Multi-omic subtypes of Alzheimer’s dementia are differentially associated with psychological traits

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Zammit, Lei Yu, Victoria Poole, Konstantinos Arfanakis, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6131485/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Psychological traits reflecting neuroticism, depressive symptoms, loneliness, and purpose in life are risk factors of AD dementia; however, the underlying biologic mechanisms of these associations remain largely unknown. In this study we examined whether pseudotime, representing molecular distance from no cognitive impairment (NCI) to AD dementia, and three distinct multi-omic brain molecular subtypes of AD dementia representing 3 omic pathways from NCI to AD dementia are differentially associated with these risk factors. Participants included 822 decedents with multi-omic data from the dorsolateral prefrontal cortex from two cohort-based studies; Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP), both ongoing longitudinal clinical pathological studies. We first ran four separate linear regressions with neuroticism, depressive symptoms, loneliness, purpose in life as the outcomes, and pseudotime as the predictor, adjusting for age, sex and education. We then ran four separate analyses of covariance (ANCOVAs) with Bonferroni-corrected post-hoc tests to test whether the three multi-omic AD subtypes are differentially associated with the four risk factors, adjusting for the same covariates. Pseudotime was positively associated ( p < 0.05) with neuroticism and loneliness. AD subtypes were differentially associated with the traits: AD subtypes 1 and 3 were associated with neuroticism; AD subtype 2 with depressive symptoms; AD subtype 3 with loneliness, and AD subtype 2 with purpose in life. Our results show that psychological risk factors might be associated with AD dementia via shared multi-omic molecular pathways. Our data provide novel insights into the biology underlying well-established associations between psychological traits and AD dementia. Biological sciences/Psychology Health sciences/Biomarkers/Diagnostic markers multi-omics AD subtypes AD dementia psychological traits Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Psychological traits, such as proneness to psychological distress (i.e., neuroticism), depressive symptoms, loneliness, and purpose in life, are well-established risk factors for prospective mental and physical health outcomes 1 – 5 such as incidence of mild cognitive impairment (MCI) and Alzheimer’s disease and related dementias (AD/ADRD) 6–10 as well as morbidity and mortality. 11 – 16 The public health significance of these traits as robust risk factors has also been well-documented. 17 – 21 We previously documented the associations of neuroticism 22 , depressive symptoms 23 , loneliness 24 , and purpose in life 25 , with cognitive decline, and incidence of MCI and AD/ADRD. These associations remained after adjusting for common age-related neuropathologic indices that cause cognitive impairment. 22 – 29 To date we have not found any associations between these traits and common markers of neurodegeneration including neuritic plaques, neurofibrillary tangles, Lewy bodies, TDP-43, and hippocampal sclerosis, though we did find weak associations between purpose in life and lacunar cerebral infarctions. 30 Thus, other molecular mechanisms are likely involved. For example, we reported that two neocortical proteins 31 and a transcriptomic co-expression module 32 mediated in part the association of neuroticism with cognition. Further, we also found some relationships linking depressive symptoms to cognition via select proteins 33 and microRNAs. 34 We recently integrated four layers of dorsolateral prefrontal cortex (DLPFC) omics data, i.e., epigenomic, transcriptomic, proteomic, and metabolomic, with multimodal contrastive Trajectories Inference (mcTI) analysis to derive multi-omic pseudotime from no cognitive impairment (NCI) to AD dementia 35 . We subsequently decomposed pseudotime into three distinct multi-omic brain molecular subtypes representing three molecular pathways from NCI to AD dementia. These subtypes differed in their multi-omic composition and key drivers. Here, we examined whether one or more of these molecular pathways would be associated with neuroticism, depressive symptoms, loneliness and/or purpose in life. Such associations would provide clues to potential molecular omic connections linking these traits to AD dementia. Subjects and Methods Study population Participants were community-based older adults from the Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP) 36 . ROS, initiated in 1994, includes older priests, nuns, and brothers from across the U.S. while MAP established in 1997, includes older men and women from across the greater Chicago metropolitan area. Participants were free of known dementia at enrollment, agreed to annual clinical evaluation and signed informed consent and Anatomic Gift Act to donate their brains at death. Both studies were approved by an Institutional Review Board of Rush University Medical Center. Assessment of psychological risk factors Neuroticism was assessed using either 12 or 6 items from the NEO Five-Factor Inventory, which was administered at baseline or near baseline as previously reported 37 . Depressive symptoms were assessed annually using a 10-item form 38 of the Center of Epidemiologic Study-Depression Scale(CES-D) 39 . Loneliness and purpose in life were assessed at baseline in MAP participants only, using a 5-item version from a modified scale of the de Jong-Gierveld Loneliness scale 40 , 41 and a 10-item scale derived from Ryff’s Scales of Psychological Wellbeing as previously described. 42 , 43 eMethods details psychometric information. Clinical Diagnoses AD dementia was diagnosed by an experienced clinician using criteria of the joint working group of the National Institute of Neurologic and Communicative Disorders/Stroke/AD and Related Disorders Association 44 , as previously described. 45 These criteria require a history of cognitive decline and evidence of impairment in at least two domains of cognitive function, one of which must be memory. 46 MCI required evidence of impairment without meeting accepted criteria for dementia, and NCI refers to individuals without dementia or MCI as previously established. 46 , 47 Neuropathologic evaluation Brain autopsy followed standardize protocols. 48 , 49 Neuropathologic evaluations systematically assessed common AD and non-AD neurodegenerative and cerebrovascular conditions including Alzheimer’s disease pathology, Lewy bodies, transactive response DNA binding protein (TDP)-43, hippocampal sclerosis, chronic macroscopic and microinfarcts, cerebral amyloid angiopathy(CAA), atherosclerosis, and arteriolosclerosis, as described in more detail in the eMethods . Postmortem Imaging A subset of 278 participants underwent a postmortem brain imaging protocol, described previously 50 . Briefly, after one month postmortem, the cerebral hemisphere selected for neuropathological examination was imaged with a multi-echo spin-echo sequence on one of four 3-Tesla MRI scanners, and the resulting images were used for deformation-based morphometry(DBM) as detailed in eMethods . Annotation of the previously reported brain multi-omic molecular AD subtypes Brain multi-omic data contain DNA methylation (DNAm) with Illumina 450 array, bulk next generation RNA sequencing, targeted protein expression with selected reaction monitoring, and metabolite level (metabolon) from DLPFC, generated as described previously. 51 – 53 We previously used the machine-learning mcTI algorithm to generate a brain molecular pseudotime of AD dementia relative to NCI, and three brain molecular subtypes reflecting different molecular pathways from NCI to AD dementia based on the numbers and types of omics. 35 Here, we generate the top (FDR p < 0.05) molecular features that characterize drivers of the three previously reported AD subtypes. 35 Statistical analyses First, as pseudotime and the subtypes are built from AD dementia as the target, we compared frequency of pathologies across subtypes to ensure that our molecular subtypes were not simply capturing different pathological features. Pathologies were grouped accordingly:i) AD, CAA; ii)non-AD neurodegenerative: neocortical Lewy bodies, TPD-43, hippocampal sclerosis; iii) total infarcts, iv)cerebral vessel pathology: arteriosclerosis, atherosclerosis. We performed chi-squared tests to determine whether observed frequencies of pathologies differ by subtype. Second, we described the proportion of the top epigenomic, transcriptomic, proteomic, and metabolomic features of the trajectory inferences per subtype, and illustrated their top 25 influential contributing features ordered by their F-value. Finally, we ran four separate analyses of variance tests, one for each omic modality (F-value as outcome and AD subtypes as predictors) using Bonferroni’s method for post-hoc analyses to calculate inter-subtype differences in the F-value. Third, we tested associations with brain morphometry by running a general linear model of voxel-wise deformation assessed using FSL PALM 54 , 55 adjusting for demographics, postmortem interval, and scanner(accounting for differences in both mean and variance across scanners). We then contrasted each group pair. P-values were computed from 500 permutations using tail approximation 56 . The threshold-free cluster enhancement (TFCE) approach was used to define clusters of significance. Associations were considered statistically significant at p ≤ .05, family-wise error rate (FWER) corrected. Fourth, we tested associations between pseudotime and the psychological traits in four separate linear regression models. Our outcomes were neuroticism, depressive symptoms, loneliness, and purpose in life. Covariates included age at death, sex, and education. The term for pseudotime indicated its association with the traits by one additional point. Finally, we ran four analyses of covariance using Bonferroni’s method for multiple comparisons to test whether the 3 subtypes are associated with the psychological traits, adjusting for the same covariates. Results A total of 822 participants had multi-omic data. Of those, 761 participants also had measures of neuroticism, 818 had CES-D, and 306 participants had measures on loneliness and purpose in life which were only available in MAP. Mean age at baseline was just over 80 years and mean age at death was close to 90 years. Over 60% were female, and most participants