Cerebrospinal fluid proteomics in patients with Alzheimer's disease reveals five molecular subtypes with distinct genetic risk profiles.

Nature Aging | Volume 4 | January 2024 | 33–47 33 nature aging https://doi.org/10.1038/s43587-023-00550-7Letter Cerebrospinal fluid proteomics in patients with Alzheimer’s disease reveals five molecular subtypes with distinct genetic risk profiles Betty M. Tijms 1,2 , Ellen M. Vromen1,2, Olav Mjaavatten3, Henne Holstege 1,2,4, Lianne M. Reus1,2,5, Sven van der Lee 1,2,6, Kirsten E. J. Wesenhagen1,2, Luigi Lorenzini7,8, Lisa Vermunt 2,9, Vikram Venkatraghavan1,2, Niccoló Tesi 6,10, Jori Tomassen1,2, Anouk den Braber1,2,11, Julie Goossens12, Eugeen Vanmechelen 12, Frederik Barkhof 7,13, Yolande A. L. Pijnenburg1,2, Wiesje M. van der Flier 1,2,14, Charlotte E. Teunissen 2,9, Frode S. Berven3 & Pieter Jelle Visser 1,2,15,16 Alzheimer’s disease (AD) is heterogenous at the molecular level. Understanding this heterogeneity is critical for AD drug development. Here we define AD molecular subtypes using mass spectrometry proteomics in cerebrospinal fluid, based on 1,058 proteins, with different levels in individuals with AD (n = 419) compared to controls (n = 187). These AD subtypes had alterations in protein levels that were associated with distinct molecular processes: subtype 1 was characterized by proteins related to neuronal hyperplasticity; subtype 2 by innate immune activation; subtype 3 by RNA dysregulation; subtype 4 by choroid plexus dysfunction; and subtype 5 by blood–brain barrier impairment. Each subtype was related to specific AD genetic risk variants, for example, subtype 1 was enriched with TREM2 R47H. Subtypes also differed in clinical outcomes, survival times and anatomical patterns of brain atrophy. These results indicate molecular heterogeneity in AD and highlight the need for personalized medicine. Alzheimer’s disease (AD) is the leading cause of dementia, affecting about 44 million people worldwide 1 . AD is histopathologically defined by amyloid plaques and hyperphosphorylated tau tangles in the brain, but its underlying pathophysiology is largely unclear. Genetic, brain tissue proteomics and gene expression studies indicated that many different pathophysiological processes are associated with amyloid and tau pathology, including but not limited to synaptic plasticity, the innate immune system, neuroinflammation, lipid metabolism, RNA metabolism, the matrisome and vascular function 2–8 . Differences between patients regarding the underlying mechanisms, together with other factors, may have contributed to limited or lack of clinical effects observed in previous AD trials 9–11 . For example, we previously found abnormally high cerebrospinal fluid (CSF) BACE1 levels in a specific AD subtype8,12, suggesting that BACE inhibition may be effective in a subgroup only, provided that other factors are optimized. This highlights the need for personalized treatments and for in vivo tools to define such molecular subtypes. CSF is the most accessible biofluid to study the molecular complexity of neurodegenerative diseases during life. It is in close contact with the brain and protein concentrations in the CSF reflect the brain’s Received: 11 September 2023 Accepted: 29 November 2023 Published online: 9 January 2024 Check for updates A full list of affiliations appears at the end of the paper. e-mail: b.[email protected] Nature Aging | Volume 4 | January 2024 | 33–47 34 Letter https://doi.org/10.1038/s43587-023-00550-7 419 individuals had AD as defined by an abnormal amyloid biomarker and included all clinical stages (that is, 107 with normal cognition, 103 with mild cognitive impairment (MCI) and 209 with dementia). The 187 controls were required to have normal cognition and normal amyloid and tau biomarkers. CSF proteins from each sample were enzymatically digested and the peptides were labeled with tandem mass tags (TMTs), fractionated and analyzed by liquid chromatography–tandem mass spectrometry (LC–MS/MS) (Methods). A total of 3,863 proteins was identified, of which 1,309 proteins (defined by 28,408 peptides) were observed across all individuals. We then tested which proteins had different levels in individuals with AD compared to controls, and repeated those analyses stratified on tau levels or disease stage because protein levels can change in a nonlinear way with these variables 12,19 . This led to the selection of 1,058 AD-related proteins for cluster analyses (Supplementary Table 2; it also includes information at the peptide level). We then clustered individuals with AD on AD-related proteins with nonnegative matrix factorization20, which is a dual clustering approach (Supplementary Fig. 1). A particular strength of the algorithm is that individuals are per definition allocated to one subtype, which is useful for diagnosis or patient stratification for trials. Five AD subtypes that differ on clinical characteristics The patients’ proteomic profiles clustered into five subtypes (Fig. 1a and Supplementary Table 3 for the fit and stability statistics): subtypes 1, 2 and 5 recapitulated our previously detected subtypes with neuronal hyperplasticity (subtype 1), innate immune activation (subtype 2) and blood–brain barrier dysfunction (subtype 5); two additional subtypes emerged: one with RNA dysregulation (subtype 3) and one with choroid plexus dysfunction (subtype 4). We tested the robustness of the subtypes by clustering the weighted protein coexpression network again with the Louvain algorithm (Methods), which also resulted in five protein clusters that were similar to the NMF protein clusters (93.5% overlap of cluster-specific proteins (Supplementary Table 4, column X). The next sections discuss each subtype in detail according to molecular, genetic and clinical characteristics. We briefly summarize the subtype differences. Subgroups were compared to controls and each other, with estimated marginal means from linear models for continuous outcomes, which were two-tailed tests, and proportions were tested with chi-squared tests and repeated for each pairwise group combination (Table 1, please note these comparisons are uncorrected for multiple testing because this is a descriptive table). Compared to controls, subtypes 1, 2 and 3 had increased CSF t-tau and p-tau levels, while subtypes 4 and 5 had mostly normal tau levels (see Table 1 and Supplementary Table 5for additional analyses stratified according to cognitive state and adjusted for sex and age). Subtypes differed according to clinical stage, sex and age; all subsequent analyses took these characteristics into account. Compared to controls, subtypes differed in the rates of progression from MCI to dementia, with subtypes 2 and 5 having the highest risk, and subtype 4 the lowest (Fig. 2d), although differences between subtypes did not reach statistical significance (Supplementary Table 6a). Subtype 3 individuals with dementia had the shortest average survival time of 5.6 years, which was shorter than