PubMed Health⌕ Search

PubMed · 9440393

Bone mass, lean mass, and fat mass: same genes or same environments?

Abstract

The contributions of genetic and environmental factors to the association among bone mineral density (BMD), lean mass, and fat mass were assessed in the Sydney Twin Study of Osteoporosis (Australia), 1995-1996, in 57 monozygotic and 55 dizygotic female twin pairs of Caucasian background, aged 52.8 (standard deviation, 13) years. In multiple regression analysis, lean mass was a significant determinant of areal BMD; however, fat mass was a principal determinant of volumetric BMD. Univariate model-fitting analyses indicated that 80% and 65% of variance of lean mass and fat mass, respectively, were attributable to genetic factors. The estimated heritability of BMD for lumbar spine, femoral neck, and total body BMD was 78%, 76%, and 79%, respectively. Multivariate analyses suggested that, while the association between lean mass and fat mass was attributable mainly to environmental factors (re = 0.53, p < 0.01), the association among the three BMD sites was attributable to both genetic and environmental factors (rg = 0.64-0.75, p < 0.001; re = 0.57-0.70, p < 0.001). Furthermore, genetic factors that affect lean mass or fat mass have minor effects on BMD. It is concluded that lean mass and fat mass, as well as bone density, are under strong genetic regulation. However, the associations between BMD and fat mass or between lean mass and fat mass appear to be mediated mainly via environmental influences.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

T V Nguyen, G M Howard, P J Kelly, J A Eisman. 1998-01-01. Bone mass, lean mass, and fat mass: same genes or same environments?. https://doi.org/10.1093/oxfordjournals.aje.a009362

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

A repeated measures concordance correlation coefficient.

The concordance correlation coefficient is commonly used to assess agreement between two raters or two methods of measuring a response when the data are measured on a continuous scale. However, the situation may arise in which repeated measurements are taken for each rater or method, e.g. longitudinal studies in clinical trials or bioassay data with subsamples. This paper proposes a coefficient for measuring agreement between two raters or two methods of measuring a response in the presence of repeated measurements. We illustrate the methodology with examples comparing (1) 1-hr versus 2-hr blood draws for measuring cortisol in an asthma clinical trial and (2) two measurements of percentage body fat, from skinfold calipers and dual energy X-ray absorptiometry.

Absorptiometry, Photon↗

Crystallization and preliminary X-ray analysis of a domain in the Runx2 transcription factor that interacts with the 1alpha,25 dihydroxy vitamin D3 receptor.

The Runx2 transcription factor is a key regulator of osteoblast differentiation. In response to 1alpha,25 dihydroxy vitamin D3, Runx2 may interact with the 1alpha,25 dihydroxy vitamin D3 receptor (VDR) in the promoter of target genes, producing a synergic activation of their transcription. Previous studies have suggested that the motifs responsible for the VDR-Runx2 interaction are contained within the 230-361 domain of Runx2. In this work, we confirmed by GST-pull down that Runx2(I(209-361)) is sufficient to interact with the VDR. To obtain structural information, GST-Runx2(I(209-361)) protein was overexpressed in Escherichia coli, purified and crystallized using the hanging-drop vapor-diffusion method and polyethyleneglycol as a precipitant. The crystals were found to diffract to a maximum resolution of 2.7 A and a complete data set to a 3.3 A resolution was collected and analyzed. The crystals belong to the tetragonal system, with a space group P4 and unit-cell parameters of a = b = 90.8, and c = 57.2 A. The presence of a monomer of the recombinant GST-Runx2(I(209-361)) in the asymmetric unit gives a V(M) of 2.7 A(3) Da(-1) and a solvent content of 54.8%.

Absorptiometry, Photon↗

Quantifying the treatment effect explained by markers in the presence of measurement error.

Surrogate markers or intermediate markers are important in identifying subjects with high risk of a serious disease or for monitoring disease progression of a subject on treatment. Quantifying the proportion of treatment effect (PTE) explained by markers has been studied extensively. Due to reasons such as biological variation, limited machine precision, etc. markers are generally measured with error. The estimated PTE ignoring the measurement error could be biased, which may lead to incorrect conclusions. In this article, we adjust for the measurement error using regression calibration to construct a less biased estimator of excess relative odds, a quantity to measure the treatment effect explained by markers. The method is applied to data from a clinical study in osteoporosis.

Absorptiometry, Photon↗