OBJECTIVE:Along with the known effects of stress on brain structure and inflammatory processes, increasing evidence suggest a role of chronic stress in the pathogenesis of Alzheimer's disease (AD). We investigated the association of accumulated stressful life events (SLEs) with AD pathologies, neuroinflammation, and gray matter (GM) volume among cognitively unimpaired (CU) individuals at heightened risk of AD. METHODS:This cross-sectional cohort study included 1,290 CU participants (aged 48-77) from the ALFA cohort with SLE, lumbar puncture (n = 393), and/or structural magnetic resonance imaging (n = 1,234) assessments. Using multiple regression analyses, we examined the associations of total SLEs with cerebrospinal fluid (1) phosphorylated (p)-tau181 and Aβ1-42/1-40 ratio, (2) interleukin 6 (IL-6), and (3) GM volumes voxel-wise. Further, we performed stratified and interaction analyses with sex, history of psychiatric disease, and evaluated SLEs during specific life periods. RESULTS:Within the whole sample, only childhood and midlife SLEs, but not total SLEs, were associated with AD pathophysiology and neuroinflammation. Among those with a history of psychiatric disease SLEs were associated with higher p-tau181 and IL-6. Participants with history of psychiatric disease and men, showed lower Aβ1-42/1-40 with higher SLEs. Participants with history of psychiatric disease and women showed reduced GM volumes in somatic regions and prefrontal and limbic regions, respectively. INTERPRETATION:We did not find evidence supporting the association of total SLEs with AD, neuroinflammation, and atrophy pathways. Instead, the associations appear to be contingent on events occurring during early and midlife, sex and history of psychiatric disease. ANN NEUROL 2024;95:1058-1068.

INTRODUCTION:Semorinemab, an anti-tau monoclonal antibody, was assessed in two Phase II trials for Alzheimer's disease (AD). Plasma and cerebrospinal fluid (CSF) biomarkers provided insights into the drug's potential mechanism of action. METHODS:Qualified assays were used to measure biomarkers of tau, amyloidosis, glial activity, neuroinflammation, synaptic function, and neurodegeneration from participant samples in Tauriel (NCT03289143) and Lauriet (NCT03828747) Phase II trials. RESULTS:Plasma phosphorylated Tau 181 (pTau181) and CSF chitinase-3-like protein 1 (YKL-40) increased following semorinemab treatment in both studies. In Lauriet, increasing plasma glial fibrillary protein (GFAP) concentrations stabilized with semorinemab, while this was not observed in Tauriel. Other AD pathophysiology biomarkers showed no consistent response to semorinemab. DISCUSSION:Increases in CSF YKL-40 suggest that semorinemab may stimulate microglia activation in the presence of AD-associated Tau pathology, but not in healthy controls. Stabilization of plasma GFAP in Lauriet indicates a possible impact on reactive gliosis in mild-to-moderate AD. TRIAL REGISTRATION:Tauriel ClinicalTrials.gov Identifier: NCT03289143. Lauriet ClinicalTrials.gov Identifier: NCT03828747. Phase 1 ClinicalTrials.gov Identifier: NCT02820896. HIGHLIGHTS:AD pathophysiology biomarkers were measured to assess the mechanism of action. Semorinemab increased CSF YKL-40 in participants with AD but not in healthy controls. Semorinemab possibly stabilized plasma GFAP in the Lauriet trial. Semorinemab treatment may activate microglia and moderate reactive gliosis.

INTRODUCTION:Recent genome-wide association studies (GWAS) have reported a genetic association with Alzheimer's disease (AD) at the TNIP1/GPX3 locus, but the mechanism is unclear. METHODS:We used cerebrospinal fluid (CSF) proteomics data to test (n = 137) and replicate (n = 446) the association of glutathione peroxidase 3 (GPX3) with CSF biomarkers (including amyloid and tau) and the GWAS-implicated variants (rs34294852 and rs871269). RESULTS:CSF GPX3 levels decreased with amyloid and tau positivity (analysis of variance P = 1.5 × 10-5) and higher CSF phosphorylated tau (p-tau) levels (P = 9.28 × 10-7). The rs34294852 minor allele was associated with decreased GPX3 (P = 0.041). The replication cohort found associations of GPX3 with amyloid and tau positivity (P = 2.56 × 10-6) and CSF p-tau levels (P = 4.38 × 10-9). DISCUSSION:These results suggest variants in the TNIP1 locus may affect the oxidative stress response in AD via altered GPX3 levels. HIGHLIGHTS:Cerebrospinal fluid (CSF) glutathione peroxidase 3 (GPX3) levels decreased with amyloid and tau positivity and higher CSF phosphorylated tau. The minor allele of rs34294852 was associated with lower CSF GPX3. levels when also controlling for amyloid and tau category. GPX3 transcript levels in the prefrontal cortex were lower in Alzheimer's disease than controls. rs34294852 is an expression quantitative trait locus for GPX3 in blood, neutrophils, and microglia.

