TREM2 Risk Variants with Alzheimer’s Disease Differ in Rate of Cognitive Decline

TREM2 Risk Variants with Alzheimer’s Disease Differ in Rate of Cognitive Decline | 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 TREM2 Risk Variants with Alzheimer’s Disease Differ in Rate of Cognitive Decline Janna Dijkstra, Lisa Vermunt, Vikram Venkatraghavan, Georgii Ozgehov, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5310076/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rare variants of the triggering receptor expressed on myeloid cell 2 ( TREM2 ) gene are major risk factors for Alzheimer’s disease (AD), and drugs targeting the TREM2 protein are being developed. However, it is unknown whether carriers of a TREM2 risk variant have a clinically distinct AD phenotype. Here we studied a full range of clinical measures in a large cohort of TREM2 variant carriers ( n = 123, 7.8%, i.e., R62H n = 66, R47H n = 26, T96K n = 16, other TREM2 variants n = 17) compared to confirmed non-carriers ( n = 1,459) with biomarker confirmed symptomatic AD from Amsterdam Dementia Cohort. TREM2 variant carriers (mean age at diagnosis 64.4 years (SD ± 7.1), 54% female) did not show distinct clinical measures of AD at presentation compared to AD patients not carrying a TREM2 variant (mean age at diagnosis 64.4 ± 7.0, 52% female). Specifically, we observed no differences in MMSE, most neuropsychological domains (except visuospatial functioning), MRI scores, CSF biomarkers, and EEG. Also, in an exploratory analysis of neuroimaging measures, including structural MRI (41 ROIs) and Tau-PET scans of four carriers (R62H, R47H, G58A, D87N), TREM2 variant carriers showed similar atrophy patterns and similar abnormal tracer binding compared to non-carriers. Despite not being different at baseline, carriers did show faster cognitive decline in follow-up. Carriers declined 0.63 ± 0.25 points on the MMSE more per year compared to non-carriers, but there was no difference in the hazard rate of death after diagnosis. Finally, we explored whether specific TREM2 variants are associated with distinct clinical measures compared to the reference group, i.e. non-carriers, within the same cohort. Notably, both R47H and T96K carriers exhibited faster cognitive decline, and R47H carriers even showed an increased rate of death after diagnosis. In contrast to the shared cognitive decline, these variants showed different results for other measures at baseline. This study presents a detailed overview of the clinical measures in AD patients carrying a TREM2 risk variant, and it shows that carriers of TREM2 risk variants cannot be distinguished based on clinical presentation at baseline. However, carriers exhibit a faster global cognitive decline compared to non-carriers. Variant-specific analyses suggest that especially R47H and T96K carriers drive the association of TREM2 variants with faster cognitive decline. TREM2 Alzheimer’s disease clinical measures Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 MAIN Rare TREM2 variants are major risk factors for Alzheimer’s disease (AD) (1–6) . The triggering receptor expressed on myeloid cell 2 ( TREM2 ) gene is situated on chromosome 6, it encodes a transmembrane protein of 230 amino acids, and it is expressed exclusively in microglia within the brain (7) . The TREM2 protein appears to be a key player in microglial function and AD development (8–10) , and is a target of disease-modifying therapies that are currently in phase II clinical trials (11–16) . To date, it is unknown whether carriers of a TREM2 risk variant have a specific clinical presentation of AD. In a retrospective study of autopsied cases, TREM2 variant carriers more often had non-amnestic syndromes compared to non-carriers, faster cognitive decline (17) , more tau accumulation, but no altered regional beta-amyloid (Ab) burden (17,18) . Another study did not find a distinct neuropsychological profile when comparing TREM2 R47H carriers with AD non-carriers (19) . All these studies were small with a maximum number of 31 TREM2 variant carriers. Therefore, the variability of results between studies may be explained by small samples (20,21) and heterogeneity of effects introduced by studying populations of different ancestry (4,21) , both making it more difficult to find associations. Another explanation why associations with clinical measures are inconclusive could be the variant-specific effects. At a molecular level, TREM2 risk variants impair TREM2 activity differently (7,22–24) . Most TREM2 risk variants are situated on exon 2 where the coding corresponds to the Ig-like V type domain (25) , suggesting an alteration in the interaction between TREM2 and its ligands (21,25) . R47H is located near the exon 2 junction, whereas T96K is located near a conserved part of the protein; thus, these variants could affect distinct functional regions on TREM2’s surface (23) . Several studies indicated that TREM2 proteins resulting from R47H showed reduced ligand binding and signalling, while conversely proteins resulting from T96K showed enhanced ligand binding (22,24) . In addition, the variants R62H and R47H associated with two different AD subtypes based on CSF proteomics (26) , which further indicates variant-specific mechanisms. Hence, TREM2 variant-specific mechanisms necessitate variant-specific studies. Studying this hypothesis requires large clinical sample sizes to be able to observe adequate numbers for variant-specific analyses. Previous research indicated that TREM2 R47H carriers seem to show a typical clinical AD profile (27) , elevated CSF-Tau (28) , and lower grey matter volume in right orbitofrontal regions compared to non-carriers (19) . However, another study did not find a significant effect on cross-sectional brain volumes (29) . TREM2 R62H and T96K carriers have not yet been studied well. Here we hypothesize that TREM2 risk variants may be associated with distinct clinical measures. Hence, we studied the association of TREM2 carriership with a full range of clinical measures at baseline (neuropsychological profile, visual MRI rating, CSF AD biomarkers, and visual EEG rating) and in follow-up (cognitive decline and survival status) in a large clinical cohort of biomarker confirmed AD patients, followed by an exploratory analysis of neuroimaging measures (structural MRI, and Tau-PET) and an analysis of the specific TREM2 variants (R47H, R62H, T96K and others). METHODS Amsterdam Dementia Cohort We included 1,582 patients with Mild Cognitive Impairment (MCI) or dementia due to AD, based on confirmed AD biomarkers (in CSF 95% and amyloid PET 5%), and with available genetic data who visited the Alzheimer Centre Amsterdam memory clinic (Fig. 1 ) ( 31 ). We identified a TREM2 risk variant in 123 AD patients, representing 7.8% of the total cohort, while 1,459 AD patients were confirmed to not carry a TREM2 risk variant. All patients underwent a standardized diagnostic trajectory ( 31 ). Information was collected on demographics, medical history, family history, neuropsychological investigation, MRI, cerebrospinal fluid (CSF), and blood. Diagnoses were determined by consensus in a multidisciplinary meeting, ensuring that diagnostic criteria were met. Patients were followed over time for reassessments and/or research purposes. Patients with a revised diagnosis at follow-up ( n = 22) were excluded. Mortality data was collected from the Central Public Administration. Patients consented to be part of the Amsterdam Dementia Cohort (ADC) to use their medical information for research and to allow their DNA to be stored in a dedicated biobank. Genotyping, Imputation of 6 Selected SNPs, and Genetic Selection Whole exome sequencing, Single-Nucleotide Polymorphism (SNP) Arrays and targeted TaqMan assay After DNA collection, most samples were genotyped with whole exome sequencing (WES) data using Illumina sequencers ( n = 1,569 of total 1,956). Some samples did not have WES but were genotyped on Illumina Global Screening Array ( n = 359). We included six missense variants that were imputed with high quality (R 2 > 0.37) (R47H, R62H, T96K, T66M, C51Y, and Q33X) ( 32 ). Standard quality control was performed. Samples were imputed with the Trans-Omics for Precision Medicine (TOPMeD) reference panel ( 33 , 34 ). Processing, quality control and variant calling has been described previously ( 3 , 35 ). For patients without WES or SNP array data available, made-to-order TaqMan assays were targeted on variants R47H and R62H ( n = 28). Patients with autosomal dominant AD, i.e., carriers of genetic mutations in PSEN1 , PSEN2 and APP , were excluded ( n = 29). Genetic selection A schematic overview of the population is presented in Fig. 1 . Whole exome sequencing (WES) ( n = 110), SNP array ( n = 10), and TaqMan assays ( n = 3) identified 123 TREM2 carriers, and WES confirmed that 1,459 AD patients did not carry a TREM2 variant (i.e., non-carriers). We included TREM2 missense variants proven to be associated with AD. These are R47H (NC_000006.12: g.41161515G > C, OR 3.1, p A, OR 1.7, p A, OR 1.2 in African GWAS, p < 0.5x10 e − 5 ) ( 4 ). One single variant was selected based on biological evidence, being D87N ( 36 ). Other rare damaging variants, including splice variants, were selected based on the burden test association. This test included a Rare Exome Variant Ensemble Learner (REVEL) score > 0.25 ( 3 , 37 ), protein truncating variants, or frameshift deletion variants. Clinical measures Neuropsychological assessment Global cognitive functioning was assessed using the Mini-Mental State Examination (MMSE) ( 38 ). MMSE data was available for 1,564 (99%) patients. In addition, neuropsychological data was available for 1,519 (96%) patients. We measured five neuropsychological domains, i.e., episodic memory, executive functioning, attention and speed, language, and visuospatial functioning using a standardized neuropsychological assessment comprising eight cognitive tests ( 31 ). Classification was based on a total of 16 variables as previously reported by Dubbelman et al. (2022) ( 39 ). Supplementary Data gives an overview of the variables used per domain. In short, each domain was assessed when at least two cognitive tests were available (range of available data per domain: 69–91%). Z-scores for each variable were calculated per cognitive domain scaled on the baseline mean and standard deviation of the total cohort. The combined domains gave a summarized z-score of global cognition. Longitudinally, we studied cognitive decline using the MMSE. MMSE data had a median follow-up of 1.0 years (interquartile range (IQR) 0.0-2.4); 42% had one measure, 21% had two measures, 15% had three measures, and 22% had more than three measures. CSF biomarkers CSF data was available for 1,522 (96%) patients. CSF Aβ42, pTau-181 and total tau were assessed with the Innotest enzyme-linked immunosorbent assay (ELISA) and Aβ42 was drift corrected, or on Elecsys ( 40 ). Amyloid status for AD was confirmed if the Innotest tau/Aβ42 ratio exceeded 0.46 ( 41 ), or the Elecsys pTau-181/Aβ42 ratio exceeded 0.20 ( 42 ). If an (additional) amyloid-PET scan was available, amyloid status was confirmed by positive amyloid-PET scan ( n = 85). To correct for variance between these assays, Elecsys results were converted based on established equations in biomarker associations ( 43 ). In addition, we employed available CSF-NfL measurements described in a previous paper defining reference values for the SIMOA NF-light assay ( 44 ). To calculate age-adjusted z-scores in CSF, we approached the reference range percentile formula with a linear model with outcome log2(NfL) and age as the predictor in the reference. This resulted in the following formula for calculating age-adjusted NfL z-scores in CSF: [log2(NfL) − (6.661 + (age × 0.045))] / 0.736. MRI clinical ratings Brain-MRI data was available for 1,210 (76%) patients. Three visual rating scales as used in clinical assessment were employed: Medial Temporal lobe Atrophy (MTA) ( 45 ), Posterior Cortical Atrophy (PCA) ( 46 ), and Fazekas score for white matter hyperintensities ( 47 ). Electroencephalogram (EEG) EEG data was available for 1,304 (82%) patients. Details of the acquisition, processing, and visual assessment of the EEG recordings have been described previously ( 48 ). The assessment was conducted utilizing a standardized severity scale (1 to 4), representing the spectrum from no abnormalities to severe abnormalities ( 49 ). We studied ‘normal’ versus ‘abnormal’ EEG scans. ‘Normal’ was defined as 1–2 on the severity scale, with or without focal abnormalities. ‘Abnormal’ were all other possibilities, including epileptiform activity and diffuse abnormalities. Neuroimaging measures MRI structural brain imaging Structural MRI data was available for 1,069 (67%) patients. Quantitative image analysis was done for several regions of interest based on Desikan Kiliany Atlas by FreeSurfer v7.1 ( 50 ). Details of the MRI data processing ( 51 ) and quality check process ( 52 ) have been described previously. MRI data were from 12 scanners. The scanner-related effects were removed using a procedure called ComBat ( 53 ). We studied 34 cortical thickness measures (mm) and seven subcortical volumes (mm 3 ). We averaged measures of the left and right hemisphere per region. [ 18 F]flortaucipir PET Tau-PET data was available for 67 (4%) patients, including four TREM2 variant carriers (R47H, R62H, G58A, and D87N). Details on the acquisition and processing of the [ 18 F]flortaucipir PET images have been described previously ( 54 – 57 ). For semi-quantification, we calculated standardized uptake value ratio (SUVr) using the gray matter cerebellum as reference region in six different composite regions of interest from the Hammers and Svarer templates: 1) a medial temporal region (including the entorhinal cortex, parahippocampal gyrus, amygdala, and fusiform gyrus), 2) a lateral temporal region (including the superior, middle and inferior temporal gyrus, and the posterior temporal lobe), 3) a medial parietal region (including the posterior cingulate), 4) a lateral parietal region (including the superior parietal gyrus and the inferolateral remainder of the parietal lobe), 5) an occipital region (including the cuneus, lingual gyrus and lateral remainder of the occipital lobe), and 6) a frontal region (including the superior, middle and inferior frontal gyrus, gyrus rectus, and orbitofrontal gyrus). In line with previous work ( 58 ), we contrasted the SUVRs for the AD cases with a TREM2 variant against the observed distribution of the AD cases without a TREM2 variant. Hippocampi could not be adequately assessed with this tracer due to off-target binding. Statistical analyses Analyses were performed in R version 4.3.0 ( 59 ) and Python version 3.9 ( 60 ). Education level was converted from the Dutch Verhage scale ( 61 ) to the Standard Classification of Education, i.e., low, medium, and high ( 62 ). For individuals with missing education level ( n = 7), the median level was imputed, i.e., high education. Baseline characteristics of TREM2 variant carriers and non-carriers were compared with Chi-squared tests for categorical variables, and with t-tests for continuous variables. In the main analysis, all TREM2 variants were combined to study the effect of TREM2 mutation status with clinical measures relative to the reference group, i.e., non-carriers. Linear regression models were used to associate TREM2 mutation status with MMSE at baseline, the five neuropsychological domains and combined global cognition, MRI features, and CSF biomarkers. All measurements were scaled for comparability. Logistic