The research team, led by Feixiong Cheng of the Cleveland Clinic Genome Center together with Yuan Hou, Yichen Li and Pengyue Zhang as co-first authors, started from a well-established observation: genetically supported drug targets roughly double the probability of success in drug development. Alzheimer’s disease is highly heritable, with heritability estimates of about 90 percent for early-onset disease and 58 to 79 percent for late-onset disease. Yet the enormous volumes of genetic and genomic data generated by projects such as the Alzheimer’s Disease Sequencing Project and the Alzheimer’s Disease Neuroimaging Initiative have not been fully exploited to guide therapeutic development. The team set out to change that by screening 1,229 druggable proteins, defined as proteins targeted by FDA-approved drugs or investigational molecules with binding affinity below one micromolar.

The core statistical engine of the study was Mendelian randomization, a technique that uses naturally occurring genetic variants as instrumental variables to test whether altered levels of a protein or gene product causally influence disease risk, rather than merely correlating with it. The researchers drew instrumental variables from three protein quantitative trait locus datasets and nine expression quantitative trait locus datasets spanning five brain regions, including resources from the Religious Orders Study and Memory and Aging Project, the MetaBrain consortium and the Mayo Clinic Biobank. These were tested against seven Alzheimer’s genome-wide association study datasets comprising 275,540 cases and 1.55 million controls of European and African ancestry. Five complementary Mendelian randomization models were applied to strengthen rigor, and the team distilled the results into a consensus metric they call the alzMR score, which aggregates evidence across datasets and methods. Simulations and a Cauchy combination test confirmed that the score reliably ranks genes by the strength of their causal evidence, even when participant cohorts partially overlap.

The analysis yielded 19 high-confidence drug targets for individuals of European ancestry and seven suggestive targets for African ancestry. Targets were classified as antagonistic, where inhibition could be beneficial, or agonistic, where activation could help, based on the direction of the genetic signal. Among the strongest antagonistic targets were EPHX2, EGFR, PRKCB, FOLH1 and ADAM10, while ACE and eukaryotic translation initiation factor 4E emerged as leading agonistic targets. Colocalization and fine-mapping analyses, which test whether the same genetic variant drives both protein levels and disease risk, lent further support to targets including ACE, ADAM10, CTSH and EGFR. Encouragingly, the method independently recovered targets already under clinical investigation, such as BACE2 and APH1B in the amyloid pathway and CHRNE and GABBR1 in neuromodulation, suggesting the pipeline captures biologically meaningful signals. Notably, many of the prioritized targets act on inflammation, metabolism and vascular pathways, extending well beyond the classical amyloid and tau biology that has dominated the field.

The standout target was EPHX2, which encodes soluble epoxide hydrolase, an enzyme that breaks down protective lipid metabolites called epoxy fatty acids and promotes neuroinflammation and synapse damage. Elevated EPHX2 protein levels in the brain were strongly associated with increased Alzheimer’s risk across multiple datasets. Crucially, the team identified a protective missense variant, p.Arg287Gln, carried at the rs751141 locus, which reduces EPHX2 protein levels and protects against the disease. Functional work in induced pluripotent stem cell-derived neurons and cerebral organoids from patients with Alzheimer’s showed that this variant lowers levels of phosphorylated tau, a hallmark pathology. Using prime editing to introduce the protective mutation directly into patient cells, the researchers confirmed that it reduces EPHX2 protein and activates neuroprotective, neurogenesis and anti-inflammatory gene programs, including the PI3K-Akt pathway that restrains GSK-3β, a major tau kinase.

To test whether pharmacological inhibition could deliver the same benefit, the team treated 5xFAD mice, a transgenic model of Alzheimer’s disease, with EC5026, a picomolar-potency EPHX2 inhibitor already in phase I trials. Over eight months of treatment, the drug significantly reduced amyloid-beta plaque burden and microglia-associated inflammation, enhanced lysosomal function and preserved synapse-related pathways in excitatory neurons and oligodendrocytes, as revealed by single-nucleus RNA sequencing. Most strikingly, treated mice performed better on tests of learning and memory, including novel object recognition, contextual fear conditioning and the Morris water maze. Pharmacokinetic analysis confirmed that EC5026 reached therapeutic concentrations in the cortex with roughly 15 percent brain penetration, and mass spectrometry verified on-target enzyme inhibition. A phenome-wide scan of 624 disease phenotypes in the UK Biobank found no adverse associations with the EPHX2 genetic instruments, offering early reassurance about safety.

