Article in Obesity (Silver Spring, Md.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 33% 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
3 citing papers in PubMed, 3 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
9 authors at 2 institutions in 2 countries.
Hao MaDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.
Xuan WangDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.
Zhaoxia LiangObstetrical Department, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Xiang LiDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.ORCID 0000-0002-6637-6428
Yoriko HeianzaDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.
Jiang HeDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.
Wei ChenDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.
Lydia BazzanoDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.ORCID 0000-0002-8958-1352
Lu QiDepartment of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA.ORCID 0000-0002-8041-7791
Tulane University · USWomen's Hospital, School of Medicine, Zhejiang University · CN
Funding
Research BaseP30DK072476 · NIDDK · LSU PENNINGTON BIOMEDICAL RESEARCH CTR · PI Sujoy Ghosh · 2005 to 2026
$26.5M
Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Ning Liu · 2016 to 2026
$25.3M
RISK FACTORS FOR CVD IN WOMENR01HL034594 · NHLBI · TULANE UNIVERSITY OF LOUISIANA · PI JoAnn Elisabeth Manson, Lu Qi · 1985 to 2026
$13.8M
Nutrigenetics and Nutrigenomics for Precision Weight-Loss Diet InterventionsR01DK115679 · NIDDK · TULANE UNIVERSITY OF LOUISIANA · PI Lu Qi · 2018 to 2026
$5.3M
Common Genetic Variation and Quantitative Diabetes TraitsR01DK078616 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI MEIGS, JAMES B · 2008 to 2014
$5.0M
Genetic Markers of CHD in Type 2 DiabetesR01HL071981 · NHLBI · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI QI, LU · 2003 to 2012
$4.9M
Obesity Genes, Energy Regulation in Response to Weight-Loss DietsR01DK091718 · NIDDK · TULANE UNIVERSITY OF LOUISIANA · PI QI, LU · 2012 to 2021
$4.5M
Weight-Loss Diet Intervention on Cardiometabolic Factors of Gut MicrobiotaR01DK100383 · NIDDK · TULANE UNIVERSITY OF LOUISIANA · PI QI, LU · 2014 to 2024
$4.2M
TOPMed Omics of Type 2 Diabetes and Quantitative TraitsUM1DK078616 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI MANNING, ALISA KNODLE · 2021 to 2025
$3.8M
Rare Sequence Variation and Diabetes Quantitative TraitsU01DK078616 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI MEIGS, JAMES B · 2015 to 2019
$3.6M
Genome-wide interactions with diet patterns on long-term weight changeR21HL126024 · NHLBI · TULANE UNIVERSITY OF LOUISIANA · PI QI, LU · 2015 to 2016
$421k
Dissecting the impact of forced displacement on Type Two DiabetesR21TW010790 · FIC · LEBANESE AMERICAN UNIVERSITY · PI QI, LU, ZALLOUA, PIERRE · 2018 to 2019
objectiveThis study investigated whether changes in DNA methylation (DNAm) at TXNIP are associated with glycemic changes and whether such an association differs with early-life adiposity changes.
methodsA total of 594 Bogalusa Heart Study participants who had blood DNAm measurements at two time points in midlife were included. Of them, 353 participants had at least four BMI measurements during childhood and adolescence. The incremental area under the curve was calculated as a measure of long-term trends of BMI during childhood and adolescence.
resultsIncrease in DNAm at TXNIP was significantly associated with decrease in fasting plasma glucose (FPG) independent of covariates (p < 0.001). The study found that the strength of this relationship was significantly modified by a trend of increasing BMI during childhood and adolescence (p-interaction = 0.003). Each 1% increase in DNAm at TXNIP was associated with a 2.90- (0.77) mg/dL decrease in FPG among participants with the highest tertile of BMI incremental area under the curve and a 0.96- (0.38) mg/dL decrease among those with the middle tertile, whereas no association was observed among participants with the lowest tertile.
conclusionsThese results indicate that changes in blood DNAm at TXNIP are significantly associated with changes in FPG in midlife, and this association was modified by BMI trends during childhood and adolescence.
Indexed as
AdiposityBirth WeightBody Mass IndexCarrier ProteinsEpigenesis, GeneticGlucoseBlood GlucoseChildDNA MethylationHumansBlood GlucoseCarrier ProteinsGlucoseTXNIP protein, human
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.
BMI change during childhood, DNA methylation change at TXNIP, and glucose change during midlife. · full record | OpenQuestion