subtype 1 with the longest average survival time of 8.9 years (P = 0.04; Fig. 2 and ongoing (patho)physiological processes. We previously discovered and replicated three distinct molecular AD subtypes by investigating the 707 and 204 proteins in the CSF8. The proteins involved in these subtypes represent distinct biological processes, such as neuronal plasticity, innate immune activation and blood–brain barrier dysfunction8. Subtype-specific molecular alterations were already present at a very early stage of AD, when cognition was still intact and neuronal damage still limited. Many of these molecular processes were also previously identified in AD postmortem tissue proteomics or gene expression studies 2,3,5,7 . This supports the value of CSF proteomics to detect AD pathophysiological processes in living patients3. Proteomic techniques have greatly improved since and can detect thousands of proteins in the CSF, which provides an opportunity to dissect the molecular processes associated with AD subtypes in detail. In this study, we used these techniques and detected more than 3,000 proteins in the CSF in another independent cohort of 609 individuals to replicate and refine the existing subtypes, to test if the higher complexity allows us to uncover additional AD subtypes and to study the underlying genetic factors of these subtypes. In our previous studies, we compared CSF AD subtypes on APOE e4 carriership (the strongest genetic risk factor for sporadic AD) 8,13 and on AD polygenic risk scores (PRS). In the current study, we further extended the genetic analyses and compared subtypes on AD risk variants from a recent genome-wide association study (GWAS) 4 . Moreover, we enriched for the relatively rare TREM2 R47H and R62H mutations because these are associated with a 2.3 and 1.4-fold increased risk for AD 4 . TREM2 R47H and R62H impair microglial activation in AD 14 . Therefore, we hypothesized that TREM2 carriers could be grouped together in a subtype with impaired microglial activation. A small number of patients (n = 6) carried an autosomal dominant mutation in PSEN1 or APP; we performed an exploratory analysis to identify which subtypes these genetic variants were associated with. This large-scale CSF proteomic study revealed five molecular AD subtypes. Three subtypes recapitulated our previously identified three subtypes (hyperplasticity, innate immune activation and blood–brain barrier dysfunction) 8 . We further identified two additional AD subtypes: one with RNA dysregulation and one with choroid plexus dysfunction. All subtypes were associated with distinct genetic risk profiles, providing further biological validation for AD subtypes. The proteomic signatures associated with AD subtypes were present already at the preclinical stage and largely remained stable with increasing disease severity. Subtypes differed in the amount and pattern of cortical atrophy, cell typespecific expression of proteins, vascular damage and clinical outcomes. These results highlight the importance of neuronal plasti city, microglial impairment, innate immune activation, RNA processing, choroid plexus and blood–brain barrier dysfunction in AD pathogenesis, and provide a comprehensive resource that informs on the proteins and pathways that are dysregulated in patients with a specific AD subtype. Results We analyzed CSF samples from 609 individuals that were selected from the Alzheimer Center Amsterdam related studies 15–18 (for clinical details, see Methods and Supplementary Table 1). Of this sample, Fig. 1 | Biological description of AD subtypes. a, Patient subtypes projected to the uniform manifold approximation and projection (UMAP) space. b, CSF protein levels (rows) averaged across individuals within subtypes (columns). c, Cell-type-specificity signatures for proteins associated with the AD subtypes for proteins with increased (top row) and decreased (bottom row) level. The left circle diagram shows all cell types associated with a subtype combined. Proteins that could not be assigned to a specific cell type were not plotted (no colour to 100% in the left circle diagrams). The circle diagrams to the right zoom into the subcategories of specific cell types (neurons, glia, immune cells and endothelial cells). Cell-type specificity was determined according to the Human Protein Atlas. d, Top transcription factors associated with subtypes from the CHEA and ENCODE databases. e, Gene Ontology (GO) biological pathways associated with subtypes (see Supplementary Table 9 for all pathways). f, AD genetic risk factors associated with specific subtypes; white indicates not statistically significant. Differences between subtypes and controls were determined from linear regression models with estimated marginal means, providing a two-tailed test for group comparisons, uncorrected for multiple testing because this is a post hoc comparison. Supplementary Tables 4 and 9–11 list all the proteins, pathways, and transcription and genetic factors tested with the statistical metrics. NS, not statistically significant; S1, subtype 1 (hyperplasticity); S2, subtype 2 (innate immune activation); S3, subtype 3 (RNA dysregulation); S4, subtype 4 (choroid plexus dysfunction); S5, subtype 5 (blood–brain barrier dysfunction). Nature Aging | Volume 4 | January 2024 | 33–47 35 Letter https://doi.org/10.1038/s43587-023-00550-7 Subtype 1 n = 137 (32.7%) AD patient subtypes Subtype 2: n = 124 (29.6%) Subtype 3 n= 24 (5.7%) Subtype 4 n = 78 (18.6%) Subtype 5 n = 56 (13.4%) NMF UMAP 1 NMF UMAP 2 Acute inflammatory response (GO:0002526) Blood coagulation (GO:0007596) B cell-mediated immunity (GO:0019724) Regulation of BMP signaling pathway (GO:0030510) Regulation of pathway-restricted SMAD protein (GO:0060393) Sphingolipid catabolic process (GO:0030149) Extracellular matrix organization (GO:0030198) Regulation of mRNA stability (GO:0043488) Protein folding (GO:0006457) Posttrans regulation gene expression (GO:0010608) Axonal transport (GO:0098930) Cytoskeleton organization (GO:0007010) Cholesterol efflux (GO:0033344) Regulation of ERK1 and ERK2 cascade (GO:0070372) Regulation of cytokine production (GO:0001817) Leukocyte activation (GO:0045321) Angiogenesis (GO:0001525) Regulation of MAPK cascade (GO:0043408) Gliogenesis (GO:0042063) Regulation of synaptic plasticity (GO:0048167) Synapse assembly (GO:0007416) Myelination (GO:0042552) Axon guidance (GO:0007411) Neurogenesis (GO:0022008) 60 30 NS 30 –log10 pFDR Increased protein levelsDecreased protein levels GO biological pathways Protein levels in the CSF S1 S2 S3 S4 S5 Protein differences between subtypes Biological pathway associations Cell-type signatures Decreased Increased –log10 pFDR TRIM28 CHEA TP63 CHEA SMAD4 CHEA MYC ENCODE TAF1 ENCODE KLF4 CHEA NFE2L2 CHEA SUZ12 CHEA REST CHEA 40 20 NS 20 Transcription factors Subtype S1 S2 S3 S4 S5 a b c e d f Subtype S1 S2 S3 S4 S5 S1 AD genetic variants S2 S3 S4 S5 –log(P) 10 20 30 40/0 10 20 30 40/0 10 20 30 40/0 10 20 30 40/0 Neurons Glia Immune