Genetic Validation of ABI3 p.Ser209Phe Variant and Its effects On Early Brain Pathology in Asymptomatic Elderly Individuals

Genetic Validation of ABI3 p.Ser209Phe Variant and Its effects On Early Brain Pathology in Asymptomatic Elderly Individuals | 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 Validation of ABI3 p.Ser209Phe Variant and Its effects On Early Brain Pathology in Asymptomatic Elderly Individuals Mikko Koivumäki, Henna Martiskainen, Mari Takalo, Jenni Lehtisalo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8113148/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Feb, 2026 Read the published version in Alzheimer's Research & Therapy → Version 1 posted 10 You are reading this latest preprint version Abstract Background Alzheimer’s disease (AD) has a strong genetic component, with APOE ε4 being the most established risk factor through its effects on beta-amyloid (Aβ) metabolism and microglial function. Recent genetic studies have also implicated microglial genes, such as the ABI3 S209F variant, to increased AD risk. As APOE ε4 and ABI3 S209F influence microglial pathways through distinct mechanisms, their combined analysis may provide novel insights into AD pathophysiology. Therefore, we investigated ABI3 S209F in the Finnish FinnGen cohort and in an imaging study of cognitively healthy older adults Methods We used FinnGen R12 data (> 500,000 individuals), including 8,490 ABI3 S209F carriers and 511,670 non-carriers, with survival analyses matched by sex and birth year. Disease endpoints (AD, dementia, neurodegenerative disorder) were defined from national health registries using harmonized ICD codes, medication, and reimbursement records. For the imaging study, 58 participants aged ≥ 50 years were recruited into three genotype-based groups (ABI3 S209F / APOE ε4, ABI3 S209F / APOE ε3, non-carriers). All imaging participants underwent structural MRI, [ 11 C]PiB PET for amyloid beta, [ 11 C]PK11195 PET for microglial activity, and a comprehensive neuropsychological battery. Results ABI3 S209F was significantly associated with increased risk of AD (OR = 1.22, p = 0.0012) and neurodegenerative disorders (OR = 1.21, p = 0.00023), but not with dementia (OR = 1.10, p = 0.06). Survival analyses indicated that ABI3 S209F carriers developed AD at an earlier age than non-carriers with the same APOE genotype. The carriers of ABI3 S209F and APOE ε4 had higher brain Aβ burden when compared to the ABI3 S209F carriers without APOE ε4 (SUVR 2.0 (0.7) vs. 1.67 (0.5); SUVR 2.0 (0.7) vs. 1.67 (0.5); mean (sd), p = 0.017). ABI3 S209F was not associated with global neuroinflammation, although subtle regional increases in [ 11 C]PK11195 binding were observed in ABI3 S209F ε4 carriers. No differences were found in brain volumes or cognition. Conclusions ABI3 S209F increases AD risk and is associated with earlier disease onset. The variant alone does not significantly influence cortical Aβ deposition, neuroinflammation, or brain structure. Its effect may be pronounced in combination with APOEε4. Alzheimer’s disease ABI3S209F APOE ε4 Microglia β-amyloid- PET imaging FinnGen Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Alzheimer's disease (AD) has a strong genetic component, with the Apolipoprotein E ε4 (APOEε4) being the most common and well-established genetic risk factor (Corder et al., 1993 ; Guerreiro, Gustafson, & Hardy, 2012 ). APOEε4 is associated with a higher burden of beta-amyloid (Aβ) plaques in the brain in a dose-dependent manner (Castellano et al., 2011 ; Reiman et al., 2009 ), and it is thought to impair Aβ clearance (Verghese et al., 2013 ). In recent years, genome-wide association studies (GWAS) have identified several additional risk genes for AD (C. Bellenguez et al., 2022 ; Jansen et al., 2019 ; D. P. Wightman et al., 2021 ), including ones with strong expression in microglia, such as ABI3 . The ABI3 p.Ser209Phe variant (ABI3 S209F ) was first linked to increased AD risk in 2017 with an odds ratio (OR) of 1.43 (Sims et al., 2017 ), and this association has been replicated in multiple cohorts (Conway et al., 2018 ; Dalmasso et al., 2019 ; Olive et al., 2020 ; Douglas P. Wightman et al., 2021 ). ABI3 is highly expressed in microglial cells, with limited expression in neurons and other glial cell types (Conway et al., 2018 ; Satoh et al., 2017 ). Microglia have dual roles in AD pathology, initially protective through Aβ clearance, but later potentially harmful due to sustained inflammation (Z. Fan, Brooks, Okello, & Edison, 2017 ; Leng & Edison, 2021 ). ABI3 is upregulated in the cortex of AD patients and in Aβ mouse models (Castillo et al., 2017 ), and is thought to modulate microglial responses through interferon signalling (Sims et al., 2017 ), and actin cytoskeletal reorganization via the WAVE2 complex (Davidson, Ura, Thomason, Kalna, & Insall, 2013 ; Sekino et al., 2015 ). However, findings from murine Abi3 knockout models have been inconsistent as some show increased Aβ deposition (Karahan et al., 2021 ; Karahan et al., 2023 ), while others note a transient reduction (Ibanez et al., 2022 ). Despite the replicated association between ABI3 S209F and AD, evidence for its impact on in vivo AD biomarkers is limited. Notably, Olive et al. ( 2020 ) found no significant association between the variant and CSF Aβ42 or Aβ PET signal in cognitively normal individuals. APOEε4 does not only influence Aβ accumulation but also directly modulates microglial function (Krasemann et al., 2017 ; Rodriguez, Tai, Ladu, & Rebeck, 2014 ; Ulrich et al., 2018 ). The APOEε4 isoform has been shown to alter microglial responses by limiting their ability to adopt a neuroprotective phenotype, impairing Aβ clearance and promoting their pro-inflammatory state (Liu et al., 2023 ; Nguyen et al., 2020 ; Yin et al., 2023 ). The dual role of APOEε4 in Aβ pathology and immune regulation makes it a particularly relevant modifier when exploring microglial activity and gene–gene interactions in AD. Given that both APOEε4 and ABI3 influence microglial function, albeit through distinct molecular pathways, their combined analysis may provide a more comprehensive understanding of microglial contributions to AD pathophysiology. Investigating these genes in parallel allows for the exploration of potential gene–gene interactions, additive effects, or pathway convergence that may influence cortical Aβ deposition and neuroinflammation. Also, due to the genetic and pathological heterogeneity of AD, replication of risk variants in distinct populations is essential to validate their generalizability and potential clinical relevance (Bellenguez et al., 2022 ). Studying the ABI3 S209F in a Finnish cohort provides an opportunity to confirm its association with AD risk in a genetically and environmentally distinct population, thereby strengthening the evidence for its role in disease pathogenesis (Sims et al., 2017 ; Wightman et al., 2021 ). In this study, we aim to confirm the association of ABI3 S209F with AD in the Finnish FinnGen cohort, and to investigate how this variant, alone or in combination with APOEε4, modulates cortical Aβ burden, neuroinflammation, and brain morphology in cognitively healthy older adults using positron emission tomography (PET) and magnetic resonance imaging (MRI). Materials and methods Study population The FinnGen Study is a large biobank-scale project that combines genome data and longitudinal register-based healthcare data of > 500,000 Finns (Kurki et al., 2023 ). In this study we used data from FinnGen release R12, which included 8,490 heterozygous carriers of ABI3 S209F and 511,670 non-carriers. For survival analysis, each ABI3 S209F carrier was assigned with up to five non-carrier controls matched for sex and the year of birth. For the imaging part of this study, we recruited 58 subjects of ≥ 50 years of age, based on power calculations on previous studies. The participants of this cross-sectional study were recruited in collaboration with the local Auria biobank and from the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) (Kivipelto et al., 2013 ) cohort. Genotype data was available for a subset of the biobank cohort through FinnGen study, allowing the biobank to directly contact persons with the ABI3 S209F and APOE ε4/ε3 or APOE ε3/ε3 genotype who had previously signed a biobank consent and an additional informed consent allowing the biobank to contact them if they are suitable for participating in a research study. Main exclusion criteria were dementia or cognitive impairment, any degenerative neurological disease, chronic inflammatory condition, and contraindication for MRI or PET imaging. Our study involved three distinct groups for comparative analysis (n = 19 + 19 + 20): 1) individuals possessing both the ABI3 S209F variant and the APOE ε4/ε3 genotype (ABI3 S209F /ε4), 2) those with the ABI3 S209F variant and the APOE ε3/ε3 genotype (ABI3 S209F /ε3), and 3) a non-carrier group with the major ABI3 S209F allele and APOE ε3/ε3 genotype (NC). The study was approved by the Ethical Committee of the Hospital District of Southwest Finland. All participants signed written informed consent. Genotyping ABI3 S209F is encoded by a single nucleotide substitution NM_016428.3:c.626T > C (rs616338). Although the T allele (encoding phenylalanine) is the reference allele, it is the rare allele in this locus. Thus, we refer to the common C allele (encoding serine) as the major allele and the rare T allele as the minor (effect) allele, consistent with previous reports as well as allele frequency and evolutionary conservation of the serine residue across multiple species (Sims et al., 2017 ). The FinnGen cohort has been genotyped with Illumina (Illumina Inc., San Diego, USA) and Affymetrix (Thermo Fisher Scientific, Santa Clara, CA, USA) chip arrays as part of the FinnGen Study. Chip genotype data were imputed using the Finnish population-specific imputation reference panel Sequencing Initiative Suomi project (SISu v4.2, Institute for Molecular Medicine Finland, University of Helsinki, Finland, http://sisuproject.fi ). Majority of the FinnGen cohort (92%) had been directly genotyped for rs616338, but for a broader coverage, imputed genotypes were used in all analyses. Imputation INFO score for rs616338 was 0.995. FINGER cohort (Kivipelto et al., 2013 ) was genotyped with Illumina Infinium Global Screening Array and imputed with TOPMed reference panel as described before (Céline Bellenguez et al., 2022 ). To confirm the ABI3 genotypes in a subset of 42 individuals enrolled to the imaging study, venous blood was collected and genomic DNA was extracted from whole blood using QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany). ABI3 rs616338 was genotyped with TaqMan assay (C_2270073_20, Applied Biosystems). All genotypes were consistent with those observed with chip genotyping or imputation. Definition of disease endpoints To define the disease endpoints, we utilized FinnGen core endpoints, which are based on digital health record data from Finnish health registries. Diagnoses are based on International Classification of Diseases (ICD) codes and have been harmonized over ICD-8, ICD-9 and ICD-10 codes. AD was defined as a diagnosis in the hospital discharge or cause of death registries with the ICD codes G30 (ICD-10) or 3310 (ICD-9). AD onset age for the survival analysis was defined as the age of the first diagnosis. The remaining individuals were considered as controls. For the survival analysis, end of follow-up for controls was defined as death, moving abroad, or the latest update of the registry data, whichever came first. Neurodegenerative disorder was defined as at least three entries with the following criteria: diagnosis in the hospital discharge or cause of death registries with the ICD codes F00*, G30 (ICD-10), 3310 (ICD-9), or 29010 (ICD-8); Finnish Social Insurance Institution (Kela) reimbursement for F00* or G30 (ICD-10) or reimbursement code 307; or prescription medicine purchases with ATC class N06D. Remaining individuals, excluding cases with AD, were considered as controls. Dementia was defined as at least three entries with the following criteria: a diagnosis in the hospital discharge or cause of death registries with the ICD codes F00-F09 (ICD-10), 290, 3310, or 4378A (ICD-9) or 290 (ICD-8), Finnish Social Insurance Institution (Kela) reimbursement code 307, or prescription medicine purchases with ATC class N06D. Dementia cases included individuals with vascular dementia, dementia in other diseases classified elsewhere, or unspecified dementia. Remaining individuals, excluding cases with organic mental disorders, were considered as controls. Brain imaging All subjects underwent a structural brain MRI including T1-weighted sequences. Structural brain images were acquired with the Philips Ingenuity 3.0 T TF PET/MRI (Philips Healthcare, Amsterdam, the Netherlands). MRI was used to acquire volumetric variables for hippocampus, parahippocampus, entorhinal cortex and amygdala. PET scans were acquired using an ECAT high-resolution research tomograph (HRRT, Siemens Medical solutions, Knoxville, TN). To estimate brain Aβ accumulation, [ 11 C]PiB scans (n = 56) were acquired 40 to 90 min post injection (mean injected dose 493 (standard deviation (SD) 42) MBq). To estimate microglial activity, we used TSPO imaging with [ 11 C]PK11195. Dynamic [ 11 C]PK11195 scans (n = 56) were acquired for 60 min post injection (mean injected dose 476 (SD 41) MBq). All images were reconstructed with 3D ordinary Poisson ordered subset expectation maximization algorithm (OP-OSEM3D), and list mode data was histogrammed into 8 (6 × 5 + 2 × 10 min, [ 11 C]PiB) and 17 (2 × 15; 3 × 30; 3 × 60; 7 × 300; 2 × 600 s, [ 11 C]PK11195) time frames. Cognitive testing A thorough neurocognitive test battery was administered to the participants by trained psychology students. The test battery included parts from the Finnish version of the Wechsler Memory Scale (WMS-R) and the Wechsler Adult Intelligence Scale (WAIS-R), Boston Naming Test (BNT), Trail Making Tests A and