Associations of VGF with Neuropathologies and Cognitive Health in Older Adults.

OBJECTIVE: VGF is proposed as a potential therapeutic target for Alzheimer's (AD) and other neurodegenerative conditions. The cell-type specific and, separately, peptide specific associations of VGF with pathologic and cognitive outcomes remain largely unknown. We leveraged gene expression and protein data from the human neocortex and investigated the VGF associations with common neuropathologies and late-life cognitive decline. METHODS: Community-dwelling older adults were followed every year, died, and underwent brain autopsy. Cognitive decline was captured via annual cognitive testing. Common neurodegenerative and cerebrovascular conditions were assessed during neuropathologic evaluations. Bulk brain RNASeq and targeted proteomics analyses were conducted using frozen tissues from dorsolateral prefrontal cortex of 1,020 individuals. Cell-type specific gene expressions were quantified in a subsample (N = 424) following single nuclei RNASeq analysis from the same cortex. RESULTS: The bulk brain VGF gene expression was primarily associated with AD and Lewy bodies. The VGF gene association with cognitive decline was in part accounted for by neuropathologies. Similar associations were observed for the VGF protein. Cell-type specific analyses revealed that, while VGF was differentially expressed in most major cell types in the cortex, its association with neuropathologies and cognitive decline was restricted to the neuronal cells. Further, the peptide fragments across the VGF polypeptide resembled each other in relation to neuropathologies and cognitive decline. INTERPRETATION: Multiple pathways link VGF to cognitive health in older age, including neurodegeneration. The VGF gene functions primarily in neuronal cells and its protein associations with pathologic and cognitive outcomes do not map to a specific peptide. ANN NEUROL 2023;94:232-244.

Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants.

The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms.

Proteins linking APOE ɛ4 with Alzheimer's disease.

INTRODUCTION: The ɛ4 allele of the apolipoprotein E gene (APOE ɛ4) is the strongest genetic risk factor for Alzheimer's disease (AD), but the mechanisms connecting APOE ɛ4 to AD are not clear. METHODS: Participants (n = 596) were from two clinical-pathological studies. Tissues from dorsolateral prefrontal cortex were examined to identify 8425 proteins. Post mortem pathological assessment used immunohistochemistry to obtain amyloid beta (Aß) load and tau tangle density. RESULTS: In separate models, APOE ɛ4 was associated with 18 proteins, which were associated with Aß and tau tangles. Examining the proteins in a single model identified Netrin-1 and secreted frizzled-related protein 1 (SFRP1) as the two proteins linking APOE ɛ4 with Aß with the largest effect sizes and Netrin-1 and testican-3 linking APOE ɛ4 with tau tangles. DISCUSSION: We identified Netrin-1, SFRP1, and testican-3 as the most promising proteins that link APOE ɛ4 with Aß and tau tangles. HIGHLIGHTS: Of 8425 proteins extracted from prefrontal cortex, 18 were related to APOE ɛ4. The 18 proteins were also related to amyloid beta (Aß) and tau. The 18 proteins were more related to APOE ɛ4 than other AD genetic risk variants. Netrin-1 and secreted frizzled-related protein 1 were the two most promising proteins linking APOE ɛ4 with Aß. Netrin-1 and testican-3 were two most promising proteins linking APOE ɛ4 with tau.

Associations of cortical SPP1 and ITGAX with cognition and common neuropathologies in older adults.

INTRODUCTION: The secreted phosphoprotein 1 (SPP1) gene expressed by CD11c+ cells is known to be associated with microglia activation and neuroinflammatory diseases. As most studies rely on mouse models, we investigated these genes and proteins in the cortical brain tissue of older adults and their role in Alzheimer's disease (AD) and related disorders. METHODS: We leveraged protein measurements, single-nuclei, and RNASeq data from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) of over 1200 samples for association analysis. RESULTS: Expression of SPP1 and its encoded protein osteopontin were associated with faster cognitive decline and greater odds of common neuropathologies. At single-cell resolution,  integrin subunit alpha X (ITGAX) was highly expressed in microglia, where specific subpopulations were associated with AD and cerebral amyloid angiopathy. DISCUSSION: The study provides evidence of SPP1 and ITGAX association with cognitive decline and common neuropathologies identifying a microglial subset associated with disease.

Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain.

Deciphering the environmental contexts at which genetic effects are most prominent is central for making full use of GWAS results in follow-up experiment design and treatment development. However, measuring a large number of environmental factors at high granularity might not always be feasible. Instead, here we propose extracting cellular embedding of environmental factors from gene expression data by using latent variable (LV) analysis and taking these LVs as environmental proxies in detecting gene-by-environment (GxE) interaction effects on gene expression, i.e., GxE expression quantitative trait loci (eQTLs). Applying this approach to two largest brain eQTL datasets (n = 1,100), we show that LVs and GxE eQTLs in one dataset replicate well in the other dataset. Combining the two samples via meta-analysis, 895 GxE eQTLs are identified. On average, GxE effect explains an additional ∼4% variation in expression of each gene that displays a GxE effect. Ten of these 52 genes are associated with cell-type-specific eQTLs, and the remaining genes are multi-functional. Furthermore, after substituting LVs with expression of transcription factors (TF), we found 91 TF-specific eQTLs, which demonstrates an important use of our brain GxE eQTLs.

Seasonal plasticity of cognition and related biological measures in adults with and without Alzheimer disease: Analysis of multiple cohorts.

BACKGROUND: There are few data concerning the association between season and cognition and its neurobiological correlates in older persons-effects with important translational and therapeutic implications for the diagnosis and treatment of Alzheimer disease (AD). We aimed to measure these effects. METHODS AND FINDINGS: We analyzed data from 3,353 participants from 3 observational community-based cohort studies of older persons (the Rush Memory and Aging Project [MAP], the Religious Orders Study [ROS], and the Minority Aging Research Study [MARS]) and 2 observational memory-clinic-based cohort studies (Centre de Neurologie Cognitive [CNC] study at Lariboisière Hospital and the Sunnybrook Dementia Study [SDS]). We performed neuropsychological testing and, in subsets of participants, evaluated cerebrospinal fluid AD biomarkers, standardized structured autopsy measures, and/or prefrontal cortex gene expression by RNA sequencing. We examined the association between season and these variables using nested multiple linear and logistic regression models. There was a robust association between season and cognition that was replicated in multiple cohorts (amplitude = 0.14 SD [a measure of the magnitude of seasonal variation relative to overall variability; 95% CI 0.07-0.23], p = 0.007, in the combined MAP, ROS, and MARS cohorts; amplitude = 0.50 SD [95% CI 0.07-0.66], p = 0.017, in the SDS cohort). Average composite global cognitive function was higher in the summer and fall compared to winter and spring, with the difference equivalent in cognitive effect to 4.8 years' difference in age (95% CI 2.1-8.4, p = 0.002). Further, the odds of meeting criteria for mild cognitive impairment or dementia were higher in the winter and spring (odds ratio 1.31 [95% CI 1.10-1.57], p = 0.003). These results were robust against multiple potential confounders including depressive symptoms, sleep, physical activity, and thyroid status and persisted in cases with AD pathology. Moreover, season had a marked effect on cerebrospinal fluid Aß 42 level (amplitude 0.30 SD [95% CI 0.10-0.64], p = 0.003), which peaked in the summer, and on the brain expression of 4 cognition-associated modules of co-expressed genes (m6: amplitude = 0.44 SD [95% CI 0.21-0.65], p = 0.0021; m13: amplitude = 0.46 SD [95% CI 0.27-0.76], p = 0.0009; m109: amplitude = 0.43 SD [95% CI 0.24-0.67], p = 0.0021; and m122: amplitude 0.46 SD [95% CI 0.20-0.71], p = 0.0012), which were in phase or anti-phase to the rhythms of cognition and which were in turn associated with binding sites for several seasonally rhythmic transcription factors including BCL11A, CTCF, EGR1, MEF2C, and THAP1. Limitations include the evaluation of each participant or sample once per annual cycle, reliance on self-report for measurement of environmental and behavioral factors, and potentially limited generalizability to individuals in equatorial regions or in the southern hemisphere. CONCLUSIONS: Season has a clinically significant association with cognition and its neurobiological correlates in older adults with and without AD pathology. There may be value in increasing dementia-related clinical resources in the winter and early spring, when symptoms are likely to be most pronounced. Moreover, the persistence of robust seasonal plasticity in cognition and its neurobiological correlates, even in the context of concomitant AD pathology, suggests that targeting environmental or behavioral drivers of seasonal cognitive plasticity, or the key transcription factors and genes identified in this study as potentially mediating these effects, may allow us to substantially improve cognition in adults with and without AD.