completed college(Table 1 ). Table 1 Characteristics of the whole sample and stratified by controls and AD subtypes. Mean (SD) or % (n) Characteristics Whole sample NCI Subtype 1 Subtype 2 Subtype 3 N 822 274 189 197 162 Age at baseline, mean years, (SD) 82.3 (6.7) 79.2 (6.9) 81.9 (6.3) 82.2 (6.8) 82.9 (6.9) Age at death, mean years (SD) 88.4 (6.6) 86.2 (6.5) 89.3 (6.4) 89.1 (6.6) 90.4 (6.1) Female, n (%) 532 (64) 171 (32) 130 (24) 122 (22) 109 (20) Educational attainment, mean years (SD) 16.3 (3.5) 16.3 (3.6) 16.4 (3.6) 16.2(3.5) 16.3 (3.2) Neuroticism, baseline, (SD) 16.8 (6.6) 15.76 (6.68) 17.83 (6.61) 16.72 (6.39) 17.53 (6.26) Depressive symptoms, mean score (SD) 1.44 (1.37) 1.26 (1.26) 1.52 (1.44) 1.58 (1.37) 1.48 (1.46) Loneliness, baseline (SD) 2.4 (0.6) 2.2 (0.6) 2.4 (0.6) 2.3 (0.5) 2.5 (0.7) Purpose in life, baseline (SD) 3.6 (0.5) 3.6 (0.5) 3.5 (0.4) 3.5 (0.4) 3.5 (0.4) Pseudotime, mean (SD) 0.4 (0.21) 0.23 (0.13) 0.38 (0.13) 0.45 (0.09) 0.68 (0.18) Global cognition, baseline, mean (SD) -0.23 (0.7) 0.17 (0.4) -0.40 (0.6) -0.45 (0.7) -0.43 (0.9) Global cognition, last valid, mean (SD) -0.96 (1.2) 0.08 (0.4) -1.45 (1.1) -1.50 (1.1) -1.50 (1.1) Mild cognitive impairment proximate to death, n (%) 197 (23) 0 63 (31) 78 (39) 56 (28) AD dementia proximate to death, n (%) 351 (42) 0 126 (35) 119 (33) 106 (30) Neuropathology Pathologic AD (NIA-AA), n (%) 517 (62.9) 118 (43.1) 141 (74.6) 138 (70.1) 120 (74.1) Neocortical Lewy bodies, n (%) 84 (10.2) 13 (4.7) 18 (9.5) 28 (14.2) 25 (15.4) TDP-43, n (%) 238 (30.4) 43 (16.5) 64 (36.4) 72 (38.3) 59 (37.3) Hippocampal sclerosis, n (%) 60 (7.4) 6 (2.2) 17 (9.0) 25 (12.8) 12 (7.5) Gross chronic infarcts, n (%) 294 (35.8) 61 (22.3) 83 (43.9) 77 (39.1) 73 (45.1) Micro chronic infarcts, n (%) 225 (27.4) 63 (23.0) 60 (31.7) 56 (28.4) 46 (28.4) Arteriosclerosis, moderate to severe, n (%) 317 (38.9) 78 (28.7) 74 (39.4) 99 (50.8) 66 (41.3) Atherosclerosis, moderate to severe, n (%) 346 (42.2) 86 (31.7) 84 (44.4) 98 (49.7) 78 (48.1) Cerebral amyloid angiopathy, moderate, n (%) 281 (35.1) 75 (28.2) 69 (37.1) 73 (38.4) 64 (40.3) Frequency of AD/ADRD pathologies across AD subtypes The reference NCI group had less pathology, as expected (median = 2 pathologies, interquartile range (IQR) = 2). AD subtypes had a median of 3 pathologies (IQRs:subtype 1 = 2; subtypes 2/3 = 3). Over half of NCI participants had 1 or 2 pathologies, while a third had three or more. Meanwhile over two thirds of participants in all AD subtypes had three or more pathologies, which is expected since mixed pathologies commonly drive AD dementia (eTable 1). Over 70% of individuals across all subtypes had pathologic AD and/or CAA (χ 2 (2, 535) = 1.96, p = 0.38), and almost half had non-AD neurodegenerative neuropathology (χ 2 (2, 517) = 4.09, p = 0.66). There were no differences in the distribution of infarcts ((χ 2 (2, 548) = 4.01, p = 0.14) or in frequency of cerebral vessel neuropathology (χ 2 (4, 543) = 2.89, p = 0.24)(Fig. 1 ).Therefore, our molecular subtypes are not simply capturing different neuropathological features. Proportion of omic features for each AD subtype The relative contribution of each omic dimension was related to abundance with the greatest number of features: DNAm, followed by bulk RNAseq, and targeted proteomics which had the fewest. Thus, most frequent omic features across all subtypes were epigenomic alterations, followed by RNA alterations which is expected as they are the two with by far the greatest number of features. Still, there were significant differences in omic proportions amongst the AD subtypes (χ 2 (6,569) = 30.4, p < 0.001)(Fig. 2 a). For example, while 15% of the top features in subtype 1 were metabolites, less than 5% in subtypes 2 and 3 were metabolites. Almost 43% of top features in subtype 2 were RNA alterations while in subtypes 1 and 3 these alterations amounted to about 30%. Finally, while over 60% of epigenomic alterations characterized subtype 3, these were less then 50% in subtypes 1 and 2. Proteomic abnormalities contributed the least, as expected given the small number of features. Top omic features for each AD subtype Top influential contributing omic features with an F-value > 50% can be seen in Fig. 2 b. Interestingly, almost all the strong drivers were metabolites. The strongest contributors (F-value > 90%) differentiating these AD subtypes from NCI were three phospholipids and a major excitatory neurotransmitter. Specifically, phosphatidylcholine acyl-alkyl (PCae)C38:4 for AD subtype 1; lysophosphatidylcholine (lysoPC)acyl C20:3 and PC acyl-acyl (aa)C36:6 for AD subtype 2; and glutamate for AD subtype 3 (full list in eTable 2) . Subtype-subtype differences of top influential omic features by modality We then compared each omic modality’s F-values amongst subtypes; higher F-values indicate stronger differences from NCI relative to other subtypes. AD subtype 2 consistently had significantly higher F-values across all omic features (Fig. 3 ). Specifically, epigenomic alterations were significantly higher in AD subtype 2 relative to subtypes 1 and 3 as were RNA alterations, proteomic alterations, and metabolomic alterations. eTable 3 lists means and 95%CIs. In sum, the top omic features in subtype 2 differentiated this subtype from NCI more strongly than either of the omic features in subtypes 1 and 3. Yet, specific omic features at the individual level were different per subtype. eTable 4 details mean differences. Brain morphology across AD subtypes To further characterize the subtypes, we examined differences in brain morphometry across a subset of 278 participants (NCI = 91, AD subtypes: 1 n = 74; 2 n = 57, 3 n = 56) with postmortem MRI. As expected, all AD subtypes exhibited greater cortical atrophy, as indicated by smaller postmortem brain volumes, relative to NCI ( eFigure 1 ). AD subtype 1 had the most extensive atrophy, as observed by smaller volumes in the medial temporal lobe and across several other temporal, frontal, and parietal regions. AD subtype 2 exhibited temporal lobe atrophy that extended to the temporal pole. Finally, AD subtype 3 exhibited the least atrophy, which was largely localized to the temporal lobe and insular cortex. Both subtypes 1 and 2 also had extensive ventricular enlargement, which indicated central brain atrophy, while subtype 3 had minimal enlargement of the temporal horn. While the differences are intriguing, none of the subtypes were statistically different from one another in this small subsample. Association of pseudotime with psychological traits Mean pseudotime in the NCIs was almost 0.25 while across the AD subtypes this ranged from about 0.4 for subtype 1 to 0.5 for subtype 2 and 0.7 for subtype 3, with higher mean pseudotime indicating closer proximity to AD dementia(Table 1 ). Higher pseudotime was positively associated with neuroticism (beta = 2.86, 95%CI = 0.62–5.10, p = 0.013) and loneliness (beta = 0.35, 95%CI = 0.04–0.66, p = 0.029). Pseudotime explained 4% of variance in neuroticism, and 3% of variance in reported loneliness after accounting for age, sex, and level of education. There was no association between pseudotime and depressive symptoms (beta = 0.402, 95%CI = -0.052–0.856, p = 0.082), or purpose in life(beta=-0.15, 95%CI=-0.38-0.08, p = 0.212). Association of AD subtypes with psychological traits ANCOVA showed significant effects of the subtypes on all traits (neuroticism: F(3,760) = 5.9, p < 0.001; depressive symptoms: F(3,817) = 3.9, p = 0.009; loneliness: F(3,305) = 5.6, p = 0.014; purpose in life, F(3,305) = 3.5, p = 0.016). Post-hoc comparisons indicated differential associations between AD subtypes and the traits relative to NCI; however, there were no inter-subtype differences(Fig. 4 ). Subtypes 1 and 3 had higher neuroticism scores than NCI, while subtype 2 had higher depressive symptoms. Subtype 3 had higher loneliness; subtype 2 had marginally lower purpose in life score( eTable 5 ). Discussion To better understand the mechanistic basis of the relation of four psychological traits to AD dementia, we leveraged prior work in 822 older adults who subsequently died and had brain autopsy, providing brain multi-omic molecular pseudotime, and three distinct molecular subtypes representing pathways from NCI to AD dementia. We found that pseudotime was associated with neuroticism and loneliness, and subsequently AD multi-omic brain molecular subtypes were differentially associated with neuroticism, depressive symptoms, loneliness, and purpose in life. The results provide novel data on shared top multi-omic features between molecular subtypes of AD dementia and well-established AD/ADRD risk factors, suggesting common underlying mechanisms driving associations between psychological traits and AD/ADRD. Previous studies primarily focused on single-omic associations. We previously showed that neuroticism is associated with 18 cortical transcriptomic co-expressed modules, three (m6, m7, and m127) of which partially mediated the association of higher neuroticism on faster cognitive decline 32 . We previously also identified two cortical proteins, 40S ribosomal protein S3 and BCKDHB, that were associated with both neuroticism and cognitive decline independent of common AD/ADRD pathologic indices. 31 These two proteins strongly mediated the association of higher neuroticism on cognitive decline independent of common AD/ADRD pathologic indices, and further explained 25% of the variance in this association. Together, our prior studies provide evidence that neuroticism has strong and widespread effects on the transcriptome, and on specific cortical proteins in the aged prefrontal cortex, both of which affect downstream AD-related outcomes. Similarly, in prior work we identified a cortical protein, IGFBP-5, that was associated with both depressive symptoms and cognition, and further explained 10% of the association between depressive symptoms and cognitive decline. 33 We also previously identified four microRNAs associated with depressive symptoms, of which miR-484 targets were enriched in a co-expression module involved in synaptic function and plasticity and were associated with higher risk of AD dementia. 