B (TMT-A and TMT-B), S-fluency, categorical fluency, and Stroop (Battery, 1944 ; Kaplan, Goodglass, & Weintraub, 1983 ; Stroop, 1935 ; Wechsler, 1981 , 1987 ). Domain-specific neurocognitive test z-scores, based on an a priori hypothesis (Lezak, Howieson, Bigler, & Tranel, 2012 ), were calculated for executive functions, processing speed, language, and episodic memory, with higher scores indicating better performance. The executive function domain included the Trail Making Test A and B (TMT-B minus TMT-A), Stroop test (inhibition minus naming), digit span backward, and S-fluency. The processing speed domain consisted of TMT-A and digit symbol tests. The episodic memory domain included the WMS-R delayed logic memory and delayed verbal recall. The language domain included categorical fluency, the Boston naming test, and WAIS-R similarities. Brain image analysis Both PET and MRI images were analysed for region of interest (ROI) and voxel-wise differences between the study groups. PET and MRI image preprocessing and analysis were performed using an automated pipeline at Turku PET Centre (Karjalainen et al., 2020 ), which executed the PET data frame by frame realignment, PET-MRI co-registration, FreeSurfer (Freesurfer v6, https://surfer.nmr.mgh.harvard.edu/ ) ROI parcellation and PET data kinetic modelling. Regional and voxel level [ 11 C]PiB binding was quantified as standardized uptake value ratios (SUVR) calculated for 60 to 90 min post injection using the cerebellar cortex as the reference region. A composite neocortical [ 11 C]PiB score was calculated as the volume weighted average of the [ 11 C]PiB region-to-cerebellar cortex SUVRs for the lateral frontal, lateral temporal, and parietal cortices as well as the posterior cingulate, anterior cingulate, and precuneus. This composite [ 11 C]PiB score was used to estimate brain Aβ load. [ 11 C]PK11195-binding was quantified from the same composite region, as distribution volume ratios (DVR) within 20–60 min post injection using a reference tissue input Logan’s method with pseudo-reference region extracted using supervised clustering algorithm (Turkheimer et al., 2007 ; Yaqub et al., 2012 ). Voxel-level kinetic modeling for [ 11 C]PK11195 was carried out using basis function implementation of simplified reference tissue model with respect to the aforementioned clustered pseudo-reference region and with 300 basis functions calculated within the Θ3 parameter limits 0.06 ≤ Θ3 ≤ 0.6 (Gunn, Lammertsma, Hume, & Cunningham, 1997 ). Partial volume effect (PVE)-corrected data was used for all [ 11 C]PK11195 analysis to minimize the effect of PK binding in sinuses to cortical regions. PVE correction was carried out using PETPVE12 toolbox (Gonzalez-Escamilla, Lange, Teipel, Buchert, & Grothe, 2017 ) in both ROI (geometric transfer matrix method) and voxel-level (Muller-Gartner method) data. The brain MRI images were also analysed for volumetric differences extracted from the FreeSurfer results. Here we focused on the hippocampus, parahippocampus, and the entorhinal cortex due to their known association with neurodegeneration related to AD (Chandra, Dervenoulas, & Politis, 2019 ). All voxel-wise analyses were conducted using statistical parametric mapping (SPM12 v12; Wellcome Trust Centre for Neuroimaging, London, UK) running on MATLAB R2021b (Math-Works, Natick, MA, USA). Statistical analysis Association of rs616338-T allele with AD, neurodegenerative disorder, and dementia were assessed in FinnGen data with chi-square test using R 4.3.2. The data are presented as OR with 95% confidence intervals (CI). Survival analysis comparing AD-free survival of ABI3 S209F carriers and age- and sex-matched controls was performed in R 4.3.2 where Kaplan-Meier curves were generated using package survminer v0.4.9 and cox proportional hazards models were fitted with survival package v3.2-7 (T Therneau, 2024 ; TM Therneau & Grambsch, 2000 ). The proportional hazards assumption was assessed using the Schoenfeld residuals test, which indicated a violation for the APOE genotype. To account for this, the Cox model was stratified by APOE genotype. The data is presented as hazard ratio (HR) with 95% CI. Results with p < 0.05 were considered statistically significant. The sample size for the imaging part of this study was based on a power analysis calculated from previous research results obtained with the same radiotracers (Aalto et al., 2009 ; Z. Fan et al., 2017 ). The assumption was that the study would have 90% power (1–β = 0.9, α = 0.05) to detect, depending on the tracer, a 10–20% difference in regional binding ratios between the ABI3 S209F /ε4 and NC groups. The final sample size also aimed to account for potential dropouts. The ABI3 S209F /ε3 group was assumed to show an effect, with results expected to fall between those of the primary comparison groups. Statistical analyses of the imaging data were performed using JMP Pro 16.0.0 (SAS Institute Inc., Cary, North Carolina, USA). All data following a normal distribution are presented as mean (SD), otherwise as median (interquartile range, IQR). The normality of the data was evaluated visually from the distribution and with the Shapiro-Wilk test. Differences in continuous variables between the three groups were tested using linear regression models, adjusting for age and sex. The MRI variables were also adjusted for total intracranial volume. If a significant effect was found, all pairs were compared using the post hoc Tukey’s honest significance test for multiple comparisons. Volumetric differences in T1 MRI, [ 11 C]PIB, and [ 11 C]PK11195-binding at the voxel level were assessed using ANCOVA, using age and sex as covariates for [ 11 C]PIB and [ 11 C]PK11195, but also total intracranial volume for MRI. This was followed by post-hoc pairwise comparisons in SPM12. Voxel-wise differences between the groups were tested using linear regression in SPM12 to evaluate if differences were present also outside the a priori chosen brain regions. Uncorrected p < 0.001 combined with a cluster-level false discovery rate (FDR) correction for multiple comparisons was considered statistically significant in the voxel-based analyses. When significant FDR corrected clusters were found we applied family wise error (FWE) correction with p < 0.05 to see if the results survived the tighter threshold. To combine the scores of different neurocognitive tests into domain-specific scores, z-scores for the tests were calculated by standardizing the raw test scores to the study population’s mean and standard deviation. To achieve a normal distribution, one outlier per raw test were excluded before the z-transformation (1 outlier in Boston naming test; and 1 outlier in Stroop test). All outliers performed worse on the cognitive tests than − 2 SD of the present study population. A skewness of − 1 to 1 was accepted for the raw test score distribution. Domain-specific z-scores were determined by averaging all the z-scores within each domain. For tests with a reverse scale (Stroop, TMT-A, and TMT-B), reciprocal numbers were used to ensure higher scores indicated better performance. Participants with missing test results were excluded. Results ABI3 S209F associates with increased risk and earlier onset age of AD in FinnGen cohort ABI3 S209F (rs616338-T) was significantly associated with increased risk of AD (OR = 1.22, 95% CI: 1.09–1.38, p = 0.0012) and neurodegenerative disorder (OR = 1.21, 95% CI: 1.10–1.34, p = 0.00023) in FinnGen (Fig. 1 A). Association with dementia was not statistically significant (OR = 1.10, 95% CI: 1.00-1.20, p = 0.06). To investigate the impact of ABI3 S209F and APOE ε4 on the age of onset of AD, we performed survival analysis for age at AD diagnosis. Kaplan-Meier survival curves indicated an earlier onset of AD among the ABI3 S209F carriers when compared to the non-carriers with the same APOE genotype (Fig. 1 B). Stratified Cox proportional hazards models were used to account for the violation of the proportional hazard assumption by APOE genotype. Within the APOE ε3 stratum, ABI3 S209F carriers exhibited a significantly increased hazard of AD onset (HR = 1.31, 95% CI: 1.09–1.59, p = 0.0046). Similarly, in the APOE ε4 stratum, ABI3 S209F carriers showed an elevated risk (HR = 1.32, 95% CI: 1.09–1.60, p = 0.0045). These findings suggest that the ABI3 S209F variant is associated with earlier AD onset, independent of APOE genotype. Demographics of the PET–MRI subjects Baseline demographic, clinical, and imaging characteristics of the PET–MRI subsample are presented in Table 1 . Across the three genotype-defined study groups (ABI3 S209F /ε4, ABI3 S209F /ε3, and NC), no statistically significant differences were observed in sex distribution, age, height, or hippocampal volume (P > 0.07 for all). Cognitive performance, as assessed by MMSE, was similar across groups. Importantly, a significant group difference was observed in cortical Aβ burden, estimated as composite [¹¹C]PiB SUVR, with the ABI3 S209F /ε3 group showing a lower median binding score than the ABI3 S209F /ε4 group (p = 0.01). No differences were observed in microglial activation ([¹¹C]PK11195 DVR). Table 1 Subject demographics and descriptive data ABI3 S209F /ε4 ABI3 S209F /ε3 NC Group difference N 19 19 20 Sex female 11 8 11 0.58 male 8 11 9 Age Mean (sd) 69.5 (6.2) 71.4 (6.9) 71.4 (5.1) 0.55 Height Mean (sd) 168.9 (9.4) 172.2 (9.6) 168.3 (7.9) 0.39 Weight Mean (sd) 81.8 (12.7) 83.4 (18) 73.1 (13.2) 0.073 BMI Mean (sd) 28.8 (4.5) 27.9 (4.2) 25.7 (3.7) 0.066 [ 11 C]PiB score Mean (sd)) 2.0 (0.7) 1.67 (0.5)* 1.75 (0.6) 0.01 [ 11 C]PK DVR Mean (sd) 1.3 (0.1) 1.3 (0.1) 1.3 (0.1) 0.83 Hippocampal volume Mean (sd) 7.7 (0.8) 7.7 (1.0) 7.2 (0.6) 0.26 MMSE Median (IQR) 28.5 (28–29) 29 (28–29) 29 (28–30) 0.83 Data are presented as mean (standard deviation) or median (interquartile range) depending on the distribution. Differences between groups were tested with ANCOVA with Tukey’s honest significance test, Kruskal–Wallis test with Dunn’s method for multiple or linear models. χ2 test was used for testing categorical variables. P value presents overall difference between groups. Significant differences in pairwise comparisons to ABI+/e4e3 (p < 0.05 = *) are presented. APOEε4, but not ABI3 S209F , increases brain Aβ load In the ROI analysis, the ABI3 S209F /ε4 had higher composite cortical [ 11 C]PiB score when compared to the ABI3 S209F /ε3 (2.0 (0.7) vs. 1.67 (0.5); mean (sd), p = 0.017 (HST)) (Fig. 2 a). The voxel-level analysis showed higher [ 11 C]PiB binding across the cortex in the ABI3 S209F /ε4 group than in the two other groups (Fig. 3 ). No global, but subtle regional neuroinflammatory differences in ABI3 S209F /ε4 carriers. The [ 11 C]PK11195 analysis revealed no significant differences between the groups in the composite cortical ROI (Fig. 2 b). However, voxel-level analysis detected minor differences between the ABI3 S209F /ε4 and both the ABI3 S209F /ε3 and NC groups, but not between the ABI3 S209F /ε3 and NC groups (Fig. 4 ). The ABI3 S209F /ε4 showed higher [ 11 C]PK11195 binding in the precuneus and parieto-occipital regions compared to the ABI3 S209F /ε3 group. Additionally, higher [ 11 C]PK11195 binding was observed in the parieto-occipital and calcarine regions of the ABI3 S209F /ε4 compared to the NC group. While no significant ROI-level correlations were found between the PET tracers, some voxel-wise associations were identified mainly in subcortical regions in the white matter. In addition, a weak correlation (r = 0.27, p = 0.043) was observed between [¹¹C]PK11195 binding and cortical volume. Full correlation results are provided in the Supplementary Materials (Table S1 , Figure S1 ). ABI3 S209F does not significantly affect brain region volumes In the prechosen ROIs we found no differences between the groups in the volumes of cerebral cortex, hippocampus, parahippocampus, entorhinal cortex or amygdala (Supplementary material, Table S2). Subsequent VBM analysis found that the ABI3 S209F /ε3 had lower volumes than the other groups mainly in the superior temporal gyrus, when correcting for FDR, but these findings did not survive the FEW correction (Supplementary material, Figure S2). ABI3 S209F has no effect on cognition Global cognition was assessed with the MMSE, and domain-specific performance was summarized as z-standardized composite. One-way ANOVA followed by Tukey’s honest significant difference test revealed no overall between-group differences for MMSE (Table 1 ) or any cognitive domain (Table 2 , Fig. 5 ). Table 2 Cognitive test results ABI3 S209F /ε4 ABI3 S209F /ε3 NC Group difference Executive functions Mean (sd) 0.041 (0.41) -0.046 (0.61) 0.035 (0.47) 0.47 Processing speed Mean (sd) 0.024 (0.79) 0.30 (0.86) 0.21 (0.87) 0.85 Episodic memory Mean (sd) -0.34 (0.73) -0.11 (0.65) -0.10 (0.52) 0.63 Language functions Mean (sd) -0.16 (0.58) 0.12 (0.66) 0.14 (0.53) 0.23 Data are presented as mean (standard deviation). Differences between groups were tested with one-way ANOVA with Tukey’s honest significance test. P value presents overall difference between groups. Significant differences in pairwise comparisons to ABI+/e4e3 (p < 0.05 = *) are presented Discussion This study investigated the ABI3 S209F genetic variant as a risk factor for AD using data from the large Finnish FinnGen cohort. In addition, we experimentally assessed its impact, alone or combined with the APOEε4, on cortical Aβ deposition, neuroinflammation, brain morphology, and cognitive performance in a separate subsample of cognitively healthy older adults. Genetic analysis confirmed ABI3 S209F as a significant AD risk variant (OR = 1.22), though with a somewhat smaller effect size compared to previous findings (Conway et al., 2018 ; Dalmasso et al., 2019 ; Olive et al., 2020 ; Satoh et al., 2017 ; Sims et al., 2017 ; Wightman et al., 2021 ). Additionally, survival analyses indicated that ABI3 S209F carriers developed AD earlier than non-carriers. In the experimental analyses, ABI3 S209F alone did not influence cortical Aβ accumulation, neuroinflammation