cells Endothelial cells 10 20 30 0/40 10 20 30 0/40 10 20 30 0/40 10 20 30 0/40 Aspecific Excitatory Inhibitory Aspecific Microglia Astrocytes Oligodendrocytes Oligodendrocyte precursor cells Aspecific Macrophages Monocytes B cells T cells Endothelial cells Decreased Increased Decreased Increased Decreased Increased Decreased Increased S5 S4 S3 S2 S1 S5 S4 S3 S2 S1 S5 S4 S3 S2 S1 S5 S4 S3 S2 S1 S5 S4 S3 S2 S1 S5 S4 S3 S2 S1 S5 S4 S3 S2 S1 S5 S4 S3 S2 S1 All cell types Increased 25 50 75 100/0 Decreased 25 50 75 0/100 S5 S4 S3 S2 S1 S5 S4 S3 S2 S1 Neurons Glia Immune cells Endothelial cells Zoom in per cell type > Zoom in per cell type > 4 5 6 7 8 rs2154481.APP rs7912495.ECHDC3 rs4985556.IL34 rs3851179.PICALM rs12151021.ABCA7 rs7384878.SPDYE3 rs143332484.TREM2.R62H rs3848143.SNX1 rs6733839.BIN1 rs889555.KAT8 rs7225151.SCIMP rs6846529.CLNK rs3822030.IDUA rs2245466.RHOH rs587709.LILRB2 rs75932628.TREM2.R47H Z 3 2 1 0 –1 Nature Aging | Volume 4 | January 2024 | 33–47 36 Letter https://doi.org/10.1038/s43587-023-00550-7 Supplementary Table 6b), and steeper decline on Mini Mental State Examination (MMSE), language and memory tests (Extended Data Figs. 1 and 2 and Supplementary Table 7). These results suggests that different underlying molecular processes may explain a part of betweenpatient variability in decline. Analysis of magnetic resonance imaging (MRI) scans in individuals with dementia (n = 159) indicated that subtypes differed in the degree and anatomical location of cortical atrophy (Fig. 2 and Supplementary Table 8). All subtypes had a higher prevalence of the APOE e4 genotype than controls, and a higher AD PRS, supporting their underlying AD genetic risk architecture. However, subtypes had distinct AD genetic risk profiles (discussed in detail below). We next examined the molecular processes associated with the AD subtypes. For each subtype, we compared the levels of 2,878 proteins against the control group (Fig. 1b and Supplementary Table 4). Proteins with different levels between a subtype and the control group were included in the enrichment analyses to study associated biological processes and transcription factors. To aid comparability with the gene expression literature, we report gene names for proteins (see Supplementary Table 4 for the UniProt codes). Stratification according to clinical stage resulted in similar differences to controls (correlations of effects ranging between 0.85 and 0.98; Extended Data Fig. 4), further supporting that AD subtypes reflect specific disease traits8,13. Below, we highlight subtype-specific associations with biological processes, cell types, AD genetic risk variants and atrophy patterns (see Supplementary Tables 4–11 for detailed results). Subtype 1 hyperplasticity Subtype 1 individuals (n = 137, 32.7%) had 827 proteins with increased CSF levels and 408 proteins with decreased levels compared to controls. Of all the subtypes, subtype 1 had the highest proportion of proteins specific for neurons, astrocytes, oligodendrocytes and oligodendrocyte precursor cells (Fig. 1c). Proteins with increased levels were associated with neuronal plasticity processes, including synapse assembly, axon guidance, neurogenesis and gliogenesis (Fig. 1e; see Supplementary Table 9 for all biological processes enriched). In addition, this neuronal hyperplasticity subtype had high BACE1, amyloid-β1–40 and tau CSF levels (Table 1), as we previously observed in the hyperplasticity subtype 8 . While high tau levels were previously thought to reflect neuronal loss due to tangle formation, more studies are indicating that this may also reflect other processes21,22. For example, neurons with increased activity secrete more amyloid and tau 23–27 ; such hyperactive neurons have been observed near plaques 28 . Fragments of amyloid and tau may in turn drive hyperplasticity through enhanced gene transcription 29 . Indeed, proteins increased in subtype 1 were enriched for the transcription factors REST (P adjusted = 0.018 × 10 −13 ; Fig. 1d and Supplementary Table 7) and SUZ12 (P adjusted = 0.016 × 10 −12 ), which regulate plasticity-related processes through repression of neuronal differentiation genes 30,31 . Previous studies pointed towards REST de-repression and increases of tau and plasticity-related processes in AD brain tissue 7,32 , induced pluripotent stem cell (iPSC) neurons 33,34 and tau tangle-bearing neurons35. Comparing subtype 1 increased proteins with those studies, we found an overlap five of six from Lu et al. 32 , 65 of 173 from Meyer et al. 33 and 46 of 127 from Otero-Gracia 35 (Supplementary Table 4, columns AZ-BB). Moreover, with the higher number of proteins that we measured compared to our previous study, we found additional mechanisms that may contribute to the plasti city response observed in this subtype. For example, subtype 1 had the highest CSF levels of the lysosomal protein PLD3. High PLD3 levels have been reported in dystrophic neurites associated with ‘amyloid Table 1 | Comparison of subtypes according to clinical characteristics Characteristic Controls n = 187 Subtype 1 n = 137 Subtype 2 n = 124 Subtype 3 n = 24 Subtype 4 n = 78 Subtype 5 n = 56 Cognitive state, n (%) Normal cognition 187 (100) 51 (37) 32 (26) 1 (4)a,b 15 (19)a8 (14)a MCI 037 (27) 28 (23) 3 (12) 16 (21) 19 (34) Dementia 049 (36) 64 (52)a20 (83)a,b 47 (60)a29 (52)c Age 64.01 (11.83) 64.71 (6.82) 69.38 (8.35)a,d 64.46 (8.73)b64.28 (8.09)b66.16 (8.11)b Men, n (%) 111 (59) 62 (45)d61 (49) 10 (42) 51 (65)a,b 41 (73%)a,b,c Years of education, mean (s.d.) 12.4 (3.2) 12.1 (3.4) 11.2 (3.2)d11.7 (2.9) 11.9 (3.4) 11.8 (3.3) ≥1 APOE e4 allele, n (%) 51 (28) 88 (68)d73 (62)d15 (65)d47 (64)d40 (74%)d AD PRS, mean (s.d.) 5.6 (0.37) 5.8 (0.41)d5.8 (0.31)d6.0 (0.41)d,b 5.8 (0.46)d5.8 (0.34)d CSF total tau, pg ml−1, mean (s.d.) 199 (88) 592 (340)d765 (447)a,d 882 (367)a,d 301 (166)a,b,c,d 469 (297)a,b,c,d,e CSF p-tau 181, z-score, mean (s.d.) 0 (0.99) 3.4 (2.5)d5.1 (3.1)a,d 5.0 (2.5)a,d 0.6 (1.4)a,b,c,d 2.1 (2.3)a,b,c,d,e CSF BACE1, pg ml−1, mean (s.d.) 1931.9 (643.49) 2203.8 (479.07)d2478.49 (687.98)a,d 2185.33 (573.92)b,d 1391.36 (323.74)a,b,c,d 1819.55 (560.55)a,b,c,e CSF Abeta40, pg ml−1, mean (s.d.) 7135.12 (2134.68) 7825.31 (1722.1)d8519.82 (2264.92)a,d 6817.46 (1783.25)a,b 4610.78 (1286.96)a,b,c,d 5943.02 (1543.58)a,b,c,d,e CSF NRGN, pg ml−1, mean (s.d.) 317.49 (148.07) 488.01 (178.95)d634.44 (300.5)a,d 561 (175.88)b,d 244.09 (97.64)a,b,c,d 370.91 (166.83)a,b,c,e CSF NEFL, pg ml−1, mean (s.d.) 360.1 (275.1) 447.01 (187.34)d620.08 (341.41)a,d 630.12 (293.84)a,d 453.61 (292.84)b,c,d 594.16 (371.67)a,d,e CSF VAMP2, pg ml−1, mean (s.d.) 162.17 (70.43) 196.32 (61.39)d233.44 (79.91)a,d 188.6 (61.77)b100.1 (39.8)a,b,c,d 141.94 (52.35)a,b,c,d,e Microbleed count on MRI, mean (s.d.) 0.91 (2.55) 1.89 (9.80) 1.16 (3.74) 1.65 (3.18) 2.07 (8.02) 4.40 (17.94)b,d aDiffers from subtype 1 with P < 0.05. bDiffers from subtype 2 with P < 0.05. cDiffers from subtype 3 with P < 0.05. dDiffers from controls with P < 