Evidence map›Paper›PMID 35598895›Full record

ArticleObesity (Silver Spring, Md.)2022

A multivariant recall-by-genotype study of the metabolomic signature of BMI.

Si Fang, Kaitlin H Wade, David A Hughes, Sophie Fitzgibbon, Vikki Yip, Nicholas J Timpson, Laura J Corbin

Erratum issuedOpen access · hybridAbstract read
In one paragraph

Article in Obesity (Silver Spring, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.6field-weighted citation impact, top 19% 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, 17 citations in OpenAlex.

  1. Trial
  2. Article
  3. Leveraging Multiomic Signatures to Predict Body Composition.Advances in nutrition (Bethesda, Md.) · 2026
    Review
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Si FangMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.ORCID 0000-0003-4934-1212
Kaitlin H WadeMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.ORCID 0000-0003-3362-6280
David A HughesMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.
Sophie FitzgibbonBristol Bioresource Laboratories, Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.
Vikki YipBristol Bioresource Laboratories, Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK.
Nicholas J TimpsonMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.ORCID 0000-0002-7141-9189
Laura J CorbinMRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.ORCID 0000-0002-4032-9500
University of Bristol · GB

Funding

Cancer Research UK C18281/A29019Department of HealthMedical Research Council MC_UU_00011/1Medical Research Council MC_UU_12013/3Wellcome Trust 108902/Z/15/ZWellcome Trust 202802/Z/16/ZWellcome Trust 204813/Z/16/ZWellcome Trust 217065/Z/19/Z
6 · The paper itself

Abstract

objectiveThis study estimated the effect of BMI on circulating metabolites in young adults using a recall-by-genotype study design.

methodsA recall-by-genotype study was implemented in the Avon Longitudinal Study of Parents and Children. Samples from 756 participants were selected for untargeted metabolomics analysis based on low versus high genetic liability for higher BMI defined by a genetic risk score (GRS). Regression analyses were performed to investigate associations between BMI GRS group and relative abundance of 973 metabolites.

resultsAfter correction for multiple testing, 29 metabolites were associated with BMI GRS group. Bilirubin was among the most strongly associated metabolites, with reduced levels measured in individuals in the high-BMI GRS group (β = -0.32, 95% CI: -0.46 to -0.18, Benjamini-Hochberg adjusted p = 0.005). This study observed associations between BMI GRS group and the levels of several potentially diet-related metabolites, including hippurate, which had lower mean abundance in individuals in the high-BMI GRS group (β = -0.29, 95% CI: -0.44 to -0.15, Benjamini-Hochberg adjusted p = 0.008).

conclusionsTogether with existing literature, these results suggest that a genetic predisposition to higher BMI captures differences in metabolism leading to adiposity gain. In the absence of prospective data, separating these effects from the downstream consequences of weight gain is challenging.

Indexed as

Genetic Predisposition to DiseasePolymorphism, Single NucleotideBody Mass IndexChildGenotypeHumansLongitudinal StudiesMetabolomicsProspective StudiesRisk FactorsYoung Adult

Identifiers

PMID35598895
PMCPMC9324973
OpenAlexW4281289230

What OpenQuestion 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 OpenQuestion graph.