or regional brain volumes. However, subtle regional effects emerged in voxel-wise analyses, particularly for ABI3 S209F combined with APOEε4, suggesting region-specific Aβ deposition and modest neuroinflammation not detected by broader ROI analyses. Collectively, these findings replicate and extend prior results, highlighting the complexity and context-dependent nature of ABI3 S209F ’s contribution to AD pathology. In a genetic association analysis in FinnGen, the ABI3 S209F variant significantly associated with AD and more broadly defined neurodegenerative disorder, with comparable ORs across the endpoints. Interestingly, no significant association was observed with the broadest endpoint definition, dementia. This likely reflects differences in definitions of the broader endpoints: while the neurodegenerative disorder endpoint may more specifically capture AD and related pathologies, the dementia endpoint encompasses a heterogeneous group of conditions, including vascular and unspecified dementias. The lack of association with dementia may therefore result from dilution of AD-specific genetic signals within this phenotypically diverse group. Beyond AD risk, ABI3 S209F also appears to influence the timing of disease onset. Survival analysis revealed that carriers of the variant developed AD at an earlier age compared to non-carriers, with consistent effect observed across APOE ε3/ε3 and ε3/ε4 backgrounds. These findings suggest that ABI3 S209F may act as a disease modifier, accelerating the onset of AD independently of APOE genotype. Taken together, these results reinforce the specificity of the association of ABI3 S209F variant with AD and highlight its potential role in modulating disease progression. In the imaging part of the study, we examined the effect of ABI3 S209F , in the presence or absence of the APOEε4, on brain Aβ deposition, neuroinflammation, brain structures, and cognitive functions in cognitively healthy older adults. Although ABI3 S209F has previously been associated with increased AD risk in several studies, including replication in our current study, we did not observe increased cortical Aβ deposition in ABI3 S209F /ε3 carriers when compared to NC, either in ROI-based or voxel-wise analyses. This suggests that ABI3 S209F alone, without the APOE ε4 allele, may have limited functional impact on Aβ accumulation in cognitively normal individuals. Interestingly, the highest cortical Aβ levels in ROI analysis were observed in participants carrying both ABI3 S209F and APOEε4. However, these were only significantly different compared to the ABI3 S209F /ε3 group, not to NC. Voxel-wise analysis, in contrast, revealed region-specific increases in [ 11 C]PiB signal in the ABI3 S209F /ε4 group compared to both other groups, particularly in frontal, temporal, and posterior cortices. Previous work has shown that one APOEε4 alone does not consistently induce such voxel-level differences (Snellman et al., 2023 ; Reiman et al., 2009 ), further supporting a gene–gene interaction hypothesis. The ROI findings align with previous reports showing no significant association between ABI3 S209F and CSF Aβ42 levels or Aβ-PET signal in cognitively healthy individuals (Olive et al., 2020 ). The voxel-level differences suggest that the combination of ABI3 S209F and APOEε4 may lead to regionally specific Aβ accumulation not captured by conventional ROI analyses, supporting a synergistic gene–gene interaction. Animal studies have yielded mixed results regarding ABI3’s role in Aβ pathology. While ABI3 knockout has been associated with increased Aβ deposition in some studies (Karahan et al., 2021 ), others have reported reduced deposition, particularly at early time points (Ibanez et al., 2022 ). In the latter case, the reduction was transient, and Aβ levels eventually caught up with NC as the animals aged. To our knowledge, this is the first study to examine TSPO PET imaging in cognitively normal individuals carrying the ABI3 S209F variant. Previous studies with various PET-ligands have reported increased TSPO binding between AD and NC (Cagnin et al., 2001 ; Edison et al., 2008 ; Zhen Fan, Okello, Brooks, & Edison, 2015 ), and also in Aβ-positive MCI (Okello et al., 2009 ; Parbo et al., 2017 ) and Aβ-positive NC (Z. Fan et al., 2017 ; Zou et al., 2020 ). However, when comparing a composite ROI covering the entire cortical grey matter, we found no significant differences in [¹¹C]PK11195 binding between the genotype groups, although Aβ accumulation was clearly highest in the ABI3 S209F /ε4 group in the same region. Voxel-wise analysis revealed a subtle increase in [¹¹C]PK11195 binding in the parieto-occipital cortex of the ABI3 S209F /ε4 group compared to the other groups. Although this region also showed elevated Aβ signal, the highest Aβ accumulation was observed in the frontal cortex, where no corresponding increase in [¹¹C]PK11195 binding was detected. Our findings suggest that while the combination of ABI3 S209F and APOEε4 is associated with increased cortical Aβ accumulation, this does not correspond to a parallel increase in microglial activation as measured by [¹¹C]PK11195. A similar dissociation has previously been reported among APOEε4 carriers with varying allele loads (Snellman et al., 2023 ). This mismatch may reflect the limited sensitivity of first-generation TSPO tracers such as [¹¹C]PK11195, which are known to suffer from poor signal-to-noise ratio and high nonspecific binding (Vivash & O'Brien, 2016 ; Yokokura et al., 2017 ) or a temporal lag between Aβ deposition and immune response, or the functional heterogeneity of microglial activation, which is not fully captured by TSPO PET imaging (Z. Fan et al., 2017 ). Overall, while the voxel- and ROI-level data diverge in some respects, both support a nuanced and genotype-dependent role for ABI3 S209F in AD pathology. The strength of this study is the combination of population-scale genetic data with in vivo multimodal imaging, enabling a rare genotype-specific characterization of early AD-related pathology in cognitively normal individuals. The use of the large and well-characterized FinnGen cohort allowed us to validate the ABI3 S209F AD association in a genetically distinct Northern European population, adding valuable replication evidence to earlier findings. A major practical strength of this study was the ability to efficiently recruit participants with specific genotypes through biobank collaboration. Without access to genotype-based preselection, recruiting a sufficient number of ABI3 S209F carriers would have required genotyping and screening several hundred individuals. The biobank-based recruitment thus greatly increased feasibility and resource efficiency. Additionally, the integration of PET imaging for both Aβ and microglial activity, alongside MRI-derived structural metrics and neuropsychological testing, enabled a multidimensional assessment of functional consequences at an early disease stage. However, some limitations should be noted. The use of the first-generation TSPO tracer [¹¹C]PK11195 may have constrained sensitivity to microglial activation due to its limited signal-to-noise ratio and high nonspecific binding. Also, the sample size in our imaging part was modest, although it was based on power calculations on previous studies there is a chance of type II error. Furthermore, although voxel-wise analysis increases spatial precision, it also introduces multiple comparison challenges and increases the risk of type I error, despite statistical correction. Finally, while the study design allowed for the evaluation of ABI3 S209F effects in isolation and in combination with APOEε4, the cross-sectional nature of the imaging component precludes firm conclusions about temporal dynamics or causality. Despite these limitations, the present findings highlight the importance of considering gene–gene interactions and regional brain vulnerability in understanding AD risk. Further studies using larger imaging cohorts, more sensitive second-generation tracers, and longitudinal follow-up will be essential to clarify the mechanisms by which ABI3 and APOEε4 interact to influence early neuropathological changes. Conclusions This study confirms the association of the ABI3 S209F variant with increased risk for AD. The variant was also associated with an earlier age of AD onset. ABI3 S209F alone does not significantly affect cortical Aβ deposition or neuroinflammation. However, the combination of ABI3 S209F and APOEε4 may contribute to region-specific Aβ accumulation and increased microglial activation. Our findings emphasize the complex and context-dependent role of ABI3 S209F in AD pathophysiology, possibly acting as a modifier rather than a primary driver of pathology. The lack of consistent differences in cognitive performance and brain volumes supports the notion that ABI3 S209F -related changes may precede symptomatic disease or require additional genetic or environmental interactions to manifest. Further longitudinal and mechanistic studies are needed to elucidate the functional consequences of this variant and its potential as a therapeutic target or biomarker in preclinical stages of AD. Abbreviations AD Alzheimer’s disease ApoE Apolipoprotein E APOEε4 APOE ε4 allelle Aβ Beta-amyloid ABI3 Abelson interactor family member 3 ABI3 S209F ABI3 p.Ser209Phe variant APP Amyloid precursor protein ATC Anatomical Therapeutic Chemical BNT Boston Naming Test CSF Cerebrospinal fluid DVR Distribution Volume Ratio FDR False discovery rate FINGER Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability FinnGen Finnish genetic research project combining genomic and health registry data FWE Family-wise error GWAS Genome-wide association study HRRT High-resolution research tomograph ICD International Classification of Diseases IQR Interquartile range MBq Megabecquerel MCI Mild cognitive impairment MRI Magnetic resonance imaging PET Positron emission tomography PVE Partial volume effect ROI Region of interest SD Standard deviation SPM Statistical parametric mapping SUVR Standardized uptake value ratio TSPO Translocator protein Declarations Ethics approval and consent to participate The brain imaging study was approved by the Ethical Committee of the Hospital District of Southwest Finland. Biobank participant recontacting was approved by the Scientific Steering Committee of Auria Biobank. All participants signed a written informed consent. The study subjects in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, separate research cohorts, collected prior the Finnish Biobank Act came into effect (in September 2013) and start of FinnGen (August 2017), were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Fimea (Finnish Medicines Agency), the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Fimea. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS) statement number for the FinnGen study is Nr HUS/990/2017. The FinnGen study is approved by Finnish Institute for Health and Welfare (permit numbers: THL/2031/6.02.00/2017, THL/1101/5.05.00/2017, THL/341/6.02.00/2018, THL/2222/6.02.00/2018, THL/283/6.02.00/2019, THL/1721/5.05.00/2019 and THL/1524/5.05.00/2020), Digital and population data service agency (permit numbers: VRK43431/2017-3, VRK/6909/2018-3, VRK/4415/2019-3), the Social Insurance Institution (permit numbers: KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019, KELA 98/522/2019, KELA 134/522/2019, KELA 138/522/2019, KELA 2/522/2020, KELA 16/522/2020), Findata permit numbers THL/2364/14.02/2020, THL/4055/14.06.00/2020, THL/3433/14.06.00/2020, THL/4432/14.06/2020, THL/5189/14.06/2020, THL/5894/14.06.00/2020, THL/6619/14.06.00/2020, THL/209/14.06.00/2021, THL/688/14.06.00/2021, THL/1284/14.06.00/2021, THL/1965/14.06.00/2021, THL/5546/14.02.00/2020, THL/2658/14.06.00/2021, THL/4235/14.06.00/2021, Statistics Finland (permit numbers: TK-53-1041-17 and TK/143/07.03.00/2020 (earlier TK-53-90-20) TK/1735/07.03.00/2021, TK/3112/07.03.00/2021) and Finnish Registry for Kidney Diseases permission/extract from the meeting minutes on 4th July 2019. The Biobank Access Decisions for FinnGen samples and data utilized in FinnGen Data Freeze 12 include: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7, BB2019_8, BB2019_26, BB2020_1, BB2021_65, Finnish Red Cross Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, HUS/248/2020, HUS/430/2021 §28, §29, HUS/150/2022 §12, §13, §14, §15, §16, §17, §18, §23, §58, §59, HUS/128/2023 §18, Auria Biobank AB17-5154 and amendment #1 (August 17 2020) and amendments BB_2021-0140, BB_2021-0156 (August 26 2021, Feb 2 2022), BB_2021-0169, BB_2021-0179, BB_2021-0161, AB20-5926 and amendment #1 (April 23 2020) and it´s modifications (Sep 22 2021), BB_2022-0262, BB_2022-0256, Biobank Borealis of Northern Finland_2017_1013, 2021_5010, 2021_5010 Amendment, 2021_5018, 2021_5018 Amendment, 2021_5015, 2021_5015 Amendment, 2021_5015 Amendment_2, 2021_5023, 2021_5023 Amendment, 2021_5023 Amendment_2, 2021_5017, 2021_5017 Amendment, 2022_6001, 2022_6001 Amendment, 2022_6006 Amendment, 2022_6006 Amendment, 2022_6006 Amendment_2, BB22-0067, 2022_0262, 2022_0262 Amendment, Biobank of Eastern Finland 1186/2018 and amendment 22§/2020, 53§/2021, 13§/2022, 14§/2022, 15§/2022, 27§/2022, 28§/2022, 29§/2022, 33§/2022, 35§/2022, 36§/2022, 37§/2022, 39§/2022, 7§/2023, 32§/2023, 33§/2023, 34§/2023, 35§/2023, 36§/2023, 37§/2023, 38§/2023, 39§/2023, 40§/2023, 41§/2023, Finnish Clinical Biobank Tampere MH0004 and amendments (21.02.2020 & 06.10.2020), BB2021-0140 8§/2021, 9§/2021, §9/2022, §10/2022, §12/2022, 13§/2022, §20/2022, §21/2022, §22/2022, §23/2022, 28§/2022, 29§/2022, 30§/2022, 31§/2022, 32§/2022, 38§/2022, 40§/2022, 42§/2022, 1§/2023, Central Finland Biobank 1-2017, BB_2021-0161, BB_2021-0169, BB_2021-0179, BB_2021-0170, BB_2022-0256, BB_2022-0262, BB22-0067, Decision allowing to continue data processing until 31st Aug 2024 for projects: BB_2021-0179, BB22-0067,BB_2022-0262, BB_2021-0170, BB_2021-0164, BB_2021-0161, and BB_2021-0169, and Terveystalo Biobank STB 2018001 and amendment 25th Aug 2020, Finnish Hematological Registry and Clinical Biobank decision 18th June 2021, Arctic biobank P0844: ARC_2021_1001. Data availability The brain imaging datasets used and analysed during the current study are available from the corresponding author on reasonable request. Summary statistics from FinnGen data release 12 are publicly available and can be accessed at https://www.finngen.fi/en/access_results and https://r12.finngen.fi. Access for individual level genotype data can be applied for via the Fingenious portal (https://site.fingenious.fi/en/) hosted by the Finnish Biobank Cooperative FinBB (https://finbb.fi/en/). Access to Finnish Health register data can be applied from Findata (https://findata.fi/en/data/). Competing interests Funding MK was supported by Yrjö Jahnsson Foundation. HM and MH were supported by the Research Council of Finland (grants #355604, #338182). MH was supported by the Sigrid Jusélius Foundation, Jane and Aatos Erkko Foundation, the Strategic Neuroscience Funding of the University of Eastern Finland, Faculty of Health Sciences of University of Eastern Finland, Alzheimer's Association (ADSF-24-1284326-C and AARG-22-926152). FinnGen is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and the following industry partners: AbbVie Inc., AstraZeneca UK Ltd, Biogen MA Inc., Bristol Myers Squibb (and Celgene Corporation & Celgene International II Sàrl), Genentech Inc., Merck Sharp & Dohme LCC, Pfizer Inc., GlaxoSmithKline Intellectual Property Development Ltd., Sanofi US Services Inc., Maze Therapeutics Inc., Janssen Biotech Inc, Novartis Pharma AG, and Boehringer Ingelheim International GmbH. Authors' contributions MK, JOR and HM designed the study concept. MK drafted the manuscript. MK contributed to data collection. MK and HM analysed the data. 