ACE and VP were genotyped as part of the Genome Research at Ace Alzheimer center (GR@ACE) and Dementia Genetics Spanish Consortium (DEGESCO) genetic initiatives [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Genotyping was conducted using the Axiom 815K Spanish biobank array (according to manufacturer\u0026rsquo;s instructions - Axiom\u0026trade; 2.0 Assay Manual Workflow, Thermo Fisher) at the Spanish National Center for Genotyping (CeGEN, Santiago de Compostela, Spain). Details on genotyping and quality control procedures are provided elsewhere [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eADNI samples were genotyped with the Illumina Human 610-Quad BeadChip (Illumina, Inc., San Diego, CA, USA), for ADNI-1 and with the OmniExpress BeadChip for ADNI-GO/2 individuals (ADNI-2) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenomic data quality control (QC) was performed uniformly in three parallel batches for ADNI-1, ADNI-2 and ACE-VP. QC included removal of samples with low-quality genotyping, excess of heterozygosity or high missingness and variants with call rate below 95% or deviation from the Hardy\u0026ndash;Weinberg equilibrium (\u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e). Additionally, samples with discordant genetic sex annotation, family members (PI-HAT\u0026thinsp;\u0026gt;\u0026thinsp;0.1875), or with a non-European ancestry (as per 1000 Genomes Project) were excluded from the analysis.\u003c/p\u003e \u003cp\u003eTo maximize the AD-PRS coverage, imputed data was generated with the Trans-Omics for Precision Medicine (TOPMed) reference panel [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] on genome build GRCh38. Rare variants (minor allele frequency; MAF\u0026thinsp;\u0026lt;\u0026thinsp;1%) and variants with low imputation quality (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.3) were excluded.\u003c/p\u003e \u003cp\u003eA weighted individual AD-PRS was calculated based on the 83 genome-wide significant (GWS, \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;5E-08) variants reported by the European Alzheimer\u0026rsquo;s and Dementia Biobank (EADB) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. AD-PRS was generated by multiplying the genotype dosage of each risk allele for each variant by its respective weight and then summing across all variants. Due to its large effect, we excluded \u003cem\u003eAPOE\u003c/em\u003e variants (rs429358 and rs7412) from the AD-PRS calculation as well as the \u003cem\u003eABI3\u003c/em\u003e locus (rs616338) because it was not properly imputed in the ADNI-2 dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Structural MRI image acquisition\u003c/h2\u003e \u003cp\u003eThe image acquisition process was slightly different for each project of the ACE cohort included in this study. MRI scans from FACEHBI were performed on a 1.5T Siemens MAGNETOM Aera (Erlangen, Germany), while for for BIOFACE, EPAD and MOPEAD images were acquired with a Siemens MAGNETOM VIDA 3T scanner (Erlangen, Germany) using a 32-channel head coil at Cl\u0026iacute;nica Corachan, Barcelona. Anatomical T1-weighted images were acquired using a three-dimensional (3D) magnetization-prepared rapid gradient-echo (MPRAGE) sequence with different parameters for FACEHBI [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] (repetition time (TR) 2.200 ms, echo time (TE) 2.66 ms, inversion time (TI) 900 ms, slip angle 8\u0026deg;, field of view (FOV) 250 mm, slice thickness 1 mm, and isotropic voxel size 1 \u0026times; 1 \u0026times; 1 mm), BIOFACE [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] (TR 2.200ms, TE 2.23 ms, TI 968ms, 1.2 mm slice thickness, FOV 270 mm, and voxel measurement 1.1 \u0026times; 1.1 \u0026times; 1.2mm), MOPEAD [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] (TR 2.200 ms, TE 2.33 ms, TI 968ms, slip angle 8\u0026deg;, FOV 270 mm, slice thickness 1.2 mm, and isotropic voxel size 1.1 \u0026times; 1.1 \u0026times; 1.2 mm) and EPAD [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] (TR 2.300 ms, TE 2.93 ms, TI 900ms, slip angle 9\u0026deg;, FOV 270 mm, slice thickness 1.2 mm, and isotropic voxel size 1.1 \u0026times; 1.1 \u0026times; 1.2 mm). Axial T2-weighted, 3D isotropic fast fluid-attenuated inversion recovery (FLAIR) and axial T2*-weighted sequences were also acquired to detect significant vascular brain damage or microbleeds.\u003c/p\u003e \u003cp\u003eAll the subjects with existing GWAS information were chosen from ADNI cohorts (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://adni.loni.usc.edu/\u003c/span\u003e\u003cspan address=\"http://adni.loni.usc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The closest MRI to the baseline was selected for each subject. All MRI T1-weighted images were downloaded in NIfTI format [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Since the ADNI protocol for MRI included one non-accelerated and one accelerated version of the scan, if available, more than one T1-weighted MRI for the same experiment was downloaded, in order to perform movement correction in the Freesurfer image processing pipeline (described below).\u003c/p\u003e \u003cp\u003eFor the VP cohort, all T1-weighted images (3D fast spoiled gradient echo with inversion recovery preparation) were acquired using a 3T MRI (Signa HDxt GEHC, Waukesha, USA) with a phased array 8 channel head coil and the following parameters: TR 10 ms, TE 4.5 ms, TI 600 ms, FOV 240 mm, matrix 288x288 and slice thickness 1 mm, yielding 0.5x0.5x1 mm voxel size. All MRI scans were reported by a neuroradiologist.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Hippocampal subfields extraction\u003c/h2\u003e \u003cp\u003eIn order to extract the hippocampal subfield volumes, subjects from ACE and ADNI were processed the same way. First, cortical reconstruction and volumetric segmentation for MRI images was performed with the Freesurfer 7.2 image analysis suite, which is documented and freely available for download online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The technical details of these procedures are described in prior publications [\u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Freesurfer segmentation was performed using both T1 and T2-weighted images for the ACE samples, while only T1-weighted images were used for the ADNI cohort.