34 While there is some evidence on associations of loneliness 57 , and purpose in life 58 , with altered DNAm, and expression of mRNAs and proteins, their shared mechanisms with AD/ADRD are still largely unexplored. We extend prior work by showing here that including multiple molecular modalities to identify common correlates of subtypes representing distinct molecular signatures of AD dementia can be leveraged to potentially identify the molecular basis of psychological traits associated with AD dementia. To our knowledge, this is the first study that has leveraged multi-omics data to directly explore these associations. This approach allowed us to identify distinct molecular AD subtypes that are differentially associated with four psychological traits. AD subtypes 1 and 3 showed strong associations with neuroticism, while subtype 2 was exclusively associated with depressive symptoms. AD subtype 1 had higher proportions of metabolomic alterations relative to subtypes 2 and 3, while subtype 3 had higher proportions of epigenomic alterations. By contrast, subtype 2 had relatively higher proportions of transcriptomic alterations. Loneliness and purpose in life had somewhat weaker associations with subtypes 3 and 2 respectively likely due to low statistical power since these two measures were only available in MAP participants. Much work remains to expand our findings in better-powered studies, with additional and larger multi-layered omic subtypes. Our findings suggest that the various AD molecular mechanisms that are subserving cognition are also related to psychological traits. Metabolomic alterations were the topmost features that significantly differentiated these AD subtypes from NCI. Top alterations in subtypes 1 and 2 were phosphatidylcholines (PCs), a major class of phospholipids that form a crucial component of cell membranes. PC acyl-acyl (AA) and acyl-ether (AE) lipids are involved in signaling and maintenance of membrane structure; the ether bond in the AE lipids further promote resistance to oxidative damage. Disruption in lipid metabolism, including significant reductions in PC ae c38:4 and PC aa C36:6 are significantly associated with clinical AD. 59 60, 61 LysoPCs are proinflammatory lipids and their concentration is substantially higher in clinical AD. 62 Psychological distress has also been associated with altered lipid metabolism via a cascade of dysregulation in PCs and lysoPCs that eventually cause cell membrane destabilization and inflammatory responses. 63 The top alteration in AD subtype 3 was glutamate which is the primary excitatory neurotransmitter in the central nervous system. Overactivation of glutamate receptors are implicated in neuronal damage and death via a cascade of intracellular events including oxidative stress, mitochondrial dysfunction, and activation of destructive enzymes. 64 Chronic distress and depression have also been implicated in the malfunctioning of the glutamatergic neurotransmitter system primarily affecting glutamate receptor expression in the prefrontal cortex, hippocampus and amygdala 65 , 66 . Further studies that will collect more omics data will be better able to characterize multi-level omic subtypes and determine mediating effects of top contributors of AD subtypes on the associations between enduring psychological traits and AD/ADRD outcomes. This study has limitations and strengths. The brief neuroticism measure used here may have underestimated associations, though we have previously reported a correlation of .90 between shorter-(6-and 12-) item versions of the NEO 22 testifying to its validity. Nonetheless associations might be slightly stronger than what we observed here. Similarly, the depressive symptoms measure is also based on a brief psychometric measure, however, its reliability has been previously established 67 . For CES-D we used each person’s average score across all evaluations as previously 68 , which capture stable enduring tendencies, and reduce random error. Measures of loneliness and purpose in life were only available in MAP participants, potentially reducing statistical power. All omics came from one brain region and the number of epigenomic features and transcriptomic were disproportionally higher than those for metabolites or proteins 35 , which might have overestimated the noted proportions of epigenomic features within subtypes. However, the DLPFC is a known region for its associations with learning, stress response and emotion regulation, and the continuous collection of omics data within our cohorts will allow for further multilevel characterization of complex traits and replication of results. Our novel multi-omic approach provides insights into the molecular basis of psychological AD/ADRD risk factors. Other approaches have been used to identify molecular subtypes of AD; however, they have been agnostic to any clinical trait. 69 – 72 High participation rates in the clinical evaluations and brain autopsy in the ROS and MAP cohorts minimize bias due to selective attrition. Declarations Conflict of interest We declare no competing interests. Data sharing The data are available via the Rush Alzheimer’s Disease Center Research Resource Sharing Hub. Qualified applicants should complete an application including study premises and a brief description of the research plan. Brain multi-omic data are available in the AMP-AD knowledge portal ( www.synapse.org ), using the following Synapse IDs:syn3157275 (epigenomic data), syn3800853 (transcriptomic), syn10468856 (proteomic), and syn10235595 and syn10235594 (metabolomic). Contributors ARZ, YIT and DAB were responsible for the conception and design of the current study. ARZ performed the literature review and wrote the first draft of the manuscript. LY conducted the data analysis and contributed to writing the statistical analysis section and revising the manuscript. LY supervised the data analysis and contributed to revising the manuscript. KA is the director of the neuroimaging core of Rush Alzheimer’s Disease Center. KA and VNP supervised and oversaw the neuroimaging analyses. JAS is the director of pathology core of Rush Alzheimer’s Disease Center that collects indices of brain pathologies. She is also the director of Rush Alzheimer’s Disease Research Center that oversees data collection of the ROS cohort. VAP selected the protein targets and designed the peptides. PLD performed LC-SRM targeted proteomic assays and provided quantitative datasets of the peptide. RKD leads Alzheimer Disease Metabolomics Consortium and Alzheimer Gut Microbiome Project funded by NIA that contributed metabolomics data generated by Metabolon Inc. where metabolomics was conducted by chromatography spectrometry. DAB is the principal investigator of MAP and was involved in data collection. LY, VNP, KA, JAS, VAP, PLD, YIT and DAB contributed to revising the manuscript. YIT and DAB had full access to all the data in the study, accessed and verified the data, and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors had access to the data and had final responsibility for the decision to submit for publication. Acknowledgements We appreciate the participants of MAP for the time generously given for data collection and for consenting for brain donation. We also acknowledge staff of Rush Alzheimer’s Disease Center for data collection, management, and analyses. This work was supported by National Institutes of Health: R01AG17917, P30AG10161, P30AG72975, R01AG015819, U01AG61356, U01 NS100599, R01 AG064233, RF1 NS139975, P30 AG072975. We would also like to thank Michael Urbut and the Paul M. Angell Family Foundation for their generous financial support given towards this work. The funding organizations had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. References Malouff JM, Thorsteinsson EB, Schutte NS. The Relationship Between the Five-Factor Model of Personality and Symptoms of Clinical Disorders: A Meta-Analysis. 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Grodstein F, Yu L, de Jager PL, Levey A, Seyfried NT, Bennett DA. Exploring Cortical Proteins Underlying the Relation of Neuroticism to Cognitive Resilience. Aging Brain 2022;2. De Jager CH, White CC, Bennett DA, Ma Y. Neuroticism alters the transcriptome of the frontal cortex to contribute to the cognitive decline and onset of Alzheimer's disease. Transl Psychiatry 2021;11:139. Capuano AW, Wilson RS, Honer WG, et al. Brain IGFBP-5 modifies the relation of depressive symptoms to decline in cognition in older persons. J Affect Disord 2019;250:313-318. Wingo TS, Yang J, Fan W, et al. Brain microRNAs associated with late-life depressive symptoms are also associated with cognitive trajectory and dementia. NPJ Genom Med 2020;5:6. Iturria-Medina Y, Adewale Q, Khan AF, et al. Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity. Sci Adv 2022;8:eabo6764. Bennett DA, Buchman AS, Boyle PA, Barnes LL, Wilson RS, Schneider JA. Religious Orders Study and Rush Memory and Aging Project. Journal of Alzheimer's Disease 2018;64:S161-S189. Wilson RS, Schneider JA, Boyle PA, Arnold SE, Tang Y, Bennett DA. Chronic distress and incidence of mild cognitive impairment. Neurology 2007;68:2085-2092. Kohout FJ, Berkman LF, Evans DA, Cornoni-Huntley J. Two shorter forms of the CES-D (Center for Epidemiological Studies Depression) depression symptoms index. J Aging Health 1993;5:179-193. Radloff LS. The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement 1977;1:385-401. de Jong-Gierveld J, Kamphuls F. The Development of a Rasch-Type Loneliness Scale. Applied Psychological Measurement 1985;9:289-299. de Jong-Gierveld J. Developing and testing a model of loneliness. J Pers Soc Psychol 1987;53:119-128. Boyle PA, Barnes LL, Buchman AS, Bennett DA. Purpose in life is associated with mortality among community-dwelling older persons. Psychosom Med 2009;71:574-579. Ryff CD, Keyes CL. The structure of psychological well-being revisited. J Pers Soc Psychol 1995;69:719-727. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology 1984;34:939-944. Bennett DA, Schneider JA, Aggarwal NT, et al. Decision rules guiding the clinical diagnosis of Alzheimer's disease in two community-based cohort studies compared to standard practice in a clinic-based cohort study. Neuroepidemiology 2006;27:169-176. Bennett DA, Wilson RS, Schneider JA, et al. Natural history of mild cognitive impairment in older persons. Neurology 2002;59:198-205. Bennett DA, Schneider JA, Arvanitakis Z, Kelly JF, Aggarwal NT, Shah RC, Wilson RS. Neuropathology of older persons without cognitive impairment from two community-based studies. Neurology 2006;66:1837-1844. Schneider JA, Arvanitakis Z, Bang W, Bennett DA. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology 2007;69:2197-2204. Boyle PA, Wilson RS, Yu L, Barr AM, Honer WG, Schneider JA, Bennett DA. Much of late life cognitive decline is not due to common neurodegenerative pathologies. Ann Neurol 2013;74:478-489. Arfanakis K, Evia AM, Leurgans SE, et al. Neuropathologic Correlates of White Matter Hyperintensities in a Community-Based Cohort of Older Adults. J Alzheimers Dis 2020;73:333-345. De Jager PL, Ma Y, McCabe C, et al. A multi-omic atlas of the human frontal cortex for aging and Alzheimer's disease research. Sci Data 2018;5:180142. Bennett DA, Yu L, De Jager PL. Building a pipeline to discover and validate novel therapeutic targets and lead compounds for Alzheimer's disease. Biochemical Pharmacology 2014;88:617-630. De Jager PL, Srivastava G, Lunnon K, et al. Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci 2014;17:1156-1163. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. Neuroimage 2014;92:381-397. Winkler AM, Webster MA, Vidaurre D, Nichols TE, Smith SM. Multi-level block permutation. Neuroimage 2015;123:253-268. Winkler AM, Ridgway GR, Douaud G, Nichols TE, Smith SM. Faster permutation inference in brain imaging. Neuroimage 2016;141:502-516. Arzate-Mejía RG, Lottenbach Z, Schindler V, Jawaid A, Mansuy IM. Long-Term Impact of Social Isolation and Molecular Underpinnings. Frontiers in Genetics 2020;11. Kim ES, Sun JK, Park N, Kubzansky LD, Peterson C. Purpose in life and reduced risk of myocardial infarction among older U.S. adults with coronary heart disease: a two-year follow-up. J Behav Med 2013;36:124-133. Fiandaca MS, Zhong X, Cheema AK, et al. Plasma 24-metabolite Panel Predicts Preclinical Transition to Clinical Stages of Alzheimer's Disease. Front Neurol 2015;6:237. Li D, Misialek JR, Boerwinkle E, et al. Plasma phospholipids and prevalence of mild cognitive impairment and/or dementia in the ARIC Neurocognitive Study (ARIC-NCS). Alzheimers Dement (Amst) 2016;3:73-82. Klavins K, Koal T, Dallmann G, Marksteiner J, Kemmler G, Humpel C. The ratio of phosphatidylcholines to lysophosphatidylcholines in plasma differentiates healthy controls from patients with Alzheimer's disease and mild cognitive impairment. Alzheimers Dement (Amst) 2015;1:295-302. Dorninger F, Moser AB, Kou J, et al. Alterations in the Plasma Levels of Specific Choline Phospholipids in Alzheimer's Disease Mimic Accelerated Aging. J Alzheimers Dis 2018;62:841-854. Walther A, Cannistraci CV, Simons K, Durán C, Gerl MJ, Wehrli S, Kirschbaum C. Lipidomics in Major Depressive Disorder. Front Psychiatry 2018;9:459. Dong X-x, Wang Y, Qin Z-h. Molecular mechanisms of excitotoxicity and their relevance to pathogenesis of neurodegenerative diseases. Acta Pharmacologica Sinica 2009;30:379-387. Popoli M, Yan Z, McEwen BS, Sanacora G. The stressed synapse: the impact of stress and glucocorticoids on glutamate transmission. Nature Reviews Neuroscience 2012;13:22-37. Pal MM. Glutamate: The Master Neurotransmitter and Its Implications in Chronic Stress and Mood Disorders. Frontiers in Human Neuroscience 2021;15. Shrout PE, Yager TJ. Reliability and validity of screening scales: Effect of reducing scale length. Journal of Clinical Epidemiology 1989;42:69-78. Wilson RS, Schneider JA, Bienias JL, Arnold SE, Evans DA, Bennett DA. Depressive symptoms, clinical AD, and cortical plaques and tangles in older persons. Neurology 2003;61:1102-1107. Green GS, Fujita M, Yang HS, et al. Cellular communities reveal trajectories of brain ageing and Alzheimer's disease. Nature 2024;633:634-645. Higginbotham L, Carter EK, Dammer EB, et al. Unbiased classification of the elderly human brain proteome resolves distinct clinical and pathophysiological subtypes of cognitive impairment. Neurobiol Dis 2023;186:106286. Neff RA, Wang M, Vatansever S, et al. Molecular subtyping of Alzheimer's disease using RNA sequencing data reveals novel mechanisms and targets. Sci Adv 2021;7. Yang M, Matan-Lithwick S, Wang Y, De Jager PL, Bennett DA, Felsky D. Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing. Brain Commun 2023;5:fcad110. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files eMethods022025.docx eMethods SupplementaryMaterial.docx Supplemental results Cite Share Download PDF Status: Under Review Version 1 posted Unknown event 05 Jan, 2026 Editorial decision: Reject after peer review 14 Aug, 2025 Reviewer # 3 agreed at journal 28 Jul, 2025 Reviewer # 2 agreed at journal 22 Jun, 2025 Review # 1 received at journal 25 Mar, 2025 Reviewer # 1 agreed at journal 18 Mar, 2025 Reviewers invited by journal 18 Mar, 2025 Editor assigned by journal 06 Mar, 2025 Submission checks completed at journal 04 Mar, 2025 First submitted to journal 03 Mar, 2025 Unknown event 03 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-6131485","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":430633230,"identity":"3ecb743b-4cdb-47ad-b9d4-2aca6f2e083c","order_by":0,"name":"Andrea R. Zammit","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACNgYGAxBdzyB/gAHEloMIMjDj1MIP1ZLYwADWYmBMUItkA4oWBgMgg4AWg9vN2z58qAFqYWx+uulGwZ/07TPSnz1gqLAG6cWu5c6x4pkzjgH9wsxmdjvHwCB3zo0ccwOGM+m4tdzIMWbmYWOoAboGomWGRA6bBGPbYZxa7EFa/vxjSGPgYf8G0pIuIZH+TILxH24tYFsY2xiSGSR4wLYkSEgkmEkwNuDTklbM2NsH9L4ETxlQi7HhDJ43ZhIJx9KNcWtJ3szw4xtQi/zxbbdz/sjJS7ADHfahxloWlxYo+M9gfwCZn4Bf+SgYBaNgFIwCAgAAhZVXmVapfi4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0003-5560-9542","institution":"Rush Alzheimer's Disease Center","correspondingAuthor":true,"prefix":"","firstName":"Andrea","middleName":"R.","lastName":"Zammit","suffix":""},{"id":430633231,"identity":"9dbe0874-470a-45b1-946f-8334fe97b216","order_by":1,"name":"Lei Yu","email":"","orcid":"https://orcid.org/0000-0002-0237-8758","institution":"Rush University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Yu","suffix":""},{"id":430633232,"identity":"0f7dc5d2-8e96-45ce-9de9-1a191e3028bd","order_by":2,"name":"Victoria Poole","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"","lastName":"Poole","suffix":""},{"id":430633233,"identity":"e4932765-9776-46b9-bc03-292ce37cd693","order_by":3,"name":"Konstantinos Arfanakis","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Konstantinos","middleName":"","lastName":"Arfanakis","suffix":""},{"id":430633234,"identity":"849bfc30-74c9-40fa-9db0-789c61d8dfa0","order_by":4,"name":"Julie Schneider","email":"","orcid":"https://orcid.org/0000-0002-9482-1752","institution":"Rush University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Julie","middleName":"","lastName":"Schneider","suffix":""},{"id":430633235,"identity":"c6e31e9e-4568-461f-9b75-b308812919fe","order_by":5,"name":"Vlad Petyuk","email":"","orcid":"https://orcid.org/0000-0003-4076-151X","institution":"PNNL","correspondingAuthor":false,"prefix":"","firstName":"Vlad","middleName":"","lastName":"Petyuk","suffix":""},{"id":430633236,"identity":"3a54bd6a-99d1-4b5c-b988-8ec28416c5f6","order_by":6,"name":"Philip De Jager","email":"","orcid":"https://orcid.org/0000-0002-8057-2505","institution":"Columbia University Irving Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"","lastName":"De Jager","suffix":""},{"id":430633237,"identity":"92becd68-28da-4d62-a04e-f35b7413689e","order_by":7,"name":"Rima Kaddurah-Daouk","email":"","orcid":"https://orcid.org/0000-0003-1858-5732","institution":"Duke University","correspondingAuthor":false,"prefix":"","firstName":"Rima","middleName":"","lastName":"Kaddurah-Daouk","suffix":""},{"id":430633238,"identity":"ee2887eb-439a-470b-8d13-766eb18af45a","order_by":8,"name":"Yasser Iturria Medina","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yasser","middleName":"Iturria","lastName":"Medina","suffix":""},{"id":430633239,"identity":"0a61f3f7-2d72-4b78-b2f1-c3f84142c9d8","order_by":9,"name":"David Bennett","email":"","orcid":"","institution":"Rush University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Bennett","suffix":""}],"badges":[],"createdAt":"2025-02-28 21:45:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6131485/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6131485/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79348937,"identity":"edc08e1c-9e49-4071-a178-076babb0cedc","added_by":"auto","created_at":"2025-03-27 10:04:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":337071,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage of individuals with ADRD pathologic indices stratified by No cognitive impairment and multi-omic brain molecular AD subtypes. \u003c/strong\u003ePathologic indices include diagnosis of AD, non-AD neurodegenerative pathology (neocortical Lewy bodies, TPD-43, and hippocampal sclerosis), cerebral (gross and/or micro) infarcts, and cerebral vessel pathology (arteriosclerosis, atherosclerosis, and cerebral amyloid angiopathy).\u003cstrong\u003e \u003c/strong\u003eDifferent colored bars represent the percentage of individuals within the no cognitive impairment (NCI) group and each AD subtype and add up to 100% for each individual group.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Gray = no cognitive impairment; Orange= AD subtype 1; Red=AD subtype 2; Purple=AD subtype 3.\u003c/p\u003e","description":"","filename":"fig1pathUPDATED1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6131485/v1/6d5ec299cb4b96cc7752b100.jpg"},{"id":79350639,"identity":"bd88c413-150f-40aa-b57d-efbff2fa0fe9","added_by":"auto","created_at":"2025-03-27 10:20:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":234502,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModality-specific omic contributions and top contributing omic markers per AD subtype. Panel A. Modality-specific contributions (in percentages) of epigenomic, transcriptomic, proteomic, and metabolomic \u0026nbsp;alterations relative to the three identified AD subtypes. Each colored bar adds up to 100% for that particular AD subtype.\u003c/strong\u003e Therefore, while epigenomic features were the most common omic modality in all subtypes, relative to each other, subtype 1 was characterized by metabolomic alterations, subtype 2 by transcriptomic alterations, and subtype 3 by epigenomic alterations. Only markers significantly associated at FDR \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 were considered in this study. \u003cstrong\u003ePanel B. Top molecular omics contributions to AD molecular subtypes in the DLPFC of the postmortem human brain. \u003c/strong\u003eTop influential metabolomic, transcriptomic, epigenomic and proteomic markers per AD subtype (F-values in percentages, only F-values\u0026gt;50% of omic signals are shown). In all, while epigenomic features were the most frequent data modality across subtypes, the strongest contributors as determined by the F-value, consistently were metabolites.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6131485/v1/fb0f7ec1a2430e74436f4849.jpg"},{"id":79348940,"identity":"65c5c168-d8f4-4120-a58c-b80579b8af20","added_by":"auto","created_at":"2025-03-27 10:04:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":343314,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox and whisker plots of inter-subtype molecular differences.