regression models were used to associate TREM2 mutation status with an EEG abnormality score. Regression analyses were conducted separately and were adjusted for age and sex as dependent variables, and MMSE at baseline, neuropsychological profiles and EEG scores were also adjusted for education level (i.e., low, middle, high) ( 62 ) and disease stage (i.e., MCI or dementia). With Cox proportional hazards models, we associated TREM2 mutation status with time in years between diagnosis and death adjusted for age at diagnosis, sex, education level, disease stage, and MMSE at baseline. Linear mixed models were used to study associations of TREM2 variants and change in MMSE scores over time, and were adjusted for age at diagnosis, sex, education level, and disease stage. The models included a random intercept and interaction effect over time. A p-value corrected for a false discovery rate (FDR) < 0.10 was considered statistically significant. In the exploratory analysis of neuroimaging outcomes, we tested with linear regression models the effect of TREM2 status with 41 regions of interest (ROI) from structural MRI, and models were adjusted for age, sex, and estimated intracranial volume. In the subsequent exploratory analysis, TREM2 variants were grouped to associate variant-specific TREM2 effects with each clinical measure relative to the reference group, comparing (i) R62H carriers vs. non-carriers, (ii) R47H carriers vs. non-carriers, (iii) T96K carriers vs. non-carriers, and (iv) other TREM2 carriers vs. non-carriers. In the cox regression models and linear mixed models, we tested a separate TREM2 effect relative to the reference group comparing categorical (R62H, R47H, T96K, other) carriers vs. non-carriers. Carriers of two different mutations were categorized for the variant conferring the highest risk. A p-value < 0.05 was considered statistically significant. In the sensitivity analysis, we excluded patients of non-European descent (determined through 1000 Genomes clustering) ( 63 ) and patients who had a familial relationship (identity-by-descent ≥ 0.2). All the models described above were further adjusted for three principal components. RESULTS Population and TREM2 characteristics Ten different TREM2 risk variants were identified (Table 1 ). All genetic variants were located on exon 2 (of 5 exons). The most prevalent mutations were R62H ( n = 66, 54%), R47H ( n = 26, 21%; among which one also carried R62H) and T96K ( n = 16, 13%, among which two were homozygous). We further observed rare mutations in seventeen patients (14% of TREM2 carriers) that carried one of the following heterozygous missense, splice, or protein truncating mutations: p.Ser31Phe (R31F), p.Gln33Ter (Q33X; among which one also carrier R62H), p.Arg47Gly (R47G), p.Cys51Tyr (C51Y), p.Gly58Ala (G58A), p.Asp87Asn (D87N), and p.Ala105Val (A105V). Table 1 Genetic descriptives of TREM2 variants identified in the cohort at baseline. Abbrev. Location* Exon HGVSc HGVSp R 2 Consequence GMAF REVEL CADD OR** n*** R62H 6:41161469 2/5 c.185G > A p.Arg62His Genotyped Missense variant 0.0050 0.039 11.4 1.7 1 66 R47H 6:41161514 2/5 c.140G > A p.Arg47His 0.852 Missense variant 0.0020 0.335 26.1 3.09 2 26 T96K 6:41161367 2/5 c.287C > A p.Thr96Lys 0.975 Missense variant 0.0409 0.261 22.7 1.2 2 16 D87N 6:41161395 2/5 c.259G > A p.Asp87Asn 0.816 Missense variant 0.0006 0.2 19.8 1.71 2 8 Q33X 6:41161557 2/5 c.97C > T p.Gln33Ter 0.465 Stop-gained UNK NA 32.0 13.9 2 3 G58A 6:41161481 2/5 c.173G > C p.Gly58Ala NA Missense variant UNK 0.363 18.0 UNK 2 C51Y 6:41161502 2/5 c.152G > A p.Cys51Tyr 0.379 Missense variant UNK 0.602 26.5 UNK 1 R47G 6:41161515 2/5 c.139C > G p.Arg47Gly NA Missense variant UNK 0.416 23.9 UNK 1 S31F 6:41161562 2/5 c.92C > T p.Ser31Phe NA Missense variant UNK 0.49 24.1 UNK 1 A105V 6:41161340 2/5 c.314C > T p.Ala105Val NA Missense variant UNK 0.381 UNK UNK 1 *GRCh38 assembly, EnsGENE database annotation from 2023.0316, MANE SELECT: NM_018965.4. **Based on 1. Sims et al. ( 6 ), 2. Holstege et al. ( 3 ), and 3. Sherva et al. ( 4 ). ***total n = 123; n = 4 carriers with double mutation: one R62H/R47H carrier, one R62H/Q33X carrier, and two T96K homozygote carriers. Abbreviations: Abbrev. = Abbreviation; AD = Alzheimer’s Disease; CADD = Combined Annotation Dependent Depletion; HGVSc = Human Genome Variation Society coding; HGVSc = Human Genome Variation Society protein; GMAF = Global Minor Allele Frequency; miss. = missense; OR = Odd’s Ratio; p. = protein; R 2 = imputation quality; REVEL = Rare Exome Variant Ensemble Learner; TREM2 = Triggering Receptor Expressed on Myeloid Cells 2; UNK = Unknown. Cohort demographics and data availability per clinical measure TREM2 variant carriers had a mean age at diagnosis of 64.4 years (standard deviation (SD) ± 7.1), 67 were female (54%), and 71 died (58%) with a mean age at death of 70.4 ± 7.9 years (Table 2 ). Non-carriers had a mean age at diagnosis of 64.4 ± 7.0 years, 757 were female (52%), and 878 died (60%) with a mean age at death of 71.1 ± 7.7 years. In addition, 57% of TREM2 variant carriers had a positive family history (i.e., having an affected first-degree relative) compared to 45% of non-carriers (Chi-squared (X 2 ) P = 2.1×10 − 2 ), and 71% of TREM2 carriers carried an APOE-ε4 allele compared to 69% of non-carriers (χ 2 P = 0.71). Furthermore, 82% of TREM2 variant carriers were diagnosed with dementia and 18% with MCI, compared to 87% of non-carriers with dementia and 13% with MCI (χ 2 P = 0.). Table 2 Demographics of cohort at baseline stratified by TREM2 variant carriership. Main analysis Exploratory analysis TREM2 variant carriers Total Non-carriers All TREM2 carriers p-value R62H R47H T96K Other d Total 1582 (100) 1459 (92) 123 ( 8 ) 66 ( 4 ) 26 ( 2 ) 16 ( 1 ) 17 ( 1 ) AD-dementia 1364 (86) 1263 (87) 101 (82) 0.215 53 (80) 23 (88) 15 (94) 12 (71) MCI-AD 218 ( 14 ) 196 ( 13 ) 22 ( 18 ) 13 ( 20 ) 3 ( 12 ) 1 ( 6 ) 5 ( 29 ) Female 824 ( 52 ) 757 ( 52 ) 67 ( 54 ) 0.647 36 ( 55 ) 11 ( 42 ) 12 (75) 9 ( 53 ) Education, median (IQR) 1 (0–2) 1 (0–2) 1 (0–2) 0.697 1 (0–2) 1 (0–2) 1 (0.75-1) 1 (0–2) Positive family history a,e 691 ( 46 ) 623 ( 45 ) 68 ( 57 ) 0.021* 35 ( 53 ) 15 ( 62 ) 9 ( 56 ) 11 ( 69 ) ApoE-ε4 carrier b 1076 ( 69 ) 990 ( 69 ) 86 (71) 0.708 47 (72) 16 ( 62 ) 13 (87) 11 ( 65 ) Age at diagnosis, mean ± SD 64.4 ± 7.0 64.4 ± 7.0 64.4 ± 7.1 0.924 65.2 ± 6.5 64.6 ± 9 61.4 ± 6.7 64.4 ± 5.8 Died 949 ( 60 ) 878 ( 60 ) 71 ( 58 ) 0.662 35 ( 53 ) 22 (85) 7 ( 44 ) 8 ( 47 ) Age at death c , mean ± SD 71 ± 7.7 71.1 ± 7.7 70.4 ± 7.9 0.496 70.4 ± 6.8 70.5 ± 9.7 69.9 ± 9.7 71.7 ± 6.5 Table shows n (%) unless otherwise specified. TREM2 variant carriers and non-carriers were compared with Chi-squared tests for categorical variables, and with t-tests for continuous variables. *p-value < 0.05. Total n : a = 1497, b = 1556, c = 948; d = Other TREM2 variants include: D87N, G58A, Q33X, C51Y, R47G, R31F, and A105V; e = Positive family history, i.e. affected first-degree relative. Abbreviations: AD = Alzheimer’s Disease; ApoE-ε4 = Apolipoprotein E ε4; IQR = Interquartile Range; MCI = Mild Cognitive Impairment; NA = Not Available; R47H = p.Arg47His; R62H = p.Arg62His; T96K = p.Thr96Lys; SD = Standard Deviation; TREM2 = Triggering Receptor Expressed on Myeloid Cells 2. Main analysis Effects of all TREM2 variants combined on clinical outcomes Figure 2 shows a heatmap of all outcomes from the main analysis and Fig. 3 summarizes all findings. TREM2 variant carriers did not associate with MMSE at baseline or most neuropsychological domains compared to AD patients not carrying a TREM2 variant, however they did show less impaired scores in visuospatial functioning (standardized β (std β ) 0.21, ± standard error (se) 0.08, P fdr =9.4×10 − 2 ). TREM2 carriership did not associate with MRI clinical ratings or CSF AD biomarker levels. In the longitudinal analysis, TREM2 carriers showed a faster cognitive decline compared to non-carriers; non-carriers declined on average 1.80 points on MMSE per year of follow-up, whereas TREM2 carriers declined on average 2.43 points ( β -difference − 0.63 ± 0.25, P fdr =9.4×10 − 2 ) (Table 3 , Fig. 5 A). TREM2 carriers were not at increased risk of mortality (Hazard Ratio (HR) 1.12, 95% Confidence Interval (CI) 0.9–1.4, P fdr =0.667) (Supplementary Table 3, Supplementary Fig. 1). Table 3 Effect of TREM2 variants on cognitive decline (in MMSE) stratified by TREM2 mutation carriers compared to non-carriers. Estimate Standard Error p-value p-value FDR-corrected✦ Model 1 Time * TREM2 mutation carrier -0.63 0.25 1.10×10 − 2 * 9.37×10 − 2✦ Model 2 Time * R62H 0.58 0.62 0.35 NA Time * R47H -1.4 0.61 2.07×10 − 2 * NA Time * T96K -0.54 0.31 8.52×10 − 2 NA Time * Other TREM2 variants -1.75 0.74 1.82×10 − 2 * NA Shown here are betas derived from linear mixed models with standard error adjusted for age, sex, education level, and disease stage (MCI/dementia), and represent the group * time interaction with non-carriers serving as the reference group. Model 1 is TREM2 mutation carriers versus non-carriers. Model 2 is categories of TREM2 mutation carriers versus non-carriers. ✦ FDR-corrected p < 0.10, *p < 0.05. Other TREM2 variants include: D87N, G58A, Q33X, C51Y, R47G, R31F, and A105V. Abbreviations: FDR = False Discovery Rate; MMSE = Mini-Mental State Examination; NA = Not Available; R47H = p.Arg47His; R62H = p.Arg62His; T96K = p.Thr96Lys; TREM2 = Triggering Receptor Expressed on Myeloid Cells 2. Exploratory analysis of neuroimaging measures Effects of TREM2 variants combined on structural MRI and tau-PET imaging On structural MRI, TREM2 variant carriers had smaller amygdala (std β 0.19 ± 0.10, P = 4.7×10 − 2 ) compared to AD patients not carrying a TREM2 variant (Fig. 5 ). There was no difference in 34 cortical thickness regions or the six other subcortical volumes. Figure 6 A shows the tau-PET scans of each of the TREM2 variant carriers as well as the average tau-PET scan of the non-carrier group. On visual inspection, each of the TREM2 carriers showed clear increased tracer binding in temporoparietal regions, similarly to the average non-carrier AD group. Exploratory analysis of specific TREM2 variants Effects of TREM2 R62H Carriers of the R62H variant ( n = 66, 54% of TREM2 carriers) showed less impaired scores in attention and speed compared to AD patients not carrying a TREM2 variant (std β − 0.28 ± 0.14, P = 0.042). R62H carriers did not show different CSF core AD biomarker levels. On MRI, R62H carriers had less white matter intensities, and less atrophy in the temporal pole compared to non-carriers (Fazekas std β − 0.27 ± 0.13, P = 3.5×10 − 2 ; std β 0.29 ± 0.14, P = 3.7×10 − 2 ), but no difference in other atrophy measures (i.e., MTA, PCA, 40 ROIs). R62H carriership did not associate with EEG abnormality. Longitudinally, the R62H variant did not show a significant effect on cognitive decline as measured by MMSE ( β -0.54 ± 0.31, P = 8.5×10 − 2 ), or on time between diagnosis and death (HR P = 0.76). Effects of TREM2 R47H Carriers of the R47H variant ( n = 26, 21% of TREM2 carriers) showed more impaired scores in language and global cognition compared to AD patients not carrying a TREM2 variant (std β − 0.38 ± 0.17, P = 2.7×10 − 2 and std β − 0.56 ± 0.16, P = 4.4×10 − 4 ). R47H carriers had higher CSF-pTau181 and t-tau levels compared to non-carriers (std β 0.60 ± 0.20, P = 2.7×10 − 3 and std β 0.47 ± 0.20, P = 1.8×10 − 2 ). No effect was found in Aβ42 or NfL levels. On MRI, R47H carriers had less atrophy in the hippocampus and amygdala (std β 0.49 ± 0.20, P = 1.6×10 − 2 and std β 0.69 ± 0.21, P = 9.5×10 − 4 ), whereas the global temporal regions tended to show (non-significantly) more atrophy compared to non-carriers. The lingual and cuneus regions in the occipital lobe showed less cortical atrophy compared to non-carriers (std β 0.47 ± 0.24, P = 4.9×10 − 2 and std β 0.49 ± 0.24, P = 3.7×10 − 2 ). R47H carriership did not associate with MRI visual ratings or EEG visual scores. Longitudinally, R47H carriers showed a faster cognitive decline compared to non-carriers (-3.2 points decline per year of follow-up, β -difference − 1.4 ± 0.61, P = 2.1×10 − 2 , Fig. 4 B). Moreover, R47H carriers were at increased risk of mortality (HR 1.60, 95% CI 1.0-2.5, P = 3.4×10 − 2 ). Effects of TREM2 T96K Carriers of the T96K variant ( n = 16, 13% of TREM2 carriers) had more impaired memory and language compared to AD patients not carrying a TREM2 variant (std β − 0.42 ± 0.18, P = 1.8×10 − 2 and std β − 0.37 ± 0.18, P = 3.4×10 − 2 ). T96K carriership was associated with lower levels of CSF-Aβ42, pTau-181, and t-tau (std β − 0.64 ± 0.21, P = 2.8×10 − 3 , std β − 0.50 ± 0.21, P = 1.9×10 − 2 and std β − 0.45 ± 0.21, P = 3.5×10 − 2 ), but not with different NfL levels compared to non-carriers. On MRI, we observed that T96K carriers had better parietal cortical atrophy scores (std β − 0.56 ± 0.23, P = 1.3×10 − 2 ). T96K carriers showed more hippocampal atrophy (std β − 0.64 ± 0.22, P = 3.3×10 − 3 ), which tended to expand (non-significantly) into the temporal lobe. T96K carriership did not associated with EEG abnormality. Longitudinally, carriers of T96K showed faster cognitive decline ( β -difference − 1.75 ± 0.74, P = 1.8×10 − 2 ). The T96K variant did not show a significant effect on time between diagnosis and death (HR 0.66, 95% CI 0.3–1.5, P = 3.1×10 − 1 ). Effects of other TREM2 risk variants Carriers of other TREM2 variants ( n = 17, 14% of TREM2 carriers; D87N, G58A, Q33X, C51Y, R47G, R31F, and A105V) were grouped due to small sample sizes. These carriers were not significantly different on neuropsychological domains, MRI visual ratings, or the CSF core AD biomarker levels than AD patients not carrying a TREM2 variant. On structural MRI, carriers of other TREM2 variants showed more atrophy in the frontal region (pars orbitalis: std β − 0.59 ± 0.27, P = 3.3×10 − 2 , frontal pole: std β − 0.56 ± 0.28, P = 4.2×10 − 2 ) and posterior cingulate region than non-carriers (isthmus cingulate: std β − 0.61 ± 0.28, P = 2.7×10 − 2 ). Carriers of other TREM2 variants were not associated with EEG abnormality. Longitudinally, other TREM2 variants did not show a significant effect on cognitive decline ( β -difference 0.58 ± 0.62, P = 3.5×10 − 1 ), or on time between diagnosis and death ( P = 7.1×10 − 1 ). Sensitivity analysis After removal of population outliers, familial relations, and adjusting the models for three principal components, the cohort consisted of n = 103 TREM2 variant carriers and n = 1,341 non-carriers. In the main analysis, the TREM2 effect of less impaired visuospatial functioning and faster cognitive decline remained, albeit non-significantly (std β 0.22 ± 0.1, P = 1.4×10 − 1 , and β -0.52 ± 0.3, P = 2.5×10 − 1 ; Supplementary Table 6). In the exploratory analysis, the association of R62H ( n = 60 carriers) with less white matter intensities (std β − 0.30 ± 0.1, P = 2.5×10 − 2 ) remained. The less impaired attention and speed, and less atrophy in the temporal pole were non-significant but in the same direction (std β 0.08 ± 0.1; std β 0.25 ± 0.1). The effects seen in R47H ( n = 26 carriers) remained, i.e., more impaired global cognition (std β − 0.56 ± 0.2, P = 4.4×10 − 4 ), higher pTau-181 and t-tau levels (std β 0.60 ± 0.2, P = 2.6×10 − 3 , std β 0.46 ± 0.2, P = 2.0×10 − 2 ), less atrophy in the hippocampus and amygdala, and less atrophy in the posterior lobe (std β 0.47 ± 0.2, P = 2.3×10 − 2 , std β 0.70 ± 0.2, P = 1.0×10 − 2 , cuneus: std β 0.48 ± 0.2, P = 4.1×10 − 2 ), faster cognitive decline ( β -1.4 ± 0.6, P = 2.2×10 − 2 ), and less time between diagnosis and death ( β 0.46 ± 0.2, P = 3.8×10 − 2 ). The effects seen in T96K