\u003c/p\u003e \u003cp\u003eThen, the hippocampal subfields segmentation (HSF) was carried out using the hippocampal parcellation method included in Freesurfer 7.2 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Individual hippocampal subfield volumes for cornu ammonis (CA) were extracted out from results and grouped into a table. The following subfields of the hippocampal formation were used for the analyses in this study: CA1 body CA1 head, CA3 body, CA3 head, CA4 body, CA4 head, Granule Cell and Molecular Layer of the Dentate Gyrus (GC-ML-DG) body, GC-ML-DG head, hippocampus-amygdala-transition-area (HATA), whole hippocampal body and head (BH), hippocampal tail, whole hippocampus, fimbria, hippocampal fissure, molecular layer of the hippocampus body, molecular layer of the hippocampal head, parasubiculum, presubiculum body, presubiculum head, subiculum body and subiculum head. Also, estimated total intracranial volumes (TIV) were extracted, from prior Freesurfer analyses, in order to make further volumetric corrections.\u003c/p\u003e \u003cp\u003eFor the VP cohort, automatic segmentation of the hippocampus was performed on each participant\u0026rsquo;s T1-weighted image using FreeSurfer v.6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Technical details of the whole-brain segmentation methods have been described previously [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Hippocampal volumes were extracted using the hippocampal subfields module in FreeSurfer 6.0 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and segmentations for all participants were visually inspected for accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Association of HS-by-proxy with AD-PRS\u003c/h2\u003e \u003cp\u003eHippocampal subfield volumes and AD-PRS data were standardized by project using the scale function in R (Supplementary Fig.\u0026nbsp;1) and independent t-tests were conducted to assess differences between AD-PRS means in individuals classified by diagnosis and \u003cem\u003eAPOE\u003c/em\u003e ɛ4 carriers. For hippocampal subfield volumes, outliers differing\u0026thinsp;\u0026plusmn;\u0026thinsp;3 SD from the mean were removed. As has been frequently applied to MRI data analysis [\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], we conducted hierarchical agglomerative clustering to group highly correlated hippocampal subfields in order to increase statistical power. We used the average agglomeration method on the Euclidean distance matrix of hippocampal subfield volumes (Supplementary Figs.\u0026nbsp;2 \u0026amp; 3). After dimension reduction of these variables, analyses were conducted using the volumes of the following four hippocampal subfields used as HS-by-proxy: hippocampal fissure, parasubiculum, fimbria and whole hippocampal BH. All analyses were performed using R version 4.1.2 software.\u003c/p\u003e \u003cp\u003eAssociation of AD-PRS with HS-by-proxy was assessed using linear regression for each project. HS-by-proxy and AD-PRS were set as dependent and independent variables respectively, with sex, age, age\u003csup\u003e2\u003c/sup\u003e (to account for the non-linear effect of age), years of education, diagnosis (when applicable), TIV and first ten genetic principal components (PC1-10; to adjust genetic variability for population structure) as covariates in the following regression model:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:HS\\:by\\:proxy\\:\\sim\\:PRS+Sex+\\:Age+{Age}^{2}+Education+Diagnosis+TIV+PCs$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ePearson\u0026rsquo;s correlation analysis was conducted to study the association between each SNP included in the AD-PRS calculation and HS-by-proxy phenotype previously found to be significantly associated with AD-PRS. SNPs presenting higher and significant correlations (\u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;.05 and Pearson\u0026rsquo;s r\u0026thinsp;\u0026gt;\u0026thinsp;.05) were later employed as independent variants for the estimation of linear regression models adjusted by the same covariates as before:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:HS\\:by\\:proxy\\:\\sim\\:SNP\\:dose+Age+{Age}^{2}+Sex+Education+Diagnosis+TIV+PCs$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eResults for both regression models were then combined across projects with a fixed-effect meta-analysis (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;75%) for each hippocampal subfield representing HS-by-proxy phenotype, using the inverse variance weighted approach implemented with the \u003cem\u003emeta\u003c/em\u003e package in R [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Co-regulatory network analysis\u003c/h2\u003e \u003cp\u003eTo identify co-regulatory networks of genes associated with HS-aging, we used GeneFriends [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], a bioinformatics pipeline previously reported by our team [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. These lists of genes were based on the Sequence Read Archive (SRA) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and only genes highly co-expressed (co-expression value\u0026thinsp;\u0026gt;\u0026thinsp;0.5) in neuron and/or brain tissue were included in further analyses. Next, WebGestalt [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] was used to identify potential enrichments in the previously identified co-regulated gene lists. We used over-representation enrichment analysis (ORA) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in humans using protein-coding regions of the genome as reference set. Those lists of co-expressed genes showing significantly enriched KEGG pathways were further analyzed using STRING v11.5 [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] in order to find known and predicted protein-protein interactions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. AD-PRS and HS-by-proxy phenotype\u003c/h2\u003e \u003cp\u003eIn the meta-analysis presented here, we included data from six different projects, which consisted of 1,130 participants without dementia with MRI and GWAS data. Their demographic composition was as follows: 46.2% were female, 58.9% had MCI and 41.1% were individuals without dementia (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average age of the participants was 72.3 (\u0026plusmn;\u0026thinsp;3.84) years. Additionally, an AD-PRS was calculated for these participants, which ranged from \u0026minus;\u0026thinsp;3.60 to 3.73 on a scaled min-max basis (Supplementary Fig.\u0026nbsp;4). MCI showed a tendency toward a higher AD genetic load compared to controls (Supplementary Fig.