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Record W2887620501 · doi:10.1093/ajcn/nqy107

Body mass index is negatively associated with telomere length: a collaborative cross-sectional meta-analysis of 87 observational studies

2018· review· en· W2887620501 on OpenAlexaff
Marij Gielen, Geja J. Hageman, Evangelia E. Antoniou, Katarina Nordfjäll, Massimo Mangino, Muthuswamy Balasubramanyam, Tim De Meyer, Audrey E. Hendricks, Erik J. Giltay, Steven C. Hunt, Jennifer A. Nettleton, Klelia D. Salpea, Vanessa A. Díaz, Ramin Farzaneh‐Far, Gil Atzmon, Sarah E. Harris, Lifang Hou, David Gilley, Iiris Hovatta, Jeremy D. Kark, Hisham Nassar, David J. Kurz, Karen A. Mather, Peter Willeit, Yun‐Ling Zheng, Sofia Pavanello, Ellen W. Demerath, Line Rode, Daniel Bunout, Andrew Steptoe, Amelia Martí, Belinda L. Needham, Wei Zheng, Rosalind Ramsey‐Goldman, Andrew J. Pellatt, Jaakko Kaprio, Christian Gieger, Giuseppe Paolisso, Jacob Hjelmborg, Teresa E. Seeman, Jason Wong, Pim van der Harst, Linda Broer, Florian Kronenberg, Barbara Kollerits, Timo Strandberg, Dan T. A. Eisenberg, Catherine Duggan, Josine E. Verhoeven, Roxanne Schaakxs, Raffaela Zannolli, Rosana M. R. dos Reis, Fadi J. Charchar, Maciej Tomaszewski, Ute Mons, Ilja Demuth, Andrea Elena Iglesias Molli, Guo Cheng, Dmytro Krasnienkov, Bianca D’Antono, Marek Kasielski, Barry J. McDonnell, Richard P. Ebstein, Kristina Sundquist, Guillaume Paré, Michael Chong, Maurice P. Zeegers

Bibliographic record

VenueAmerican Journal of Clinical Nutrition · 2018
Typereview
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsMcMaster UniversityPopulation Health Research InstituteMontreal Heart InstituteInstitute of Aging
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Research ResourcesNational Institute of Environmental Health SciencesNational Institute on Drug AbuseBiotechnology and Biological Sciences Research CouncilNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute on AgingBritish Heart FoundationNational Cancer InstituteNational Institutes of HealthNational Center for Advancing Translational SciencesWellcome Trust
KeywordsObservational studyTelomereBody mass indexCross-sectional studyMeta-analysisIndex (typography)MedicinePsychologyInternal medicineGeneticsStatisticsBiologyMathematicsComputer scienceDNAWorld Wide Web

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.006
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.015
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.445
GPT teacher head0.534
Teacher spread0.089 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations176
Published2018
Admission routes1
Has abstractno

Explore more

Same venueAmerican Journal of Clinical NutritionSame topicTelomeres, Telomerase, and SenescenceFrench-language works237,207