could not be calculated as only three T96K carriers were of European ancestry. This was anticipated as T96K is more prevalent in individuals with an African genetic ancestry. The effects seen in other TREM2 variants ( n = 16 carriers) with atrophy in the frontal region (pars orbitalis: std β − 0.55 ± 0.28, and frontal pole: std β − 0.54 ± 0.28, albeit non-significantly) and posterior cingulate region (isthmus cingulate: std β − 0.61 ± 0.28) remained. DISCUSSION This study gives an overview of TREM2 -associated and variant-specific clinical measures in symptomatic Alzheimer’s disease ( n = 1,582 including 7.8% TREM2 variant carriers). Our primary finding was that TREM2 variant carriers do not show a clinically distinct profile at baseline measures compared to patients with AD who do not carry a TREM2 variant, however they do show faster cognitive decline in follow-up. This was most obvious in R47H and T96K carriers who progress nearly twice as fast as non-carriers. The more pronounced cognitive decline in R47H carriers ( n = 26) was accompanied by a shorter time between diagnosis and death, more impaired global cognition, higher CSF-pTau181 and t-tau levels, but with relative sparing of the hippocampal volume, as was previously observed in post-mortem studies ( 17 ). The more pronounced cognitive decline in T96K carriers ( n = 16) was accompanied by more impaired language, lower levels of the core AD biomarkers CSF-Aβ42, pTau-181 and t-tau, and more hippocampal and temporal atrophy. In summary, TREM2 variants carriers, especially R47H and T96K, seemed to have a more aggressive form of AD and the underlying biological mechanism of faster progression could differ between TREM2 variants. This knowledge could help us understand the effects of the TREM2 gene, enrich clinical trials for fast progressors, and inform the development of future TREM2 therapies. TREM2 variant carriers as fast progressors Our findings support that TREM2 variation is involved in processes relevant for cognitive decline. Already in the discovery of TREM2 , R47H showed worse cognition as a function of age than non-carriers ( 1 ), although this study did not differentiate between progression after a diagnosis of AD dementia. Another study by Kim et al. (2022) also reported faster cognitive decline in TREM2 variant carriers ( n = 12 of whom n = 8 R47H carriers) compared to AD non-carriers ( 17 ). R47H and T96K carriers showed twice as fast cognitive decline than non-carriers. Hence, this suggests that specifically R47H and T96K carriers, as fast progressors, are interesting candidates for enrichment in TREM2 -targeting clinical trials for AD. As T96K carriership is common in African ancestry (12.5% of the African (American) population) ( 64 ), targeting this subgroup could offer valuable insights within diverse populations affected by AD. To conclude, the observed TREM2 effects should be considered in studies of disease progression such as clinical trials. This is particularly important when treatment groups are enriched with individuals of African ancestry, as the faster progression induced by the T96K variant, present in over 12% of this population, may even mask a treatment effect. TREM2 carriers present as typical AD The pattern and severity of cognitive impairments can vary among individuals with AD ( 65 ). Our study did not identify any distinct patterns of AD. However, we did observe less impaired visuospatial functioning among carriers of a TREM2 variant compared to non-carriers. One possible explanation for this discrepancy could be that visuospatial difficulties manifest later in the disease progression of TREM2 variant carriers compared to non-carriers. The mechanism underlying the observed differences in visuospatial functioning among TREM2 variant carriers with AD remains unclear and warrants further research, especially considering the complex interplay of brain regions and networks involved in visuospatial functioning ( 66 ) and the borderline significance of the FDR-corrected p-value. TREM2 R47H effect on tau R47H carriers in our cohort showed higher CSF-pTau181 and t-tau levels compared to other variants in TREM2 . This is in line with a GWAS study on CSF, which reported a strong association between AD patients carrying this variant and higher levels of CSF-pTau181 and tau compared to AD non-carriers ( 67 ). In terms of brain atrophy, we found preserved volumes of hippocampus and amygdala and less occipital atrophy than non-carriers. Pathology findings on TREM2 variant carriers align with our R47H findings and reported an overall higher tau burden than AD non-carriers, no altered Aβ burden, and a significantly lower tau burden in hippocampal regions ( 17 , 18 ). Together, this could suggest faster tau accumulation in the brain of R47H carriers than non-carriers ( 68 ), as well as a stronger down-stream effect of amyloid. As tau is a predictor of disease progression as shown in tau-PET studies ( 69 , 70 ), this makes TREM2 an interesting target for disease-modifying therapies to slow progression of disease by enhancing TREM2 activation ( 15 ). However, even though R47H carriers showed higher CSF-pTau181 and t-tau levels, T96K carriers showed lower pTau181 and t-tau levels suggesting another mechanism of tau processing. As TREM2 variants impact tau, this genetic factor could modify the effect of disease-modifying therapies. Hence, TREM2 variants could be considered when evaluating the effect of such therapies. Strengths and limitations The main strength of the study is the use of a large monocentre clinical dataset of TREM2 variant carriers with available data from all clinical measures. This dataset facilitated precise estimations of TREM2 effects on multilayered phenotypes and disease progression. The large sample also enabled exploratory analyses of the separate TREM2 variants. In addition, the consistency of the diagnostic trajectory across all patients from 2000 to 2023 prevents ascertainment bias ( 31 ). Moreover, the strict inclusion criteria, limited to amyloid-confirmed AD patients, and the thorough identification of non-carriers through WES of the TREM2 gene increased the homogeneity of the data and likely the reliability of results. Moving forward, the results of the exploratory analysis should be replicated in other cohorts including more diverse populations. Specifically, the T96K effect on cognitive decline should be replicated by comparing carriers and non-carriers of African ancestry. Our findings suggest that specific TREM2 variants can influence the disease phenotype and progression, highlighting the importance of genetic factors in AD. This knowledge can enhance the understanding of the molecular mechanisms underlying AD and support the development of targeted therapies. Additionally, the observed TREM2 variant-specific effects could be considered as a factor to be included in inclusion criteria to improve clinical trial design and evaluation of the effect of such therapies, potentially leading to more personalized treatment approaches. Declarations ACKNOWLEDGEMENTS We thank all study participants and all personnel involved in data collection for the contributing studies. S.L. was funded for this study by NWO (#733050512, PROMO-GENODE: a PROspective study of MOnoGEnic causes Of Dementia) a substantial donation by Edwin Bouw Fonds, Dioraphte and YOD-INCLUDED (ZonMW project no. 10510032120002), and S.L. is part of the Dutch Dementia Research Programme. S.L. further received funding for the GeneMINDS consortium, which is powered by Health~Holland, Top Sector Life Sciences & Health. Research of Alzheimer Center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting Steun Alzheimercentrum Amsterdam. The chair W.F. is supported by the Pasman stichting. W.F., S.L., H.H., M.H. are recipients of ABOARD, which is a public-private partnership receiving funding from ZonMW (#73305095007) and Health~Holland, Topsector Life Sciences & Health (PPP-allowance; #LSHM20106). More than 30 partners participate in ABOARD. ABOARD also receives funding from de Hersenstichting, Edwin Bouw Fonds and Gieskes-Strijbisfonds. Array genotyping was performed in the context of EADB (European Alzheimer DNA biobank) funded by the JPco-fuND FP-829-029 (ZonMW projectnumber 733051061). V.V. is supported by JPND-funded E-DADS project (ZonMW project #733051106). The work in this manuscript was carried out on the Snellius supercomputer, which is embedded in the Dutch national e-infrastructure with the support of SURF Cooperative. Computing hours were granted in 2016, 2017, 2018 and 2019 to H.H. by the Dutch Research Council (project name: ‘100plus’; project numbers 15318 and 17232). This work also used the Dutch national e-infrastructure with the support of the SURF Cooperative using grant no. EINF-2044 and EINF-5353, granted to V.V. F.B. is supported by the NIHR biomedical research centre at UCLH. AUTHOR INFORMATION These authors jointly supervised this work: Lisa Vermunt, Sven van der Lee. Genomics of Neurodegenerative Diseases and Aging, Department of Human Genetics, Amsterdam UMC location VUmc, Amsterdam, The Netherlands Janna I.R. Dijkstra, Henne Holstege, Marc Hulsman, Georgii Ozgehov, and Sven van der Lee Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC location VUmc, The Netherlands Janna I.R. Dijkstra, Lisa Vermunt, Wiesje M. van der Flier, Henne Holstege, Marc Hulsman, Rik Ossenkoppele, Vikram Venkatraghavan, Sietske Sikkes, Betty Tijms, Everard G.B. Vijverberg, Yolande A.L. Pijnenburg, Alida A. Gouw, Willem de Haan, and Sven van der Lee Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam UMC location VUmc, The Netherlands Lisa Vermunt, Charlotte E. Teunissen Department of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands Wiesje M. van der Flier Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands Emma Coomans, Rik Ossenkoppele, Elsmarieke van de Giessen, Frederik Barkhof Clinical Genetics, Department of Human Genetics, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands Christa M. de Geus Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands Alida A. Gouw, Willem de Haan Clinical Memory Research Unit, Department of Clinical Sciences Mälmo, Lund University, Lund, Sweden Rik Ossenkoppele Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands Marc Hulsman, Georgii Ozgehov Department of Clinical, Neuro- and Developmental Psychology, VU University, Amsterdam, the Netherlands Sietske Sikkes Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, UK Frederik Barkhof Author’s contributions J.D., L.V., and S.v.d.L. designed the study, had full access to the raw data, carried out the final statistical analyses, wrote the manuscript, and had the final responsibility to submit for publication. All authors contributed either demographic, clinical, genetic, biomarker, or neuroimaging data. All authors contributed to the interpretation of the results, critically reviewed the manuscript, and approved the final manuscript. ETHICS DECLARATIONS Competing interests S.L. is part of the GeneMINDS consortium, which is powered by Health~Holland, Top Sector Life Sciences & Health and receives co-financing from Vigil Neuroscience, Prevail therapeutics and Brain Research Center. All funding is paid to his institution. L.V. is supported by grant funding/collaborative study and consultancy/speaker fees from ZonMw (VENI grant), Amsterdam UMC (Startergrant) Stichting Dioraphte (biobank DemenTree), Olink, Lilly, and Roche; all paid to her institution. Research programs of W.F. have been funded by ZonMW, NWO, EU-JPND, EU-IHI, Alzheimer Nederland, Hersenstichting CardioVascular Onderzoek Nederland, Health~Holland, Topsector Life Sciences & Health, stichting Dioraphte, Gieskes-Strijbis fonds, stichting Equilibrio, Edwin Bouw fonds, Pasman stichting, stichting Alzheimer & Neuropsychiatrie Foundation, Philips, Biogen MA Inc, Novartis-NL, Life-MI, AVID, Roche BV, Fujifilm, Eisai, Combinostics. W.F. holds the Pasman chair. W.F. is recipient of TAP-dementia (www.tap-dementia.nl), receiving funding from ZonMw (#10510032120003). TAP-dementia receives co-financing from Avid Radiopharmaceuticals and Amprion. All funding is paid to her institution. W.F. has been an invited speaker at Biogen MA Inc, Danone, Eisai, WebMD Neurology (Medscape), NovoNordisk, Springer Healthcare, European Brain Council. All funding is paid to her institution. W.F. is consultant to Oxford Health Policy Forum CIC, Roche, Biogen MA Inc, and Eisai. All funding is paid to her institution. W.F. participated in advisory boards of Biogen MA Inc, Roche, and Eli Lilly. W.F. is member of the steering committee of EVOKE/EVOKE+ (NovoNordisk). All funding is paid to her institution. W.F. is member of the steering committee of PAVE, and Think Brain Health. W.F. was associate editor of Alzheimer, Research & Therapy in 2020/2021. W.F. is associate editor at Brain. R.O. has received research funding from European Research Council, ZonMw, NWO, National Institute of Health, Alzheimer Association, Alzheimer Nederland, Stichting Dioraphte, Cure Alzheimer’s fund, Health Holland, ERA PerMed, Alzheimerfonden, Hjarnfonden (all paid to the institutions). R.O. has received research support from Avid Radiopharmaceuticals, Janssen Research & Development, Roche, Quanterix and Optina Diagnostics, and has given lectures in symposia sponsored by GE Healthcare. He is an advisory board member for Asceneuron and Bristol Myers Squibb. All the aforementioned has been paid to the institutions. He is an editorial board member of Alzheimer’s Research & Therapy and the European Journal of Nuclear Medicine and Molecular Imaging. F.B. is Steering committee or Data Safety Monitoring Board member for Biogen, Merck, Eisai and Prothena. F.B. is advisory board member for Combinostics, Scottish Brain Sciences. F.B. is consultant for Roche, Celltrion, Rewind Therapeutics, Merck, Bracco. F.B. has research agreements with ADDI, Merck, Biogen, GE Healthcare, Roche. F.B. is co-founder and shareholder of Queen Square Analytics LTD. Ethics approval and consent to participate The study was approved by the Medical Ethical Committee of Amsterdam UMC, location VUmc. All patients provided written informed consent for their clinical data to be used for research purposes. Consent was obtained according to the Declaration of Helsinki. DATA AVAILABILITY Data is provided within the manuscript or supplementary information files. The dataset used and/or the analyses performed can be provided upon reasonable request from data manager of the ADC (W.F.). References Jonsson T, Stefansson H, Steinberg S, Jonsdottir I, Jonsson P V, Snaedal J, et al. Variant of TREM2 Associated with the Risk of Alzheimer’s Disease. N Engl J Med. 2013;368(2):107–23. 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GWAS of cerebrospinal fluid tau levels identifies novel risk variants for Alzheimer’s disease. Neuron. 2013;78(2):256–68. Sanchez-Mejias E, Navarro V, Jimenez S, Sanchez-Mico M, Sanchez-Varo R, Nuñez-Diaz C, et al. Soluble phospho-tau from Alzheimer’s disease hippocampus drives microglial degeneration. Acta Neuropathol. 2016;132(6):897–916. Ossenkoppele R, Pichet Binette A, Groot C, Smith R, Strandberg O, Palmqvist S, et al. Amyloid and tau PET-positive cognitively unimpaired individuals are at high risk for future cognitive decline. Nat Med. 2022;28(11):2381–7. Stephanie J. B. Vos, Frans Verhey, Lutz Frölich, Johannes Kornhuber, Jens Wiltfang, Wolfgang Maier, et al. New criteria for Alzheimer’s disease: Which, when and why? Brain. 