\u0026nbsp;4A). This trend may be due to significant differences in AD-PRS observed between \u003cem\u003eAPOE\u003c/em\u003e ɛ4 MCI carriers and controls (\u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;.000084; Supplementary Fig.\u0026nbsp;4B \u0026amp; C).\u003c/p\u003e \u003cp\u003eWe found a study-wise significant association between the AD-PRS and hippocampal BH (OR [95% CI]\u0026thinsp;=\u0026thinsp;0.91 [0.87\u0026ndash;0.96]; \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;.000142) and fimbria (OR [95% CI]\u0026thinsp;=\u0026thinsp;0.92 [0.87\u0026ndash;0.97]; \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;.00213) as HS-by-proxy phenotypes after Bonferroni correction (\u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;.0125; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeta-analysis results of the hippocampal subfield volumes association with AD-PRS [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampal subfield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDiscovery (N\u0026thinsp;=\u0026thinsp;1,128)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParasubiculum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;01\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampal fissure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.16 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;01\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFimbria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.13 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;03\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampal BH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.42 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;04\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReplication (N\u0026thinsp;=\u0026thinsp;728)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFimbria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;01\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampal BH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.55\u0026times;10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;02\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinal (N\u0026thinsp;=\u0026thinsp;1,856)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFimbria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.12 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;03\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampal BH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.77 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;05\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Significant \u003cem\u003eP\u003c/em\u003e values after Bonferroni correction in bold (\u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;.0125; replication \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;.05).\u003c/p\u003e \u003cp\u003e\u0026dagger; OR: Odds ratio; CI: Confidence interval; BH: Body and head.\u003c/p\u003e \u003cp\u003eNext, the Vallecas replication cohort (N\u0026thinsp;=\u0026thinsp;728 healthy controls, 68.1% females and mean age 74.7 years with an AD-PRS range scaled \u003csub\u003emin\u0026minus;max\u003c/sub\u003e from \u0026minus;\u0026thinsp;3.11 to 2.96) confirmed a significant and independent association between AD-PRS and hippocampal BH (OR [95% CI]\u0026thinsp;=\u0026thinsp;0.94 [0.88\u0026ndash;1.00]; \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;.0355) validating our discovery finding (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCombining all three cohorts in an extended meta-analysis (N\u0026thinsp;=\u0026thinsp;1,856) showed the same effect size and improved the statistical significance of the association between the AD-PRS and the HS-by-proxy phenotypes (hippocampal BH, OR [95% CI]\u0026thinsp;=\u0026thinsp;0.92 [0.89\u0026ndash;0.96]; \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;1.77\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e; fimbria, OR [95% CI]\u0026thinsp;=\u0026thinsp;0.93 [0.89\u0026ndash;0.97]; \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;.00112; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) suggesting that participants without dementia with higher genetic burden of AD have less volume in these hippocampal subfields. All subsequent values reported here correspond to results of analyses of all cohorts combined. The impact of covariates in each model is presented in Supplementary Table\u0026nbsp;1 for hippocampal BH and Supplementary Table\u0026nbsp;2 for fimbria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. AD SNPs and HS-by-proxy phenotype\u003c/h2\u003e \u003cp\u003eFor those HS-by-proxy phenotypes for which we found a significant association with AD-PRS (hippocampal BH and fimbria) we tested for correlation with the AD loci reported by Bellenguez et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] composing the AD-PRS. The purpose of this analysis is to reduce the dimensionality of gene data associated with AD for a more specific assessment of its relation with HS-aging (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Variants in \u003cem\u003eSHARPIN\u003c/em\u003e (rs34173062), \u003cem\u003eGRN\u003c/em\u003e (rs5848) and \u003cem\u003eTNIP1\u003c/em\u003e (rs871269) loci were found to be significantly associated with hippocampal BH (rs34173062, OR [95% CI]\u0026thinsp;=\u0026thinsp;0.84 [0.77\u0026ndash;0.92], \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;1.51\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e; rs871269, OR [95% CI]\u0026thinsp;=\u0026thinsp;0.90 [0.85\u0026ndash;0.96], \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;3.57\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e) and/or fimbria (rs34173062, OR [95% CI]\u0026thinsp;=\u0026thinsp;0.81 [0.73\u0026ndash;0.90], \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;6.63\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e; rs5848, OR[95% CI]\u0026thinsp;=\u0026thinsp;0.89 [0.83\u0026ndash;0.95], \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;2.84\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) after Bonferroni correction (\u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;6.02\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e) in the meta-analysis. Based on the correlation of AD-PRS loci with HS-by-proxy, we found three clusters of AD loci showing an unequal association profile to HS-aging which supports the existence of a loci set more related to specific hippocampal pathobiology (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The