0.05. eDiffers from subtype 4 with P < 0.05. Nature Aging | Volume 4 | January 2024 | 33–47 37 Letter https://doi.org/10.1038/s43587-023-00550-7 axonal spheroids’ 36 . Such spheroids trigger axonal remodeling and local hyperactivity36. Dystrophic neurites also accumulate BACE1, which is associated with increased APP metabolism37, and may explain the elevated BACE1 and amyloid-β levels in this subtype. Next, we tested which AD genetic risk variants4 were overrepresented in this subtype compared to controls. We found enrichment for TREM2 R47H and variants in LILRB2, RHOH and APP (Fig. 1f and Supplementary Table 11a). This subtype also included three of the four PSEN1 carriers and three of the four NCK2 carriers (Supplementary Table 11b). TREM2 is a transmembrane protein that can activate microglia when ligands, including amyloid fibrils, bind to its extracellular part 14 . The R47H variant alters the extracellular part, decreasing its ability to bind ligands, resulting in dampened microglia activation 38,39 . LILRB2 mediates TREM2 signaling and has also been associated with dampened immune activation40,41. RHOH and NCK2 encode signaling molecules downstream from TREM2 that influence cytoskeleton rearrangement of microglia, which enables migration toward pathogens and amyloid plaques42. Normally, activated microglia form a tight barrier around plaques, which decreases plaque surface and minimizes plaque contact with neurites14,38,43. When microglial activation is dampened, as observed in carriers of TREM2 variants, amyloid plaques are less compact, with toxic oligomers sticking out that could damage nearby neurites44 and may lead to axonal dystrophy44, possibly triggering a plasticity response as an attempt to repair. TREM2 has also been implicated in impaired microglial synaptic pruning, which could further contribute to the hyperplasticity signature observed in Cortical thickness of subtypes in dementia stage compared to controls Subtype 1 Subtype 2 Subtype 3 Subtype 4 Subtype 5 S1 S2 S3 S4 S5 0.2 0.3 0.4 Volume (percentage of TIV) S1 S2 S3 S4 S5 0.02 0.04 0.06 0.08 0.10 Volume (percentage of TIV) −0.4 −0.2 0 0.2 β 0 0.25 0.50 0.75 1.00 0 1 2 3 4 5 Time in years Prodromal progression to dementia Without dementia 6 3 5 5 Control S1 S2 S4 0 1 2 3 4 5 105 31 20 15 S5 14 101 28 15 15 13 75 21 9 12 10 57 15 7 8 42 7 2 28 2 2 2 Time in years 0 0.25 0.50 0.75 1.00 0 2 4 6 8 10 Time in years 172 166 141 83 49 28 48 46 37 19 9 2 64 63 40 17 8 6 20 20 16 8 4 0 46 45 37 18 9 3 29 29 24 13 6 4 S5 S4 S3 S2 S1 Control 0 2 4 6 8 10 Time in years Survival curves in dementia Survival a b c d Hippocampus Choroid plexus Fig. 2 | AD subtype comparisons on MRI and clinical outcomes. a, Median hippocampal volume as the percentage of total intracranial volume (TIV) compared to subtypes in the dementia stage. b, Choroid plexus volume as the percentage of TIV compared to subtypes in the dementia stage. c, Cortical atrophy associated with AD subtypes in the dementia stage compared to controls (n = 160). β indicates mean cortical thickness in mm, averaged over the right and left hemispheres and adjusted for age and sex. d, Clinical progression from MCI to dementia according to subtype (left; excluding subtype 3 due to n = 2) and time from dementia to death according to subtypes (right). All atrophy measures are based on individuals with dementia only. a,b, The boxplots depict the median in the center; the boundaries indicate the first and third quartiles, while the whiskers extend up and down to 1.5 times the interquartile range (limited to actual observed data points), and the points indicate individual person values (subtype 1, n = 37; subtype 2, n = 45; subtype 3, n = 12; subtype 4, n = 40; subtype 5, n = 25). See Supplementary Tables 6a,b and 8 for the detailed statistical metrics. Nature Aging | Volume 4 | January 2024 | 33–47 38 Letter https://doi.org/10.1038/s43587-023-00550-7 this subtype 45–47 . Such an excess of synapses was previously associated with milder atrophy in TREM2 mouse models45. MRI analyses in our data indicated that this subtype had less atrophy compared to the other subtypes (Extended Data Fig. 3 and Supplementary Table 9), and was restricted to the temporal and parietal lobes. Together, our results provide further support for a hyperplasticity subtype in AD, and provide additional insights into the underlying mechanisms, such as that this subtype could be related to a dampened microglial response. Therapies boosting TREM2 activation are under development48. We argue that individuals with this subtype may also respond to such treatments, even without carrying the TREM2 R47H variant. Subtype 2 innate immune activation Subtype 2 individuals (n = 124, 29.6%) had, compared to controls, 986 proteins with increased CSF levels and 506 with decreased levels. A high proportion of proteins increased in subtype 2 was specific to microglia. Proteins with increased levels were associated with innate immune activation, including regulation of cytokine production. These included proteins from the complement complex (C1QA, C1QB, C1QC, C1S and C1R), as well as APOE and LPL, in line with our previous findings. Additionally, we now observed that this subtype also had increased levels of the microglial Tyro3, Axl and Mer (TAM) receptors AXL and MERTK, and GAS6 (a MERTK ligand), which can detect and engulf plaques 49 . We further found increased PYCARD levels specifically in subtype 2. PYCARD is also known as apoptosis-associated speck-like protein containing a CARD (ASC), and is released by microglia with NLRP3 inflammasome activation 50,51 . PYCARD can form ASC specks, which are fibrils that worsen amyloid aggregation51 and induce tau phosphorylation52, providing a potential mechanism through which microglial activation may aggravate AD pathology. Indeed, subtype 2 individuals had higher p-tau levels than seen in subtype 1 (Table 1). Other subtype 2 increased proteins were related to neuron-microglia signaling, including CSF1, CSF1R and CX3CL1. Neuroimmune signaling occurs during normal neuronal development when microglia prune immature synapses 53–55 . In AD, activated microglia near diffuse and neuritic plaques may lead to excessive synaptic pruning53. This could lead to exacerbated atrophy as shown in mouse models56. In line with those models, subtype 2 was one of the two subtypes with the most severe and widespread cortical atrophy on MRI compared to subtypes 1, 3 and 5 (Extended Data Fig. 2). Still, despite this severe atrophy, the levels of proteins related to neuroplasticity were increased in this subtype and these proteins overlapped with subtype 1; they were also enriched for the transcription factors REST and SUZ12. Possibly, the increase of plasticity-related proteins may reflect an attempt to repair synaptic contacts, which succumbs in the presence of activated microglia. Alternatively, increased protein levels may reflect neuronal loss. The AD genetic variants associated with this subtype were IDUA, CLNK and