\u003c/strong\u003e Each omic data feature shows a comparison amongst the three AD subtypes. Each plot corresponds to the percent of significantly different features in percent F-value for each omic modality, based on ANOVA tests with subtype as the grouping measure. The dots in each plot represent a feature within that omic modality; the top and bottom of the boxes represent quartiles 1 and 3, and the whiskers show the inter-quartile range. The middle horizontal line represents the mean percent of data features that are abnormal for that subtype.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6131485/v1/410d31daa5670ae4b02036e3.jpg"},{"id":79350237,"identity":"a9c3cc34-fe58-4b82-85de-0653042a7073","added_by":"auto","created_at":"2025-03-27 10:12:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":219824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar charts illustrating distance from the mean of each psychological risk factor (panels A -D).\u003c/strong\u003e Results are stratified by the no cognitive impairment (NCI) group and the three AD subtypes. The measures are z-transformed for illustration purposes. \u0026nbsp;The means are adjusted for age, sex, and education.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6131485/v1/91b586875e3c5750fec3d284.jpg"},{"id":89048496,"identity":"a38d1a1a-f002-4498-bcfa-dc49360f0198","added_by":"auto","created_at":"2025-08-14 07:17:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3454152,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6131485/v1/60b3fcd3-5c49-4dbd-820c-de01a603a9e3.pdf"},{"id":79348938,"identity":"84ebc892-e560-4f30-9716-8e0fc095f840","added_by":"auto","created_at":"2025-03-27 10:04:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":35159,"visible":true,"origin":"","legend":"eMethods","description":"","filename":"eMethods022025.docx","url":"https://assets-eu.researchsquare.com/files/rs-6131485/v1/bcd32abe07998e8a77f8c5f3.docx"},{"id":79348941,"identity":"ab9ed222-ddd8-4140-98f6-5417fa3878f0","added_by":"auto","created_at":"2025-03-27 10:04:14","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":660448,"visible":true,"origin":"","legend":"Supplemental results","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6131485/v1/e57bf9390583f1268bdba9a2.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Multi-omic subtypes of Alzheimer’s dementia are differentially associated with psychological traits","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePsychological traits, such as proneness to psychological distress (i.e., neuroticism), depressive symptoms, loneliness, and purpose in life, are well-established risk factors for prospective mental and physical health outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e such as incidence of mild cognitive impairment (MCI) and Alzheimer\u0026rsquo;s disease and related dementias (AD/ADRD)\u003csup\u003e6\u0026ndash;10\u003c/sup\u003e as well as morbidity and mortality.\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e The public health significance of these traits as robust risk factors has also been well-documented.\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe previously documented the associations of neuroticism\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, depressive symptoms\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, loneliness\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and purpose in life\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, with cognitive decline, and incidence of MCI and AD/ADRD. These associations remained after adjusting for common age-related neuropathologic indices that cause cognitive impairment.\u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24 CR25 CR26 CR27 CR28\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e To date we have not found any associations between these traits and common markers of neurodegeneration including neuritic plaques, neurofibrillary tangles, Lewy bodies, TDP-43, and hippocampal sclerosis, though we did find weak associations between purpose in life and lacunar cerebral infarctions.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Thus, other molecular mechanisms are likely involved. For example, we reported that two neocortical proteins\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and a transcriptomic co-expression module\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e mediated in part the association of neuroticism with cognition. Further, we also found some relationships linking depressive symptoms to cognition via select proteins\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and microRNAs.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe recently integrated four layers of dorsolateral prefrontal cortex (DLPFC) omics data, i.e., epigenomic, transcriptomic, proteomic, and metabolomic, with multimodal contrastive Trajectories Inference (mcTI) analysis to derive multi-omic pseudotime from no cognitive impairment (NCI) to AD dementia\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. We subsequently decomposed pseudotime into three distinct multi-omic brain molecular subtypes representing three molecular pathways from NCI to AD dementia. These subtypes differed in their multi-omic composition and key drivers. Here, we examined whether one or more of these molecular pathways would be associated with neuroticism, depressive symptoms, loneliness and/or purpose in life. Such associations would provide clues to potential molecular omic connections linking these traits to AD dementia.\u003c/p\u003e"},{"header":"Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eParticipants were community-based older adults from the Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP)\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. ROS, initiated in 1994, includes older priests, nuns, and brothers from across the U.S. while MAP established in 1997, includes older men and women from across the greater Chicago metropolitan area. Participants were free of known dementia at enrollment, agreed to annual clinical evaluation and signed informed consent and Anatomic Gift Act to donate their brains at death. Both studies were approved by an Institutional Review Board of Rush University Medical Center.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssessment of psychological risk factors\u003c/h3\u003e\n\u003cp\u003eNeuroticism was assessed using either 12 or 6 items from the NEO Five-Factor Inventory, which was administered at baseline or near baseline as previously reported\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Depressive symptoms were assessed annually using a 10-item form\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e of the Center of Epidemiologic Study-Depression Scale(CES-D)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Loneliness and purpose in life were assessed at baseline in MAP participants only, using a 5-item version from a modified scale of the de Jong-Gierveld Loneliness scale\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e and a 10-item scale derived from Ryff\u0026rsquo;s Scales of Psychological Wellbeing as previously described.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e\u003cb\u003eeMethods\u003c/b\u003e details psychometric information.\u003c/p\u003e\n\u003ch3\u003eClinical Diagnoses\u003c/h3\u003e\n\u003cp\u003eAD dementia was diagnosed by an experienced clinician using criteria of the joint working group of the National Institute of Neurologic and Communicative Disorders/Stroke/AD and Related Disorders Association\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, as previously described.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e These criteria require a history of cognitive decline and evidence of impairment in at least two domains of cognitive function, one of which must be memory.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e MCI required evidence of impairment without meeting accepted criteria for dementia, and NCI refers to individuals without dementia or MCI as previously established. \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eNeuropathologic evaluation\u003c/h3\u003e\n\u003cp\u003eBrain autopsy followed standardize protocols.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e Neuropathologic evaluations systematically assessed common AD and non-AD neurodegenerative and cerebrovascular conditions including Alzheimer\u0026rsquo;s disease pathology, Lewy bodies, transactive response DNA binding protein (TDP)-43, hippocampal sclerosis, chronic macroscopic and microinfarcts, cerebral amyloid angiopathy(CAA), atherosclerosis, and arteriolosclerosis, as described in more detail in the \u003cb\u003eeMethods\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003ePostmortem Imaging\u003c/h3\u003e\n\u003cp\u003eA subset of 278 participants underwent a postmortem brain imaging protocol, described previously\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Briefly, after one month postmortem, the cerebral hemisphere selected for neuropathological examination was imaged with a multi-echo spin-echo sequence on one of four 3-Tesla MRI scanners, and the resulting images were used for deformation-based morphometry(DBM) as detailed in \u003cb\u003eeMethods\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnnotation of the previously reported brain multi-omic molecular AD subtypes\u003c/h2\u003e \u003cp\u003eBrain multi-omic data contain DNA methylation (DNAm) with Illumina 450 array, bulk next generation RNA sequencing, targeted protein expression with selected reaction monitoring, and metabolite level (metabolon) from DLPFC, generated as described previously. \u003csup\u003e\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e We previously used the machine-learning mcTI algorithm to generate a brain molecular pseudotime of AD dementia relative to NCI, and three brain molecular subtypes reflecting different molecular pathways from NCI to AD dementia based on the numbers and types of omics.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Here, we generate the top (FDR \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) molecular features that characterize drivers of the three previously reported AD subtypes.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eFirst, as pseudotime and the subtypes are built from AD dementia as the target, we compared frequency of pathologies across subtypes to ensure that our molecular subtypes were not simply capturing different pathological features. Pathologies were grouped accordingly:i) AD, CAA; ii)non-AD neurodegenerative: neocortical Lewy bodies, TPD-43, hippocampal sclerosis; iii) total infarcts, iv)cerebral vessel pathology: arteriosclerosis, atherosclerosis. We performed chi-squared tests to determine whether observed frequencies of pathologies differ by subtype.