SNPs in cluster A (blue cluster) show an overall negative correlation with our HS-by-proxy phenotype, meaning that they are generally correlated with smaller fimbria and hippocampal BH volumes, thus potentially specific to tissue atrophy. What is more, all SNPs found significantly associated with HS-by-proxy (\u003cem\u003eSHARPIN\u003c/em\u003e, \u003cem\u003eGRN\u003c/em\u003e, \u003cem\u003eTNIP1\u003c/em\u003e) gather together in cluster A. SNPs in cluster B and C were not significantly associated with our HS-by-proxy phenotype, with an overall positive correlation among the SNPs in these clusters and hippocampal volume. This suggests a tendency toward greater hippocampal volumes for carriers of these cluster B and C variants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeta-analysis results of the HS-by-proxy association with the selected SNPs in the AD-PRS [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoci\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHippocampal BH\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eADAM17\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers72777026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84\u0026ndash;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.0049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBCKDK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers889555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00-1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGRN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers5848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.0068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHS3ST5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers785129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMAPT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers199515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePRDM7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers56407236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSHARPIN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers34173062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u0026ndash;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.51 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;04\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTNIP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers871269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.57 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;04\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFimbria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eADAM17\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers72777026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBCKDK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers889555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGRN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers5848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.84 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;04\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHS3ST5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers785129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMAPT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers199515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.0013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePRDM7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers56407236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u0026ndash;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.0049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSHARPIN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers34173062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u0026ndash;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.63 \u0026times; 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;05\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTNIP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers871269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e* Significant \u003cem\u003eP\u003c/em\u003e values after Bonferroni correction in bold (\u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;6.02 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e\u0026dagger; OR: Odds ratio; CI: Confidence interval; BH: Body and head.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The LUBAC complex as represented pathway in the AD SNPs associated with HS-by-proxy phenotype\u003c/h2\u003e \u003cp\u003eUsing the STRING software, we found that cluster A (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; blue) which includes \u003cem\u003eSHARPIN, GRN\u003c/em\u003e and \u003cem\u003eTNIP1\u003c/em\u003e, was significantly enriched in the linear ubiquitin chain assembly complex (LUBAC) cellular component (Gene Ontology (GO) Term: GO:0071797) responsible for producing linear polyubiquitin chains and regulating the NF-κB pathway [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], which plays a critical role in inflammatory and immune responses. The biological processes enriched by this cluster are: protein linear polyubiquitination (GO:0097039) and the negative regulation of biological process (GO:0048519). Next, the top three significantly enriched biological process in cluster B (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) were the regulation of neurofibrillary tangle assembly (GO:1902996), microglial cell proliferation (GO:0061518) and positive regulation of dendritic cell cytokine production (GO:0002732). In cluster C, the top three significantly enriched biological processes were the positive regulation of engulfment of apoptotic cell (GO:1901076), neuropeptide catabolic process (GO:0010813) and negative regulation of aspartic-type endopeptidase activity involved in amyloid precursor protein catabolic process (GO:1902960). To point out, the B and C clusters are both significantly enriched in their top ten pathways by the positive regulation of immune system process (GO:0002684). Another 23 biological processes are enriched by loci within both clusters B and C, with some of them being related to amyloid β (Aβ) formation or clearance. However, none of the pathways enriched for genes in cluster A are also enriched for loci in cluster B or C. Summaries of the biological processes enriched by each cluster are reported on Supplementary Tables\u0026nbsp;3, 4 \u0026amp; 5.