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Neurobiology of aging, 85 , 11-21. doi:10.1016/j.neurobiolaging.2019.09.019 Additional Declarations No competing interests reported. Supplementary Files AlztransABISupplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 19 Feb, 2026 Read the published version in Alzheimer's Research & Therapy → Version 1 posted Editorial decision: Revision requested 12 Jan, 2026 Reviews received at journal 05 Jan, 2026 Reviewers agreed at journal 18 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers invited by journal 15 Dec, 2025 Editor assigned by journal 17 Nov, 2025 Submission checks completed at journal 17 Nov, 2025 First submitted to journal 14 Nov, 2025 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. 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1","display":"","copyAsset":false,"role":"figure","size":79439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eABI3\u003c/em\u003e\u003csup\u003e\u003cem\u003eS209F\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e associates with increased risk and earlier onset age of AD in FinnGen. \u0026nbsp;\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eA)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Forest plot showing ABI3\u003c/em\u003e\u003csup\u003e\u003cem\u003eS209F\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e association with selected disease endpoints in FinnGen cohort (ABI3\u003c/em\u003e\u003csup\u003e\u003cem\u003eS209F\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e n=8,490; ABI3 n=511,670). Odds ratio (OR) with 95 % confidence intervals. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Kaplan-Meier survival curves of time to AD diagnosis in different ABI3 and APOE genotype groups in FinnGen cohort. Shading indicates 95 % confidence intervals. X-axis indicates age at the first diagnosis for cases and age at the end of follow-up for controls. N(\u003c/em\u003eABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3)=5141, N(ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4)=2326, N(ABI3/ε3)=47234, N(ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4)=21484.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8113148/v1/29d0dcf1ece6e944994d0592.png"},{"id":98780580,"identity":"a44af4b8-b937-47e5-bb40-a0d92c713aaf","added_by":"auto","created_at":"2025-12-22 12:31:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36490,"visible":true,"origin":"","legend":"\u003cp\u003ea) [\u003csup\u003e11\u003c/sup\u003eC]PiB score and b) [\u003csup\u003e11\u003c/sup\u003eC]PK11195 DVR stratified by the three study groups. Median, first and third quartile and range are presented by the box plot. *= p= 0.017 (Tukey´s honest significance test). P-value below the figures presents overall difference between the three groups (ANCOVA).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8113148/v1/09c570be6a00c50f1965ca80.png"},{"id":98778236,"identity":"4c3ac74f-10dc-4082-acc8-b2063f00537c","added_by":"auto","created_at":"2025-12-22 12:29:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":312864,"visible":true,"origin":"","legend":"\u003cp\u003eVoxel-wise analysis showing areas with statistically significant increase in [\u003csup\u003e11\u003c/sup\u003eC]PiB score between the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4, ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3 and NC groups. FDR corrected p \u0026lt; 0.001 in green colour. FWE corrected p\u0026lt;0.05 in red-yellow.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8113148/v1/fe29138fc7a8c35ebea5a44b.png"},{"id":98756905,"identity":"4b072b51-cbee-4e57-8d6f-d7c8287097d5","added_by":"auto","created_at":"2025-12-22 09:36:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209986,"visible":true,"origin":"","legend":"\u003cp\u003eABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 group had higher [\u003csup\u003e11\u003c/sup\u003eC]PK11195 binding than the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3 or NC groups. FDR corrected p\u0026lt;0.001 in green colour.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8113148/v1/0f3644dfa82e149c0e80ff4d.png"},{"id":98756908,"identity":"6a5c45d7-b014-4946-b2c5-59114d8b8b64","added_by":"auto","created_at":"2025-12-22 09:36:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":74377,"visible":true,"origin":"","legend":"\u003cp\u003eCognitive test results\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8113148/v1/0587618d760ed0e1b4015474.png"},{"id":103252599,"identity":"99cb492f-91dd-4b01-ab1b-959bd6a57a00","added_by":"auto","created_at":"2026-02-23 16:15:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1467018,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8113148/v1/74754679-a54c-4d81-8894-545bab7094ca.pdf"},{"id":98756906,"identity":"fa6bd43c-08fe-4dc9-8417-c5f9be99f89b","added_by":"auto","created_at":"2025-12-22 09:36:00","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":884182,"visible":true,"origin":"","legend":"","description":"","filename":"AlztransABISupplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8113148/v1/9e97573e1d7afaeff39ca5ff.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic Validation of ABI3 p.Ser209Phe Variant and Its effects On Early Brain Pathology in Asymptomatic Elderly Individuals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer's disease (AD) has a strong genetic component, with the Apolipoprotein E ε4 (APOEε4) being the most common and well-established genetic risk factor (Corder et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Guerreiro, Gustafson, \u0026amp; Hardy, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). APOEε4 is associated with a higher burden of beta-amyloid (Aβ) plaques in the brain in a dose-dependent manner (Castellano et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Reiman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and it is thought to impair Aβ clearance (Verghese et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, genome-wide association studies (GWAS) have identified several additional risk genes for AD (C. Bellenguez et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jansen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; D. P. Wightman et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), including ones with strong expression in microglia, such as \u003cem\u003eABI3\u003c/em\u003e. The ABI3 p.Ser209Phe variant (ABI3\u003csup\u003eS209F\u003c/sup\u003e) was first linked to increased AD risk in 2017 with an odds ratio (OR) of 1.43 (Sims et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and this association has been replicated in multiple cohorts (Conway et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dalmasso et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Olive et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Douglas P. Wightman et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eABI3 is highly expressed in microglial cells, with limited expression in neurons and other glial cell types (Conway et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Satoh et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Microglia have dual roles in AD pathology, initially protective through Aβ clearance, but later potentially harmful due to sustained inflammation (Z. Fan, Brooks, Okello, \u0026amp; Edison, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Leng \u0026amp; Edison, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). ABI3 is upregulated in the cortex of AD patients and in Aβ mouse models (Castillo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and is thought to modulate microglial responses through interferon signalling (Sims et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and actin cytoskeletal reorganization via the WAVE2 complex (Davidson, Ura, Thomason, Kalna, \u0026amp; Insall, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sekino et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, findings from murine Abi3 knockout models have been inconsistent as some show increased Aβ deposition (Karahan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Karahan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while others note a transient reduction (Ibanez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite the replicated association between ABI3\u003csup\u003eS209F\u003c/sup\u003e and AD, evidence for its impact on in vivo AD biomarkers is limited. Notably, Olive et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found no significant association between the variant and CSF Aβ42 or Aβ PET signal in cognitively normal individuals.\u003c/p\u003e \u003cp\u003eAPOEε4 does not only influence Aβ accumulation but also directly modulates microglial function (Krasemann et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rodriguez, Tai, Ladu, \u0026amp; Rebeck, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ulrich et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The APOEε4 isoform has been shown to alter microglial responses by limiting their ability to adopt a neuroprotective phenotype, impairing Aβ clearance and promoting their pro-inflammatory state (Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The dual role of APOEε4 in Aβ pathology and immune regulation makes it a particularly relevant modifier when exploring microglial activity and gene\u0026ndash;gene interactions in AD.\u003c/p\u003e \u003cp\u003eGiven that both APOEε4 and ABI3 influence microglial function, albeit through distinct molecular pathways, their combined analysis may provide a more comprehensive understanding of microglial contributions to AD pathophysiology. Investigating these genes in parallel allows for the exploration of potential gene\u0026ndash;gene interactions, additive effects, or pathway convergence that may influence cortical Aβ deposition and neuroinflammation. Also, due to the genetic and pathological heterogeneity of AD, replication of risk variants in distinct populations is essential to validate their generalizability and potential clinical relevance (Bellenguez et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studying the ABI3\u003csup\u003eS209F\u003c/sup\u003e in a Finnish cohort provides an opportunity to confirm its association with AD risk in a genetically and environmentally distinct population, thereby strengthening the evidence for its role in disease pathogenesis (Sims et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wightman et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we aim to confirm the association of ABI3\u003csup\u003eS209F\u003c/sup\u003e with AD in the Finnish FinnGen cohort, and to investigate how this variant, alone or in combination with APOEε4, modulates cortical Aβ burden, neuroinflammation, and brain morphology in cognitively healthy older adults using positron emission tomography (PET) and magnetic resonance imaging (MRI).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eThe FinnGen Study is a large biobank-scale project that combines genome data and longitudinal register-based healthcare data of \u0026gt;\u0026thinsp;500,000 Finns (Kurki et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study we used data from FinnGen release R12, which included 8,490 heterozygous carriers of ABI3\u003csup\u003eS209F\u003c/sup\u003e and 511,670 non-carriers. For survival analysis, each ABI3\u003csup\u003eS209F\u003c/sup\u003e carrier was assigned with up to five non-carrier controls matched for sex and the year of birth.