\u003c/p\u003e \u003cp\u003eSecond, we described the proportion of the top epigenomic, transcriptomic, proteomic, and metabolomic features of the trajectory inferences per subtype, and illustrated their top 25 influential contributing features ordered by their F-value. Finally, we ran four separate analyses of variance tests, one for each omic modality (F-value as outcome and AD subtypes as predictors) using Bonferroni\u0026rsquo;s method for post-hoc analyses to calculate inter-subtype differences in the F-value.\u003c/p\u003e \u003cp\u003eThird, we tested associations with brain morphometry by running a general linear model of voxel-wise deformation assessed using FSL PALM\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e adjusting for demographics, postmortem interval, and scanner(accounting for differences in both mean and variance across scanners). We then contrasted each group pair. P-values were computed from 500 permutations using tail approximation\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. The threshold-free cluster enhancement (TFCE) approach was used to define clusters of significance. Associations were considered statistically significant at p\u0026thinsp;\u0026le;\u0026thinsp;.05, family-wise error rate (FWER) corrected.\u003c/p\u003e \u003cp\u003eFourth, we tested associations between pseudotime and the psychological traits in four separate linear regression models. Our outcomes were neuroticism, depressive symptoms, loneliness, and purpose in life. Covariates included age at death, sex, and education. The term for pseudotime indicated its association with the traits by one additional point.\u003c/p\u003e \u003cp\u003eFinally, we ran four analyses of covariance using Bonferroni\u0026rsquo;s method for multiple comparisons to test whether the 3 subtypes are associated with the psychological traits, adjusting for the same covariates.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 822 participants had multi-omic data. Of those, 761 participants also had measures of neuroticism, 818 had CES-D, and 306 participants had measures on loneliness and purpose in life which were only available in MAP. Mean age at baseline was just over 80 years and mean age at death was close to 90 years. Over 60% were female, and most participants completed college(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eCharacteristics of the whole sample and stratified by controls and AD subtypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eMean (SD) or % (n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhole sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSubtype 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubtype 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSubtype 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at baseline, mean years, (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82.3 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.2 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.9 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82.2 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.9 (6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at death, mean years (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.4 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.2 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.3 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.1 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.4 (6.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e532 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109 (20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational attainment, mean years (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.3 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.3 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.4 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.2(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.3 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuroticism, baseline, (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.8 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.76 (6.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.83 (6.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.72 (6.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.53 (6.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive symptoms, mean score (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52 (1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.58 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48 (1.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness, baseline (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5 (0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePurpose in life, baseline (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6 (0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.5 (0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudotime, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68 (0.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal cognition, baseline, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.23 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.40 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.45 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.43 (0.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal cognition, last valid, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.96 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.45 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.50 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.50 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild cognitive impairment proximate to death, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56 (28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAD dementia proximate to death, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e106 (30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuropathology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic AD (NIA-AA), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e517 (62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 (74.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e138 (70.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120 (74.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeocortical Lewy bodies, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (15.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDP-43, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72 (38.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59 (37.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHippocampal sclerosis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross chronic infarcts, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e294 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73 (45.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicro chronic infarcts, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46 (28.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArteriosclerosis, moderate to severe, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66 (41.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtherosclerosis, moderate to severe, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e346 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78 (48.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral amyloid angiopathy, moderate, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64 (40.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFrequency of AD/ADRD pathologies across AD subtypes\u003c/h2\u003e \u003cp\u003eThe reference NCI group had less pathology, as expected (median\u0026thinsp;=\u0026thinsp;2 pathologies, interquartile range (IQR)\u0026thinsp;=\u0026thinsp;2). AD subtypes had a median of 3 pathologies (IQRs:subtype 1\u0026thinsp;=\u0026thinsp;2; subtypes 2/3\u0026thinsp;=\u0026thinsp;3). Over half of NCI participants had 1 or 2 pathologies, while a third had three or more. Meanwhile over two thirds of participants in all AD subtypes had three or more pathologies, which is expected since mixed pathologies commonly drive AD dementia\u003cb\u003e(eTable 1).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOver 70% of individuals across all subtypes had pathologic AD and/or CAA (χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e(2, 535)\u0026thinsp;=\u0026thinsp;1.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.38), and almost half had non-AD neurodegenerative neuropathology (χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e(2, 517)\u0026thinsp;=\u0026thinsp;4.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66). There were no differences in the distribution of infarcts ((χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e(2, 548)\u0026thinsp;=\u0026thinsp;4.01,\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14) or in frequency of cerebral vessel neuropathology (χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e(4, 543)\u0026thinsp;=\u0026thinsp;2.89,\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24)(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).Therefore, our molecular subtypes are not simply capturing different neuropathological features.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eProportion of omic features for each AD subtype\u003c/h2\u003e \u003cp\u003eThe relative contribution of each omic dimension was related to abundance with the greatest number of features: DNAm, followed by bulk RNAseq, and targeted proteomics which had the fewest. Thus, most frequent omic features across all subtypes were epigenomic alterations, followed by RNA alterations which is expected as they are the two with by far the greatest number of features. Still, there were significant differences in omic proportions amongst the AD subtypes (χ\u003csup\u003e2\u003c/sup\u003e(6,569)\u0026thinsp;=\u0026thinsp;30.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). For example, while 15% of the top features in subtype 1 were metabolites, less than 5% in subtypes 2 and 3 were metabolites. Almost 43% of top features in subtype 2 were RNA alterations while in subtypes 1 and 3 these alterations amounted to about 30%. Finally, while over 60% of epigenomic alterations characterized subtype 3, these were less then 50% in subtypes 1 and 2. Proteomic abnormalities contributed the least, as expected given the small number of features.