SCIMP, which are all involved in immune processes4,57. Together, these results give additional detailed insights into the innate immune activation AD subtype and suggest that an overactive innate immune system worsens the disease. Subtype 3 RNA dysregulation Subtype 3 (n = 24, 5.7%) emerged as one of the two additional subtypes. Compared to controls, this subtype had increased CSF levels for 516 proteins and decreased levels for 757 proteins. Proteins with increased levels were associated with cytoskeleton organization, axonal transport, and proteasome and protein folding (Supplementary Tables 4 and 9). This subtype had the highest t-tau and NEFL CSF levels. BACE1 levels were higher than in controls (Table 1), but unlike subtypes 1 and 2, amyloid-β40 levels were similar to controls, suggesting a different mechanism associated with higher BACE1 levels for this subtype. Proteins specifically increased in subtype 3 included heterogenous nuclear ribonucleoproteins (hnRNPs) and other RNA-binding proteins, which may point to RNA dysregulation. HnRNPs are involved in the maturation of pre-mRNAs, mRNA stabilization during transport and local mRNA translation for many RNAs, including those important for cytoskeleton organization 58 . Disruptions in hnRNPs and mRNA have been associated with tau tangles in previous proteomic studies 59 . Mislocalized hnRNPs could result in dysfunctional proteins due to mis-splicing or cryptic splicing60. For example, TDP43 mislocalization can lead to cryptic splicing of STMN2 (ref. 61), resulting in shorter proteins and decreased STMN2 levels in tissue 62 . STMN2 was detected in a subset (n = 84) of our sample, and subtype 3 had decreased levels of STMN2 compared to controls. Transcription factors associated with subtype 3 increased proteins were KLF4 (Padjusted = 0.02 × 10−15), which is associated with axon regeneration 63 , and TAF1 (P adjusted = 0.008 × 10 −13 ) and MYC (P adjusted = 0.02 × 10 −10 ), which are interacting factors in cell differentiation processes 64,65 . A previous gene expression study in brain tissue found a similar AD subtype with increased TAF1 and MYC signaling and aberrant synapse organization7. When testing AD genetic risk factors, we found enrichment for BIN1, which is known as ‘Myc-box-dependent interaction protein’. One of BIN1’s functions is to physically inhibit MYC66. BIN1 mainly localizes in axons and has many isoforms arising from splicing 66 . BIN1 mis-splicing has been associated with de-inhibition of MYC and cytoskeleton disruption67. TREM2 R62H was also associated with this subtype. Other genetic risk variants associated with subtype 3 included SPDYE3, involved in the cell cycle, SNX1, important for endosome sorting, and KAT8, a lysine acetyltransferase4,57. While RNA dysfunction has been mainly observed in frontotemporal dementia 68 , these processes have also been observed in AD in tissue 5 and tau tangle proteomic studies 59 ; our results suggests that this can be detected in the CSF of a specific AD subtype. Subtype 4 choroid plexus dysfunction Subtype 4 (n = 78, 18.6%) was the other additional subtype. Compared to controls, this subtype had increased CSF levels of 467 proteins and decreased levels of 626 proteins. A high proportion of proteins increased in subtype 4 were specific to microglia and other immune cells. Moreover, a large subset of proteins with increased levels (45%) was associated with high expression in the lateral ventricle choroid plexus (Supplementary Table 4), including TTR, SPARC and extracellular matrix proteins such as DCN, LUM and COLA12. Biological processes associated with subtype 4 included cell adhesion, and BMP and SMAD pathways, which are involved in choroid plexus development 69 . The choroid plexus is located along the ventricles, where it produces CSF and is responsible for nutrient, lipid and protein transfer across the blood–CSF barrier69. It consists of a highly developed extracellular matrix that connects a dense vasculature to its epithelial cells69. On MRI, subtype 4 had the largest choroid plexus volume (Fig. 2b). Increased choroid plexus volume has been associated with inflammation and structural alterations in AD 70,71 . Although this subtype most often had normal t-tau and p-tau levels (Table 1), it had worse atrophy than subtypes 1, 3 and 5, with specific involvement of anterior cingulate areas (Extended Data Fig. 3). Furthermore, proteins increased in subtype 4 were enriched for fibroblasts (Supplementary Table 4), which produce extracellular matrix proteins and provide structural support to the choroid plexus. Other proteins increased in subtype 4 included cytokines, such as CCL2, CCL21 and CCL15, which can attract monocytes and T lymphocytes 72 . Of note, proteins with decreased levels in subtype 4 were related to axonal outgrowth and synaptic plasticity (for example, BDNF), in part overlapping with proteins increased in subtypes 1 and 2, and were also enriched for REST and SUZ12. This suggests that subtype 2 is also characterized by neuronal hypoplasticity. When testing AD genetic risk variants, we found enrichment for ABCA7, PICALM, IL-34 and CLNK. While ABCA7 and IL-34 are expressed in the choroid plexus73,74, PICALM is expressed in the blood–brain barrier75. Both ABCA7 and PICALM have a role in lipid metabolism76,77 and Nature Aging | Volume 4 | January 2024 | 33–47 39 Letter https://doi.org/10.1038/s43587-023-00550-7 have been associated with amyloid clearance in combination with LRP1 (ref. 78). IL-34 has been associated with impaired macrophage function79, which could interfere with the macrophage uptake of amyloid fibrils80. Together with the decreased levels of BACE1 and amyloid-β40 in this subtype compared to controls (Table 1), suggesting decreased amyloid metabolism, these genetic factors suggests that impaired clearance mechanisms contribute to AD pathogenesis in subtype 4. Taken together, these results suggest that choroid plexus dysfunction is another contributor to AD, in a specific subgroup of patients. Subtype 5 blood–brain barrier dysfunction Subtype 5 (n = 56, 13.4%) was highly similar to our previously identified blood–brain barrier dysfunction subtype with increased levels of 640 proteins that included blood proteins such as albumin, fibrinogens, plasminogen, prothrombin and many immunoglobulins, such as IgG1, all proteins that leak into the brain when the blood–brain barrier is compromised 81 . Pathways associated with increased proteins included blood coagulation, B cell-mediated immunity and acute inflammatory response. No transcription factor enrichment was observed for proteins with increased CSF levels. On MRI, this subtype had more microbleeds than controls (P = 0.01; Table 1), unlike the other subtypes. Most proteins associated with subtype 5 (1,013, 61%) had, however, decreased CSF levels compared to controls, and these were associated with neuroplasticity and converged on the transcription factors SUZ12 and REST. This suggests that the blood–brain barrier