\u003c/p\u003e \u003cp\u003eRegarding disease-gene associations found using STRING, genes in clusters B and C, which are associated with higher hippocampal subfield volumes, showed a significant enrichment for AD (DOID:10652; B cluster FDR\u0026thinsp;=\u0026thinsp;5.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;03\u003c/sup\u003e; C cluster FDR\u0026thinsp;=\u0026thinsp;8.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e). In contrast to the cluster A, which includes genes correlated with smaller hippocampal subfield volumes and is only significantly enriched in nominal aphasia (DOID:4541; FDR\u0026thinsp;=\u0026thinsp;1.84\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;02\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Network of genes associated with SNPs related to HS-by-proxy phenotype\u003c/h2\u003e \u003cp\u003eLooking for a gene-network and pathways enriched for the genes associated with hippocampal BH and fimbria, we extracted the genes co-expressed with \u003cem\u003eSHARPIN, GRN\u003c/em\u003e and \u003cem\u003eTNIP1\u003c/em\u003e in brain tissue and neurons using Genefriends. For this purpose, only transcripts displaying a high expression correlation between them were selected (Pearson\u0026rsquo;s r\u0026thinsp;\u0026gt;\u0026thinsp;0.5). We detected a 3.57% overlap among genes co-expressed with \u003cem\u003eSHARPIN, GRN\u003c/em\u003e and \u003cem\u003eTNIP1\u003c/em\u003e which were further tested in a pathway enrichment analysis (Supplementary Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eNine significantly enriched KEGG pathways were identified by the enrichment analysis with WebGestalt and 10 pathways (FDR\u0026thinsp;\u0026le;\u0026thinsp;0.05) with STRING in brain (Supplementary Table\u0026nbsp;6\u0026ndash;7), No significant pathways were detected in neurons (Supplementary Table\u0026nbsp;8). Four of the most significantly enriched pathways concordant between the STRING and the WebGestalt strategies could be connected to biological mechanisms of HS-aging: protein processing in endoplasmic reticulum (hsa04141), other glycan degradation (hsa00511), B cell receptor signaling pathway (hsa04662) and lysosome (hsa04142).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eCharacterization of the genetic landscape of complex diseases provides a unique opportunity for a better understanding of their associated physiopathological processes. Although GWAS analyses have significantly helped improve our current understanding of AD genetic architecture, the fact that AD often co-occurs with other pathologies, and the massive sample sizes required for this kind of studies comes with the increased risk of adding higher rates of individuals with co-pathologies or doubtful diagnoses in these analyses. Approximately 75% of older adults examined \u003cem\u003epost mortem\u003c/em\u003e exhibit multiple brain pathologies, commonly referred to as mixed neuropathologies [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur recent study of AD in a Spanish histopathological cohort [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], suggested a stronger association of the AD-PRS with AD-mixed pathologies than with AD alone. The replication of this analysis using an updated version of the AD-PRS with the 83 SNPs from Bellenguez et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], reveals an increase of the effect of this trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These results indicate that the AD association of some AD-PRS variants might be due to their relation with other neuropathologies, like HS-aging, rather than AD alone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur hypothesis arose from our previous findings [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] where AD-PRS was found to be more strongly associated with mixed pathologies (AD\u0026thinsp;+\u0026thinsp;HS-aging) than with pure AD pathology. We hypothesized that some SNPs associated with AD in large GWAS might be more specifically associated to HS-aging hippocampal atrophy patterns than AD. For this purpose, we used the AD-related SNPs extracted from the recently published meta-GWAS for AD risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] to assess their association with hippocampal subfield volumes as HS-by-proxy phenotype in individuals without dementia, since previous studies suggest that hippocampal atrophy seen in HS-aging begins early prior to dementia compared to AD and support its use as a biomarker for HS-aging [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn line with previous research that associate AD-PRS with hippocampal atrophy in individuals without dementia [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], the main findings of this study agree with our hypothesis: higher values of AD-PRS are associated not only with AD but more specifically with HS-by-proxy represented by small hippocampal subfields volumes in fimbria and hippocampus BH regions in individuals without clinical AD dementia. This is also in keeping with the recent demonstration [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] that \u003cem\u003ein vivo\u003c/em\u003e MRI correlates of HS-aging, derived from postmortem histopathology, are primarily reductions in grey matter in anterior hippocampus (i.e., head extending into body). Regarding the loss of volume of with matter (i.e., the fimbria), we speculate that this occurs secondary to degeneration of the CA1 and subiculum. Moreover, our results show two new variants that are part of the AD-PRS but might be more specific to HS-aging (rs34173062 and rs871269). These variants drive the effect of the association between HS-aging and AD-PRS together with rs5848 in \u003cem\u003eGRN\u003c/em\u003e, for which we replicate the association with HS-aging.\u003c/p\u003e \u003cp\u003ePrevious HS-aging studies have consistently identified four loci (\u003cem\u003eGRN\u003c/em\u003e, \u003cem\u003eTMEM106B\u003c/em\u003e, \u003cem\u003eABCC9\u003c/em\u003e, and \u003cem\u003eKCNMB2\u003c/em\u003e) [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Specifically, variant rs5848 in \u003cem\u003eGRN\u003c/em\u003e has been associated with HS-aging [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. \u003cem\u003eGRN\u003c/em\u003e (Granulin Precursor) is a gene that encodes for granulins, a family of glycosylated peptides. Glycosylation happens in the endoplasmic reticulum (ER) and disruptions on the protein processing in the ER (KEGG pathway has04141, enriched by genes commonly co-expressed with \u003cem\u003eSHARPIN, GRN\u003c/em\u003e and \u003cem\u003eTNIP1\u003c/em\u003e) have been associated with neurodegenerative diseases. After being attached to proteins in the ER, glycans are degraded (KEGG pathway hsa00511) in the lysosome (KEGG pathway hsa04142) by autophagy, both pathways enriched by genes co-expressed with \u003cem\u003eSHARPIN\u003c/em\u003e and \u003cem\u003eTNIP1\u003c/em\u003e. Autophagy eliminates inflammatory triggers (i.e., cytokines) and regulates the organelle function in immune cells (i.e., B cells). The B cell receptor signaling pathway (KEGG pathway has04662), involved in inflammation [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], is enriched in our results by genes co-expressed with the loci linked with HS-aging.