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTop omic features for each AD subtype\u003c/h2\u003e \u003cp\u003eTop influential contributing omic features with an F-value\u0026thinsp;\u0026gt;\u0026thinsp;50% can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb. Interestingly, almost all the strong drivers were metabolites. The strongest contributors (F-value\u0026thinsp;\u0026gt;\u0026thinsp;90%) differentiating these AD subtypes from NCI were three phospholipids and a major excitatory neurotransmitter. Specifically, phosphatidylcholine acyl-alkyl (PCae)C38:4 for AD subtype 1; lysophosphatidylcholine (lysoPC)acyl C20:3 and PC acyl-acyl (aa)C36:6 for AD subtype 2; and glutamate for AD subtype 3 (full list in \u003cb\u003eeTable 2)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSubtype-subtype differences of top influential omic features by modality\u003c/h2\u003e \u003cp\u003eWe then compared each omic modality\u0026rsquo;s F-values amongst subtypes; higher F-values indicate stronger differences from NCI relative to other subtypes. AD subtype 2 consistently had significantly higher F-values across all omic features (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, epigenomic alterations were significantly higher in AD subtype 2 relative to subtypes 1 and 3 as were RNA alterations, proteomic alterations, and metabolomic alterations. \u003cb\u003eeTable 3\u003c/b\u003e lists means and 95%CIs. In sum, the top omic features in subtype 2 differentiated this subtype from NCI more strongly than either of the omic features in subtypes 1 and 3. Yet, specific omic features at the individual level were different per subtype. \u003cb\u003eeTable 4\u003c/b\u003e details mean differences.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBrain morphology across AD subtypes\u003c/h2\u003e \u003cp\u003eTo further characterize the subtypes, we examined differences in brain morphometry across a subset of 278 participants (NCI\u0026thinsp;=\u0026thinsp;91, AD subtypes: 1 n\u0026thinsp;=\u0026thinsp;74; 2 n\u0026thinsp;=\u0026thinsp;57, 3 n\u0026thinsp;=\u0026thinsp;56) with postmortem MRI. As expected, all AD subtypes exhibited greater cortical atrophy, as indicated by smaller postmortem brain volumes, relative to NCI (\u003cb\u003eeFigure 1\u003c/b\u003e). AD subtype 1 had the most extensive atrophy, as observed by smaller volumes in the medial temporal lobe and across several other temporal, frontal, and parietal regions. AD subtype 2 exhibited temporal lobe atrophy that extended to the temporal pole. Finally, AD subtype 3 exhibited the least atrophy, which was largely localized to the temporal lobe and insular cortex. Both subtypes 1 and 2 also had extensive ventricular enlargement, which indicated central brain atrophy, while subtype 3 had minimal enlargement of the temporal horn. While the differences are intriguing, none of the subtypes were statistically different from one another in this small subsample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of pseudotime with psychological traits\u003c/h2\u003e \u003cp\u003eMean pseudotime in the NCIs was almost 0.25 while across the AD subtypes this ranged from about 0.4 for subtype 1 to 0.5 for subtype 2 and 0.7 for subtype 3, with higher mean pseudotime indicating closer proximity to AD dementia(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigher pseudotime was positively associated with neuroticism (beta\u0026thinsp;=\u0026thinsp;2.86, 95%CI\u0026thinsp;=\u0026thinsp;0.62\u0026ndash;5.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) and loneliness (beta\u0026thinsp;=\u0026thinsp;0.35, 95%CI\u0026thinsp;=\u0026thinsp;0.04\u0026ndash;0.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). Pseudotime explained 4% of variance in neuroticism, and 3% of variance in reported loneliness after accounting for age, sex, and level of education. There was no association between pseudotime and depressive symptoms (beta\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.402, 95%CI = -0.052\u0026ndash;0.856, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.082), or purpose in life(beta=-0.15, 95%CI=-0.38-0.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.212).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of AD subtypes with psychological traits\u003c/h2\u003e \u003cp\u003eANCOVA showed significant effects of the subtypes on all traits (neuroticism: F(3,760)\u0026thinsp;=\u0026thinsp;5.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; depressive symptoms: F(3,817)\u0026thinsp;=\u0026thinsp;3.9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009; loneliness: F(3,305)\u0026thinsp;=\u0026thinsp;5.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014; purpose in life, F(3,305)\u0026thinsp;=\u0026thinsp;3.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e \u003cp\u003ePost-hoc comparisons indicated differential associations between AD subtypes and the traits relative to NCI; however, there were no inter-subtype differences(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Subtypes 1 and 3 had higher neuroticism scores than NCI, while subtype 2 had higher depressive symptoms. Subtype 3 had higher loneliness; subtype 2 had marginally lower purpose in life score(\u003cb\u003eeTable 5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo better understand the mechanistic basis of the relation of four psychological traits to AD dementia, we leveraged prior work in 822 older adults who subsequently died and had brain autopsy, providing brain multi-omic molecular pseudotime, and three distinct molecular subtypes representing pathways from NCI to AD dementia. We found that pseudotime was associated with neuroticism and loneliness, and subsequently AD multi-omic brain molecular subtypes were differentially associated with neuroticism, depressive symptoms, loneliness, and purpose in life. The results provide novel data on shared top multi-omic features between molecular subtypes of AD dementia and well-established AD/ADRD risk factors, suggesting common underlying mechanisms driving associations between psychological traits and AD/ADRD.\u003c/p\u003e \u003cp\u003ePrevious studies primarily focused on single-omic associations. We previously showed that neuroticism is associated with 18 cortical transcriptomic co-expressed modules, three (m6, m7, and m127) of which partially mediated the association of higher neuroticism on faster cognitive decline\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. We previously also identified two cortical proteins, 40S ribosomal protein S3 and BCKDHB, that were associated with both neuroticism and cognitive decline independent of common AD/ADRD pathologic indices.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e These two proteins strongly mediated the association of higher neuroticism on cognitive decline independent of common AD/ADRD pathologic indices, and further explained 25% of the variance in this association. Together, our prior studies provide evidence that neuroticism has strong and widespread effects on the transcriptome, and on specific cortical proteins in the aged prefrontal cortex, both of which affect downstream AD-related outcomes. Similarly, in prior work we identified a cortical protein, IGFBP-5, that was associated with both depressive symptoms and cognition, and further explained 10% of the association between depressive symptoms and cognitive decline.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e We also previously identified four microRNAs associated with depressive symptoms, of which miR-484 targets were enriched in a co-expression module involved in synaptic function and plasticity and were associated with higher risk of AD dementia.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e While there is some evidence on associations of loneliness\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, and purpose in life\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, with altered DNAm, and expression of mRNAs and proteins, their shared mechanisms with AD/ADRD are still largely unexplored.\u003c/p\u003e \u003cp\u003eWe extend prior work by showing here that including multiple molecular modalities to identify common correlates of subtypes representing distinct molecular signatures of AD dementia can be leveraged to potentially identify the molecular basis of psychological traits associated with AD dementia. To our knowledge, this is the first study that has leveraged multi-omics data to directly explore these associations. This approach allowed us to identify distinct molecular AD subtypes that are differentially associated with four psychological traits. AD subtypes 1 and 3 showed strong associations with neuroticism, while subtype 2 was exclusively associated with depressive symptoms. AD subtype 1 had higher proportions of metabolomic alterations relative to subtypes 2 and 3, while subtype 3 had higher proportions of epigenomic alterations. By contrast, subtype 2 had relatively higher proportions of transcriptomic alterations. Loneliness and purpose in life had somewhat weaker associations with subtypes 3 and 2 respectively likely due to low statistical power since these two measures were only available in MAP participants. Much work remains to expand our findings in better-powered studies, with additional and larger multi-layered omic subtypes.\u003c/p\u003e \u003cp\u003eOur findings suggest that the various AD molecular mechanisms that are subserving cognition are also related to psychological traits. Metabolomic alterations were the topmost features that significantly differentiated these AD subtypes from NCI. Top alterations in subtypes 1 and 2 were phosphatidylcholines (PCs), a major class of phospholipids that form a crucial component of cell membranes. PC acyl-acyl (AA) and acyl-ether (AE) lipids are involved in signaling and maintenance of membrane structure; the ether bond in the AE lipids further promote resistance to oxidative damage. Disruption in lipid metabolism, including significant reductions in PC ae c38:4 and PC aa C36:6 are significantly associated with clinical AD.\u003csup\u003e59 60, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e LysoPCs are proinflammatory lipids and their concentration is substantially higher in clinical AD.\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e Psychological distress has also been associated with altered lipid metabolism via a cascade of dysregulation in PCs and lysoPCs that eventually cause cell membrane destabilization and inflammatory responses.