INTRODUCTION:Apolipoprotein E (APOE) ε4-carrier status or ε4 allele count are included in analyses to account for the APOE genetic effect on Alzheimer's disease (AD); however, this does not account for protective effects of APOE ε2 or heterogeneous effect of ε2, ε3, and ε4 haplotypes. METHODS:We leveraged results from an autopsy-confirmed AD study to generate a weighted risk score for APOE (APOE-npscore). We regressed cerebrospinal fluid (CSF) amyloid and tau biomarkers on APOE variables from the Wisconsin Registry for Alzheimer's Prevention (WRAP), Wisconsin Alzheimer's Disease Research Center (WADRC), and Alzheimer's Disease Neuroimaging Initiative (ADNI). RESULTS:The APOE-npscore explained more variance and provided a better model fit for all three CSF measures than APOE ε4-carrier status and ε4 allele count. These findings were replicated in ADNI and observed in subsets of cognitively unimpaired (CU) participants. DISCUSSION:The APOE-npscore reflects the genetic effect on neuropathology and provides an improved method to account for APOE in AD-related analyses.

Recency refers to the information learned at the end of a study list or task. Recency forgetting, as tracked by the ratio between recency recall in immediate and delayed conditions, i.e., the recency ratio (Rr), has been applied to list-learning tasks, demonstrating its efficacy in predicting cognitive decline, conversion to mild cognitive impairment (MCI), and cerebrospinal fluid (CSF) biomarkers of neurodegeneration. However, little is known as to whether Rr can be effectively applied to story recall tasks. To address this question, data were extracted from the database of the Alzheimer's Disease Research Center at the University of Wisconsin – Madison. A total of 212 participants were included in the study. CSF biomarkers were amyloid-beta (Aβ) 40 and 42, phosphorylated (p) and total (t) tau, neurofilament light (NFL), neurogranin (Ng), and α-synuclein (a-syn). Story Recall was measured with the Logical Memory Test (LMT). We carried out Bayesian regression analyses with Rr, and other LMT scores as predictors; and CSF biomarkers (including the Aβ42/40 and p-tau/Aβ42 ratios) as outcomes. Results showed that models including Rr consistently provided best fits with the data, with few exceptions. These findings demonstrate the applicability of Rr to story recall and its sensitivity to CSF biomarkers of neurodegeneration, and encourage its inclusion when evaluating risk of neurodegeneration with story recall.

Vascular dysfunction often occurs concurrently with Alzheimer’s disease (AD). Breakdown of the blood–brain barrier (BBB) and vascular injury may be related to amyloid and tau pathology. In preliminary analyses, we examined the relationship between cerebrospinal fluid (CSF) biomarkers of AD, neurodegeneration, and glial activation, and two novel CSF markers of vascular dysfunction: soluble platelet-derived growth factor receptor beta (sPDGFRβ) a marker of BBB integrity, and angiopoietin-2 (Ang2), a marker of vascular injury. CSF samples from participants enrolled in the Wisconsin Registry for Alzheimer’s Prevention (WRAP) or the Wisconsin Alzheimer’s Disease Research Center (WADRC) studies were assayed for markers of AD, neurodegeneration, and gliosis, using Roche NeuroToolKit® immunoassays (Roche Diagnostics International Ltd, Switzerland). A subset of serially sampled participants ( N = 209, N ob s = 531) who spanned the AD clinical spectrum (cognitively unimpaired, MCI, dementia) underwent sPDGFRβ measurement (ELISA); a sample of N = 121 ( N obs = 276) was also assayed for Ang2 (ELISA) (Table 1). Pearson correlations were calculated for all CSF biomarkers. Linear mixed-effects models with random intercepts, age-at-lumbar-puncture as the measure of time, and CSF vascular biomarker as the outcome were used to test associations with tau positivity (>24.8 pg/mL), and cognitive status. sPDGFRβ correlated with all CSF biomarkers ( r s range .18 to .45, p s<.001, Figure 1A) except AB 42/40 . Ang2 correlated with all CSF biomarkers ( r s range .21 to .45 p s <.001, Figure 1B) except AB 42/40 , and S100B. sPDGFRβ and Ang2 correlated most strongly with Aβ 40 and biomarkers of synaptic function (neurogranin and α-synuclein). Tau positivity was associated with sPDGFRβ in the whole sample (estimate = 64.5, p<.001) and excluding participants with MCI/dementia (estimate = 77.7, p<.001). Tau positivity was associated with Ang2 (estimate = 18.1, p = .007), however the association was not significant after excluding participants with MCI/dementia. There was no difference in sPDGFRβ across the AD clinical spectrum. Ang2 was higher among participants with MCI than cognitively unimpaired participants (estimate = 21.0, p = .037, Figure 2). CSF markers of vascular injury were elevated in MCI and were moderately related to tau pathology and markers of neuronal injury and neuroinflammation, but not amyloid pathology, providing insight into the association of of vascular injury to AD related disease

