In one paragraphArticle in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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0citing papers in PubMed
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1 · What the graph read from itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
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4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
21 authors.
Bharadwaj MarellaInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
Patrick WeinischInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.ORCID 0000-0001-8430-2150 Josef J BlessInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
Shannon L RisacherDepartment of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana, USA.ORCID 0000-0002-3304-7943 Naama KaruTasmanian Independent Metabolomics and Analytical Chemistry Solutions (TIMACS), Hobart, Tasmania, Australia.ORCID 0000-0001-8005-0726 Karel KaleckýCenter of Metabolomics, Institute of Metabolic Disease, Baylor Scott and White Research Institute, Dallas, TX, USA.ORCID 0000-0002-4212-6811 Tingting WangBaker Heart and Diabetes Institute, Melbourne, Australia.
Kevin HuynhBaker Heart and Diabetes Institute, Melbourne, Australia.
Alexandra Kueider-PaisleyDepartment of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.ORCID 0000-0001-7132-5003 J Will ThompsonMove Analytical, Carrboro, NC, USA.
Alzheimer’s Disease Neuroimaging Initiative
Alzheimer’s Disease Metabolomics Consortium
Teodoro BottiglieriCenter of Metabolomics, Institute of Metabolic Disease, Baylor Scott and White Research Institute, Dallas, TX, USA.
Kwangsik NhoDepartment of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana, USA.
P Murali DoraiswamyDepartment of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.ORCID 0000-0003-0697-3893 Andrew J SaykinDepartment of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana, USA.ORCID 0000-0002-1376-8532 Gabi KastenmüllerInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.ORCID 0000-0002-2368-7322 Rima Kaddurah-DaoukDepartment of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.ORCID 0000-0003-1858-5732 Matthias ArnoldInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.ORCID 0000-0002-4666-0923 Funding
Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0MProject 4 - Mechanistic studies on the role of the gut microbiome in models for Alzheimer's diseaseU19AG063744 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Rima F Kaddurah-Daouk · 2019 to 2026
$54.1MMetabolomic Signatures for Disease Sub-classification and Target Prioritization in AMP-ADU01AG061359 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, KASTENMULLER, GABI · 2018 to 2022
$10.0MThe Role of Chemical Exposures in Alzheimer's Disease (AD) and its TrajectoryU01AG088562 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Oliver Fiehn, LEE E. GOLDSTEIN · 2024 to 2026
$7.2MMetabolic Networks and Pathways Predictive of Sex Differences in AD Risk and Responsiveness to TreatmentRF1AG059093 · NIA · DUKE UNIVERSITY · PI BRINTON, ROBERTA EILEEN, CHANG, RUI · 2018 to 2018
$5.9MMetabolic Networks and Pathways in Alzheimer's DiseaseR01AG046171 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F · 2014 to 2017
$4.4MTargetAD: A systems multi-omics approach to drug repositioning in Alzheimer's diseaseR01AG069901 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Matthias Arnold, Jan Krumsiek · 2021 to 2026
$3.7MMetabolic Network Analysis of Biochemical Trajectories in Alzheimer's DiseaseRF1AG057452 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, KASTENMULLER, GABI · 2017 to 2017
$3.5MGut Liver Brain Biochemical Axis in Alzheimer's DiseaseRF1AG058942 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, VAN DUIJN, CORNELIA MARJA · 2018 to 2018
$3.4MMetabolic age to define influences of the lipidome on brain aging in Alzheimer's diseaseR01AG081322 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Matthias Arnold, Rima F Kaddurah-Daouk · 2023 to 2026
$2.6MPrecision Brain Health Monitoring for Alzheimer's Disease Risk Detection in the Framingham StudyR01AG072654 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Rhoda Au, LEE E. GOLDSTEIN · 2025 to 2026
$1.1MNIA NIH HHS R01 AG046171NIA NIH HHS R01 AG069901NIA NIH HHS R01 AG072654NIA NIH HHS R01 AG081322NIA NIH HHS RF1 AG057452NIA NIH HHS RF1 AG058942NIA NIH HHS RF1 AG059093NIA NIH HHS U01 AG024904NIA NIH HHS U01 AG061359NIA NIH HHS U01 AG088562NIA NIH HHS U19 AG063744
6 · The paper itselfAbstract
Metabolic dysregulation is a hallmark of Alzheimer's disease (AD), yet the temporal nature of metabolite-phenotype associations remains poorly understood. We systematically evaluated 506 serum metabolites across 4,063 longitudinal samples from 1,430 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), applying cross-sectional single-timepoint analyses, multi-timepoint meta-analysis, and time-interaction analysis. Across 15 AD-related phenotypes, we identified 311 metabolites to be significantly associated with disease. Of those, 281 emerged from the multi-timepoint meta-analysis, 243 (216 overlapping/27 additional) from cross-sectional analyses, and 19 (16 overlapping/3 additional) metabolites that showed a significant evolution of their association with AD over time. In total, 128 metabolites (41%) showed persistent associations over time, providing evidence for chronic and systemic metabolic dysregulation in the disease. This, together with the comparably small number of metabolites showing evolving changes, suggests that many metabolic alterations in AD do not change substantially anymore once they manifested. Our findings confirm impaired fatty acid and energy metabolism, disrupted neurotransmitter systems, and oxidative stress as key metabolic features of AD. We demonstrate broad replication of the reported metabolite associations in prior studies and an independent lipidomics dataset in ADNI. In summary, this work expands previous metabolomics studies in AD and provides novel leads regarding timing and persistence of metabolic alterations across the disease trajectory.
Identifiers
PMID41409665
PMCPMC12706616
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