Article in Annals of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.1field-weighted citation impact, top 12% 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
6 citing papers in PubMed, 16 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
7 authors at 3 institutions in 1 country.
Luis A RodriguezUniversity of California, San Francisco, Department of Epidemiology & Biostatistics, San Francisco, CA; Kaiser Permanente Northern California, Division of Research, Oakland, CA. Electronic address: Luis.Rodriguez@ucsf.edu.
Alka M KanayaUniversity of California, San Francisco, Department of Epidemiology & Biostatistics, San Francisco, CA; University of California, San Francisco, Division of General Internal Medicine, San Francisco, CA.
Stephen C ShiboskiUniversity of California, San Francisco, Department of Epidemiology & Biostatistics, San Francisco, CA.
Alicia FernandezUniversity of California, San Francisco, Department of Medicine, San Francisco, CA.
David HerringtonWake Forest School of Medicine, Department of Internal Medicine, Winston-Salem, NC.
Jingzhong DingWake Forest School of Medicine, Sticht Center on Aging, Winston-Salem, NC.
Patrick T BradshawUniversity of California, Berkeley, School of Public Health, Division of Epidemiology & Biostatistics, Berkeley, CA.
University of California, San Francisco · USWake Forest University · USUniversity of California, Berkeley · US
Funding
Institute for Clinical and Translational ResearchUL1TR001079 · NCATS · JOHNS HOPKINS UNIVERSITY · PI FORD, DANIEL ERNEST · 2013 to 2017
$60.1M
Translational Research Support CoreP30ES000002 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI JAIME ELIZABETH HART · 1985 to 2026
$44.6M
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
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
UCSF Nutrition Obesity Research CenterP30DK098722 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHRISTIAN VAISSE · 2015 to 2026
$14.6M
Translational Research Core - Health Engagement & Action Translational (HEAT)P30DK092924 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI Alyce Sophia Adams, HILARY Kessler SELIGMAN · 2011 to 2026
$9.2M
Task 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
$5.1M
TO 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
$4.5M
Task 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
$4.4M
Task 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 fundin75N92020D00003 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI POST, WENDY S · 2020 to 2025
$3.8M
Task 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 fundin75N92020D00007 · NHLBI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI BERTONI, ALAIN GERALD · 2020 to 2025
purposeTo estimate the effect of obesity on type 2 diabetes (T2DM) risk and evaluate to what extent non-alcoholic fatty liver disease (NAFLD) mediates this association.
methodsData came from 4,522 adults ages 45-84 participating in the Multi-Ethnic Study of Atherosclerosis cohort. Baseline obesity was defined using established BMI categories. NAFLD was measured by CT scans at baseline and incident T2DM defined as fasting glucose ≥126 mg/dL or use of diabetes medications.
resultsOver a median 9.1 years of follow-up between 2000 and 2012, 557 new cases of T2DM occurred. After adjusting for age, sex, race/ethnicity, education, diet and exercise, those with obesity had 4.5 times the risk of T2DM compared to normal weight (hazard ratio [HR] = 4.5, 95% confidence interval [CI]: 3.0, 5.9). The mediation analysis suggested that NAFLD accounted for ~36% (95% CI: 27, 44) of the effect (direct effect HR = 3.2, 95% CI: 2.3, 4.6; indirect effect through NAFLD, HR = 1.4, 95% CI: 1.3, 1.5).
conclusionsThese data suggest that the association between obesity and T2DM risk is partially explained by the presence of NAFLD. Future studies should evaluate if NAFLD could be an effective target to reduce the effect of obesity on T2DM.
Indexed as
AtherosclerosisDiabetes Mellitus, Type 2Non-alcoholic Fatty Liver DiseaseAdultAgedAged, 80 and overEthnicityHumansMiddle AgedObesityRisk FactorsCausal mediation analysisDiabetes mellitusMarginal Structural ModelNAFLDObesity
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 Socratic graph.
Does NAFLD mediate the relationship between obesity and type 2 diabetes risk? evidence from the multi-ethnic study of atherosclerosis (MESA). · full record | Socratic