Evidence map›Paper›PMID 28096530›Full record

ArticleInternational journal of obesity (2005)2017

Associations between body mass index-related genetic variants and adult body composition: The Fenland cohort study.

E A D Clifton, F R Day, E De Lucia Rolfe, N G Forouhi, S Brage, S J Griffin, N J Wareham, K K Ong

Abstract read
In one paragraph

Article in International journal of obesity (2005), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
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

8 authors.

E A D CliftonMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
F R DayMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
E De Lucia RolfeMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
N G ForouhiMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
S BrageMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
S J GriffinMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
N J WarehamMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.
K K OngMRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge, UK.

Funding

Medical Research Council MC_EX_MR/L100002/1Medical Research Council MC_PC_13046Medical Research Council MC_U106179471Medical Research Council MC_UU_12015/1Medical Research Council MC_UU_12015/2
6 · The paper itself

Abstract

BACKGROUND/

objectiveBody mass index (BMI) is a surrogate measure of adiposity but does not distinguish fat from lean or bone mass. The genetic determinants of BMI are thought to predominantly influence adiposity but this has not been confirmed. Here we characterise the association between BMI-related genetic variants and body composition in adults. SUBJECTS/

methodsAmong 9667 adults aged 29-64 years from the Fenland study, a genetic risk score for BMI (BMI-GRS) was calculated for each individual as the weighted sum of BMI-increasing alleles across 96 reported BMI-related variants. Associations between the BMI-GRS and body composition, estimated by dual-energy X-ray absorptiometry (DXA) scans, were examined using age-adjusted linear regression models, separately by sex.

resultsThe BMI-GRS was positively associated with all fat, lean and bone variables. Across body regions, associations of the greatest magnitude were observed for adiposity variables, for example, for each s.d. increase in BMI-GRS predicted BMI, we observed a 0.90 s.d. (95% confidence interval (CI): 0.71, 1.09) increase in total fat mass for men (P=3.75 × 10

conclusionsIn adults, genetic susceptibility to elevated BMI influences adiposity more than lean or bone mass. This mirrors the association between BMI and body composition. The BMI-GRS can be used to model the effects of measured BMI and adiposity on health and other outcomes.

Indexed as

Body Mass IndexAbsorptiometry, PhotonAdultBody CompositionBone DensityCohort StudiesEnglandFemaleGenetic Predisposition to DiseaseGenetic VariationHumansLinear ModelsMaleMiddle AgedObesityPolymorphism, Single Nucleotide

Identifiers

PMID28096530
PMCPMC5382973

What OpenQuestion holds

Textmetadata
LicenceTDM
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 OpenQuestion graph.