BACKGROUND Wordlist and story recall tests are routinely employed in clinical practice for dementia diagnosis. In this study, our aim was to establish how well-standard clinical metrics compared to process scores derived from wordlist and story recall tests in predicting biomarker determined Alzheimer's disease, as defined by CSF ptau/Aβ42 ratio. METHODS Data from 295 participants (mean age = 65 ± 9.) were drawn from the University of Wisconsin - Madison Alzheimer's Disease Research Center (ADRC) and Wisconsin Registry for Alzheimer's Prevention (WRAP). Rey's Auditory Verbal Learning Test (AVLT; wordlist) and Logical Memory Test (LMT; story) data were used. Bayesian linear regression analyses were carried out with CSF ptau/Aβ42 ratio as outcome. Sensitivity analyses were carried out with logistic regressions to assess diagnosticity. RESULTS LMT generally outperformed AVLT. Notably, the best predictors were primacy ratio, a process score indexing loss of information learned early during test administration, and recency ratio, which tracks loss of recently learned information. Sensitivity analyses confirmed this conclusion. CONCLUSIONS Our study shows that story recall tests may be better than wordlist tests for detection of dementia, especially when employing process scores alongside conventional clinical scores.

INTRODUCTION: Our objective was determining the optimal combinations of cerebrospinal fluid (CSF) biomarkers for predicting disease progression in Alzheimer's disease (AD) and other neurodegenerative diseases. METHODS: We included 1,983 participants from three different cohorts with longitudinal cognitive and clinical data, and baseline CSF levels of Aβ42, Aβ40, p-tau, NfL, neurogranin, α-synuclein, sTREM2, GFAP, YKL-40, S100b and IL-6 (Elecsys® NeuroToolKit). RESULTS: Change of modified Preclinical Alzheimer's Cognitive Composite (mPACC) in cognitively unimpaired (CU) was best predicted by p-tau/Aβ42 alone (R2≥0.31) or together with neurofilament light ([NfL],R2=0.25), while p-tau/Aβ42 (R2≥0.19) was sufficient to accurately predict change of the Mini-Mental State Examination (MMSE) in mild cognitive impairment (MCI) patients. P-tau/Aβ42 (AUC≥0.87) and p-tau/Aβ42 with NfL (AUC≥0.75) were the best predictors of conversion to AD and all-cause dementia, respectively. DISCUSSION: P-tau/Aβ42 is sufficient for predicting progression in AD, with very high accuracy. Adding NfL improves the prediction of all-cause dementia conversion and cognitive decline.

Introduction:Metabolomics technology facilitates studying associations between small molecules and disease processes. Correlating metabolites in cerebrospinal fluid (CSF) with Alzheimer's disease (AD) CSF biomarkers may elucidate additional changes that are associated with early AD pathology and enhance our knowledge of the disease. Methods:The relative abundance of untargeted metabolites was assessed in 161 individuals from the Wisconsin Registry for Alzheimer's Prevention. A metabolome-wide association study (MWAS) was conducted between 269 CSF metabolites and protein biomarkers reflecting brain amyloidosis, tau pathology, neuronal and synaptic degeneration, and astrocyte or microglial activation and neuroinflammation. Linear mixed-effects regression analyses were performed with random intercepts for sample relatedness and repeated measurements and fixed effects for age, sex, and years of education. The metabolome-wide significance was determined by a false discovery rate threshold of 0.05. The significant metabolites were replicated in 154 independent individuals from then Wisconsin Alzheimer's Disease Research Center. Mendelian randomization was performed using genome-wide significant single nucleotide polymorphisms from a CSF metabolites genome-wide association study. Results:Metabolome-wide association study results showed several significantly associated metabolites for all the biomarkers except Aβ42/40 and IL-6. Genetic variants associated with metabolites and Mendelian randomization analysis provided evidence for a causal association of metabolites for soluble triggering receptor expressed on myeloid cells 2 (sTREM2), amyloid β (Aβ40), α-synuclein, total tau, phosphorylated tau, and neurogranin, for example, palmitoyl sphingomyelin (d18:1/16:0) for sTREM2, and erythritol for Aβ40 and α-synuclein. Discussion:This study provides evidence that CSF metabolites are associated with AD-related pathology, and many of these associations may be causal.

