Top

Diabetologia

Published in:

01-09-2007 | Article

Are there common genetic and environmental factors behind the endophenotypes associated with the metabolic syndrome?

Authors: B. Benyamin, T. I. A. Sørensen, K. Schousboe, M. Fenger, P. M. Visscher, K. O. Kyvik

Published in: Diabetologia | Issue 9/2007

Abstract

Aims/hypothesis

The cluster of obesity, insulin resistance, dyslipidaemia and hypertension, called the metabolic syndrome, has been suggested as a risk factor for cardiovascular disease and type 2 diabetes. The aim of the present study was to evaluate whether there are common genetic and environmental factors influencing this cluster in a general population of twin pairs.

Materials and methods

A multivariate genetic analysis was performed on nine endophenotypes associated with the metabolic syndrome from 625 adult twin pairs of the GEMINAKAR study of the Danish Twin Registry.

Results

All endophenotypes showed moderate to high heritability (0.31–0.69) and small common environmental variance (0.05–0.21). In general, genetic and phenotypic correlations between the endophenotypes were strong only within sets of physiologically similar endophenotypes, but weak to moderate for other pairs of endophenotypes. However, moderate correlations between insulin resistance indices and either obesity-related endophenotypes or triacylglycerol levels indicated that some common genetic backgrounds are shared between those components.

Conclusions/interpretation

We demonstrated that, in a general population, the endophenotypes associated with the metabolic syndrome apparently do not share a substantial common genetic or familial environmental background.

Available only for authorised users

Eckel RH, Grundy SM, Zimmet PZ (2005) The metabolic syndrome. Lancet 365:1415–1428PubMedCrossRef

Roche HM, Phillips C, Gibney MJ (2005) The metabolic syndrome: the crossroads of diet and genetics. Proc Nutr Soc 64:371–377PubMedCrossRef

Shaw DI, Hall WL, Williams CM (2005) Metabolic syndrome: what is it and what are the implications? Proc Nutr Soc 64:349–357PubMedCrossRef

Laaksonen DE, Lakka HM, Niskanen LK, Kaplan GA, Salonen JT, Lakka TA (2002) Metabolic syndrome and development of diabetes mellitus: application and validation of recently suggested definitions of the metabolic syndrome in a prospective cohort study. Am J Epidemiol 156:1070–1077PubMedCrossRef

Girman CJ, Dekker JM, Rhodes T et al (2005) An exploratory analysis of criteria for the metabolic syndrome and its prediction of long-term cardiovascular outcomes: the Hoorn Study. Am J Epidemiol 162:438–447PubMedCrossRef

Sundstrom J, Riserus U, Byberg L, Zethelius B, Lithell H, Lind L (2006) Clinical value of the metabolic syndrome for long term prediction of total and cardiovascular mortality: prospective, population based cohort study. BMJ 332:878–882PubMedCrossRef

Cameron AJ, Shaw JE, Zimmet PZ (2004) The metabolic syndrome: prevalence in worldwide populations. Endocrinol Metab Clin North Am 33:351–375PubMedCrossRef

Ford ES, Giles WH (2003) A comparison of the prevalence of the metabolic syndrome using two proposed definitions. Diabetes Care 26:575–581PubMedCrossRef

Freeman MS, Mansfield MW, Barrett JH, Grant PJ (2002) Heritability of features of insulin resistance syndrome in a community-based study of healthy families. Diabet Med 19:994–999PubMedCrossRef

10.

Li JKY, Ng MCY, So WY et al (2006) Phenotypic and genetic clustering of the metabolic syndrome in Chinese families with type 2 diabetes mellitus. Diabetes Metab Res Rev 22:46–52PubMedCrossRef

11.

Lin H-F, Boden-Albala B, Juo SH, Park N, Rundek T, Sacco RL (2005) Heritabilities of the metabolic syndrome and its components in the Northern Manhattan Family Study. Diabetologia 48:2006–2012PubMedCrossRef

12.

Martin LJ, North KE, Dyer T, Blangero J, Comuzzie AG, Williams J (2003) Phenotypic, genetic, and genome-wide structure in the metabolic syndrome. BMC Genet 4(Suppl.1):S95PubMedCrossRef

13.

