Literature DB >> 17823430

Genetic and environmental determination of tracking in subcutaneous fat distribution during adolescence.

Maarten W Peeters1, Gaston P Beunen, Hermine H Maes, Ruth J F Loos, Albrecht L Claessens, Robert Vlietinck, Martine A Thomis.   

Abstract

BACKGROUND: The distribution of fat and adipose tissue is an important predictor of disease risk. Variation in fat distribution during adolescence is correlated with fat distribution in adulthood.
OBJECTIVE: The objective was to gain insight into the relative contribution of genes and environment to the stability of subcutaneous fat distribution from early adolescence into young adulthood.
DESIGN: Ratio of trunk to extremity skinfold thickness (TER) data from the Leuven Longitudinal Twin Study (n = 105 Belgian twin pairs followed from 10 to 18 y of age) was entered into a longitudinal path analysis.
RESULTS: The best-fitting model included additive genetic sources of variance and nonshared environment. Heritabilities ranged between 84.3% (95% CI: 63.9-92.3%) and 88.6% (95% CI: 76.5-94.1%) in boys and between 78.4% (95% CI: 59.3-88.3%) and 88.3% (95% CI: 77.0-93.8%) in girls. The majority of the phenotypic tracking (boys: 0.40-0.78; girls: 0.38-0.72) could be attributed to the moderate-to-high genetic correlations (rG) (between 0.27-0.84 and 0.38-0.80 for the various age intervals in boys and girls, respectively). This rG could be attributed to both genetic sources of variance, which are the same throughout adolescence, as well as genetic sources of variance that are "switched-on" at a certain age, the effect of which is then transmitted to subsequent observations. Environmental correlations (rE) in boys ranged between 0.51 and 0.70 but contributed relatively little to phenotypic tracking because the amount of variance explained by the environment was low (11.4-15.7%). In girls rE was low to moderate at best (0.09-0.48).
CONCLUSION: Phenotypic tracking in subcutaneous fat distribution during adolescence is predominantly explained by additive genetic sources of variance.

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Mesh:

Year:  2007        PMID: 17823430     DOI: 10.1093/ajcn/86.3.652

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


  5 in total

Review 1.  The genetics of fat distribution.

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Journal:  Diabetologia       Date:  2014-03-16       Impact factor: 10.122

2.  Genome-wide association study of anthropometric traits in Korcula Island, Croatia.

Authors:  Ozren Polasek; Ana Marusić; Kresimir Rotim; Caroline Hayward; Veronique Vitart; Jennifer Huffman; Susan Campbell; Stipan Janković; Mladen Boban; Zrinka Biloglav; Ivana Kolcić; Vjekoslav Krzelj; Janos Terzić; Lana Matec; Gordan Tometić; Dijana Nonković; Jasna Nincević; Marina Pehlić; Jurica Zedelj; Vedran Velagić; Danica Juricić; Iva Kirac; Sanja Belak Kovacević; Alan F Wright; Harry Campbell; Igor Rudan
Journal:  Croat Med J       Date:  2009-02       Impact factor: 1.351

3.  The association of ACE, ACTN3 and PPARA gene variants with strength phenotypes in middle school-age children.

Authors:  Ildus I Ahmetov; Dmitry N Gavrilov; Irina V Astratenkova; Anastasiya M Druzhevskaya; Alexandr V Malinin; Elena E Romanova; Victor A Rogozkin
Journal:  J Physiol Sci       Date:  2012-09-16       Impact factor: 2.781

4.  Bidirectional cross-sectional and prospective associations between physical activity and body composition in adolescence: birth cohort study.

Authors:  Pedro C Hallal; Felipe F Reichert; Ulf Ekelund; Samuel C Dumith; Ana M Menezes; Cesar G Victora; Jonathan Wells
Journal:  J Sports Sci       Date:  2011-12-05       Impact factor: 3.337

5.  Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery.

Authors:  Elliot D K Cha; Yogasudha Veturi; Chirag Agarwal; Aalpen Patel; Mohammad R Arbabshirani; Sarah A Pendergrass
Journal:  J Obes       Date:  2018-09-27
  5 in total

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