More than 57 million people globally suffer from neurodegenerative diseases, a figure expected to double every 20 years. Despite this growing burden, there are currently no cures, and treatment options remain limited due to disease heterogeneity, prolonged preclinical and prodromal phases, poor understanding of disease mechanisms, and diagnostic challenges. Identifying novel biomarkers is crucial for improving early detection, prognosis, staging and subtyping of these conditions. High-dimensional molecular studies in biofluids (‘omics’) offer promise for scalable biomarker discovery, but challenges in assembling large, diverse datasets hinder progress. To address this, the Global Neurodegeneration Proteomics Consortium (GNPC)—a public–private partnership—established one of the world’s largest harmonized proteomic datasets. It includes approximately 250 million unique protein measurements from multiple platforms from more than 35,000 biofluid samples (plasma, serum and cerebrospinal fluid) contributed by 23 partners, alongside associated clinical data spanning Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). This dataset is accessible to GNPC members via the Alzheimer’s Disease Data Initiative’s AD Workbench, a secure cloud-based environment, and will be available to the wider research community on 15 July 2025. Here we present summary analyses of the plasma proteome revealing disease-specific differential protein abundance and transdiagnostic proteomic signatures of clinical severity. Furthermore, we describe a robust plasma proteomic signature of APOE ε4 carriership, reproducible across AD, PD, FTD and ALS, as well as distinct patterns of organ aging across these conditions. This work demonstrates the power of international collaboration, data sharing and open science to accelerate discovery in neurodegeneration research.

The APOE ε4 genetic variant is the strongest genetic risk factor for late-onset Alzheimer’s disease (AD) and is increasingly being implicated in other neurodegenerative diseases. Using the Global Neurodegeneration Proteomics Consortium SomaScan dataset covering 1,346 cerebrospinal fluid (CSF) and 9,924 plasma samples, we used machine learning-based proteome profiling to identify an APOE ε4 proteomic signature shared across individuals with AD, frontotemporal dementia (FTD), Parkinson’s disease dementia (PDD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS) and nonimpaired controls. This signature was enriched in pro-inflammatory immune and infection pathways as well as immune cells, including monocytes, T cells and natural killer cells. Analysis of the dorsolateral prefrontal cortex proteome for 262 donors from the Accelerating Medicines Partnership for AD UPenn Proteomics Study revealed a consistent APOE ε4 phenotype, independent of neurodegenerative pathology, including amyloid-β tau and gliosis for all diseases, as well as TDP-43 in ALS and FTD cases, and α-synuclein in PD and PDD cases. While systemic proteomic changes were consistent across APOE ε4 carriers, their relationship with clinical and lifestyle factors, such as hypertension and smoking, varied by disease. These findings suggest APOE ε4 confers a systemic biological vulnerability that is necessary but not sufficient for neurodegeneration, emphasizing the need to consider gene–environment interactions. Overall, our study reveals a conserved APOE ε4-associated pro-inflammatory immune signature persistent across the brain, CSF and plasma irrespective of neurodegenerative disease, highlighting a fundamental, disease-independent biological vulnerability to neurodegeneration. This work reframes APOE ε4 as a pleiotropic immune modulator rather than an AD-specific risk gene, providing a foundation for precision biomarker development and early intervention strategies across neurodegenerative diseases.

Spinal cord injury (SCI) occurs when the spinal cord is damaged from either a traumatic event or disease. SCI is characterised by multiple injury phases that affect the transmission of sensory and motor signals and lead to temporary or long-term functional deficits. There are few treatments for SCI. Estrogens and estrogenic compounds, however, may effectively mitigate the effects of SCI and therefore represent viable treatment options. This review systematically examines the pre-clinical literature on estrogen and estrogenic compound neuroprotection after SCI. Several estrogens were examined by the included studies: estrogen, estradiol benzoate, Premarin, isopsoralen, genistein, and selective estrogen receptor modulators. Across these pharmacotherapies, we find significant evidence that estrogens indeed offer protection against myriad pathophysiological effects of SCI and lead to improvements in functional outcomes, including locomotion. A STRING functional network analysis of proteins modulated by estrogen after SCI demonstrated that estrogen simultaneously upregulates known neuroprotective pathways, such as HIF-1, and downregulates pro-inflammatory pathways, including IL-17. These findings highlight the strong therapeutic potential of estrogen and estrogenic compounds after SCI.

Alzheimer’s disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

Proteome changes associated with APOE4 variant carriage that are independent of Alzheimer's disease (AD) pathology and diagnosis are unknown. This study investigated APOE4 proteome changes in people with AD, mild cognitive impairment, and no impairment. Clinical, APOE genotype, and cerebrospinal fluid (CSF) proteome and AD biomarker data was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Proteome profiling was done using supervised machine learning. We found an APOE4‐specific proteome signature that was independent of cognitive diagnosis and AD pathological biomarkers, and increased the risk of progression to cognitive impairment. Proteins were enriched in brain regions including the caudate and cortex and cells including endothelial cells, oligodendrocytes, and astrocytes. Enriched peripheral immune cells included T cells, macrophages, and B cells. APOE4 carriers have a unique CSF proteome signature associated with a strong brain and peripheral immune and inflammatory phenotype that likely underlies APOE4 carriers' vulnerability to cognitive decline and AD as they age.