Poulsen P, Vaag A, Kyvik K, Beck-Nielsen H (2001) Genetic versus environmental aetiology of the metabolic syndrome among male and female twins. Diabetologia 44:537–543PubMedCrossRef

14.

Schousboe K, Visscher PM, Henriksen JE, Hopper JL, Sørensen TI, Kyvik KO (2003) Twin study of genetic and environmental influences on glucose tolerance and indices of insulin sensitivity and secretion. Diabetologia 46:1276–1283PubMedCrossRef

15.

Henkin L, Bergman RN, Bowden DW et al (2003) Genetic epidemiology of insulin resistance and visceral adiposity: the IRAS Family Study design and methods. Ann Epidemiol 13:211–217PubMedCrossRef

16.

Samaras K, Nguyen TV, Jenkins AB et al (1999) Clustering of insulin resistance, total and central abdominal fat: same genes or same environment? Twin Res 2:218–225PubMedCrossRef

17.

Hong Y, Pedersen NL, Brismar K, Faire UD (1997) Genetic and environmental architecture of the features of the insulin-resistance syndrome. Am J Hum Genet 60:143–152PubMed

18.

Nelson TL, Vogler GP, Pedersen NL, Hong Y, Miles TP (2000) Genetic and environmental influences on body fat distribution, fasting insulin levels and CVD: are the influences shared? Twin Res 3:43–50PubMedCrossRef

19.

Tregouet DA, Herbeth B, Juhan-Vague I, Siest G, Ducimetiere P, Tiret L (1999) Bivariate familial correlation analysis of quantitative traits by use of estimating equation: application to a familial analysis of the insulin resistance syndrome. Genet Epidemiol 16:69–83PubMedCrossRef

20.

Perusse L, Rice T, Despres JP, Rao DC, Bouchard C (1997) Cross-trait familial resemblance for body fat and blood lipids: familial correlations in the Quebec Family Study. Arterioscler Thromb Vasc Biol 17:3270–3277PubMed

21.

Rainwater DL, Mitchell BD, Mahaney MC, Haffner SM (1997) Genetic relationship between measures of HDL phenotypes and insulin concentrations. Arterioscler Thromb Vasc Biol 17:3414–3419PubMed

22.

Kahn R, Buse J, Ferrannini E, Stern M (2005) The metabolic syndrome: time for a critical appraisal. Joint statement from the American Association and the European Association for the Study of Diabetes. Diabetes Care 28:2289–2304PubMedCrossRef

23.

Schousboe K, Visscher PM, Erbas B et al (2004) Twin study of genetic and environmental influences on adult body size, shape and composition. Int J Obes 28:39–48CrossRef

24.

Hansen T, Drivsholm T, Urhammer SA et al (2007) The BIGTT test: a novel test for simultaneous measurement of pancreatic beta-cell function, insulin sensitivity, and glucose tolerance. Diabetes Care 30:257–262PubMedCrossRef

25.

Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28:412–419PubMedCrossRef

26.

Diabetes Trials Unit, HOMA calculator. Available at http://www.dtu.ox.ac.uk, accessed 16 June 2007

27.

Neale MC, Boker SM, Xie G, Maes HH (2002) Mx: statistical modelling. Department of Psychiatry, Virginia Commonwealth University, Richmond. Available at http://www.vcu.edu/mx/documentation.html, accessed 16 June 2007

28.

Kendler KS, Neale MC, Kessler RC, Heath AC, Eaves LJ (1993) A test of the equal-environment assumption in twin studies of psychiatric illness. Behav Genet 23:21–27PubMedCrossRef

29.

Scarr S (1968) Environmental bias in twin studies. Eugen Q 15:34–40PubMed

30.

Edwards KL, Newman B, Mayer E, Selby JV, Krauss RM, Austin MA (1997) Heritability of factors of the insulin resistance syndrome in women twins. Genet Epidemiol 14:241–253PubMedCrossRef

31.

Mitchell BD, Kammerer CM, Mahaney MC et al (1996) Pleiotropic effects of genes influencing insulin levels on lipoprotein and obesity measures. Arterioscler Thromb Vasc Biol 16:281–288PubMed

32.

