Evidence map›Paper›PMID 25628644›Full record

ArticleFrontiers in genetics2014

A statistical framework for testing the causal effects of fetal drive.

Nianjun Liu, Edward Archer, Vinodh Srinivasasainagendra, David B Allison

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.7field-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

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.

  1. Pooled it
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  5. The Third Annual Symposium of the Midwest Aging Consortium.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024
    Article
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  7. Observational
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4 · The record

Corrections and comments

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

4 authors at 2 institutions in 1 country.

Nianjun LiuDepartment of Biostatistics, School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA.
Edward ArcherOffice of Energetics, School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA.
Vinodh SrinivasasainagendraDepartment of Biostatistics, School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA.
David B AllisonDepartment of Biostatistics, School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA ; Office of Energetics, School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA.
Energetics (United States) · USUniversity of Alabama at Birmingham · US

Funding

Why is the prevalence of obesity so high in U.S. Southern States? Regional predictors of BMI and obesity treatment response.P30DK056336 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI BARBARA A GOWER · 2000 to 2026
$31.9M
The molecular genetic analysis of human obesityR01DK052431 · NIDDK · ROCKEFELLER UNIVERSITY · PI CHUNG, WENDY K, LEIBEL, RUDOLPH L · 1996 to 2020
$11.6M
Predictors of hemorrhage among patients on direct acting oral anticoagulantsR01HL092173 · NHLBI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI LIMDI, NITA A · 2008 to 2023
$10.4M
UAB Obesity Training ProgramT32DK062710 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI James O Hill, Brooks C Wingo · 2004 to 2026
$5.5M
UAB Multidisciplinary Clinical Research CenterP60AR064172 · NIAMS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI BRIDGES, S LOUIS · 2013 to 2018
$4.5M
Genome Wide Haplotype Association AnalysisR01GM081488 · NIGMS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI LIU, NIANJUN · 2008 to 2012
$1.3M
The Mathematical Sciences in Obesity ResearchR25DK099080 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DAVID B ALLISON, Andrew W Brown · 2013 to 2026
$1.2M
Dell high performance computing cluster and associated Hitachi terabyte data storS10RR026723 · NCRR · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI ALLISON, DAVID B · 2010 to 2010
$500k
NCRR NIH HHS S10 RR026723NHLBI NIH HHS R01 HL092173NIAMS NIH HHS P60 AR064172NIDDK NIH HHS P30 DK056336NIDDK NIH HHS R01 DK052431NIDDK NIH HHS R25 DK099080NIDDK NIH HHS T32 DK062710NIGMS NIH HHS R01 GM081488
6 · The paper itself

Abstract

Maternal genetic and phenotypic characteristics (e.g., metabolic and behavioral) affect both the intrauterine milieu and lifelong health trajectories of their fetuses. Yet at the same time, fetal genotype may affect processes that alter pre and postnatal maternal physiology, and the subsequent health of both fetus and mother. We refer to these latter effects as 'fetal drive.' If fetal genotype is driving physiologic, metabolic, and behavioral phenotypic changes in the mother, there is a possibility of differential effects with different fetal genomes inducing different long-term effects on both maternal and fetal health, mediated through intrauterine environment. This proposed mechanistic path remains largely unexamined and untested. In this study, we offer a statistical method to rigorously test this hypothesis and make causal inferences in humans by relying on the (conditional) randomization inherent in the process of meiosis. For illustration, we apply this method to a dataset from the Framingham Heart Study.

Indexed as

causal inferencefetal effectsgeneticshumanintrauterine environmentstatistics

Identifiers

PMID25628644
PMCPMC4292723
OpenAlexW2013460541

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.