{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T19:56:31Z","timestamp":1780775791225,"version":"3.54.1"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>An increasing body of evidence suggests that neuroinflammation is one of the key drivers of late-onset Alzheimer\u2019s disease (LOAD) pathology. Due to the increased permeability of the blood\u2013brain barrier (BBB) in older adults, peripheral plasma proteins can infiltrate the central nervous system (CNS) and drive neuroinflammation through interactions with neurons and glial cells. Because these inflammatory factors are heritable, a greater understanding of their genetic relationship with LOAD could identify new biomarkers that contribute to LOAD pathology or offer protection against it.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used a genome-wide association study (GWAS) of 90 different plasma proteins (<jats:italic>n<\/jats:italic>\u2009=\u200917,747) to create polygenic scores (PGSs) in an independent discovery (cases\u2009=\u20091,852 and controls\u2009=\u20091,990) and replication (cases\u2009=\u2009799 and controls\u2009=\u2009778) cohort. Multivariate logistic regression was used to associate the plasma protein PGSs with LOAD diagnosis while controlling for age, sex, principal components 1\u20132, and the number of <jats:italic>APOE<\/jats:italic>-e4 alleles as covariates. After meta-analyzing the PGS-LOAD associations between the two cohorts, we then performed a two-sample Mendelian randomization (MR) analysis using the summary statistics of significant plasma protein level PGSs in the meta-analysis as an exposure, and a GWAS of clinically diagnosed LOAD (cases\u2009=\u200921,982, controls\u2009=\u200941,944) as an outcome to explore possible causal relationships between the two.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We identified four plasma protein level PGSs that were significantly associated (FDR-adjusted <jats:italic>p<\/jats:italic>\u2009&amp;lt;\u20090.05) with LOAD in a meta-analysis of the discovery and replication cohorts: CX3CL1, hepatocyte growth factor (HGF), TIE2, and matrix metalloproteinase-3 (MMP-3). When these four plasma proteins were used as exposures in MR with LOAD liability as the outcome, plasma levels of HGF were inferred to have a negative causal relationship with the disease when single-nucleotide polymorphisms (SNPs) used as instrumental variables were not restricted to cis-variants (OR\/95%CI\u2009=\u20090.945\/0.906\u20130.984, <jats:italic>p<\/jats:italic>\u2009=\u20090.005).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our results show that plasma HGF has a negative causal relationship with LOAD liability that is driven by pleiotropic SNPs possibly involved in other pathways. These findings suggest a low transferability between PGS and MR approaches, and future research should explore ways in which LOAD and the plasma proteome may interact.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnins.2024.1404377","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T05:24:59Z","timestamp":1721712299000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Polygenic scores and Mendelian randomization identify plasma proteins causally implicated in Alzheimer\u2019s disease"],"prefix":"10.3389","volume":"18","author":[{"given":"Davis B.","family":"Cammann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yimei","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jerome I.","family":"Rotter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexis C.","family":"Wood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingchun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e95192","DOI":"10.1371\/journal.pone.0095192","article-title":"Homogenous 96-Plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability","volume":"9","author":"Assarsson","year":"2014","journal-title":"PLoS One"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"7571","DOI":"10.1038\/nature15393","article-title":"A global reference for human genetic variation","volume":"526","author":"Auton","year":"2015","journal-title":"Nature"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"e14","DOI":"10.3389\/fdgth.2020.00014","article-title":"Polygenic risk scores in Alzheimer\u2019s disease: current applications and future directions","volume":"2","author":"Baker","year":"2020","journal-title":"Front. 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