\u003c/p\u003e \u003cp\u003eConsistent with our findings, variant rs34173062 in \u003cem\u003eSHARPIN\u003c/em\u003e has been suggested to be a genetic modifier of neuroanatomical variation in the limbic system through a GWAS of imaging that used a much larger sample size (N\u0026thinsp;=\u0026thinsp;8,428) of younger individuals (age 49\u0026ndash;69) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Additionally, a rare variant in the \u003cem\u003eSHARPIN\u003c/em\u003e gene (rs77359862), which is in linkage disequilibrium with the variant examined in this study (rs34173062, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.003, D\u0026rsquo;= 1, and Minor Allele Frequency (MAF)\u0026thinsp;=\u0026thinsp;3.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e) and located 4,325 base pairs away, has been previously identified to have a genome-wide significant association with MRI traits in a Korean cohort [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. \u003cem\u003eSHARPIN\u003c/em\u003e (SHANK Associated RH Domain Interactor) is a gene coding for a postsynaptic density protein of excitatory synapses which is part of the NF-κB-activating the LUBAC complex in the nervous system[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. NF-κB induces the expression of various pro-inflammatory genes, including those encoding cytokines and chemokines, and participates in inflammasome regulation [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Further supporting our results, variant rs871269 in \u003cem\u003eTNIP1\u003c/em\u003e has been formerly associated with HS-aging pathology in 2,831 individuals with European ancestry [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. \u003cem\u003eTNIP1\u003c/em\u003e (TNFAIP3 Interacting Protein 1) is also a gene implicated in NF-κB activation and NF-κB-dependent gene expression involved in the anti-inflammatory response. Functionally, TNIP1 protein is an inflammation modulatory protein that exerts its influence by regulating nuclear factor κB activation [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll three variants in \u003cem\u003eSHARPIN\u003c/em\u003e, \u003cem\u003eGRN\u003c/em\u003e and \u003cem\u003eTNIP1\u003c/em\u003e disclosed in this study, cluster with SNPs showing a negative correlation with hippocampal subfield volumes representing HS-by-proxy (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In addition, the only pathway enriched for this cluster, the LUBAC complex pathway, is associated with neurodegeneration via inflammatory pathways but neither with Aβ nor with Tau pathology. This, implies that these loci are potentially more specific to brain atrophy in general or hippocampal pathology in particular than to other AD pathological hallmarks. It is known that ubiquitination by the LUBAC complex is a key checkpoint in death receptor signaling [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Moreover, recent investigations demonstrate the impact of LUBAC-mediated linear polyubiquitination on the aggregation of disease associated proteins linked to various neurodegenerative diseases, such as TDP-43 proteinpathy which has been seen to improve after LUBAC inhibition [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Moreover, linear polyubiquitination by LUBAC complex leads the abnormal TDP-43 aggregates to autophagic proteolysis, via failed protein degradation system and subsequent NF-κB activation [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Given that \u003cem\u003eSHARPIN\u003c/em\u003e is part of LUBAC and \u003cem\u003eTNIP1\u003c/em\u003e is implicated in NF-κB activation, we speculate that these loci might be more prominently associated with TDP-43 and HS pathologies.\u003c/p\u003e \u003cp\u003eA previous study [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] adds evidence to the relation of these variants with the immune-mediated component of hippocampal atrophy. This study reported a pathway enrichment analysis of AD-loci by Bellenguez et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] to the clusters obtained as a result of associating these AD SNPs with levels of Aβ42 and phosphorylated Tau (pTau) in cerebrospinal fluid (CSF). Variants in \u003cem\u003eGRN\u003c/em\u003e and \u003cem\u003eTNIP1\u003c/em\u003e cluster together separated from the variant located in \u003cem\u003eSHARPIN\u003c/em\u003e. No GO-terms were enriched by the SNPs that cluster with \u003cem\u003eSHARPIN\u003c/em\u003e, but \u003cem\u003eSHARPIN\u003c/em\u003e alone shows a significant association with pTau in CSF [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], which may be related to pathologies other than AD. A total of eight GO-terms were enriched by \u003cem\u003eGRN\u003c/em\u003e and \u003cem\u003eTNIP1\u003c/em\u003e cluster. The common denominator of the names of the pathways enriched by this cluster is \u0026ldquo;immune\u0026rdquo;, of which \u003cem\u003eGRN\u003c/em\u003e is one of the most frequents contributor. Aside from \u003cem\u003eGRN\u003c/em\u003e, a high number of genes included in the \u0026ldquo;immune\u0026rdquo; cluster have been related to dementia types other than AD [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. This aligns with the results presented in this study, which further confirm that these loci may be more closely related to dysfunction in the immune response involved in multiple neurodegenerative diseases rather than specifically to AD pathology [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe concept of the immune-brain axis has gained attention, highlighting bidirectional communication between the immune system and the central nervous system [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Since both age and neurodegeneration