Literature DB >> 18541558

Familial influences and obesity-associated metabolic risk factors contribute to the variation in resting energy expenditure: the Kiel Obesity Prevention Study.

Anja Bosy-Westphal1, Andreas Wolf, Frederike Bührens, Britta Hitze, Norbert Czech, Heiner Mönig, Oliver Selberg, Uta Settler, Maria Pfeuffer, Jürgen Schrezenmeir, Michael Krawczak, Manfred J Müller.   

Abstract

BACKGROUND: A low metabolic rate may be inherited and predispose to obesity, whereas a higher metabolic rate in obesity may be acquired by obesity-associated cardiometabolic risk.
OBJECTIVE: We aimed to explain the interindividual variation in resting energy expenditure (REE) by assessing 1) the association between REE and body composition, thyroid hormones, and obesity-related cardiometabolic risk factors, and 2) the familial (genetic and environmental) contribution to REE.
DESIGN: REE and metabolic risk factors (ie, blood pressure and plasma insulin, glucose, and C-reactive protein concentrations) were assessed in 149 two- or three-generation families, including at least one overweight or obese member. Heritability of REE, respiratory quotient (RQ), thyroid hormones [thyrotropin (TSH), free triiodothyronine (FT3) and free thyroxine (FT4)], and body composition (fat-free mass and fat mass) were estimated by using variance components-based quantitative genetic models.
RESULTS: REE adjusted for body composition, sex, and age (REEadj) significantly correlated with systolic and diastolic blood pressure, plasma insulin and glucose concentrations, and the homeostasis model assessment (HOMA) (r = 0.14-0.31, P < 0.05). Thyroid hormones had a modest influence on REE variance only. Heritability was 0.30 +/- 0.07 for REEadj and 0.29 +/- 0.08 for REE after additional adjustment for thyroid hormones and metabolic risk. Furthermore, heritability was estimated to be 0.22 +/- 0.08 for RQ, 0.37 +/- 0.08 for TSH, 0.68 +/- 0.06 for FT4, and 0.69 +/- 0.05 for FT3 (all significantly larger than zero).
CONCLUSIONS: Obesity-related cardiometabolic risk factors contribute to interindividual variation in REE, with hypertension and insulin resistance being associated with a higher REE. REE was moderately heritable, independent of body composition, sex, age, thyroid function, and cardiometabolic risk.

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Mesh:

Year:  2008        PMID: 18541558     DOI: 10.1093/ajcn/87.6.1695

Source DB:  PubMed          Journal:  Am J Clin Nutr        ISSN: 0002-9165            Impact factor:   7.045


  20 in total

1.  Lower resting energy expenditure and fat oxidation in Native American and Hispanic infants born to mothers with diabetes.

Authors:  Kevin R Short; April M Teague; David A Fields; Timothy Lyons; Steven D Chernausek
Journal:  J Pediatr       Date:  2015-01-31       Impact factor: 4.406

2.  Impact of Protein Intake during Weight Loss on Preservation of Fat-Free Mass, Resting Energy Expenditure, and Physical Function in Overweight Postmenopausal Women: A Randomized Controlled Trial.

Authors:  Isabell Englert; Anja Bosy-Westphal; Stephan C Bischoff; Kathrin Kohlenberg-Müller
Journal:  Obes Facts       Date:  2021-05-11       Impact factor: 3.942

3.  Acquired differences in brain responses among monozygotic twins discordant for restrained eating.

Authors:  Ellen A Schur; Natalia M Kleinhans; Jack Goldberg; Dedra S Buchwald; Janet Polivy; Angelo Del Parigi; Kenneth R Maravilla
Journal:  Physiol Behav       Date:  2011-09-17

4.  Body circumferences are predictors of weight adjusted resting energy expenditure in older people.

Authors:  K Khalaj Hedayati; M Dittmar
Journal:  J Nutr Health Aging       Date:  2011-12       Impact factor: 4.075

Review 5.  Human energy expenditure: advances in organ-tissue prediction models.

Authors:  S B Heymsfield; C M Peterson; B Bourgeois; D M Thomas; D Gallagher; B Strauss; M J Müller; A Bosy-Westphal
Journal:  Obes Rev       Date:  2018-07-23       Impact factor: 9.213

6.  'Functional' body composition: differentiating between benign and non-benign obesity.

Authors:  Manfred J Müller; Anja Bosy-Westphal; Martin Heller
Journal:  F1000 Biol Rep       Date:  2009-10-14

Review 7.  Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations.

Authors:  N S Sabounchi; H Rahmandad; A Ammerman
Journal:  Int J Obes (Lond)       Date:  2013-01-15       Impact factor: 5.095

8.  Prediction of basal metabolic rate in obese children and adolescents considering pubertal stages and anthropometric characteristics or body composition.

Authors:  S Lazzer; A Patrizi; A De Col; A Saezza; A Sartorio
Journal:  Eur J Clin Nutr       Date:  2014-03-05       Impact factor: 4.016

Review 9.  Examining variations of resting metabolic rate of adults: a public health perspective.

Authors:  Robert G McMurray; Jesus Soares; Carl J Caspersen; Thomas McCurdy
Journal:  Med Sci Sports Exerc       Date:  2014-07       Impact factor: 5.411

10.  Exome Sequencing Identifies A Nonsense Variant in DAO Associated With Reduced Energy Expenditure in American Indians.

Authors:  Paolo Piaggi; Çiğdem Köroğlu; Anup K Nair; Jeff Sutherland; Yunhua L Muller; Pankaj Kumar; Wen-Chi Hsueh; Sayuko Kobes; Alan R Shuldiner; Hye In Kim; Nehal Gosalia; Cristopher V Van Hout; Marcus Jones; William C Knowler; Jonathan Krakoff; Robert L Hanson; Clifton Bogardus; Leslie J Baier
Journal:  J Clin Endocrinol Metab       Date:  2020-11-01       Impact factor: 5.958

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