Age-related disease may be mediated by low levels of chronic inflammation (“inflammaging”). Recent work suggests that gut microbes can contribute to inflammation via degradation of the intestinal barrier. While aging and age-related diseases including Alzheimer’s disease (AD) are linked to altered microbiome composition and higher levels of gut microbial components in systemic circulation, the role of intestinal inflammation remains unclear. To investigate whether greater gut inflammation is associated with advanced age and AD pathology, we assessed fecal samples from older adults to measure calprotectin, an established marker of intestinal inflammation which is elevated in diseases of gut barrier integrity. Multiple regression with maximum likelihood estimation and Satorra–Bentler corrections were used to test relationships between fecal calprotectin and clinical diagnosis, participant age, cerebrospinal fluid biomarkers of AD pathology, amyloid burden measured using 11 C-Pittsburgh compound B positron emission tomography (PiB PET) imaging, and performance on cognitive tests measuring executive function and verbal learning and recall. Calprotectin levels were elevated in advanced age and were higher in participants diagnosed with amyloid-confirmed AD dementia. Additionally, among individuals with AD dementia, higher calprotectin was associated with greater amyloid burden as measured with PiB PET. Exploratory analyses indicated that calprotectin levels were also associated with cerebrospinal fluid markers of AD, and with lower verbal memory function even among cognitively unimpaired participants. Taken together, these findings suggest that intestinal inflammation is linked with brain pathology even in the earliest disease stages. Moreover, intestinal inflammation may exacerbate the progression toward AD.

Abstract Chronic systemic inflammation increases the risk of neurodegeneration, but the mechanisms remain unclear. Part of the challenge in reaching a nuanced understanding is the presence of multiple risk factors that interact to potentiate adverse consequences. To address modifiable risk factors and mitigate downstream effects, it is necessary, although difficult, to tease apart the contribution of an individual risk factor by accounting for concurrent factors such as advanced age, cardiovascular risk, and genetic predisposition. Using a case-control design, we investigated the influence of asthma, a highly prevalent chronic inflammatory disease of the airways, on brain health in participants recruited to the Wisconsin Alzheimer’s Disease Research Center (31 asthma patients, 186 non-asthma controls, aged 45–90 years, 62.2% female, 92.2% cognitively unimpaired), a sample enriched for parental history of Alzheimer’s disease. Asthma status was determined using detailed prescription information. We employed multi-shell diffusion weighted imaging scans and the three-compartment neurite orientation dispersion and density imaging model to assess white and gray matter microstructure. We used cerebrospinal fluid biomarkers to examine evidence of Alzheimer’s disease pathology, glial activation, neuroinflammation and neurodegeneration. We evaluated cognitive changes over time using a preclinical Alzheimer cognitive composite. Using permutation analysis of linear models, we examined the moderating influence of asthma on relationships between diffusion imaging metrics, CSF biomarkers, and cognitive decline, controlling for age, sex, and cognitive status. We ran additional models controlling for cardiovascular risk and genetic risk of Alzheimer’s disease, defined as a carrier of at least one apolipoprotein E (APOE) ε4 allele. Relative to controls, greater Alzheimer’s disease pathology (lower amyloid-β42/amyloid-β40, higher phosphorylated-tau-181) and synaptic degeneration (neurogranin) biomarker concentrations were associated with more adverse white matter metrics (e.g. lower neurite density, higher mean diffusivity) in patients with asthma. Higher concentrations of the pleiotropic cytokine IL-6 and the glial marker S100B were associated with more salubrious white matter metrics in asthma, but not in controls. The adverse effects of age on white matter integrity were accelerated in asthma. Finally, we found evidence that in asthma, relative to controls, deterioration in white and gray matter microstructure was associated with accelerated cognitive decline. Taken together, our findings suggest that asthma accelerates white and gray matter microstructural changes associated with aging and increasing neuropathology, that in turn, are associated with more rapid cognitive decline. Effective asthma control, on the other hand, may be protective and slow progression of cognitive symptoms.

Background: Genetic scores for late-onset Alzheimer’s disease (LOAD) have been associated with preclinical cognitive decline and biomarker variations. Compared with an overall polygenic risk score (PRS), a pathway-specific PRS (p-PRS) may be more appropriate in predicting a specific biomarker or cognitive component underlying LOAD pathology earlier in the lifespan. Objective: In this study, we leveraged longitudinal data from the Wisconsin Registry for Alzheimer’s Prevention and explored changing patterns in cognition and biomarkers at various age points along six biological pathways. Methods: PRS and p-PRSs with and without APOE were constructed separately based on the significant SNPs associated with LOAD in a recent genome-wide association study meta-analysis and compared to APOE alone. We used a linear mixed-effects model to assess the association between PRS/p-PRSs and cognitive trajectories among 1,175 individuals. We also applied the model to the outcomes of cerebrospinal fluid biomarkers in a subset. Replication analyses were performed in an independent sample. Results: We found p-PRSs and the overall PRS can predict preclinical changes in cognition and biomarkers. The effects of PRS/p-PRSs on rate of change in cognition, amyloid-β, and tau outcomes are dependent on age and appear earlier in the lifespan when APOE is included in these risk scores compared to when APOE is excluded. Conclusion: In addition to APOE, the p-PRSs can predict age-dependent changes in amyloid-β, tau, and cognition. Once validated, they could be used to identify individuals with an elevated genetic risk of accumulating amyloid-β and tau, long before the onset of clinical symptoms.