are associated with inflammatory processes, we speculate that in HS-aging, inflammatory responses in the hippocampus may influence autophagy events [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA limitation of our study is the heterogeneity of the MRI data due to varying acquisition parameters and Freesurfer versions across centers. T2-weighted images were used mainly to exclude individuals with vascular damage or microbleeding, while hippocampal segmentation was performed using only T1-weighted images. Moreover, despite combining cohorts, our sample size remains limited, and a larger one is needed to increase statistical power and validate the present findings. Nevertheless, the main limitation of this study is the reliance on a proxy for HS-aging, as direct pathological confirmation is currently not possible \u003cem\u003ein vivo\u003c/em\u003e. While hippocampal atrophy measured with MRI has consistently been proposed as a promising \u003cem\u003ein vivo\u003c/em\u003e biomarker for HS-aging, it is not as accurate as \u003cem\u003epost mortem\u003c/em\u003e diagnosis of the pathology. However, disentangling the genetic risk factors associated different neuropathologies remains a necessary area of investigation. The findings presented here contribute to this effort, and suggest promising new directions for future research. A longitudinal follow-up of the present study to identify patients developing HS-aging, or future validations in HS-aging autopsy cohorts, are required to confirm our results. Nevertheless, this study yielded significant results and include independent and consistent replication results supporting our hypothesis.\u003c/p\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003eIn conclusion, AD-PRS and some AD-variants showed correlation with hippocampal subfields volume which might be used as a proxy for HS-aging. By studying preclinical hippocampal subfield atrophy we identified predominant genetic signals that might be instrumental for the detection of premature dementia. Specifically, variants in \u003cem\u003eSHARPIN, GRN\u003c/em\u003e and \u003cem\u003eTNIP1\u003c/em\u003e might be more related to hippocampal subfield atrophy caused by HS-aging rather than to AD alone. Our study highlights the importance of precise phenotyping in genetic studies to generate disease-specific PRS. Dissecting the molecular pathways, cell types, and brain regions associated with each AD locus is crucial to translate genetic observations into clinical benefits.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAce Alzheimer Center Barcelona (ACE)\u003c/p\u003e\n\u003cp\u003eAD polygenic risk score (AD-PRS)\u003c/p\u003e\n\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD)\u003c/p\u003e\n\u003cp\u003eAlzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI)\u003c/p\u003e\n\u003cp\u003eAmyloid \u0026beta; (A\u0026beta;)\u003c/p\u003e\n\u003cp\u003eBody and head (BH)\u003c/p\u003e\n\u003cp\u003eCornu Ammonis (CA)\u003c/p\u003e\n\u003cp\u003eDementia Genetics Spanish Consortium (DEGESCO)\u003c/p\u003e\n\u003cp\u003eEcho time (TE)\u003c/p\u003e\n\u003cp\u003eEuropean Alzheimer\u0026rsquo;s and Dementia Biobank (EADB)\u003c/p\u003e\n\u003cp\u003eEuropean Prevention of Alzheimer\u0026apos;s Dementia (EPAD)\u003c/p\u003e\n\u003cp\u003eFalse Discovery Rate (FDR)\u003c/p\u003e\n\u003cp\u003eField of view (FOV)\u003c/p\u003e\n\u003cp\u003eFundaci\u0026oacute; ACE Healthy Brain Initiative (FACEHBI)\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO)\u003c/p\u003e\n\u003cp\u003eGenome Research at Ace Alzheimer center (GR@ACE)\u003c/p\u003e\n\u003cp\u003eGenome-wide association studies (GWAS)\u003c/p\u003e\n\u003cp\u003eGenome-wide significant (GWS)\u003c/p\u003e\n\u003cp\u003eGranule Cell and Molecular Layer of the Dentate Gyrus (GC-ML-DG)\u003c/p\u003e\n\u003cp\u003eHippocampal sclerosis of aging (HS-aging)\u003c/p\u003e\n\u003cp\u003eHippocampal subfields segmentation (HSF)\u003c/p\u003e\n\u003cp\u003eHippocampus-amygdala-transition-area (HATA)\u003c/p\u003e\n\u003cp\u003eInversion time (TI)\u003c/p\u003e\n\u003cp\u003eKyoto Encyclopedia of Genes and Genomes (KEGG)\u003c/p\u003e\n\u003cp\u003eLimbic-predominant age-related TDP-43 encephalopathy (LATE)\u003c/p\u003e\n\u003cp\u003eLinear ubiquitin chain assembly complex (LUBAC)\u003c/p\u003e\n\u003cp\u003eMagnetic resonance imaging (MRI)\u003c/p\u003e\n\u003cp\u003eMagnetization-prepared rapid gradient-echo (MPRAGE)\u003c/p\u003e\n\u003cp\u003eMild cognitive impairment (MCI)\u003c/p\u003e\n\u003cp\u003eMinor allele frequency (MAF)\u003c/p\u003e\n\u003cp\u003eModels of Patient Engagement for Alzheimer\u0026rsquo;s Disease (MOPEAD)\u003c/p\u003e\n\u003cp\u003eOdd ratio (OR)\u003c/p\u003e\n\u003cp\u003eOver-representation enrichment analysis (ORA)\u003c/p\u003e\n\u003cp\u003ePolygenic risk scores (PRS)\u003c/p\u003e\n\u003cp\u003ePrincipal Component (PC)\u003c/p\u003e\n\u003cp\u003eQuality Control (QC)\u003c/p\u003e\n\u003cp\u003eRepetition time (TR)\u003c/p\u003e\n\u003cp\u003eSequence Read Archive (SRA)\u003c/p\u003e\n\u003cp\u003eSpanish National Center for Genotyping (CeGEN)\u003c/p\u003e\n\u003cp\u003eThree-dimensional (3D)\u003c/p\u003e\n\u003cp\u003eTotal intracranial volumes (TIV)\u003c/p\u003e\n\u003cp\u003eTrans-Omics for Precision Medicine (TOPMed)\u003c/p\u003e\n\u003cp\u003eVallecas Project (VP)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll projects passed the ethics committee and written informed consent was obtained from all human participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board Statement\u003c/p\u003e\n\u003cp\u003eAll protocols were approved by the Clinical Research Ethics Commission of the Hospital Clinic, Barcelona, Spain (reference: HCB/2014/0494) in accordance with the current Spanish regulations in the field of biomedical research and the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAll authors critically revised the manuscript for important intellectual content and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study from ADNI (ADNI-1 and ADNI-2) can be accessed upon request through their respective repositories. Datasets from ACE (BIOFACE, FACEHBI, EPAD and MOPEAD) and VP dataset can be accessed upon request through the corresponding author of this paper.