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Set points, settling points and some alternative models: theoretical options to understand how genes and environments combine to regulate body adiposity

Speakman, John R., Levitsky, David A., Allison, David B., Bray, Molly S., de Castro, John M., Clegg, Deborah J., Clapham, John C., Dulloo, Abdul G., Gruer, Laurence, Haw, Sally, Hebebrand, Johannes, Hetherington, Marion M., Higgs, Susanne, Jebb, Susan A., Loos, Ruth J. F., Luckman, Simon, Luke, Amy, Mohammed-Ali, Vidya, O’Rahilly, Stephen, Pereira, Mark, Perusse, Louis, Robinson, Tom N., Rolls, Barbara, Symonds, Michael E., Westerterp-Plantenga, Margriet S.·2011·Disease Models & Mechanisms

Abstract

The close correspondence between energy intake and expenditure over prolonged time periods, coupled with an apparent protection of the level of body adiposity in the face of perturbations of energy balance, has led to the idea that body fatness is regulated via mechanisms that control intake and energy expenditure. Two models have dominated the discussion of how this regulation might take place. The set point model is rooted in physiology, genetics and molecular biology, and suggests that there is an active feedback mechanism linking adipose tissue (stored energy) to intake and expenditure via a set point, presumably encoded in the brain. This model is consistent with many of the biological aspects of energy balance, but struggles to explain the many significant environmental and social influences on obesity, food intake and physical activity. More importantly, the set point model does not effectively explain the ‘obesity epidemic’ – the large increase in body weight and adiposity of a large proportion of individuals in many countries since the 1980s. An alternative model, called the settling point model, is based on the idea that there is passive feedback between the size of the body stores and aspects of expenditure. This model accommodates many of the social and environmental characteristics of energy balance, but struggles to explain some of the biological and genetic aspects. The shortcomings of these two models reflect their failure to address the gene-by-environment interactions that dominate the regulation of body weight. We discuss two additional models – the general intake model and the dual intervention point model – that address this issue and might offer better ways to understand how body fatness is controlled.

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  • There are two set points with a settling between them in body weight regulation.

    “and (ii) there are two set points with a settling between them.”

    From Changes in energy expenditure with weight gain and weight loss in humans (2016) · cites this paper as [77]

    The dual intervention point model section describes upper and lower intervention points with passive settling-point regulation between them. It does not call those boundaries two set points: it distinguishes the model from a set-point system because there is no defined target. (LLM, conf=0.86)

    Experimental — accuracy not yet measured
  • A subsequent "settling point" theory evolved based on the possibility of "settling" into a new body weight, on the basis of changing lifestyle and habits.

    “While this might help explain the high rate of weight regain among dieters, a subsequent “settling point” theory evolved based on the possibility of “settling” into a new body weight, on the basis of changing lifestyle and habits [219].”
  • Adaptive thermogenesis is part of body weight regulation.

    “the data give rise to the idea that (i) AT is part of body weight regulation”

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Metadata

Authors
Speakman, J. R., Levitsky, D. A., Allison, D. B., Bray, M. S., de Castro, J. M., Clegg, D. J., Clapham, J. C., Dulloo, A. G., Gruer, L., Haw, S., Hebebrand, J., Hetherington, M. M., Higgs, S., Jebb, S. A., Loos, R. J. F., Luckman, S., Luke, A., Mohammed-Ali, V., O’Rahilly, S., Pereira, M., Perusse, L., Robinson, T. N., Rolls, B., Symonds, M. E., & Westerterp-Plantenga, M. S.
Affiliations
  • Institute of Biological and Environmental Sciences (IBES), University of Aberdeen, Aberdeen, Scotland, AB39 2PN, UK
  • Division of Nutritional Sciences and the Department of Psychology, Cornell University, Ithaca, NY 14850, USA
  • Office of Energetics, Ryals Public Health Building, Rm 140J, School of Public Health, University of Alabama at Birmingham, 1665 University Boulevard, Birmingham, AL 35294, USA
  • Heflin Center for Genomic Sciences Illumina Core Lab, University of Alabama at Birmingham, 1530 3rd Avenue S, RPHB 230H, Birmingham, AL 35294, USA
  • College of Humanities and Social Sciences, Sam Houston State University, Box 2509, Huntsville, TX 77341-2509, USA
  • Touchstone Diabetes Research Center, Department of Internal Medicine, 5323 Harry Hines Blvd, Dallas, TX 75390-8854, USA
  • Research and Development (CVGI), AstraZeneca, Alderley Park, Cheshire, SK10 4TG, UK
  • Department of Medicine/Physiology, University of Fribourg, Fribourg CH 1700, Switzerland
  • NHS Health Scotland, 65 West Regent St, Glasgow, G2 2AF, UK
  • Centre for Public Health and Population Health Research, School of Nursing, Midwifery and Health, University of Stirling, Scotland, FK9 4LA, UK
  • Department of Child and Adolescent Psychiatry, University Duisburg-Essen, Virchowstrasse 174, 45147, Essen, Germany
  • Institute of Psychological Sciences, University of Leeds, Leeds, LS2 9JT, UK
  • School of Psychology, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
  • MRC Human Nutrition Research, Elsie Widdowson Laboratory, Fulbourn Road, Cambridge, CB1 9NL, UK
  • MRC Epidemiology Unit, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 0QQ, UK
  • Faculty of Life Sciences, University of Manchester, Manchester, M13 9PT, UK
  • Department of Preventive Medicine and Epidemiology, Loyola University Chicago, 2160 South First Avenue, Maywood, IL 60153, USA
  • University College London Division of Medicine, Rayne Building, 5 University Street, London, WC1E 6JF, UK
  • University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke’s Hospital, Hills Road, Cambridge, CB2 OQQ, UK
  • Division of Epidemiology and Community Health, University of Minnesota, 1300 South Second Street, Suite 300, Minneapolis, MN 55454, USA
  • Division of Kinesiology, Department of Preventive Medicine, Laval University, Sainte-Foy, Quebec, G1K 7P4, Canada
  • Lucile Packard Children’s Hospital, Stanford University School of Medicine, 725 Welch Road, Palo Alto, CA 94304, USA
  • Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA 16802, USA
  • Early Life Nutrition Research Unit, Academic Division of Child Health, School of Clinical Sciences, University Hospital, Nottingham, NG7 2UH, UK
  • Department of Human Biology, Nutrim, FHML, MUMC, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands
Venue
Disease Models & Mechanisms
Published
2011-11-01
Volume
4
Issue
6
Pages
733-745
ISSN
  • 1754-8411
  • 1754-8403
Cited by
290
References
19
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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