Cerebrospinal fluid (CSF) concentration of soluble TREM2 (sTREM2), a potential biomarker for microglial activation, is associated with attenuated longitudinal neurodegeneration and cognitive decline in Alzheimer’s disease (AD), but data in early disease are lacking. This study’s purpose was to use longitudinal volumetric imaging to assess the association of sTREM2 with age- and preclinical AD-related grey matter (GM) changes. Cognitively unimpaired participants (N = 384; amyloid-positive N = 82) from the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin ADRC clinical core studies with baseline CSF biomarker and subsequent longitudinal T1-weighted magnetic resonance imaging data were analyzed. CSF sTREM2 and phosphorylated-tau 181 /amyloid-beta 1-42 ratio (pTau/Aβ42) were measured using the NeuroToolKit panel of robust prototype assays (Roche Diagnostics International Ltd, Rotkreuz, Switzerland). T1-weighted images were longitudinally registered to intra-subject templates and segmented to create 58 GM regions of interest (ROIs) via the CAT12 longitudinal segmentation pipeline. Linear mixed-effects models (random participant intercepts and age slopes) testing a three-way interaction between time-varying age, sTREM2, and pTau/Aβ 42 to predict regional grey matter changes with all potential two-way interactions and simple effects (adjusted for gender, years of education, intracranial volume, and head coil) were tested. In the event of a non-significant three-way interaction, the three-way interaction was dropped and the model was reinterpreted. Statistical significance was considered at p < .05, uncorrected for multiple comparisons. Age had a negative effect on regional GM volume across the brain and showed widespread interactions with pTau/Aβ42, indicative of accelerated decline with AD pathology. Negative three-way interactions between sTREM2, pTau/Aβ42, and age were evident in the angular, supramarginal, lingual, and middle occipital gyri, predicting accelerated AD-related longitudinal neurodegeneration with higher sTREM2 concentration. Negative two-way interactions between pTau/Aβ42 and sTREM2 were evident in the supplementary motor cortex and superior frontal gyrus, indicating worse effects of AD-pathology with higher sTREM2, regardless of age. Overall, higher sTREM2 may be associated with accelerated AD-related neurodegeneration over time in the context of preclinical AD, particularly in posterior ROIs. Higher sTREM2 and underlying microglial activation may denote individuals at higher risk of experiencing the deleterious effects of early AD pathology on the brain.

Lower measures of myelin have been associated with abnormal levels of AD biomarkers and APOE4 carriage. However, most human studies have been cross-sectional, leaving the relationship between AD and myelin changes unclear. The purpose of this study was to determine the association between AD pathology, APOE4, glial activation, and longitudinal changes in quantitative R1 (qR1), a marker sensitive to myelination obtained via MPnRAGE MRI. Participants from the Wisconsin Registry for Alzheimer’s Prevention (WRAP) and Wisconsin Alzheimer’s Disease Research Center (ADRC) were selected based on available longitudinal MPnRAGE, clinical diagnosis, and APOE genotyping (N = 446). A subset (N = 141) included those with available baseline CSF (AB42/40, pTau181), and gliosis markers (sTREM2, GFAP, YKL40) measured using a robust prototype assay as part of the Roche NeuroToolKit research platform (Roche International). Longitudinal mean qR1 was estimated from white matter regions from the ICBM-DTI-81 atlas. In the overall sample, linear mixed effects models predicting regional qR1 (FDR-corrected) with random intercepts and slopes were estimated controlling for sex, age centered within-subjects (agec), age averaged within-subjects (agegm), cognitive impairment, and APOE4 carriage as covariates. Two-way interactions between impairment and APOE4 with agec were tested in the overall sample as were moderating effects of CSF amyloid-positivity, pTau181, and gliosis markers on agec in the CSF sample. In the overall sample, older agec, agegm, and female sex were associated with lower qR1 in a majority of tracts indicating substantial longitudinal declines in qR1 over time and lower qR1 with advanced age and female sex. Sex, impairment, and APOE4 did not show significant agec interactions. Likewise, none of the CSF biomarkers moderated the effect of agec in the CSF subsample. While aging is robustly associated with longitudinal demyelination as potentially indexed by qR1, these rates do not appear to be moderated by AD pathology, cognitive impairment, APOE4, or glial activation in early disease. Given the capacity for re-myelination in the context of injury, longitudinal studies are critical for parsing myelin and AD relationships. Established associations between myelin and AD risk may reflect later disease processes, particularly in samples more enriched for tau pathology.

