In one paragraphArticle in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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, 5 citations in OpenAlex.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
17 authors at 7 institutions in 3 countries.
Katlyn E McGrawColumbia University Mailman School of Public Health, Department of Environmental Health Science, 722 W 168th St, New York, NY 10032.ORCID 0000-0003-1143-3920 Kathrin SchillingColumbia University Mailman School of Public Health, Department of Environmental Health Science, 722 W 168th St, New York, NY 10032.
Ronald A GlabonjatColumbia University Mailman School of Public Health, Department of Environmental Health Science, 722 W 168th St, New York, NY 10032.
Marta Galvez-FernandezColumbia University Mailman School of Public Health, Department of Environmental Health Science, 722 W 168th St, New York, NY 10032.ORCID 0000-0003-1996-7425 Arce Domingo-RellosoColumbia University Mailman School of Public Health, Department of Biostatistics, 722 W 168th St, New York, NY 10032.ORCID 0000-0001-6928-8290 Irene Martinez-MorataColumbia University Mailman School of Public Health, Department of Environmental Health Science, 722 W 168th St, New York, NY 10032.ORCID 0000-0003-3165-4931 Miranda R JonesJohns Hopkins University School of Medicine, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Baltimore MD 21057.ORCID 0000-0003-4863-104X Wendy S PostJohns Hopkins University School of Medicine, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Baltimore MD 21057.ORCID 0000-0002-8655-5204 Maria Tellez-PlazaNational Center for Epidemiology, Instituto de Salud Carlos III, Madrid, Spain, Department of Chronic Diseases Epidemiology.ORCID 0000-0002-3850-1228 Linda ValeriColumbia University Mailman School of Public Health, Department of Biostatistics, 722 W 168th St, New York, NY 10032.
Graham R BarrColumbia University Irving Medical Center, Departments of Medicine and Epidemiology.
Ana Navas-AcienColumbia University Mailman School of Public Health, Department of Environmental Health Science, 722 W 168th St, New York, NY 10032.ORCID 0000-0001-9824-7797 Tiffany R SanchezColumbia University Mailman School of Public Health, Department of Environmental Health Science, 722 W 168th St, New York, NY 10032.
Columbia University · USColumbia University Irving Medical Center · USJohns Hopkins University · USNew York University · USUniversity of Washington · USCape Town HVTN Immunology Laboratory / Hutchinson Centre Research Institute of South Africa · ZAInstituto de Salud Carlos III · ES
Funding
Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1MTraining CoreP42ES010349 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI NAVAS-ACIEN, ANA · 2000 to 2020
$53.6MTrue Metal fac coreP30ES009089 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Steven N. Chillrud · 1998 to 2026
$44.7MWake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3MClinical and Translational Science AwardUL1TR000040 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI GINSBERG, HENRY N · 2012 to 2015
$26.2MSuperfund Training CoreP42ES023716 · NIEHS · UNIVERSITY OF LOUISVILLE · PI HEIN, DAVID W · 2017 to 2025
$18.1MTask 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
$17.2MSupplemental Training in Making Data FAIR and AI/ML ReadyT32ES007322 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Pam R Factor-Litvak, Allison Kupsco · 2000 to 2026
$12.1MResearch Experience and Training Coordination CoreP42ES033719 · NIEHS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Ana Navas-Acien · 2022 to 2026
$11.9MTask Area A shall encompass annual follow-up of cohort members, clinical events investigations, study operations, and data analysis and manuscript writing. If implemented, Task A.1 will provide fundin75N92020D00005 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI WATSON, KAROL E · 2020 to 2025
$5.1MTO EXERCISE OPTION PERIOD ONE (1) FOR TASK AREA A - MESA CORE OPERATIONS, FIELD CENTER.75N92020D00004 · NHLBI · NORTHWESTERN UNIVERSITY · PI SIEGEL, JONATHAN H · 2020 to 2025
$4.5MTask Area A shall encompass annual follow-up of cohort members, clinical events investigations, study operations, and data analysis and manuscript writing. If implemented, Task A.1 will provide fundin75N92020D00006 · NHLBI · UNIVERSITY OF MINNESOTA · PI PANKOW, JAMES S · 2020 to 2025
$4.4MNCATS NIH HHS UL1 TR000040NCATS NIH HHS UL1 TR001079NCATS NIH HHS UL1 TR001420NHLBI NIH HHS 75N92020D00001NHLBI NIH HHS 75N92020D00002NHLBI NIH HHS 75N92020D00003NHLBI NIH HHS 75N92020D00004NHLBI NIH HHS 75N92020D00005NHLBI NIH HHS 75N92020D00006NHLBI NIH HHS 75N92020D00007NHLBI NIH HHS HHSN268201500003CNHLBI NIH HHS HHSN268201500003INHLBI NIH HHS N01 HC095159NHLBI NIH HHS N01 HC095160NHLBI NIH HHS N01 HC095161NHLBI NIH HHS N01 HC095162NHLBI NIH HHS N01 HC095163NHLBI NIH HHS N01 HC095164NHLBI NIH HHS N01 HC095165NHLBI NIH HHS N01 HC095166NHLBI NIH HHS N01 HC095167NHLBI NIH HHS N01 HC095168NHLBI NIH HHS N01 HC095169NHLBI NIH HHS R01 HL155576NIEHS NIH HHS P30 ES009089NIEHS NIH HHS P42 ES010349NIEHS NIH HHS P42 ES023716NIEHS NIH HHS P42 ES033719NIEHS NIH HHS R01 ES029967NIEHS NIH HHS T32 ES007322
6 · The paper itselfAbstract
Objective: Growing evidence indicates that exposure to metals are risk factors for cardiovascular disease (CVD). We hypothesized that higher urinary levels of metals with prior evidence of an association with CVD, including non-essential (cadmium , tungsten, and uranium) and essential (cobalt, copper, and zinc) metals are associated with baseline and rate of change of coronary artery calcium (CAC) progression, a subclinical marker of atherosclerotic CVD. Methods: We analyzed data from 6,418 participants in the Multi-Ethnic Study of Atherosclerosis (MESA) with spot urinary metal levels at baseline (2000-2002) and 1-4 repeated measures of spatially weighted coronary calcium score (SWCS) over a ten-year period. SWCS is a unitless measure of CAC highly correlated to the Agatston score but with numerical values assigned to individuals with Agatston score=0. We used linear mixed effect models to assess the association of baseline urinary metal levels with baseline SWCS, annual change in SWCS, and SWCS over ten years of follow-up. Urinary metals (adjusted to μg/g creatinine) and SWCS were log transformed. Models were progressively adjusted for baseline sociodemographic factors, estimated glomerular filtration rate, lifestyle factors, and clinical factors. Results: At baseline, the median and interquartile range (25 Conclusion: Higher levels of cadmium, tungsten, uranium, cobalt, copper, and zinc, as measured in urine, were associated with subclinical CVD at baseline and at follow-up. These findings support the hypothesis that metals are pro-atherogenic factors.
Indexed as
cadmiumcardiovascular diseasecobaltcoppercoronary artery calcificationlongitudinalMetalsmixed modelsprospectivetungstenuraniumzinc
Identifiers
PMID37961623
PMCPMC10635251
OpenAlexW4388124423
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