2015;138(5):1134–7. Additional Declarations Competing interest reported. S.L. is part of the GeneMINDS consortium, which is powered by Health~Holland, Top Sector Life Sciences & Health and receives co-financing from Vigil Neuroscience, Prevail therapeutics and Brain Research Center. All funding is paid to his institution. L.V. is supported by grant funding/collaborative study and consultancy/speaker fees from ZonMw (VENI grant), Amsterdam UMC (Startergrant) Stichting Dioraphte (biobank DemenTree), Olink, Lilly, and Roche; all paid to her institution. Research programs of W.F. have been funded by ZonMW, NWO, EU-JPND, EU-IHI, Alzheimer Nederland, Hersenstichting CardioVascular Onderzoek Nederland, Health~Holland, Topsector Life Sciences & Health, stichting Dioraphte, Gieskes-Strijbis fonds, stichting Equilibrio, Edwin Bouw fonds, Pasman stichting, stichting Alzheimer & Neuropsychiatrie Foundation, Philips, Biogen MA Inc, Novartis-NL, Life-MI, AVID, Roche BV, Fujifilm, Eisai, Combinostics. W.F. holds the Pasman chair. W.F. is recipient of TAP-dementia ( www.tap-dementia.nl ), receiving funding from ZonMw (#10510032120003). TAP-dementia receives co-financing from Avid Radiopharmaceuticals and Amprion. All funding is paid to her institution. W.F. has been an invited speaker at Biogen MA Inc, Danone, Eisai, WebMD Neurology (Medscape), NovoNordisk, Springer Healthcare, European Brain Council. All funding is paid to her institution. W.F. is consultant to Oxford Health Policy Forum CIC, Roche, Biogen MA Inc, and Eisai. All funding is paid to her institution. W.F. participated in advisory boards of Biogen MA Inc, Roche, and Eli Lilly. W.F. is member of the steering committee of EVOKE/EVOKE+ (NovoNordisk). All funding is paid to her institution. W.F. is member of the steering committee of PAVE, and Think Brain Health. W.F. was associate editor of Alzheimer, Research & Therapy in 2020/2021. W.F. is associate editor at Brain. R.O. has received research funding from European Research Council, ZonMw, NWO, National Institute of Health, Alzheimer Association, Alzheimer Nederland, Stichting Dioraphte, Cure Alzheimer’s fund, Health Holland, ERA PerMed, Alzheimerfonden, Hjarnfonden (all paid to the institutions). R.O. has received research support from Avid Radiopharmaceuticals, Janssen Research & Development, Roche, Quanterix and Optina Diagnostics, and has given lectures in symposia sponsored by GE Healthcare. He is an advisory board member for Asceneuron and Bristol Myers Squibb. All the aforementioned has been paid to the institutions. He is an editorial board member of Alzheimer’s Research & Therapy and the European Journal of Nuclear Medicine and Molecular Imaging. F.B. is Steering committee or Data Safety Monitoring Board member for Biogen, Merck, Eisai and Prothena. F.B. is advisory board member for Combinostics, Scottish Brain Sciences. F.B. is consultant for Roche, Celltrion, Rewind Therapeutics, Merck, Bracco. F.B. has research agreements with ADDI, Merck, Biogen, GE Healthcare, Roche. F.B. is co-founder and shareholder of Queen Square Analytics LTD. Supplementary Files 240722SuppTREM2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5310076","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":368971317,"identity":"39cf2974-0799-4d8a-9807-11b98709ae85","order_by":0,"name":"Janna Dijkstra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACxgbGBiDFzMPYA+bbgMQaDxCrBcRKA4vh1QIFzAwMPGAth8FcvFqYG4CwgsFahrnn+PPHFTXn7da2HwbaUmMTjc9hjGcY0nkYe3sMG88cu5287UwiUMuxtNwGfFoaGA7zMPbzMDY2sN1ONjsA1MLYcJgYLewPGxv+nUs2O/+QWC29DYaNjW0H7MxuELKlmbHhYIMB0C89ZwxnNvYlJ5jdANqSgMcvhu3tDx82VFjbG/akP/jY8M3O3ux8+sMHH2pscGtpBsWCAZABVZEIphNwKAcBeXSGPR7Fo2AUjIJRMEIBAMrHYU2/6DFyAAAAAElFTkSuQmCC","orcid":"","institution":"Genomics of Neurodegenerative Diseases and Aging, Department of Human Genetics, Amsterdam UMC location VUmc","correspondingAuthor":true,"prefix":"","firstName":"Janna","middleName":"","lastName":"Dijkstra","suffix":""},{"id":368971318,"identity":"24acb3b3-51af-45b2-b00b-f685c66d981b","order_by":1,"name":"Lisa Vermunt","email":"","orcid":"","institution":"Neurochemistry Laboratory, Department of Laboratory Medicine, Amsterdam Neuroscience, Amsterdam UMC Vrije Universiteit","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Vermunt","suffix":""},{"id":368971319,"identity":"5ba48a2e-b715-43cf-ad2d-42f350979ee5","order_by":2,"name":"Vikram Venkatraghavan","email":"","orcid":"","institution":"Alzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC location VUmc","correspondingAuthor":false,"prefix":"","firstName":"Vikram","middleName":"","lastName":"Venkatraghavan","suffix":""},{"id":368971320,"identity":"daec0375-937c-44d8-8dd7-e342d5a69c51","order_by":3,"name":"Georgii Ozgehov","email":"","orcid":"","institution":"Genomics of Neurodegenerative Diseases and Aging, Department of Human Genetics, Amsterdam UMC location VUmc","correspondingAuthor":false,"prefix":"","firstName":"Georgii","middleName":"","lastName":"Ozgehov","suffix":""},{"id":368971321,"identity":"62246016-3842-4a95-9374-420166643dc6","order_by":4,"name":"Emma M. 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S.L. is part of the GeneMINDS consortium, which is powered by Health~Holland, Top Sector Life Sciences \u0026 Health and receives co-financing from Vigil Neuroscience, Prevail therapeutics and Brain Research Center. All funding is paid to his institution. L.V. is supported by grant funding/collaborative study and consultancy/speaker fees from ZonMw (VENI grant), Amsterdam UMC (Startergrant) Stichting Dioraphte (biobank DemenTree), Olink, Lilly, and Roche; all paid to her institution.\n\nResearch programs of W.F. have been funded by ZonMW, NWO, EU-JPND, EU-IHI, Alzheimer Nederland, Hersenstichting CardioVascular Onderzoek Nederland, Health~Holland, Topsector Life Sciences \u0026 Health, stichting Dioraphte, Gieskes-Strijbis fonds, stichting Equilibrio, Edwin Bouw fonds, Pasman stichting, stichting Alzheimer \u0026 Neuropsychiatrie Foundation, Philips, Biogen MA Inc, Novartis-NL, Life-MI, AVID, Roche BV, Fujifilm, Eisai, Combinostics. W.F. holds the Pasman chair. W.F. is recipient of TAP-dementia (www.tap-dementia.nl), receiving funding from ZonMw (#10510032120003). TAP-dementia receives co-financing from Avid Radiopharmaceuticals and Amprion. All funding is paid to her institution. W.F. has been an invited speaker at Biogen MA Inc, Danone, Eisai, WebMD Neurology (Medscape), NovoNordisk, Springer Healthcare, European Brain Council. All funding is paid to her institution. W.F. is consultant to Oxford Health Policy Forum CIC, Roche, Biogen MA Inc, and Eisai. All funding is paid to her institution. W.F. participated in advisory boards of Biogen MA Inc, Roche, and Eli Lilly. W.F. is member of the steering committee of EVOKE/EVOKE+ (NovoNordisk). All funding is paid to her institution. W.F. is member of the steering committee of PAVE, and Think Brain Health. W.F. was associate editor of Alzheimer, Research \u0026 Therapy in 2020/2021. W.F. is associate editor at Brain. \n\nR.O. has received research funding from European Research Council, ZonMw, NWO, National Institute of Health, Alzheimer Association, Alzheimer Nederland, Stichting Dioraphte, Cure Alzheimer’s fund, Health Holland, ERA PerMed, Alzheimerfonden, Hjarnfonden (all paid to the institutions). R.O. has received research support from Avid Radiopharmaceuticals, Janssen Research \u0026 Development, Roche, Quanterix and Optina Diagnostics, and has given lectures in symposia sponsored by GE Healthcare. He is an advisory board member for Asceneuron and Bristol Myers Squibb. All the aforementioned has been paid to the institutions. He is an editorial board member of Alzheimer’s Research \u0026 Therapy and the European Journal of Nuclear Medicine and Molecular Imaging.\n\nF.B. is Steering committee or Data Safety Monitoring Board member for Biogen, Merck, Eisai and Prothena. F.B. is advisory board member for Combinostics, Scottish Brain Sciences. F.B. is consultant for Roche, Celltrion, Rewind Therapeutics, Merck, Bracco. F.B. has research agreements with ADDI, Merck, Biogen, GE Healthcare, Roche. F.B. is co-founder and shareholder of Queen Square Analytics LTD.","formattedTitle":"TREM2 Risk Variants with Alzheimer’s Disease Differ in Rate of Cognitive Decline","fulltext":[{"header":"MAIN","content":"\u003cp\u003eRare \u003cem\u003eTREM2\u003c/em\u003e variants are major risk factors for Alzheimer\u0026rsquo;s disease (AD) \u003cspan lang=\"EN-GB\"\u003e(1\u0026ndash;6)\u003c/span\u003e.\u0026nbsp;The triggering receptor expressed on myeloid cell 2 (\u003cem\u003eTREM2\u003c/em\u003e) gene is situated on chromosome 6, it encodes a transmembrane protein of 230 amino acids, and it is expressed exclusively in microglia within the brain \u003cspan lang=\"EN-GB\"\u003e(7)\u003c/span\u003e. The TREM2 protein appears to be a key player in microglial function and AD development \u003cspan lang=\"EN-GB\"\u003e(8\u0026ndash;10)\u003c/span\u003e, and is a target of disease-modifying therapies that are currently in phase II clinical trials \u003cspan lang=\"EN-GB\"\u003e(11\u0026ndash;16)\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eTo date, it is unknown whether carriers of a \u003cem\u003eTREM2\u003c/em\u003e risk variant have a specific clinical presentation of AD. In a retrospective study of autopsied cases, \u003cem\u003eTREM2\u0026nbsp;\u003c/em\u003evariant carriers more often had non-amnestic syndromes compared to non-carriers, faster cognitive decline \u003cspan lang=\"EN-GB\"\u003e(17)\u003c/span\u003e, more tau accumulation, but no altered regional beta-amyloid (Ab) burden \u003cspan lang=\"EN-GB\"\u003e(17,18)\u003c/span\u003e. Another study did not find a distinct neuropsychological profile when comparing \u003cem\u003eTREM2\u0026nbsp;\u003c/em\u003eR47H carriers with AD non-carriers \u003cspan lang=\"EN-GB\"\u003e(19)\u003c/span\u003e. All these studies were small with a maximum number of 31 \u003cem\u003eTREM2\u003c/em\u003e variant carriers. Therefore, the variability of results between studies may be explained by small samples \u003cspan lang=\"EN-GB\"\u003e(20,21)\u003c/span\u003e and heterogeneity of effects introduced by studying populations of different ancestry \u003cspan lang=\"EN-GB\"\u003e(4,21)\u003c/span\u003e, both making it more difficult to find associations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother explanation why associations with clinical measures are inconclusive could be the variant-specific effects. At a molecular level, \u003cem\u003eTREM2\u003c/em\u003e risk variants impair TREM2 activity differently \u003cspan lang=\"EN-GB\"\u003e(7,22\u0026ndash;24)\u003c/span\u003e. Most \u003cem\u003eTREM2\u0026nbsp;\u003c/em\u003erisk variants are situated on exon 2 where the coding corresponds to the Ig-like V type domain \u003cspan lang=\"EN-GB\"\u003e(25)\u003c/span\u003e, suggesting an alteration in the interaction between TREM2 and its ligands \u003cspan lang=\"EN-GB\"\u003e(21,25)\u003c/span\u003e. R47H is located near the exon 2 junction, whereas T96K is located near a conserved part of the protein; thus, these variants could affect distinct functional regions on TREM2\u0026rsquo;s surface \u003cspan lang=\"EN-GB\"\u003e(23)\u003c/span\u003e. Several studies indicated that TREM2 proteins resulting from R47H showed reduced ligand binding and signalling, while conversely proteins resulting from T96K showed enhanced ligand binding \u003cspan lang=\"EN-GB\"\u003e(22,24)\u003c/span\u003e. In addition, the variants R62H and R47H associated with two different AD subtypes based on CSF proteomics \u003cspan lang=\"EN-GB\"\u003e(26)\u003c/span\u003e, which further indicates variant-specific mechanisms. Hence, \u003cem\u003eTREM2\u0026nbsp;\u003c/em\u003evariant-specific mechanisms necessitate variant-specific studies. Studying this hypothesis requires large clinical sample sizes to be able to observe adequate numbers for variant-specific analyses. Previous research indicated that \u003cem\u003eTREM2\u0026nbsp;\u003c/em\u003eR47H carriers seem to show a typical clinical AD profile \u003cspan lang=\"EN-GB\"\u003e(27)\u003c/span\u003e, elevated CSF-Tau \u003cspan lang=\"EN-GB\"\u003e(28)\u003c/span\u003e, and lower grey matter volume in right orbitofrontal regions compared to non-carriers \u003cspan lang=\"EN-GB\"\u003e(19)\u003c/span\u003e. However, another study did not find a significant effect on cross-sectional brain volumes \u003cspan lang=\"EN-GB\"\u003e(29)\u003c/span\u003e. \u003cem\u003eTREM2\u0026nbsp;\u003c/em\u003eR62H and T96K carriers have not yet been studied well.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere we hypothesize that \u003cem\u003eTREM2\u003c/em\u003e risk variants may be associated with distinct clinical measures. Hence, we studied the association of \u003cem\u003eTREM2\u0026nbsp;\u003c/em\u003ecarriership\u003cem\u003e\u0026nbsp;\u003c/em\u003ewith a full range of clinical measures at baseline (neuropsychological profile, visual MRI rating, CSF AD biomarkers, and visual EEG rating) and in follow-up (cognitive decline and survival status) in a large clinical cohort of biomarker confirmed AD patients, followed by an exploratory analysis of neuroimaging measures (structural MRI, and Tau-PET) and an analysis of the specific \u003cem\u003eTREM2\u003c/em\u003e variants (R47H, R62H, T96K and others).\u003c/p\u003e"},{"header":"METHODS","content":"\u003ch3\u003eAmsterdam Dementia Cohort\u003c/h3\u003e\u003cp\u003eWe included 1,582 patients with Mild Cognitive Impairment (MCI) or dementia due to AD, based on confirmed AD biomarkers (in CSF 95% and amyloid PET 5%), and with available genetic data who visited the Alzheimer Centre Amsterdam memory clinic (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). We identified a \u003cem\u003eTREM2\u003c/em\u003e risk variant in 123 AD patients, representing 7.8% of the total cohort, while 1,459 AD patients were confirmed to not carry a \u003cem\u003eTREM2\u003c/em\u003e risk variant. All patients underwent a standardized diagnostic trajectory (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Information was collected on demographics, medical history, family history, neuropsychological investigation, MRI, cerebrospinal fluid (CSF), and blood. Diagnoses were determined by consensus in a multidisciplinary meeting, ensuring that diagnostic criteria were met. Patients were followed over time for reassessments and/or research purposes. Patients with a revised diagnosis at follow-up (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22) were excluded. Mortality data was collected from the Central Public Administration. Patients consented to be part of the Amsterdam Dementia Cohort (ADC) to use their medical information for research and to allow their DNA to be stored in a dedicated biobank.