An P, Freedman BI, Hanis CL et al (2005) Genome-wide linkage scans for fasting glucose, insulin, and insulin resistance in the National Heart, Lung, and Blood Institute Family Blood Pressure Program. Diabetes 54:909–914PubMedCrossRef

33.

Kissebah AH, Sonnenberg GE, Myklebust J et al (2000) Quantitative trait loci on chromosome 3 and 17 influence phenotypes of the metabolic syndrome. PNAS 97:14478–14483PubMedCrossRef

34.

Rich SS, Bowden DW, Haffner SM et al (2005) A genome scan for fasting insulin and fasting glucose identifies a quantitative trait locus on chromosome 17p: the Insulin Resistance Atherosclerosis Study (IRAS) Family Study. Diabetes 54:290–295PubMedCrossRef

35.

Shearman AM, Ordovas JM, Cupples LA et al (2000) Evidence for a gene influencing the TG/HDL-C ratio on chromosome 7q32.3-qter: a genome-wide scan in the Framingham Study. Hum Mol Genet 9:1315–1320PubMedCrossRef

36.

Jowett JB, Elliot KS, Curran JE et al (2004) Genetic variation in BEACON influences quantitative variation in metabolic syndrome-related phenotypes. Diabetes 53:2467–2472PubMedCrossRef

37.

Goldin LR, Camp NJ, Keen KJ et al (2003) Analysis of metabolic syndrome phenotypes in Framingham Heart Study families from genetic analysis workshop 13. Genet Epidemiol 25:S78–S89PubMedCrossRef

38.

Loos RJF, Katzmarzyk PT, Rao DC et al (2003) Genome-wide linkage scan for the metabolic syndrome in the HERITAGE Family Study. J Clin Endocrinol Metab 88:5935–5943PubMedCrossRef

39.

McQueen MB, Bertram L, Rimm EB, Blacker D, Santangelo SL (2003) A QTL genome scan of the metabolic syndrome and its component traits. BMC Genet 4(Suppl.1):S96PubMedCrossRef

40.

Ng MCY, So WY, Lam VKL et al (2004) Genome-wide scan for metabolic syndrome and related quantitative traits in Hong Kong Chinese and confirmation of a susceptibility locus in chromosome 1q21–q25. Diabetes 53:2676–2683PubMedCrossRef

41.

Tang W, Miller MB, Rich SS et al (2003) Linkage analysis of a composite factor for the multiple metabolic syndrome. Diabetes 52:2840–2847PubMedCrossRef

Title: Are there common genetic and environmental factors behind the endophenotypes associated with the metabolic syndrome?
Authors: B. Benyamin
T. I. A. Sørensen
K. Schousboe
M. Fenger
P. M. Visscher
K. O. Kyvik
Publication date: 01-09-2007
Publisher: Springer-Verlag
Published in: Diabetologia / Issue 9/2007
Print ISSN: 0012-186X
Electronic ISSN: 1432-0428
DOI: https://doi.org/10.1007/s00125-007-0758-1

Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Reserve your place

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin

Prof. Florian Limbourg

Prof. Anoop Chauhan

Developed by: Springer Medicine

Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

2024 ASCO Annual Meeting

Springer Medicine

Abstract

Aims/hypothesis

Materials and methods

Results

Conclusions/interpretation

Please log in to get access to this content

Other articles of this Issue 9/2007

A 10-s sprint performed prior to moderate-intensity exercise prevents early post-exercise fall in glycaemia in individuals with type 1 diabetes

Independent associations of physical activity and cardiorespiratory fitness with metabolic risk factors in children: the European youth heart study

Apolipoprotein A-I stimulates AMP-activated protein kinase and improves glucose metabolism

Collagen I induction by high glucose levels is mediated by epidermal growth factor receptor and phosphoinositide 3-kinase/Akt signalling in mesangial cells

Brain-derived neurotrophic factor (BDNF) and type 2 diabetes. Reply to Lambert GW et al (letter)

Variations in the four and a half LIM domains 1 gene (FHL1) are associated with fasting insulin and insulin sensitivity responses to regular exercise

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

At a glance: The STEP trials