Metabolically Healthy Overweight and Obesity
Bibliographic record
Abstract
Letters1 April 2014Metabolically Healthy Overweight and ObesityDorit Samocha-Bonet, PhD, Antony D. Karelis, PhD, and Rémi Rabasa-Lhoret, MD, PhDDorit Samocha-Bonet, PhDFrom Garvan Institute of Medical Research and Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia; Université du Québec à Montréal, Montreal, Québec, Canada; and Institut de Recherches Cliniques de Montréal, Montreal, Québec, Canada.Search for more papers by this author, Antony D. Karelis, PhDFrom Garvan Institute of Medical Research and Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia; Université du Québec à Montréal, Montreal, Québec, Canada; and Institut de Recherches Cliniques de Montréal, Montreal, Québec, Canada.Search for more papers by this author, and Rémi Rabasa-Lhoret, MD, PhDFrom Garvan Institute of Medical Research and Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia; Université du Québec à Montréal, Montreal, Québec, Canada; and Institut de Recherches Cliniques de Montréal, Montreal, Québec, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/L14-5007-2 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITORKramer and colleagues’ (1) data suggest that metabolically healthy obese persons have increased cardiovascular and all-cause mortality risk compared with their normal-weight counterparts. However, the relative risk (RR) reported for the metabolically healthy obese group is approximately one half that reported for the metabolically unhealthy obese group (1.24 vs. 2.65, respectively). This finding suggests that metabolically healthy obese persons are relatively protected from some adverse outcomes of obesity.Cross-sectional studies suggest that metabolic health in obesity is associated with “healthy” adipose tissue that is capable of storing fat away from insulin-sensitive tissues, primarily the liver (2, 3). ...References1. Kramer CK, Zinman B, Retnakaran R. Are metabolically healthy overweight and obesity benign conditions?: A systematic review and meta-analysis. Ann Intern Med. 2013;159:758-69. [PMID: 24297192] LinkGoogle Scholar2. Samocha-Bonet D, Chisholm DJ, Tonks K, Campbell LV, Greenfield JR. Insulin-sensitive obesity in humans—a ‘favorable fat' phenotype? Trends Endocrinol Metab. 2012;23:116-24. [PMID: 22284531] CrossrefMedlineGoogle Scholar3. Primeau V, Coderre L, Karelis AD, Brochu M, Lavoie ME, Messier V, et al. Characterizing the profile of obese patients who are metabolically healthy. Int J Obes (Lond). 2011;35:971-81. [PMID: 20975726] CrossrefMedlineGoogle Scholar4. Messier V, Karelis AD, Prud'homme D, Primeau V, Brochu M, Rabasa-Lhoret R. Identifying metabolically healthy but obese individuals in sedentary postmenopausal women. Obesity (Silver Spring). 2010;18:911-7. [PMID: 19851302] CrossrefMedlineGoogle Scholar5. Hinnouho GM, Czernichow S, Dugravot A, Batty GD, Kivimaki M, Singh-Manoux A. Metabolically healthy obesity and risk of mortality: does the definition of metabolic health matter? Diabetes Care. 2013;36:2294-300. [PMID: 23637352] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From Garvan Institute of Medical Research and Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia; Université du Québec à Montréal, Montreal, Québec, Canada; and Institut de Recherches Cliniques de Montréal, Montreal, Québec, Canada.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=L14-0023. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoAre Metabolically Healthy Overweight and Obesity Benign Conditions? Caroline K. Kramer , Bernard Zinman , and Ravi Retnakaran Metabolically Healthy Overweight and Obesity Jean-Philippe Chaput and Arya M. Sharma Metabolically Healthy Overweight and Obesity Nathalie Esser , André J. Scheen , and Nicolas Paquot Metabolically Healthy Overweight and Obesity Gerson T. Lesser Metabolically Healthy Overweight and Obesity Juhee Cho , Yoosoo Chang , and Seungho Ryu Metabolically Healthy Overweight and Obesity Katherine M. Flegal Metabolically Healthy Overweight and Obesity Caroline K. Kramer , Bernard Zinman , and Ravi Retnakaran Metabolically Healthy Overweight and Obesity Nathalie Esser , André J. Scheen , and Nicolas Paquot Metabolically Healthy Overweight and Obesity Caroline K. Kramer , Bernard Zinman , and Ravi Retnakaran Metabolically Healthy Overweight and Obesity Gerson T. Lesser Metabolically Healthy Overweight and Obesity Katherine M. Flegal Metabolically Healthy Overweight and Obesity Juhee Cho , Yoosoo Chang , and Seungho Ryu Metabolically Healthy Overweight and Obesity Jean-Philippe Chaput and Arya M. Sharma Metrics Cited byObesity, Metabolic Abnormality, and Progression of CKD 1 April 2014Volume 160, Issue 7Page: 513-514KeywordsBody mass indexLongitudinal studiesMetabolic syndromeObesityOverweightPhenotypesRisk managementType 2 diabetes ePublished: 1 April 2014 Issue Published: 1 April 2014 Copyright & PermissionsCopyright © 2014 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.086 | 0.016 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".