Article in Obesity (Silver Spring, Md.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.1field-weighted citation impact, top 52% 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
1 citing paper in PubMed, 1 synthesis or guideline pooled it, 2 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 1 institution in 1 country.
Yunhua L MullerPhoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, USA.ORCID 0000-0001-5007-2107
Robert L HansonPhoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, USA.
Darin MahkeePhoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, USA.
Paolo PiaggiPhoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, USA.ORCID 0000-0003-2774-9161
Sayuko KobesPhoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, USA.
Wen-Chi HsuehPhoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, USA.
William C KnowlerPhoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, USA.
Clifton BogardusPhoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, USA.ORCID 0000-0003-1472-3376
Leslie J BaierPhoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona, USA.
National Institutes of Health · US
Funding
Genetic Epidemiology of Diabetes and ObesityZIADK069028 · NIDDK · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES · PI HANSON, ROBERT · 2009 to 2025
$7.5M
Epidemiology of Type 2 Diabetes Mellitus in the Gila River Indian CommunityZIADK069000 · NIDDK · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES · PI KNOWLER, WILLIAM C · 2009 to 2021
$3.4M
Structural Analysis Of Genes For Type NIDDM &ObesityZ01DK069071 · NIDDK · NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES · PI BAIER, LESLIE J · 1997 to 2008
$976k
Intramural NIH HHS Z01 DK069071NIDDK NIH HHS the intramural research programme of the National
6 · The paper itself
Abstract
objectiveInsulinlike growth factor II (IGF-II) regulates metabolism and growth. In humans, both positive and negative relationships have been reported between serum IGF-II levels and obesity. This study assessed the relationship between serum IGF-II levels and BMI and determined whether IGF-II levels predict weight gain.
methodsSerum samples were available from 911 American Indians with a recorded BMI. IGF-II was measured using enzyme-linked immunosorbent assay.
resultsSerum IGF-II levels were negatively correlated with BMI (r = -0.17, P = 4.4 × 10
conclusionsThere is a negative relationship between IGF-II levels and BMI, in which the correlation is stronger at older ages. The association between genetic risk for BMI and IGF-II levels suggests that this correlation may be due to an effect of obesity on IGF-II.
Indexed as
Body Mass IndexAdultAgedCross-Sectional StudiesFemaleGenotypeHumansIndians, North AmericanInsulin-Like Growth Factor ILongitudinal StudiesMaleMiddle AgedYoung AdultInsulin-Like Growth Factor I
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.