\u003c/p\u003e \u003cp\u003eFor the imaging part of this study, we recruited 58 subjects of \u0026ge;\u0026thinsp;50 years of age, based on power calculations on previous studies. The participants of this cross-sectional study were recruited in collaboration with the local Auria biobank and from the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) (Kivipelto et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) cohort. Genotype data was available for a subset of the biobank cohort through FinnGen study, allowing the biobank to directly contact persons with the ABI3\u003csup\u003eS209F\u003c/sup\u003e and \u003cem\u003eAPOE\u003c/em\u003e ε4/ε3 or \u003cem\u003eAPOE\u003c/em\u003e ε3/ε3 genotype who had previously signed a biobank consent and an additional informed consent allowing the biobank to contact them if they are suitable for participating in a research study. Main exclusion criteria were dementia or cognitive impairment, any degenerative neurological disease, chronic inflammatory condition, and contraindication for MRI or PET imaging. Our study involved three distinct groups for comparative analysis (n\u0026thinsp;=\u0026thinsp;19\u0026thinsp;+\u0026thinsp;19\u0026thinsp;+\u0026thinsp;20): 1) individuals possessing both the ABI3\u003csup\u003eS209F\u003c/sup\u003e variant and the \u003cem\u003eAPOE\u003c/em\u003e ε4/ε3 genotype (ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4), 2) those with the ABI3\u003csup\u003eS209F\u003c/sup\u003e variant and the \u003cem\u003eAPOE\u003c/em\u003e ε3/ε3 genotype (ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3), and 3) a non-carrier group with the major ABI3\u003csup\u003eS209F\u003c/sup\u003e allele and \u003cem\u003eAPOE\u003c/em\u003e ε3/ε3 genotype (NC). The study was approved by the Ethical Committee of the Hospital District of Southwest Finland. All participants signed written informed consent.\u003c/p\u003e \u003cp\u003eGenotyping\u003c/p\u003e \u003cp\u003eABI3\u003csup\u003eS209F\u003c/sup\u003e is encoded by a single nucleotide substitution NM_016428.3:c.626T\u0026thinsp;\u0026gt;\u0026thinsp;C (rs616338). Although the T allele (encoding phenylalanine) is the reference allele, it is the rare allele in this locus. Thus, we refer to the common C allele (encoding serine) as the major allele and the rare T allele as the minor (effect) allele, consistent with previous reports as well as allele frequency and evolutionary conservation of the serine residue across multiple species (Sims et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe FinnGen cohort has been genotyped with Illumina (Illumina Inc., San Diego, USA) and Affymetrix (Thermo Fisher Scientific, Santa Clara, CA, USA) chip arrays as part of the FinnGen Study. Chip genotype data were imputed using the Finnish population-specific imputation reference panel Sequencing Initiative Suomi project (SISu v4.2, Institute for Molecular Medicine Finland, University of Helsinki, Finland, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sisuproject.fi\u003c/span\u003e\u003cspan address=\"http://sisuproject.fi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Majority of the FinnGen cohort (92%) had been directly genotyped for rs616338, but for a broader coverage, imputed genotypes were used in all analyses. Imputation INFO score for rs616338 was 0.995. FINGER cohort (Kivipelto et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) was genotyped with Illumina Infinium Global Screening Array and imputed with TOPMed reference panel as described before (C\u0026eacute;line Bellenguez et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To confirm the \u003cem\u003eABI3\u003c/em\u003e genotypes in a subset of 42 individuals enrolled to the imaging study, venous blood was collected and genomic DNA was extracted from whole blood using QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany). \u003cem\u003eABI3\u003c/em\u003e rs616338 was genotyped with TaqMan assay (C_2270073_20, Applied Biosystems). All genotypes were consistent with those observed with chip genotyping or imputation.\u003c/p\u003e \u003cp\u003eDefinition of disease endpoints\u003c/p\u003e \u003cp\u003eTo define the disease endpoints, we utilized FinnGen core endpoints, which are based on digital health record data from Finnish health registries. Diagnoses are based on International Classification of Diseases (ICD) codes and have been harmonized over ICD-8, ICD-9 and ICD-10 codes.\u003c/p\u003e \u003cp\u003eAD was defined as a diagnosis in the hospital discharge or cause of death registries with the ICD codes G30 (ICD-10) or 3310 (ICD-9). AD onset age for the survival analysis was defined as the age of the first diagnosis. The remaining individuals were considered as controls. For the survival analysis, end of follow-up for controls was defined as death, moving abroad, or the latest update of the registry data, whichever came first.\u003c/p\u003e \u003cp\u003eNeurodegenerative disorder was defined as at least three entries with the following criteria: diagnosis in the hospital discharge or cause of death registries with the ICD codes F00*, G30 (ICD-10), 3310 (ICD-9), or 29010 (ICD-8); Finnish Social Insurance Institution (Kela) reimbursement for F00* or G30 (ICD-10) or reimbursement code 307; or prescription medicine purchases with ATC class N06D. Remaining individuals, excluding cases with AD, were considered as controls.\u003c/p\u003e \u003cp\u003eDementia was defined as at least three entries with the following criteria: a diagnosis in the hospital discharge or cause of death registries with the ICD codes F00-F09 (ICD-10), 290, 3310, or 4378A (ICD-9) or 290 (ICD-8), Finnish Social Insurance Institution (Kela) reimbursement code 307, or prescription medicine purchases with ATC class N06D. Dementia cases included individuals with vascular dementia, dementia in other diseases classified elsewhere, or unspecified dementia. Remaining individuals, excluding cases with organic mental disorders, were considered as controls.\u003c/p\u003e \u003cp\u003eBrain imaging\u003c/p\u003e \u003cp\u003eAll subjects underwent a structural brain MRI including T1-weighted sequences. Structural brain images were acquired with the Philips Ingenuity 3.0 T TF PET/MRI (Philips Healthcare, Amsterdam, the Netherlands). MRI was used to acquire volumetric variables for hippocampus, parahippocampus, entorhinal cortex and amygdala.\u003c/p\u003e \u003cp\u003ePET scans were acquired using an ECAT high-resolution research tomograph (HRRT, Siemens Medical solutions, Knoxville, TN). To estimate brain Aβ accumulation, [\u003csup\u003e11\u003c/sup\u003eC]PiB scans (n\u0026thinsp;=\u0026thinsp;56) were acquired 40 to 90 min post injection (mean injected dose 493 (standard deviation (SD) 42) MBq). To estimate microglial activity, we used TSPO imaging with [\u003csup\u003e11\u003c/sup\u003eC]PK11195. Dynamic [\u003csup\u003e11\u003c/sup\u003eC]PK11195 scans (n\u0026thinsp;=\u0026thinsp;56) were acquired for 60 min post injection (mean injected dose 476 (SD 41) MBq). All images were reconstructed with 3D ordinary Poisson ordered subset expectation maximization algorithm (OP-OSEM3D), and list mode data was histogrammed into 8 (6 \u0026times; 5\u0026thinsp;+\u0026thinsp;2 \u0026times; 10 min, [\u003csup\u003e11\u003c/sup\u003eC]PiB) and 17 (2 \u0026times; 15; 3 \u0026times; 30; 3 \u0026times; 60; 7 \u0026times; 300; 2 \u0026times; 600 s, [\u003csup\u003e11\u003c/sup\u003eC]PK11195) time frames.\u003c/p\u003e \u003cp\u003eCognitive testing\u003c/p\u003e \u003cp\u003eA thorough neurocognitive test battery was administered to the participants by trained psychology students. The test battery included parts from the Finnish version of the Wechsler Memory Scale (WMS-R) and the Wechsler Adult Intelligence Scale (WAIS-R), Boston Naming Test (BNT), Trail Making Tests A and B (TMT-A and TMT-B), S-fluency, categorical fluency, and Stroop (Battery, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1944\u003c/span\u003e; Kaplan, Goodglass, \u0026amp; Weintraub, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Stroop, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1935\u003c/span\u003e; Wechsler, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1981\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Domain-specific neurocognitive test z-scores, based on an a priori hypothesis (Lezak, Howieson, Bigler, \u0026amp; Tranel, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), were calculated for executive functions, processing speed, language, and episodic memory, with higher scores indicating better performance. The executive function domain included the Trail Making Test A and B (TMT-B minus TMT-A), Stroop test (inhibition minus naming), digit span backward, and S-fluency. The processing speed domain consisted of TMT-A and digit symbol tests. The episodic memory domain included the WMS-R delayed logic memory and delayed verbal recall. The language domain included categorical fluency, the Boston naming test, and WAIS-R similarities.\u003c/p\u003e \u003cp\u003eBrain image analysis\u003c/p\u003e \u003cp\u003eBoth PET and MRI images were analysed for region of interest (ROI) and voxel-wise differences between the study groups. PET and MRI image preprocessing and analysis were performed using an automated pipeline at Turku PET Centre (Karjalainen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which executed the PET data frame by frame realignment, PET-MRI co-registration, FreeSurfer (Freesurfer v6, \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) ROI parcellation and PET data kinetic modelling. Regional and voxel level [\u003csup\u003e11\u003c/sup\u003eC]PiB binding was quantified as standardized uptake value ratios (SUVR) calculated for 60 to 90 min post injection using the cerebellar cortex as the reference region. A composite neocortical [\u003csup\u003e11\u003c/sup\u003eC]PiB score was calculated as the volume weighted average of the [\u003csup\u003e11\u003c/sup\u003eC]PiB region-to-cerebellar cortex SUVRs for the lateral frontal, lateral temporal, and parietal cortices as well as the posterior cingulate, anterior cingulate, and precuneus. This composite [\u003csup\u003e11\u003c/sup\u003eC]PiB score was used to estimate brain Aβ load. [\u003csup\u003e11\u003c/sup\u003eC]PK11195-binding was quantified from the same composite region, as distribution volume ratios (DVR) within 20\u0026ndash;60 min post injection using a reference tissue input Logan\u0026rsquo;s method with pseudo-reference region extracted using supervised clustering algorithm (Turkheimer et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Yaqub et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Voxel-level kinetic modeling for [\u003csup\u003e11\u003c/sup\u003eC]PK11195 was carried out using basis function implementation of simplified reference tissue model with respect to the aforementioned clustered pseudo-reference region and with 300 basis functions calculated within the Θ3 parameter limits 0.06\u0026thinsp;\u0026le;\u0026thinsp;Θ3\u0026thinsp;\u0026le;\u0026thinsp;0.6 (Gunn, Lammertsma, Hume, \u0026amp; Cunningham, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Partial volume effect (PVE)-corrected data was used for all [\u003csup\u003e11\u003c/sup\u003eC]PK11195 analysis to minimize the effect of PK binding in sinuses to cortical regions. PVE correction was carried out using PETPVE12 toolbox (Gonzalez-Escamilla, Lange, Teipel, Buchert, \u0026amp; Grothe, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in both ROI (geometric transfer matrix method) and voxel-level (Muller-Gartner method) data.\u003c/p\u003e \u003cp\u003eThe brain MRI images were also analysed for volumetric differences extracted from the FreeSurfer results. Here we focused on the hippocampus, parahippocampus, and the entorhinal cortex due to their known association with neurodegeneration related to AD (Chandra, Dervenoulas, \u0026amp; Politis, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). All voxel-wise analyses were conducted using statistical parametric mapping (SPM12 v12; Wellcome Trust Centre for Neuroimaging, London, UK) running on MATLAB R2021b (Math-Works, Natick, MA, USA).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAssociation of rs616338-T allele with AD, neurodegenerative disorder, and dementia were assessed in FinnGen data with chi-square test using R 4.3.2. The data are presented as OR with 95% confidence intervals (CI). Survival analysis comparing AD-free survival of ABI3\u003csup\u003eS209F\u003c/sup\u003e carriers and age- and sex-matched controls was performed in R 4.3.2 where Kaplan-Meier curves were generated using package survminer v0.4.9 and cox proportional hazards models were fitted with survival package v3.2-7 (T Therneau, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; TM Therneau \u0026amp; Grambsch, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The proportional hazards assumption was assessed using the Schoenfeld residuals test, which indicated a violation for the \u003cem\u003eAPOE\u003c/em\u003e genotype. To account for this, the Cox model was stratified by \u003cem\u003eAPOE\u003c/em\u003e genotype. The data is presented as hazard ratio (HR) with 95% CI. Results with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003cp\u003eThe sample size for the imaging part of this study was based on a power analysis calculated from previous research results obtained with the same radiotracers (Aalto et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Z. Fan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The assumption was that the study would have 90% power (1\u0026ndash;β\u0026thinsp;=\u0026thinsp;0.9, α\u0026thinsp;=\u0026thinsp;0.05) to detect, depending on the tracer, a 10\u0026ndash;20% difference in regional binding ratios between the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 and NC groups. The final sample size also aimed to account for potential dropouts. The ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3 group was assumed to show an effect, with results expected to fall between those of the primary comparison groups.\u003c/p\u003e \u003cp\u003eStatistical analyses of the imaging data were performed using JMP Pro 16.0.0 (SAS Institute Inc., Cary, North Carolina, USA). All data following a normal distribution are presented as mean (SD), otherwise as median (interquartile range, IQR). The normality of the data was evaluated visually from the distribution and with the Shapiro-Wilk test. Differences in continuous variables between the three groups were tested using linear regression models, adjusting for age and sex. The MRI variables were also adjusted for total intracranial volume. If a significant effect was found, all pairs were compared using the \u003cem\u003epost hoc\u003c/em\u003e Tukey\u0026rsquo;s honest significance test for multiple comparisons.