MRI-derived brain-age prediction is a promising biomarker of biological brain aging. Accelerated brain aging has been found in Alzheimer’s disease (AD) and other neurodegenerative diseases. However, no previous studies have investigated the relationship between specific pathophysiological pathways in AD and biological brain aging. Here, we studied whether glial activation and synaptic dysfunction are associated with biological brain aging in the earliest stages of the Alzheimer’s continuum. We included 418 cognitively unimpaired individuals (CU) from the ALFA+ study with available structural MRI, and CSF biomarkers of amyloid-ß (Aß42/40) and tau pathology (p-tau181), synaptic dysfunction (neurogranin, GAP43, SYT1, SNAP25), glial activation (sTREM2, YKL40, GFAP, interleukin-6 and S100b) and a-synuclein (Table 1). Aß42/40, neurogranin and the glial activation biomarkers were measured using the Roche NeuroToolKit. We computed brain-age delta as the difference between chronological and predicted brain-age. The latter was estimated using a previously pretrained machine learning algorithm on cerebral morphological measurements on individuals from the UKBioBank cohort (N = 22.000). General linear modeling was used to test the associations between CSF biomarkers and brain-age delta, adjusting by p-tau, age, APOE status and sex. For the biomarkers whose associations were significant, we evaluated the interaction term “biomarker” × AT status while adjusting by age, APOE status and sex. AT staging was performed using pre-established cut-off values. We then used hippocampal volume as a marker of AD-related neurodegeneration and repeated the same association studies with CSF biomarkers, adjusting by p-tau, age, APOE status, sex and TIV. Brain-age delta was negatively associated with CSF sTREM2 (Padjusted<0.001), meaning that younger-appearing brains showed higher levels of this biomarker (Table 1). None of the other biomarkers survived multiple comparisons. Hippocampal volume was not significantly associated with any of the CSF biomarkers (Table 2). There was no significant interaction between AT status and CSF sTREM2 for brain-age delta, nor for hippocampal volume. These results showed that higher levels of CSF sTREM2 were associated with younger-appearing brains in CU individuals independently of AT status, which might indicate a protective effect of this microglial phenotype in brain aging. This effect might not be AD-related.

Modifiable factors can influence the risk for Alzheimer’s disease (AD) and serve as targets for intervention; however, the biological mechanisms linking these factors to AD are unknown. This study aims to identify plasma metabolites associated with modifiable factors for AD, including MIND diet, physical activity, smoking, and caffeine intake, and test their association with AD endophenotypes to identify their potential roles in pathophysiological mechanisms. The association between each of the 757 plasma metabolites and four modifiable factors was tested in the wisconsin registry for Alzheimer’s prevention cohort of initially cognitively unimpaired, asymptomatic middle-aged adults. After Bonferroni correction, the significant plasma metabolites were tested for association with each of the AD endophenotypes, including twelve cerebrospinal fluid (CSF) biomarkers, reflecting key pathophysiologies for AD, and four cognitive composite scores. Finally, causal mediation analyses were conducted to evaluate possible mediation effects. Analyses were performed using linear mixed-effects regression. A total of 27, 3, 23, and 24 metabolites were associated with MIND diet, physical activity, smoking, and caffeine intake, respectively. Potential mediation effects include beta-cryptoxanthin in the association between MIND diet and preclinical Alzheimer cognitive composite score, hippurate between MIND diet and immediate learning, glutamate between physical activity and CSF neurofilament light, and beta-cryptoxanthin between smoking and immediate learning. Our study identified several plasma metabolites that are associated with modifiable factors. These metabolites can be employed as biomarkers for tracking these factors, and they provide a potential biological pathway of how modifiable factors influence the human body and AD risk.

