» Articles » PMID: 25338273

Nutrigenomics of Body Weight Regulation: a Rationale for Careful Dissection of Individual Contributors

Overview
Journal Nutrients
Date 2014 Oct 23
PMID 25338273
Citations 4
Authors
Affiliations
Soon will be listed here.
Abstract

Body weight stability may imply active regulation towards a certain physiological condition, a body weight setpoint. This interpretation is ill at odds with the world-wide increase in overweight and obesity. Until now, a body weight setpoint has remained elusive and the setpoint theory did not provide practical clues for body weight reduction interventions. For this an alternative theoretical model is necessary, which is available as the settling point model. The settling point model postulates that there is little active regulation towards a predefined body weight, but that body weight settles based on the resultant of a number of contributors, represented by the individual's genetic predisposition, in interaction with environmental and socioeconomic factors, such as diet and lifestyle. This review refines the settling point model and argues that by taking body weight regulation from a settling point perspective, the road will be opened to careful dissection of the various contributors to establishment of body weight and its regulation. This is both necessary and useful. Nutrigenomic technologies may help to delineate contributors to body weight settling. Understanding how and to which extent the different contributors influence body weight will allow the design of weight loss and weight maintenance interventions, which hopefully are more successful than those that are currently available.

Citing Articles

FADS1 and FADS2 Gene Polymorphisms Modulate the Relationship of Omega-3 and Omega-6 Fatty Acid Plasma Concentrations in Gestational Weight Gain: A NISAMI Cohort Study.

Santana J, Pereira M, Carvalho G, Gouveia Peluzio M, Louro I, Dos Santos D Nutrients. 2022; 14(5).

PMID: 35268031 PMC: 8912382. DOI: 10.3390/nu14051056.


Recent advances in understanding body weight homeostasis in humans.

Muller M, Geisler C, Heymsfield S, Bosy-Westphal A F1000Res. 2018; 7.

PMID: 30026913 PMC: 6039924. DOI: 10.12688/f1000research.14151.1.


Maternal and postnatal high-fat diet consumption programs energy balance and hypothalamic melanocortin signaling in nonhuman primate offspring.

Sullivan E, Rivera H, True C, Franco J, Baquero K, Dean T Am J Physiol Regul Integr Comp Physiol. 2017; 313(2):R169-R179.

PMID: 28404581 PMC: 5582949. DOI: 10.1152/ajpregu.00309.2016.


Nutritional Strategies for the Individualized Treatment of Non-Alcoholic Fatty Liver Disease (NAFLD) Based on the Nutrient-Induced Insulin Output Ratio (NIOR).

Stachowska E, Ryterska K, Maciejewska D, Banaszczak M, Milkiewicz P, Milkiewicz M Int J Mol Sci. 2016; 17(7).

PMID: 27455252 PMC: 4964561. DOI: 10.3390/ijms17071192.

References
1.
Astrup A, Grunwald G, Melanson E, Saris W, Hill J . The role of low-fat diets in body weight control: a meta-analysis of ad libitum dietary intervention studies. Int J Obes Relat Metab Disord. 2000; 24(12):1545-52. DOI: 10.1038/sj.ijo.0801453. View

2.
Volkow N, Wang G, Baler R . Reward, dopamine and the control of food intake: implications for obesity. Trends Cogn Sci. 2010; 15(1):37-46. PMC: 3124340. DOI: 10.1016/j.tics.2010.11.001. View

3.
Stern J, Hirsch J, Drewnowski A, Sullivan A, Johnson P, Cohn C . Glycerol kinase activity in adipose tissue of obese rats and mice: effects of diet composition. J Nutr. 1983; 113(3):714-20. DOI: 10.1093/jn/113.3.714. View

4.
Fine E, Feinman R . Thermodynamics of weight loss diets. Nutr Metab (Lond). 2004; 1(1):15. PMC: 543577. DOI: 10.1186/1743-7075-1-15. View

5.
Tschop M, Speakman J, Arch J, Auwerx J, Bruning J, Chan L . A guide to analysis of mouse energy metabolism. Nat Methods. 2011; 9(1):57-63. PMC: 3654855. DOI: 10.1038/nmeth.1806. View