subtype also has hypoplasticity, like the choroid plexus subtype. Neuronal plasticity processes can be impaired by leakage of blood proteins, including fibrin, which were specifically increased in this subtype 82 . Furthermore, compared to our previous study, we found additional proteins altered in the blood–brain barrier subtype, which were associated with pericytes, cells that normally cover capillaries, and with particular vascular cell types, such as lower levels of PDGFRB, CDH2 (N-cadherin), MFGE8 (medin), HTRA1, LAMB1 (laminin), EDN1, LRP1 and JAM3, as well as increased levels of CDH5 (VE-cadherin), ANXA3, ICAM1, AMBP, VWF and PTPRB (Supplementary Table 4, columns AV-AX). All of these have been previously associated with deposition of blood proteins in the parenchyma 75,83–87 . The low PDGFRb levels we observed may reflect loss of pericytes, which is in line with brain tissue measures of PDGFRb in rodent models and postmortem AD83,88. A decreased number of pericytes might also explain the decreased levels of LRP1 we observed in this subtype, which may impede amyloid clearance across the blood–brain barrier78. Alternatively, the low concentrations we observed in the blood–brain barrier subtype could reflect loss of other vascular cells, such as arterial smooth muscle cells, which also express PDGFRb86. In terms of genetic risk, this subtype had the highest proportion of APOE e4 carriers, although the difference with other subtypes did not reach statistical significance (Table 1). This subtype was further enriched for the IL-34, ECHDC3 and APP variants. IL-34 was also associated with the choroid plexus subtype, suggesting that it contributes to AD pathogenesis through processes related to the brain barrier. ECHDC3 has been associated with lipid metabolism57. Some variants in APP have been associated with vascular disruption or increased occurrence of cerebral amyloid angiopathy, through amyloid fragments that are more difficult to clear89. The notion that this subtype has blood–brain barrier dysfunction, suggests that this common APP variant may contribute to AD risk through vascular integrity. Together, these data provide additional insights into the underlying pathophysiological processes associated with the blood–brain barrier dysfunction AD subtype. Predicting AD subtypes in replication cohorts We then studied if the subtypes could be identified in six independent replication datasets with available CSF TMT MS. To this end, we trained random forest classifiers on the current dataset and then used these classifiers to predict subtype labels for individuals in the replication cohorts, including patients from over nine different countries in Europe and the USA (Methods). All five subtypes were observed in most replication cohorts with high subtype-specific probabilities and comparable frequencies as in the discovery cohort: on average 27.9% had subtype 1, 35.5% subtype 2, 5.8% subtype 3, 17.1% subtype 4 and 16.6% subtype 5 (Fig. 3a and Supplementary Table 12). Subtype comparisons on CSF t-tau and p-tau levels, as well as age and sex, indicated mostly similar differences as observed in the main analyses for the replication cohorts (Fig. 3b–d and Supplementary Table 13). Replication cohort 4 also had CSF over the serum albumin ratios available (Fig. 3e), which is a marker for blood–brain barrier leakage; this was increased in the blood–brain barrier subtype only (P < 0.0001). Overall, these results suggests that the subtypes we discovered in the main analyses are also present in other cohorts. Discussion In this study, we dissected AD disease heterogeneity in patients with CSF proteomics, at a level of detail that approaches the level of complexity achieved in tissue proteomics 2,6 . Our analyses included more proteins and individuals than in our previous study and this led to a more indepth characterization of three previously identified CSF subtypes (that is, hyperplasticity, innate immune activation and blood–brain barrier dysfunction); two additional subtypes emerged, one with RNA dysregulation, which showed the most aggressive disease course, and one with choroid plexus dysfunction. Notably, we found that each subtype was associated with distinct AD genetic risk factors, further supporting that each CSF AD subtype reflects specific underlying molecular mechanisms. The subtypes also differed in cortical atro - phy patterns and survival times, underscoring their clinical relevance. A potential limitation of our study is that AD was defined with biomarkers, which correlate strongly with pathology but sometimes may be inaccurate. Another limitation is that we were unable to test if subtypes would have a different treatment response because we did not have access to samples from trials. Given the distinct patterns of molecular processes and AD genetic risk profiles, it is likely that AD subtypes will require specific treatments. For example, subtype 1 individuals may benefit from TREM2-activating treatments, subtype 2 from innate immune inhibitors, subtype 3 from antisense oligonucleotides that restore RNA processing, subtype 4 from inhibition of monocyte infiltration and subtype 5 from cerebrovascular treatments. At the same time, side effects arising from certain treatments may also depend on subtype. For example, while antibodies may more easily cross the blood–brain barrier in subtype 5, these individuals may be at increased risk for cerebral bleeding that can occur with antibody treatment. Future studies should aim to (re)analyze proteomics in clinical trial samples to test whether particular treatments have subtype-specific effects. To conclude, CSF-based subtyping may be useful to select individuals for a specific therapeutic treatment, either for a priori subject stratification or for responder and side effect analysis in clinical trials. Methods Participants For this study, we selected individuals from studies performed at the Alzheimer Center Amsterdam under similar protocols (that is, Amsterdam Dementia Cohort (ADC) 15 , EMIF-AD preclinAD 16 and 90+ studies 17 , and Amsterdam site participants who coenrolled in the ADC biobank and the EPAD study18) who provided written informed consent to use their data and biospecimens for research purposes. None of the individuals overlapped with our previous CSF proteomics study8. AD was defined based on the presence of an abnormal amyloid marker (n = 419), and controls were required to have normal cognition with normal amyloid markers (described in the ‘CSF ELISA measures’ section). Among the group with abnormal amyloid markers, 107 individuals had normal cognition, 103 had MCI and 209 had dementia according to international consensus criteria 15–18 . When more individuals met Nature Aging | Volume 4 | January 2024 | 33–47 40 Letter https://doi.org/10.1038/s43587-023-00550-7 0 0.25 0.50 0.75 1.00 Subtype probability Replication cohort 1 - EMIF-AD MBD (ref. 8) S1 n = 77 (33%) Replication cohort 2 (ref. 104) Subtype probability S1 S2 S3 S4 S5 Subtype