The recently reported TNIP1 / GPX3 locus from AD GWAS studies was investigated. Using proteomics and other functional omics data, we identified evidence for a functional mechanism linking variants in this locus to decreased CSF GPX3 levels as AD progresses, suggesting a new potential target for intervention. ### Competing Interest Statement Author CC receives research support from Biogen, EISAI, Alector, GSK and Parabon; these funders of the study had no role in the collection, analysis, or interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. Author CC is a member of the advisory board of Vivid Genomics, Halia Therapeutics and ADx Healthcare. Author HZ has served at scientific advisory boards and/or as a consultant for Abbvie, Alector, Annexon, Apellis, Artery Therapeutics, AZTherapies, CogRx, Denali, Eisai, Nervgen, Novo Nordisk, Pinteon Therapeutics, Red Abbey Labs, Passage Bio, Roche, Samumed, Siemens Healthineers, Triplet Therapeutics, and Wave, has given lectures in symposia sponsored by Cellectricon, Fujirebio, Alzecure, Biogen, and Roche, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. Author KB has served as a consultant, at advisory boards, or at data monitoring committees for Abcam, Axon, BioArctic, Biogen, Julius Clinical, Lilly, MagQu, Novartis, Roche Diagnostics, and Siemens Healthineers, and is a co-founder of Brain Biomarker Solutions in Gothenburg AB (BBS), which is a part of the GU Ventures Incubator Program. Author GK is a full-time employee of Roche Diagnostics GmbH. Author IS is a full-time employee and shareholder of Roche Diagnostics International Ltd. Author NW is a full-time employee of Roche Diagnostics GmbH. Author SCJ serves as a consultant to Roche Diagnostics and receives research funding from Cerveau Technologies. Author MPS is a cofounder and scientific advisor of Personalis, SensOmics, Qbio, January AI, Fodsel, Filtricine, Protos, RTHM, Iollo, Marble Therapeutics and Mirvie. He is a scientific advisor of Genapsys, Jupiter, Neuvivo, Swaza, and Mitrix. Other authors have no competing interests to declare. ### Funding Statement This research is supported by National Institutes of Health (NIH) grants R01AG27161 (Wisconsin Registry for Alzheimer Prevention: Biomarkers of Preclinical AD), R01AG054047 (Genomic and Metabolomic Data Integration in a Longitudinal Cohort at Risk for Alzheimer's Disease), P41GM108538 (National Center for Quantitative Biology of Complex Systems), R01AG037639 (White Matter Degeneration: Biomarkers in Preclinical Alzheimer's Disease), R01AG021155 (The Longitudinal Course of Imaging Biomarkers in People at Risk of AD), R21AG067092 (Identifying Metabolomic Risk Factors in Plasma and CSF for Alzheimer's Disease), and P50AG033514 and P30AG062715 (Wisconsin Alzheimer's Disease Research Center Grant), the Clinical and Translational Science Award (CTSA) program through the NIH National Center for Advancing Translational Sciences (NCATS) grant UL1TR000427, and the University of Wisconsin-Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation. Computational resources were supported by a core grant to the Center for Demography and Ecology at the University of Wisconsin-Madison (P2CHD047873). We also acknowledge use of the facilities of the Center for Demography of Health and Aging at the University of Wisconsin-Madison, funded by NIA Center grant P30AG017266. Author LMR was funded by the Memorabel fellowship "Identifying biological and clinical relevance of (epi)genetic risk factors in sporadic FTD" (ZonMW project number: 10510022110012). Author YKD was supported by a training grant from the National Institute on Aging (T32AG000213). Author PJV was supported by grants from the European Commission, IMI (AMYPAD: 115952; RADAR-AD: 806999; and EPND: 101034344) and the ZonMW (Redefining Alzheimer's disease, #733050824736, and NCDC, #73305095005). Author HZ is a Wallenberg Scholar supported by grants from the Swedish Research Council (#2018-02532), the European Research Council (#101053962), Swedish State Support for Clinical Research (#ALFGBG-720931), the Alzheimer Drug Discovery Foundation (ADDF), USA (#201809-2016862), the AD Strategic Fund and the Alzheimer's Association (#ADSF-21-831376-C, #ADSF-21-831381-C and #ADSF-21-831377-C), the Olav Thon Foundation, the Erling-Persson Family Foundation, Stiftelsen för Gamla Tjänarinnor, Hjärnfonden, Sweden (#FO2019-0228), the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860197 (MIRIADE), and the UK Dementia Research Institute at UCL (#UKDRI-1003). Author KB is supported by the Swedish Research Council (#2017-00915), the Swedish Alzheimer Foundation (#AF-930351, #AF-939721, and #AF-968270), Hjärnfonden, Sweden (#FO2017-0243 and #ALZ2022-0006), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986 and #ALFGBG-965240), and the Alzheimer's Association 2021 Zenith Award (ZEN-21-848495). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author JG is supported by Alzheimerfonden (AF-930934) and the Foundation of Gamla Tjänarinnor. Author CC receives support from the National Institutes of Health (R01AG044546, R01AG064877, RF1AG053303, R01AG058501, U01AG058922, R01AG064614, 1RF1AG074007), and the Chuck Zuckerberg Initiative (CZI). The recruitment and clinical characterization of research participants at Washington University were supported by NIH P30AG066444, and P01AG003991. This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the NeuroGenomics and Informatics Center (NGI: https://neurogenomics.wustl.edu/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine. Authors LMR, PJV, and BMT are supported by the ZonMW Memorabel grant programma (#733050824), and author PJV is additionally supported by the Innovative Medicines Initiative Joint Undertaking under the EMIF grant agreement (#115372). ELECSYS, COBAS and COBAS E are trademarks of Roche. The Roche NeuroToolKit robust prototype assays are for investigational purposes only and are not approved for clinical use. We thank the University of Wisconsin Madison Biotechnology Center Gene Expression Center for providing Illumina Infinium genotyping services. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: University of Wisconsin data sets: This study was performed as part of the GeneRations Of WRAP (GROW) study, which was approved by the University of Wisconsin Health Sciences Institutional Review Board. Participants in the ADRC and WRAP studies provided written informed consent. The institutional review boards of all participating institutions approved the procedures for this study. EMIF-AD MBD cohort data sets: Written informed consent was obtained from all participants or surrogates, and the procedures for this study were approved by the institutional review boards of all participating institutions, including the following (see Bos et al 2018 for full listing): Aristotle University of Thessaloniki Medical School Ethics Committee; Ethics Committee of the Medical Faculty Mannheim, University of Heidelberg; Ethic and Clinical Research Committee Donostia; Ethics committee Inserm and Aix Marseille University; The Healthcare Ethics Committee of the Hospital Clínic; Central Clinical Research and Clinical Trials Unit (UICEC Sant Pau); INSERM Ethical Committee; Ethic Committee of the IRCCS San Giovanni di Dio FBF; Comitato Etico IRCCS Pascale - Napoli; Ethics Committee at Karolinska Institutet; Ethische commissie onderzoek UZ/KU Leuven; Research Ethics Committee Lausanne University Hospital; Medical ethical committee Maastricht University Medical Center; Committee on Health Research Ethics, Region of Denmark; Ethics committee of Mediterranean University; University of Lille Ethics committee; Ethical Committee at the Medical Faculty, Leipzig University; Ethical Committee at the Medical Faculty, University Hospital Essen; Ethics committee University of Antwerp; Ethical Committee of University of Genoa; Ethics Committee, University of Gothenburg; Human ethics Committee of the University of Perugia; and the Medical ethics committee VU Medical Center. Washington University in St. Louis cohort data sets: The study was approved by an Institutional Review Board at Washington University School of Medicine in St. Louis. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data sets analyzed from the Wisconsin ADRC and WRAP studies may be requested at https://www.adrc.wisc.edu/apply-resources. Microglia eQTL summary statistics may be requested through the European Genome-Phenome Archive (EGAD00001005736) and the Wellcome Sanger Institute Data Access Committee. The EMIF-AD proteomics data may be requested from the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier 10.6019/PXD019910. The Knight ADRC proteomic data is available at NIAGADS: NG00102 collection and can be interactively explored at http://ngi.pub:3838/ONTIME_Proteomics/.