\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e The top alteration in AD subtype 3 was glutamate which is the primary excitatory neurotransmitter in the central nervous system. Overactivation of glutamate receptors are implicated in neuronal damage and death via a cascade of intracellular events including oxidative stress, mitochondrial dysfunction, and activation of destructive enzymes.\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e Chronic distress and depression have also been implicated in the malfunctioning of the glutamatergic neurotransmitter system primarily affecting glutamate receptor expression in the prefrontal cortex, hippocampus and amygdala\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Further studies that will collect more omics data will be better able to characterize multi-level omic subtypes and determine mediating effects of top contributors of AD subtypes on the associations between enduring psychological traits and AD/ADRD outcomes.\u003c/p\u003e \u003cp\u003eThis study has limitations and strengths. The brief neuroticism measure used here may have underestimated associations, though we have previously reported a correlation of .90 between shorter-(6-and 12-) item versions of the NEO\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e testifying to its validity. Nonetheless associations might be slightly stronger than what we observed here. Similarly, the depressive symptoms measure is also based on a brief psychometric measure, however, its reliability has been previously established\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. For CES-D we used each person\u0026rsquo;s average score across all evaluations as previously\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, which capture stable enduring tendencies, and reduce random error. Measures of loneliness and purpose in life were only available in MAP participants, potentially reducing statistical power. All omics came from one brain region and the number of epigenomic features and transcriptomic were disproportionally higher than those for metabolites or proteins\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, which might have overestimated the noted proportions of epigenomic features within subtypes. However, the DLPFC is a known region for its associations with learning, stress response and emotion regulation, and the continuous collection of omics data within our cohorts will allow for further multilevel characterization of complex traits and replication of results. Our novel multi-omic approach provides insights into the molecular basis of psychological AD/ADRD risk factors. Other approaches have been used to identify molecular subtypes of AD; however, they have been agnostic to any clinical trait.\u003csup\u003e\u003cspan additionalcitationids=\"CR70 CR71\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e High participation rates in the clinical evaluations and brain autopsy in the ROS and MAP cohorts minimize bias due to selective attrition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eWe declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eData sharing\u003c/h2\u003e \u003cp\u003eThe data are available via the Rush Alzheimer\u0026rsquo;s Disease Center Research Resource Sharing Hub. Qualified applicants should complete an application including study premises and a brief description of the research plan.\u003c/p\u003e \u003cp\u003eBrain multi-omic data are available in the AMP-AD knowledge portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.synapse.org\u003c/span\u003e\u003cspan address=\"http://www.synapse.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), using the following Synapse IDs:syn3157275 (epigenomic data), syn3800853 (transcriptomic), syn10468856 (proteomic), and syn10235595 and syn10235594 (metabolomic).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eContributors\u003c/h2\u003e \u003cp\u003eARZ, YIT and DAB were responsible for the conception and design of the current study. ARZ performed the literature review and wrote the first draft of the manuscript. LY conducted the data analysis and contributed to writing the statistical analysis section and revising the manuscript. LY supervised the data analysis and contributed to revising the manuscript. KA is the director of the neuroimaging core of Rush Alzheimer\u0026rsquo;s Disease Center. KA and VNP supervised and oversaw the neuroimaging analyses. JAS is the director of pathology core of Rush Alzheimer\u0026rsquo;s Disease Center that collects indices of brain pathologies. She is also the director of Rush Alzheimer\u0026rsquo;s Disease Research Center that oversees data collection of the ROS cohort. VAP selected the protein targets and designed the peptides. PLD performed LC-SRM targeted proteomic assays and provided quantitative datasets of the peptide. RKD leads Alzheimer Disease Metabolomics Consortium and Alzheimer Gut Microbiome Project funded by NIA that contributed metabolomics data generated by Metabolon Inc. where metabolomics was conducted by chromatography spectrometry. DAB is the principal investigator of MAP and was involved in data collection. LY, VNP, KA, JAS, VAP, PLD, YIT and DAB contributed to revising the manuscript. YIT and DAB had full access to all the data in the study, accessed and verified the data, and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors had access to the data and had final responsibility for the decision to submit for publication.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe appreciate the participants of MAP for the time generously given for data collection and for consenting for brain donation. We also acknowledge staff of Rush Alzheimer\u0026rsquo;s Disease Center for data collection, management, and analyses.\u003c/p\u003e \u003cp\u003eThis work was supported by National Institutes of Health: R01AG17917, P30AG10161, P30AG72975, R01AG015819, U01AG61356, U01 NS100599, R01 AG064233, RF1 NS139975, P30 AG072975. We would also like to thank Michael Urbut and the Paul M. Angell Family Foundation for their generous financial support given towards this work.\u003c/p\u003e \u003cp\u003eThe funding organizations had no role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMalouff JM, Thorsteinsson EB, Schutte NS. The Relationship Between the Five-Factor Model of Personality and Symptoms of Clinical Disorders: A Meta-Analysis. Journal of Psychopathology and Behavioral Assessment 2005;27:101-114.\u003c/li\u003e\n\u003cli\u003eSmith TW, MacKenzie J. Personality and risk of physical illness. Annu Rev Clin Psychol 2006;2:435-467.\u003c/li\u003e\n\u003cli\u003eHarshfield EL, Pennells L, Schwartz JE, et al. Association Between Depressive Symptoms and Incident Cardiovascular Diseases. JAMA 2020;324:2396-2405.\u003c/li\u003e\n\u003cli\u003ePark C, Majeed A, Gill H, et al. 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Brain Commun 2023;5:fcad110.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"multi-omics, AD subtypes, AD dementia, psychological traits","lastPublishedDoi":"10.21203/rs.3.rs-6131485/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6131485/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePsychological traits reflecting neuroticism, depressive symptoms, loneliness, and purpose in life are risk factors of AD dementia; however, the underlying biologic mechanisms of these associations remain largely unknown. In this study we examined whether pseudotime, representing molecular distance from no cognitive impairment (NCI) to AD dementia, and three distinct multi-omic brain molecular subtypes of AD dementia representing 3 omic pathways from NCI to AD dementia are differentially associated with these risk factors. Participants included 822 decedents with multi-omic data from the dorsolateral prefrontal cortex from two cohort-based studies; Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP), both ongoing longitudinal clinical pathological studies. We first ran four separate linear regressions with neuroticism, depressive symptoms, loneliness, purpose in life as the outcomes, and pseudotime as the predictor, adjusting for age, sex and education. We then ran four separate analyses of covariance (ANCOVAs) with Bonferroni-corrected post-hoc tests to test whether the three multi-omic AD subtypes are differentially associated with the four risk factors, adjusting for the same covariates. Pseudotime was positively associated (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with neuroticism and loneliness. AD subtypes were differentially associated with the traits: AD subtypes 1 and 3 were associated with neuroticism; AD subtype 2 with depressive symptoms; AD subtype 3 with loneliness, and AD subtype 2 with purpose in life. Our results show that psychological risk factors might be associated with AD dementia via shared multi-omic molecular pathways. Our data provide novel insights into the biology underlying well-established associations between psychological traits and AD dementia.\u003c/p\u003e","manuscriptTitle":"Multi-omic subtypes of Alzheimer’s dementia are differentially associated with psychological traits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 10:04:09","doi":"10.21203/rs.3.rs-6131485/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"transferred","content":"Translational Psychiatry","date":"2026-01-05T17:40:39+00:00","index":"","fulltext":""},{"type":"decision","content":"Reject after peer review","date":"2025-08-14T07:02:14+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-07-28T12:28:11+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-23T03:32:05+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-03-25T19:39:10+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-03-18T18:39:25+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-03-18T17:05:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-06T11:43:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-04T11:43:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Psychiatry","date":"2025-03-03T13:32:22+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-03-03T12:33:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab5c54c9-4ac8-4a12-bd83-d4c5c2a9d5fa","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":45865897,"name":"Biological sciences/Psychology"},{"id":45865898,"name":"Health sciences/Biomarkers/Diagnostic markers"}],"tags":[],"updatedAt":"2026-01-12T16:25:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-27 10:04:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6131485","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6131485","identity":"rs-6131485","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}