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe Genome Research @ Ace Alzheimer Center Barcelona project (GR@ACE) is supported by Grifols SA, Fundacion bancaria La Caixa, Ace Alzheimer Center Barcelona and CIBERNED. Ace Alzheimer Center Barcelona is one of the participating centers of the Dementia Genetics Spanish Consortium (DEGESCO). The FACEHBI study is supported by funds from Ace Alzheimer Center Barcelona, Grifols, Life Molecular Imaging, Araclon Biotech, Alkahest, Laboratorio de analisis Echevarne and IrsiCaixa. MB, AR, MM acknowledge the support of the Spanish Ministry of Science and Innovation, Proyectos de Generacion de Conocimiento grants PID2021-122473OA-I00, PID2021-123462OB-I00 and PID2019-106625RB-I00. ISCIII, Accion Estrategica en Salud, integrated in the Spanish National R+D+I Plan and financed by ISCIII Subdireccion General de Evaluacion and the Fondo Europeo de Desarrollo Regional (FEDER Una manera de hacer Europa) grants PI13/02434, PI16/01861, PI17/01474, PI19/00335, PI19/01240, PI19/01301, PI12019/08-1, PI22/01403, PI22/00258 and the ISCIII national grant PMP22/00022, funded by the European Union (NextGenerationEU). The support of CIBERNED (ISCIII) under the grants CB06/05/2004 and CB18/05/00010. The support from the ADAPTED and MOPEAD projects, European Union/EFPIA Innovative Medicines Initiative Joint (grant numbers 115975 and 115985, respectively); from PREADAPT project, Joint Program for Neurodegenerative Diseases (JPND) grant No AC19/00097 and No AC23_2/00038; from HARPONE project, Agency for Innovation and Entrepreneurship (VLAIO) grant No PR067/21 and Janssen. DESCARTES project is funded by German Research Foundation (DFG). Additionally, IdR and CO are supported by the Instituto de Salud Carlos III (ISCIII) under the grant FI20/00215 and FI24/00029 respectively. PGG is supported by CIBERNED employment plan (CNV-304-PRF-866). ACF received support from the ISCIII under the grant Sara Borrell (CD22/00125).\u003c/p\u003e\n\u003cp\u003eAuthor\u0026rsquo;s contributions\u003c/p\u003e\n\u003cp\u003eC.O. and I.dR. contributed to data acquisition, analysis, interpreted the data and co-wrote the manuscript, supplementary materials and prepared figures and tables. O.S.G. and L.Z. contributed to MRI data analysis. A.Ru., M.V.F. and I.dR. designed, conceptualized, supervised the study and interpreted the data. L.T., S.V., M.Ma., P.S.J., M.B., B.S., M.V.F. and A.Ru. contributed to the critical revision of the paper.\u0026nbsp;Data generation, sample contribution: C.O., I.dR., L.Z., O.S.G., I.Q., P.G.G., R.P., F.G.G., L.M., M.C.B., A.Can., A.M., J.B.F., M.C., A.Ra., M.T.A., A.B.P., T.dS., M.Me. and A.Car. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe would like to extend our gratitude to participants and their families for their contribution of time and samples to this study. We are indebted to the Biobank-Hospital Clinic-FRCB-IDIBAPS for samples and data procurement. The present work has been performed as part of the doctoral program of C. Oliv\u0026eacute; at the Universitat de Barcelona (Barcelona, Spain).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNelson PT, Smith CD, Abner EL, et al. Hippocampal sclerosis of aging, a prevalent and high-morbidity brain disease. Acta Neuropathol. 2013;126:161\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtega-Cruz D, Iglesias JE, Rabano A, Strange BA. Hippocampal sclerosis of aging at post-mortem is evident on MRI more than a decade prior. Alzheimer\u0026rsquo;s Dement. 2023;19:5307\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoodworth DC, Nguyen HL, Khan Z, Kawas CH, Corrada MM, Sajjadi SA. Utility of MRI in the identification of hippocampal sclerosis of aging. Alzheimer\u0026rsquo;s Dement. 2021;17:847\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePao WC, Dickson DW, Crook JE, Finch NA, Rademakers R, Graff-Radford NR. 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Nat Genet. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1101/2020.10.01.20200659\u003c/span\u003e\u003cspan address=\"10.1101/2020.10.01.20200659\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hippocampal sclerosis of aging, Alzheimer’s disease, Magnetic Resonance Imaging, Polygenic risk score","lastPublishedDoi":"10.21203/rs.3.rs-6429978/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6429978/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKROUND\u003c/strong\u003e: Hippocampal sclerosis of aging (HS-aging) is frequently present in individuals over 85 who die with dementia. Recent studies suggest that some loci associated with Alzheimer’s disease (AD) may be more related to HS-aging. We aimed to find AD-associated SNPs potentially related to HS-aging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS\u003c/strong\u003e: We used different regression models to assess the relation of the AD polygenic risk score (AD-PRS) with hippocampal subfield volumes assessed by magnetic resonance imaging (MRI) as HS-by-proxy in 1,130 participants without dementia. We meta-analyzed 1,708 individuals to associate their AD-PRS (83 variants) with AD alongside HS-aging. We also performed co-regulatory network analyses and over-representation enrichment analyses in order to identify biological pathways enriched with co-regulatory networks of genes associated with HS-aging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS\u003c/strong\u003e: HS-by-proxy measures of fimbria and hippocampal body and head show association with AD-PRS, \u003cem\u003eSHARPIN\u003c/em\u003e, \u003cem\u003eGRN\u003c/em\u003e and \u003cem\u003eTNIP1\u003c/em\u003e, also after replication. Our results also show an association of the LUBAC complex with our proxy phenotype. We replicated the stronger AD-PRS association with AD in the presence of HS-aging compared to AD alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS\u003c/strong\u003e: Results show association between some AD-SNPs and HS-proxy, enriched in immune-brain axis pathways, differentiating HS-aging from AD. This insight aids in understanding their interrelationships and identifying specific therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Genetic dissection of hippocampal sclerosis of ageing using magnetic resonance imaging surrogates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 10:03:48","doi":"10.21203/rs.3.rs-6429978/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7c2b7af-7e02-45b7-a1ee-c8fe3433fbfc","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T09:23:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 10:03:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6429978","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6429978","identity":"rs-6429978","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}