Multiomic disease signatures converge to cytotoxic CD8 T cells in primary Sjögren's syndrome.

OBJECTIVES: Multiomics study was conducted to elucidate the crucial molecular mechanisms of primary Sjögren's syndrome (SS) pathology. METHODS: We generated multiple data set from well-defined patients with SS, which includes whole-blood transcriptomes, serum proteomes and peripheral immunophenotyping. Based on our newly generated data, we performed an extensive bioinformatic investigation. RESULTS: Our integrative analysis identified SS gene signatures (SGS) dysregulated in widespread omics layers, including epigenomes, mRNAs and proteins. SGS predominantly involved the interferon signature and ADAMs substrates. Besides, SGS was significantly overlapped with SS-causing genes indicated by a genome-wide association study and expression trait loci analyses. Combining the molecular signatures with immunophenotypic profiles revealed that cytotoxic CD8 -T cells- were associated with SGS. Further, we observed the activation of SGS in cytotoxic CD8 T cells isolated from patients with SS. CONCLUSIONS: Our multiomics investigation identified gene signatures deeply associated with SS pathology and showed the involvement of cytotoxic CD8 T cells. These integrative relations across multiple layers will facilitate our understanding of SS at the system level.

Polygenic risk associated with Alzheimer's disease and other traits influences genes involved in T cell signaling and activation.

Introduction: T cells, known for their ability to respond to an enormous variety of pathogens and other insults, are increasingly recognized as important mediators of pathology in neurodegeneration and other diseases. T cell gene expression phenotypes can be regulated by disease-associated genetic variants. Many complex diseases are better represented by polygenic risk than by individual variants. Methods: We first compute a polygenic risk score (PRS) for Alzheimer's disease (AD) using genomic sequencing data from a cohort of Alzheimer's disease (AD) patients and age-matched controls, and validate the AD PRS against clinical metrics in our cohort. We then calculate the PRS for several autoimmune disease, neurological disorder, and immune function traits, and correlate these PRSs with T cell gene expression data from our cohort. We compare PRS-associated genes across traits and four T cell subtypes. Results: Several genes and biological pathways associated with the PRS for these traits relate to key T cell functions. The PRS-associated gene signature generally correlates positively for traits within a particular category (autoimmune disease, neurological disease, immune function) with the exception of stroke. The trait-associated gene expression signature for autoimmune disease traits was polarized towards CD4+ T cell subtypes. Discussion: Our findings show that polygenic risk for complex disease and immune function traits can have varying effects on T cell gene expression trends. Several PRS-associated genes are potential candidates for therapeutic modulation in T cells, and could be tested in in vitro applications using cells from patients bearing high or low polygenic risk for AD or other conditions.

Digital quantification of the MMSE interlocking pentagon areas: a three-stage algorithm.