\u003c/p\u003e\n\u003ch3\u003eGenotyping, Imputation of 6 Selected SNPs, and Genetic Selection\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eWhole exome sequencing, Single-Nucleotide Polymorphism (SNP) Arrays and targeted TaqMan assay\u003c/h2\u003e \u003cp\u003eAfter DNA collection, most samples were genotyped with whole exome sequencing (WES) data using Illumina sequencers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,569 of total 1,956). Some samples did not have WES but were genotyped on Illumina Global Screening Array (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;359). We included six missense variants that were imputed with high quality (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.37) (R47H, R62H, T96K, T66M, C51Y, and Q33X) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Standard quality control was performed. Samples were imputed with the Trans-Omics for Precision Medicine (TOPMeD) reference panel (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Processing, quality control and variant calling has been described previously (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). For patients without WES or SNP array data available, made-to-order TaqMan assays were targeted on variants R47H and R62H (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28). Patients with autosomal dominant AD, i.e., carriers of genetic mutations in \u003cem\u003ePSEN1\u003c/em\u003e, \u003cem\u003ePSEN2\u003c/em\u003e and \u003cem\u003eAPP\u003c/em\u003e, were excluded (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenetic selection\u003c/h3\u003e\n\u003cp\u003eA schematic overview of the population is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Whole exome sequencing (WES) (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;110), SNP array (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10), and TaqMan assays (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) identified 123 \u003cem\u003eTREM2\u003c/em\u003e carriers, and WES confirmed that 1,459 AD patients did not carry a \u003cem\u003eTREM2\u003c/em\u003e variant (i.e., non-carriers). We included \u003cem\u003eTREM2\u003c/em\u003e missense variants proven to be associated with AD. These are R47H (NC_000006.12: g.41161515G\u0026thinsp;\u0026gt;\u0026thinsp;C, OR 3.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.5x10\u003csup\u003ee\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/sup\u003e) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), R62H (NC_000006.12: g.41161470G\u0026thinsp;\u0026gt;\u0026thinsp;A, OR 1.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.5x10\u003csup\u003ee\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/sup\u003e) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and T96K (NC_000006.12: g.41129105 C\u0026thinsp;\u0026gt;\u0026thinsp;A, OR 1.2 in African GWAS, p\u0026thinsp;\u0026lt;\u0026thinsp;0.5x10\u003csup\u003ee\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/sup\u003e) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). One single variant was selected based on biological evidence, being D87N (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Other rare damaging variants, including splice variants, were selected based on the burden test association. This test included a Rare Exome Variant Ensemble Learner (REVEL) score\u0026thinsp;\u0026gt;\u0026thinsp;0.25 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), protein truncating variants, or frameshift deletion variants.\u003c/p\u003e\n\u003ch3\u003eClinical measures\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNeuropsychological assessment\u003c/h2\u003e \u003cp\u003eGlobal cognitive functioning was assessed using the Mini-Mental State Examination (MMSE) (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). MMSE data was available for 1,564 (99%) patients. In addition, neuropsychological data was available for 1,519 (96%) patients. We measured five neuropsychological domains, i.e., episodic memory, executive functioning, attention and speed, language, and visuospatial functioning using a standardized neuropsychological assessment comprising eight cognitive tests (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Classification was based on a total of 16 variables as previously reported by Dubbelman et al. (2022) (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Supplementary Data gives an overview of the variables used per domain. In short, each domain was assessed when at least two cognitive tests were available (range of available data per domain: 69\u0026ndash;91%). Z-scores for each variable were calculated per cognitive domain scaled on the baseline mean and standard deviation of the total cohort. The combined domains gave a summarized z-score of global cognition. Longitudinally, we studied cognitive decline using the MMSE. MMSE data had a median follow-up of 1.0 years (interquartile range (IQR) 0.0-2.4); 42% had one measure, 21% had two measures, 15% had three measures, and 22% had more than three measures.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCSF biomarkers\u003c/h3\u003e\n\u003cp\u003eCSF data was available for 1,522 (96%) patients. CSF Aβ42, pTau-181 and total tau were assessed with the Innotest enzyme-linked immunosorbent assay (ELISA) and Aβ42 was drift corrected, or on Elecsys (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Amyloid status for AD was confirmed if the Innotest tau/Aβ42 ratio exceeded 0.46 (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), or the Elecsys pTau-181/Aβ42 ratio exceeded 0.20 (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). If an (additional) amyloid-PET scan was available, amyloid status was confirmed by positive amyloid-PET scan (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;85). To correct for variance between these assays, Elecsys results were converted based on established equations in biomarker associations (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). In addition, we employed available CSF-NfL measurements described in a previous paper defining reference values for the SIMOA NF-light assay (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). To calculate age-adjusted z-scores in CSF, we approached the reference range percentile formula with a linear model with outcome log2(NfL) and age as the predictor in the reference. This resulted in the following formula for calculating age-adjusted NfL z-scores in CSF: [log2(NfL) \u0026minus; (6.661 + (age \u0026times; 0.045))] / 0.736.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMRI clinical ratings\u003c/h2\u003e \u003cp\u003eBrain-MRI data was available for 1,210 (76%) patients. Three visual rating scales as used in clinical assessment were employed: Medial Temporal lobe Atrophy (MTA) (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), Posterior Cortical Atrophy (PCA) (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), and Fazekas score for white matter hyperintensities (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eElectroencephalogram (EEG)\u003c/h3\u003e\n\u003cp\u003eEEG data was available for 1,304 (82%) patients. Details of the acquisition, processing, and visual assessment of the EEG recordings have been described previously (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). The assessment was conducted utilizing a standardized severity scale (1 to 4), representing the spectrum from no abnormalities to severe abnormalities (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). We studied \u0026lsquo;normal\u0026rsquo; versus \u0026lsquo;abnormal\u0026rsquo; EEG scans. \u0026lsquo;Normal\u0026rsquo; was defined as 1\u0026ndash;2 on the severity scale, with or without focal abnormalities. \u0026lsquo;Abnormal\u0026rsquo; were all other possibilities, including epileptiform activity and diffuse abnormalities.\u003c/p\u003e\n\u003ch3\u003eNeuroimaging measures\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMRI structural brain imaging\u003c/h2\u003e \u003cp\u003eStructural MRI data was available for 1,069 (67%) patients. Quantitative image analysis was done for several regions of interest based on Desikan Kiliany Atlas by FreeSurfer v7.1 (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Details of the MRI data processing (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) and quality check process (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) have been described previously. MRI data were from 12 scanners. The scanner-related effects were removed using a procedure called ComBat (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). We studied 34 cortical thickness measures (mm) and seven subcortical volumes (mm\u003csup\u003e3\u003c/sup\u003e). We averaged measures of the left and right hemisphere per region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e[\u003csup\u003e18\u003c/sup\u003eF]flortaucipir PET\u003c/h2\u003e \u003cp\u003eTau-PET data was available for 67 (4%) patients, including four \u003cem\u003eTREM2\u003c/em\u003e variant carriers (R47H, R62H, G58A, and D87N). Details on the acquisition and processing of the [\u003csup\u003e18\u003c/sup\u003eF]flortaucipir PET images have been described previously (\u003cspan additionalcitationids=\"CR55 CR56\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). For semi-quantification, we calculated standardized uptake value ratio (SUVr) using the gray matter cerebellum as reference region in six different composite regions of interest from the Hammers and Svarer templates: 1) a medial temporal region (including the entorhinal cortex, parahippocampal gyrus, amygdala, and fusiform gyrus), 2) a lateral temporal region (including the superior, middle and inferior temporal gyrus, and the posterior temporal lobe), 3) a medial parietal region (including the posterior cingulate), 4) a lateral parietal region (including the superior parietal gyrus and the inferolateral remainder of the parietal lobe), 5) an occipital region (including the cuneus, lingual gyrus and lateral remainder of the occipital lobe), and 6) a frontal region (including the superior, middle and inferior frontal gyrus, gyrus rectus, and orbitofrontal gyrus). In line with previous work (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), we contrasted the SUVRs for the AD cases with a \u003cem\u003eTREM2\u003c/em\u003e variant against the observed distribution of the AD cases without a TREM2 variant. Hippocampi could not be adequately assessed with this tracer due to off-target binding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAnalyses were performed in R version 4.3.0 (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) and Python version 3.9 (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Education level was converted from the Dutch Verhage scale (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e) to the Standard Classification of Education, i.e., low, medium, and high (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). For individuals with missing education level (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7), the median level was imputed, i.e., high education. Baseline characteristics of \u003cem\u003eTREM2\u003c/em\u003e variant carriers and non-carriers were compared with Chi-squared tests for categorical variables, and with t-tests for continuous variables.\u003c/p\u003e \u003cp\u003eIn the main analysis, all \u003cem\u003eTREM2\u003c/em\u003e variants were combined to study the effect of \u003cem\u003eTREM2\u003c/em\u003e mutation status with clinical measures relative to the reference group, i.e., non-carriers. Linear regression models were used to associate \u003cem\u003eTREM2\u003c/em\u003e mutation status with MMSE at baseline, the five neuropsychological domains and combined global cognition, MRI features, and CSF biomarkers. All measurements were scaled for comparability. Logistic regression models were used to associate \u003cem\u003eTREM2\u003c/em\u003e mutation status with an EEG abnormality score. Regression analyses were conducted separately and were adjusted for age and sex as dependent variables, and MMSE at baseline, neuropsychological profiles and EEG scores were also adjusted for education level (i.e., low, middle, high) (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e) and disease stage (i.e., MCI or dementia). With Cox proportional hazards models, we associated \u003cem\u003eTREM2\u003c/em\u003e mutation status with time in years between diagnosis and death adjusted for age at diagnosis, sex, education level, disease stage, and MMSE at baseline. Linear mixed models were used to study associations of \u003cem\u003eTREM2\u003c/em\u003e variants and change in MMSE scores over time, and were adjusted for age at diagnosis, sex, education level, and disease stage. The models included a random intercept and interaction effect over time. A p-value corrected for a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.10 was considered statistically significant.\u003c/p\u003e \u003cp\u003eIn the exploratory analysis of neuroimaging outcomes, we tested with linear regression models the effect of \u003cem\u003eTREM2\u003c/em\u003e status with 41 regions of interest (ROI) from structural MRI, and models were adjusted for age, sex, and estimated intracranial volume.\u003c/p\u003e \u003cp\u003eIn the subsequent exploratory analysis, \u003cem\u003eTREM2\u003c/em\u003e variants were grouped to associate variant-specific \u003cem\u003eTREM2\u003c/em\u003e effects with each clinical measure relative to the reference group, comparing (i) R62H carriers vs. non-carriers, (ii) R47H carriers vs. non-carriers, (iii) T96K carriers vs. non-carriers, and (iv) other \u003cem\u003eTREM2\u003c/em\u003e carriers vs. non-carriers. In the cox regression models and linear mixed models, we tested a separate \u003cem\u003eTREM2\u003c/em\u003e effect relative to the reference group comparing categorical (R62H, R47H, T96K, other) carriers vs. non-carriers. Carriers of two different mutations were categorized for the variant conferring the highest risk. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eIn the sensitivity analysis, we excluded patients of non-European descent (determined through 1000 Genomes clustering) (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e) and patients who had a familial relationship (identity-by-descent\u0026thinsp;\u0026ge;\u0026thinsp;0.2). All the models described above were further adjusted for three principal components.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003ePopulation and\u003c/b\u003e \u003cb\u003eTREM2\u003c/b\u003e \u003cb\u003echaracteristics\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTen different \u003cem\u003eTREM2\u003c/em\u003e risk variants were identified (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All genetic variants were located on exon 2 (of 5 exons). The most prevalent mutations were R62H (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66, 54%), R47H (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26, 21%; among which one also carried R62H) and T96K (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16, 13%, among which two were homozygous). We further observed rare mutations in seventeen patients (14% of \u003cem\u003eTREM2\u003c/em\u003e carriers) that carried one of the following heterozygous missense, splice, or protein truncating mutations: p.Ser31Phe (R31F), p.Gln33Ter (Q33X; among which one also carrier R62H), p.Arg47Gly (R47G), p.Cys51Tyr (C51Y), p.Gly58Ala (G58A), p.Asp87Asn (D87N), and p.Ala105Val (A105V).\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\u003eGenetic descriptives of \u003cem\u003eTREM2\u003c/em\u003e variants identified in the cohort at baseline.