Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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.
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
6 authors.
Kaori FujishiroDivision of Surveillance, Hazard Evaluations, and Field Studies, National Institute for Occupational Safety and Health, Cincinnati, Ohio, United States of America.ORCID 0000-0003-1743-625X
Belinda L NeedhamDepartment of Epidemiology and Center for Social Epidemiology and Population Health, University of Michigan, Ann Arbor, Michigan, United States of America.
Paul A LandsbergisDepartment of Environmental and Occupational Health Sciences, State University of New York Downstate Medical Center, Brooklyn, New York, United States of America.
Teresa SeemanDepartment of Medicine, University of California Los Angeles, Los Angeles, California, United States of America.
Nancy Swords JennyDepartment of Pathology, University of Vermont, Burlington, Vermont, United States of America.
Ana V Diez RouxDepartment of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania, United States of America.
Funding
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
USC/UCLA Center on Biodemography &Population HealthP30AG017265 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI JENNIFER A AILSHIRE, HIRAM BELTRAN-SANCHEZ · 1999 to 2026
$17.9M
UCLA OLDER AMERICANS INDEPENDENCE CENTERP30AG028748 · NIA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI MOORE, ALISON AHERN · 2006 to 2017
$9.8M
Stress, Gene-Environment Interaction and Cardiovascular DiseaseR01HL101161 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DIEZ ROUX, ANA VICTORIA, NEEDHAM, BELINDA L. · 2010 to 2013
$4.1M
Socioeconimic Patterning of Inflammation and HemostasisR01HL076831 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DIEZ-ROUX, ANA V. · 2004 to 2007
$2.5M
SUBCLINICAL CARDIOVASCULAR DISEASE STUDYN01HC095166 · HC · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI TRACY, RUSSELL P · 1999 to 2001
$758k
SUBCLINICAL CARDIOVASCULAR DISEASE STUDY-FIELD CENTERN01HC095162 · HC · JOHNS HOPKINS UNIVERSITY · PI SZKLO, MOYSES A · 1999 to 2000
$694k
SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTERN01HC095161 · HC · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SHEA, STEVEN · 1999 to 2001
$642k
SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTERN01HC095165 · HC · WAKE FOREST UNIVERSITY · PI BURKE, GREGORY L · 1999 to 2001
$616k
SUBCLINICAL CARDIOVASCULAR DISEASE STUDY--FIELD CENTERN01HC095160 · HC · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SAAD, MOHAMMED F · 1999 to 2001
Telomere length (TL) is considered as a marker of cell senescence, but factors influencing the rate of TL attrition are not well understood. While one previous study reported the association of occupation and TL, many subsequent studies have failed to find the association. This may be due to heterogeneity within the samples and cross-sectional designs. This longitudinal study examines two occupational characteristics, occupational complexity and hazardous conditions, as predictors of TL attrition in gender- and race/ethnicity-stratified analysis. Leukocyte TL (expressed as T/S ratio) was measured twice over a 10-year period in a multi-racial sample (n = 914). Linear mixed effect models were used to estimate TL attrition associated with occupational complexity and hazardous conditions. Analysis was stratified by gender and race/ethnicity (white, African American, and Latino) and controlled for baseline age, baseline TL, and time since baseline. Higher occupational complexity was associated with slower rates of TL attrition only among white men. Hazardous conditions were not associated with TL attrition for any gender-and-race/ethnicity stratified group. Occupational complexity may influence TL attrition, but the different findings for white men and other groups suggest that a more comprehensive framework is needed to better understand the potential link between occupational characteristics and biological aging.
Indexed as
OccupationsAgedAged, 80 and overAgingAtherosclerosisBlack or African AmericanEthnicityFemaleHispanic or LatinoHumansLeukocytesLongitudinal StudiesMaleMiddle AgedSex FactorsTelomere Shortening
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.
Selected occupational characteristics and change in leukocyte telomere length over 10 years: The Multi-Ethnic Study of Atherosclerosis (MESA). · full record | OpenQuestion