estimates of sarcopenia prevalence vary from 9.9 to 40.4%, depending on the definition used. Significant differences in prevalence exist within definitions across populations. This lack of agreement between definitions needs to be better understood before sarcopenia can be appropriately used in a clinical context.
The goal of this review article is to provide a conceptual based summary of how heritability estimates for complex traits such as obesity are determined and to explore the future directions of research in the heritability field. The target audience are researchers who use heritability data rather than those conducting heritability studies. The article provides an introduction to key concepts critical to understanding heritability studies including: i) definitions of heritability: broad sense versus narrow sense heritability; ii) how data for heritability studies are collected: twin, adoption, family and population-based studies; and iii) analytical techniques: path analysis, structural equations and mixed or regressive models of complex segregation analysis. For each section, a discussion of how the different definitions and methodologies influence heritability estimates is provided. The general limitations of heritability studies are discussed including the issue of “missing heritability” in which heritability estimates are significantly higher than the variance explained by known genetic variants. Potential causes of missing heritability include restriction of many genetic association studies to single nucleotide polymorphisms, gene by gene interactions, epigenetics, and gene by environment interactions. Innovative strategies of accounting for missing heritability including modeling techniques and improved software are discussed.
Dietary patterns containing nuts are associated with a lower risk of CVD mortality, and increased nut consumption has been shown to have beneficial effects on CVD risk factors including serum lipid levels. Recent studies have reported on the relationship between nut intake and CVD outcomes and mortality. Our objective was to systematically review the literature and quantify associations between nut consumption and CVD outcomes and all-cause mortality. Five electronic databases (through July 2015), previous reviews and bibliographies of qualifying articles were searched. In the twenty included prospective cohort studies (n 467 389), nut consumption was significantly associated with a lower risk of all-cause mortality (ten studies; risk ratio (RR) 0·81; 95 % CI 0·77, 0·85 for highest v. lowest quantile of intake, P het = 0·04, I 2 = 43 %), CVD mortality (five studies; RR 0·73; 95 % CI 0·68, 0·78; P het = 0·31, I 2 = 16 %), all CHD (three studies; RR 0·66; 95 % CI 0·48, 0·91; P het = 0·0002, I 2 = 88 %) and CHD mortality (seven studies; RR 0·70; 95 % CI 0·64, 0·76; P het = 0·65, I 2 = 0 %), as well as a statistically non-significant reduction in the risk of nonfatal CHD (three studies; RR 0·71; 95 % CI 0·49, 1·03; P het = 0·03, I 2 = 72 %) and stroke mortality (three studies; RR 0·83; 95 % CI 0·69, 1·00; P het = 0·54, I 2 = 0 %). No evidence of association was found for total stroke (two studies; RR 1·05; 95 % CI 0·69, 1·61; P het = 0·04, I 2 = 77 %). Data on total CVD and sudden cardiac death were available from one cohort study, and they were significantly inversely associated with nut consumption. In conclusion, we found that higher nut consumption is associated with a lower risk of all-cause mortality, total CVD, CVD mortality, total CHD, CHD mortality and sudden cardiac death.
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