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbbrev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocation*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHGVSc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHGVSp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConsequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eREVEL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCADD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOR**\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003en***\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR62H\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:41161469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.185G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.Arg62His\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenotyped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.7\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR47H\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:41161514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.140G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.Arg47His\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.09\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT96K\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:41161367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.287C\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.Thr96Lys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.2\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eD87N\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:41161395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.259G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.Asp87Asn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.71\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ33X\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:41161557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.97C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.Gln33Ter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStop-gained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.9\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eG58A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:41161481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.173G\u0026thinsp;\u0026gt;\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.Gly58Ala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC51Y\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:41161502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.152G\u0026thinsp;\u0026gt;\u0026thinsp;A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.Cys51Tyr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR47G\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:41161515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.139C\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.Arg47Gly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS31F\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:41161562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.92C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.Ser31Phe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eA105V\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6:41161340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ec.314C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.Ala105Val\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMissense variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eUNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e*GRCh38 assembly, EnsGENE database annotation from 2023.0316, MANE SELECT: NM_018965.4.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e**Based on 1. Sims et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), 2. Holstege et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), and 3. Sherva et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e***total \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;123; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4 carriers with double mutation: one R62H/R47H carrier, one R62H/Q33X carrier, and two T96K homozygote carriers.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eAbbreviations: Abbrev. = Abbreviation; AD\u0026thinsp;=\u0026thinsp;Alzheimer\u0026rsquo;s Disease; CADD\u0026thinsp;=\u0026thinsp;Combined Annotation Dependent Depletion; HGVSc\u0026thinsp;=\u0026thinsp;Human Genome Variation Society coding; HGVSc\u0026thinsp;=\u0026thinsp;Human Genome Variation Society protein; GMAF\u0026thinsp;=\u0026thinsp;Global Minor Allele Frequency; miss. = missense; OR\u0026thinsp;=\u0026thinsp;Odd\u0026rsquo;s Ratio; p. = protein; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;imputation quality; REVEL\u0026thinsp;=\u0026thinsp;Rare Exome Variant Ensemble Learner; \u003cem\u003eTREM2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Triggering Receptor Expressed on Myeloid Cells 2; UNK\u0026thinsp;=\u0026thinsp;Unknown.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCohort demographics and data availability per clinical measure\u003c/h2\u003e \u003cp\u003e \u003cem\u003eTREM2\u003c/em\u003e variant carriers had a mean age at diagnosis of 64.4 years (standard deviation (SD)\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1), 67 were female (54%), and 71 died (58%) with a mean age at death of 70.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9 years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Non-carriers had a mean age at diagnosis of 64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0 years, 757 were female (52%), and 878 died (60%) with a mean age at death of 71.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7 years. In addition, 57% of \u003cem\u003eTREM2\u003c/em\u003e variant carriers had a positive family history (i.e., having an affected first-degree relative) compared to 45% of non-carriers (Chi-squared (X\u003csup\u003e2\u003c/sup\u003e) \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), and 71% of \u003cem\u003eTREM2\u003c/em\u003e carriers carried an \u003cem\u003eAPOE-ε4\u003c/em\u003e allele compared to 69% of non-carriers (χ\u003csup\u003e2\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71). Furthermore, 82% of \u003cem\u003eTREM2\u003c/em\u003e variant carriers were diagnosed with dementia and 18% with MCI, compared to 87% of non-carriers with dementia and 13% with MCI (χ\u003csup\u003e2\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.).\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\u003eDemographics of cohort at baseline stratified by \u003cem\u003eTREM2\u003c/em\u003e variant carriership.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\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\u003eMain analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eExploratory analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eTREM2\u003c/em\u003e variant carriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\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\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-carriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAll \u003cem\u003eTREM2\u003c/em\u003e carriers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR62H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR47H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eT96K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eOther\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1582 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1459 (92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e16 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e17 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAD-dementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1364 (86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1263 (87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e15 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e12 (71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCI-AD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e218 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e5 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e824 (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e757 (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36 (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11 (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e12 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e9 (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, \u003cem\u003emedian (IQR)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1 (0.75-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive family history\u003csup\u003ea,e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e691 (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e623 (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35 (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15 (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e9 (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e11 (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoE-ε4 carrier\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1076 (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e990 (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16 (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e13 (87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e11 (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis, \u003cem\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e64.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e61.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e949 (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e878 (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35 (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22 (85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7 (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e8 (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at death\u003csup\u003ec\u003c/sup\u003e, \u003cem\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e69.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e71.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eTable shows n (%) unless otherwise specified. \u003cem\u003eTREM2\u003c/em\u003e variant carriers and non-carriers were compared with Chi-squared tests for categorical variables, and with t-tests for continuous variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e*p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eTotal \u003cem\u003en\u003c/em\u003e: a\u0026thinsp;=\u0026thinsp;1497, b\u0026thinsp;=\u0026thinsp;1556, c\u0026thinsp;=\u0026thinsp;948; d\u0026thinsp;=\u0026thinsp;Other \u003cem\u003eTREM2\u003c/em\u003e variants include: D87N, G58A, Q33X, C51Y, R47G, R31F, and A105V; e\u0026thinsp;=\u0026thinsp;Positive family history, i.e. affected first-degree relative.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eAbbreviations: AD\u0026thinsp;=\u0026thinsp;Alzheimer\u0026rsquo;s Disease; ApoE-ε4\u0026thinsp;=\u0026thinsp;Apolipoprotein E ε4; IQR\u0026thinsp;=\u0026thinsp;Interquartile Range; MCI\u0026thinsp;=\u0026thinsp;Mild Cognitive Impairment; NA\u0026thinsp;=\u0026thinsp;Not Available; R47H\u0026thinsp;=\u0026thinsp;p.Arg47His; R62H\u0026thinsp;=\u0026thinsp;p.Arg62His; T96K\u0026thinsp;=\u0026thinsp;p.Thr96Lys; SD\u0026thinsp;=\u0026thinsp;Standard Deviation; \u003cem\u003eTREM2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Triggering Receptor Expressed on Myeloid Cells 2.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMain analysis\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eEffects of all TREM2 variants combined on clinical outcomes\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows a heatmap of all outcomes from the main analysis and Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes all findings. \u003cem\u003eTREM2\u003c/em\u003e variant carriers did not associate with MMSE at baseline or most neuropsychological domains compared to AD patients not carrying a \u003cem\u003eTREM2\u003c/em\u003e variant, however they did show less impaired scores in visuospatial functioning (standardized \u003cem\u003eβ\u003c/em\u003e (std\u003cem\u003eβ\u003c/em\u003e) 0.21, \u0026plusmn; standard error (se) 0.08, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003efdr\u003c/em\u003e\u003c/sub\u003e=9.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). \u003cem\u003eTREM2\u003c/em\u003e carriership did not associate with MRI clinical ratings or CSF AD biomarker levels. In the longitudinal analysis, \u003cem\u003eTREM2\u003c/em\u003e carriers showed a faster cognitive decline compared to non-carriers; non-carriers declined on average 1.80 points on MMSE per year of follow-up, whereas \u003cem\u003eTREM2\u003c/em\u003e carriers declined on average 2.43 points (\u003cem\u003eβ\u003c/em\u003e-difference \u0026minus;\u0026thinsp;0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003efdr\u003c/em\u003e\u003c/sub\u003e=9.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). \u003cem\u003eTREM2\u003c/em\u003e carriers were not at increased risk of mortality (Hazard Ratio (HR) 1.12, 95% Confidence Interval (CI) 0.9\u0026ndash;1.4, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003efdr\u003c/em\u003e\u003c/sub\u003e=0.667) (Supplementary Table\u0026nbsp;3, Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of \u003cem\u003eTREM2\u003c/em\u003e variants on cognitive decline (in MMSE) stratified by \u003cem\u003eTREM2\u003c/em\u003e mutation carriers compared to non-carriers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value FDR-corrected✦\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime * \u003cem\u003eTREM2\u003c/em\u003e mutation carrier\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.37\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2✦\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime * R62H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime * R47H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.07\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime * T96K\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.52\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime * Other \u003cem\u003eTREM2\u003c/em\u003e variants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eShown here are betas derived from linear mixed models with standard error adjusted for age, sex, education level, and disease stage (MCI/dementia), and represent the group * time interaction with non-carriers serving as the reference group. Model 1 is \u003cem\u003eTREM2\u003c/em\u003e mutation carriers versus non-carriers. Model 2 is categories of \u003cem\u003eTREM2\u003c/em\u003e mutation carriers versus non-carriers.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e✦ FDR-corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.10, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eOther \u003cem\u003eTREM2\u003c/em\u003e variants include: D87N, G58A, Q33X, C51Y, R47G, R31F, and A105V.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: FDR\u0026thinsp;=\u0026thinsp;False Discovery Rate; MMSE\u0026thinsp;=\u0026thinsp;Mini-Mental State Examination; NA\u0026thinsp;=\u0026thinsp;Not Available; R47H\u0026thinsp;=\u0026thinsp;p.Arg47His; R62H\u0026thinsp;=\u0026thinsp;p.Arg62His; T96K\u0026thinsp;=\u0026thinsp;p.Thr96Lys; \u003cem\u003eTREM2\u003c/em\u003e\u0026thinsp;=\u0026thinsp;Triggering Receptor Expressed on Myeloid Cells 2.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eExploratory analysis of neuroimaging measures\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eEffects of TREM2 variants combined on structural MRI and tau-PET imaging\u003c/h2\u003e \u003cp\u003eOn structural MRI, \u003cem\u003eTREM2\u003c/em\u003e variant carriers had smaller amygdala (std\u003cem\u003eβ\u003c/em\u003e 0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) compared to AD patients not carrying a \u003cem\u003eTREM2\u003c/em\u003e variant (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e). There was no difference in 34 cortical thickness regions or the six other subcortical volumes. Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e6\u003c/span\u003eA shows the tau-PET scans of each of the \u003cem\u003eTREM2\u003c/em\u003e variant carriers as well as the average tau-PET scan of the non-carrier group. On visual inspection, each of the \u003cem\u003eTREM2\u003c/em\u003e carriers showed clear increased tracer binding in temporoparietal regions, similarly to the average non-carrier AD group.