The Mini-Mental State Examination (MMSE) is a widely employed screening tool for the severity of cognitive impairment. Among the MMSE items, the pentagon copying test (PCT) requires participants to accurately replicate a sample of two interlocking pentagons. While the PCT is traditionally scored on a binary scale, there have been limited developments of granular scoring scale to assess task performance. In this paper, we present a novel three-stage algorithm, called Quantification of Interlocking Pentagons (QIP) which quantifies PCT performance by computing the areas of individual pentagons and their intersection areas, and a balance ratio between the areas of the two individual pentagons. The three stages of the QIP algorithm include: (1) detection of line segments, (2) unraveling of the interlocking pentagons, and (3) quantification of areas. A set of 497 PCTs from 84 participants including their baseline and follow-up PCTs from the Rush Memory and Aging Project was selected blinded about their cognitive and clinical status. Analysis of the quantified data revealed a significant inverse relationship between age and balance ratio (beta = - 0.49, p = 0.0033), indicating that older age was associated with a smaller balance ratio. In addition, balance ratio was associated with perceptual speed (r = 0.71, p = 0.0135), vascular risk factors (beta = - 3.96, p = 0.0269), and medical conditions (beta = - 2.78, p = 0.0389). The QIP algorithm can serve as a useful tool for enhancing the scoring of performance in the PCT.

SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning.

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.

G-protein coupled estrogen receptor 1, amyloid-ß, and tau tangles in older adults.

Accumulation of amyloid-ß (Aß) and tau tangles are hallmarks of Alzheimer's disease. Aß is extracellular while tau tangles are typically intracellular, and it is unknown how these two proteinopathies are connected. Here, we use data of 1206 elders and test that RNA expression levels of GPER1, a transmembrane protein, modify the association of Aß with tau tangles. GPER1 RNA expression is related to more tau tangles (p = 0.001). Moreover, GPER1 expression modifies the association of immunohistochemistry-derived Aß load with tau tangles (p = 0.044). Similarly, GPER1 expression modifies the association between Aß proteoforms and tau tangles: total Aß protein (p = 0.030) and Aß38 peptide (p = 0.002). Using single nuclei RNA-seq indicates that GPER1 RNA expression in astrocytes modifies the relation of Aß load with tau tangles (p = 0.002), but not GPER1 in excitatory neurons or endothelial cells. We conclude that GPER1 may be a link between Aß and tau tangles driven mainly by astrocytic GPER1 expression.

Explainable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons.

Hand drawing, which requires multiple neural systems for planning and controlling sequential movements, is a useful cognitive test for older adults. However, the conventional visual assessment of these drawings only captures limited attributes and overlooks subtle details that could help track cognitive states. Here, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3111 participants in three aging cohorts, explained 23.3% of the variance in the global cognitive scores, 1.92 times more than the conventional rating. This accuracy improvement was due to capturing additional drawing features associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that deep learning models can extract novel drawing metrics to improve the assessment and monitoring of cognitive decline and dementia in older adults.

Interpretable deep learning approach for extracting cognitive features from hand-drawn images of intersecting pentagons in older adults.

Hand drawing involves multiple neural systems for planning and precise control of sequential movements, making it a valuable cognitive test for older adults. However, conventional visual assessment of drawings may not capture intricate nuances that could help track cognitive states. To address this issue, we utilized a deep-learning model, PentaMind, to examine cognition-related features from hand-drawn images of intersecting pentagons. PentaMind, trained on 13,777 images from 3,111 participants in three aging cohorts, explained 23.3% of the variance in global cognitive scores, a comprehensive hour-long cognitive battery. The model’s performance, which was 1.92 times more accurate than conventional visual assessment, significantly improved the detection of cognitive decline. The improvement in accuracy was due to capturing additional drawing features that we found to be associated with motor impairments and cerebrovascular pathologies. By systematically modifying the input images, we discovered several important drawing attributes for cognition, including line waviness. Our results demonstrate that hand-drawn images can provide rich cognitive information, enabling rapid assessment of cognitive decline and suggesting potential clinical implications in dementia.