\u003c/p\u003e \u003cp\u003eVolumetric differences in T1 MRI, [\u003csup\u003e11\u003c/sup\u003eC]PIB, and [\u003csup\u003e11\u003c/sup\u003eC]PK11195-binding at the voxel level were assessed using ANCOVA, using age and sex as covariates for [\u003csup\u003e11\u003c/sup\u003eC]PIB and [\u003csup\u003e11\u003c/sup\u003eC]PK11195, but also total intracranial volume for MRI. This was followed by post-hoc pairwise comparisons in SPM12. Voxel-wise differences between the groups were tested using linear regression in SPM12 to evaluate if differences were present also outside the \u003cem\u003ea priori\u003c/em\u003e chosen brain regions. Uncorrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 combined with a cluster-level false discovery rate (FDR) correction for multiple comparisons was considered statistically significant in the voxel-based analyses. When significant FDR corrected clusters were found we applied family wise error (FWE) correction with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to see if the results survived the tighter threshold.\u003c/p\u003e \u003cp\u003eTo combine the scores of different neurocognitive tests into domain-specific scores, z-scores for the tests were calculated by standardizing the raw test scores to the study population\u0026rsquo;s mean and standard deviation. To achieve a normal distribution, one outlier per raw test were excluded before the z-transformation (1 outlier in Boston naming test; and 1 outlier in Stroop test). All outliers performed worse on the cognitive tests than \u0026minus;\u0026thinsp;2 SD of the present study population. A skewness of \u0026minus;\u0026thinsp;1 to 1 was accepted for the raw test score distribution. Domain-specific z-scores were determined by averaging all the z-scores within each domain. For tests with a reverse scale (Stroop, TMT-A, and TMT-B), reciprocal numbers were used to ensure higher scores indicated better performance. Participants with missing test results were excluded.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eABI3\u003csup\u003eS209F\u003c/sup\u003e associates with increased risk and earlier onset age of AD in FinnGen cohort\u003c/p\u003e \u003cp\u003eABI3\u003csup\u003eS209F\u003c/sup\u003e (rs616338-T) was significantly associated with increased risk of AD (OR\u0026thinsp;=\u0026thinsp;1.22, 95% CI: 1.09\u0026ndash;1.38, p\u0026thinsp;=\u0026thinsp;0.0012) and neurodegenerative disorder (OR\u0026thinsp;=\u0026thinsp;1.21, 95% CI: 1.10\u0026ndash;1.34, p\u0026thinsp;=\u0026thinsp;0.00023) in FinnGen (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Association with dementia was not statistically significant (OR\u0026thinsp;=\u0026thinsp;1.10, 95% CI: 1.00-1.20, p\u0026thinsp;=\u0026thinsp;0.06).\u003c/p\u003e \u003cp\u003eTo investigate the impact of ABI3\u003csup\u003eS209F\u003c/sup\u003e and \u003cem\u003eAPOE\u003c/em\u003e ε4 on the age of onset of AD, we performed survival analysis for age at AD diagnosis. Kaplan-Meier survival curves indicated an earlier onset of AD among the ABI3\u003csup\u003eS209F\u003c/sup\u003e carriers when compared to the non-carriers with the same \u003cem\u003eAPOE\u003c/em\u003e genotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Stratified Cox proportional hazards models were used to account for the violation of the proportional hazard assumption by \u003cem\u003eAPOE\u003c/em\u003e genotype. Within the \u003cem\u003eAPOE\u003c/em\u003e ε3 stratum, ABI3\u003csup\u003eS209F\u003c/sup\u003e carriers exhibited a significantly increased hazard of AD onset (HR\u0026thinsp;=\u0026thinsp;1.31, 95% CI: 1.09\u0026ndash;1.59, p\u0026thinsp;=\u0026thinsp;0.0046). Similarly, in the \u003cem\u003eAPOE\u003c/em\u003e ε4 stratum, ABI3\u003csup\u003eS209F\u003c/sup\u003e carriers showed an elevated risk (HR\u0026thinsp;=\u0026thinsp;1.32, 95% CI: 1.09\u0026ndash;1.60, p\u0026thinsp;=\u0026thinsp;0.0045). These findings suggest that the ABI3\u003csup\u003eS209F\u003c/sup\u003e variant is associated with earlier AD onset, independent of \u003cem\u003eAPOE\u003c/em\u003e genotype.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDemographics of the PET\u0026ndash;MRI subjects\u003c/p\u003e \u003cp\u003eBaseline demographic, clinical, and imaging characteristics of the PET\u0026ndash;MRI subsample are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Across the three genotype-defined study groups (ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4, ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3, and NC), no statistically significant differences were observed in sex distribution, age, height, or hippocampal volume (P\u0026thinsp;\u0026gt;\u0026thinsp;0.07 for all). Cognitive performance, as assessed by MMSE, was similar across groups. Importantly, a significant group difference was observed in cortical Aβ burden, estimated as composite [\u0026sup1;\u0026sup1;C]PiB SUVR, with the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3 group showing a lower median binding score than the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 group (p\u0026thinsp;=\u0026thinsp;0.01). No differences were observed in microglial activation ([\u0026sup1;\u0026sup1;C]PK11195 DVR).\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\u003eSubject demographics and descriptive data\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eABI3\u003c/em\u003e\u003csup\u003e\u003cem\u003eS209F\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/ε4\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eABI3\u003c/em\u003e\u003csup\u003e\u003cem\u003eS209F\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/ε3\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGroup difference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \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\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \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\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.5 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.4 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e71.4 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168.9 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172.2 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e168.3 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.8 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.4 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e73.1 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.8 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.9 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e25.7 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003csup\u003e11\u003c/sup\u003eC]PiB score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (sd))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67 (0.5)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.75 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003csup\u003e11\u003c/sup\u003eC]PK DVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.3 (0.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampal volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.7 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e7.2 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.5 (28\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (28\u0026ndash;29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e29 (28\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eData are presented as mean (standard deviation) or median (interquartile range) depending on the distribution. Differences between groups were tested with ANCOVA with Tukey\u0026rsquo;s honest significance test, Kruskal\u0026ndash;Wallis test with Dunn\u0026rsquo;s method for multiple or linear models. χ2 test was used for testing categorical variables. P value presents overall difference between groups. Significant differences in pairwise comparisons to ABI+/e4e3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 = *) are presented.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAPOEε4, but not ABI3\u003csup\u003eS209F\u003c/sup\u003e, increases brain Aβ load\u003c/p\u003e \u003cp\u003eIn the ROI analysis, the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 had higher composite cortical [\u003csup\u003e11\u003c/sup\u003eC]PiB score when compared to the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3 (2.0 (0.7) vs. 1.67 (0.5); mean (sd), p\u0026thinsp;=\u0026thinsp;0.017 (HST)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The voxel-level analysis showed higher [\u003csup\u003e11\u003c/sup\u003eC]PiB binding across the cortex in the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 group than in the two other groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNo global, but subtle regional neuroinflammatory differences in ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 carriers.\u003c/p\u003e \u003cp\u003eThe [\u003csup\u003e11\u003c/sup\u003eC]PK11195 analysis revealed no significant differences between the groups in the composite cortical ROI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). However, voxel-level analysis detected minor differences between the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 and both the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3 and NC groups, but not between the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3 and NC groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 showed higher [\u003csup\u003e11\u003c/sup\u003eC]PK11195 binding in the precuneus and parieto-occipital regions compared to the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3 group. Additionally, higher [\u003csup\u003e11\u003c/sup\u003eC]PK11195 binding was observed in the parieto-occipital and calcarine regions of the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 compared to the NC group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile no significant ROI-level correlations were found between the PET tracers, some voxel-wise associations were identified mainly in subcortical regions in the white matter. In addition, a weak correlation (r\u0026thinsp;=\u0026thinsp;0.27, p\u0026thinsp;=\u0026thinsp;0.043) was observed between [\u0026sup1;\u0026sup1;C]PK11195 binding and cortical volume. Full correlation results are provided in the Supplementary Materials (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eABI3\u003csup\u003eS209F\u003c/sup\u003e does not significantly affect brain region volumes\u003c/p\u003e \u003cp\u003eIn the prechosen ROIs we found no differences between the groups in the volumes of cerebral cortex, hippocampus, parahippocampus, entorhinal cortex or amygdala (Supplementary material, Table S2). Subsequent VBM analysis found that the \u003cem\u003eABI3\u003c/em\u003e\u003csup\u003e\u003cem\u003eS209F\u003c/em\u003e\u003c/sup\u003e/ε3 had lower volumes than the other groups mainly in the superior temporal gyrus, when correcting for FDR, but these findings did not survive the FEW correction (Supplementary material, Figure S2).\u003c/p\u003e \u003cp\u003eABI3\u003csup\u003eS209F\u003c/sup\u003e has no effect on cognition\u003c/p\u003e \u003cp\u003eGlobal cognition was assessed with the MMSE, and domain-specific performance was summarized as z-standardized composite. One-way ANOVA followed by Tukey\u0026rsquo;s honest significant difference test revealed no overall between-group differences for MMSE (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) or any cognitive domain (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\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\u003eCognitive test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eABI3\u003c/em\u003e\u003csup\u003e\u003cem\u003eS209F\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/ε4\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eABI3\u003c/em\u003e\u003csup\u003e\u003cem\u003eS209F\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/ε3\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNC\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroup difference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive functions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.046 (0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessing speed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30 (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpisodic memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.34 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.10 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage functions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.16 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eData are presented as mean (standard deviation). Differences between groups were tested with one-way ANOVA with Tukey\u0026rsquo;s honest significance test. P value presents overall difference between groups. Significant differences in pairwise comparisons to ABI+/e4e3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 = *) are presented\u003c/em\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 \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the ABI3\u003csup\u003eS209F\u003c/sup\u003e genetic variant as a risk factor for AD using data from the large Finnish FinnGen cohort. In addition, we experimentally assessed its impact, alone or combined with the APOEε4, on cortical Aβ deposition, neuroinflammation, brain morphology, and cognitive performance in a separate subsample of cognitively healthy older adults.