probability S1 S2 S3 S4 S5 Replication cohort 3 (ref. 105) Subtype probability S1 S2 S3 S4 S5 Replication cohort 4 (ref. 106)Replication cohort 5 (ref. 107) Replication cohort 6 (ref. 107) Subtype probability S1 S2 S3 S4 S5 Subtype probability S1 S2 S3 S4 S5 Subtype probability S1 S2 S3 S4 S5 Subtype S1 S2 S3 S4 S5 Subtype Age Albumin CSF/serum Discovery cohort Replication 1 Replication 2 Replication 3 Replication 6 Replication 4 NA NA NA NA NA NA d e a S2 n = 60 (25%) S3 n = 2 (1%) S4 n = 40 (17%) S5 n = 52 (22%) S1 n = 31 (24%) S2 n = 35 (28%) S4 n = 27 (21%) S5 n = 29 (23%) S3 n = 5 (4%) S1 n = 8 (44%) S3 1 (6%) S4 n = 3 (17%) S1 n = 15 (24%) S2 n = 14 (23%) S3 n = 3 (5%) S4 n = 20 (33%) S5 n = 10 (16%) S2 n = 6 (33%) S1 n = 7 (14%) S2 n = 32 (61%) S3 n = 8 (15%) S4 n = 2 (4%) S5 n = 3 (6%) S1 n = 41 (28%) S2 n = 62 (42%) S3 n = 6 (4%) S4 n = 17 (12%) S5 n = 21 (14%) Control 1 2 3 4 5 0 500 1,000 1,500 2,000 0 3 6 250 500 750 1,000 100 200 300 0 500 1,000 1,500 50 100 150 200 250 0 100 200 300 Subtype t-tau pg ml−1 Discovery cohort bCSF t-tau Replication 6 Replication 3 Replication 2 Replication 1 Control 1 2 3 4 5 0 100 200 −2 0 2 4 30 60 90 25 50 75 100 200 300 20 40 60 80 40 80 120 Subtype p-tau pg ml−1 Replication 4 Replication 5 Discovery cohort Replication 6 Replication 3 Replication 2 Replication 1 Replication 4 CSF p-tau c Replication 5 Control 1 2 3 4 5 40 60 80 40 60 80 40 60 80 40 60 80 40 60 80 40 60 80 Age NA Discovery cohort Replication 6 Replication 3 Replication 2 Replication 1 Replication 4 Replication 5 5 10 Control 1 2 3 4 5 Subtype Albumin CSF/serum Fig. 3 | Replication of AD subtypes in six cohorts. a, Subtype probability for each individual in six replication cohorts. Most individuals showed high probability for one subtype only. b–e, Subtype comparisons on CSF t-tau levels (b), p-tau (c), age (d) and the CSF over serum albumin ratio (e) for each replication cohort when available. b–e, The boxplots depict the median in the center, the boundaries indicate the first and third quartiles, the whiskers extend up and down to 1.5 times the interquartile range (limited to actual observed data points) and the points indicate individual person values. The number of individuals per group in the boxplots are listed in Supplementary Tables 12 and 13, and provides the statistical metrics for the comparisons. NA, not applicable. Nature Aging | Volume 4 | January 2024 | 33–47 41 Letter https://doi.org/10.1038/s43587-023-00550-7 criteria for inclusion, preference was given to individuals with a known TREM2 R47H (n = 8) or R62H mutation (n = 28; see the details in the ‘Genetic data’ section), to individuals without dementia with clinical follow-up (n = 216) and to individuals with available tau positron emission tomography (PET) (n = 36). Mortality information was obtained from the Dutch Municipal Register for ADC and EMIF-AD participants. All studies were approved by the ethics committee of the Amsterdam UMC (location VUmc), the Biobank Research Ethics Committee of the Amsterdam UMC (location VUmc) and the Ethics Committee of the University of Norway. CSF collection and preparation for MS CSF was collected by lumber puncture between the L3/L4, L4/L5 or L5/S1 intervertebral space with a 25-gauge needle and syringe and collected in polypropylene tubes 15 . For all participants, CSF sample processing and biobank storage at the Alzheimer center biobank at the department of Laboratory Medicine was performed according to international guidelines15. The 610 samples were randomized over seven 96-well plates using random sampling as implemented in R to determine the layouts, 100 µl CSF in each well and stored at −80 °C. Researchers measuring the CSF samples were blinded to the diagnosis. Each sample plate was thawed on ice and 30 µg protein (separate 96-well plates containing 40 µl CSF were used to carry out the bicinchoninic acid (BCA) protein assay on 2 × 10 µl CSF for the protein concentration measurements) from each well was transferred to 1.5 ml protein LoBind tubes and immediately frozen on dry ice. All samples were lyophilized using a freeze dryer and kept at −80 °C before digestion. Urea protein digestion was performed as follows. Each day until there were no more samples to process, 28 samples together with one quality control sample and two reference samples were added (20 µl of 8 M urea/20 mM methylamine), vortexed for 5 min at 1,000 r.p.m. and sonicated for 30 s in ice-cold water. The urea solution was diluted with 20 µl of 50 mM Tris HCl/1 mM CaCl2, pH 7.6, followed by cysteine reduction (0.4 µmol dithiothreitol (DTT), 1 h incubation at room temperature) and alkylation (1 µmol iodoacetamide, 1 h incubation in the dark at room temperature). To avoid protease alkylation, excess iodoacetamide was allowed to react with DTT by adding 0.08 µmol of the reagent before diluting the urea to 1 M with 50 mM Tris HCl/1 mM CaCl2, pH 7.6. Trypsin digestion was performed for 16 h at 37 °C after adding 0.6 mg of the protease (porcine trypsin, Promega Corporation). Sample cleanup was performed using a reverse-phase Oasis 96-well HLB µElution Plate 30 µm (2 mg HLB sorbent, Waters). After lyophilization, the quality control samples were resuspended in 25 µl 2% acetonitrile (ACN)/0.5% formic acid. All other samples were resuspended in 20 µl of 50 mM HEPES buffer, pH 8.2, 4-(2-hydroxyethyl) 2-piperazin-1-ylethanesulfonic acid) before TMT labeling. Samples were vortexed for 30 s at 1,500 r.p.m. and sonicated for 30 s in an ultrasonic bath. Each reporter in a 5-mg TMTpro 16plex reagent set was dissolved in 1 ml anhydrous ACN. The 610 samples were labeled in 44 experiments, where each experiment contained 14 samples and 2 reference samples. For each sample, the 20-µl label was added. The labeling reaction was stopped by adding 5 µl 5% hydroxylamine after 75 min. The 16 labeled samples for each experiment were combined and lyophilized (about 240 µg protein); approximately 150 µg were desalted using a reverse-phase Oasis 96-well HLB Elution Plate 30 µm (10 mg HLB sorbent, Waters). After lyophilization, the 44 samples were dissolved in 150 µl of 10 mM ammonium formate, pH 7.9; 65 µl were fractionated using an off-line high-performance liquid chromatography (HPLC) system (Agilent 1260 infinity, Agilent Technologies) equipped with a reverse-phase column (XSelect CSH C18, 130 Å, 3.5 µm, 1 × 150 mm, Waters). Using high-pH reverse-phase chromatography, peptides were separated during a biphasic ACN gradient from two HPLC pumps (flow rate of 50 µl min−1). Solvents A and B were 10 mM ammonium formate, pH 7.9, in water and 90% ACN/10% water, respectively. The gradient composition was 5% B during trapping (2 min) followed by 5–12% B over 1 min, 12–44% B for the next 35 min, 44–70% B over 10 min and 70–95% B over 2 min. Elution of very hydrophobic peptides and conditioning of the column were performed for a 5-min isocratic elution with 95% B and a 12-min isocratic elution with 5% B, respectively. Peptides were collected