Unconventional Source of Neurotoxic Protein Aggregation from Organelle Off-Target Bax∆2 in Alzheimer's Disease.

Protein aggregates are a hallmark of Alzheimer's disease (AD). Extensive studies have focused on ß-amyloid plaques and Tau tangles. Here, we illustrate a novel source of protein aggregates in AD neurons from organelle off-target proteins. Bax is a mitochondrial pore-forming pro-death protein. What happens to Bax if it fails to target mitochondria? We previously showed that a mitochondrial target-deficient alternatively spliced variant, Bax∆2, formed large cytosolic protein aggregates and triggered caspase 8-mediated cell death. Bax∆2 protein levels were low in most normal organs and the proteins were quickly degraded in cancer. Here, we found that 85% of AD patients had Bax∆2 required alternative splicing. Increased Bax∆2 proteins were mostly accumulated in neurons of AD-susceptible brain regions. Intracellularly, Bax∆2 aggregates distributed independently of Tau tangles. Interestingly, Bax∆2 aggregates triggered the formation of stress granules (SGs), a large protein-RNA complex involved in AD pathogenesis. Although the functional domains required for aggregation and cell death are the same as in cancer cells, Bax∆2 relied on SGs, not caspase 8, for neuronal cell death. These results imply that the aggregation of organelle off-target proteins, such as Bax∆2, broadens the scope of traditional AD pathogenic proteins that contribute to the neuronal stress responses and AD pathogenesis.

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Deep learning methods have recently become the state-of-the-art in a variety of regulatory genomic tasks1-6 including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions, however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluates their utility as personal DNA interpreters. We used paired Whole Genome Sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learnt sequence motif grammar, and suggest new model training strategies to improve performance.

Differential gene expression analysis based on linear mixed model corrects false positive inflation for studying quantitative traits.

Differential gene expression (DGE) analysis has been widely employed to identify genes expressed differentially with respect to a trait of interest using RNA sequencing (RNA-Seq) data. Recent RNA-Seq data with large samples pose challenges to existing DGE methods, which were mainly developed for dichotomous traits and small sample sizes. Especially, existing DGE methods are likely to result in inflated false positive rates. To address this gap, we employed a linear mixed model (LMM) that has been widely used in genetic association studies for DGE analysis of quantitative traits. We first applied the LMM method to the discovery RNA-Seq data of dorsolateral prefrontal cortex (DLPFC) tissue (n = 632) with four continuous measures of Alzheimer's Disease (AD) cognitive and neuropathologic traits. The quantile-quantile plots of p-values showed that false positive rates were well calibrated by LMM, whereas other methods not accounting for sample-specific mixed effects led to serious inflation. LMM identified 37 potentially significant genes with differential expression in DLPFC for at least one of the AD traits, 17 of which were replicated in the additional RNA-Seq data of DLPFC, supplemental motor area, spinal cord, and muscle tissues. This application study showed not only well calibrated DGE results by LMM, but also possibly shared gene regulatory mechanisms of AD traits across different relevant tissues.

Robust, scalable, and informative clustering for diverse biological networks.

Clustering molecular data into informative groups is a primary step in extracting robust conclusions from big data. However, due to foundational issues in how they are defined and detected, such clusters are not always reliable, leading to unstable conclusions. We compare popular clustering algorithms across thousands of synthetic and real biological datasets, including a new consensus clustering algorithm-SpeakEasy2: Champagne. These tests identify trends in performance, show no single method is universally optimal, and allow us to examine factors behind variation in performance. Multiple metrics indicate SpeakEasy2 generally provides robust, scalable, and informative clusters for a range of applications.