Glial activation is one of the earliest mechanisms to be altered in Alzheimer's disease (AD). Glial fibrillary acidic protein (GFAP) relates to reactive astrogliosis and can be measured in both cerebrospinal fluid (CSF) and blood. Plasma GFAP has been suggested to become altered earlier in AD than its CSF counterpart. Although astrocytes consume approximately half of the glucose-derived energy in the brain, the relationship between reactive astrogliosis and cerebral glucose metabolism is poorly understood. Here, we aimed to investigate the association between fluorodeoxyglucose ([18F]FDG) uptake and reactive astrogliosis, by means of GFAP quantified in both plasma and CSF for the same participants.We included 314 cognitively unimpaired participants from the ALFA + cohort, 112 of whom were amyloid-β (Aβ) positive. Associations between GFAP markers and [18F]FDG uptake were studied. We also investigated whether these associations were modified by Aβ and tau status (AT stages).Plasma GFAP was positively associated with glucose consumption in the whole brain, while CSF GFAP associations with [18F]FDG uptake were only observed in specific smaller areas like temporal pole and superior temporal lobe. These associations persisted when accounting for biomarkers of Aβ pathology but became negative in Aβ-positive and tau-positive participants (A + T +) in similar areas of AD-related hypometabolism.Higher astrocytic reactivity, probably in response to early AD pathological changes, is related to higher glucose consumption. With the onset of tau pathology, the observed uncoupling between astrocytic biomarkers and glucose consumption might be indicative of a failure to sustain the higher energetic demands required by reactive astrocytes.

Gene‐environment interactions are important in understanding Alzheimer’s disease (AD) etiology. Current research is limited, possibly due to weak effects of individual genetic variants. We analysed interaction between genetics of hippocampal volume, environmental exposures and levels of AD biomarkers in cognitively unimpaired individuals at increased risk of AD.