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExploratory analysis of specific\u003c/b\u003e \u003cb\u003eTREM2\u003c/b\u003e \u003cb\u003evariants\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEffects of TREM2 R62H\u003c/h2\u003e \u003cp\u003eCarriers of the R62H variant (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66, 54% of \u003cem\u003eTREM2\u003c/em\u003e carriers) showed less impaired scores in attention and speed compared to AD patients not carrying a \u003cem\u003eTREM2\u003c/em\u003e variant (std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). R62H carriers did not show different CSF core AD biomarker levels. On MRI, R62H carriers had less white matter intensities, and less atrophy in the temporal pole compared to non-carriers (Fazekas std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e; std\u003cem\u003eβ\u003c/em\u003e 0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), but no difference in other atrophy measures (i.e., MTA, PCA, 40 ROIs). R62H carriership did not associate with EEG abnormality. Longitudinally, the R62H variant did not show a significant effect on cognitive decline as measured by MMSE (\u003cem\u003eβ\u003c/em\u003e -0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), or on time between diagnosis and death (HR \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eEffects of TREM2 R47H\u003c/h2\u003e \u003cp\u003eCarriers of the R47H variant (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26, 21% of \u003cem\u003eTREM2\u003c/em\u003e carriers) showed more impaired scores in language and global cognition compared to AD patients not carrying a \u003cem\u003eTREM2\u003c/em\u003e variant (std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). R47H carriers had higher CSF-pTau181 and t-tau levels compared to non-carriers (std\u003cem\u003eβ\u003c/em\u003e 0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and std\u003cem\u003eβ\u003c/em\u003e 0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). No effect was found in Aβ42 or NfL levels. On MRI, R47H carriers had less atrophy in the hippocampus and amygdala (std\u003cem\u003eβ\u003c/em\u003e 0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.6\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and std\u003cem\u003eβ\u003c/em\u003e 0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), whereas the global temporal regions tended to show (non-significantly) more atrophy compared to non-carriers. The lingual and cuneus regions in the occipital lobe showed less cortical atrophy compared to non-carriers (std\u003cem\u003eβ\u003c/em\u003e 0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.9\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and std\u003cem\u003eβ\u003c/em\u003e 0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). R47H carriership did not associate with MRI visual ratings or EEG visual scores. Longitudinally, R47H carriers showed a faster cognitive decline compared to non-carriers (-3.2 points decline per year of follow-up, \u003cem\u003eβ\u003c/em\u003e-difference \u0026minus;\u0026thinsp;1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Moreover, R47H carriers were at increased risk of mortality (HR 1.60, 95% CI 1.0-2.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eEffects of TREM2 T96K\u003c/h2\u003e \u003cp\u003eCarriers of the T96K variant (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16, 13% of \u003cem\u003eTREM2\u003c/em\u003e carriers) had more impaired memory and language compared to AD patients not carrying a \u003cem\u003eTREM2\u003c/em\u003e variant (std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). T96K carriership was associated with lower levels of CSF-Aβ42, pTau-181, and t-tau (std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.9\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e and std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), but not with different NfL levels compared to non-carriers. On MRI, we observed that T96K carriers had better parietal cortical atrophy scores (std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). T96K carriers showed more hippocampal atrophy (std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), which tended to expand (non-significantly) into the temporal lobe. T96K carriership did not associated with EEG abnormality. Longitudinally, carriers of T96K showed faster cognitive decline (\u003cem\u003eβ\u003c/em\u003e-difference \u0026minus;\u0026thinsp;1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). The T96K variant did not show a significant effect on time between diagnosis and death (HR 0.66, 95% CI 0.3\u0026ndash;1.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eEffects of other TREM2 risk variants\u003c/h2\u003e \u003cp\u003eCarriers of other \u003cem\u003eTREM2\u003c/em\u003e variants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17, 14% of \u003cem\u003eTREM2\u003c/em\u003e carriers; D87N, G58A, Q33X, C51Y, R47G, R31F, and A105V) were grouped due to small sample sizes. These carriers were not significantly different on neuropsychological domains, MRI visual ratings, or the CSF core AD biomarker levels than AD patients not carrying a \u003cem\u003eTREM2\u003c/em\u003e variant. On structural MRI, carriers of other \u003cem\u003eTREM2\u003c/em\u003e variants showed more atrophy in the frontal region (pars orbitalis: std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, frontal pole: std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) and posterior cingulate region than non-carriers (isthmus cingulate: std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). Carriers of other \u003cem\u003eTREM2\u003c/em\u003e variants were not associated with EEG abnormality. Longitudinally, other \u003cem\u003eTREM2\u003c/em\u003e variants did not show a significant effect on cognitive decline (\u003cem\u003eβ\u003c/em\u003e-difference 0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), or on time between diagnosis and death (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eAfter removal of population outliers, familial relations, and adjusting the models for three principal components, the cohort consisted of \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;103 \u003cem\u003eTREM2\u003c/em\u003e variant carriers and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,341 non-carriers. In the main analysis, the \u003cem\u003eTREM2\u003c/em\u003e effect of less impaired visuospatial functioning and faster cognitive decline remained, albeit non-significantly (std\u003cem\u003eβ\u003c/em\u003e 0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and \u003cem\u003eβ\u003c/em\u003e -0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Supplementary Table\u0026nbsp;6). In the exploratory analysis, the association of R62H (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;60 carriers) with less white matter intensities (std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) remained. The less impaired attention and speed, and less atrophy in the temporal pole were non-significant but in the same direction (std\u003cem\u003eβ\u003c/em\u003e 0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1; std\u003cem\u003eβ\u003c/em\u003e 0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1). The effects seen in R47H (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26 carriers) remained, i.e., more impaired global cognition (std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), higher pTau-181 and t-tau levels (std\u003cem\u003eβ\u003c/em\u003e 0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.6\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, std\u003cem\u003eβ\u003c/em\u003e 0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.0\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), less atrophy in the hippocampus and amygdala, and less atrophy in the posterior lobe (std\u003cem\u003eβ\u003c/em\u003e 0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, std\u003cem\u003eβ\u003c/em\u003e 0.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.0\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e, cuneus: std\u003cem\u003eβ\u003c/em\u003e 0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), faster cognitive decline (\u003cem\u003eβ\u003c/em\u003e -1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), and less time between diagnosis and death (\u003cem\u003eβ\u003c/em\u003e 0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e). The effects seen in T96K could not be calculated as only three T96K carriers were of European ancestry. This was anticipated as T96K is more prevalent in individuals with an African genetic ancestry. The effects seen in other \u003cem\u003eTREM2\u003c/em\u003e variants (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16 carriers) with atrophy in the frontal region (pars orbitalis: std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28, and frontal pole: std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28, albeit non-significantly) and posterior cingulate region (isthmus cingulate: std\u003cem\u003eβ\u003c/em\u003e \u0026minus;\u0026thinsp;0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28) remained.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study gives an overview of \u003cem\u003eTREM2\u003c/em\u003e-associated and variant-specific clinical measures in symptomatic Alzheimer\u0026rsquo;s disease (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,582 including 7.8% \u003cem\u003eTREM2\u003c/em\u003e variant carriers). Our primary finding was that \u003cem\u003eTREM2\u003c/em\u003e variant carriers do not show a clinically distinct profile at baseline measures compared to patients with AD who do not carry a \u003cem\u003eTREM2\u003c/em\u003e variant, however they do show faster cognitive decline in follow-up. This was most obvious in R47H and T96K carriers who progress nearly twice as fast as non-carriers. The more pronounced cognitive decline in R47H carriers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26) was accompanied by a shorter time between diagnosis and death, more impaired global cognition, higher CSF-pTau181 and t-tau levels, but with relative sparing of the hippocampal volume, as was previously observed in post-mortem studies (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The more pronounced cognitive decline in T96K carriers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16) was accompanied by more impaired language, lower levels of the core AD biomarkers CSF-Aβ42, pTau-181 and t-tau, and more hippocampal and temporal atrophy. In summary, \u003cem\u003eTREM2\u003c/em\u003e variants carriers, especially R47H and T96K, seemed to have a more aggressive form of AD and the underlying biological mechanism of faster progression could differ between \u003cem\u003eTREM2\u003c/em\u003e variants. This knowledge could help us understand the effects of the \u003cem\u003eTREM2\u003c/em\u003e gene, enrich clinical trials for fast progressors, and inform the development of future TREM2 therapies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTREM2\u003c/b\u003e \u003cb\u003evariant carriers as fast progressors\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur findings support that \u003cem\u003eTREM2\u003c/em\u003e variation is involved in processes relevant for cognitive decline. Already in the discovery of \u003cem\u003eTREM2\u003c/em\u003e, R47H showed worse cognition as a function of age than non-carriers (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), although this study did not differentiate between progression after a diagnosis of AD dementia. Another study by Kim et al. (2022) also reported faster cognitive decline in \u003cem\u003eTREM2\u003c/em\u003e variant carriers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12 of whom \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8 R47H carriers) compared to AD non-carriers (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). R47H and T96K carriers showed twice as fast cognitive decline than non-carriers. Hence, this suggests that specifically R47H and T96K carriers, as fast progressors, are interesting candidates for enrichment in \u003cem\u003eTREM2\u003c/em\u003e-targeting clinical trials for AD. As T96K carriership is common in African ancestry (12.5% of the African (American) population) (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e), targeting this subgroup could offer valuable insights within diverse populations affected by AD. To conclude, the observed \u003cem\u003eTREM2\u003c/em\u003e effects should be considered in studies of disease progression such as clinical trials. This is particularly important when treatment groups are enriched with individuals of African ancestry, as the faster progression induced by the T96K variant, present in over 12% of this population, may even mask a treatment effect.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTREM2\u003c/b\u003e \u003cb\u003ecarriers present as typical AD\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe pattern and severity of cognitive impairments can vary among individuals with AD (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Our study did not identify any distinct patterns of AD. However, we did observe less impaired visuospatial functioning among carriers of a \u003cem\u003eTREM2\u003c/em\u003e variant compared to non-carriers. One possible explanation for this discrepancy could be that visuospatial difficulties manifest later in the disease progression of \u003cem\u003eTREM2\u003c/em\u003e variant carriers compared to non-carriers. The mechanism underlying the observed differences in visuospatial functioning among \u003cem\u003eTREM2\u003c/em\u003e variant carriers with AD remains unclear and warrants further research, especially considering the complex interplay of brain regions and networks involved in visuospatial functioning (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) and the borderline significance of the FDR-corrected p-value.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTREM2\u003c/b\u003e \u003cb\u003eR47H effect on tau\u003c/b\u003e\u003c/p\u003e \u003cp\u003eR47H carriers in our cohort showed higher CSF-pTau181 and t-tau levels compared to other variants in \u003cem\u003eTREM2\u003c/em\u003e. This is in line with a GWAS study on CSF, which reported a strong association between AD patients carrying this variant and higher levels of CSF-pTau181 and tau compared to AD non-carriers (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). In terms of brain atrophy, we found preserved volumes of hippocampus and amygdala and less occipital atrophy than non-carriers. Pathology findings on \u003cem\u003eTREM2\u003c/em\u003e variant carriers align with our R47H findings and reported an overall higher tau burden than AD non-carriers, no altered Aβ burden, and a significantly lower tau burden in hippocampal regions (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Together, this could suggest faster tau accumulation in the brain of R47H carriers than non-carriers (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e), as well as a stronger down-stream effect of amyloid.\u003c/p\u003e \u003cp\u003eAs tau is a predictor of disease progression as shown in tau-PET studies (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e), this makes TREM2 an interesting target for disease-modifying therapies to slow progression of disease by enhancing TREM2 activation (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, even though R47H carriers showed higher CSF-pTau181 and t-tau levels, T96K carriers showed lower pTau181 and t-tau levels suggesting another mechanism of tau processing. As \u003cem\u003eTREM2\u003c/em\u003e variants impact tau, this genetic factor could modify the effect of disease-modifying therapies. Hence, \u003cem\u003eTREM2\u003c/em\u003e variants could be considered when evaluating the effect of such therapies.