Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-26T10:59:50.261Z Has data issue: false hasContentIssue false

A Twin Study Approach Towards Understanding Genetic Contributions to Body Size and Metabolic Rate

Published online by Cambridge University Press:  01 August 2014

J.K. Hewitt*
Affiliation:
Department of Human Genetics, Medical College of Virginia, Richmond, USA Department of Psychology, University of Birmingham, UK
A.J. Stunkard
Affiliation:
Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
D. Carroll
Affiliation:
Department of Psychology, University of Birmingham, UK
J. Sims
Affiliation:
Department of Occupational Health, University of Birmingham, UK
J.R. Turner
Affiliation:
Department of Psychology, University of Birmingham, UK Department of Psychiatry, University of North Carolina, Chapel Hill, USA
*
Department of Human Genetics, Medical College of Virginia, Richmond, VA 23298-0003, USA

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The genetic and environmental determinants of a brief assessment of metabolic rate at rest and under psychological stress were studied in 40 pairs of monozygotic and 40 pairs of dizygotic young adult male twins. Height, weight and age were employed as covariates. Univariate analyses showed a high heritability for height and weight and moderate heritability for metabolic rate. Classical twin analyses and multivariate genetic modeling indicated that genetic influences on resting metabolic rate were entirely explained by body weight: there was no independent genetic contribution to resting metabolic rate. Metabolic rate under psychological stress, on the other hand, showed a significant genetic effect. The exponent (3/4) in the power function relating body weight to resting metabolic rate was the same as that found in a wide variety of animal species, a value that has been proposed as defining a body weight set point. We speculate that an adult body weight set point is genetically transmitted. Independent genetic effects on resting metabolic rate would be observed only when the normal equilibrium between body weight and metabolic rate is unbalanced during development, aging or disease. The study illustrates the use of multivariate genetic analyses of twin data which may be readily applied to widely used metabolic rate assessments.

Type
Research Article
Copyright
Copyright © The International Society for Twin Studies 1991

