Article in PLoS genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 34% of its field
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
16 authors at 7 institutions in 3 countries.
Manish D ParanjpeProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.ORCID 0000-0002-6463-8965
Mark ChaffinProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.ORCID 0000-0002-1234-5562
Sohail ZahidProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.
Scott RitchieCambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0002-8454-9548
Jerome I RotterThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-University of California, Los Angeles Medical Center, Torrance, California, United States of America.ORCID 0000-0001-7191-1723
Stephen S RichCenter for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America.ORCID 0000-0003-3872-7793
Robert GersztenProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.
Xiuqing GuoThe Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-University of California, Los Angeles Medical Center, Torrance, California, United States of America.ORCID 0000-0002-5264-5068
Susan HeckbertDepartment of Epidemiology, University of Washington, Seattle, Washington, United States of America.ORCID 0000-0002-7100-512X
Russ TracyDepartment of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, Vermont, United States of America.
John DaneshBritish Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
Eric S LanderProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.
Michael InouyeCambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0001-9413-6520
Sekar KathiresanProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.ORCID 0000-0002-3711-7101
Adam S ButterworthBritish Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
Amit V KheraProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America.
Broad Institute · USBaker Heart and Diabetes Institute · AUUniversity of California, Los Angeles · USUniversity of Cambridge · GBUniversity of Vermont · USUniversity of Virginia · USUniversity of Washington · US
Funding
Large Scale Sequencing and Analysis of GenomesU54HG003067 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI GABRIEL, STACEY, LANDER, ERIC S · 2004 to 2015
$568.6M
Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Clinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2M
SUBCLINICAL CARDIOVASCULAR DISEASE COORDINATING CENTERN01HC95159 · NHLBI · UNIVERSITY OF WASHINGTON · PI KRONMAL, RICHARD A · 2007 to 2014
$19.6M
Task Area A Core Study Operations.Task Area A shall encompass annual follow-up of cohort members, clinical endpoints ascertainment, study coordination activities, maintenance of the database and biosp75N92020D00001 · NHLBI · UNIVERSITY OF WASHINGTON · PI MCCLELLAND, ROBYN LEAGH · 2020 to 2025
$17.2M
Studies of Rare Genetic Variation in the Isolated Population of SardiniaR01HL117626 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABECASIS, GONCALO · 2013 to 2016
$10.5M
SNP HEALTH ASSOCIATION RESOURCE (SHARE)N02HL64278 · NHLBI · PI CHIN, SIANG · 2007 to 2007
$9.2M
Rare variants and NHLBI traits in deeply phenotyped cohortsR01HL120393 · NHLBI · UNIVERSITY OF WASHINGTON · PI PSATY, BRUCE M, RICE, KENNETH M. · 2014 to 2016
$8.9M
SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTERN01HC95160 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI WATSON, KAROL · 2007 to 2015
$7.7M
SUBCLINICAL CARDIOVASCULAR DISEASE STUDY-FIELD CENTERN01HC95164 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI LIU, KIANG · 2007 to 2015
$7.0M
Proteomics of Cardiometabolic and Renal Traits in African AmericansR01HL133870 · NHLBI · UNIVERSITY OF MISSISSIPPI MED CTR · PI ROBERT E GERSZTEN · 2017 to 2026
For Alzheimer's disease-a leading cause of dementia and global morbidity-improved identification of presymptomatic high-risk individuals and identification of new circulating biomarkers are key public health needs. Here, we tested the hypothesis that a polygenic predictor of risk for Alzheimer's disease would identify a subset of the population with increased risk of clinically diagnosed dementia, subclinical neurocognitive dysfunction, and a differing circulating proteomic profile. Using summary association statistics from a recent genome-wide association study, we first developed a polygenic predictor of Alzheimer's disease comprised of 7.1 million common DNA variants. We noted a 7.3-fold (95% CI 4.8 to 11.0; p < 0.001) gradient in risk across deciles of the score among 288,289 middle-aged participants of the UK Biobank study. In cross-sectional analyses stratified by age, minimal differences in risk of Alzheimer's disease and performance on a digit recall test were present according to polygenic score decile at age 50 years, but significant gradients emerged by age 65. Similarly, among 30,541 participants of the Mass General Brigham Biobank, we again noted no significant differences in Alzheimer's disease diagnosis at younger ages across deciles of the score, but for those over 65 years we noted an odds ratio of 2.0 (95% CI 1.3 to 3.2; p = 0.002) in the top versus bottom decile of the polygenic score. To understand the proteomic signature of inherited risk, we performed aptamer-based profiling in 636 blood donors (mean age 43 years) with very high or low polygenic scores. In addition to the well-known apolipoprotein E biomarker, this analysis identified 27 additional proteins, several of which have known roles related to disease pathogenesis. Differences in protein concentrations were consistent even among the youngest subset of blood donors (mean age 33 years). Of these 28 proteins, 7 of the 8 proteins with concentrations available were similarly associated with the polygenic score in participants of the Multi-Ethnic Study of Atherosclerosis. These data highlight the potential for a DNA-based score to identify high-risk individuals during the prolonged presymptomatic phase of Alzheimer's disease and to enable biomarker discovery based on profiling of young individuals in the extremes of the score distribution.
Indexed as
Alzheimer DiseaseAdultAgedBiomarkersCross-Sectional StudiesGenome-Wide Association StudyHumansMiddle AgedProteomicsBiomarkers
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Neurocognitive trajectory and proteomic signature of inherited risk for Alzheimer's disease. · full record | OpenQuestion