The third pillar of the study brought the findings into the clinic through drugome-wide association studies, a pharmacoepidemiologic approach that asks whether people taking drugs against genetically prioritized targets show altered disease incidence. Drawing on electronic health data from 111,680 patients with mild cognitive impairment, the team evaluated 210 frequently prescribed drugs using four complementary exposure models with propensity score matching and adjustment for age, sex, race and comorbidities. Twelve drugs were consistently associated with reduced Alzheimer’s incidence, including trazodone, an ADRA1A antagonist linked to a 14 percent risk reduction in a dose-dependent manner and a 22 percent reduction against the active comparator escitalopram in an independent claims database, as well as baclofen, a GABBR1 agonist, and amlodipine, a CACNA1D inhibitor associated with a 12 percent risk reduction. Half of these drugs acted on targets with significant alzMR scores, providing converging genetic and real-world evidence.

The study also made a deliberate effort to address a long-standing gap in Alzheimer’s research: the underrepresentation of people of African ancestry, who face different disease prevalence, risk factors and clinical courses yet have been largely excluded from genetic studies and trials. Using an African ancestry GWAS dataset of 2,784 cases and 5,222 controls, the team identified seven suggestive targets, including DHFR, SLC7A11, TRPV3, ABL1 and SETD7 as antagonistic targets and ALDH2 and PPARG as agonistic targets, with more than half tied to metabolic pathways. DHFR expression rose with Braak stage and cognitive decline, and methotrexate, an approved DHFR inhibitor, was associated with reduced Alzheimer’s risk in African ancestry patients with mild cognitive impairment in a dose-dependent fashion, but not in European ancestry patients. The authors argue that such ancestry-specific findings are scientifically and medically essential for populations underserved by existing genetic datasets, and they plan to extend the approach using the All of Us Research Program, which includes 45 percent racial and ethnic minorities.

The authors are candid about limitations. Overlapping participants across European ancestry GWAS cohorts can inflate statistical evidence, a problem they mitigated with the Cauchy combination test, and diagnoses in real-world claims data are rarely confirmed by PET imaging or cerebrospinal fluid biomarkers. Individual variation, including APOE genotype and the protective EPHX2 variant itself, will likely modulate drug response and should inform patient stratification, much as APOE ε4 homozygosity shapes the risk of amyloid-related imaging abnormalities with lecanemab. Even so, the study delivers something the field has lacked: a compendium of drug targets backed simultaneously by human genetics, experimental validation and population-scale clinical data, with EPHX2 standing out as a genetically indicated, experimentally confirmed anti-inflammatory target whose inhibitors already have established safety profiles. If the framework scales as promised, the long, costly road from Alzheimer’s genetic discovery to effective medicine may finally be getting a reliable map.

Subject of Research: Identification and validation of druggable Alzheimer's disease targets using Mendelian randomization, multiomics data and electronic health records

Article Title: Alzheimer’s disease target and drug discovery by leveraging multiomics and electronic health data

Article References: Hou, Y., Li, Y., Zhang, P., Lorincz-Comi, N., Gohel, D., Fan, F., Qiu, Y., Yang, J., Chen, X., Song, W., Yang, X., Tan, Z., Liu, Z., Fang, X., Rivera Paz, I., Martin, W., Feng, Y., Zhou, Y., Xu, J., … Cheng, F. (2026). Alzheimer’s disease target and drug discovery by leveraging multiomics and electronic health data. Nature Neuroscience. https://doi.org/10.1038/s41593-026-02472-0

Image Credits: AI Generated

DOI: 10.1038/s41593-026-02472-0

Keywords: Alzheimer's disease, Mendelian randomization, drug repurposing, EPHX2, multiomics, electronic health records, GWAS, neuroinflammation, tau pathology, precision medicine, drug targets, 5xFAD mice