Evidence map›Paper›PMID 40036808›Full record

ArticleThe Journal of clinical endocrinology and metabolism2025

Effects of Rare Coding Variants in Severe Early-Onset Obesity Genes in the Population-Based UK Biobank Study.

Raina Y Jia, Sam Lockhart, Brian Y H Lam, Yajie Zhao, Katherine A Kentistou, Eugene J Gardner, I Sadaf Farooqi, Stephen O'Rahilly, Felix R Day, Ken K Ong and 1 more

Abstract read
In one paragraph

Article in The Journal of clinical endocrinology and metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
–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

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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.

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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

11 authors.

Raina Y JiaUniversity of Cambridge, Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge CB2 0SL, UK.ORCID 0000-0003-2234-8238
Sam LockhartUniversity of Cambridge, Medical Research Council Metabolic Diseases Unit, Institute of Metabolic Science-Metabolic Research Laboratories, Cambridge CB2 0QQ, UK.ORCID 0000-0003-2092-4350
Brian Y H LamUniversity of Cambridge, Medical Research Council Metabolic Diseases Unit, Institute of Metabolic Science-Metabolic Research Laboratories, Cambridge CB2 0QQ, UK.
Yajie ZhaoUniversity of Cambridge, Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge CB2 0SL, UK.ORCID 0000-0002-2747-0219
Katherine A KentistouUniversity of Cambridge, Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge CB2 0SL, UK.ORCID 0000-0002-5816-664X
Eugene J GardnerUniversity of Cambridge, Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge CB2 0SL, UK.
I Sadaf FarooqiUniversity of Cambridge, Medical Research Council Metabolic Diseases Unit, Institute of Metabolic Science-Metabolic Research Laboratories, Cambridge CB2 0QQ, UK.ORCID 0000-0001-7609-3504
Stephen O'RahillyUniversity of Cambridge, Medical Research Council Metabolic Diseases Unit, Institute of Metabolic Science-Metabolic Research Laboratories, Cambridge CB2 0QQ, UK.ORCID 0000-0003-2199-4449
Felix R DayUniversity of Cambridge, Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge CB2 0SL, UK.ORCID 0000-0003-3789-7651
Ken K OngUniversity of Cambridge, Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge CB2 0SL, UK.ORCID 0000-0003-4689-7530
John R B PerryUniversity of Cambridge, Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Cambridge CB2 0SL, UK.ORCID 0000-0001-6483-3771

Funding

Medical Research Council MC_UU_00006/2NIHR Cambridge Comprehensive Biomedical Research Centre
6 · The paper itself

Abstract

contextClinical case-based studies have identified rare pathogenic variants in several genes as causes of severe early-onset obesity, but their penetrance and interaction with polygenic susceptibility in the general population remain unclear.

objectiveWe analyzed the United Kingdom Biobank (UKBB) whole-exome sequence data to assess the effects of heterozygous variants in 9 previously reported genes on adult body mass index (BMI) and recalled childhood adiposity.

methodsAmong 419 581 UKBB participants, we identified heterozygous carriers of coding variants that were (1) experimentally characterized as loss of function (LoF), or (2) bioinformatically predicted as rare (minor allele frequency <0.1%) LoF. We assessed variant-level and gene-level population penetrance of obesity and associations with adult BMI and recalled childhood adiposity, and tested the statistical interaction between rare variant carriage and a BMI polygenic score.

resultsConsidering experimentally characterized LoF variants (excluding MC4R), we identified 22 heterozygous and 2 homozygous variants in 3 autosomal recessive genes (POMC, PCSK1, LEPR), and 3 autosomal dominant genes (SH2B1, SIM1, KSR2) with at least 10 carriers in the UKBB. Obesity penetrance among carriers ranged from 8% to 29% (median 23%), and none was significantly different from noncarriers (24%, all P > .05). For bioinformatically predicted rare LoF variants, gene-based burden tests showed that carriage of heterozygous variants in MC4R, PCSK1, and POMC was associated with higher adult BMI (effect sizes ranged from 0.5 to 2.5 kg/m2, all P < .003), with no significant interaction effects with common variant polygenic risk of BMI.

conclusionThis study provides the population-specific report of variant penetrance of known obesity genes and confirmed the heterozygous rare variant effects in MC4R, POMC, and PCSK1. We also underscore the utility of population-based studies in supporting variant classifications.

Indexed as

Obesity, MorbidPediatric ObesityAdultAge of OnsetBiological Specimen BanksBody Mass IndexChildExome SequencingFemaleGenetic Predisposition to DiseaseHeterozygoteHumansLoss of Function MutationMaleMiddle AgedMultifactorial InheritancePCSK1 protein, humanPro-OpiomelanocortinProprotein Convertase 1obesitypopulation geneticsvariant classificationswhole-exome sequencing

Identifiers

PMID40036808
PMCPMC12527455

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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.