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks1-6, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the full spectrum of genetic variation observed in personal genomes. Previous evaluation strategies have assessed their predictions of gene expression across genomic regions; however, systematic benchmarking is lacking to assess their predictions across individuals, which would directly evaluate their utility as personal DNA interpreters. We used paired whole genome sequencing and gene expression from 839 individuals in the ROSMAP study7 to evaluate the ability of current methods to predict gene expression variation across individuals at varied loci. Our approach identifies a limitation of current methods to correctly predict the direction of variant effects. We show that this limitation stems from insufficiently learned sequence motif grammar and suggest new model training strategies to improve performance.

Proteome-Wide Discovery of Cortical Proteins That May Provide Motor Resilience to Offset the Negative Effects of Pathologies in Older Adults.

BACKGROUND: Motor resilience proteins have not been identified. This proteome-wide discovery study sought to identify proteins that may provide motor resilience. METHODS: We studied the brains of older decedents with annual motor testing, postmortem brain pathologies, and proteome-wide data. Parkinsonism was assessed using 26 items of a modified United Parkinson Disease Rating Scale. We used linear mixed-effect models to isolate motor resilience, defined as the person-specific estimate of progressive parkinsonism after controlling for age, sex, and 10 brain pathologies. A total of 8 356 high-abundance proteins were quantified from dorsal lateral prefrontal cortex using tandem mass tag and liquid chromatography-mass spectrometry. RESULTS: There were 391 older adults (70% female), mean age 80 years at baseline and 89 years at death. Five proteins were associated with motor resilience: A higher level of AP1B1 (Estimate -0.504, SE 0.121, p = 3.12 × 10-5) and GNG3 (Estimate -0.276, SE 0.068, p = 4.82 × 10-5) was associated with slower progressive parkinsonism. By contrast, a higher level of TTC38 (Estimate 0.140, SE 0.029, p = 1.87 × 10-6), CARKD (Estimate 0.413, SE 0.100, p = 3.50 × 10-5), and ABHD14B (Estimate 0.175, SE 0.044, p = 6.48 × 10-5) was associated with faster progressive parkinsonism. Together, these 5 proteins accounted for almost 25% of the variance of progressive parkinsonism above the 17% accounted for by 10 indices of brain pathologies. DISCUSSION: Cortical proteins may provide more or less motor resilience in older adults. These proteins are high-value therapeutic targets for drug discovery that may lead to interventions that maintain motor function despite the accumulation of as yet untreatable brain pathologies.

The MIND diet, brain transcriptomic alterations, and dementia.

Identifying novel mechanisms underlying dementia is critical to improving prevention and treatment. As an approach to mechanistic discovery, we investigated whether MIND diet (Mediterranean-DASH Diet Intervention for Neurodegenerative Delay), a consistent risk factor for dementia, is correlated with a specific profile of cortical gene expression, and whether such a transcriptomic profile is associated with dementia, in the Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP). RNA sequencing (RNA-Seq) was conducted in postmortem dorsolateral prefrontal cortex tissue from 1,204 deceased participants; neuropsychological assessments were performed annually prior to death. In a subset of 482 participants, diet was assessed ~6 years before death using a validated food-frequency questionnaire; in these participants, using elastic net regression, we identified a transcriptomic profile, consisting of 50 genes, significantly correlated with MIND diet score (P=0.001). In multivariable analysis of the remaining 722 individuals, higher transcriptomic score of MIND diet was associated with slower annual rate of decline in global cognition (ß=0.011 per standard deviation increment in transcriptomic profile score, P=0.003) and lower odds of dementia (odds ratio [OR] =0.76, P=0.0002). Cortical expression of several genes appeared to mediate the association between MIND diet and dementia, including TCIM, whose expression in inhibitory neurons and oligodendrocytes was associated with dementia in a subset of 424 individuals with single-nuclei RNA-seq data. In a secondary Mendelian randomization analysis, genetically predicted transcriptomic profile score was associated with dementia (OR=0.93, P=0.04). Our study suggests that associations between diet and cognitive health may involve brain molecular alterations at the transcriptomic level. Investigating brain molecular alterations related to diet may inform the identification of novel pathways underlying dementia.