References

REFERENCES

1.Bogardus, C, Lillioja, S, Ravussin, E, Abbott, W, Zawadzki, JK, Young, A, Knowler, WC, Jacobowitz, R, Moll, PP (1986): Familial dependence of the resting metabolic rate. New Engl J Med 315: 96100CrossRefGoogle ScholarPubMed
2.Bouchard, C, Tremblay, A, Nadeau, A, Despres, JP, Theriault, G, Boulay, MR, Lortie, G, Leblanc, C, Fournier, G (1989): Genetic effect in resting and exercise metabolic rate. Metabolism 38: 364370.Google Scholar
3.Carroll, D, Hewitt, JK, Last, KA, Turner, JR, Sims, J (1985): A twin study of cardiac reactivity and its relationship to parental blood pressure. Physiol & Behav 34: 103106.CrossRefGoogle ScholarPubMed
4.Fontaine, E, Savard, R, Tremblay, A, Despres, JP, Poehlman, E, Bouchard, C (1985): Resting metabolic rate in monozygotic and dizygotic twins. Acta Genet Med Gemellol 34: 4147.Google ScholarPubMed
5.Griffiths, M, Payne, PR (1976): Energy expenditure in small children of obese and non-obese parents. Nature 260: 698700.Google Scholar
6.Griffiths, M, Payne, PR, Rivers, JPW, Stunkard, AJ, Cox, M (1990): Low metabolic rate and physical development. Lancet 336: 7678.CrossRefGoogle ScholarPubMed
7.Heath, AC, Neale, MC, Hewitt, JK, Eaves, LJ, Fulker, DW (1989): Testing structural equation models for twin data using LISREL-VI. Behav Genet 19: 935.Google Scholar
8.James, WPT, Davies, HL, Dailey, J, Dauncey, MJ (1978): Elevated metabolic rates in obesity. Lancet 1: 11221125.CrossRefGoogle ScholarPubMed
9.Joreskog, KG, Sorbom, D, (1985): LISREL VI: Analysis of Linear Structural Relationships by Maximum Likelihood, Instrumental Variables and Least Squares Methods. Mooresville: Scientific Software, Inc.Google Scholar
10.Kasriel, J, Eaves, L (1976): The zygosity of twins: Further evidence on the agreement between diagnosis by blood groups and written questionnaires. J Biosocial Sci 8: 263266.Google Scholar
11.Keesey, RE, Corbett, SW (1984): Metabolic defense of the body weight set-point. In Stunkard, AJ, Stellar, E (eds): Eating and its Disorders. New York: Raven Press, pp 8796.Google Scholar
12.Kleiber, M (1975): The Fire of Life. New York: Robert E. Krieger.Google Scholar
13.Li, CC (1975): Path Analysis: A Primer. Pacific Grove, CA: Boxwood Press.Google Scholar
14.Wright, S (1921): Correlation and causation. J Agricultural Res 20: 557583.Google Scholar
15.Morton, NE (1974): Analysis of family resemblance. I. Introduction. Am J Hum Genet 26: 318330.Google Scholar
16.Neale, MC, Heath, AC, Hewitt, JK, Eaves, LJ, Fulker, DW (1989): Fitting genetic models with LISREL: Hypothesis testing. Behav Genet 19: 3749.Google Scholar
17.Prentice, AM, Black, AE, Coward, WEet al (1986): High levels of energy expenditure in obese woman. Brit Med J 292: 983987.CrossRefGoogle Scholar
18.Price, RA, Cadoret, RJ, Stunkard, AJ, Troughton, E (1987): Genetic contribution to human obesity: An adoption study. Am J Psychiat 144: 10031008.Google Scholar
19.Price, RA, Stunkard, AJ (1989): A commingling analysis of human obesity. Hum Hered 39: 121135.CrossRefGoogle Scholar
20.Ravussin, E, Burnand, B, Schutz, Y, Jequier, E (1982): Twenty-four hour expenditure and resting metabolic rate in obese, moderately obese and control subjects. Am J Clin Nutr 35: 566573.Google Scholar
21.Ravussin, E, Lillioja, S, Knowler, WC, Christin, L, Freymond, D, Abbott, WGH, Boyce, V, Howard, BV, Bogardus, C (1988): Reduced rate of energy expenditure as a risk factor for body-weight gain. New Engl J Med 318: 462472.Google Scholar
22.Roberts, SB, Savage, J, Coward, WE, Chew, B, Lucas, A (1988): Energy expenditure and intake in infants born to lean and overweight mothers. New Engl J Med 318: 461466.Google Scholar
23.Schieken, RM, Eaves, LJ, Hewitt, JK, Mosteller, M, Bodurtha, JN, Moskowitz, WB, Nance, WE (1989): The univariate genetic analysis of blood pressure and heart rate in children: The MCV study. Am J Cardiology 64: 13331337.Google Scholar
24.Sorensen, TIA, Price, RA, Stunkard, AJ, Schulsinger, F (1989): Genetics of human obesity in adult adoptees and their biological siblings. Brit Med J 298: 8790.CrossRefGoogle ScholarPubMed
25.Stunkard, AJ, Foch, TT, Hrubec, Z (1986): A twin study of human obesity. JAMA 256: 5154.Google Scholar
26.Stunkard, AJ, Sorensen, TIA, Hanis, C, Teasdale, TW, Chakraborty, R, Schull, WJ, Schulsinger, F (1986): An adoption study of human obesity. New Engl J Med 314: 193198.CrossRefGoogle ScholarPubMed
27.Turner, JR, Carroll, D (1985): Heart rate and oxygen consumption during mental arithmetic, a video game and graded exercise: Further evidence of metabolically-exaggerated cardiac adjustements? Psychophysiol 22: 261267.CrossRefGoogle Scholar
28.Turner, JR, Carroll, D, Courtney, H (1983): Cardiac and metabolic responses to “space invaders”: An instance of metabolically-exaggerated cardiac adjustment? Psychophysiol 20: 544549.CrossRefGoogle ScholarPubMed
29.Weir, JB V de (1949): New methods for calculating metabolic rate with special reference to protein metabolism. J Physiol 109: 19.CrossRefGoogle ScholarPubMed