Background
Multi-analyte plasma proteomic panels that can accurately detect initial Alzheimer’s disease (AD) pathology in preclinical populations and simultaneously measure related biological processes relevant for disease risk are critical for advancing early detection and prognosis.

Methods
Using the NULISAseq™ CNS panel, we measured plasma proteomics from 315 clinically unimpaired (CU) older adults across two independent cohorts: the Stanford Aging and Memory Study (SAMS; n = 193) with paired cerebrospinal fluid (CSF) and plasma analyzed with Lumipulse, and the Attention, Memory, and Aging Study at Stanford (AMASS; n = 122) with paired florbetaben (FBB) amyloid PET. We evaluated correspondence of core AD-relevant biomarkers pTau217, pTau181, Aβ42/Aβ40, pTau217/Aβ42, GFAP, and NfL measured using multiplex NULISAseq and single-plex Lumipulse immunoassays. ROC curve analyses compared performance for detecting amyloid-positivity (A+) (a) across platforms in SAMS and (b) across brain-derived (BD) and total-pTau assays in AMASS, leveraging novel NULISAseq immunoassays. Linear models were applied across all NULISAseq CNS proteins to explore proteomic abundance patterns associated with age, sex, APOE-ε4, amyloid burden (CSF Aβ42/Aβ40, amyloid PET), and tau burden (CSF pTau181, PI-2620 tau PET) using an FDR-corrected p-value of < 0.05 to identify significant targets. Results In SAMS, moderate to high correlations were observed between NULISAseq and Lumipulse plasma biomarkers. NULISAseq pTau217/Aβ42 (AUC: 0.940) and pTau217 (AUC: 0.879) performed as well as corresponding single-plex Lumipulse assays (pTau217/Aβ42, AUC: 0.907; pTau217, AUC: 0.838) for detecting CSF A+ in SAMS. In AMASS, BD-pTau217 (AUC: 0.920) and BD-pTau181 (AUC: 0.920) exhibited the highest performance in discriminating PET A+, providing significant performance gains compared to total-pTau measures (pTau217, AUC: 0.861; pTau181, AUC: 0.763). Exploratory proteomic abundance analyses across NULISA CNS targets revealed pTau isoforms as most differentially expressed with amyloid burden across cohorts, together with upregulation of GFAP and downregulation of Aβ42 in SAMS. Tau burden was associated with upregulation of plasma pTau217, independent of amyloid burden, together with proteins related to astrocyte activation, inflammation, and synaptic integrity. Conclusions NULISAseq multiplex immunoassays, including novel BD-pTau assays, accurately detect AD pathology among CU older adults and identify multiple biological pathways related to aging and early biomarker abnormality that may become dysregulated in preclinical AD.

Rates of cognitive decline in Alzheimer’s disease (AD) are extremely heterogeneous. Although biomarkers for amyloid-beta (Aβ) and tau proteins, the hallmark AD pathologies, have improved pathology-based diagnosis, they explain only 20-40% of the variance in AD-related cognitive impairment (CI). To discover novel biomarkers of CI in AD, we performed cerebrospinal fluid (CSF) proteomics on 3,397 individuals from six major prospective AD case-control cohorts. Synapse proteins emerged as the strongest correlates of CI, independent of Aβ and tau. Using machine learning, we derived the CSF YWHAG:NPTX2 synapse protein ratio, which explained 27% of the variance in CI beyond CSF pTau₁₈₁:Aβ₄₂, 11% beyond tau positron emission tomography, and 28% beyond CSF neurofilament, growth-associated protein 43 and neurogranin in Aβ⁺ and phosphorylated tau⁺ (A+T₁+) individuals. CSF YWHAG:NPTX2 also increased with normal aging and 20 years before estimated symptom onset in carriers of autosomal dominant AD mutations. Regarding cognitive prognosis, CSF YWHAG:NPTX2 predicted conversion from A+T₁+ cognitively normal to mild cognitive impairment (standard deviation increase hazard ratio = 3.0, P = 7.0 × 10^-4) and A+T₁+ mild cognitive impairment to dementia (standard deviation increase hazard ratio = 2.2, P = 8.2 × 10^-16) over a 15-year follow-up, adjusting for CSF pTau₁₈₁:Aβ₄₂, CSF neurofilament, CSF neurogranin, CSF growth-associated protein 43, age, APOE4 and sex. We also developed a plasma proteomic signature of CI, which we evaluated in 13,401 samples, which partly recapitulated CSF YWHAG:NPTX2. Overall, our findings underscore CSF YWHAG:NPTX2 as a robust prognostic biomarker for cognitive resilience versus AD onset and progression, highlight the potential of plasma proteomics in replacing CSF measurement and further implicate synapse dysfunction as a core driver of AD dementia.

Human genetics implicate defective myeloid responses in the development of late-onset Alzheimer disease. A decline in peripheral and brain myeloid metabolism, triggering maladaptive immune responses, is a feature of aging. The role of TREM1, a pro-inflammatory factor, in neurodegenerative diseases is unclear. Here we show that Trem1 deficiency prevents age-dependent changes in myeloid metabolism, inflammation and hippocampal memory function in mice. Trem1 deficiency rescues age-associated declines in ribose 5-phosphate. In vitro, Trem1-deficient microglia are resistant to amyloid-β₄₂ oligomer-induced bioenergetic changes, suggesting that amyloid-β₄₂ oligomer stimulation disrupts homeostatic microglial metabolism and immune function via TREM1. In the 5XFAD mouse model, Trem1 haploinsufficiency prevents spatial memory loss, preserves homeostatic microglial morphology, and reduces neuritic dystrophy and changes in the disease-associated microglial transcriptomic signature. In aging APP^Swe mice, Trem1 deficiency prevents hippocampal memory decline while restoring synaptic mitochondrial function and cerebral glucose uptake. In postmortem Alzheimer disease brain, TREM1 colocalizes with Iba1⁺ cells around amyloid plaques and its expression is associated with Alzheimer disease clinical and neuropathological severity. Our results suggest that TREM1 promotes cognitive decline in aging and in the context of amyloid pathology.

Animal studies show aging varies between individuals as well as between organs within an individual^1-4, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20-50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer’s disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. ⁵), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

This analysis compares plasma biomarkers of tau and neurodegeneration during major cardiac and noncardiac surgeries.