\u003c/p\u003e \u003cp\u003eGenetic analysis confirmed ABI3\u003csup\u003eS209F\u003c/sup\u003e as a significant AD risk variant (OR\u0026thinsp;=\u0026thinsp;1.22), though with a somewhat smaller effect size compared to previous findings (Conway et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dalmasso et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Olive et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Satoh et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sims et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wightman et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, survival analyses indicated that ABI3\u003csup\u003eS209F\u003c/sup\u003e carriers developed AD earlier than non-carriers. In the experimental analyses, ABI3\u003csup\u003eS209F\u003c/sup\u003e alone did not influence cortical Aβ accumulation, neuroinflammation or regional brain volumes. However, subtle regional effects emerged in voxel-wise analyses, particularly for ABI3\u003csup\u003eS209F\u003c/sup\u003e combined with APOEε4, suggesting region-specific Aβ deposition and modest neuroinflammation not detected by broader ROI analyses. Collectively, these findings replicate and extend prior results, highlighting the complexity and context-dependent nature of ABI3\u003csup\u003eS209F\u003c/sup\u003e\u0026rsquo;s contribution to AD pathology.\u003c/p\u003e \u003cp\u003eIn a genetic association analysis in FinnGen, the ABI3\u003csup\u003eS209F\u003c/sup\u003e variant significantly associated with AD and more broadly defined neurodegenerative disorder, with comparable ORs across the endpoints. Interestingly, no significant association was observed with the broadest endpoint definition, dementia. This likely reflects differences in definitions of the broader endpoints: while the neurodegenerative disorder endpoint may more specifically capture AD and related pathologies, the dementia endpoint encompasses a heterogeneous group of conditions, including vascular and unspecified dementias. The lack of association with dementia may therefore result from dilution of AD-specific genetic signals within this phenotypically diverse group.\u003c/p\u003e \u003cp\u003eBeyond AD risk, ABI3\u003csup\u003eS209F\u003c/sup\u003e also appears to influence the timing of disease onset. Survival analysis revealed that carriers of the variant developed AD at an earlier age compared to non-carriers, with consistent effect observed across \u003cem\u003eAPOE\u003c/em\u003e ε3/ε3 and ε3/ε4 backgrounds. These findings suggest that ABI3\u003csup\u003eS209F\u003c/sup\u003e may act as a disease modifier, accelerating the onset of AD independently of \u003cem\u003eAPOE\u003c/em\u003e genotype. Taken together, these results reinforce the specificity of the association of ABI3\u003csup\u003eS209F\u003c/sup\u003e variant with AD and highlight its potential role in modulating disease progression.\u003c/p\u003e \u003cp\u003eIn the imaging part of the study, we examined the effect of ABI3\u003csup\u003eS209F\u003c/sup\u003e, in the presence or absence of the APOEε4, on brain Aβ deposition, neuroinflammation, brain structures, and cognitive functions in cognitively healthy older adults. Although ABI3\u003csup\u003eS209F\u003c/sup\u003e has previously been associated with increased AD risk in several studies, including replication in our current study, we did not observe increased cortical Aβ deposition in ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3 carriers when compared to NC, either in ROI-based or voxel-wise analyses. This suggests that ABI3\u003csup\u003eS209F\u003c/sup\u003e alone, without the \u003cem\u003eAPOE\u003c/em\u003e ε4 allele, may have limited functional impact on Aβ accumulation in cognitively normal individuals.\u003c/p\u003e \u003cp\u003eInterestingly, the highest cortical Aβ levels in ROI analysis were observed in participants carrying both ABI3\u003csup\u003eS209F\u003c/sup\u003e and APOEε4. However, these were only significantly different compared to the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε3 group, not to NC. Voxel-wise analysis, in contrast, revealed region-specific increases in [\u003csup\u003e11\u003c/sup\u003eC]PiB signal in the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 group compared to both other groups, particularly in frontal, temporal, and posterior cortices. Previous work has shown that one APOEε4 alone does not consistently induce such voxel-level differences (Snellman et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Reiman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), further supporting a gene\u0026ndash;gene interaction hypothesis. The ROI findings align with previous reports showing no significant association between ABI3\u003csup\u003eS209F\u003c/sup\u003e and CSF Aβ42 levels or Aβ-PET signal in cognitively healthy individuals (Olive et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The voxel-level differences suggest that the combination of ABI3\u003csup\u003eS209F\u003c/sup\u003e and APOEε4 may lead to regionally specific Aβ accumulation not captured by conventional ROI analyses, supporting a synergistic gene\u0026ndash;gene interaction. Animal studies have yielded mixed results regarding ABI3\u0026rsquo;s role in Aβ pathology. While ABI3 knockout has been associated with increased Aβ deposition in some studies (Karahan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), others have reported reduced deposition, particularly at early time points (Ibanez et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the latter case, the reduction was transient, and Aβ levels eventually caught up with NC as the animals aged.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study to examine TSPO PET imaging in cognitively normal individuals carrying the ABI3\u003csup\u003eS209F\u003c/sup\u003e variant. Previous studies with various PET-ligands have reported increased TSPO binding between AD and NC (Cagnin et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Edison et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zhen Fan, Okello, Brooks, \u0026amp; Edison, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and also in Aβ-positive MCI (Okello et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Parbo et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Aβ-positive NC (Z. Fan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zou et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, when comparing a composite ROI covering the entire cortical grey matter, we found no significant differences in [\u0026sup1;\u0026sup1;C]PK11195 binding between the genotype groups, although Aβ accumulation was clearly highest in the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 group in the same region. Voxel-wise analysis revealed a subtle increase in [\u0026sup1;\u0026sup1;C]PK11195 binding in the parieto-occipital cortex of the ABI3\u003csup\u003eS209F\u003c/sup\u003e/ε4 group compared to the other groups. Although this region also showed elevated Aβ signal, the highest Aβ accumulation was observed in the frontal cortex, where no corresponding increase in [\u0026sup1;\u0026sup1;C]PK11195 binding was detected. Our findings suggest that while the combination of ABI3\u003csup\u003eS209F\u003c/sup\u003e and APOEε4 is associated with increased cortical Aβ accumulation, this does not correspond to a parallel increase in microglial activation as measured by [\u0026sup1;\u0026sup1;C]PK11195. A similar dissociation has previously been reported among APOEε4 carriers with varying allele loads (Snellman et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This mismatch may reflect the limited sensitivity of first-generation TSPO tracers such as [\u0026sup1;\u0026sup1;C]PK11195, which are known to suffer from poor signal-to-noise ratio and high nonspecific binding (Vivash \u0026amp; O'Brien, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yokokura et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) or a temporal lag between Aβ deposition and immune response, or the functional heterogeneity of microglial activation, which is not fully captured by TSPO PET imaging (Z. Fan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Overall, while the voxel- and ROI-level data diverge in some respects, both support a nuanced and genotype-dependent role for ABI3\u003csup\u003eS209F\u003c/sup\u003e in AD pathology.\u003c/p\u003e \u003cp\u003eThe strength of this study is the combination of population-scale genetic data with in vivo multimodal imaging, enabling a rare genotype-specific characterization of early AD-related pathology in cognitively normal individuals. The use of the large and well-characterized FinnGen cohort allowed us to validate the ABI3\u003csup\u003eS209F\u003c/sup\u003e AD association in a genetically distinct Northern European population, adding valuable replication evidence to earlier findings. A major practical strength of this study was the ability to efficiently recruit participants with specific genotypes through biobank collaboration. Without access to genotype-based preselection, recruiting a sufficient number of ABI3\u003csup\u003eS209F\u003c/sup\u003e carriers would have required genotyping and screening several hundred individuals. The biobank-based recruitment thus greatly increased feasibility and resource efficiency. Additionally, the integration of PET imaging for both Aβ and microglial activity, alongside MRI-derived structural metrics and neuropsychological testing, enabled a multidimensional assessment of functional consequences at an early disease stage.\u003c/p\u003e \u003cp\u003eHowever, some limitations should be noted. The use of the first-generation TSPO tracer [\u0026sup1;\u0026sup1;C]PK11195 may have constrained sensitivity to microglial activation due to its limited signal-to-noise ratio and high nonspecific binding. Also, the sample size in our imaging part was modest, although it was based on power calculations on previous studies there is a chance of type II error. Furthermore, although voxel-wise analysis increases spatial precision, it also introduces multiple comparison challenges and increases the risk of type I error, despite statistical correction. Finally, while the study design allowed for the evaluation of ABI3\u003csup\u003eS209F\u003c/sup\u003e effects in isolation and in combination with APOEε4, the cross-sectional nature of the imaging component precludes firm conclusions about temporal dynamics or causality.\u003c/p\u003e \u003cp\u003eDespite these limitations, the present findings highlight the importance of considering gene\u0026ndash;gene interactions and regional brain vulnerability in understanding AD risk. Further studies using larger imaging cohorts, more sensitive second-generation tracers, and longitudinal follow-up will be essential to clarify the mechanisms by which ABI3 and APOEε4 interact to influence early neuropathological changes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study confirms the association of the ABI3\u003csup\u003eS209F\u003c/sup\u003e variant with increased risk for AD. The variant was also associated with an earlier age of AD onset. ABI3\u003csup\u003eS209F\u003c/sup\u003e alone does not significantly affect cortical Aβ deposition or neuroinflammation. However, the combination of ABI3\u003csup\u003eS209F\u003c/sup\u003e and APOEε4 may contribute to region-specific Aβ accumulation and increased microglial activation. Our findings emphasize the complex and context-dependent role of ABI3\u003csup\u003eS209F\u003c/sup\u003e in AD pathophysiology, possibly acting as a modifier rather than a primary driver of pathology. The lack of consistent differences in cognitive performance and brain volumes supports the notion that ABI3\u003csup\u003eS209F\u003c/sup\u003e-related changes may precede symptomatic disease or require additional genetic or environmental interactions to manifest. Further longitudinal and mechanistic studies are needed to elucidate the functional consequences of this variant and its potential as a therapeutic target or biomarker in preclinical stages of AD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eApoE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eApolipoprotein E\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPOEε4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAPOE ε4 allelle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAβ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBeta-amyloid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABI3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbelson interactor family member 3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABI3\u003csup\u003eS209F\u003c/sup\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eABI3 p.Ser209Phe variant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmyloid precursor protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnatomical Therapeutic Chemical\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBNT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBoston Naming Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCerebrospinal fluid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDVR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDistribution Volume Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFINGER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFinnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFinnGen\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFinnish genetic research project combining genomic and health registry data\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFWE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFamily-wise error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-wide association study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHRRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-resolution research tomograph\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMBq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMegabecquerel\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMild cognitive impairment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePositron emission tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePVE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePartial volume effect\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegion of interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical parametric mapping\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSUVR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandardized uptake value ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTSPO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranslocator protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe brain imaging study was approved by the Ethical Committee of the Hospital District of Southwest Finland. Biobank participant recontacting was approved by the Scientific Steering Committee of Auria Biobank. All participants signed a written informed consent.