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe main strength of the study is the use of a large monocentre clinical dataset of \u003cem\u003eTREM2\u003c/em\u003e variant carriers with available data from all clinical measures. This dataset facilitated precise estimations of \u003cem\u003eTREM2\u003c/em\u003e effects on multilayered phenotypes and disease progression. The large sample also enabled exploratory analyses of the separate \u003cem\u003eTREM2\u003c/em\u003e variants. In addition, the consistency of the diagnostic trajectory across all patients from 2000 to 2023 prevents ascertainment bias (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Moreover, the strict inclusion criteria, limited to amyloid-confirmed AD patients, and the thorough identification of non-carriers through WES of the \u003cem\u003eTREM2\u003c/em\u003e gene increased the homogeneity of the data and likely the reliability of results. Moving forward, the results of the exploratory analysis should be replicated in other cohorts including more diverse populations. Specifically, the T96K effect on cognitive decline should be replicated by comparing carriers and non-carriers of African ancestry.\u003c/p\u003e \u003cp\u003eOur findings suggest that specific \u003cem\u003eTREM2\u003c/em\u003e variants can influence the disease phenotype and progression, highlighting the importance of genetic factors in AD. This knowledge can enhance the understanding of the molecular mechanisms underlying AD and support the development of targeted therapies. Additionally, the observed \u003cem\u003eTREM2\u003c/em\u003e variant-specific effects could be considered as a factor to be included in inclusion criteria to improve clinical trial design and evaluation of the effect of such therapies, potentially leading to more personalized treatment approaches.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all study participants and all personnel involved in data collection for the contributing studies. S.L. was funded for this study by NWO (#733050512, PROMO-GENODE: a PROspective study of MOnoGEnic causes Of Dementia) a substantial donation by Edwin Bouw Fonds, Dioraphte and YOD-INCLUDED (ZonMW project no. 10510032120002), and S.L. is part of the Dutch Dementia Research Programme. S.L. further received funding for the GeneMINDS consortium, which is powered by Health~Holland, Top Sector Life Sciences \u0026amp; Health. Research of Alzheimer Center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting Steun Alzheimercentrum Amsterdam. The chair W.F. is supported by the Pasman stichting. W.F., S.L., H.H., M.H. are recipients of ABOARD, which is a public-private partnership receiving funding from ZonMW (#73305095007) and Health~Holland, Topsector Life Sciences \u0026amp; Health (PPP-allowance; #LSHM20106). More than 30 partners participate in ABOARD. ABOARD also receives funding from de Hersenstichting, Edwin Bouw Fonds and Gieskes-Strijbisfonds. Array genotyping was performed in the context of EADB (European Alzheimer DNA biobank) funded by the JPco-fuND FP-829-029 (ZonMW projectnumber 733051061). V.V. is supported by JPND-funded E-DADS project (ZonMW project #733051106). The work in this manuscript was carried out on the Snellius supercomputer, which is embedded in the Dutch national e-infrastructure with the support of SURF Cooperative. Computing hours were granted in 2016, 2017, 2018 and 2019 to H.H. by the Dutch Research Council (project name: \u0026lsquo;100plus\u0026rsquo;; project numbers 15318 and 17232). This work also used the Dutch national e-infrastructure with the support of the SURF Cooperative using grant no. EINF-2044 and EINF-5353, granted to V.V. F.B. is supported by the NIHR biomedical research centre at UCLH.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThese authors jointly supervised this work: Lisa Vermunt, Sven van der Lee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics of Neurodegenerative Diseases and Aging, Department of Human Genetics, Amsterdam UMC location VUmc, Amsterdam, The Netherlands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJanna I.R. Dijkstra, Henne Holstege, Marc Hulsman, Georgii Ozgehov, and Sven van der Lee\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlzheimer Center Amsterdam, Department of Neurology, Amsterdam UMC location VUmc, The Netherlands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJanna I.R. Dijkstra, Lisa Vermunt, Wiesje M. van der Flier, Henne Holstege, Marc Hulsman, Rik Ossenkoppele, Vikram Venkatraghavan, Sietske Sikkes, Betty Tijms, Everard G.B. Vijverberg, Yolande A.L. Pijnenburg, Alida A. Gouw, Willem de Haan, and Sven van der Lee\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam UMC location VUmc, The Netherlands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLisa Vermunt, Charlotte E. Teunissen\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWiesje M. van der Flier\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, the Netherlands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmma Coomans, Rik Ossenkoppele, Elsmarieke van de Giessen, Frederik Barkhof\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Genetics, Department of Human Genetics, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChrista M. de Geus\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlida A. Gouw, Willem de Haan\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Memory Research Unit, Department of Clinical Sciences M\u0026auml;lmo, Lund University, Lund, Sweden\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRik Ossenkoppele\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDelft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarc Hulsman, Georgii Ozgehov\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Clinical, Neuro- and Developmental Psychology, VU University, Amsterdam, the Netherlands\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSietske Sikkes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQueen Square Institute of Neurology and Centre for Medical Image Computing, University College London, UK\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrederik Barkhof\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.D., L.V., and S.v.d.L. designed the study, had full access to the raw data, carried out the final statistical analyses, wrote the manuscript, and had the final responsibility to submit for publication. All authors contributed either demographic, clinical, genetic, biomarker, or neuroimaging data. All authors contributed to the interpretation of the results, critically reviewed the manuscript, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS DECLARATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.L. is part of the GeneMINDS consortium, which is powered by Health~Holland, Top Sector Life Sciences \u0026amp; Health and receives co-financing from Vigil Neuroscience, Prevail therapeutics and Brain Research Center. All funding is paid to his institution. L.V. is supported by grant funding/collaborative study and consultancy/speaker fees from ZonMw (VENI grant), Amsterdam UMC (Startergrant) Stichting Dioraphte (biobank DemenTree), Olink, Lilly, and Roche; all paid to her institution.\u003c/p\u003e\n\u003cp\u003eResearch programs of W.F. have been funded by ZonMW, NWO, EU-JPND, EU-IHI, Alzheimer Nederland, Hersenstichting CardioVascular Onderzoek Nederland, Health~Holland, Topsector Life Sciences \u0026amp; Health, stichting Dioraphte, Gieskes-Strijbis fonds, stichting Equilibrio, Edwin Bouw fonds, Pasman stichting, stichting Alzheimer \u0026amp; Neuropsychiatrie Foundation, Philips, Biogen MA Inc, Novartis-NL, Life-MI, AVID, Roche BV, Fujifilm, Eisai, Combinostics. W.F. holds the Pasman chair. W.F. is recipient of TAP-dementia (www.tap-dementia.nl), receiving funding from ZonMw (#10510032120003). TAP-dementia receives co-financing from Avid Radiopharmaceuticals and Amprion. All funding is paid to her institution. W.F. has been an invited speaker at Biogen MA Inc, Danone, Eisai, WebMD Neurology (Medscape), NovoNordisk, Springer Healthcare, European Brain Council. All funding is paid to her institution. W.F. is consultant to Oxford Health Policy Forum CIC, Roche, Biogen MA Inc, and Eisai. All funding is paid to her institution. W.F. participated in advisory boards of Biogen MA Inc, Roche, and Eli Lilly. W.F. is member of the steering committee of EVOKE/EVOKE+ (NovoNordisk). All funding is paid to her institution. W.F. is member of the steering committee of PAVE, and Think Brain Health. W.F. was associate editor of Alzheimer, Research \u0026amp; Therapy in 2020/2021. W.F. is associate editor at Brain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eR.O. has received research funding from European Research Council, ZonMw, NWO, National Institute of Health, Alzheimer Association, Alzheimer Nederland, Stichting Dioraphte, Cure Alzheimer\u0026rsquo;s fund, Health Holland, ERA PerMed, Alzheimerfonden, Hjarnfonden (all paid to the institutions). R.O. has received research support from Avid Radiopharmaceuticals, Janssen Research \u0026amp; Development, Roche, Quanterix and Optina Diagnostics, and has given lectures in symposia sponsored by GE Healthcare. He is an advisory board member for Asceneuron and Bristol Myers Squibb. All the aforementioned has been paid to the institutions. He is an editorial board member of Alzheimer\u0026rsquo;s Research \u0026amp; Therapy and the European Journal of Nuclear Medicine and Molecular Imaging.\u003c/p\u003e\n\u003cp\u003eF.B. is Steering committee or Data Safety Monitoring Board member for Biogen, Merck, Eisai and Prothena. F.B. is advisory board member for Combinostics, Scottish Brain Sciences. F.B. is consultant for Roche, Celltrion, Rewind Therapeutics, Merck, Bracco. F.B. has research agreements with ADDI, Merck, Biogen, GE Healthcare, Roche. F.B. is co-founder and shareholder of Queen Square Analytics LTD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Medical Ethical Committee of Amsterdam UMC, location VUmc. All patients provided written informed consent for their clinical data to be used for research purposes. Consent was obtained according to the Declaration of Helsinki.\u003c/p\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e \u003cp\u003e Data is provided within the manuscript or supplementary information files. The dataset used and/or the analyses performed can be provided upon reasonable request from data manager of the ADC (W.F.).\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJonsson T, Stefansson H, Steinberg S, Jonsdottir I, Jonsson P V, Snaedal J, et al. Variant of TREM2 Associated with the Risk of Alzheimer\u0026rsquo;s Disease. N Engl J Med. 2013;368(2):107\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuerreiro R, Wojtas A, Bras J, Carrasquillo M, Rogaeva E, Majounie E, et al. TREM2 Variants in Alzheimer\u0026rsquo;s Disease. N Engl J Med. 2013;368(2):117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolstege H, Hulsman M, Charbonnier C, Grenier-Boley B, Quenez O, Grozeva D, et al. Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer\u0026rsquo;s disease. 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Brain. 2015;138(5):1134\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TREM2, Alzheimer’s disease, clinical measures","lastPublishedDoi":"10.21203/rs.3.rs-5310076/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5310076/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRare variants of the triggering receptor expressed on myeloid cell 2 (\u003cem\u003eTREM2\u003c/em\u003e) gene are major risk factors for Alzheimer\u0026rsquo;s disease (AD), and drugs targeting the TREM2 protein are being developed. However, it is unknown whether carriers of a \u003cem\u003eTREM2\u003c/em\u003e risk variant have a clinically distinct AD phenotype. Here we studied a full range of clinical measures in a large cohort of \u003cem\u003eTREM2\u003c/em\u003e variant carriers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;123, 7.8%, i.e., R62H \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;66, R47H \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;26, T96K \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16, other \u003cem\u003eTREM2\u003c/em\u003e variants \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;17) compared to confirmed non-carriers (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,459) with biomarker confirmed symptomatic AD from Amsterdam Dementia Cohort.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTREM2\u003c/em\u003e variant carriers (mean age at diagnosis 64.4 years (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1), 54% female) did not show distinct clinical measures of AD at presentation compared to AD patients not carrying a \u003cem\u003eTREM2\u003c/em\u003e variant (mean age at diagnosis 64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0, 52% female). Specifically, we observed no differences in MMSE, most neuropsychological domains (except visuospatial functioning), MRI scores, CSF biomarkers, and EEG. Also, in an exploratory analysis of neuroimaging measures, including structural MRI (41 ROIs) and Tau-PET scans of four carriers (R62H, R47H, G58A, D87N), \u003cem\u003eTREM2\u003c/em\u003e variant carriers showed similar atrophy patterns and similar abnormal tracer binding compared to non-carriers. Despite not being different at baseline, carriers did show faster cognitive decline in follow-up. Carriers declined 0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 points on the MMSE more per year compared to non-carriers, but there was no difference in the hazard rate of death after diagnosis.\u003c/p\u003e \u003cp\u003eFinally, we explored whether specific \u003cem\u003eTREM2\u003c/em\u003e variants are associated with distinct clinical measures compared to the reference group, i.e. non-carriers, within the same cohort. Notably, both R47H and T96K carriers exhibited faster cognitive decline, and R47H carriers even showed an increased rate of death after diagnosis. In contrast to the shared cognitive decline, these variants showed different results for other measures at baseline.\u003c/p\u003e \u003cp\u003eThis study presents a detailed overview of the clinical measures in AD patients carrying a \u003cem\u003eTREM2\u003c/em\u003e risk variant, and it shows that carriers of \u003cem\u003eTREM2\u003c/em\u003e risk variants cannot be distinguished based on clinical presentation at baseline. However, carriers exhibit a faster global cognitive decline compared to non-carriers. Variant-specific analyses suggest that especially R47H and T96K carriers drive the association of \u003cem\u003eTREM2\u003c/em\u003e variants with faster cognitive decline.\u003c/p\u003e","manuscriptTitle":"TREM2 Risk Variants with Alzheimer’s Disease Differ in Rate of Cognitive Decline","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-08 14:34:30","doi":"10.21203/rs.3.rs-5310076/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9a1e446-31d1-4f35-a46c-a9d4ab2417f8","owner":[],"postedDate":"November 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-15T00:53:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-08 14:34:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5310076","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5310076","identity":"rs-5310076","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}