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Record W4400484024 · doi:10.1212/wnl.0000000000209620

Association of Body Mass Index and Parkinson Disease

2024· article· en· W4400484024 on OpenAlexfundno aff
Cloé Domenighetti, Pierre‐Emmanuel Sugier, Ashwin Ashok Kumar Sreelatha, Claudia Schulte, Sandeep Grover, Berta Portugal, Pei‐Chen Lee, Patrick May, Dheeraj Reddy Bobbili, Milena Radivojkov Blagojevic, Peter Lichtner, Andrew Singleton, Dena Hernández, Connor Edsall, George D. Mellick, Alexander Zimprich, Walter Pirker, Ekaterina Rogaeva, Anthony E. Lang, Sulev Kõks, Pille Taba, Suzanne Lesage, Alexis Brice, Jean‐Christophe Corvol, Marie‐Christine Chartier‐Harlin, Eugénie Mutez, Kathrin Brockmann, Angela Deutschländer, Georgios M. Hadjigeorgiou, Efthimios Dardiotis, Leonidas Stefanis, Athina Maria Simitsi, Enza Maria Valente, Simona Petrucci, Letizia Straniero, Anna Zecchinelli, Gianni Pezzoli, Laura Brighina, Carlo Ferrarese, Grazia Annesi, Andrea Quattrone, Monica Gagliardi, Hirotaka Matsuo, Akiyoshi Nakayama, Nobutaka Hattori, Kenya Nishioka, Sun Ju Chung, Yun Joong Kim, Pierre Kolber, Bart P.C. van de Warrenburg, Bastiaan R. Bloem, Mathias Toft, Lasse Pihlstrøm, Leonor Correia Guedes, Joaquim J. Ferreira, Soraya Bardien, Jonathan Carr, Eduardo Tolosa, Mario Ezquerra, Pau Pástor, Mónica Díez-Fairén, Karin Wirdefeldt, Nancy L. Pedersen, Caroline Ran, Andrea Carmine Belin, Andreas Puschmann, Clara Hellberg, Carl E Clarke, Karen Morrison, Manuela Tan, Dimitri Krainc, Lena F. Burbulla, Matthew J. Farrer, Rejko Krüger, Thomas Gasser, Manu Sharma, Alexis Elbaz

Bibliographic record

VenueNeurology · 2024
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersFaculty of Medicine and Health, University of SydneyInstituto de Salud Carlos IIIMSD K.K.Pfizer JapanNovartis PharmaParkinsonfondenAllerganNational Institutes of HealthIpsenParkinson VerenigingSun PharmaFP Pharmaceutical CorporationMitsubishi Tanabe Pharma CorporationMedical Research CouncilServierSkånes universitetssjukhusAssociation France ParkinsonNorges ForskningsrådEuropean CommissionFondazione Pierfranco e Luisa MarianiMinistero della SaluteHelse Sør-Øst RHFSanofi K.K.Eesti TeadusagentuurDaiichi Sankyo EuropeKarolinska InstitutetZonMwBiogenAgence Nationale de la RechercheIdorsia PharmaceuticalsH. Lundbeck A/SUniversité Paris-SaclayDepartment of Science and Technology, Ministry of Science and Technology, IndiaFondation Roger de SpoelberchSouth African Medical Research CouncilHersenstichtingEli Lilly and CompanyRadboud UniversiteitNational Research FoundationCentro de Investigación Biomédica en Red sobre Enfermedades NeurodegenerativasMinisterio de Ciencia e InnovaciónUniversity College LondonVerily Life SciencesUniversiteit StellenboschParkinson's UKHellenic Foundation for Research and InnovationGeneral Secretariat for Research and TechnologyMultiple System Atrophy CoalitionBundesministerium für Bildung und ForschungNasjonalforeningen for FolkehelsenBoehringer Ingelheim JapanFondazione CariploKisseiAlexion PharmaceuticalsEVER Neuro PharmaNihon Medi-PhysicsPfizerUniversité du LuxembourgEU Joint Programme – Neurodegenerative Disease ResearchSunovionDeutsche ForschungsgemeinschaftJapan Agency for Medical Research and DevelopmentBoston Scientific CorporationMylanMultiple Sclerosis Society of Western AustraliaConsortium canadien en neurodégénérescence associée au vieillissementUCB PharmaMeiji Seika PharmaMichael J. Fox Foundation for Parkinson's ResearchTeva Pharmaceutical IndustriesU.S. Department of DefenseSanofiJapan Society for the Promotion of ScienceAstellas PharmaHorizon 2020 Framework ProgrammeVetenskapsrådetEisaiBoston Scientific JapanU.S. Department of Health and Human ServicesGlaxoSmithKlineOno PharmaceuticalTakeda Pharmaceutical Company
KeywordsMendelian randomizationParkinson's diseaseBody mass indexDiseaseMedicineInternal medicineAssociation (psychology)PsychologyOncologyBiologyGeneticsGenotypeGenetic variantsGene

Abstract

fetched live from OpenAlex

Background and Objectives The role of body mass index (BMI) in Parkinson disease (PD) is unclear. Based on the Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in PD (Courage-PD) consortium, we used 2-sample Mendelian randomization (MR) to replicate a previously reported inverse association of genetically predicted BMI with PD and investigated whether findings were robust in analyses addressing the potential for survival and incidence-prevalence biases. We also examined whether the BMI-PD relation is bidirectional by performing a reverse MR. Methods We used summary statistics from a genome-wide association study (GWAS) to extract the association of 501 single-nucleotide polymorphisms (SNPs) with BMI and from the Courage-PD and international Parkinson Disease Genomics Consortium (iPDGC) to estimate their association with PD. Analyses are based on participants of European ancestry. We used the inverse-weighted method to compute odds ratios (OR IVW per 4.8 kg/m 2 [95% CI]) of PD and additional pleiotropy robust methods. We performed analyses stratified by age, disease duration, and sex. For reverse MR, we used SNPs associated with PD from 2 iPDGC GWAS to assess the effect of genetic liability toward PD on BMI. Results Summary statistics for BMI are based on 806,834 participants (54% women). Summary statistics for PD are based on 8,919 (40% women) cases and 7,600 (55% women) controls from Courage-PD, and 19,438 (38% women) cases and 24,388 (51% women) controls from iPDGC. In Courage-PD, we found an inverse association between genetically predicted BMI and PD (OR IVW 0.82 [0.70–0.97], p = 0.012) without evidence for pleiotropy. This association tended to be stronger in younger participants (≤67 years, OR IVW 0.71 [0.55–0.92]) and cases with shorter disease duration (≤7 years, OR IVW 0.75 [0.62–0.91]). In pooled Courage-PD + iPDGC analyses, the association was stronger in women (OR IVW 0.85 [0.74–0.99], p = 0.032) than men (OR IVW 0.92 [0.80–1.04], p = 0.18), but the interaction was not statistically significant ( p -interaction = 0.48). In reverse MR, there was evidence for pleiotropy, but pleiotropy robust methods showed a significant inverse association. Discussion Using an independent data set (Courage-PD), we replicate an inverse association of genetically predicted BMI with PD, not explained by survival or incidence-prevalence biases. Moreover, reverse MR analyses support an inverse association between genetic liability toward PD and BMI, in favor of a bidirectional relation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations12
Published2024
Admission routes1
Has abstractyes

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