MétaCan
Menu
Back to cohort
Record W4210516353 · doi:10.7554/elife.72452

Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation

2021· article· en· W4210516353 on OpenAlexafffund
Susan F. Martin, Jessica Tyrrell, E. Louise Thomas, Matthew J. Bown, Andrew R. Wood, Robin N. Beaumont, Lam C. Tsoi, Philip E. Stuart, James T. Elder, Philip Law, Richard S. Houlston, Christopher Kabrhel, Nikos Papadimitriou, Marc J. Gunter, Caroline J. Bull, Joshua A. Bell, Emma E. Vincent, Naveed Sattar, Malcolm G. Dunlop, Ian Tomlinson, Sara Lindström, Jimmy D. Bell, Timothy M. Frayling, Hanieh Yaghootkar

Bibliographic record

VenueeLife · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsInstitute of Cancer Research
FundersOffice of Research Infrastructure Programs, National Institutes of HealthNational Center for Advancing Translational SciencesNational Institute of Environmental Health SciencesNational Institute on AgingMedicinska fakulteten, Umeå UniversitetInstituto de Salud Carlos IIINational Human Genome Research InstituteCancer Council VictoriaOntario Ministry of Research and InnovationNational Health and Medical Research CouncilMedical Research CouncilNational Institutes of HealthCenters for Disease Control and PreventionChonnam National University Hwasun HospitalNational Institute of Arthritis and Musculoskeletal and Skin DiseasesHellenic Health FoundationJunta de Castilla y LeónWereld Kanker Onderzoek FondsXunta de GaliciaInnovative Medicines InitiativeMutuelle Générale de l'Education NationaleDeutsche KrebshilfeAssociazione Italiana per la Ricerca sul CancroNordForskVetenskapsrådetStockholms Läns LandstingBritish Heart FoundationBundesministerium für Bildung und ForschungMinisterio de Economía y CompetitividadNational Cancer InstituteFundación Científica Asociación Española Contra el CáncerCanadian Cancer Society Research InstituteInstitut National de la Santé et de la Recherche MédicaleNational Institute of Diabetes and Digestive and Kidney DiseasesWorld Cancer Research FundKnut och Alice Wallenbergs StiftelseDamon Runyon Cancer Research FoundationUniversity of BristolChonnam National UniversityCentres de Recerca de CatalunyaUmeå UniversitetMinisterstvo Zdravotnictví Ceské RepublikyEngineering and Physical Sciences Research CouncilGroupement des Entreprises Françaises dans la lutte contre le CancerCentre Hospitalier Universitaire de NantesEuropean CommissionUniversity of ExeterAcademy of Medical SciencesCancerfondenCanadian Institutes of Health ResearchDiabetes UKGeneralitat de CatalunyaFood Standards AgencyXarxa de Bancs de Tumors de CatalunyaWorld Health OrganizationWageningen University and ResearchCancer Research UKUniversity of CambridgeMoffitt Cancer CenterPelotoniaConseil Régional des Pays de la LoireGénome QuébecInstitut Gustave-RoussyGrantová Agentura České RepublikyJohns Hopkins UniversityCentre International de Recherche sur le CancerWorld Cancer Research Fund InternationalMemorial Sloan-Kettering Cancer CenterDivision of Cancer Prevention, National Cancer InstituteAgència de Gestió d'Ajuts Universitaris i de RecercaNational Institute for Health and Care ResearchAssociation Anne de Bretagne GenetiqueNational Heart, Lung, and Blood InstituteFlorida Department of HealthMcGill UniversityFred Hutchinson Cancer Research CenterCancer Research Foundation in Northern SwedenLigue Contre le CancerDeutsches KrebsforschungszentrumUniversity of South FloridaU.S. Department of Health and Human Services
KeywordsDiseaseMendelian inheritanceMendelian randomizationAdverse effectObesityMedicineBiologyPhysiologyBioinformaticsInternal medicineGeneticsGeneGenetic variantsGenotype

Abstract

fetched live from OpenAlex

Background: Some individuals living with obesity may be relatively metabolically healthy, whilst others suffer from multiple conditions that may be linked to adverse metabolic effects or other factors. The extent to which the adverse metabolic component of obesity contributes to disease compared to the non-metabolic components is often uncertain. We aimed to use Mendelian randomisation (MR) and specific genetic variants to separately test the causal roles of higher adiposity with and without its adverse metabolic effects on diseases. Methods: We selected 37 chronic diseases associated with obesity and genetic variants associated with different aspects of excess weight. These genetic variants included those associated with metabolically 'favourable adiposity' (FA) and 'unfavourable adiposity' (UFA) that are both associated with higher adiposity but with opposite effects on metabolic risk. We used these variants and two sample MR to test the effects on the chronic diseases. Results: MR identified two sets of diseases. First, 11 conditions where the metabolic effect of higher adiposity is the likely primary cause of the disease. Here, MR with the FA and UFA genetics showed opposing effects on risk of disease: coronary artery disease, peripheral artery disease, hypertension, stroke, type 2 diabetes, polycystic ovary syndrome, heart failure, atrial fibrillation, chronic kidney disease, renal cancer, and gout. Second, 9 conditions where the non-metabolic effects of excess weight (e.g. mechanical effect) are likely a cause. Here, MR with the FA genetics, despite leading to lower metabolic risk, and MR with the UFA genetics, both indicated higher disease risk: osteoarthritis, rheumatoid arthritis, osteoporosis, gastro-oesophageal reflux disease, gallstones, adult-onset asthma, psoriasis, deep vein thrombosis, and venous thromboembolism. Conclusions: Our results assist in understanding the consequences of higher adiposity uncoupled from its adverse metabolic effects, including the risks to individuals with high body mass index who may be relatively metabolically healthy. Funding: Diabetes UK, UK Medical Research Council, World Cancer Research Fund, National Cancer Institute.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.279
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations43
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueeLifeSame topicGenetic Associations and EpidemiologyFrench-language works237,207