\u003c/p\u003e\n\u003cp\u003eThe study subjects in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Alternatively, separate research cohorts, collected prior the Finnish Biobank Act came into effect (in September 2013) and start of FinnGen (August 2017), were collected based on study-specific consents and later transferred to the Finnish biobanks after approval by Fimea (Finnish Medicines Agency), the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Fimea. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS) statement number for the FinnGen study is Nr HUS/990/2017.\u003c/p\u003e\n\u003cp\u003eThe FinnGen study is approved by Finnish Institute for Health and Welfare (permit numbers: THL/2031/6.02.00/2017, THL/1101/5.05.00/2017, THL/341/6.02.00/2018, THL/2222/6.02.00/2018, THL/283/6.02.00/2019, THL/1721/5.05.00/2019 and THL/1524/5.05.00/2020), Digital and population data service agency (permit numbers: VRK43431/2017-3, VRK/6909/2018-3, VRK/4415/2019-3), the Social Insurance Institution (permit numbers: KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019, KELA 98/522/2019, KELA 134/522/2019, KELA 138/522/2019, KELA 2/522/2020, KELA 16/522/2020), Findata permit numbers THL/2364/14.02/2020, THL/4055/14.06.00/2020, THL/3433/14.06.00/2020, THL/4432/14.06/2020, THL/5189/14.06/2020, THL/5894/14.06.00/2020, THL/6619/14.06.00/2020, THL/209/14.06.00/2021, THL/688/14.06.00/2021, THL/1284/14.06.00/2021, THL/1965/14.06.00/2021, THL/5546/14.02.00/2020, THL/2658/14.06.00/2021, THL/4235/14.06.00/2021, Statistics Finland (permit numbers: TK-53-1041-17 and TK/143/07.03.00/2020 (earlier TK-53-90-20) TK/1735/07.03.00/2021, TK/3112/07.03.00/2021) and Finnish Registry for Kidney Diseases permission/extract from the meeting minutes on 4th July 2019.\u003c/p\u003e\n\u003cp\u003eThe Biobank Access Decisions for FinnGen samples and data utilized in FinnGen Data Freeze 12 include: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7, BB2019_8, BB2019_26, BB2020_1, BB2021_65, Finnish Red Cross Blood Service Biobank 7.12.2017, Helsinki Biobank HUS/359/2017, HUS/248/2020, HUS/430/2021 \u0026sect;28, \u0026sect;29, HUS/150/2022 \u0026sect;12, \u0026sect;13, \u0026sect;14, \u0026sect;15, \u0026sect;16, \u0026sect;17, \u0026sect;18, \u0026sect;23, \u0026sect;58, \u0026sect;59, HUS/128/2023 \u0026sect;18, Auria Biobank AB17-5154 and amendment #1 (August 17 2020) and amendments BB_2021-0140, BB_2021-0156 (August 26 2021, Feb 2 2022), BB_2021-0169, BB_2021-0179, BB_2021-0161, AB20-5926 and amendment #1 (April 23 2020) and it\u0026acute;s modifications (Sep 22 2021), BB_2022-0262, BB_2022-0256, Biobank Borealis of Northern Finland_2017_1013, 2021_5010, 2021_5010 Amendment, 2021_5018, 2021_5018 Amendment, 2021_5015, 2021_5015 Amendment, 2021_5015 Amendment_2, 2021_5023, 2021_5023 Amendment, 2021_5023 Amendment_2, 2021_5017, 2021_5017 Amendment, 2022_6001, 2022_6001 Amendment, 2022_6006 Amendment, 2022_6006 Amendment, 2022_6006 Amendment_2, BB22-0067, 2022_0262, 2022_0262 Amendment, Biobank of Eastern Finland 1186/2018 and amendment 22\u0026sect;/2020, 53\u0026sect;/2021, 13\u0026sect;/2022, 14\u0026sect;/2022, 15\u0026sect;/2022, 27\u0026sect;/2022, 28\u0026sect;/2022, 29\u0026sect;/2022, 33\u0026sect;/2022, 35\u0026sect;/2022, 36\u0026sect;/2022, 37\u0026sect;/2022, 39\u0026sect;/2022, 7\u0026sect;/2023, 32\u0026sect;/2023, 33\u0026sect;/2023, 34\u0026sect;/2023, 35\u0026sect;/2023, 36\u0026sect;/2023, 37\u0026sect;/2023, 38\u0026sect;/2023, 39\u0026sect;/2023, 40\u0026sect;/2023, 41\u0026sect;/2023, Finnish Clinical Biobank Tampere MH0004 and amendments (21.02.2020 \u0026amp; 06.10.2020), BB2021-0140 8\u0026sect;/2021, 9\u0026sect;/2021, \u0026sect;9/2022, \u0026sect;10/2022, \u0026sect;12/2022, 13\u0026sect;/2022, \u0026sect;20/2022, \u0026sect;21/2022, \u0026sect;22/2022, \u0026sect;23/2022, 28\u0026sect;/2022, 29\u0026sect;/2022, 30\u0026sect;/2022, 31\u0026sect;/2022, 32\u0026sect;/2022, 38\u0026sect;/2022, 40\u0026sect;/2022, 42\u0026sect;/2022, 1\u0026sect;/2023, Central Finland Biobank 1-2017, BB_2021-0161, BB_2021-0169, BB_2021-0179, BB_2021-0170, BB_2022-0256, BB_2022-0262, BB22-0067, Decision allowing to continue data processing until 31st Aug 2024 for projects: BB_2021-0179, BB22-0067,BB_2022-0262, BB_2021-0170, BB_2021-0164, BB_2021-0161, and BB_2021-0169, and Terveystalo Biobank STB 2018001 and amendment 25th Aug 2020, Finnish Hematological Registry and Clinical Biobank decision 18th June 2021, Arctic biobank P0844: ARC_2021_1001.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe brain imaging datasets used and analysed during the current study are available from the corresponding author on reasonable request. Summary statistics from FinnGen data release 12 are publicly available and can be accessed at https://www.finngen.fi/en/access_results and https://r12.finngen.fi. Access for individual level genotype data can be applied for via the Fingenious portal (https://site.fingenious.fi/en/) hosted by the Finnish Biobank Cooperative FinBB (https://finbb.fi/en/). Access to Finnish Health register data can be applied from Findata (https://findata.fi/en/data/).\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eMK was supported by Yrj\u0026ouml; Jahnsson Foundation. HM and MH were supported by the Research Council of Finland (grants #355604, #338182). MH was supported by the Sigrid Jus\u0026eacute;lius Foundation, Jane and Aatos Erkko Foundation, the Strategic Neuroscience Funding of the University of Eastern Finland, Faculty of Health Sciences of University of Eastern Finland, Alzheimer\u0026apos;s Association (ADSF-24-1284326-C and AARG-22-926152).\u003c/p\u003e\n\u003cp\u003eFinnGen is funded by two grants from Business Finland (HUS 4685/31/2016 and UH 4386/31/2016) and the following industry partners: AbbVie Inc., AstraZeneca UK Ltd, Biogen MA Inc., Bristol Myers Squibb (and Celgene Corporation \u0026amp; Celgene International II S\u0026agrave;rl), Genentech Inc., Merck Sharp \u0026amp; Dohme LCC, Pfizer Inc., GlaxoSmithKline Intellectual Property Development Ltd., Sanofi US Services Inc., Maze Therapeutics Inc., Janssen Biotech Inc, Novartis Pharma AG, and Boehringer Ingelheim International GmbH.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eMK, JOR and HM designed the study concept. MK drafted the manuscript. MK contributed to data collection. MK and HM analysed the data. MK, JOR, MT, AS and HM contributed to study design and interpretation of the data. JOR, AS, and MH supervised the study. All authors read and critically revised the manuscript for its content and approved the final submitted version.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eAuthors would like to thank Dr. Christian Haass (DZNE, Germany) for his valuable comments on the manuscript. We thank the participants and researchers of FinnGen. We thank Merja Per\u0026auml;l\u0026auml; from Auria Biobank for her valuable work in recruiting the subjects.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAalto, S., Scheinin, N. M., Kemppainen, N. M., N\u0026aring;gren, K., Kailaj\u0026auml;rvi, M., Leinonen, M., . . . Rinne, J. O. (2009). 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Microglial activation, but not tau pathology, is independently associated with amyloid positivity and memory impairment. \u003cem\u003eNeurobiology of aging, 85\u003c/em\u003e, 11-21. doi:10.1016/j.neurobiolaging.2019.09.019\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"alzheimers-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"azrt","sideBox":"Learn more about [Alzheimer's Research and Therapy](http://alzres.biomedcentral.com/)","snPcode":"13195","submissionUrl":"https://submission.nature.com/new-submission/13195/3","title":"Alzheimer's Research \u0026 Therapy","twitterHandle":"@AlzheimersRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer’s disease, ABI3S209F, APOE ε4, Microglia, β-amyloid-, PET imaging, FinnGen","lastPublishedDoi":"10.21203/rs.3.rs-8113148/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8113148/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) has a strong genetic component, with \u003cem\u003eAPOE\u003c/em\u003e ε4 being the most established risk factor through its effects on beta-amyloid (Aβ) metabolism and microglial function. Recent genetic studies have also implicated microglial genes, such as the ABI3\u003csup\u003eS209F\u003c/sup\u003e variant, to increased AD risk. As \u003cem\u003eAPOE\u003c/em\u003e ε4 and ABI3\u003csup\u003eS209F\u003c/sup\u003e influence microglial pathways through distinct mechanisms, their combined analysis may provide novel insights into AD pathophysiology. Therefore, we investigated ABI3\u003csup\u003eS209F\u003c/sup\u003e in the Finnish FinnGen cohort and in an imaging study of cognitively healthy older adults\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used FinnGen R12 data (\u0026gt;\u0026thinsp;500,000 individuals), including 8,490 ABI3\u003csup\u003eS209F\u003c/sup\u003e carriers and 511,670 non-carriers, with survival analyses matched by sex and birth year. Disease endpoints (AD, dementia, neurodegenerative disorder) were defined from national health registries using harmonized ICD codes, medication, and reimbursement records. For the imaging study, 58 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;50 years were recruited into three genotype-based groups (ABI3\u003csup\u003eS209F\u003c/sup\u003e/\u003cem\u003eAPOE\u003c/em\u003e ε4, ABI3\u003csup\u003eS209F\u003c/sup\u003e/\u003cem\u003eAPOE\u003c/em\u003e ε3, non-carriers). All imaging participants underwent structural MRI, [\u003csup\u003e11\u003c/sup\u003eC]PiB PET for amyloid beta, [\u003csup\u003e11\u003c/sup\u003eC]PK11195 PET for microglial activity, and a comprehensive neuropsychological battery.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eABI3\u003csup\u003eS209F\u003c/sup\u003e was significantly associated with increased risk of AD (OR\u0026thinsp;=\u0026thinsp;1.22, p\u0026thinsp;=\u0026thinsp;0.0012) and neurodegenerative disorders (OR\u0026thinsp;=\u0026thinsp;1.21, p\u0026thinsp;=\u0026thinsp;0.00023), but not with dementia (OR\u0026thinsp;=\u0026thinsp;1.10, p\u0026thinsp;=\u0026thinsp;0.06). Survival analyses indicated that ABI3\u003csup\u003eS209F\u003c/sup\u003e carriers developed AD at an earlier age than non-carriers with the same \u003cem\u003eAPOE\u003c/em\u003e genotype. The carriers of ABI3\u003csup\u003eS209F\u003c/sup\u003e and \u003cem\u003eAPOE\u003c/em\u003e ε4 had higher brain Aβ burden when compared to the ABI3\u003csup\u003eS209F\u003c/sup\u003e carriers without \u003cem\u003eAPOE\u003c/em\u003e ε4 (SUVR 2.0 (0.7) vs. 1.67 (0.5); SUVR 2.0 (0.7) vs. 1.67 (0.5); mean (sd), p\u0026thinsp;=\u0026thinsp;0.017). ABI3\u003csup\u003eS209F\u003c/sup\u003e was not associated with global neuroinflammation, although subtle regional increases in [\u003csup\u003e11\u003c/sup\u003eC]PK11195 binding were observed in ABI3\u003csup\u003eS209F\u003c/sup\u003e ε4 carriers. No differences were found in brain volumes or cognition.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eABI3\u003csup\u003eS209F\u003c/sup\u003e increases AD risk and is associated with earlier disease onset. The variant alone does not significantly influence cortical Aβ deposition, neuroinflammation, or brain structure. Its effect may be pronounced in combination with APOEε4.\u003c/p\u003e","manuscriptTitle":"Genetic Validation of ABI3 p.Ser209Phe Variant and Its effects On Early Brain Pathology in Asymptomatic Elderly Individuals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 09:35:56","doi":"10.21203/rs.3.rs-8113148/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-12T08:24:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T13:27:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278606040256284925489735857878370287907","date":"2025-12-18T07:57:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T11:46:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305081939103454888264301583945438755473","date":"2025-12-16T06:19:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276604251893168911216271603721074598562","date":"2025-12-16T02:48:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-15T14:55:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-17T23:57:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-17T23:56:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Alzheimer's Research \u0026 Therapy","date":"2025-11-14T10:04:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"alzheimers-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"azrt","sideBox":"Learn more about [Alzheimer's Research and Therapy](http://alzres.biomedcentral.com/)","snPcode":"13195","submissionUrl":"https://submission.nature.com/new-submission/13195/3","title":"Alzheimer's Research \u0026 Therapy","twitterHandle":"@AlzheimersRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d370d74-cd8a-48ce-81bb-6640d6c39527","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:13:17+00:00","versionOfRecord":{"articleIdentity":"rs-8113148","link":"https://doi.org/10.1186/s13195-026-01984-y","journal":{"identity":"alzheimers-research-and-therapy","isVorOnly":false,"title":"Alzheimer's Research \u0026 Therapy"},"publishedOn":"2026-02-19 15:59:03","publishedOnDateReadable":"February 19th, 2026"},"versionCreatedAt":"2025-12-22 09:35:56","video":"","vorDoi":"10.1186/s13195-026-01984-y","vorDoiUrl":"https://doi.org/10.1186/s13195-026-01984-y","workflowStages":[]},"version":"v1","identity":"rs-8113148","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8113148","identity":"rs-8113148","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}