in a 500-µl protein LoBind 96-well plate during peptide elution; ten fractions were collected. The first fraction was collected in two wells from 5 to 16 min (5.5 min per well, merged into one fraction); the next eight fractions (2.7 min per fraction) were collected between 16 and 37.6 min; the last fraction was collected in two wells between 37.6 and 53.6 min (8 min per well, merged into one fraction). Fractions were lyophilized and resuspended in 10 µl 2% ACN/0.5% formic acid, and peptide concentrations were measured with a NanoDrop UV-Vis spectrophotometer (Thermo Fisher Scientific) before LC–MS/MS analysis. LC–MS/MS About 0.5 µg tryptic TMT-labeled peptides were injected into an Ultimate 3000 RSLC system (Thermo Fisher Scientific) connected online to a Exploris 480 mass spectrometer (Thermo Fisher Scientific) equipped with an EASY-Spray nano-electrospray ion source. Peptides were desalted on a precolumn (Acclaim PepMap 100, 2 cm × 75 µm ID nanoViper column, packed with 3 µm C18 beads) at a flow rate of 5 µl min −1 for 5 min with 0.1% trifluoroacetic acid before separation on a 50-cm analytical column (PepMap RSLC, 50 cm × 75 µm ID EASY-Spray column, packed with 2 µm C18 beads). During a biphasic ACN gradient from two nanoflow UPLC pumps (solvent A and B were 0.1% formic acid (vol/vol) in water and 100% ACN, respectively), peptides were separated through the reverse-phase column at a flow rate of 200 nl min−1. The gradient composition was 5% B during trapping (5 min) followed by 5–8% B over 1 min, 8–30% B for the next 104 min, 30–40% B over 15 min and 40–80% B over 3 min. Elution of very hydrophobic peptides and conditioning of the column was performed for a 9-min isocratic elution with 80% B and a 10-min isocratic elution with 5% B. The LC was controlled through an SII for Xcalibur 1.6 (Thermo Fisher Scientific). Peptides eluted from the column were detected in the Exploris 480 mass spectrometer (capillary temperature at 275 °C and ion spray voltage at 2100 V) with high-field asymmetric waveform ion mobility spectrometry (FAIMS) enabled using two compensation voltages (CVs) (−50V and −70V), and ‘advanced peak determination’ on. During each CV, the mass spectrometer was operated in the data-dependent acquisition (DDA) mode to automatically switch between one full scan MS and MS/MS acquisition. Instrument control was through an Orbitrap Exploris 480 Tune 3.1 and Xcalibur 4.4. The cycle time was maintained at 1.5 s per CV. The FAIMS filter performed gas-phase fractionation, enabling the preferred accumulation of multiply charged ions to maximize the efficiency of the DDA. FAIMS results in less precursor coisolation and cleaner MS/MS spectra. MS spectra were acquired in the scan range of 375–1,500 m/z with resolution R = 60,000 at 200 m/z, automatic gain control target of 3 × 106 and a maximum injection time at auto (depending on the transient length in the Orbitrap). The most intense eluting peptides with charge states 2–6 and above an intensity threshold of 2 × 104 were sequentially isolated to a standard target value of 2 × 105, or a maximum injection time of 120 ms in the C-trap, and isolation width maintained at 0.7 m/z (quadrupole isolation), before fragmentation in the higher-energy collision dissociation. Fragmentation was performed with a normalized collision energy of 32%; fragments were detected in the Orbitrap at a resolution of 45,000 at 200 m/z, with first mass fixed at 110 m/z. One MS/MS spectrum of a precursor mass was allowed before dynamic exclusion for 45 s with ‘exclude isotopes’ on. Lock-mass internal calibration was not enabled. The resulting .raw files were processed using Proteome Discoverer 2.5. The database file used for the search using Sequest HT was Swiss-Prot with 20,395 entries (v.20210413.fasta). The following modifications were made in the database search: precursor mass tolerance: 10 ppm; fragment mass tolerance: 0.02 Da; static peptide N terminus: TMTpro/ + 304.207 Da (any N terminus); static modification: Nature Aging Letter https://doi.org/10.1038/s43587-023-00550-7 Extended Data Fig. 1 | Trajectories of repeated cognitive test scores over time for AD subtypes. Analyses were stratified according to clinical stage. In all plots the grey line represents the trajectory of the control group (that is, individuals with intact cognition and normal CSF markers). * indicates that the slope differs from zero with p < 0.05. a indicates slope different from controls, b indicates slope different from subtype 1, c indicates slope different from subtype 2, d indicates slope different from subtype 3, e indicates slope different from subtype 4 and f indicates slope different from subtype 5. See Supplementary Table 7 for statistical metrics. Nature Aging Letter https://doi.org/10.1038/s43587-023-00550-7 Extended Data Fig. 2 | Trajectories of repeated cognitive scores for subtype 3 individuals without dementia. Grey lines indicate individuals with dementia and subtype 3. MMSE includes one individual with normal cognition (blue line), all other tests include two individuals with MCI (red lines). No statistics were performed for predementia individuals due to small sample size. Nature Aging Letter https://doi.org/10.1038/s43587-023-00550-7 Extended Data Fig. 3 | Comparing subtypes on cortical thickness in dementia stage. ß indicates mean cortical thickness in mm, averaged over right and left hemispheres and adjusted for age and sex. a) Cortical thickness compared between AD subtypes (in dementia stage) with controls. b) Cortical thickness comparisons between AD subtypes within the dementia stage. Negative values indicate thinner cortex in the subtype indicated in the row as compared to the subtype indicated in the column. Analyses were adjusted for age and sex. Nature Aging Letter https://doi.org/10.1038/s43587-023-00550-7 Extended Data Fig. 4 | Comparing Alzheimer’s disease (AD) subtypes on protein levels against controls, plotted separately according to clinical stage. Left for cognitively normal with abnormal amyloid, middle for MCI with abnormal amyloid and right for AD dementia and abnormal amyloid. Plots indicates highly similar subtype patterns, suggesting that protein levels reflect particular AD related traits. See Supplementary Table 5 columns CY to HU for statistical metrics of subtype comparisons within each clinical stage. All proteins were scaled according to the mean and standard deviation of the control group, such that positive values indicate higher levels than controls, and negative values lower levels than controls. Nature Aging Letter https://doi.org/10.1038/s43587-023-00550-7 Extended Data Fig. 5 | Batch correction of TMT experiments. a) Biplot of first two principal components on unnormalized protein abundances have batch effects between TMT experiments as indicated by the non-overlapping circles that correspond to all 44 TMT experiments. b) Batch effects were successfully removed with the Internal Reference Scaling method as described in the online methods.